Showing 1–50 of 51 results for author: Martínez, F

Inducing Spatial Locality in Vision Transformers through the Training Protocol

Authors: Eduardo Santiago Toledo, Asael Fabian Martínez

Abstract: We investigate whether the training protocol can induce spatial locality in the early layers of a Vision Transformer (ViT) trained from scratch, without large-scale pretraining. Keeping the architecture and optimization procedure fixed, we compare a Baseline protocol with a Modern protocol (AutoAugment/ColorJitter, CutMix, and Label Smoothing) on CIFAR-10, CIFAR-100, and Tiny-ImageNet, characteriz… ▽ More We investigate whether the training protocol can induce spatial locality in the early layers of a Vision Transformer (ViT) trained from scratch, without large-scale pretraining. Keeping the architecture and optimization procedure fixed, we compare a Baseline protocol with a Modern protocol (AutoAugment/ColorJitter, CutMix, and Label Smoothing) on CIFAR-10, CIFAR-100, and Tiny-ImageNet, characterizing each attention head via Mean Attention Distance (MAD) and normalized entropy. Across all three datasets, the Modern protocol produces more local and more concentrated attention in early layers; on CIFAR-100, the minimum MAD drops from 0.316 (Baseline) to 0.008 (Modern). To identify the source of this effect, we conduct an ablation study on CIFAR-100 by adding or removing each component individually. The results identify CutMix as the determining component within our experiments: all conditions with CutMix exhibit MAD 0.024, while all conditions without CutMix remain at MAD 0.210. AutoAugment and Label Smoothing show no independent effect on locality. Taken together, these findings suggest that the pressure to classify from partial image regions, induced by CutMix, can promote the emergence of local attention in Vision Transformers. △ Less

Submitted 11 May, 2026; originally announced May 2026.

An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing

Authors: Lianrui Zuo, Yihao Liu, Gaurav Rudravaram, Karthik Ramadass, Aravind R. Krishnan, Michael D. Phillips, Yelena G. Bodien, Mayur B. Patel, Paula Trujillo, Yency Forero Martinez, Stephen A. Deppen, Eric L. Grogan, Fabien Maldonado, Kevin McGann, Hudson M. Holmes, Laurie E. Cutting, Yuankai Huo, Bennett A. Landman

Abstract: Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends beyond model design to require dataset-aware workflow configuration and provenance tracking. Two requirements therefore become central: \textbf{adaptability}, the ability to configure workflows according to dataset-speci… ▽ More Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends beyond model design to require dataset-aware workflow configuration and provenance tracking. Two requirements therefore become central: \textbf{adaptability}, the ability to configure workflows according to dataset-specific conditions and evolving analytical goals; and \textbf{reproducibility}, the guarantee that all transformations and decisions are explicitly recorded and re-executable. Here, we present an artifact-based agent framework that introduces a semantic layer to augment medical image processing. The framework formalizes intermediate and final outputs through an artifact contract, enabling structured interrogation of workflow state and goal-conditioned assembly of configurations from a modular rule library. Execution is delegated to a workflow executor to preserve deterministic computational graph construction and provenance tracking, while the agent operates locally to comply with most privacy constraints. We evaluate the framework on real-world clinical CT and MRI cohorts, demonstrating adaptive configuration synthesis, deterministic reproducibility across repeated executions, and artifact-grounded semantic querying. These results show that adaptive workflow configuration can be achieved without compromising reproducibility in heterogeneous clinical environments. △ Less

Submitted 31 March, 2026; originally announced April 2026.

A Geometric Multimodal Foundation Model Integrating Bp-MRI and Clinical Reports in Prostate Cancer Classification

Authors: Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Abstract: Prostate cancer (PCa) is one of the most common cancers in men worldwide. Bi-parametric MRI (bp-MRI) and clinical variables are crucial for PCa identification and improving treatment decisions. However, this process is subjective to expert interpretations. Furthermore, most existing computer-aided diagnosis methods focus on imaging-based models, overlooking the clinical context and suffering from… ▽ More Prostate cancer (PCa) is one of the most common cancers in men worldwide. Bi-parametric MRI (bp-MRI) and clinical variables are crucial for PCa identification and improving treatment decisions. However, this process is subjective to expert interpretations. Furthermore, most existing computer-aided diagnosis methods focus on imaging-based models, overlooking the clinical context and suffering from data scarcity, limiting their ability to learn robust representations. We propose a geometric multimodal Foundation Model (FM), named MFM-Geom, that learns representations from bp-MRI and clinical reports, encoding visual findings and information from the context of clinical variables. In the representations classification head, the approach leverages symmetric positive definite (SPD) matrices and Riemannian deep learning to integrate imaging-text representations from a biomedical multimodal FM. Using 10% of the training data, MFM-Geom outperformed baseline class token embedding-based classification (+8.3%, AUC-PR of 90.67). Generalization on external dataset confirmed the robustness of fine-tuning biomedical FM, achieving an AUC-PR of 90.6. △ Less

Submitted 30 January, 2026; originally announced February 2026.

Comments: Accepted at IEEE International Symposium on Biomedical Imaging (ISBI) 2026

Journal ref: 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)

Automated univariate time series forecasting with regression trees

Authors: Francisco Martínez, María P. Frías

Abstract: This paper describes a methodology for automated univariate time series forecasting using regression trees and their ensembles: bagging and random forests. The key aspects that are addressed are: the use of an autoregressive approach and recursive forecasts, how to select the autoregressive features, how to deal with trending series and how to cope with seasonal behavior. Experimental results show… ▽ More This paper describes a methodology for automated univariate time series forecasting using regression trees and their ensembles: bagging and random forests. The key aspects that are addressed are: the use of an autoregressive approach and recursive forecasts, how to select the autoregressive features, how to deal with trending series and how to cope with seasonal behavior. Experimental results show a forecast accuracy comparable with well-established statistical models such as exponential smoothing or ARIMA. Furthermore, a publicly available software implementing all the proposed strategies has been developed and is described in the paper. △ Less

Submitted 21 January, 2026; originally announced February 2026.

Comments: 23 pages, 17 figures

RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT

Authors: John M. Oyer, Ali Namvar, Benjamin A. Hoff, Wassim W. Labaki, Ella A. Kazerooni, Charles R. Hatt, Fernando J. Martinez, MeiLan K. Han, Craig J. Galbán, Sundaresh Ram

Abstract: Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically i… ▽ More Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically informed topology correction. The segmentation network produces an initial airway mask, after which a skeleton-based algorithm identifies potential discontinuities and proposes reconnections. A 1D convolutional classifier then determines which candidate links correspond to true anatomical branches versus false or obstructed paths. We evaluate RepAir on two distinct datasets: ATM'22, comprising annotated CT scans from predominantly healthy subjects and AeroPath, encompassing annotated scans with severe airway pathology. Across both datasets, RepAir outperforms existing 3D U-Net-based approaches such as Bronchinet and NaviAirway on both voxel-level and topological metrics, and produces more complete and anatomically consistent airway trees while maintaining high segmentation accuracy. △ Less

Submitted 11 February, 2026; v1 submitted 18 November, 2025; originally announced November 2025.

