Showing 1–50 of 90 results for author: Zheng, M

Recent Sharp Rise in Inhomogeneous Hydrological Extremes Stress Vegetation Growth in China

Authors: Shengyuan Liu, Jeremy Cheuk-Hin Leung, Jianjun Xu, Kunlun Xiang, Shifei Tu, Meiying Zheng, Daosheng Xu, Yi Li, Banglin Zhang

Abstract: The intensifying spatial inhomogeneity of rainfall under greenhouse warming implies that more inhomogeneous hydrological extremes (IHEs), i.e., coexistence of extreme rainfall or drought, may be triggered. While vegetation growth in China is sensitive to hydrological hazards, the variability of IHEs and their ecological impacts remain underexplored. Here, we find a significant increase in IHEs dur… ▽ More The intensifying spatial inhomogeneity of rainfall under greenhouse warming implies that more inhomogeneous hydrological extremes (IHEs), i.e., coexistence of extreme rainfall or drought, may be triggered. While vegetation growth in China is sensitive to hydrological hazards, the variability of IHEs and their ecological impacts remain underexplored. Here, we find a significant increase in IHEs during China's growing season since 2000 (+2.1 events or +14.52 days per decade), with a rapid sharp rise to an annual average of 6.4 events or 42.0 days in the past five years. The primary driver is the enhanced inhomogeneity of moisture-dynamic coupled weather conditions, overlapping with a northward shift of climatological precipitation distribution. This results in a "Wet-North and Dry-South" pattern of IHE impacts, which poses severe and asymmetric threats to vegetation growth in China, with the expansion of drought areas exerts stronger stress on vegetation than the compensatory effects of rainfall. Our findings suggest that the sharp rises of IHEs tend to yield net negative impacts on vegetation growth, highlighting the need for stronger hydrological management to reduce future risks. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: 21 pages, 5 figures

36Cl Concentrations from Polar Ice Cores Set New Constraints on the Carrington Event

Authors: F. Mekhaldi, C. I. Paleari, A. M. Smith, A. Aldahan, J. Beer, M. Christl, C. Vockenhuber, H. Hayakawa, M. Curran, T. Erhardt, C. Plummer, K. Simon, K. Wilcken, M. Zheng, R. Muscheler

Abstract: The Carrington event of 1859 CE is considered as one of the largest geomagnetic storms of the observational era, and often used as a benchmark for a worst-case scenario. Yet, there exists no robust evidence of an associated solar energetic particle event of a significant magnitude, based on measurements of cosmogenic radionuclides 10Be and 14C from ice cores and tree rings, respectively. In this s… ▽ More The Carrington event of 1859 CE is considered as one of the largest geomagnetic storms of the observational era, and often used as a benchmark for a worst-case scenario. Yet, there exists no robust evidence of an associated solar energetic particle event of a significant magnitude, based on measurements of cosmogenic radionuclides 10Be and 14C from ice cores and tree rings, respectively. In this study, we present two 36Cl records from Greenland with 2-year and 4-year resolution from the EGRIP and NGRIP ice-core sites, together with semi-annual 10Be data from EGRIP, as well as annual 10Be and 36Cl concentrations from the Dome Summit Site, Law Dome, East Antarctica. We observe no significant 36Cl concentration increase around 1859 CE in the three records. This allows us to rule out an extreme solar energetic particle event hitting Earth associated with the Carrington event in terms of fluence above 30 MeV. Based on these ice core 36Cl measurements, we can suggest two scenarios: i) a soft SEP event with a maximum fluence above 30 MeV up to three times larger than any Space Age event or, ii) the possibility that there was no Earth-bound SEP event. △ Less

Submitted 29 April, 2026; originally announced April 2026.

Comments: 16 pages, 2 figures, and 2 tables

Journal ref: Philosophical Transactions of the Royal Society A, 2026

Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks

Authors: Yanlin Zhang, Yan Zhang, Muhua Zheng, Kesheng Xu

Abstract: Experimental evidence indicates that intrinsic temporal dynamics operating across multiple time scales are closely associated with the emergence of periodic spatial activity of increasing complexity. However, how information encoded in grid-like firing patterns for path integration is processed across these intrinsic time scales remains unclear. To address this question, we introduce adaptive time… ▽ More Experimental evidence indicates that intrinsic temporal dynamics operating across multiple time scales are closely associated with the emergence of periodic spatial activity of increasing complexity. However, how information encoded in grid-like firing patterns for path integration is processed across these intrinsic time scales remains unclear. To address this question, we introduce adaptive time scales through a leak term in recurrent neural networks (RNNs), forming leaky RNNs discretized from the continuous attractors of firing rate models. Our results demonstrate that leaky RNNs substantially enhance the emergence of well-defined and highly regular hexagonal firing patterns. Compared with vanilla RNNs lacking a leak term, the trained leaky RNNs produce more accurate position estimates while generating reliable grid-cell-like representations. Furthermore, under identical noise conditions, leaky RNNs consistently exhibit more stable dynamics and better-defined grid structures. The learned dynamics also give rise to stable torus attractors with a clear central hole, supporting robust and regular grid-like activity. Overall, the dynamic leak acts as a low-pass filtering mechanism that protects recurrent neural circuitry from noise, stabilizes network dynamics, and improves path-integration accuracy in recurrent neural networks. △ Less

Submitted 17 April, 2026; originally announced April 2026.

Comments: 13 figures and 15 pages

Journal ref: Nonlinear Dynamics 114, no. 12 (2026): 849

Role of chloride concentration in modulating seizure transitions in excitatory and inhibitory networks

Authors: Qianchen Gong, Yingpeng Liu, Yan Zhang, Muhua Zheng, Kesheng Xu

Abstract: Experimental evidence indicates that intracellular chloride concentration regulates the excitation and inhibition (EI) balance, yet the mechanisms by which activity-dependent chloride dynamics drive seizure evolution and stage transitions remain unclear. We present a conductance-based neuronal network in which EI balance emerges from chloride homeostasis via channel-mediated influx and transporter… ▽ More Experimental evidence indicates that intracellular chloride concentration regulates the excitation and inhibition (EI) balance, yet the mechanisms by which activity-dependent chloride dynamics drive seizure evolution and stage transitions remain unclear. We present a conductance-based neuronal network in which EI balance emerges from chloride homeostasis via channel-mediated influx and transporter-mediated extrusion. We show that the fraction of inhibitory synaptic conductance contributing to channel-mediated influx acts as a control parameter that organizes seizure dynamics into distinct stages,pre-ictal, ictal-tonic, and ictal-clonic,distinguished by characteristic amplitude and frequency signatures. Decreasing this fraction shortens ictal activity and suppresses seizure initiation, whereas high fraction promotes the emergence of ictal-tonic and ictal-clonic stages and spiral-wave dynamics, rendering seizure dynamics largely insensitive to inhibition. At intermediate values, seizures bypass the ictal-tonic stage and emerge directly as the icta,clonic stage. Moreover, joint variation of fractions with synaptic strengths reveals that recurrent excitation expands the tonic-clonic seizure, while recurrent inhibition prolongs pre-ictal states and suppresses ictal-clonic activity. △ Less

Submitted 17 April, 2026; originally announced April 2026.

Comments: 16 pages, 15 figures

Journal ref: Physical Review E 113, 034401 (2026)

Robustness and size-dependence of circadian rhythms in multiscale suprachiasmatic-nucleus networks

Authors: Youhao Zhuo, Yingpeng Liu, Jiao Wu, Kesheng Xu, Muhua Zheng

Abstract: Understanding how multi-scale network structure influences circadian rhythms in the suprachiasmatic nucleus (SCN) is essential for uncovering the principles of rhythmic robustness and synchronization. Previous studies using synthetic SCN networks suggested a size-dependent phenomenon, in which rhythmic activity initially strengthens with network size and then saturates, but it remains unclear whet… ▽ More Understanding how multi-scale network structure influences circadian rhythms in the suprachiasmatic nucleus (SCN) is essential for uncovering the principles of rhythmic robustness and synchronization. Previous studies using synthetic SCN networks suggested a size-dependent phenomenon, in which rhythmic activity initially strengthens with network size and then saturates, but it remains unclear whether this occurs in real SCN networks. Here, we apply geometric branch growth (GBG) and geometric renormalization (GR) to generate self-similar scaled-up and scaled-down replicas from a single-scale functional mouse SCN network. Unlike synthetic models, these SCN replicas do not exhibit size-dependent rhythms: average period, amplitude, and synchronization remain stable across scales. By increasing the average degree with network size, we reproduce size-dependent rhythms and show that they arise from network connectivity, whereas low-degree networks fragment and fail to sustain oscillations. Disrupting clustering self-similarity slightly reduces synchronization, but circadian rhythms remain robust, indicating that average degree, rather than clustering, is the dominant structural driver. These results highlight the resilience of SCN rhythms to network scaling and provide a framework for linking multi-scale network structure to biological timekeeping. △ Less

Submitted 7 March, 2026; originally announced March 2026.

