Showing 1–50 of 84 results for author: Ghasemi, A

Row-Local Spectral Certificates and Analog Courant Metrics for Finite-Bandwidth Feedback-Coupled Solvers

Authors: Arash Ghasemi

Abstract: This paper identifies a dynamical constraint on analog-computing approaches in which a row of the matrix is represented by an impedance network. It shows that the fastest normalized mode is no more than 2$π$ times the largest combined unity-gain bandwidth (CUGBW) among all the circuit rows. The CUGBW of a row equals its finite-gain-adjusted unity-gain bandwidth plus the contributions of all rows c… ▽ More This paper identifies a dynamical constraint on analog-computing approaches in which a row of the matrix is represented by an impedance network. It shows that the fastest normalized mode is no more than 2$π$ times the largest combined unity-gain bandwidth (CUGBW) among all the circuit rows. The CUGBW of a row equals its finite-gain-adjusted unity-gain bandwidth plus the contributions of all rows coupled to it. Each contribution is the square root of the product of the two rows' unity-gain bandwidths multiplied by their coupling conductance and divided by the square root of the product of their total conductance loadings. This bound plays a role analogous to the Courant-number restriction in time-stepping methods by limiting the operator rates that analog hardware can physically represent and resolve at its outputs. It is shown that in mixed-signal approach, the Courant metrics become stability criteria, and they must be less than 2 in order for the solver to remain stable. The theory is validated using large-scale LTspice simulations across architectures ranging from CMOS to thermionic vacuum-tube circuits. The benchmark circuits implement a one-dimensional heat equation, a graph-based semi-supervised learning problem, and a graph-regularized regression. △ Less

Submitted 14 August, 2026; v1 submitted 29 July, 2026; originally announced July 2026.

Comments: Revised Version of the Original Manuscript

AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation

Authors: D. -M. Mei, K. Acharya, C. M. Adhikari, M. Adhikari, S. Aryal, B. V. Benson, K. Bhatta, S. Bhattarai, N. Budhathoki, A. M. Castillo, D. Chakraborty, S. Chhetri, S. Choudhury, T. A. Chowdhury, R. D. Cruz, B. Cui, S. Dhital, K. -M. Dong, R. Gapuz, A. Ghasemi, E. Z. Gnimpieba, B. D. S. Gurung, H. A. Hashim, R. I. Harry, K. -E. Hasin , et al. (29 additional authors not shown)

Abstract: Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological response, manufacturability, safety, and governance constraints. Existing materials and biomedical data ecosystems are powerful but remain poorly coupled for AI-guided discovery. Here we present AIMBio, a conceptual framework for an AI-native, FAIR, and g… ▽ More Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological response, manufacturability, safety, and governance constraints. Existing materials and biomedical data ecosystems are powerful but remain poorly coupled for AI-guided discovery. Here we present AIMBio, a conceptual framework for an AI-native, FAIR, and governance-aware decision layer that links materials provenance, biomedical context, knowledge graphs, uncertainty-aware machine learning, and human-in-the-loop active learning. The framework formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty and introduces practical requirements for metadata, model documentation, risk-tiered governance, evaluation metrics, and phased implementation. To make the roadmap testable, we add a minimum viable prototype specification and a worked pilot for AI-guided nanomaterials for drug delivery. AIMBio is positioned as exploratory and preclinical discovery infrastructure, not as clinical decision-support software; any clinical or regulated-device use would require separate validation, change control, and regulatory review. The central contribution is a publishable platform blueprint for converting fragmented materials and biomedical records into auditable, experimentally actionable, and translationally responsible discovery workflows. △ Less

Submitted 20 May, 2026; originally announced May 2026.

Comments: 35 pages, 4 figures, and 12 tables

Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains

Authors: D. -M. Mei, K. Acharya, C. M. Adhikari, M. Adhikari, S. Aryal, B. V. Benson, K. Bhatta, S. Bhattarai, N. Budhathoki, A. M. Castillo, D. Chakraborty, S. Chhetri, S. Choudhury, T. A. Chowdhury, R. D. Cruz, B. Cui, S. Dhital, K. -M. Dong, R. Gapuz, A. Ghasemi, E. Z. Gnimpieba, B. D. S. Gurung, H. A. Hashim, R. I. Harry, K. -E. Hasin , et al. (30 additional authors not shown)

Abstract: Data-centric materials science is changing how materials are discovered, optimized, manufactured, and qualified, yet many deployment-limiting materials problems still depend on experimental, processing-rich, device-level, and field-relevant data that are difficult to capture in conventional materials databases. This perspective argues that the Great Plains and adjacent interior research corridor c… ▽ More Data-centric materials science is changing how materials are discovered, optimized, manufactured, and qualified, yet many deployment-limiting materials problems still depend on experimental, processing-rich, device-level, and field-relevant data that are difficult to capture in conventional materials databases. This perspective argues that the Great Plains and adjacent interior research corridor can make a distinctive national contribution by organizing distributed experimental assets into a trusted regional materials-data ecosystem. The proposed model emphasizes FAIR metadata, provenance, persistent sample identifiers, uncertainty-aware modeling, semi-closed-loop workflows, stackable workforce training, and tiered governance for academic, public, controlled-access, and industry-protected data. We identify five coupled barriers -- fragmented data, weak algorithm--laboratory translation, uneven access to cyberinfrastructure and technical staff, workforce gaps at the materials--data interface, and insufficient incentives for sharing and reuse -- and propose a staged roadmap for addressing them. A high-purity germanium pilot illustrates how regional strengths can be converted into reusable datasets, benchmark models, trained personnel, and decision-improving workflows. The broader message is that regional leadership in data-centric materials science will depend less on geographic concentration than on trustworthy data practices, interoperable infrastructure, cross-trained people, and application-driven materials challenges. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Comments: 33 pages, 6 figures, and 8 tables

Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching

Authors: Ehsan Aghazadeh, Masoud Malekzadeh, Ahmad Ghasemi, Hossein Pishro-Nik

Abstract: Designing continuous trajectories whose time-averaged occupancy provably matches a prescribed spatial density (the \emph{ergodic coverage} problem) is central to UAV-assisted data collection and sensing, robotic exploration, and mobile monitoring. For flying agents in particular, this challenge is acute: trajectories must balance coverage fidelity against tight energy budgets, no-fly zones, and ac… ▽ More Designing continuous trajectories whose time-averaged occupancy provably matches a prescribed spatial density (the \emph{ergodic coverage} problem) is central to UAV-assisted data collection and sensing, robotic exploration, and mobile monitoring. For flying agents in particular, this challenge is acute: trajectories must balance coverage fidelity against tight energy budgets, no-fly zones, and acceleration limits. Existing methods either re-optimize each trajectory online (with cost growing in the horizon and re-running for every target, agent, and realization) or rely on bespoke analytical constructions that must be re-derived for each new constraint. We propose a \emph{epushforward} framework that decouples ergodicity from density matching: an analytic latent trajectory provides exact uniform ergodicity on a simple annular domain, and a single map, learned offline by optimal-transport conditional flow matching, transports this latent occupancy onto the prescribed target density. The composed trajectory is then asymptotically ergodic with respect to the learned pushforward distribution, with deviation from the target controlled by the flow-matching training loss. Once trained for a given target density and constraint set, the map serves an unbounded number of trajectories and a multi-agent fleet without per-agent retraining, and many differentiable operational constraints (no-fly zones, acceleration ceilings, or fairness penalties) enter as additive soft penalties in the training loss without re-deriving the design. We prove three results (an acceleration-energy bound, an $O(1/\sqrt{K})$ ergodic convergence rate in the number of trajectory cycles $K$, and an approximation-error bound) that combine into an end-to-end coverage bound estimable from CFM training diagnostics (certified given an architectural Lipschitz bound on $v_θ$). △ Less

Submitted 13 May, 2026; originally announced May 2026.

Broken Symmetry, Conservation Law, and Scaling in Accumulated Stock Returns -- a Modified Jones-Faddy Skew t-Distribution Perspective

Authors: Arshia Ghasemi, Siqi Shao, R. A. Serota

Abstract: We analyze historic S&P500 multi-day returns: from daily returns to those accumulated over up to ten days. Despite symmetry breaking between gains and losses in the distribution of returns, resulting in its positive mean and negative skew, realized variance (volatility squared) exhibits remarkably good linear dependence on the number of days of accumulation. Mean of the distribution also shows nea… ▽ More We analyze historic S&P500 multi-day returns: from daily returns to those accumulated over up to ten days. Despite symmetry breaking between gains and losses in the distribution of returns, resulting in its positive mean and negative skew, realized variance (volatility squared) exhibits remarkably good linear dependence on the number of days of accumulation. Mean of the distribution also shows near perfect linear dependence as well. We analyze this phenomenon both analytically and numerically using a modified Jones-Faddy skew t-distribution. △ Less

Submitted 16 April, 2026; originally announced April 2026.