Comments: 4 pages, 3 figures, 1 table. Oral presentation accepted to SSIAI 2026 Conference on Jan 20, 2026

ACM Class: I.2.6; I.4.6

A Second-Order Attention Mechanism For Prostate Cancer Segmentation and Detection in Bi-Parametric MRI

Authors: Mateo Ortiz, Juan Olmos, Fabio Martínez

Abstract: The detection of clinically significant prostate cancer lesions (csPCa) from biparametric magnetic resonance imaging (bp-MRI) has emerged as a noninvasive imaging technique for improving accurate diagnosis. Nevertheless, the analysis of such images remains highly dependent on the subjective expert interpretation. Deep learning approaches have been proposed for csPCa lesions detection and segmentat… ▽ More The detection of clinically significant prostate cancer lesions (csPCa) from biparametric magnetic resonance imaging (bp-MRI) has emerged as a noninvasive imaging technique for improving accurate diagnosis. Nevertheless, the analysis of such images remains highly dependent on the subjective expert interpretation. Deep learning approaches have been proposed for csPCa lesions detection and segmentation, but they remain limited due to their reliance on extensively annotated datasets. Moreover, the high lesion variability across prostate zones poses additional challenges, even for expert radiologists. This work introduces a second-order geometric attention (SOGA) mechanism that guides a dedicated segmentation network, through skip connections, to detect csPCa lesions. The proposed attention is modeled on the Riemannian manifold, learning from symmetric positive definitive (SPD) representations. The proposed mechanism was integrated into standard U-Net and nnU-Net backbones, and was validated on the publicly available PI-CAI dataset, achieving an Average Precision (AP) of 0.37 and an Area Under the ROC Curve (AUC-ROC) of 0.83, outperforming baseline networks and attention-based methods. Furthermore, the approach was evaluated on the Prostate158 dataset as an independent test cohort, achieving an AP of 0.37 and an AUC-ROC of 0.75, confirming robust generalization and suggesting discriminative learned representations. △ Less

Submitted 7 November, 2025; originally announced November 2025.

Comments: Accepted at the 28th Iberoamerican Congress on Pattern Recognition (CIARP 2025). To appear in Lecture Notes in Computer Science (LNCS), Springer

ACM Class: I.4.6; I.5.4

Federative ischemic stroke segmentation as alternative to overcome domain-shift multi-institution challenges

Authors: Edgar Rangel, Fabio Martinez

Abstract: Stroke is the second leading cause of death and the third leading cause of disability worldwide. Clinical guidelines establish diffusion resonance imaging (DWI, ADC) as the standard for localizing, characterizing, and measuring infarct volume, enabling treatment support and prognosis. Nonetheless, such lesion analysis is highly variable due to different patient demographics, scanner vendors, and e… ▽ More Stroke is the second leading cause of death and the third leading cause of disability worldwide. Clinical guidelines establish diffusion resonance imaging (DWI, ADC) as the standard for localizing, characterizing, and measuring infarct volume, enabling treatment support and prognosis. Nonetheless, such lesion analysis is highly variable due to different patient demographics, scanner vendors, and expert annotations. Computational support approaches have been key to helping with the localization and segmentation of lesions. However, these strategies are dedicated solutions that learn patterns from only one institution, lacking the variability to generalize geometrical lesions shape models. Even worse, many clinical centers lack sufficient labeled samples to adjust these dedicated solutions. This work developed a collaborative framework for segmenting ischemic stroke lesions in DWI sequences by sharing knowledge from deep center-independent representations. From 14 emulated healthcare centers with 2031 studies, the FedAvg model achieved a general DSC of $0.71 \pm 0.24$, AVD of $5.29 \pm 22.74$, ALD of $2.16 \pm 3.60$ and LF1 of $0.70 \pm 0.26$ over all centers, outperforming both the centralized and other federated rules. Interestingly, the model demonstrated strong generalization properties, showing uniform performance across different lesion categories and reliable performance in out-of-distribution centers (with DSC of $0.64 \pm 0.29$ and AVD of $4.44 \pm 8.74$ without any additional training). △ Less

Submitted 22 August, 2025; originally announced August 2025.

Comments: 11 pages, 4 figures, 3 tables, source code available

Stackelberg Coupling of Online Representation Learning and Reinforcement Learning

Authors: Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen

Abstract: Deep Q-learning jointly learns representations and values within monolithic networks, promising beneficial co-adaptation between features and value estimates. Although this architecture has attained substantial success, the coupling between representation and value learning creates instability as representations must constantly adapt to non-stationary value targets, while value estimates depend on… ▽ More Deep Q-learning jointly learns representations and values within monolithic networks, promising beneficial co-adaptation between features and value estimates. Although this architecture has attained substantial success, the coupling between representation and value learning creates instability as representations must constantly adapt to non-stationary value targets, while value estimates depend on these shifting representations. This is compounded by high variance in bootstrapped targets, which causes bias in value estimation in off-policy methods. We introduce Stackelberg Coupled Representation and Reinforcement Learning (SCORER), a framework for value-based RL that views representation and Q-learning as two strategic agents in a hierarchical game. SCORER models the Q-function as the leader, which commits to its strategy by updating less frequently, while the perception network (encoder) acts as the follower, adapting more frequently to learn representations that minimize Bellman error variance given the leader's committed strategy. Through this division of labor, the Q-function minimizes MSBE while perception minimizes its variance, thereby reducing bias accordingly, with asymmetric updates allowing stable co-adaptation, unlike simultaneous parameter updates in monolithic solutions. Our proposed SCORER framework leads to a bi-level optimization problem whose solution is approximated by a two-timescale algorithm that creates an asymmetric learning dynamic between the two players. Extensive experiments on DQN and its variants demonstrate that gains stem from algorithmic insight rather than model complexity. △ Less

Submitted 28 January, 2026; v1 submitted 10 August, 2025; originally announced August 2025.

Comments: The Fourteenth International Conference on Learning Representations (ICLR 2026)

A Flexible Instruction Set Architecture for Efficient GEMMs

Authors: Alexandre de Limas Santana, Adrià Armejach, Francesc Martinez, Erich Focht, Marc Casas

Abstract: GEneral Matrix Multiplications (GEMMs) are recurrent in high-performance computing and deep learning workloads. Typically, high-end CPUs accelerate GEMM workloads with Single-Instruction Multiple Data (SIMD) or vector Instruction Set Architectures (ISAs). Since these ISAs face significant issues when running GEMM workloads, particularly when dealing with small, tall, or skinny matrices, matrix ISA… ▽ More GEneral Matrix Multiplications (GEMMs) are recurrent in high-performance computing and deep learning workloads. Typically, high-end CPUs accelerate GEMM workloads with Single-Instruction Multiple Data (SIMD) or vector Instruction Set Architectures (ISAs). Since these ISAs face significant issues when running GEMM workloads, particularly when dealing with small, tall, or skinny matrices, matrix ISAs have been proposed and implemented by major hardware vendors in the last years. Although these matrix ISAs deliver larger throughput when running GEMMs than their SIMD/vector counterparts, they are rigid solutions unable to dynamically adapt themselves to application-specific aspects like the data format. This paper demonstrates that the state-of-the-art matrix ISAs deliver suboptimal performance when running the most commonly used convolution and transformer models. This paper proposes the Matrix Tile Extension (MTE), the first matrix ISA that completely decouples the instruction set architecture from the microarchitecture and seamlessly interacts with existing vector ISAs. MTE incurs minimal implementation overhead since it only requires a few additional instructions and a 64-bit Control Status Register (CSR) to keep its state. Specifically, MTE can i) vectorize GEMMs across the three dimensions M, N, and K; ii) leverage the capacity of the existing vector register file; and iii) decouple the tile shape from the underlying microarchitecture. MTE achieves speed-ups of 1.35x over the best state-of-the-art matrix ISA. △ Less

Submitted 4 July, 2025; originally announced July 2025.