Comments: 20 pages, 14 figures

Journal ref: Phys. Rev. E 113, 034304 (2026)

High-Performance Quantum Frequency Conversion from Ultraviolet to Telecom Band

Authors: Yi Yang, Bin Wang, Ji-Chao Lin, Yang Gao, Xin Li, Jiu-Peng Chen, Lei Hou, Ye Wang, Yong Wan, Xiu-Ping Xie, Ming-Yang Zheng, Qiang Zhang, Jian-Wei Pan

Abstract: Quantum frequency conversion (QFC) is essential for bridging the spectral gap between stationary qubits and low-loss optical communication channels. In this work, we demonstrate a short-wavelength-pumping QFC with the first-order quasi-phase matching period of 3.07 um on thin-film lithium niobate, converting ultraviolet photons to the telecom C-band. By constructing a theoretical model that correl… ▽ More Quantum frequency conversion (QFC) is essential for bridging the spectral gap between stationary qubits and low-loss optical communication channels. In this work, we demonstrate a short-wavelength-pumping QFC with the first-order quasi-phase matching period of 3.07 um on thin-film lithium niobate, converting ultraviolet photons to the telecom C-band. By constructing a theoretical model that correlates the normalized conversion efficiency with domain defects in the short-period phase-matched waveguide, we found the critical tolerance of domain defects along the waveguide should be $\le 2$ (excluding the ends). Based on this, we achieved a theoretical limit normalized conversion efficiency of 839%/(W*cm^2) for the fundamental guided mode through fabrication optimization. Furthermore, we propose a robust noise suppression strategy for short-wavelength pumping by utilizing the counter-tuning behaviors of difference-frequency generation and spontaneous parametric down-conversion. By combining these advances with ultra-narrowband filtering, we achieve a record-high external efficiency of 28.8% and an ultra-low noise of 35 counts per second. This high-performance QFC connecting ultraviolet and telecom bands satisfies the stringent requirements for long-lived remote ion-ion entanglement in scalable quantum networks [W.-Z. Liu et al., Nature (2026)]. △ Less

Submitted 2 March, 2026; originally announced March 2026.

Comments: 13 pages, 10 figures

Device-independent quantum key distribution over 100 km with single atoms

Authors: Bo-Wei Lu, Chao-Wei Yang, Run-Qi Wang, Bo-Feng Gao, Yi-Zheng Zhen, Zhen-Gang Wang, Jia-Kai Shi, Zhong-Qi Ren, Thomas A. Hahn, Ernest Y. -Z. Tan, Xiu-Ping Xie, Ming-Yang Zheng, Xiao Jiang, Jun Zhang, Feihu Xu, Qiang Zhang, Xiao-Hui Bao, Jian-Wei Pan

Abstract: Device-independent quantum key distribution (DI-QKD) is a key application of the quantum internet. We report the realization of DI-QKD between two single-atom nodes linked by 100-km fibers. To improve the entangling rate, single-photon interference is leveraged for entanglement heralding, and quantum frequency conversion is used to reduce fiber loss. A tailored Rydberg-based emission scheme suppre… ▽ More Device-independent quantum key distribution (DI-QKD) is a key application of the quantum internet. We report the realization of DI-QKD between two single-atom nodes linked by 100-km fibers. To improve the entangling rate, single-photon interference is leveraged for entanglement heralding, and quantum frequency conversion is used to reduce fiber loss. A tailored Rydberg-based emission scheme suppresses the photon recoil effect on the atom without introducing noise. We achieved high-fidelity atom-atom entanglement and positive asymptotic key rates for fiber lengths up to 100 km. At 11 km, 1.2 million heralded Bell pairs were prepared over 624 hours, yielding an estimated extractable finite-size secure key rate of 0.112 bits per event against general attacks. Our results close the gap between proof-of-principle quantum network experiments and real-world applications. △ Less

Submitted 10 February, 2026; originally announced February 2026.

Comments: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution

Journal ref: Science 391, 592-597 (2026)

A building block of quantum repeaters for scalable quantum networks

Authors: Wen-Zhao Liu, Ya-Bin Zhou, Jiu-Peng Chen, Bin Wang, Ao Teng, Xiao-Wen Han, Guang-Cheng Liu, Zhi-Jiong Zhang, Yi Yang, Feng-Guang Liu, ChaoHui Xue, Bo-Wen Yang, Jin Yang, Chao Zeng, Du-Ruo Pan, Ming-Yang Zheng, Xing-Jian Zhang, Cao Shen, Yi-Zheng Zhen, You Xiao, Hao Li, Li-Xing You, XiongFeng Ma, Qi Zhao, Feihu Xu , et al. (4 additional authors not shown)

Abstract: Quantum networks, integrating quantum communication, quantum metrology, and distributed quantum computing, could provide secure and efficient information transfer, high-resolution sensing, and an exponential speed-up in information processing. Deterministic entanglement distribution over long distances is a prerequisite for scalable quantum networks, enabling the utilization of device-independent… ▽ More Quantum networks, integrating quantum communication, quantum metrology, and distributed quantum computing, could provide secure and efficient information transfer, high-resolution sensing, and an exponential speed-up in information processing. Deterministic entanglement distribution over long distances is a prerequisite for scalable quantum networks, enabling the utilization of device-independent quantum key distribution (DI-QKD) and quantum teleportation to achieve secure and efficient information transfer. However, the exponential photon loss in optical fibres prohibits efficient and deterministic entanglement distribution. Quantum repeaters, incorporating entanglement swapping and entanglement purification with quantum memories, offer the most promising means to overcome this limitation in fibre-based quantum networks. Despite numerous pioneering efforts toward realizing quantum repeaters, a critical bottleneck remains, as remote memory-memory entanglement suffers from decoherence more rapidly than it can be established and purified over long distances. We overcome this by developing long-lived trapped-ion memories, an efficient telecom interface, and a high-visibility single-photon entanglement protocol. This allows us to establish and maintain memory-memory entanglement over a 10 km fibre within the average entanglement establishment time for the same distance. As a direct application, we demonstrate metropolitan-scale DI-QKD, distilling 1,917 secret keys out of 4.05*10^5 Bell pairs over 10 km. We further report a positive key rate over 101 km in the asymptotic limit, extending the achievable distance by more than two orders of magnitude. Our work provides a critical building block for quantum repeaters and marks an important step toward scalable quantum networks. △ Less

Submitted 9 February, 2026; originally announced February 2026.

Comments: Accepted for publication in Nature (https://doi.org/10.1038/s41586-026-10177-4). This is the original submitted version

Metrology-grade mid-infrared spectroscopy for multi-dimensional perception

Authors: Baoqi Shi, Chenxi Zhang, Ming-Yang Zheng, Yue Hu, Zeying Zhong, Zhenyuan Shang, Wenbo Ma, Xiu-Ping Xie, Xue Bai, Yi-Han Luo, Anting Wang, Hairun Guo, Qiang Zhang, Junqiu Liu

Abstract: The mid-infrared spectral window is essential for molecular fingerprinting and atmospheric sensing, yet unlocking its full potential is currently constrained by a fundamental instrumental trade-off: existing systems cannot simultaneously deliver broad bandwidth, high photon flux, and metrological frequency fidelity. Here, we resolve this bottleneck by demonstrating a metrology-grade spectroscopic… ▽ More The mid-infrared spectral window is essential for molecular fingerprinting and atmospheric sensing, yet unlocking its full potential is currently constrained by a fundamental instrumental trade-off: existing systems cannot simultaneously deliver broad bandwidth, high photon flux, and metrological frequency fidelity. Here, we resolve this bottleneck by demonstrating a metrology-grade spectroscopic system based on difference frequency generation, driven by widely tunable, near-infrared diode lasers traceable to atomic standards. Our system achieves continuous tunability across the 3-3.7 $μ$m atmospheric window and delivers output power exceeding 45 mW with an absolute frequency accuracy of 7.2 MHz. We harness this convergence to overcome a critical barrier in integrated photonics, unambiguously identifying and eliminating hydrogen-induced absorption in silicon nitride microresonators to achieve an 88-fold reduction in optical loss. We further reveal multi-phonon absorption in the silica cladding as the fundamental limit to mid-infrared integrated photonics. Finally, we demonstrate the system's versatility through scattering-resilient LiDAR capable of penetrating optically dense fog, and dual-modality sensing that simultaneously retrieves target distance and chemical composition. By unifying the rigor of frequency metrology with the versatility of broadband sensing, this architecture establishes a new paradigm for multi-dimensional perception in complex environments. △ Less

Submitted 31 January, 2026; originally announced February 2026.