Comments: 26 pages, 24 figures, 4 tables

Journal ref: Foundations 2026, 6(2), 23

Approximation Algorithms for Action-Reward Query-Commit Matching

Authors: Mahsa Derakhshan, Andisheh Ghasemi, Calum MacRury

Abstract: Matching problems under uncertainty arise in applications such as kidney exchange, hiring, and online marketplaces. A decision-maker must sequentially explore potential matches under local exploration constraints, while committing irrevocably to successful matches as they are revealed. The query-commit matching problem captures these challenges by modeling edges that succeed independently with kno… ▽ More Matching problems under uncertainty arise in applications such as kidney exchange, hiring, and online marketplaces. A decision-maker must sequentially explore potential matches under local exploration constraints, while committing irrevocably to successful matches as they are revealed. The query-commit matching problem captures these challenges by modeling edges that succeed independently with known probabilities and must be accepted upon success, subject to vertex patience (time-out) constraints limiting the number of incident queries. In this work, we introduce the action-reward query-commit matching problem, a strict generalization of query-commit matching in which each query selects an action from a known action space, determining both the success probability and the reward of the queried edge. If an edge is queried using a chosen action and succeeds, it is irrevocably added to the matching, and the corresponding reward is obtained; otherwise, the edge is permanently discarded. We study the design of approximation algorithms for this problem on bipartite graphs. This model captures a broad class of stochastic matching problems, including the sequential pricing problem introduced by Pollner, Roghani, Saberi, and Wajc (EC~2022). On the positive side, Pollner et al. designed a polynomial-time approximation algorithm achieving a ratio of $0.426$ in the one-sided patience setting, which degrades to $0.395$ when both sides have bounded patience. In this work, we design computationally efficient algorithms for the action-reward query-commit in one-sided and two-sided patience settings, achieving approximation ratios of $1-1/e \approx 0.63$ and $\frac{1}{27}\!\left(19-67/e^3\right) \approx 0.58$ respectively. These results improve the state of the art for the sequential pricing problem, surpassing the previous guarantees of $0.426$ and $0.395$. △ Less

Submitted 13 March, 2026; originally announced March 2026.

ACM Class: F.2.2; G.2.2

Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks

Authors: Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu

Abstract: The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention netw… ▽ More The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Comments: 13 pages, 10 figures. Submitted to IEEE Transactions on Machine Learning in Communications and Networking

Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand

Authors: Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu

Abstract: In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space a… ▽ More In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70\% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands. △ Less

Submitted 10 March, 2026; originally announced March 2026.

Comments: 7 pages, 5 figures. Presented at IEEE VTC 2024, Washington, DC. Published in the IEEE conference proceedings

A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks

Authors: Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi

Abstract: The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed… ▽ More The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability. △ Less

Submitted 10 March, 2026; originally announced March 2026.

Comments: 7 pages, 6 figures. Presented at IEEE GLOBECOM 2025, Taiwan. To appear in the conference proceedings

Comparing Mixture, Box, and Wasserstein Ambiguity Sets in Distributionally Robust Asset Liability Management

Authors: Alireza Ghahtarani, Ahmed Saif, Alireza Ghasemi

Abstract: Asset Liability Management (ALM) represents a fundamental challenge for financial institutions, particularly pension funds, which must navigate the tension between generating competitive investment returns and ensuring the solvency of long-term obligations. To address the limitations of traditional frameworks under uncertainty, this paper implements Distributionally Robust Optimization (DRO), an e… ▽ More Asset Liability Management (ALM) represents a fundamental challenge for financial institutions, particularly pension funds, which must navigate the tension between generating competitive investment returns and ensuring the solvency of long-term obligations. To address the limitations of traditional frameworks under uncertainty, this paper implements Distributionally Robust Optimization (DRO), an emergent paradigm that accounts for a broad spectrum of potential probability distributions. We propose and evaluate three distinct DRO formulations: mixture ambiguity sets with discrete scenarios, box ambiguity sets of discrete distribution functions, and Wasserstein metric ambiguity sets. Utilizing empirical data from the Canada Pension Plan (CPP), we conduct a comparative analysis of these models against traditional stochastic programming approaches. Our results demonstrate that DRO formulations, specifically those utilizing Wasserstein and box ambiguity sets, consistently outperform both mixture-based DRO and stochastic programming in terms of funding ratios and overall fund returns. These findings suggest that incorporating distributional robustness significantly enhances the resilience and performance of pension fund management strategies. △ Less

Submitted 8 February, 2026; originally announced February 2026.

Comments: 25 pages, 4 Figures

Broken Symmetry of Stock Returns -- a Modified Jones-Faddy Skew t-Distribution

Authors: Siqi Shao, Arshia Ghasemi, Hamed Farahani, R. A. Serota

Abstract: We argue that negative skew and positive mean of the distribution of stock returns are largely due to the broken symmetry of stochastic volatility governing gains and losses. Starting with stochastic differential equations for stock returns and for stochastic volatility we argue that the distribution of stock returns can be effectively split in two -- for gains and losses -- assuming difference in… ▽ More We argue that negative skew and positive mean of the distribution of stock returns are largely due to the broken symmetry of stochastic volatility governing gains and losses. Starting with stochastic differential equations for stock returns and for stochastic volatility we argue that the distribution of stock returns can be effectively split in two -- for gains and losses -- assuming difference in parameters of their respective stochastic volatilities. A modified Jones-Faddy skew t-distribution utilized here allows to reflect this in a single organic distribution which tends to meaningfully capture this asymmetry. We illustrate its application on distribution of daily S&P500 returns, including analysis of its tails. △ Less

Submitted 16 February, 2026; v1 submitted 29 December, 2025; originally announced December 2025.

Comments: 19 pages, 19 figures, 2 tables

Journal ref: Economies 2026, 14(3), 84; https://www.mdpi.com/2227-7099/14/3/84

Relational Emergent Time for Quantum System: A Multi-Observer, Gravitational, and Cosmological Framework

Authors: Amir Hossein Ghasemi

Abstract: We present a relational framework in which temporal structure is not fundamental but emerges from correlations within a globally stationary quantum state. Each subsystem includes an internal clock, and conditional states evolve effectively with respect to these internal readings. The construction naturally extends to relativistic motion, gravitational redshift, and cosmological expansion, leading… ▽ More We present a relational framework in which temporal structure is not fundamental but emerges from correlations within a globally stationary quantum state. Each subsystem includes an internal clock, and conditional states evolve effectively with respect to these internal readings. The construction naturally extends to relativistic motion, gravitational redshift, and cosmological expansion, leading to a unified emergent-time functional valid across diverse physical regimes. The theory reproduces classical time dilation, predicts correlation-dependent deviations from standard evolution, and suggests that non-interacting or massless particles exhibit negligible internal time. These consequences open directions for conceptual and experimental investigations in the foundations of temporal physics, from multi-clock quantum systems to precision metrology and cosmological settings. In particular, the framework suggests measurable deviation from standard quantum evolution for highly entangled systems and predicts negligible internal time for massless particles. △ Less

Submitted 15 December, 2025; originally announced December 2025.

Comments: 10 pages, 3 figures, 1 figure (LaTeX-generated), All references and figures are included in the main LaTeX source

PISA: Prioritized Invariant Subgraph Aggregation

Authors: Ali Ghasemi, Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri

Abstract: Recent work has extended the invariance principle for out-of-distribution (OOD) generalization from Euclidean to graph data, where challenges arise due to complex structures and diverse distribution shifts in node attributes and topology. To handle these, Chen et al. proposed CIGA (Chen et al., 2022b), which uses causal modeling and an information-theoretic objective to extract a single invariant… ▽ More Recent work has extended the invariance principle for out-of-distribution (OOD) generalization from Euclidean to graph data, where challenges arise due to complex structures and diverse distribution shifts in node attributes and topology. To handle these, Chen et al. proposed CIGA (Chen et al., 2022b), which uses causal modeling and an information-theoretic objective to extract a single invariant subgraph capturing causal features. However, this single-subgraph focus can miss multiple causal patterns. Liu et al. (2025) addressed this with SuGAr, which learns and aggregates diverse invariant subgraphs via a sampler and diversity regularizer, improving robustness but still relying on simple uniform or greedy aggregation. To overcome this, the proposed PISA framework introduces a dynamic MLP-based aggregation that prioritizes and combines subgraph representations more effectively. Experiments on 15 datasets, including DrugOOD (Ji et al., 2023), show that PISA achieves up to 5% higher classification accuracy than prior methods. △ Less

Submitted 27 November, 2025; originally announced November 2025.

CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency

Authors: Ehsan Aghazadeh, Ahmad Ghasemi, Hedyeh Beyhaghi, Hossein Pishro-Nik

Abstract: Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency (Wang et al., 2023) strategy requires a fixed number of calls and fails when the correct answer is infrequent. We introduce Confidence-Guided Early Stopping (CGES), a Bayesian framework that forms posteriors over candidate answers and adap… ▽ More Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency (Wang et al., 2023) strategy requires a fixed number of calls and fails when the correct answer is infrequent. We introduce Confidence-Guided Early Stopping (CGES), a Bayesian framework that forms posteriors over candidate answers and adaptively halts sampling once one answer accumulates enough posterior mass. We prove guarantees in both an ideal calibrated regime and a realistic noisy-confidence regime under a directional drift condition. Averaged over five reasoning benchmarks, CGES reduces the average number of calls by 58% on average (from 16.0 to 6.7) while matching its accuracy within 0.4 percentage points of self-consistency. △ Less

Submitted 8 June, 2026; v1 submitted 4 November, 2025; originally announced November 2025.

Comments: Extended version. A preliminary version was accepted at the Efficient Reasoning Workshop @ NeurIPS 2025. Code: https://github.com/EhsanAghazadeh/cges

Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning

Authors: Amin Farajzadeh, Hongzhao Zheng, Sarah Dumoulin, Trevor Ha, Halim Yanikomeroglu, Amir Ghasemi

Abstract: Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements o… ▽ More Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements of emerging technologies such as advanced 5G, forthcoming 6G, and the internet of things (IoT). This paper presents an effective spatio-temporal prediction framework that leverages crowdsourced user-side key performance indicators (KPIs) and regulatory datasets to model and forecast spectrum demand. The proposed methodology achieves superior prediction accuracy and cross-regional generalizability by incorporating advanced feature engineering, comprehensive correlation analysis, and transfer learning techniques. Unlike traditional ITU models, which are often constrained by arbitrary inputs and unrealistic assumptions, this approach exploits granular, data-driven insights to account for spatial and temporal variations in spectrum utilization. Comparative evaluations against ITU estimates, as the benchmark, underscore our framework's capability to deliver more realistic and actionable predictions. Experimental results validate the efficacy of our methodology, highlighting its potential as a robust approach for policymakers and regulatory bodies to enhance spectrum management and planning. △ Less

Submitted 5 August, 2025; originally announced August 2025.

Comments: Accepted to be presented at IEEE PIMRC 2025

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs

Authors: Nasim Shirvani-Mahdavi, Devin Wingfield, Amin Ghasemi, Chengkai Li

Abstract: Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique label… ▽ More Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique labeling conventions of each KG, can make them difficult for humans to understand. In this paper, we explore the potential of large language models to generate natural language explanations for logical rules. Specifically, we extract logical rules using the AMIE 3.5.1 rule discovery algorithm from the benchmark dataset FB15k-237 and two large-scale datasets, FB-CVT-REV and FB+CVT-REV. We examine various prompting strategies, including zero- and few-shot prompting, including variable entity types, and chain-of-thought reasoning. We conduct a comprehensive human evaluation of the generated explanations based on correctness, clarity, and hallucination, and also assess the use of large language models as automatic judges. Our results demonstrate promising performance in terms of explanation correctness and clarity, although several challenges remain for future research. All scripts and data used in this study are publicly available at https://github.com/idirlab/KGRule2NL}{https://github.com/idirlab/KGRule2NL. △ Less

Submitted 31 July, 2025; originally announced July 2025.

Kinetics of Vacancy-Assisted Reversible Phase Transition in Monolayer MoTe$_2$

Authors: Fei Shuang, Daniel Ocampo, Reza Namakian, Arman Ghasemi, Poulumi Dey, Wei Gao

Abstract: We investigate the kinetics of phase transition between the 2H and 1T$^\prime$ phases in monolayer MoTe$_2$ using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data, combined with mean field kinetic theory to interpret the underlying mechanisms. The transition is found to involve both diffusive and diffusionless mechanisms. Nucleation of 1T$^\prime$ ph… ▽ More We investigate the kinetics of phase transition between the 2H and 1T$^\prime$ phases in monolayer MoTe$_2$ using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data, combined with mean field kinetic theory to interpret the underlying mechanisms. The transition is found to involve both diffusive and diffusionless mechanisms. Nucleation of 1T$^\prime$ phase is initiated by the coalescence of neighboring Te monovacancies into divacancies, which are found to be mobile and can interact with other Te vacancies to form small triangular 1T$^\prime$ islands. Growth of these islands proceeds either by incorporating pre-existing vacancies at the phase boundaries or, in their absence, by absorbing divacancies that migrate from the surrounding lattice. Once a critical island size is reached, vacancy-free growth becomes possible although with a higher activation barrier. Upon removal of external stimuli, the system reverts to 2H phase, during which Te vacancies reorganize into three-fold spoke-like vacancy lines at the island center. This reverse process and the subsequent 1T$^\prime$$\leftrightarrow$2H reversible transitions are diffusionless, rapid, do not require additional vacancies and can be driven by mild external stimuli. Although our analysis focuses on strain-induced transitions, the kinetic mechanisms are expected to be generalizable to other types of stimuli. △ Less

Submitted 16 July, 2025; originally announced July 2025.

One-way Communication Complexity of Minimum Vertex Cover in General Graphs

Authors: Mahsa Derakhshan, Andisheh Ghasemi, Rajmohan Rajaraman

Abstract: We study the communication complexity of the Minimum Vertex Cover (MVC) problem on general graphs within the \(k\)-party one-way communication model. Edges of an arbitrary \(n\)-vertex graph are distributed among \(k\) parties. The objective is for the parties to collectively find a small vertex cover of the graph while adhering to a communication protocol where each party sequentially sends a mes… ▽ More We study the communication complexity of the Minimum Vertex Cover (MVC) problem on general graphs within the \(k\)-party one-way communication model. Edges of an arbitrary \(n\)-vertex graph are distributed among \(k\) parties. The objective is for the parties to collectively find a small vertex cover of the graph while adhering to a communication protocol where each party sequentially sends a message to the next until the last party outputs a valid vertex cover of the whole graph. We are particularly interested in the trade-off between the size of the messages sent and the approximation ratio of the output solution. It is straightforward to see that any constant approximation protocol for MVC requires communicating \(Ω(n)\) bits. Additionally, there exists a trivial 2-approximation protocol where the parties collectively find a maximal matching of the graph greedily and return the subset of vertices matched. This raises a natural question: \textit{What is the best approximation ratio achievable using optimal communication of \(O(n)\)?} We design a protocol with an approximation ratio of \((2-2^{-k+1}+ε)\) and \(O(n)\) communication for any desirably small constant \(ε>0\), which is strictly better than 2 for any constant number of parties. Moreover, we show that achieving an approximation ratio smaller than \(3/2\) for the two-party case requires \(n^{1 + Ω(1/\lg\lg n)}\) communication, thereby establishing the tightness of our protocol for two parties. △ Less

Submitted 10 May, 2025; v1 submitted 30 April, 2025; originally announced May 2025.

Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review

Authors: Amirehsan Ghasemi, Soheil Hashtarkhani, David L Schwartz, Arash Shaban-Nejad

Abstract: With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligenc… ▽ More With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes. △ Less

Submitted 12 July, 2024; originally announced July 2024.