ACM Class: C.1.0

Membrane: Accelerating Database Analytics with Bank-Level DRAM-PIM Filtering

Authors: Akhil Shekar, Kevin Gaffney, Martin Prammer, Khyati Kiyawat, Lingxi Wu, Helena Caminal, Zhenxing Fan, Yimin Gao, Ashish Venkat, José F. Martínez, Jignesh Patel, Kevin Skadron

Abstract: In-memory database query processing frequently involves substantial data transfers between the CPU and memory, leading to inefficiencies due to Von Neumann bottleneck. Processing-in-Memory (PIM) architectures offer a viable solution to alleviate this bottleneck. In our study, we employ a commonly used software approach that streamlines JOIN operations into simpler selection or filtering tasks usin… ▽ More In-memory database query processing frequently involves substantial data transfers between the CPU and memory, leading to inefficiencies due to Von Neumann bottleneck. Processing-in-Memory (PIM) architectures offer a viable solution to alleviate this bottleneck. In our study, we employ a commonly used software approach that streamlines JOIN operations into simpler selection or filtering tasks using pre-join denormalization which makes query processing workload more amenable to PIM acceleration. This research explores DRAM design landscape to evaluate how effectively these filtering tasks can be efficiently executed across DRAM hierarchy and their effect on overall application speedup. We also find that operations such as aggregates are more suitably executed on the CPU rather than PIM. Thus, we propose a cooperative query processing framework that capitalizes on both CPU and PIM strengths, where (i) the DRAM-based PIM block, with its massive parallelism, supports scan operations while (ii) CPU, with its flexible architecture, supports the rest of query execution. This allows us to utilize both PIM and CPU where appropriate and prevent dramatic changes to the overall system architecture. With these minimal modifications, our methodology enables us to faithfully perform end-to-end performance evaluations using established analytics benchmarks such as TPCH and star-schema benchmark (SSB). Our findings show that this novel mapping approach improves performance, delivering a 5.92x/6.5x speedup compared to a traditional schema and 3.03-4.05x speedup compared to a denormalized schema with 9-17% memory overhead, depending on the degree of partial denormalization. Further, we provide insights into query selectivity, memory overheads, and software optimizations in the context of PIM-based filtering, which better explain the behavior and performance of these systems across the benchmarks. △ Less

Submitted 8 April, 2025; originally announced April 2025.

A multitask transformer to sign language translation using motion gesture primitives

Authors: Fredy Alejandro Mendoza López, Jefferson Rodriguez, Fabio Martínez

Abstract: The absence of effective communication the deaf population represents the main social gap in this community. Furthermore, the sign language, main deaf communication tool, is unlettered, i.e., there is no formal written representation. In consequence, main challenge today is the automatic translation among spatiotemporal sign representation and natural text language. Recent approaches are based on… ▽ More The absence of effective communication the deaf population represents the main social gap in this community. Furthermore, the sign language, main deaf communication tool, is unlettered, i.e., there is no formal written representation. In consequence, main challenge today is the automatic translation among spatiotemporal sign representation and natural text language. Recent approaches are based on encoder-decoder architectures, where the most relevant strategies integrate attention modules to enhance non-linear correspondences, besides, many of these approximations require complex training and architectural schemes to achieve reasonable predictions, because of the absence of intermediate text projections. However, they are still limited by the redundant background information of the video sequences. This work introduces a multitask transformer architecture that includes a gloss learning representation to achieve a more suitable translation. The proposed approach also includes a dense motion representation that enhances gestures and includes kinematic information, a key component in sign language. From this representation it is possible to avoid background information and exploit the geometry of the signs, in addition, it includes spatiotemporal representations that facilitate the alignment between gestures and glosses as an intermediate textual representation. The proposed approach outperforms the state-of-the-art evaluated on the CoL-SLTD dataset, achieving a BLEU-4 of 72,64% in split 1, and a BLEU-4 of 14,64% in split 2. Additionally, the strategy was validated on the RWTH-PHOENIX-Weather 2014 T dataset, achieving a competitive BLEU-4 of 11,58%. △ Less

Submitted 25 March, 2025; originally announced March 2025.

Comments: 32 pages, 10 tables, 13 figures

A digital eye-fixation biomarker using a deep anomaly scheme to classify Parkisonian patterns

Authors: Juan Niño, Luis Guayacán, Santiago Gómez, Fabio Martínez

Abstract: Oculomotor alterations constitute a promising biomarker to detect and characterize Parkinson's disease (PD), even in prodromal stages. Currently, only global and simplified eye movement trajectories are employed to approximate the complex and hidden kinematic relationships of the oculomotor function. Recent advances on machine learning and video analysis have encouraged novel characterizations of… ▽ More Oculomotor alterations constitute a promising biomarker to detect and characterize Parkinson's disease (PD), even in prodromal stages. Currently, only global and simplified eye movement trajectories are employed to approximate the complex and hidden kinematic relationships of the oculomotor function. Recent advances on machine learning and video analysis have encouraged novel characterizations of eye movement patterns to quantify PD. These schemes enable the identification of spatiotemporal segments primarily associated with PD. However, they rely on discriminative models that require large training datasets and depend on balanced class distributions. This work introduces a novel video analysis scheme to quantify Parkinsonian eye fixation patterns with an anomaly detection framework. Contrary to classical deep discriminative schemes that learn differences among labeled classes, the proposed approach is focused on one-class learning, avoiding the necessity of a significant amount of data. The proposed approach focuses only on Parkinson's representation, considering any other class sample as an anomaly of the distribution. This approach was evaluated for an ocular fixation task, in a total of 13 control subjects and 13 patients on different stages of the disease. The proposed digital biomarker achieved an average sensitivity and specificity of 0.97 and 0.63, respectively, yielding an AUC-ROC of 0.95. A statistical test shows significant differences (p < 0.05) among predicted classes, evidencing a discrimination between patients and control subjects. △ Less

Submitted 24 February, 2025; originally announced February 2025.

Comments: 6 pages, 4 images

Spatio-temporal transformer to support automatic sign language translation

Authors: Christian Ruiz, Fabio Martinez

Abstract: Sign Language Translation (SLT) systems support hearing-impaired people communication by finding equivalences between signed and spoken languages. This task is however challenging due to multiple sign variations, complexity in language and inherent richness of expressions. Computational approaches have evidenced capabilities to support SLT. Nonetheless, these approaches remain limited to cover ges… ▽ More Sign Language Translation (SLT) systems support hearing-impaired people communication by finding equivalences between signed and spoken languages. This task is however challenging due to multiple sign variations, complexity in language and inherent richness of expressions. Computational approaches have evidenced capabilities to support SLT. Nonetheless, these approaches remain limited to cover gestures variability and support long sequence translations. This paper introduces a Transformer-based architecture that encodes spatio-temporal motion gestures, preserving both local and long-range spatial information through the use of multiple convolutional and attention mechanisms. The proposed approach was validated on the Colombian Sign Language Translation Dataset (CoL-SLTD) outperforming baseline approaches, and achieving a BLEU4 of 46.84%. Additionally, the proposed approach was validated on the RWTH-PHOENIX-Weather-2014T (PHOENIX14T), achieving a BLEU4 score of 30.77%, demonstrating its robustness and effectiveness in handling real-world variations △ Less

Submitted 4 February, 2025; originally announced February 2025.

Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach

Authors: Fernando Martinez, Mariyam Mapkar, Ali Alfatemi, Mohamed Rahouti, Yufeng Xin, Kaiqi Xiong, Nasir Ghani

Abstract: Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learning-based technique for detecting DDoS attacks, a paramount cybersecurity challenge with evolving complexity and scale. Specifically, we propose a new dual-space prototypical network that leverages a unique dual-space loss… ▽ More Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learning-based technique for detecting DDoS attacks, a paramount cybersecurity challenge with evolving complexity and scale. Specifically, we propose a new dual-space prototypical network that leverages a unique dual-space loss function to enhance detection accuracy for various attack patterns through geometric and angular similarity measures. This approach capitalizes on the strengths of representation learning within the latent space (a lower-dimensional representation of data that captures complex patterns for machine learning analysis), improving the model's adaptability and sensitivity towards varying DDoS attack vectors. Our comprehensive evaluation spans multiple training environments, including offline training, simulated online training, and prototypical network scenarios, to validate the model's robustness under diverse data abundance and scarcity conditions. The Multilayer Perceptron (MLP) with Attention, trained with our dual-space prototypical design over a reduced training set, achieves an average accuracy of 94.85% and an F1-Score of 94.71% across our tests, showcasing its effectiveness in dynamic and constrained real-world scenarios. △ Less

Submitted 3 June, 2024; originally announced June 2024.

Comments: 9 pages, The 33rd International Conference on Computer Communications and Networks (ICCCN 2024)

Jina CLIP: Your CLIP Model Is Also Your Text Retriever

Authors: Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao

Abstract: Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval… ▽ More Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval systems that keep separate embeddings and models for text-only and multimodal tasks. We propose a novel, multi-task contrastive training method to address this issue, which we use to train the jina-clip-v1 model to achieve the state-of-the-art performance on both text-image and text-text retrieval tasks. △ Less

Submitted 26 June, 2024; v1 submitted 30 May, 2024; originally announced May 2024.