Memory-Assisted Nonlocal Interferometer Towards Long-Baseline Telescopes

Authors: Bin Wang, Xi-Yu Luo, Bo-Feng Gao, Jian-Long Liu, Chao-Yang Wang, Zi Yan, Qiao-Mu Ke, Da Teng, Ming-Yang Zheng, Yuan Cao, Jun Li, Cheng-Zhi Peng, Qiang Zhang, Xiao-Hui Bao, Jian-Wei Pan

Abstract: Quantum networks and remote quantum entanglement serve as vital future quantum communication resources with broad applicability. A key direction lies in extending the baseline of optical interferometers to enhance angular resolution in interferometric imaging. Here, by measuring a simulated thermal light field, we report the demonstration of a memory-assisted nonlocal interferometer achieving a fi… ▽ More Quantum networks and remote quantum entanglement serve as vital future quantum communication resources with broad applicability. A key direction lies in extending the baseline of optical interferometers to enhance angular resolution in interferometric imaging. Here, by measuring a simulated thermal light field, we report the demonstration of a memory-assisted nonlocal interferometer achieving a fiber-link baseline up to 20 km while simultaneously showing its capability to compensate for a geometric delay equivalent to 1.5 km. This result demonstrates potential for enhancing the angular resolution of interferometric imaging in the optical band with delocalized single-photon entanglement, and paves the way for future application of quantum memories in astronomical observation. △ Less

Submitted 14 November, 2025; originally announced November 2025.

Comments: 6 pages in the main text, 7 pages in the supplemental meterial

Electrically pumped ultra-efficient quantum frequency conversion on thin film lithium niobate chip

Authors: Xina Wang, Xu-Feng Jiao, Bo Cao, Yang Liu, Xiu-Ping Xie, Ming-Yang Zheng, Qiang Zhang, Jian-Wei Pan

Abstract: Quantum frequency conversion (QFC) plays a crucial role in constructing seamless interconnection between quantum systems operating at different wavelengths. To advance future quantum technology, chip-scale integrated QFC components, featuring high efficiency, small footprint, low power consumption and high scalability, are indispensable. In this work, we demonstrate the first hybrid integrated QFC… ▽ More Quantum frequency conversion (QFC) plays a crucial role in constructing seamless interconnection between quantum systems operating at different wavelengths. To advance future quantum technology, chip-scale integrated QFC components, featuring high efficiency, small footprint, low power consumption and high scalability, are indispensable. In this work, we demonstrate the first hybrid integrated QFC chip on thin film lithium niobate platform that connects the telecom and visible bands. Benefiting from the periodically poled microring resonator with ulta-high normalized conversion efficiency of 386,000 %/W, an ultra-low pump power of 360 μW is achieved which is more than two orders of magnitude lower than traditional straight waveguide scheme. By injecting current into the chip, an on-chip quantum efficiency of 57% and a noise count of ~ 7k counts per second are achieved. Such an electrically pumped, integrated and scalable QFC chip would significantly advancing the integration of quantum network and the development of chip-scale quantum optical systems. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: 20 pages, 7 figures

Electrically pumped ultrabright entangled photons on chip

Authors: Xu-Feng Jiao, Ming-Yang Zheng, Yi-Hang Chen, Bo Cao, Xina Wang, Yang Liu, Cheng-Ao Yang, Xiu-Ping Xie, Chao-Yang Lu, Zhi-Chuan Niu, Qiang Zhang, Jian-Wei Pan

Abstract: Entangled photon sources (EPS) are essential for quantum science and technology. Despite advancements in integrated optical platforms like thin-film lithium niobate, a scalable, high-performance, chip-scale EPS has remained elusive. We address this by demonstrating an electrically pumped, post-selection-free polarization-EPS, achieved through hybrid integration of a distributed feedback laser with… ▽ More Entangled photon sources (EPS) are essential for quantum science and technology. Despite advancements in integrated optical platforms like thin-film lithium niobate, a scalable, high-performance, chip-scale EPS has remained elusive. We address this by demonstrating an electrically pumped, post-selection-free polarization-EPS, achieved through hybrid integration of a distributed feedback laser with thin-film lithium niobate chip which integrates periodically poled lithium niobate waveguides, beam splitter, and polarization rotator combiner. By injecting current into the chip, we realize a high-performance EPS with a bandwidth of 73 nm and an entanglement pair generation rate of 4.5*10^10 pairs/s/mW. The polarization entanglement shows Bell-state fidelities above 96% across frequency-correlated modes. This compact, integrated EPS enables key applications, including high-speed quantum key distribution via wavelength division multiplexing, satellite-based quantum communication, and entanglement-based quantum metrology. △ Less

Submitted 20 August, 2025; originally announced August 2025.

Comments: 21 pages, 8 figures

Silicon single-photon detector achieving over 84% photon detection efficiency with flexible operation modes

Authors: Dong An, Chao Yu, Ming-Yang Zheng, Anran Guo, Junsong Wang, Ruizhi Li, Huaping Ma, Xiu-Ping Xie, Xiao-Hui Bao, Qiang Zhang, Jun Zhang, Jian-Wei Pan

Abstract: Silicon single-photon detectors (Si SPDs) play a crucial role in detecting single photons in the visible spectrum. For various applications, photon detection efficiency (PDE) is the most critical characteristic for effectively collecting photons. Here, we present a Si SPD with a remarkable PDE of up to 84.4% at 785 nm, supporting multiple operation modes. We design and fabricate a thick-junction S… ▽ More Silicon single-photon detectors (Si SPDs) play a crucial role in detecting single photons in the visible spectrum. For various applications, photon detection efficiency (PDE) is the most critical characteristic for effectively collecting photons. Here, we present a Si SPD with a remarkable PDE of up to 84.4% at 785 nm, supporting multiple operation modes. We design and fabricate a thick-junction Si single-photon avalanche diode (SPAD) that enhances the avalanche probability through a backside-illumination structure, while minimizing noise through the design of a doping-compensated avalanche region. To maximize PDE, we implement a readout circuit with a 50 V quenching voltage, enabling operation in free-running, gating, or hybrid modes. The SPAD, along with its readout circuits and affiliated circuits, is integrated into a compact SPD module. In free-running mode, the module achieves a maximum PDE of 84.4%, with a dark count rate of 260 cps, and an afterpulse probability of 2.9% at 268 K. This work provides a practical solution for applications requiring ultra-high-efficiency Si SPD with multiple operation modes. △ Less

Submitted 24 July, 2025; originally announced July 2025.

Comments: Accepted by IEEE Journal of Selected Topics in Quantum Electronics

Entangling quantum memories over 420 km in fiber

Authors: Xi-Yu Luo, Chao-Yang Wang, Ming-Yang Zheng, Bin Wang, Jian-Long Liu, Bo-Feng Gao, Jun Li, Zi Yan, Qiao-Mu Ke, Da Teng, Rui-Chun Wang, Jun Wu, Jia Huang, Hao Li, Li-Xing You, Xiu-Ping Xie, Feihu Xu, Qiang Zhang, Xiao-Hui Bao, Jian-Wei Pan

Abstract: Long-distance entanglement is pivotal for quantum communication, distributed quantum computing and sensing. Significant progresses have been made in extending the distribution distance of entangled photons, either in free space or fiber. For future quantum network applications, matter-based entanglement is more favorable since the capability of storage is essential for advanced applications. Exten… ▽ More Long-distance entanglement is pivotal for quantum communication, distributed quantum computing and sensing. Significant progresses have been made in extending the distribution distance of entangled photons, either in free space or fiber. For future quantum network applications, matter-based entanglement is more favorable since the capability of storage is essential for advanced applications. Extending entanglement distance for memory qubits was partially hindered by the mismatch of its photonic emission wavelength with the low-loss transmission window of optical fiber. By incorporating quantum frequency conversion, memory-memory entanglement has been successfully extended to several tens of kilometers. Here, we make a significant step further by reporting the entanglement between two atomic ensemble quantum memories over 420 km. We convert photons emitted from the memories to telecom S-band, which enable us to exploit the significantly low transmission loss in fiber (0.17 dB/km). We employ the DLCZ scheme for remote entanglement generation, and delicately stabilize the relative phase between the two memories by using fulltime far-off-resonant locking to reduce high-frequency noise and intermittent dual-band locking to compensate low-frequency drift jointly. We demonstrate that the memory-memory entangling probability beats the repeaterless channel capacity for direct entanglement distribution. Our experiment provides a testbed of studying quantum network applications from metropolitan scale to intercity scale. △ Less

Submitted 8 April, 2025; originally announced April 2025.