Comments: 22 Pages, 6 Figures, 13 Tables

MSC Class: 68T01

Journal ref: Cancer Innovation 2024;3:e136

Tiny Graph Neural Networks for Radio Resource Management

Authors: Ahmad Ghasemi, Hossein Pishro-Nik

Abstract: The surge in demand for efficient radio resource management has necessitated the development of sophisticated yet compact neural network architectures. In this paper, we introduce a novel approach to Graph Neural Networks (GNNs) tailored for radio resource management by presenting a new architecture: the Low Rank Message Passing Graph Neural Network (LR-MPGNN). The cornerstone of LR-MPGNN is the i… ▽ More The surge in demand for efficient radio resource management has necessitated the development of sophisticated yet compact neural network architectures. In this paper, we introduce a novel approach to Graph Neural Networks (GNNs) tailored for radio resource management by presenting a new architecture: the Low Rank Message Passing Graph Neural Network (LR-MPGNN). The cornerstone of LR-MPGNN is the implementation of a low-rank approximation technique that substitutes the conventional linear layers with their low-rank counterparts. This innovative design significantly reduces the model size and the number of parameters. We evaluate the performance of the proposed LR-MPGNN model based on several key metrics: model size, number of parameters, weighted sum rate of the communication system, and the distribution of eigenvalues of weight matrices. Our extensive evaluations demonstrate that the LR-MPGNN model achieves a sixtyfold decrease in model size, and the number of model parameters can be reduced by up to 98%. Performance-wise, the LR-MPGNN demonstrates robustness with a marginal 2% reduction in the best-case scenario in the normalized weighted sum rate compared to the original MPGNN model. Additionally, the distribution of eigenvalues of the weight matrices in the LR-MPGNN model is more uniform and spans a wider range, suggesting a strategic redistribution of weights. △ Less

Submitted 28 March, 2024; originally announced March 2024.

Comments: Accepted as a full paper by the tinyML Research Symposium 2024

Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs)

Authors: Amir Ghasemi, Paul Guinand

Abstract: Wireless spectrum regulation is a complex and demanding process due to the rapid pace of technological progress, increasing demand for spectrum, and a multitude of stakeholders with potentially conflicting interests, alongside significant economic implications. To navigate this, regulators must engage effectively with all parties, keep pace with global technology trends, conduct technical evaluati… ▽ More Wireless spectrum regulation is a complex and demanding process due to the rapid pace of technological progress, increasing demand for spectrum, and a multitude of stakeholders with potentially conflicting interests, alongside significant economic implications. To navigate this, regulators must engage effectively with all parties, keep pace with global technology trends, conduct technical evaluations, issue licenses in a timely manner, and comply with various legal and policy frameworks. In light of these challenges, this paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes. We explore various roles that LLMs can play in this context while identifying some of the challenges to address. The paper also offers practical case studies and insights, with appropriate experiments, highlighting the transformative potential of LLMs in spectrum management. △ Less

Submitted 26 March, 2024; originally announced March 2024.

Phase Diagram and Spectroscopic Signatures of a Supersolid in Quantum Ising Magnet K$_2$Co(SeO$_3$)$_2$

Authors: Tong Chen, Alireza Ghasemi, Junyi Zhang, Liyu Shi, Zhenisbek Tagay, Youzhe Chen, Lei Chen, Eun-Sang Choi, Marcelo Jaime, Minseong Lee, Yiqing Hao, Huibo Cao, Barry Winn, Andrey A. Podlesnyak, Daniel M. Pajerowski, Ruidan Zhong, Xianghan Xu, N. P. Armitage, Robert Cava, Collin Broholm

Abstract: A supersolid is a quantum-entangled state of matter exhibiting the dual characteristics of superfluidity and solidity. Theory predicts that hard-core bosons with repulsive interactions on a triangular lattice can form supersolid phases at half filling and near complete filling. Leveraging an exact mapping between bosons and spin-$\frac{1}{2}$ degrees of freedom, we investigate these phases in the… ▽ More A supersolid is a quantum-entangled state of matter exhibiting the dual characteristics of superfluidity and solidity. Theory predicts that hard-core bosons with repulsive interactions on a triangular lattice can form supersolid phases at half filling and near complete filling. Leveraging an exact mapping between bosons and spin-$\frac{1}{2}$ degrees of freedom, we investigate these phases in the spin-$\frac{1}{2}$ triangular-lattice antiferromagnet \K212 with exchange constants $J_z = 2.96(2)$~meV and $J_{\perp} = 0.21(3)$~meV. At zero field, neutron diffraction reveals the gradual development for $T<15$~K of quasi-two-dimensional $\sqrt{3}\times\sqrt{3}$ magnetic order with $Z_3$ translational symmetry breaking (solidity) albeit with 44(5)% reduced amplitude at $T=0.3$~K indicating strong quantum fluctuations. These are apparent in equidistant bands of continuum neutron scattering for $\hslashω_n\approx n\times J_z$, where $n=0,1,2,3$. The lowest energy ($n=0$) $\bf Q$-dependent continuum has a lower resonant edge and includes a quasi-elastic component at K $(\frac{1}{3}\frac{1}{3})$ consistent with broken $U(1)$ spin rotational symmetry (boson superfluidity). Competing instabilities are apparent in soft albeit finite-energy modes at M $(\frac{1}{2}0)$ and at $\frac{1}{2}$K $(\frac{1}{6}\frac{1}{6})$. For $\bf c$-axis-oriented magnetic fields $17~{\rm T} <μ_0 H< 21~{\rm T}$ that almost saturate the magnetization, corresponding to nearly filling the lattice with bosons, we find a new phase consistent with a second supersolid. These phases are separated by a pronounced 1/3 magnetization plateau that supports coherent spin waves, from which we determine the spin Hamiltonian. △ Less

Submitted 3 November, 2025; v1 submitted 24 February, 2024; originally announced February 2024.

Comments: 17 pages, 6 figures

Adversarial Attacks on Graph Neural Networks based Spatial Resource Management in P2P Wireless Communications

Authors: Ahmad Ghasemi, Ehsan Zeraatkar, Majid Moradikia, Seyed, Zekavat

Abstract: This paper introduces adversarial attacks targeting a Graph Neural Network (GNN) based radio resource management system in point to point (P2P) communications. Our focus lies on perturbing the trained GNN model during the test phase, specifically targeting its vertices and edges. To achieve this, four distinct adversarial attacks are proposed, each accounting for different constraints, and aiming… ▽ More This paper introduces adversarial attacks targeting a Graph Neural Network (GNN) based radio resource management system in point to point (P2P) communications. Our focus lies on perturbing the trained GNN model during the test phase, specifically targeting its vertices and edges. To achieve this, four distinct adversarial attacks are proposed, each accounting for different constraints, and aiming to manipulate the behavior of the system. The proposed adversarial attacks are formulated as optimization problems, aiming to minimize the system's communication quality. The efficacy of these attacks is investigated against the number of users, signal-to-noise ratio (SNR), and adversary power budget. Furthermore, we address the detection of such attacks from the perspective of the Central Processing Unit (CPU) of the system. To this end, we formulate an optimization problem that involves analyzing the distribution of channel eigenvalues before and after the attacks are applied. This formulation results in a Min-Max optimization problem, allowing us to detect the presence of attacks. Through extensive simulations, we observe that in the absence of adversarial attacks, the eigenvalues conform to Johnson's SU distribution. However, the attacks significantly alter the characteristics of the eigenvalue distribution, and in the most effective attack, they even change the type of the eigenvalue distribution. △ Less

Submitted 13 December, 2023; originally announced December 2023.

The impact of input node placement in the controllability of brain networks

Authors: Seyed Samie Alizadeh Darbandi, Alex Fornito, Abdorasoul Ghasemi

Abstract: Network control theory can be used to model how one should steer the brain between different states by driving a specific region with an input. The needed energy to control a network is often used to quantify its controllability, and controlling brain networks requires diverse energy depending on the selected input region. We use the theory of how input node placement affects the longest control c… ▽ More Network control theory can be used to model how one should steer the brain between different states by driving a specific region with an input. The needed energy to control a network is often used to quantify its controllability, and controlling brain networks requires diverse energy depending on the selected input region. We use the theory of how input node placement affects the longest control chain (LCC) in the controllability of brain networks to study the role of the architecture of white matter fibers in the required control energy. We show that the energy needed to control human brain networks is related to the LCC, i.e., the longest distance between the input region and other regions in the network. We indicate that regions that control brain networks with lower energy have small LCCs. These regions align with areas that can steer the brain around the state space smoothly. By contrast, regions that need higher energy to move the brain toward different target states have larger LCCs. We also investigate the role of the number of paths between regions in the control energy. Our results show that the more paths between regions, the lower cost needed to control brain networks. We evaluate the number of paths by counting specific motifs in brain networks since determining all paths in graphs is a difficult problem. △ Less

Submitted 2 August, 2023; v1 submitted 31 July, 2023; originally announced August 2023.