Comments: 4 pages, MFM-EAI@ICML2024

MSC Class: 68T50 ACM Class: I.2.7

Comparing E-bike and Conventional Bicycle Use Patterns in a Public Bike Share System: A Case Study of Richmond, VA

Authors: Yifan Yang, Elliott Sloate, Nashid Khadem, Celeste Chavis, Vanessa Frias Martinez

Abstract: The results show that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds, and lower rates of uphill elevation change. The origin-destination analysis considering the business, mixed use, residential, and other uses shows extremely similar trends, with a large number of trips staying within either business or residential locations or mixed use. The roadw… ▽ More The results show that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds, and lower rates of uphill elevation change. The origin-destination analysis considering the business, mixed use, residential, and other uses shows extremely similar trends, with a large number of trips staying within either business or residential locations or mixed use. The roadway use analysis shows that pedelecs are used farther outside of the city than bikes. △ Less

Submitted 28 April, 2024; originally announced April 2024.

Journal ref: Journal of Cycling and Micromobility Research (2024)

COLON: The largest COlonoscopy LONg sequence public database

Authors: Lina Ruiz, Franklin Sierra-Jerez, Jair Ruiz, Fabio Martinez

Abstract: Colorectal cancer is the third most aggressive cancer worldwide. Polyps, as the main biomarker of the disease, are detected, localized, and characterized through colonoscopy procedures. Nonetheless, during the examination, up to 25% of polyps are missed, because of challenging conditions (camera movements, lighting changes), and the close similarity of polyps and intestinal folds. Besides, there i… ▽ More Colorectal cancer is the third most aggressive cancer worldwide. Polyps, as the main biomarker of the disease, are detected, localized, and characterized through colonoscopy procedures. Nonetheless, during the examination, up to 25% of polyps are missed, because of challenging conditions (camera movements, lighting changes), and the close similarity of polyps and intestinal folds. Besides, there is a remarked subjectivity and expert dependency to observe and detect abnormal regions along the intestinal tract. Currently, publicly available polyp datasets have allowed significant advances in computational strategies dedicated to characterizing non-parametric polyp shapes. These computational strategies have achieved remarkable scores of up to 90% in segmentation tasks. Nonetheless, these strategies operate on cropped and expert-selected frames that always observe polyps. In consequence, these computational approximations are far from clinical scenarios and real applications, where colonoscopies are redundant on intestinal background with high textural variability. In fact, the polyps typically represent less than 1% of total observations in a complete colonoscopy record. This work introduces COLON: the largest COlonoscopy LONg sequence dataset with around of 30 thousand polyp labeled frames and 400 thousand background frames. The dataset was collected from a total of 30 complete colonoscopies with polyps at different stages, variations in preparation procedures, and some cases the observation of surgical instrumentation. Additionally, 10 full intestinal background video control colonoscopies were integrated in order to achieve a robust polyp-background frame differentiation. The COLON dataset is open to the scientific community to bring new scenarios to propose computational tools dedicated to polyp detection and segmentation over long sequences, being closer to real colonoscopy scenarios. △ Less

Submitted 1 March, 2024; originally announced March 2024.

Comments: 7 pages, 3 figures, 3 tables

Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

Authors: Isabelle Mohr, Markus Krimmel, Saba Sturua, Mohammad Kalim Akram, Andreas Koukounas, Michael Günther, Georgios Mastrapas, Vinit Ravishankar, Joan Fontanals Martínez, Feng Wang, Qi Liu, Ziniu Yu, Jie Fu, Saahil Ognawala, Susana Guzman, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao

Abstract: We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. By f… ▽ More We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. By focusing on bilingual models and introducing a unique multi-task learning objective, we have significantly improved the model performance on STS tasks, which outperforms the capabilities of existing multilingual models in both target language understanding and cross-lingual evaluation tasks. Moreover, our bilingual models are more efficient, requiring fewer parameters and less memory due to their smaller vocabulary needs. Furthermore, we have expanded the Massive Text Embedding Benchmark (MTEB) to include benchmarks for German and Spanish embedding models. This integration aims to stimulate further research and advancement in text embedding technologies for these languages. △ Less

Submitted 26 February, 2024; originally announced February 2024.

MSC Class: 68T50 ACM Class: I.2.7

FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning

Authors: Francisco Ardévol Martínez, Michiel Min, Daniela Huppenkothen, Inga Kamp, Paul I. Palmer

Abstract: Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between model complexity and run time. Reaching this compromise leads to the simplification of many physical and chemical processes (e.g. parameterised temperature stru… ▽ More Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between model complexity and run time. Reaching this compromise leads to the simplification of many physical and chemical processes (e.g. parameterised temperature structure). Here we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm, for exoplanet atmospheric retrievals. The goal is to speed up retrievals so they can be run with more computationally expensive atmospheric models, such as those computing the temperature structure using radiative transfer. We generate 100 synthetic observations using ARCiS (ARtful Modeling Code for exoplanet Science, an atmospheric modelling code with the flexibility to compute models in varying degrees of complexity) and perform retrievals on them to test the faithfulness of the SNPE posteriors. The faithfulness quantifies whether the posteriors contain the ground truth as often as we expect. We also generate a synthetic observation of a cool brown dwarf using the self-consistent capabilities of ARCiS and run a retrieval with self-consistent models to showcase the possibilities that SNPE opens. We find that SNPE provides faithful posteriors and is therefore a reliable tool for exoplanet atmospheric retrievals. We are able to run a self-consistent retrieval of a synthetic brown dwarf spectrum using only 50,000 forward model evaluations. We find that SNPE can speed up retrievals between $\sim2\times$ and $\geq10\times$ depending on the computational load of the forward model, the dimensionality of the observation, and the signal-to-noise ratio of the observation. We make the code publicly available for the community on Github. △ Less

Submitted 8 January, 2024; originally announced January 2024.

Comments: Accepted for publication at A&A

FODT: Fast, Online, Distributed and Temporary Failure Recovery Approach for MEC

Authors: Xin Yuan, Ning Li, Jose Fernan Martinez

Abstract: Mobile edge computing (MEC) can reduce the latency of cloud computing successfully. However, the edge server may fail due to the hardware of software issues. When the edge server failure happens, the users who offload tasks to this server will be affected. How to recover the services for these affected users quickly and effectively is challenging. Moreover, considering that the server failure is c… ▽ More Mobile edge computing (MEC) can reduce the latency of cloud computing successfully. However, the edge server may fail due to the hardware of software issues. When the edge server failure happens, the users who offload tasks to this server will be affected. How to recover the services for these affected users quickly and effectively is challenging. Moreover, considering that the server failure is continuous and temporary, and the failed server can be repaired, the previous works cannot handle this problem effectively. Therefore, in this paper, we propose the fast, online, distributed, and temporary failure recovery algorithm (FODT) for MEC. In FODT, when edge sever failure happens, only the affected APs recalculate their user-server allocation strategies and the other APs do not change their strategies. For the affected access points (Aps), the strategies before server failure are reused to reduce complexity and latency. When the failed server is repaired, the influenced APs reuse the strategies before server failure to offload task to this server. Based on this approach, the FODT can achieve better performance than previous works. To the best of knowledge, the FODT is the first failure recovery algorithm, and when compared with previous research, it has higher failure recovery efficiency and lower complexity with acceptable approximate ratio. △ Less

Submitted 16 April, 2025; v1 submitted 25 December, 2023; originally announced December 2023.