Comments: 15 pages in total, 4 figures and 1 table in the main text, 4 figures and 5 tables in the supplemental material

Strain-induced nonrelativistic altermagnetic spin splitting effect

Authors: Wancheng Zhang, Mingkun Zheng, Yong Liu, Zhenhua Zhang, Rui Xiong, Zhihong Lu

Abstract: Recent studies reveal that $\mathcal{T}$-odd spin currents generated via the nonrelativistic altermagnetic spin splitting effect (ASSE) exhibit significant potential for spintronics applications, with both computational and experimental validations. Addressing the scarcity of conductive altermagnets, we propose strain engineering as a reliable method for inducing altermagnetism. Focusing on rutile… ▽ More Recent studies reveal that $\mathcal{T}$-odd spin currents generated via the nonrelativistic altermagnetic spin splitting effect (ASSE) exhibit significant potential for spintronics applications, with both computational and experimental validations. Addressing the scarcity of conductive altermagnets, we propose strain engineering as a reliable method for inducing altermagnetism. Focusing on rutile-structured $\mathrm{OsO}_2$, first-principles calculations show that minor equibiaxial tensile strain ($\mathcal{E}_{\mathrm{ts}}$=3\%) induces nonmagnetic-to-altermagnetic transitions, achieving an ASSE-driven spin-charge conversion ratio ($θ_{\text{AS}}$) of $\sim$7\% -- far surpassing conventional spin Hall angles ($θ_{\text{IS}}$). Calculations reveal that substantial $θ_{\text{AS}}$ persists even in the absence of spin-orbit coupling, with its magnitude positively correlating to nonrelativistic spin splitting magnitude, which further confirms the strain-induced ASSE's nonrelativistic origin. Further investigation reveals that $\mathrm{RuO}_2$ exhibits analogous phenomena, which may resolve recent controversies regarding its magnetic properties. Our research opens new simple pathways for developing next-generation altermagnetic spintronic devices. △ Less

Submitted 16 July, 2025; v1 submitted 22 March, 2025; originally announced March 2025.

The recurrence of groups inhibits the information spreading under higher-order interactions

Authors: Liang Yuan, Jiao Wu, Kesheng Xu, Muhua Zheng

Abstract: Modeling social systems as networks based on pairwise interactions between individuals offers valuable insights into the mechanisms underlying their dynamics. However, the majority of social interactions occur within groups of individuals, characterized by higher-order structures. The mechanisms driving group formation and the impact of higher-order interactions, which arise from group dynamics, o… ▽ More Modeling social systems as networks based on pairwise interactions between individuals offers valuable insights into the mechanisms underlying their dynamics. However, the majority of social interactions occur within groups of individuals, characterized by higher-order structures. The mechanisms driving group formation and the impact of higher-order interactions, which arise from group dynamics, on information spreading in face-to-face interaction networks remain insufficiently understood. In this study, we examine some representative human face-to-face interaction data and find the recurrent patterns of groups. Moreover, we extend the force-directed motion (FDM) model with the forces derived from similarity distances within a hidden space to reproduce the recurrent group patterns and many key properties of face-to-face interaction networks. Furthermore, we demonstrate that the FDM model effectively predicts information-spreading behaviors under higher-order interactions. Finally, our results reveal that the recurrence of triangular groups inhibits the spread of information in face-to-face interaction networks, and the higher-order interactions will make this phenomenon more pronounced. These findings represent a significant advancement in the understanding of group formation and may open new avenues for research into the effects of group interactions on information propagation processes. △ Less

Submitted 13 February, 2025; originally announced February 2025.

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Authors: Yingpeng Liu, Jiao Wu, Kesheng Xu, Muhua Zheng

Abstract: Healthy brain networks usually show highly efficient information communication and self-sustained oscillation abilities. However, how the brain network structure affects these dynamics after an injury (stroke) is not very clear. The recovery of structure and dynamics of stroke brain networks over time is still not known precisely. Based on the analysis of a large number of strokes' brain network d… ▽ More Healthy brain networks usually show highly efficient information communication and self-sustained oscillation abilities. However, how the brain network structure affects these dynamics after an injury (stroke) is not very clear. The recovery of structure and dynamics of stroke brain networks over time is still not known precisely. Based on the analysis of a large number of strokes' brain network data, we show that stroke changes the network properties in connection weights, average degree, clustering, community, etc. Yet, they will recover gradually over time to some extent. We then adopt a simplified reaction-diffusion model to investigate stroke patients' activation propagation and self-sustained oscillation abilities. Our results reveal that the stroke slows the adoption time across different brain scales, indicating a weakened brain's activation propagation ability. In addition, we show that the lifetime of self-sustained oscillatory patterns at three months post-stroke patients' brains significantly departs from the healthy one. Finally, we examine the properties of core networks of self-sustained oscillatory patterns, in which the directed edges denote the main pathways of activation propagation. Our results demonstrate that the lifetime and recovery of self-sustaining patterns are related to the properties of core networks, and the properties in the post-stroke greatly vary from those in the healthy group. Most importantly, the strokes' activation propagation and self-sustained oscillation abilities significantly improve at one year post-stroke, driven by structural connection repair. This work may help us to understand the relationship between structure and function in brain disorders. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Comments: 20 pages, 13 figures

Phase-field lattice Boltzmann method for two-phase electrohydrodynamic flows induced by Onsager-Wien effect

Authors: Mingzhen Zheng, Lei Wang, Fang Xiong, Jiangxu Huang, Kang Luo

Abstract: The leaky dielectric model is widely used in simulating two-phase electrohydrodynamic (EHD) flows. One critical issue with this classical model is the assumption of Ohmic conduction, which makes it inadequate for describing the newly discovered EHD flows caused by the Onsager-Wien effect [Ryu et al., Phys. Rev. Lett. 104, 104502 (2010)]. In this paper, we proposed a phase-field lattice Boltzmann (… ▽ More The leaky dielectric model is widely used in simulating two-phase electrohydrodynamic (EHD) flows. One critical issue with this classical model is the assumption of Ohmic conduction, which makes it inadequate for describing the newly discovered EHD flows caused by the Onsager-Wien effect [Ryu et al., Phys. Rev. Lett. 104, 104502 (2010)]. In this paper, we proposed a phase-field lattice Boltzmann (LB) method for two-phase electrohydrodynamic flows induced by the Onsager-Wien effect. In this scheme, two LB equations are employed to resolve the incompressible Navier-Stokes equations and the conservative Allen-Cahn equation, while another three LB equations are used for solving the charge conservation equations and the electric potential equation. After we validate the developed LB method, we perform a series of numerical simulations of droplet deformation under EHD conduction phenomena. Our numerical results indicate that the presence of the Onsager-Wien effect has a significant impact on droplet deformation and charge distribution. Also, it is interesting to note that, apart from the heterocharge layers near the electrodes, a charge cloud may form between the droplet interface and the electrode in some cases. To thoroughly understand the droplet dynamics, the effects of the reference length d, the applied voltage Δψ, the permittivity ratio εr, and the ionic mobility ratio μr on droplet deformation and charge distribution are all investigated in detail. △ Less

Submitted 8 January, 2025; originally announced January 2025.

P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching

Authors: Yaowei Jin, Qi Huang, Ziyang Song, Mingyue Zheng, Dan Teng, Qian Shi

Abstract: Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation. In this study, we have developed P2DFlow, a generative model based on SE(3) flow matching, to predict the structural ensembles of proteins. We specifically designed a valuable prior for the flow process and enhanced th… ▽ More Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation. In this study, we have developed P2DFlow, a generative model based on SE(3) flow matching, to predict the structural ensembles of proteins. We specifically designed a valuable prior for the flow process and enhanced the model's ability to distinguish each intermediate state by incorporating an additional dimension to describe the ensemble data, which can reflect the physical laws governing the distribution of ensembles, so that the prior knowledge can effectively guide the generation process. When trained and evaluated on the MD datasets of ATLAS, P2DFlow outperforms other baseline models on extensive experiments, successfully capturing the observable dynamic fluctuations as evidenced in crystal structure and MD simulations. As a potential proxy agent for protein molecular simulation, the high-quality ensembles generated by P2DFlow could significantly aid in understanding protein functions across various scenarios. Code is available at https://github.com/BLEACH366/P2DFlow △ Less

Submitted 3 March, 2025; v1 submitted 26 November, 2024; originally announced November 2024.

Azimuthal imaging of rock fractures by incorporating single borehole radar and optical data

Authors: Jian Shen, Liu Liu, Shaojun Li, Zhenming Shi, Yiteng Wang, Ming Peng, Minzong Zheng

Abstract: Single borehole radar detection suffers from azimuthal ambiguity, while borehole optical tests only provide information about the borehole wall. These limitations prevent either detection method from revealing the complete spatial patterns of rock fractures on their own. In this paper, we address these challenges by proposing a joint imaging method that combines the advantages of both borehole det… ▽ More Single borehole radar detection suffers from azimuthal ambiguity, while borehole optical tests only provide information about the borehole wall. These limitations prevent either detection method from revealing the complete spatial patterns of rock fractures on their own. In this paper, we address these challenges by proposing a joint imaging method that combines the advantages of both borehole detection methods. Geological azimuthal parameters are extracted from optical images by fitting the fracture curves to sinusoidal functions. A 2D Kirchhoff time migration is then implemented using radar common offset gather. Up-dip and down-dip events are separated by the f-k transform or z-s transform, depending on their geometric relation. The complete fracture planes, including trend, dip angle, gap width, and extension length, are finally reconstructed in 3D space by mapping the migration profile using azimuthal information from optical images. The method is proven reliable and high-resolution through both numerical tests and real field data. △ Less

Submitted 15 October, 2024; v1 submitted 15 October, 2024; originally announced October 2024.