Comments: 14 pages, 8 figures

Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting

Authors: Arman Ghasemi, Amin Shojaeighadikolaei, Morteza Hashemi

Abstract: Renewable energy sources, such as wind and solar power, are increasingly being integrated into smart grid systems. However, when compared to traditional energy resources, the unpredictability of renewable energy generation poses significant challenges for both electricity providers and utility companies. Furthermore, the large-scale integration of distributed energy resources (such as PV systems)… ▽ More Renewable energy sources, such as wind and solar power, are increasingly being integrated into smart grid systems. However, when compared to traditional energy resources, the unpredictability of renewable energy generation poses significant challenges for both electricity providers and utility companies. Furthermore, the large-scale integration of distributed energy resources (such as PV systems) creates new challenges for energy management in microgrids. To tackle these issues, we propose a novel framework with two objectives: (i) combating uncertainty of renewable energy in smart grid by leveraging time-series forecasting with Long-Short Term Memory (LSTM) solutions, and (ii) establishing distributed and dynamic decision-making framework with multi-agent reinforcement learning using Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed framework considers both objectives concurrently to fully integrate them, while considering both wholesale and retail markets, thereby enabling efficient energy management in the presence of uncertain and distributed renewable energy sources. Through extensive numerical simulations, we demonstrate that the proposed solution significantly improves the profit of load serving entities (LSE) by providing a more accurate wind generation forecast. Furthermore, our results demonstrate that households with PV and battery installations can increase their profits by using intelligent battery charge/discharge actions determined by the DDPG agents. △ Less

Submitted 27 February, 2023; originally announced February 2023.

Comments: 10 pages, 12 figures

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Authors: Amir Ghasemi, Nasrin Bayat, Fatemeh Mottaghian, Akram Bayat

Abstract: Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster- RCNN, as a function of image resolution. The results reveals negative effects of low resoluti… ▽ More Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster- RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies. △ Less

Submitted 14 March, 2023; v1 submitted 23 January, 2023; originally announced January 2023.

Controllability of complex networks: input node placement restricting the longest control chain

Authors: Samie Alizadeh, Márton Pósfai, Abdorasoul Ghasemi

Abstract: The minimum number of inputs needed to control a network is frequently used to quantify its controllability. Control of linear dynamics through a minimum set of inputs, however, often has prohibitively large energy requirements and there is an inherent trade-off between minimizing the number of inputs and control energy. To better understand this trade-off, we study the problem of identifying a mi… ▽ More The minimum number of inputs needed to control a network is frequently used to quantify its controllability. Control of linear dynamics through a minimum set of inputs, however, often has prohibitively large energy requirements and there is an inherent trade-off between minimizing the number of inputs and control energy. To better understand this trade-off, we study the problem of identifying a minimum set of input nodes such that controllabililty is ensured while restricting the length of the longest control chain. The longest control chain is the maximum distance from input nodes to any network node, and recent work found that reducing its length significantly reduces control energy. We map the longest control chain-constraint minimum input problem to finding a joint maximum matching and minimum dominating set. We show that this graph combinatorial problem is NP-complete, and we introduce and validate a heuristic approximation. Applying this algorithm to a collection of real and model networks, we investigate how network structure affects the minimum number of inputs, revealing, for example, that for many real networks reducing the longest control chain requires only few or no additional inputs, only the rearrangement of the input nodes. △ Less

Submitted 9 December, 2022; originally announced December 2022.

Comments: 16 pages, 9 figures, supplementary

ACM Class: J.2

Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework

Authors: Amin Shojaeighadikolaei, Arman Ghasemi, Kailani Jones, Yousif Dafalla, Alexandru G. Bardas, Reza Ahmadi, Morteza Haashemi

Abstract: This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and re… ▽ More This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators. △ Less

Submitted 28 November, 2022; originally announced November 2022.

Comments: 16 pages, 10 figures

MSC Class: 68T07; 68T42; 68T05 ACM Class: I.2; I.6

Quantum paramagnetism in a non-Kramers rare-earth oxide: Monoclinic $\rm Pr_2Ti_2O_7$

Authors: Huiyuan Man, Alireza Ghasemi, Moein Adnani, Maxime A. Siegler, Elaf A. Anber, Yufan Li, Chia-Ling Chien, Mitra L. Taheri, Ching-Wu Chu, Collin L. Broholm, Seyed M. Koohpayeh

Abstract: Little is so far known about the magnetism of the $\rm A_2B_2O_7$ monoclinic layered perovskites that replace the spin-ice supporting pyrochlore structure for $r_A/r_B>1.78$. We show that high quality monoclinic Pr$_2$Ti$_2$O$_7$ single crystals with a three-dimensional network of non-Kramers Pr$^{3+}$ ions that interact through edge-sharing super-exchange interactions, form a singlet ground state… ▽ More Little is so far known about the magnetism of the $\rm A_2B_2O_7$ monoclinic layered perovskites that replace the spin-ice supporting pyrochlore structure for $r_A/r_B>1.78$. We show that high quality monoclinic Pr$_2$Ti$_2$O$_7$ single crystals with a three-dimensional network of non-Kramers Pr$^{3+}$ ions that interact through edge-sharing super-exchange interactions, form a singlet ground state quantum paramagnet that does not undergo any magnetic phase transitions down to at least 1.8 K. The chemical phase stability, structure, and magnetic properties of the layered perovskite Pr$_2$Ti$_2$O$_7$ were investigated using x-ray diffraction, transmission electron microscopy, and magnetization measurements. Synthesis of polycrystalline samples with the nominal compositions of Pr$_2$Ti$_{2+x}$O$_7$ ($-0.16 \leq x \leq 0.16$) showed that deviations from the Pr$_2$Ti$_2$O$_7$ stoichiometry lead to secondary phases of related, structures including the perovskite phase Pr$_{2/3}$TiO$_3$ and the orthorhombic phases Pr$_4$Ti$_9$O$_{24}$ and Pr$_2$TiO$_5$. No indications of site disordering (stuffing and anti-stuffing) or vacancy defects were observed in the Pr$_2$Ti$_2$O$_7$ majority phase. A procedure for growth of high-structural-quality, stoichiometric single crystals of Pr$_2$Ti$_2$O$_7$ by the traveling solvent floating zone (TSFZ) method is reported. Thermo-magnetic measurements of single-crystalline Pr$_2$Ti$_2$O$_7$ reveal an isolated singlet ground state that we associate with the low symmetry crystal electric field environments that split the $2J+1=9$-fold degenerate spin-orbital multiplets of the four differently coordinated Pr$^{3+}$ ions into 36 isolated singlets resulting in an anisotropic temperature independent van-Vleck susceptibility at low $T$. A small isotropic Curie term is associated with 0.96(2)\% non-interacting Pr$^{4+}$ impurities. △ Less

Submitted 16 June, 2023; v1 submitted 12 November, 2022; originally announced November 2022.

Comments: 18 pages, 7 figures, 6 tables

Journal ref: Phys. Rev. Mater. 7, 063401 (2023)

A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network

Authors: Mozhde Firoozi Pouyani, Mansour Vali, Mohammad Amin Ghasemi

Abstract: Computer analysis of Lung Sound (LS) signals has been proposed in recent years as a tool to analyze the lungs' status but there have always been main challenges, including the contamination of LS with environmental noises, which come from different sources of unlike intensities. One of the common methods in noise reduction of LS signals is based on thresholding on Discrete Wavelet Transform (DWT)… ▽ More Computer analysis of Lung Sound (LS) signals has been proposed in recent years as a tool to analyze the lungs' status but there have always been main challenges, including the contamination of LS with environmental noises, which come from different sources of unlike intensities. One of the common methods in noise reduction of LS signals is based on thresholding on Discrete Wavelet Transform (DWT) coefficients or Empirical Mode Decomposition (EMD) of the signal, however, in these methods, it is necessary to calculate the SNR value to determine the appropriate threshold for noise removal. To solve this problem, a combined model based on EMD and Artificial Neural Network (ANN) trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in this research. The model can denoise white and pink noises in the range of -2 to 20dB without thresholding or even estimating SNR, and at the same time, keep the main content of the LS signal well. The proposed method is also compared with the EMD-custom method, and the results obtained from the SNR, and fit criteria indicate the absolute superiority of the proposed method. For example, at SNR = 0dB, the combined method can improve the SNR by 9.41 and 8.23dB for white and pink noises, respectively, while the corresponding values are respectively 5.89 and 4.31dB for the EMD-Custom method. △ Less

Submitted 20 September, 2022; originally announced September 2022.