Comments: 12 pages, 7 figures

Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection

Authors: Lukas Hirsch, Yu Huang, Hernan A. Makse, Danny F. Martinez, Mary Hughes, Sarah Eskreis-Winkler, Katja Pinker, Elizabeth Morris, Lucas C. Parra, Elizabeth J. Sutton

Abstract: Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based on the current MRI, with the objective of reducing screening burden and facilitating early detection. An AI algorithm was developed on 53,858 breasts from 12,694 patients who underwent screening or diagnostic MRI and acc… ▽ More Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based on the current MRI, with the objective of reducing screening burden and facilitating early detection. An AI algorithm was developed on 53,858 breasts from 12,694 patients who underwent screening or diagnostic MRI and accrued over 12 years, with 2,331 confirmed cancers. A first U-Net was trained to segment lesions and identify regions of concern. A second convolutional network was trained to detect malignant cancer using features extracted by the U-Net. This network was then fine-tuned to estimate the risk of developing cancer within a year in cases that radiologists considered normal or likely benign. Risk predictions from this AI were evaluated with a retrospective analysis of 9,183 breasts from a high-risk screening cohort, which were not used for training. Statistical analysis focused on the tradeoff between number of omitted exams versus negative predictive value, and number of potential early detections versus positive predictive value. The AI algorithm identified regions of concern that coincided with future tumors in 52% of screen-detected cancers. Upon directed review, a radiologist found that 71.3% of cancers had a visible correlate on the MRI prior to diagnosis, 65% of these correlates were identified by the AI model. Reevaluating these regions in 10% of all cases with higher AI-predicted risk could have resulted in up to 33% early detections by a radiologist. Additionally, screening burden could have been reduced in 16% of lower-risk cases by recommending a later follow-up without compromising current interval cancer rate. With increasing datasets and improving image quality we expect this new AI-aided, adaptive screening to meaningfully reduce screening burden and improve early detection. △ Less

Submitted 18 January, 2024; v1 submitted 29 November, 2023; originally announced December 2023.

Comments: Major revisions and rewriting in progress

APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge

Authors: Santiago Gómez, Daniel Mantilla, Gustavo Garzón, Edgar Rangel, Andrés Ortiz, Franklin Sierra-Jerez, Fabio Martínez

Abstract: Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack se… ▽ More Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack sensitivity in detecting subtle ischemic changes in the acute phase. As a result, complementary diffusion-weighted MRI studies are captured to provide valuable insights, allowing to recover and quantify stroke lesions. This work introduced APIS, the first paired public dataset with NCCT and ADC studies of acute ischemic stroke patients. APIS was presented as a challenge at the 20th IEEE International Symposium on Biomedical Imaging 2023, where researchers were invited to propose new computational strategies that leverage paired data and deal with lesion segmentation over CT sequences. Despite all the teams employing specialized deep learning tools, the results suggest that the ischemic stroke segmentation task from NCCT remains challenging. The annotated dataset remains accessible to the public upon registration, inviting the scientific community to deal with stroke characterization from NCCT but guided with paired DWI information. △ Less

Submitted 26 September, 2023; originally announced September 2023.

Matrices inducing generalized metric on sequences

Authors: Eloi Araujo, Fábio V. Martinez, Carlos H. A. Higa, José Soares

Abstract: Sequence comparison is a basic task to capture similarities and differences between two or more sequences of symbols, with countless applications such as in computational biology. An alignment is a way to compare sequences, where a giving scoring function determines the degree of similarity between them. Many scoring functions are obtained from scoring matrices. However,not all scoring matrices in… ▽ More Sequence comparison is a basic task to capture similarities and differences between two or more sequences of symbols, with countless applications such as in computational biology. An alignment is a way to compare sequences, where a giving scoring function determines the degree of similarity between them. Many scoring functions are obtained from scoring matrices. However,not all scoring matrices induce scoring functions which are distances, since the scoring function is not necessarily a metric. In this work we establish necessary and sufficient conditions for scoring matrices to induce each one of the properties of a metric in weighted edit distances. For a subset of scoring matrices that induce normalized edit distances, we also characterize each class of scoring matrices inducing normalized edit distances. Furthermore, we define an extended edit distance, which takes into account a set of editing operations that transforms one sequence into another regardless of the existence of a usual corresponding alignment to represent them, describing a criterion to find a sequence of edit operations whose weight is minimum. Similarly, we determine the class of scoring matrices that induces extended edit distances for each of the properties of a metric. △ Less

Submitted 15 March, 2023; originally announced March 2023.

Comments: 40 pages, 2 figures

Journal ref: Discrete Applied Mathematics, Volume 332, 15 June 2023, Pages 135-154

The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development

Authors: Steven I. Ross, Fernando Martinez, Stephanie Houde, Michael Muller, Justin D. Weisz

Abstract: Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used within development tools, these systems typically treat each model invocation independently from all previous invocations, and only a specific limited… ▽ More Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used within development tools, these systems typically treat each model invocation independently from all previous invocations, and only a specific limited functionality is exposed within the user interface. This approach to user interaction misses an opportunity for users to more deeply engage with the model by having the context of their previous interactions, as well as the context of their code, inform the model's responses. We developed a prototype system -- the Programmer's Assistant -- in order to explore the utility of conversational interactions grounded in code, as well as software engineers' receptiveness to the idea of conversing with, rather than invoking, a code-fluent LLM. Through an evaluation with 42 participants with varied levels of programming experience, we found that our system was capable of conducting extended, multi-turn discussions, and that it enabled additional knowledge and capabilities beyond code generation to emerge from the LLM. Despite skeptical initial expectations for conversational programming assistance, participants were impressed by the breadth of the assistant's capabilities, the quality of its responses, and its potential for improving their productivity. Our work demonstrates the unique potential of conversational interactions with LLMs for co-creative processes like software development. △ Less

Submitted 14 February, 2023; originally announced February 2023.

Comments: 43 pages, 3 figures. To be published in IUI 2023

Parkinson gait modelling from an anomaly deep representation

Authors: Edgar Rangel, Fabio Martinez

Abstract: Parkinson's Disease (PD) is associated with gait movement disorders, such as bradykinesia, stiffness, tremors and postural instability, caused by progressive dopamine deficiency. Today, some approaches have implemented learning representations to quantify kinematic patterns during locomotion, supporting clinical procedures such as diagnosis and treatment planning. These approaches assumes a large… ▽ More Parkinson's Disease (PD) is associated with gait movement disorders, such as bradykinesia, stiffness, tremors and postural instability, caused by progressive dopamine deficiency. Today, some approaches have implemented learning representations to quantify kinematic patterns during locomotion, supporting clinical procedures such as diagnosis and treatment planning. These approaches assumes a large amount of stratified and labeled data to optimize discriminative representations. Nonetheless these considerations may restrict the approaches to be operable in real scenarios during clinical practice. This work introduces a self-supervised generative representation to learn gait-motion-related patterns, under the pretext of video reconstruction and an anomaly detection framework. This architecture is trained following a one-class weakly supervised learning to avoid inter-class variance and approach the multiple relationships that represent locomotion. The proposed approach was validated with 14 PD patients and 23 control subjects, and trained with the control population only, achieving an AUC of 95%, homocedasticity level of 70% and shapeness level of 70% in the classification task considering its generalization. △ Less

Submitted 29 August, 2023; v1 submitted 26 January, 2023; originally announced January 2023.

Comments: Journal not submitted to any editorial

Journal ref: Rangel, E., Martínez, F. Parkinsonian gait modelling from an anomaly deep representation. Multimed Tools Appl (2024)

A Case Study in Engineering a Conversational Programming Assistant's Persona

Authors: Steven I. Ross, Michael Muller, Fernando Martinez, Stephanie Houde, Justin D. Weisz

Abstract: The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor. Conversational capability was achieved by using an existing code-fluent Large Language Model and providing it with a prompt that establishes a conversational interaction pattern, a set of conventions, and a style of interaction appropriate for the application. A di… ▽ More The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor. Conversational capability was achieved by using an existing code-fluent Large Language Model and providing it with a prompt that establishes a conversational interaction pattern, a set of conventions, and a style of interaction appropriate for the application. A discussion of the evolution of the prompt provides a case study in how to coax an existing foundation model to behave in a desirable manner for a particular application. △ Less

Submitted 13 January, 2023; originally announced January 2023.