Comments: 13 pages,5figures;Submitted to Journal of Geophysics and Engineering

The multiscale self-similarity of the weighted human brain connectome

Authors: Laia Barjuan, Muhua Zheng, M. Ángeles Serrano

Abstract: Anatomical connectivity between different regions in the brain can be mapped to a network representation, the connectome, where the intensities of the links, the weights, influence its structural resilience and the functional processes it sustains. Yet, many features associated with the weights in the human brain connectome are not fully understood, particularly their multiscale organization. In t… ▽ More Anatomical connectivity between different regions in the brain can be mapped to a network representation, the connectome, where the intensities of the links, the weights, influence its structural resilience and the functional processes it sustains. Yet, many features associated with the weights in the human brain connectome are not fully understood, particularly their multiscale organization. In this paper, we elucidate the architecture of weights, including weak ties, in multiscale hierarchical human brain connectomes reconstructed from empirical data. Our findings reveal multiscale self-similarity in the weighted statistical properties, including the ordering of weak ties, that remain consistent across the analyzed length scales of every individual and the group representatives. This phenomenon is effectively captured by a renormalization of the weighted structure applied to hyperbolic embeddings of the connectomes, based on a unique weighted geometric model that integrates links of all weights across all length scales. This eliminates the need for separate generative weighted connectivity rules for each scale or to replicate weak and strong ties at specific scales in brain connectomes. The observed symmetry represents a distinct signature of criticality in the weighted connectivity of human brain connectomes, aligning with the fractality observed in their topology, and raises important questions for future research, like the existence of a resolution threshold where the observed symmetry breaks, or whether it is preserved in cases of neurodegenerative disease or psychiatric disorder. △ Less

Submitted 9 October, 2024; originally announced October 2024.

Journal ref: PLOS Computational Biology 21(4):e1012848 (2025)

Quantum DeepONet: Neural operators accelerated by quantum computing

Authors: Pengpeng Xiao, Muqing Zheng, Anran Jiao, Xiu Yang, Lu Lu

Abstract: In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new co… ▽ More In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerning input dimensions during evaluation. Given the progress in quantum algorithms and hardware, here we propose to utilize quantum computing to accelerate DeepONet evaluations, yielding complexity that is linear in input dimensions. Our proposed quantum DeepONet integrates unary encoding and orthogonal quantum layers. We benchmark our quantum DeepONet using a variety of PDEs, including the antiderivative operator, advection equation, and Burgers' equation. We demonstrate the method's efficacy in both ideal and noisy conditions. Furthermore, we show that our quantum DeepONet can also be informed by physics, minimizing its reliance on extensive data collection. Quantum DeepONet will be particularly advantageous in applications in outer loop problems which require exploring parameter space and solving the corresponding PDEs, such as uncertainty quantification and optimal experimental design. △ Less

Submitted 30 May, 2025; v1 submitted 23 September, 2024; originally announced September 2024.

Comments: 29 pages, 9 figures

Journal ref: Quantum 9, 1761 (2025)

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner

Authors: Wanyu Bian, Panfeng Li, Mengyao Zheng, Chihang Wang, Anying Li, Ying Li, Haowei Ni, Zixuan Zeng

Abstract: This paper analyzes conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We compare traditional analytical and adaptive techniques with advanced deep learning approaches. Key strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination, such as external EMI receiver coils, are discussed alongside deep l… ▽ More This paper analyzes conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We compare traditional analytical and adaptive techniques with advanced deep learning approaches. Key strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination, such as external EMI receiver coils, are discussed alongside deep learning methods, which show superior EMI suppression by leveraging neural networks trained on MRI data. While deep learning improves EMI elimination and diagnostic capabilities, it introduces security and safety concerns, particularly in commercial applications. A balanced approach, integrating conventional reliability with deep learning's advanced capabilities, is proposed for more effective EMI suppression in MRI systems. △ Less

Submitted 13 November, 2024; v1 submitted 22 June, 2024; originally announced June 2024.

Comments: Accepted by 2024 5th International Conference on Machine Learning and Computer Application

Journal ref: Proceedings of the 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), 2024, pp. 614-618

An alkali-referenced vector spectrum analyzer for visible-light integrated photonics

Authors: Baoqi Shi, Ming-Yang Zheng, Yunkai Zhao, Yi-Han Luo, Jinbao Long, Wei Sun, Wenbo Ma, Xiu-Ping Xie, Lan Gao, Chen Shen, Anting Wang, Wei Liang, Qiang Zhang, Junqiu Liu

Abstract: Integrated photonics has reformed our information society by offering on-chip optical signal synthesis, processing and detection with reduced size, weight and power consumption. As such, it has been successfully established in the near-infrared (NIR) telecommunication bands. With the soaring demand in miniaturized systems for biosensing, quantum information and transportable atomic clocks, extensi… ▽ More Integrated photonics has reformed our information society by offering on-chip optical signal synthesis, processing and detection with reduced size, weight and power consumption. As such, it has been successfully established in the near-infrared (NIR) telecommunication bands. With the soaring demand in miniaturized systems for biosensing, quantum information and transportable atomic clocks, extensive endeavors have been stacked on translating integrated photonics into the visible spectrum, i.e. visible-light integrated photonics. Various innovative visible-light integrated devices have been demonstrated, such as lasers, frequency combs, and atom traps, highlighting the capacity and prospect to create chip-based optical atomic clocks that can make timing and frequency metrology ubiquitous. A pillar to the development of visible-light integrated photonics is characterization techniques featuring high frequency resolution and wide spectral coverage, which however remain elusive. Here, we demonstrate a vector spectrum analyzer (VSA) for visible-light integrated photonics, offering spectral bandwidth from 766 to 795 nm and frequency resolution of 415 kHz. The VSA is rooted on a widely chirping, high-power, narrow-linewidth, mode-hop-free laser around 780 nm, which is frequency-doubled from the near-infrared via an efficient, broadband CPLN waveguide. The VSA is further referenced to hyperfine structures of rubidium and potassium atoms, enabling 8.1 MHz frequency accuracy. We apply our VSA to showcase the characterization of loss, dispersion and phase response of passive integrated devices, as well as densely spaced spectra of mode-locked lasers. Combining operation in the NIR and visible spectra, our VSA allows characterization bandwidth exceeding an octave and can be an invaluable diagnostic tool for spectroscopy, nonlinear optical processing, imaging and quantum interfaces to atomic devices. △ Less

Submitted 19 June, 2024; originally announced June 2024.

Journal ref: Nat Commun 16, 7025 (2025)

Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks

Authors: Yuxuan Song, Jingjing Gong, Yanru Qu, Hao Zhou, Mingyue Zheng, Jingjing Liu, Wei-Ying Ma

Abstract: Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geom… ▽ More Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geometry by modeling diverse modalities in the differentiable parameter space of distributions. GeoBFN maintains the SE-(3) invariant density modeling property by incorporating equivariant inter-dependency modeling on parameters of distributions and unifying the probabilistic modeling of different modalities. Through optimized training and sampling techniques, we demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D molecule generation benchmarks in terms of generation quality (90.87% molecule stability in QM9 and 85.6% atom stability in GEOM-DRUG. GeoBFN can also conduct sampling with any number of steps to reach an optimal trade-off between efficiency and quality (e.g., 20-times speedup without sacrificing performance). △ Less

Submitted 17 March, 2024; originally announced March 2024.

Comments: ICLR 2024

Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

Authors: Yaping Qi, Dan Hu, Zhenping Wu, Ming Zheng, Guanghui Cheng, Yucheng Jiang, Yong P. Chen

Abstract: Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extracti… ▽ More Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis. △ Less

Submitted 3 December, 2023; originally announced December 2023.

A multinode quantum network over a metropolitan area

Authors: Jian-Long Liu, Xi-Yu Luo, Yong Yu, Chao-Yang Wang, Bin Wang, Yi Hu, Jun Li, Ming-Yang Zheng, Bo Yao, Zi Yan, Da Teng, Jin-Wei Jiang, Xiao-Bing Liu, Xiu-Ping Xie, Jun Zhang, Qing-He Mao, Xiao Jiang, Qiang Zhang, Xiao-Hui Bao, Jian-Wei Pan

Abstract: Towards realizing the future quantum internet, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive, multi-node setups on large scales. Here, we report on the debut implementation of a multi-node entanglement-based quantum network over a metropolitan area. We equipped three quantum nodes with atomic quantum memories and… ▽ More Towards realizing the future quantum internet, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive, multi-node setups on large scales. Here, we report on the debut implementation of a multi-node entanglement-based quantum network over a metropolitan area. We equipped three quantum nodes with atomic quantum memories and their telecom interfaces, and combined them into a scalable phase-stabilized architecture through a server node. We demonstrated heralded entanglement generation between two quantum nodes situated 12.5 km apart, and the storage of entanglement exceeding the round-trip communication time. We also showed the concurrent entanglement generation on three links. Our work provides a metropolitan-scale testbed for the evaluation and exploration of multi-node quantum network protocols and starts a new stage of quantum internet research. △ Less

Submitted 31 August, 2023; originally announced September 2023.