A Double-oracle, Logic-based Benders decomposition approach to solve the K-adaptability problem

Authors: Alireza Ghahtarani, Ahmed Saif, Alireza Ghasemi, Erick Delage

Abstract: We propose a novel approach to solve K-adaptability problems with convex objective and constraints and integer first-stage decisions. A logic-based Benders decomposition is applied to handle the first-stage decisions in a master problem, thus the sub-problem becomes a min-max-min robust combinatorial optimization problem that is solved via a double-oracle algorithm that iteratively generates adver… ▽ More We propose a novel approach to solve K-adaptability problems with convex objective and constraints and integer first-stage decisions. A logic-based Benders decomposition is applied to handle the first-stage decisions in a master problem, thus the sub-problem becomes a min-max-min robust combinatorial optimization problem that is solved via a double-oracle algorithm that iteratively generates adverse scenarios and recourse decisions and assigns scenarios to K subsets of the decisions by solving p-center problems. Extensions of the proposed approach to handle parameter uncertainty in both the first-stage objective and the second-stage constraints are also provided. We show that the proposed algorithm converges to an optimal solution and terminates in finite number of iterations. Numerical results obtained from experiments on benchmark instances of the adaptive shortest path problem, the regular knapsack problem, and a generic K-adaptability problem demonstrate the performance advantage of the proposed approach when compared to state-of-the-art methods in the literature. △ Less

Submitted 7 September, 2022; originally announced September 2022.

Comments: 29 pages, 7 figures

MSC Class: G.2.1

A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

Authors: Mohammad Parvini, Amir Hossein Zarif, Ali Nouruzi, Nader Mokari, Mohammad Reza Javan, Bijan Abbasi, Amir Ghasemi, Halim Yanikomeroglu

Abstract: As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use… ▽ More As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecom vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors. △ Less

Submitted 21 March, 2022; originally announced March 2022.

Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks

Authors: Abdorasoul Ghasemi, Holger Kantz

Abstract: We use machine learning tools to model the line interaction of failure cascading in power grid networks. We first collect data sets of simulated trajectories of possible consecutive line failure following an initial random failure and considering actual constraints in a model power network until the system settles at a steady state. We use weighted $l_1$-regularized logistic regression-based model… ▽ More We use machine learning tools to model the line interaction of failure cascading in power grid networks. We first collect data sets of simulated trajectories of possible consecutive line failure following an initial random failure and considering actual constraints in a model power network until the system settles at a steady state. We use weighted $l_1$-regularized logistic regression-based models to find static and dynamic models that capture pairwise and latent higher-order lines' failure interactions using pairwise statistical data. The static model captures the failures' interactions near the steady states of the network, and the dynamic model captures the failure unfolding in a time series of consecutive network states. We test models over independent trajectories of failure unfolding in the network to evaluate their failure predictive power. We observe asymmetric, strongly positive, and negative interactions between different lines' states in the network. We use the static interaction model to estimate the distribution of cascade size and identify groups of lines that tend to fail together, and compare against the data. The dynamic interaction model successfully predicts the network state for long-lasting failure propagation trajectories after an initial failure. △ Less

Submitted 2 December, 2021; originally announced December 2021.

Comments: 10 pages, 7 figures

Large Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide Based on a Machine-Learned Interatomic Potential: Novel Crystalline Phases and Oxygen-Vacancy Ordering

Authors: Hossein Tahmasbi, Stefan Goedecker, S. Alireza Ghasemi

Abstract: Using a fast and accurate neural network potential we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric compositions, in particular on suboxides, i.e., Mg$_x$O$_{1-x}$ with $0.50<x<0.60$. Our extensive exploration demonstrates that for bulk stoichiometr… ▽ More Using a fast and accurate neural network potential we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric compositions, in particular on suboxides, i.e., Mg$_x$O$_{1-x}$ with $0.50<x<0.60$. Our extensive exploration demonstrates that for bulk stoichiometric compounds, there are several new low-energy rocksalt-like structures in which Mg atoms are octahedrally six--coordinated and form trigonal prismatic motifs with different stacking sequences. Furthermore, we find a dense spectrum of novel non-stoichiometric crystal phases of Mg$_x$O$_{1-x}$ for each composition of $x$. These structures are mostly similar to the rock salt structure with octahedral coordination and five--coordinated Mg atoms. Due to the removal of one oxygen atom, the energy landscape becomes more glass-like with oxygen-vacancy type structures that all lie very close to each other energetically. For the same number of magnesium and oxygen atoms our oxygen-deficient structures are lower in energy if the vacancies are aligned along lines or planes than rock salt structures with randomly distributed oxygen vacancies. We also found the putative global minima configurations for each composition of the non-stoichiometric suboxide structures. These structures are predominantly composed of (111) slabs of the rock salt structure which are terminated with Mg atoms at the top and bottom, and are stacked in different sequences along the $z$-direction. Like other Magnéli-type phases, these structures have properties that differ considerably from their stoichiometric counterparts such as low lattice thermal conductivity and high electrical conductivity. △ Less

Submitted 12 April, 2021; originally announced April 2021.

Comments: 11 pages, 9 figures

Journal ref: Phys. Rev. Materials 5, 083806 (2021)

Robust Portfolio Selection Problems: A Comprehensive Review

Authors: Alireza Ghahtarani, Ahmed Saif, Alireza Ghasemi

Abstract: In this paper, we provide a comprehensive review of recent advances in robust portfolio selection problems and their extensions, from both operational research and financial perspectives. A multi-dimensional classification of the models and methods proposed in the literature is presented, based on the types of financial problems, uncertainty sets, robust optimization approaches, and mathematical f… ▽ More In this paper, we provide a comprehensive review of recent advances in robust portfolio selection problems and their extensions, from both operational research and financial perspectives. A multi-dimensional classification of the models and methods proposed in the literature is presented, based on the types of financial problems, uncertainty sets, robust optimization approaches, and mathematical formulations. Several open questions and potential future research directions are identified. △ Less

Submitted 11 January, 2022; v1 submitted 23 March, 2021; originally announced March 2021.

Comments: 48 pages

MSC Class: 90-02 ACM Class: A.1

A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State

Authors: M. Amin. Ghasemi, Sadjaad Ozgoli, Ali. M. NasrAbadi

Abstract: Brain-computer interface (BCI) systems are usually designed specifically for each subject based on motor imagery. Therefore, the usability of these networks has become a significant challenge. The network has to be designed separately for each user, which is time-consuming for the user. Therefore, this study proposes a method by which the calibration time is significantly reduced while the classif… ▽ More Brain-computer interface (BCI) systems are usually designed specifically for each subject based on motor imagery. Therefore, the usability of these networks has become a significant challenge. The network has to be designed separately for each user, which is time-consuming for the user. Therefore, this study proposes a method by which the calibration time is significantly reduced while the classification accuracy is increased. In this method, we calibrated the features extracted from the motor imagery task by dividing the features extracted from the resting-state into both open-eye and closed-eye modes and the state in which the subject moves his eyes. The best classification accuracy was obtained using the SVM classifier using the resting-state signal in the open eye, which increased by 3.64% to 74.04%. In this paper, we also investigated the effect of recording time of the resting-state signal and the impact of eye state on the classification accuracy. △ Less

Submitted 17 March, 2021; originally announced March 2021.

Comments: 17 pages

Data Interpolation Accuracy Comparison: Gravity Model Versus Radial Basis Function

Authors: Amirehsan Ghasemi, Kelvin J Msechu, Arash Ghasemi, Mbakisya A. Onyango, Ignatius Fomunung, Joseph Owino

Abstract: In this paper, the accuracy of two mesh-free approximation approaches, the Gravity model and Radial Basis Function, are compared. The two schemes' convergence behaviors prove that RBF is faster and more accurate than the Gravity model. As a case study, the interpolation of temperature at different locations in Tennesse, USA, are compared. Delaunay mesh generation is used to create random points in… ▽ More In this paper, the accuracy of two mesh-free approximation approaches, the Gravity model and Radial Basis Function, are compared. The two schemes' convergence behaviors prove that RBF is faster and more accurate than the Gravity model. As a case study, the interpolation of temperature at different locations in Tennesse, USA, are compared. Delaunay mesh generation is used to create random points inside and on the border, which data can be incorporated in these locations. 49 MERRA weather stations as used as data sources to provide the temperature at a specific day and hour. The contours of interpolated temperatures provided in the result section assert RBF is a more accurate method than the Gravity model by showing a smoother and broader range of interpolated data. △ Less

Submitted 19 March, 2021; v1 submitted 15 February, 2021; originally announced February 2021.