Comments: 11 pages. Submitted to the 4th Workshop on Human-AI Co-Creation with Generative Models (HAI-GEN) at IUI 2023

EvoAAA: An evolutionary methodology for automated \neural autoencoder architecture search

Authors: Francisco Charte, Antonio J. Rivera, Francisco Martínez, María J. del Jesus

Abstract: Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering al… ▽ More Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized coding, in a reduced time. △ Less

Submitted 15 January, 2023; originally announced January 2023.

Comments: Paper submited to Integrated Computer-Aided Engineering

Three-Dimensional Coherent Diffractive Imaging of Isolated Faceted Nanostructures

Authors: Alessandro Colombo, Simon Dold, Patrice Kolb, Nils Bernhardt, Patrick Behrens, Jonathan Correa, Stefan Düsterer, Benjamin Erk, Linos Hecht, Andrea Heilrath, Robert Irsig, Norman Iwe, Jakob Jordan, Björn Kruse, Bruno Langbehn, Bastian Manschwetus, Franklin Martinez, Karl-Heinz Meiwes-Broer, Kevin Oldenburg, Christopher Passow, Christian Peltz, Mario Sauppe, Fabian Seel, Rico Mayro P. Tanyag, Rolf Treusch , et al. (7 additional authors not shown)

Abstract: The structure and dynamics of isolated nanosamples in free flight can be directly visualized via single-shot coherent diffractive imaging using the intense and short pulses of X-ray free-electron lasers. Wide-angle scattering images even encode three-dimensional morphological information of the samples, but the retrieval of this information remains a challenge. Up to now, effective three-dimension… ▽ More The structure and dynamics of isolated nanosamples in free flight can be directly visualized via single-shot coherent diffractive imaging using the intense and short pulses of X-ray free-electron lasers. Wide-angle scattering images even encode three-dimensional morphological information of the samples, but the retrieval of this information remains a challenge. Up to now, effective three-dimensional morphology reconstructions from single shots were only achieved via fitting with highly constrained models, requiring a priori knowledge about possible geometrical shapes. Here we present a much more generic imaging approach. Relying on a model that allows for any sample morphology described by a convex polyhedron, we reconstruct wide-angle diffraction patterns from individual silver nanoparticles. In addition to known structural motives with high symmetries, we retrieve imperfect shapes and agglomerates which were not accessible previously. Our results open new routes towards true 3D structure determination of single nanoparticles and, ultimately, 3D movies of ultrafast nanoscale dynamics. △ Less

Submitted 8 August, 2022; originally announced August 2022.

Comments: 10 pages, 6 figures

Journal ref: Sci. Adv.9, eade5839 (2023)

CoNet: Borderless and decentralized server cooperation in edge computing

Authors: Ning Li, Xin Yuan, Zhaoxin Zhang, Jose Fernan Martinez

Abstract: In edge computing (EC), by offloading tasks to edge server or remote cloud, the system performance can be improved greatly. However, since the traffic distribution in EC is heterogeneous and dynamic, it is difficult for an individual edge server to provide satisfactory computation service anytime and anywhere. This issue motivated the researchers to study the cooperation between edge servers. The… ▽ More In edge computing (EC), by offloading tasks to edge server or remote cloud, the system performance can be improved greatly. However, since the traffic distribution in EC is heterogeneous and dynamic, it is difficult for an individual edge server to provide satisfactory computation service anytime and anywhere. This issue motivated the researchers to study the cooperation between edge servers. The previous server cooperation algorithms have disadvantages since the cooperated region is limited within one-hop. However, the performance of EC can be improved further by releasing the restriction of cooperation region. Even some works have extended the cooperated region to multi-hops, they fail to support the task offloading which is one of the core issues of edge computing. Therefore, we propose a new decentralized and borderless server cooperation algorithm for edge computing which takes task offloading strategy into account, named CoNet. In CoNet, the cooperation region is not limited. Each server forms its own basic cooperation unit (BCU) and calculates its announced capability based on BCU. The server's capability, the processing delay, the task and calculation result forwarding delay are considered during the calculation. The task division strategy bases on the real capability of host-server and the announced capability of cooperation-servers. This cooperation process is recursive and will be terminated once the terminal condition is satisfied. The simulation results demonstrate the advantages of CoNet over previous works. △ Less

Submitted 28 July, 2022; originally announced July 2022.

Convolutional neural networks as an alternative to Bayesian retrievals

Authors: Francisco Ardevol Martinez, Michiel Min, Inga Kamp, Paul I. Palmer

Abstract: Exoplanet observations are currently analysed with Bayesian retrieval techniques. Due to the computational load of the models used, a compromise is needed between model complexity and computing time. Analysis of data from future facilities, will need more complex models which will increase the computational load of retrievals, prompting the search for a faster approach for interpreting exoplanet o… ▽ More Exoplanet observations are currently analysed with Bayesian retrieval techniques. Due to the computational load of the models used, a compromise is needed between model complexity and computing time. Analysis of data from future facilities, will need more complex models which will increase the computational load of retrievals, prompting the search for a faster approach for interpreting exoplanet observations. Our goal is to compare machine learning retrievals of exoplanet transmission spectra with nested sampling, and understand if machine learning can be as reliable as Bayesian retrievals for a statistically significant sample of spectra while being orders of magnitude faster. We generate grids of synthetic transmission spectra and their corresponding planetary and atmospheric parameters, one using free chemistry models, and the other using equilibrium chemistry models. Each grid is subsequently rebinned to simulate both HST/WFC3 and JWST/NIRSpec observations, yielding four datasets in total. Convolutional neural networks (CNNs) are trained with each of the datasets. We perform retrievals on a 1,000 simulated observations for each combination of model type and instrument with nested sampling and machine learning. We also use both methods to perform retrievals on real WFC3 transmission spectra. Finally, we test how robust machine learning and nested sampling are against incorrect assumptions in our models. CNNs reach a lower coefficient of determination between predicted and true values of the parameters. Nested sampling underestimates the uncertainty in ~8% of retrievals, whereas CNNs estimate them correctly. For real WFC3 observations, nested sampling and machine learning agree within $2σ$ for ~86% of spectra. When doing retrievals with incorrect assumptions, nested sampling underestimates the uncertainty in ~12% to ~41% of cases, whereas this is always below ~10% for the CNN. △ Less

Submitted 3 March, 2022; v1 submitted 2 March, 2022; originally announced March 2022.

Comments: Accepted for publication in A&A

Journal ref: A&A 662, A108 (2022)

Better Together? An Evaluation of AI-Supported Code Translation

Authors: Justin D. Weisz, Michael Muller, Steven I. Ross, Fernando Martinez, Stephanie Houde, Mayank Agarwal, Kartik Talamadupula, John T. Richards

Abstract: Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful i… ▽ More Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone. △ Less

Submitted 15 February, 2022; originally announced February 2022.

Comments: 35 pages, 3 figures. To be published in IUI 2022

Dynamics of polynomial maps over finite fields

Authors: José Alves Oliveira, Fabio Enrique Brochero Martínez

Abstract: Let $\mathbb{F}_q$ be a finite field with $q$ elements and let $n$ be a positive integer. In this paper, we study the digraph associated to the map $x\mapsto x^n h(x^{\frac{q-1}{m}})$, where $h(x)\in\mathbb{F}_q[x].$ We completely determine the associated functional graph of maps that satisfy a certain condition of regularity. In particular, we provide the functional graphs associated to monomial… ▽ More Let $\mathbb{F}_q$ be a finite field with $q$ elements and let $n$ be a positive integer. In this paper, we study the digraph associated to the map $x\mapsto x^n h(x^{\frac{q-1}{m}})$, where $h(x)\in\mathbb{F}_q[x].$ We completely determine the associated functional graph of maps that satisfy a certain condition of regularity. In particular, we provide the functional graphs associated to monomial maps. As a consequence of our results, the number of connected components, length of the cycles and number of fixed points of these class of maps are provided. △ Less

Submitted 3 January, 2022; originally announced January 2022.