Comments: 21 pages in total, 4 figures and 1 table in the main text, 5 figures and 8 tables in the supplementary material

Journal ref: Nature 629, 579-585 (2024)

Adaptive link dynamics drive online hate networks and their mainstream influence

Authors: Minzhang Zheng, Richard Sear, Lucia Illari, Nicholas J. Restrepo, Neil F. Johnson

Abstract: Online hate is dynamic, adaptive -- and is now surging armed with AI/GPT tools. Its consequences include personal traumas, child sex abuse and violent mass attacks. Overcoming it will require knowing how it operates at scale. Here we present this missing science and show that it contradicts current thinking. Waves of adaptive links connect the hate user base over time across a sea of smaller platf… ▽ More Online hate is dynamic, adaptive -- and is now surging armed with AI/GPT tools. Its consequences include personal traumas, child sex abuse and violent mass attacks. Overcoming it will require knowing how it operates at scale. Here we present this missing science and show that it contradicts current thinking. Waves of adaptive links connect the hate user base over time across a sea of smaller platforms, allowing hate networks to steadily strengthen, bypass mitigations, and increase their direct influence on the massive neighboring mainstream. The data suggests 1 in 10 of the global population have recently been exposed, including children. We provide governing dynamical equations derived from first principles. A tipping-point condition predicts more frequent future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as illustrations, we show our findings provide abiding insights and quantitative predictions down to the hourly scale. The expected impacts of proposed mitigations can now be reliably predicted for the first time. △ Less

Submitted 24 July, 2023; originally announced July 2023.

Geometric renormalization of weighted networks

Authors: Muhua Zheng, Guillermo García-Pérez, Marián Boguñá, M. Ángeles Serrano

Abstract: The geometric renormalization technique for complex networks has successfully revealed the multiscale self-similarity of real network topologies and can be applied to generate replicas at different length scales. In this letter, we extend the geometric renormalization framework to weighted networks, where the intensities of the interactions play a crucial role in their structural organization and… ▽ More The geometric renormalization technique for complex networks has successfully revealed the multiscale self-similarity of real network topologies and can be applied to generate replicas at different length scales. In this letter, we extend the geometric renormalization framework to weighted networks, where the intensities of the interactions play a crucial role in their structural organization and function. Our findings demonstrate that weights in real networks exhibit multiscale self-similarity under a renormalization protocol that selects the connections with the maximum weight across increasingly longer length scales. We present a theory that elucidates this symmetry, and that sustains the selection of the maximum weight as a meaningful procedure. Based on our results, scaled-down replicas of weighted networks can be straightforwardly derived, facilitating the investigation of various size-dependent phenomena in downstream applications. △ Less

Submitted 3 July, 2023; originally announced July 2023.

Comments: 5 pages, 3 figures

Computational 3D topographic microscopy from terabytes of data per sample

Authors: Kevin C. Zhou, Mark Harfouche, Maxwell Zheng, Joakim Jönsson, Kyung Chul Lee, Ron Appel, Paul Reamey, Thomas Doman, Veton Saliu, Gregor Horstmeyer, Roarke Horstmeyer

Abstract: We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across $>$110 cm$^2$ areas over multi-millimeter axial ranges. Our computational microscope, termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope), features a parallelized, 54-camera architecture with 3-axis… ▽ More We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across $>$110 cm$^2$ areas over multi-millimeter axial ranges. Our computational microscope, termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope), features a parallelized, 54-camera architecture with 3-axis translation to capture, for each sample of interest, a multi-dimensional, 2.1-terabyte (TB) dataset, consisting of a total of 224,640 9.4-megapixel images. We developed a self-supervised neural network-based algorithm for 3D reconstruction and stitching that jointly estimates an all-in-focus photometric composite and 3D height map across the entire field of view, using multi-view stereo information and image sharpness as a focal metric. The memory-efficient, compressed differentiable representation offered by the neural network effectively enables joint participation of the entire multi-TB dataset during the reconstruction process. To demonstrate the broad utility of our new computational microscope, we applied STARCAM to a variety of decimeter-scale objects, with applications ranging from cultural heritage to industrial inspection. △ Less

Submitted 5 June, 2023; originally announced June 2023.

Additively manufactured polyethylene terephthalate scaffolds for Scapholunate Interosseous Ligament Reconstruction

Authors: M. Natividad Gomez-Cerezo, Nataliya Perevoshchikova, Rui Ruan, Kevin M. Moerman, Randy Bindra, David G. Lloyd, Ming Hao Zheng, David J. Saxby, Cedryck Vaquette

Abstract: The regeneration of the ruptured scapholunate interosseous ligament (SLIL) represents a clinical challenge. Here, we propose the use of a Bone-Ligament-Bone (BLB) 3D-printed polyethylene terephthalate (PET) scaffold for achieving mechanical stabilisation of the scaphoid and lunate following SLIL rupture. The BLB scaffold featured two bone compartments bridged by aligned fibres (ligament compartmen… ▽ More The regeneration of the ruptured scapholunate interosseous ligament (SLIL) represents a clinical challenge. Here, we propose the use of a Bone-Ligament-Bone (BLB) 3D-printed polyethylene terephthalate (PET) scaffold for achieving mechanical stabilisation of the scaphoid and lunate following SLIL rupture. The BLB scaffold featured two bone compartments bridged by aligned fibres (ligament compartment) mimicking the architecture of the native tissue. The scaffold presented tensile stiffness in the range of 260+/-38 N/mm and ultimate load of 113+/-13 N, which would support physiological loading. A finite element analysis, using inverse finite element analysis for material property identification, showed an adequate fit between simulation and experimental data. The scaffold was then biofunctionalized using two different methods: injected with a Gelatin Methacryloyl solution containing human mesenchymal stem cell spheroids or seeded with tendon-derived stem cells and placed in a bioreactor to undergo cyclic deformation. The first approach demonstrated high cell viability, as cells migrated out of the spheroid and colonised the interstitial space of the scaffold. These cells adopted an elongated morphology suggesting the internal architecture of the scaffold exerted topographical guidance. The second method demonstrated the high resilience of the scaffold to cyclic deformation and the secretion of a fibroblastic related protein was enhanced by the mechanical stimulation. This process promoted the expression of relevant proteins, such as Tenomodulin, indicating mechanical stimulation may enhance cell differentiation and be useful prior to surgical implantation. In conclusion, the PET scaffold presented several promising characteristics for the immediate mechanical stabilisation of disassociated scaphoid and lunate and, in the longer-term, the regeneration of the ruptured SLIL. △ Less

Submitted 18 April, 2023; originally announced April 2023.

Journal ref: Biomaterials Advances, 149, (2023), 213397

Applications of Raman Spectroscopy in Clinical Medicine

Authors: Yaping Qi, Esther Xinyi Chen, Dan Hu, Ying Yang, Zhenping Wu, Ming Zheng, Mohammad A. Sadi, Yucheng Jiang, Kang Zhang, Zi Chen, Yong P. Chen

Abstract: Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, an… ▽ More Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research. △ Less

Submitted 16 April, 2023; originally announced April 2023.

Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second

Authors: Kevin C. Zhou, Mark Harfouche, Colin L. Cooke, Jaehee Park, Pavan C. Konda, Lucas Kreiss, Kanghyun Kim, Joakim Jönsson, Jed Doman, Paul Reamey, Veton Saliu, Clare B. Cook, Maxwell Zheng, Jack P. Bechtel, Aurélien Bègue, Matthew McCarroll, Jennifer Bagwell, Gregor Horstmeyer, Michel Bagnat, Roarke Horstmeyer

Abstract: To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simulta… ▽ More To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae. △ Less

Submitted 19 January, 2023; originally announced January 2023.

Shockwaves and turbulence across social media

Authors: Pedro D. Manrique, Frank Huo, Sara El Oud, Minzhang Zheng, Lucia Illari, Neil F. Johnson

Abstract: Online communities featuring 'anti-X' hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scal… ▽ More Online communities featuring 'anti-X' hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwave-like solutions explain how, why and when such activity rises from 'out-of-nowhere', and show how it can be delayed, re-shaped and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses. △ Less

Submitted 25 October, 2022; originally announced October 2022.