An automated approach for developing neural network interatomic potentials with FLAME

Authors: Hossein Mirhosseini, Hossein Tahmasbi, Sai Ram Kuchana, S. Alireza Ghasemi, Thomas D. Kühne

Abstract: The performance of machine learning interatomic potentials relies on the quality of the training dataset. In this work, we present an approach for generating diverse and representative training data points which initiates with \it{ab initio} calculations for bulk structures. The data generation and potential construction further proceed side-by-side in a cyclic process of training the neural netwo… ▽ More The performance of machine learning interatomic potentials relies on the quality of the training dataset. In this work, we present an approach for generating diverse and representative training data points which initiates with \it{ab initio} calculations for bulk structures. The data generation and potential construction further proceed side-by-side in a cyclic process of training the neural network and crystal structure prediction based on the developed interatomic potentials. All steps of the data generation and potential development are performed with minimal human intervention. We show the reliability of our approach by assessing the performance of neural network potentials developed for two inorganic systems. △ Less

Submitted 8 February, 2021; originally announced February 2021.

A Method to Apply Piola-Kirchhoff Stress in Molecular Statics Simulation

Authors: Arman Ghasemi, Wei Gao

Abstract: A force-based optimization method is proposed to apply the first and second kind of Piola-Kirchhoff stresses in molecular statics simulation. This method is important for finite deformation problems in which the atomistic behavior can be more accurately described using Piola-Kirchhoff stresses. The performance of the method is tested and validated using Silicon as a model material. A force-based optimization method is proposed to apply the first and second kind of Piola-Kirchhoff stresses in molecular statics simulation. This method is important for finite deformation problems in which the atomistic behavior can be more accurately described using Piola-Kirchhoff stresses. The performance of the method is tested and validated using Silicon as a model material. △ Less

Submitted 1 April, 2021; v1 submitted 6 November, 2020; originally announced November 2020.

Comments: 10 pages, 6 figures; Introduction part is modified; Fig 5 is corrected

A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids

Authors: Arman Ghasemi, Amin Shojaeighadikolaei, Kailani Jones, Morteza Hashemi, Alexandru G. Bardas, Reza Ahmadi

Abstract: This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers. Furthermore, this market model enables the grid operator to leverage prosumers storage capacity as a dispatchable asset… ▽ More This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers. Furthermore, this market model enables the grid operator to leverage prosumers storage capacity as a dispatchable asset for grid support applications. Simulation results based on the Deep QNetwork (DQN) framework demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the grid operator, as well as major reductions in grid reserve power utilization. △ Less

Submitted 22 September, 2020; originally announced September 2020.

Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning

Authors: Amin Shojaeighadikolaei, Arman Ghasemi, Kailani R. Jones, Alexandru G. Bardas, Morteza Hashemi, Reza Ahmadi

Abstract: Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle operational uncertainties and incurring customer disutility, impeding their wide spread adoption in real-world applications. This paper proposes a new multiagent… ▽ More Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle operational uncertainties and incurring customer disutility, impeding their wide spread adoption in real-world applications. This paper proposes a new multiagent Reinforcement Learning (RL) based decision-making environment for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated microgrid. The proposed technique addresses several shortcomings common to traditional DR methods and provides significant economic benefits to the grid operator and prosumers. To show its better efficacy, the proposed DR method is compared to a baseline traditional operation scenario in a small-scale microgrid system. Finally, investigations on the use of prosumers energy storage capacity in this microgrid highlight the advantages of the proposed method in establishing a balanced market setup. △ Less

Submitted 22 September, 2020; originally announced September 2020.

Modulation of Control Authority in Adaptive HapticShared Control Paradigms

Authors: Vahid Izadi, Amir H. Ghasemi

Abstract: This paper presents an adaptive haptic shared control framework wherein a driver and an automation system are physically connected through a motorized steering wheel. The automation system is modeled as an intelligent agent that is not only capable of making decisions but also monitoring the human's behavior and adjusting its behavior accordingly. To enable the automation system to smoothly exchan… ▽ More This paper presents an adaptive haptic shared control framework wherein a driver and an automation system are physically connected through a motorized steering wheel. The automation system is modeled as an intelligent agent that is not only capable of making decisions but also monitoring the human's behavior and adjusting its behavior accordingly. To enable the automation system to smoothly exchange the control authority with the human partner, this paper introduces a novel self-regulating impedance controller for the automation system. To determine an optimal modulation policy, a cost function is defined. The terms of the cost function are assigned to minimize the performance error and reduce the disagreement between the human and automation system. To solve the optimal control problem, we employed a nonlinear model predictive approach and used the continuation generalized minimum residual method to solve the nonlinear cost function. To demonstrate the effectiveness of the proposed approach, simulation studies consider a scenario where the human and the automation system both detect an obstacle and negotiate on controlling the steering wheel so that the obstacle can be avoided safely. The simulations involve four interaction modes addressing the cooperation status (cooperative and uncooperative) and the desired direction of the control transfer (active safety and autopilot). △ Less

Submitted 14 July, 2020; originally announced July 2020.

Comments: 33 pages, 9 figures, Journal of Engineering Control Practice

Lest We Forget: A Dataset of Coronavirus-Related News Headlines in Swiss Media

Authors: Alireza Ghasemi, Amina Chebira

Abstract: We release our COVID-19 news dataset, containing more than 10,000 links to news articles related to the Coronavirus pandemic published in the Swiss media since early January 2020. This collection can prove beneficial in mining and analysis of the reaction of the Swiss media and the COVID-19 pandemic and extracting insightful information for further research. We hope this dataset helps researchers… ▽ More We release our COVID-19 news dataset, containing more than 10,000 links to news articles related to the Coronavirus pandemic published in the Swiss media since early January 2020. This collection can prove beneficial in mining and analysis of the reaction of the Swiss media and the COVID-19 pandemic and extracting insightful information for further research. We hope this dataset helps researchers and the public deliver results that will help analyse the pandemic and potentially lead to a better understanding of the events. △ Less

Submitted 25 June, 2020; originally announced June 2020.

Multi-Objective Channel Allocation in Cognitive Radio Networks

Authors: Ahmad Ghasemi, Foad Ghasemi

Abstract: This paper addresses the problem of channel allocation in Cognitive Radio (CR) networks. CR has been considered as a technology which improves spectrum utilization significantly by carrying out Dynamic Spectrum Management (DSM). One issue of the DSM is the using of frequency channels by secondary/CR users that are under-utilized and/or not used by primary users. CR users eager to use them when the… ▽ More This paper addresses the problem of channel allocation in Cognitive Radio (CR) networks. CR has been considered as a technology which improves spectrum utilization significantly by carrying out Dynamic Spectrum Management (DSM). One issue of the DSM is the using of frequency channels by secondary/CR users that are under-utilized and/or not used by primary users. CR users eager to use them when they are not used by primary users. The number of these spectrum bands is limited. This scarcity leads to conflict among CR users; As many as bands used by any CR user leads to decaying objective functions of other users. Thus, they have a destructive effect on each other. The paper models this conflict as a new multi-objective problem. Pareto set is attained via a Multi-Objective Optimization (MOO) technique, namely ε - constraint method. Results show the efficiency of the method. △ Less

Submitted 15 April, 2020; v1 submitted 13 April, 2020; originally announced April 2020.

Comments: 16 pages, 7 figures

Impedance Modulation for Negotiating Control Authority in a Haptic Shared Control Paradigm

Authors: Vahid Izadi, Akshay Bhardwaj, Amir H. Ghasemi

Abstract: Communication and cooperation among team members can be enhanced significantly with physical interaction. Successful collaboration requires the integration of the individual partners' intentions into a shared action plan, which may involve a continuous negotiation of intentions and roles. This paper presents an adaptive haptic shared control framework wherein a human driver and an automation syste… ▽ More Communication and cooperation among team members can be enhanced significantly with physical interaction. Successful collaboration requires the integration of the individual partners' intentions into a shared action plan, which may involve a continuous negotiation of intentions and roles. This paper presents an adaptive haptic shared control framework wherein a human driver and an automation system are physically connected through a motorized steering wheel. By virtue of haptic feedback, the driver and automation system can monitor each other actions and can still intuitively express their control intentions. The objective of this paper is to develop a systematic model for an automation system that can vary its impedance such that the control authority can transit between the two agents intuitively and smoothly. To this end, we defined a cost function that not only ensures the safety of the collaborative task but also takes account of the assistive behavior of the automation system. We employed a predictive controller based on modified least square to modulate the automation system impedance such that the cost function is optimized. The results demonstrate the significance of the proposed approach for negotiating the control authority, specifically when humans and automation are in a non-cooperative mode. Furthermore, the performance of the adaptive haptic shared control is compared with the traditional fixed automation impedance haptic shared control paradigm. △ Less

Submitted 19 March, 2020; v1 submitted 21 January, 2020; originally announced January 2020.