Comments: Comments are welcome!

On the functional graph of $f(X)=c(X^{q+1}+aX^2)$ over quadratic extensions of finite fields

Authors: F. E. Brochero Martínez, H. R. Teixeira

Abstract: Let $\mathbb{F}_q$ be the finite field with $q$ elements and $char(\mathbb{F}_q)$ odd. In this article we will describe completely the dynamics of the map $f(X)=c(X^{q+1}+aX^2)$, for $a=\{\pm1\}$ and $c\in\mathbb{F}_q^*$, over the finite field $\mathbb{F}_{q^2}$, and give some partial results for $a\in\mathbb{F}_q^*\setminus\{\pm1\}$. Let $\mathbb{F}_q$ be the finite field with $q$ elements and $char(\mathbb{F}_q)$ odd. In this article we will describe completely the dynamics of the map $f(X)=c(X^{q+1}+aX^2)$, for $a=\{\pm1\}$ and $c\in\mathbb{F}_q^*$, over the finite field $\mathbb{F}_{q^2}$, and give some partial results for $a\in\mathbb{F}_q^*\setminus\{\pm1\}$. △ Less

Submitted 22 November, 2021; originally announced November 2021.

MSC Class: 12E20; 05C20; 37P25

Using Document Similarity Methods to create Parallel Datasets for Code Translation

Authors: Mayank Agarwal, Kartik Talamadupula, Fernando Martinez, Stephanie Houde, Michael Muller, John Richards, Steven I Ross, Justin D. Weisz

Abstract: Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying natural language processing techniques towards automating the code translation task. However, due to the paucity of parallel data in this domain, sup… ▽ More Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying natural language processing techniques towards automating the code translation task. However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages. To bypass this limitation, unsupervised neural machine translation techniques have been proposed to learn code translation using only monolingual corpora. In this work, we propose to use document similarity methods to create noisy parallel datasets of code, thus enabling supervised techniques to be applied for automated code translation without having to rely on the availability or expensive curation of parallel code datasets. We explore the noise tolerance of models trained on such automatically-created datasets and show that these models perform comparably to models trained on ground truth for reasonable levels of noise. Finally, we exhibit the practical utility of the proposed method by creating parallel datasets for languages beyond the ones explored in prior work, thus expanding the set of programming languages for automated code translation. △ Less

Submitted 11 October, 2021; originally announced October 2021.

Algorithms for normalized multiple sequence alignments

Authors: Eloi Araujo, Luiz Rozante, Diego P. Rubert, Fabio V. Martinez

Abstract: Sequence alignment supports numerous tasks in bioinformatics, natural language processing, pattern recognition, social sciences, and others fields. While the alignment of two sequences may be performed swiftly in many applications, the simultaneous alignment of multiple sequences proved to be naturally more intricate. Although most multiple sequence alignment (MSA) formulations are NP-hard, severa… ▽ More Sequence alignment supports numerous tasks in bioinformatics, natural language processing, pattern recognition, social sciences, and others fields. While the alignment of two sequences may be performed swiftly in many applications, the simultaneous alignment of multiple sequences proved to be naturally more intricate. Although most multiple sequence alignment (MSA) formulations are NP-hard, several approaches have been developed, as they can outperform pairwise alignment methods or are necessary for some applications. Taking into account not only similarities but also the lengths of the compared sequences (i.e. normalization) can provide better alignment results than both unnormalized or post-normalized approaches. While some normalized methods have been developed for pairwise sequence alignment, none have been proposed for MSA. This work is a first effort towards the development of normalized methods for MSA. We discuss multiple aspects of normalized multiple sequence alignment (NMSA). We define three new criteria for computing normalized scores when aligning multiple sequences, showing the NP-hardness and exact algorithms for solving the NMSA using those criteria. In addition, we provide approximation algorithms for MSA and NMSA for some classes of scoring matrices. △ Less

Submitted 3 December, 2021; v1 submitted 4 July, 2021; originally announced July 2021.

Comments: 24 pages, 2 figures, 5 algorithms

MSC Class: 68W25 ACM Class: F.2.2

Perfection Not Required? Human-AI Partnerships in Code Translation

Authors: Justin D. Weisz, Michael Muller, Stephanie Houde, John Richards, Steven I. Ross, Fernando Martinez, Mayank Agarwal, Kartik Talamadupula

Abstract: Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming language to another. The artifacts produced in this way may contain imperfections, suc… ▽ More Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming language to another. The artifacts produced in this way may contain imperfections, such as compilation or logical errors. We examine the extent to which software engineers would tolerate such imperfections and explore ways to aid the detection and correction of those errors. Using a design scenario approach, we interviewed 11 software engineers to understand their reactions to the use of an NMT model in the context of application modernization, focusing on the task of translating source code from one language to another. Our three-stage scenario sparked discussions about the utility and desirability of working with an imperfect AI system, how acceptance of that system's outputs would be established, and future opportunities for generative AI in application modernization. Our study highlights how UI features such as confidence highlighting and alternate translations help software engineers work with and better understand generative NMT models. △ Less

Submitted 8 April, 2021; originally announced April 2021.

Comments: 18 pages, 1 figure. To be published in IUI 2021

Quality Estimation & Interpretability for Code Translation

Authors: Mayank Agarwal, Kartik Talamadupula, Stephanie Houde, Fernando Martinez, Michael Muller, John Richards, Steven Ross, Justin D. Weisz

Abstract: Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for natural languages has come under study. However, such approaches suffer from the same problem as previous NMT approaches on natural languages, viz. the lack of an ability to estimate and evaluate the quality of the transl… ▽ More Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for natural languages has come under study. However, such approaches suffer from the same problem as previous NMT approaches on natural languages, viz. the lack of an ability to estimate and evaluate the quality of the translations; and consequently ascribe some measure of interpretability to the model's choices. In this paper, we attempt to estimate the quality of source code translations built on top of the TransCoder model. We consider the code translation task as an analog of machine translation (MT) for natural languages, with some added caveats. We present our main motivation from a user study built around code translation; and present a technique that correlates the confidences generated by that model to lint errors in the translated code. We conclude with some observations on these correlations, and some ideas for future work. △ Less

Submitted 26 April, 2021; v1 submitted 4 December, 2020; originally announced December 2020.

Comments: NeurIPS 2020 Workshop on Computer-Assisted Programming

Artin-Schreier curves given by $\mathbb F_q$-linearized polynomials

Authors: Daniela Oliveira, F. E. Brochero Martínez

Abstract: Let $\mathbb F_q$ be a finite field with $q$ elements, where $q$ is a power of an odd prime $p$. In this paper we associate circulant matrices and quadratic forms with the Artin-Schreier curve $y^q - y= x \cdot F(x) - λ,$ where $F(x)$ is a $\mathbb F_q$-linearized polynomial and $λ\in \mathbb F_q$. Our results provide a characterization of the number of affine rational points of this curve in the… ▽ More Let $\mathbb F_q$ be a finite field with $q$ elements, where $q$ is a power of an odd prime $p$. In this paper we associate circulant matrices and quadratic forms with the Artin-Schreier curve $y^q - y= x \cdot F(x) - λ,$ where $F(x)$ is a $\mathbb F_q$-linearized polynomial and $λ\in \mathbb F_q$. Our results provide a characterization of the number of affine rational points of this curve in the extension $\mathbb F_{q^r}$ of $\mathbb F_q$, for $\gcd(q,r)=1$. In the case $F(x) = x^{q^i}-x$ we give a complete description of the number of affine rational points in terms of Legendre symbols and quadratic characters. △ Less

Submitted 8 September, 2022; v1 submitted 2 December, 2020; originally announced December 2020.