Comments: Feedback welcome to neiljohnson@gwu.edu

Quantum frequency conversion and single-photon detection with lithium niobate nanophotonic chips

Authors: Xina Wang, Xufeng Jiao, Bin Wang, Yang Liu, Xiu-Ping Xie, Ming-Yang Zheng, Qiang Zhang, Jian-Wei Pan

Abstract: In the past few years, the lithium niobate on insulator (LNOI) platform has revolutionized lithium niobate materials, and a series of quantum photonic chips based on LNOI have shown unprecedented performances. Quantum frequency conversion (QFC) photonic chips, which enable quantum state preservation during frequency tuning, are crucial in quantum technology. In this work, we demonstrate a low-nois… ▽ More In the past few years, the lithium niobate on insulator (LNOI) platform has revolutionized lithium niobate materials, and a series of quantum photonic chips based on LNOI have shown unprecedented performances. Quantum frequency conversion (QFC) photonic chips, which enable quantum state preservation during frequency tuning, are crucial in quantum technology. In this work, we demonstrate a low-noise QFC process on an LNOI nanophotonic platform designed to connect telecom and near-visible bands with sum-frequency generation by long-wavelength pumping. An internal conversion efficiency of 73% and an on-chip noise count rate of 900 counts per second (cps) are achieved. Moreover, the on-chip preservation of quantum statistical properties is verified, showing that the QFC chip is promising for extensive applications of LNOI integrated circuits in quantum information. Based on the QFC chip, we construct an upconversion single-photon detector with the sum-frequency output spectrally filtered and detected by a silicon single-photon avalanche photodiode, demonstrating the feasibility of an upconversion single-photon detector on-chip with a detection efficiency of 8.7% and a noise count rate of 300 cps. The realization of a low-noise QFC device paves the way for practical chip-scale QFC-based quantum systems in heterogeneous configurations. △ Less

Submitted 18 September, 2022; originally announced September 2022.

Comments: 8pages, 6 figures, 1 table

Journal ref: npj Quantum Inf 9, 38 (2023)

Noise-induce coexisting firing patterns in hybrid-synaptic interacting networks

Authors: Xinyi Wang, Xiyun Zhang, Muhua Zheng, Leijun Xu, Kesheng Xu

Abstract: Synaptic noise plays a major role in setting up coexistence of various firing patterns, but the precise mechanisms whereby these synaptic noise contributes to coexisting firing activities are subtle and remain elusive. To investigate these mechanisms, neurons with hybrid synaptic interaction in a balanced neuronal networks have been recently put forward. Here we show that both synaptic noise inten… ▽ More Synaptic noise plays a major role in setting up coexistence of various firing patterns, but the precise mechanisms whereby these synaptic noise contributes to coexisting firing activities are subtle and remain elusive. To investigate these mechanisms, neurons with hybrid synaptic interaction in a balanced neuronal networks have been recently put forward. Here we show that both synaptic noise intensity and excitatory weights can make a greater contribution than variance of synaptic noise to the coexistence of firing states with slight modification parameters. The resulting statistical analysis of both voltage trajectories and their spike trains reveals two forms of coexisting firing patterns: time-varying and parameter-varying multistability. The emergence of time-varying multistability as a format of metstable state has been observed under suitable parameters settings of noise intensity and excitatory synaptic weight. While the parameter-varying multistability is accompanied by coexistence of synchrony state and metastable (or asynchronous firing state) with slightly varying noise intensity and excitatory weights. Our results offer a series of precise statistical explanation of the intricate effect of synaptic noise in neural multistability. This reconciles previous theoretical and numerical works, and confirms the suitability of various statistical methods to investigate multistability in a hybrid synaptic interacting neuronal networks. △ Less

Submitted 10 April, 2022; originally announced April 2022.

Comments: 15 pages,8 figures

Computational Understandings of the Cation Configuration Dependent Redox Activities and Oxygen Dimerizations in Li$_{1.22}$Ni$_{0.22}$Mn$_{0.56}$O$_2$ Cathode

Authors: Zhenming Xu, Yongyao Xia, Mingbo Zheng

Abstract: Understanding the lattice oxygen dimerization is quite essential for the optimal design for the Li-rich Mn-based cathode materials. In this work, based on the density functional theory (DFT) calculations, a Ni-honeycomb Li-Ni-Mn cation configuration for Li$_{1.22}$Ni$_{0.22}$Mn$_{0.56}$O$_2$ cathode was carefully proposed and examined, which can coexist with the well-known Li-honeycomb structure i… ▽ More Understanding the lattice oxygen dimerization is quite essential for the optimal design for the Li-rich Mn-based cathode materials. In this work, based on the density functional theory (DFT) calculations, a Ni-honeycomb Li-Ni-Mn cation configuration for Li$_{1.22}$Ni$_{0.22}$Mn$_{0.56}$O$_2$ cathode was carefully proposed and examined, which can coexist with the well-known Li-honeycomb structure in the experimentally synthesized Li$_{1.2}$Ni$_{0.2}$Mn$_{0.6}$O$_2$ samples. Li-Ni-Mn cation configurations have significant impacts on oxygen redox activities and oxygen dimerizations in the delithiated Li$_x$Ni$_{0.22}$Mn$_{0.56}$O$_2$. There is no necessary consistency between the high lattice oxygen redox activity and easy oxygen dimerization, such as the Li-honeycomb structures showing higher redox activities and higher activation energy barriers to prohibit oxygen dimerizations than Ni-honeycomb structures. Avoiding the Ni-honeycomb structures with more favorable lattice oxygen dimerization and making full use of the Li-honeycomb structures with better redox activities is important to optimally design the high-performance Li-rich Mn-based cathode materials. △ Less

Submitted 12 April, 2022; v1 submitted 7 April, 2022; originally announced April 2022.

Entangling metropolitan-distance separated quantum memories

Authors: Xi-Yu Luo, Yong Yu, Jian-Long Liu, Ming-Yang Zheng, Chao-Yang Wang, Bin Wang, Jun Li, Xiao Jiang, Xiu-Ping Xie, Qiang Zhang, Xiao-Hui Bao, Jian-Wei Pan

Abstract: Quantum internet gives the promise of getting all quantum resources connected, and it will enable applications far beyond a localized scenario. A prototype is a network of quantum memories that are entangled and well separated. Previous realizations are limited in the distance. In this paper, we report the establishment of remote entanglement between two atomic quantum memories physically separate… ▽ More Quantum internet gives the promise of getting all quantum resources connected, and it will enable applications far beyond a localized scenario. A prototype is a network of quantum memories that are entangled and well separated. Previous realizations are limited in the distance. In this paper, we report the establishment of remote entanglement between two atomic quantum memories physically separated by 12.5 km directly in a metropolitan area. We create atom-photon entanglement in one node and send the photon to a second node for storage. We harness low-loss transmission through a field-deployed fiber of 20.5 km by making use of frequency down-conversion and up-conversion. The final memory-memory entanglement is verified to have a fidelity of 90% via retrieving to photons. Our experiment paves the way to study quantum network applications in a practical scenario. △ Less

Submitted 28 January, 2022; originally announced January 2022.

Comments: 9 pages in total, 4 figures in the main text, and 5 figures in the supplementary material

Journal ref: Phys. Rev. Lett. 129, 050503 (2022)

Radioactivity control strategy for the JUNO detector

Authors: JUNO collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Antonio Bergnoli, Thilo Birkenfeld, Sylvie Blin , et al. (578 additional authors not shown)

Abstract: JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particula… ▽ More JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz in the default fiducial volume, above an energy threshold of 0.7 MeV. △ Less

Submitted 13 October, 2021; v1 submitted 8 July, 2021; originally announced July 2021.

Comments: 35 pages, 12 figures

Non-line-of-sight imaging with picosecond temporal resolution

Authors: Bin Wang, Ming-Yang Zheng, Jin-Jian Han, Xin Huang, Xiu-Ping Xie, Feihu Xu, Qiang Zhang, Jian-Wei Pan

Abstract: Non-line-of-sight (NLOS) imaging enables monitoring around corners and is promising for diverse applications. The resolution of transient NLOS imaging is limited to a centimeter scale, mainly by the temporal resolution of the detectors. Here, we construct an up-conversion single-photon detector with a high temporal resolution of ~1.4 ps and a low noise count rate of 5 counts per second (cps). Nota… ▽ More Non-line-of-sight (NLOS) imaging enables monitoring around corners and is promising for diverse applications. The resolution of transient NLOS imaging is limited to a centimeter scale, mainly by the temporal resolution of the detectors. Here, we construct an up-conversion single-photon detector with a high temporal resolution of ~1.4 ps and a low noise count rate of 5 counts per second (cps). Notably, the detector operates at room temperature, near-infrared wavelength. Using this detector, we demonstrate high-resolution and low-noise NLOS imaging. Our system can provide a 180 μm axial resolution and a 2 mm lateral resolution, which is more than one order of magnitude better than that in previous experiments. These results open avenues for high-resolution NLOS imaging techniques in relevant applications. △ Less

Submitted 29 June, 2021; originally announced June 2021.