Comments: Final Manuscript Accepted in the 2020 American Control Conference (ACC)

FLAME: a library of atomistic modeling environments

Authors: Maximilian Amsler, Samare Rostami, Hossein Tahmasbi, Ehsan Rahmatizad, Somayeh Faraji, Robabe Rasoulkhani, S. Alireza Ghasemi

Abstract: FLAME is a software package to perform a wide range of atomistic simulations for exploring the potential energy surfaces (PES) of complex condensed matter systems. The range of methods include molecular dynamics simulations to sample free energy landscapes, saddle point searches to identify transition states, and gradient relaxations to find dynamically stable geometries. In addition to such commo… ▽ More FLAME is a software package to perform a wide range of atomistic simulations for exploring the potential energy surfaces (PES) of complex condensed matter systems. The range of methods include molecular dynamics simulations to sample free energy landscapes, saddle point searches to identify transition states, and gradient relaxations to find dynamically stable geometries. In addition to such common tasks, FLAME implements a structure prediction algorithm based on the minima hopping method (MHM) to identify the ground state structure of any system given solely the chemical composition, and a framework to train a neural network potential to reproduce the PES from $\textit{ab initio}$ calculations. The combination of neural network potentials with the MHM in FLAME allows a highly efficient and reliable identification of the ground state as well as metastable structures of molecules and crystals, as well as of nano structures, including surfaces, interfaces, and two-dimensional materials. In this manuscript, we provide detailed descriptions of the methods implemented in the FLAME code and its capabilities, together with several illustrative examples. △ Less

Submitted 4 December, 2019; originally announced December 2019.

Diversity of structural controllability of complex networks with given degree sequence

Authors: Abdorasoul Ghasemi, Márton Pósfai, Raissa M. D'Souza

Abstract: We investigate to what extent the degree sequence of a directed network constrains the number of driver nodes. We develop a pair of algorithms that take a directed degree sequence as input and aim to output a network with the maximum or minimum number of driver nodes. We find an upper bound for the maximum and a lower bound for the minimum, and show that the algorithms achieve these bounds for all… ▽ More We investigate to what extent the degree sequence of a directed network constrains the number of driver nodes. We develop a pair of algorithms that take a directed degree sequence as input and aim to output a network with the maximum or minimum number of driver nodes. We find an upper bound for the maximum and a lower bound for the minimum, and show that the algorithms achieve these bounds for all real and model networks, with few exceptions characterized by tiny system size and heterogeneous degree distributions. Applying these algorithms to a broad range of real networks, we show the gap between the upper and lower bounds can vary dramatically across different degree sequences. Thus, we introduce the notion of \bl{structural control} complexity to capture how much more difficult it is to control a specific network beyond what is required given its degree sequence, suggesting additional structure is present. Using model networks, we numerically and analytically investigate how typical features of the degree distribution affect the range of required driver nodes. We find that the minimum is determined by the number of sources or sinks, while the maximum is strongly affected by the presence of hubs. △ Less

Submitted 23 March, 2020; v1 submitted 16 May, 2019; originally announced May 2019.

Comments: 12 pages, 8 figures

Predictability of missing links in complex networks

Authors: Guillermo García-Pérez, Roya Aliakbarisani, Abdorasoul Ghasemi, M. Ángeles Serrano

Abstract: Predicting missing links in real networks is an important problem in network science to which considerable efforts have been devoted, giving as a result a vast plethora of link prediction methods in the literature. In this work, we take a different point of view on the problem and study the theoretical limitations to the predictability of missing links. In particular, we hypothesise that there is… ▽ More Predicting missing links in real networks is an important problem in network science to which considerable efforts have been devoted, giving as a result a vast plethora of link prediction methods in the literature. In this work, we take a different point of view on the problem and study the theoretical limitations to the predictability of missing links. In particular, we hypothesise that there is an irreducible uncertainty in link prediction on real networks as a consequence of the random nature of their formation process. By considering ensembles defined by well-known network models, we prove analytically that even the best possible link prediction method for an ensemble, given by the ranking of the ensemble connection probabilities, yields a limited precision. This result suggests a theoretical limitation to the predictability of links in real complex networks. Finally, we show that connection probabilities inferred by fitting network models to real networks allow to estimate an upper-bound to the predictability of missing links, and we further propose a method to approximate such bound from incomplete instances of real-world networks. △ Less

Submitted 31 January, 2019; originally announced February 2019.

Synthesis, crystal growth and characterization of the pyrochlore Er2Ti2O7

Authors: Q. Wang, A. Ghasemi, A. Scheie, S. M. Koohpayeh

Abstract: Pyrochlore erbium titanate samples in the form of powders (with nominal compositions of Er2Ti2+xO7) and bulk single crystals were prepared to elucidate the effect of synthesis and growth conditions on their composition, structure, and transition temperature. All samples were characterized using X-ray diffraction and specific heat measurements. Larger lattice parameters were measured for the Ti-def… ▽ More Pyrochlore erbium titanate samples in the form of powders (with nominal compositions of Er2Ti2+xO7) and bulk single crystals were prepared to elucidate the effect of synthesis and growth conditions on their composition, structure, and transition temperature. All samples were characterized using X-ray diffraction and specific heat measurements. Larger lattice parameters were measured for the Ti-deficient stuffed powders, Er2(Ti2-xErx)O7-δ, while Ti-rich anti-stuffed powders, (Er2-xTix)Ti2O7+δ, showed a decrease in lattice parameter. The lattice parameter for the stoichiometrically synthesized powder, Er2Ti2O7, was measured to be a = 10.07522(9) Å. Single crystals grown by the conventional floating zone (FZ) technique were Ti deficient (stuffed) and darker in color due to oxygen vacancies. Using the traveling solvent floating zone (TSFZ), a high structural quality, transparent and stoichiometric Er2Ti2O7 single crystal was grown at a lower temperature using the TiO2 solvent. Heat capacity measurements of the TSFZ grown Er2Ti2O7 crystal and stoichiometric powder exhibited a very close magnetic transition temperature TC ~1.23K that was suppressed in off-stoichiometric samples, demonstrating correlations between stoichiometry and ground state magnetism in Er2Ti2O7. △ Less

Submitted 13 December, 2018; originally announced December 2018.

Partial Shading Detection and Smooth Maximum Power Point Tracking of PV Arrays under PSC

Authors: Mohammad Amin Ghasemi, Hossein Mohammadian Foroushani, Mostafa Parniani

Abstract: One of the most important issues in the operation of a photovoltaic (PV) system is extracting maximum power from the PV array, especially in partial shading condition (PSC). Under PSC, P-V characteristic of PV arrays will have multiple peak points, only one of which is global maximum. Conventional maximum power point tracking (MPPT) methods are not able to extract maximum power in this condition.… ▽ More One of the most important issues in the operation of a photovoltaic (PV) system is extracting maximum power from the PV array, especially in partial shading condition (PSC). Under PSC, P-V characteristic of PV arrays will have multiple peak points, only one of which is global maximum. Conventional maximum power point tracking (MPPT) methods are not able to extract maximum power in this condition. In this paper, a novel two-stage MPPT method is presented to overcome this drawback. In the first stage, a method is proposed to determine the occurrence of PSC, and in the second stage, using a new algorithm that is based on ramp change of the duty cycle and continuous sampling from the P-V characteristic of the array, global maximum power point of array is reached. P&O algorithm is then re-activated to trace small changes of the new MPP. Open loop operation of the proposed method makes its implementation cheap and simple. The method is robust in the face of changing environmental conditions and array characteristics, and has minimum negative impact on the connected power system. Simulations in Matlab/Simulink and experimental results validate the performance of the proposed methods. △ Less

Submitted 29 October, 2018; originally announced October 2018.

Comments: 12 pages, 11 figures, 4 tables, EEE Transactions on Power Electronics 31.9 (2016): 6281-6292

Journal ref: M. A. Ghasemi, H. M. Foroushani, M. Parniani, "Partial shading detection and smooth maximum power point tracking of PV arrays under PSC", IEEE Trans. Power Electron., vol. 31, no. 9, pp. 6281-6292, Sep. 2016