Comments: 6 pages, 4 figures

Journal ref: Phys.Rev.Lett. 127 (2021) 053602

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

Authors: Peiyuan Gao, Xiu Yang, Yu-Hang Tang, Muqing Zheng, Amity Anderson, Vijayakumar Murugesan, Aaron Hollas, Wei Wang

Abstract: The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression me… ▽ More The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined. △ Less

Submitted 15 June, 2021; originally announced June 2021.

Electronic transport property of PbS nanowire devices

Authors: Jiarui Yin, Ming Zheng, Kuo Hai

Abstract: Lead sulfide is an important photosensitive material, and its photoelectric properties have received widespread attention. We completed the preparation of PbS nanowires and single PbS nanowire devices, then we conducted electrical performance tests on the PbS nanowire devices. We found that a single lead sulfide nanowire device exhibits memristive properties that depend on the applied voltage and… ▽ More Lead sulfide is an important photosensitive material, and its photoelectric properties have received widespread attention. We completed the preparation of PbS nanowires and single PbS nanowire devices, then we conducted electrical performance tests on the PbS nanowire devices. We found that a single lead sulfide nanowire device exhibits memristive properties that depend on the applied voltage and the power density of light. From this we studied the electrical transport properties of single lead sulfide nanodevices. △ Less

Submitted 30 September, 2023; v1 submitted 26 May, 2021; originally announced May 2021.

Comments: We have updated the experimental and theoretical interpretation

The Design and Sensitivity of JUNO's scintillator radiopurity pre-detector OSIRIS

Authors: JUNO Collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Guangpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Antonio Bergnoli, Thilo Birkenfeld , et al. (582 additional authors not shown)

Abstract: The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $10^{-16}$ g/g of… ▽ More The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $10^{-16}$ g/g of $^{238}$U and $^{232}$Th requires a large ($\sim$20 m$^3$) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup. △ Less

Submitted 31 March, 2021; originally announced March 2021.

Comments: 32 pages, 22 figures

Epidemic spreading under mutually independent intra- and inter-host pathogen evolution

Authors: Xiyun Zhang, Zhongyuan Ruan, Muhua Zheng, Jie Zhou, Stefano Boccaletti, Baruch Barzel

Abstract: The dynamics of epidemic spreading is often reduced to the single control parameter $R_0$, whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, $R_0$ may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of t… ▽ More The dynamics of epidemic spreading is often reduced to the single control parameter $R_0$, whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, $R_0$ may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the network spreading patterns with the intra-host evolutionary dynamics. For many pathogens these two processes, intra- and inter-host, are driven by different selection forces. And yet here we show that even in the extreme case when these two forces are mutually independent, mutations can still fundamentally alter the pandemic phase-diagram, whose transitions are now shaped, not just by $R_0$, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can break through to gain widespread prevalence. △ Less

Submitted 4 November, 2022; v1 submitted 19 February, 2021; originally announced February 2021.

Journal ref: Nat Commun 13, 6218 (2022)

Improved Spatial Resolution Achieved by Chromatic Intensity Interferometry

Authors: Lu-Chuan Liu, Luo-Yuan Qu, Cheng Wu, Jordan Cotler, Fei Ma, Ming-Yang Zheng, Xiu-Ping Xie, Yu-Ao Chen, Qiang Zhang, Frank Wilczek, Jian-Wei Pan

Abstract: Interferometers are widely used in imaging technologies to achieve enhanced spatial resolution, but require that the incoming photons be indistinguishable. In previous work, we built and analyzed color erasure detectors which expand the scope of intensity interferometry to accommodate sources of different colors. Here we experimentally demonstrate how color erasure detectors can achieve improved s… ▽ More Interferometers are widely used in imaging technologies to achieve enhanced spatial resolution, but require that the incoming photons be indistinguishable. In previous work, we built and analyzed color erasure detectors which expand the scope of intensity interferometry to accommodate sources of different colors. Here we experimentally demonstrate how color erasure detectors can achieve improved spatial resolution in an imaging task, well beyond the diffraction limit. Utilizing two 10.9 mm-aperture telescopes and a 0.8 m baseline, we measure the distance between a 1063.6 nm source and a 1064.4 nm source separated by 4.2 mm at a distance of 1.43 km, which surpasses the diffraction limit of a single telescope by about 40 times. Moreover, chromatic intensity interferometry allows us to recover the phase of the Fourier transform of the imaged objects - a quantity that is, in the presence of modest noise, inaccessible to conventional intensity interferometry. △ Less

Submitted 3 February, 2021; originally announced February 2021.

Comments: 5 pages, 3 figures

Journal ref: Phys. Rev. Lett. 127, 103601 (2021)

Surface Electromyography as a Natural Human-Machine Interface: A Review

Authors: Mingde Zheng, Michael S. Crouch, Michael S. Eggleston

Abstract: Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the last two decades, with significant advances in both the hardware and signal processing methods used to collect and analyze sEMG signals. While early work focuse… ▽ More Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the last two decades, with significant advances in both the hardware and signal processing methods used to collect and analyze sEMG signals. While early work focused mainly on medical applications, there has been growing interest in utilizing sEMG as a sensing modality to enable next-generation, high-bandwidth, and natural human-machine interfaces. In the first part of this review, we briefly overview the human skeletomuscular physiology that gives rise to sEMG signals followed by a review of developments in sEMG acquisition hardware. Special attention is paid towards the fidelity of these devices as well as form factor, as recent advances have pushed the limits of user comfort and high-bandwidth acquisition. In the second half of the article, we explore work quantifying the information content of natural human gestures and then review the various signal processing and machine learning methods developed to extract information in sEMG signals. Finally, we discuss the future outlook in this field, highlighting the key gaps in current methods to enable seamless natural interactions between humans and machines. △ Less

Submitted 5 April, 2021; v1 submitted 12 January, 2021; originally announced January 2021.

Comments: 30 pages, 11 figures, preprint v2: Update author name, add references

Calibration Strategy of the JUNO Experiment

Authors: JUNO collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Guangpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Eric Baussan, Marco Bellato, Antonio Bergnoli, Enrico Bernieri, Thilo Birkenfeld , et al. (571 additional authors not shown)

Abstract: We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector ca… ▽ More We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector can achieve a better than 1% energy linearity and a 3% effective energy resolution, required by the neutrino mass ordering determination. △ Less

Submitted 20 January, 2021; v1 submitted 12 November, 2020; originally announced November 2020.

Full description of dipole orientation in organic light-emitting diodes

Authors: Lingjie Fan, Tongyu Li, Jiao Chu, Maoxiong Zhao, Tangyao Shen, Minjia Zheng, Fang Guan, Haiwei Yin, Lei Shi, Jian Zi

Abstract: Considerable progress has been made in organic light-emitting diodes (OLEDs) to achieve high external quantum efficiency (EQE), among which the dipole orientation of OLED emitters has a remarkable effect. In most cases, EQE of the OLED emitter is theoretically predicted with only one orientation factor to match with corresponding experiments. Here, we develop a distribution theory with three indep… ▽ More Considerable progress has been made in organic light-emitting diodes (OLEDs) to achieve high external quantum efficiency (EQE), among which the dipole orientation of OLED emitters has a remarkable effect. In most cases, EQE of the OLED emitter is theoretically predicted with only one orientation factor to match with corresponding experiments. Here, we develop a distribution theory with three independent parameters to fully describe the relationship between dipole orientations and power densities. Furthermore, we propose an optimal experiment configuration for measuring such distribution parameters. Measuring the unpolarized spectrum can dig more information of dipole orientation distributions with a rather simple way. Our theory provides a universal plot of the OLED dipole orientation, paving the way for designing more complicated OLED structures. △ Less

Submitted 23 August, 2021; v1 submitted 22 September, 2020; originally announced September 2020.

Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector

Authors: Daya Bay, JUNO collaborations, :, A. Abusleme, T. Adam, S. Ahmad, S. Aiello, M. Akram, N. Ali, F. P. An, G. P. An, Q. An, G. Andronico, N. Anfimov, V. Antonelli, T. Antoshkina, B. Asavapibhop, J. P. A. M. de André, A. Babic, A. B. Balantekin, W. Baldini, M. Baldoncini, H. R. Band, A. Barresi, E. Baussan , et al. (642 additional authors not shown)

Abstract: To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were… ▽ More To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were increased in 12 steps from 0.5 g/L and <0.01 mg/L to 4 g/L and 13 mg/L, respectively. The numbers of total detected photoelectrons suggest that, with the optically purified solvent, the bis-MSB concentration does not need to be more than 4 mg/L. To bridge the one order of magnitude in the detector size difference between Daya Bay and JUNO, the Daya Bay data were used to tune the parameters of a newly developed optical model. Then, the model and tuned parameters were used in the JUNO simulation. This enabled to determine the optimal composition for the JUNO LS: purified solvent LAB with 2.5 g/L PPO, and 1 to 4 mg/L bis-MSB. △ Less

Submitted 1 July, 2020; originally announced July 2020.

Comments: 13 pages, 8 figures