MSC Class: 12E20; 11T06

Learning language variations in news corpora through differential embeddings

Authors: Carlos Selmo, Julian F. Martinez, Mariano G. Beiró, J. Ignacio Alvarez-Hamelin

Abstract: There is an increasing interest in the NLP community in capturing variations in the usage of language, either through time (i.e., semantic drift), across regions (as dialects or variants) or in different social contexts (i.e., professional or media technolects). Several successful dynamical embeddings have been proposed that can track semantic change through time. Here we show that a model with a… ▽ More There is an increasing interest in the NLP community in capturing variations in the usage of language, either through time (i.e., semantic drift), across regions (as dialects or variants) or in different social contexts (i.e., professional or media technolects). Several successful dynamical embeddings have been proposed that can track semantic change through time. Here we show that a model with a central word representation and a slice-dependent contribution can learn word embeddings from different corpora simultaneously. This model is based on a star-like representation of the slices. We apply it to The New York Times and The Guardian newspapers, and we show that it can capture both temporal dynamics in the yearly slices of each corpus, and language variations between US and UK English in a curated multi-source corpus. We provide an extensive evaluation of this methodology. △ Less

Submitted 13 November, 2020; originally announced November 2020.

Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans

Authors: Lukas Hirsch, Yu Huang, Shaojun Luo, Carolina Rossi Saccarelli, Roberto Lo Gullo, Isaac Daimiel Naranjo, Almir G. V. Bitencourt, Natsuko Onishi, Eun Sook Ko, Doris Leithner, Daly Avendano, Sarah Eskreis-Winkler, Mary Hughes, Danny F. Martinez, Katja Pinker, Krishna Juluru, Amin E. El-Rowmeim, Pierre Elnajjar, Elizabeth A. Morris, Hernan A. Makse, Lucas C Parra, Elizabeth J. Sutton

Abstract: Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented betw… ▽ More Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P <= .001 for both; n = 250). Conclusion: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI. △ Less

Submitted 12 April, 2022; v1 submitted 21 September, 2020; originally announced September 2020.

CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking

Authors: Dimitris Perdios, Manuel Vonlanthen, Florian Martinez, Marcel Arditi, Jean-Philippe Thiran

Abstract: Thanks to its capability of acquiring full-view frames at multiple kilohertz, ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical phenomena in the human body, with pioneering applications such as ultrasensitive flow imaging in the cardiovascular system or shear-wave elastography. The accuracy achievable with these motion estimation techniques is strongly contingent upon… ▽ More Thanks to its capability of acquiring full-view frames at multiple kilohertz, ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical phenomena in the human body, with pioneering applications such as ultrasensitive flow imaging in the cardiovascular system or shear-wave elastography. The accuracy achievable with these motion estimation techniques is strongly contingent upon two contradictory requirements: a high quality of consecutive frames and a high frame rate. Indeed, the image quality can usually be improved by increasing the number of steered ultrafast acquisitions, but at the expense of a reduced frame rate and possible motion artifacts. To achieve accurate motion estimation at uncompromised frame rates and immune to motion artifacts, the proposed approach relies on single ultrafast acquisitions to reconstruct high-quality frames and on only two consecutive frames to obtain 2-D displacement estimates. To this end, we deployed a convolutional neural network-based image reconstruction method combined with a speckle tracking algorithm based on cross-correlation. Numerical and in vivo experiments, conducted in the context of plane-wave imaging, demonstrate that the proposed approach is capable of estimating displacements in regions where the presence of side lobe and grating lobe artifacts prevents any displacement estimation with a state-of-the-art technique that relies on conventional delay-and-sum beamforming. The proposed approach may therefore unlock the full potential of ultrafast ultrasound, in applications such as ultrasensitive cardiovascular motion and flow analysis or shear-wave elastography. △ Less

Submitted 21 December, 2020; v1 submitted 3 September, 2020; originally announced September 2020.

Comments: 11 pages, 4 figures. Animation and slideshow of Figure 4 are provided as ancillary files. This version has been accepted for publication in the IEEE Transactions on Medical Imaging

CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging

Authors: Dimitris Perdios, Manuel Vonlanthen, Florian Martinez, Marcel Arditi, Jean-Philippe Thiran

Abstract: Ultrafast ultrasound (US) revolutionized biomedical imaging with its capability of acquiring full-view frames at over 1 kHz, unlocking breakthrough modalities such as shear-wave elastography and functional US neuroimaging. Yet, it suffers from strong diffraction artifacts, mainly caused by grating lobes, side lobes, or edge waves. Multiple acquisitions are typically required to obtain a sufficient… ▽ More Ultrafast ultrasound (US) revolutionized biomedical imaging with its capability of acquiring full-view frames at over 1 kHz, unlocking breakthrough modalities such as shear-wave elastography and functional US neuroimaging. Yet, it suffers from strong diffraction artifacts, mainly caused by grating lobes, side lobes, or edge waves. Multiple acquisitions are typically required to obtain a sufficient image quality, at the cost of a reduced frame rate. To answer the increasing demand for high-quality imaging from single unfocused acquisitions, we propose a two-step convolutional neural network (CNN)-based image reconstruction method, compatible with real-time imaging. A low-quality estimate is obtained by means of a backprojection-based operation, akin to conventional delay-and-sum beamforming, from which a high-quality image is restored using a residual CNN with multiscale and multichannel filtering properties, trained specifically to remove the diffraction artifacts inherent to ultrafast US imaging. To account for both the high dynamic range and the oscillating properties of radio frequency US images, we introduce the mean signed logarithmic absolute error (MSLAE) as a training loss function. Experiments were conducted with a linear transducer array, in single plane-wave (PW) imaging. Trainings were performed on a simulated dataset, crafted to contain a wide diversity of structures and echogenicities. Extensive numerical evaluations demonstrate that the proposed approach can reconstruct images from single PWs with a quality similar to that of gold-standard synthetic aperture imaging, on a dynamic range in excess of 60 dB. In vitro and in vivo experiments show that trainings carried out on simulated data perform well in experimental settings. △ Less

Submitted 1 April, 2022; v1 submitted 28 August, 2020; originally announced August 2020.

Comments: Main: 14 pages (6 figures). Supplement: 9 pages (10 figures). Video provided as ancillary file. This version has been accepted for publication in the the IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Natural family-free genomic distance

Authors: Diego P. Rubert, Fábio V. Martinez, Marília D. V. Braga

Abstract: A classical problem in comparative genomics is to compute the rearrangement distance, that is the minimum number of large-scale rearrangements required to transform a given genome into another given genome. While the most traditional approaches in this area are family-based, i.e., require the classification of DNA fragments into families, more recently an alternative family-free approach was pro… ▽ More A classical problem in comparative genomics is to compute the rearrangement distance, that is the minimum number of large-scale rearrangements required to transform a given genome into another given genome. While the most traditional approaches in this area are family-based, i.e., require the classification of DNA fragments into families, more recently an alternative family-free approach was proposed, and consists of studying the rearrangement distances without prior family assignment. On the one hand the computation of genomic distances in the family-free setting helps to match occurrences of duplicated genes and find homologies, but on the other hand this computation is NP-hard. In this paper, by letting structural rearrangements be represented by the generic double cut and join (DCJ) operation and also allowing insertions and deletions of DNA segments, we propose a new and more general family-free genomic distance, providing an efficient ILP formulation to solve it. Our experiments show that the ILP produces accurate results and can handle not only bacterial genomes, but also fungi and insects, or subsets of chromosomes of mammals and plants. △ Less

Submitted 14 July, 2020; v1 submitted 7 July, 2020; originally announced July 2020.

Comments: Workshop on Algorithms in Bioinformatics (WABI) 2020, 24 pages, 10 figures, 8 tables

ACM Class: G.1.6; J.3

On motifs in colored graphs

Authors: Diego P Rubert, Eloi Araujo, Marco A Stefanes, Jens Stoye, Fábio V Martinez

Abstract: One of the most important concepts in biological network analysis is that of network motifs, which are patterns of interconnections that occur in a given network at a frequency higher than expected in a random network. In this work we are interested in searching and inferring network motifs in a class of biological networks that can be represented by vertex-colored graphs. We show the computationa… ▽ More One of the most important concepts in biological network analysis is that of network motifs, which are patterns of interconnections that occur in a given network at a frequency higher than expected in a random network. In this work we are interested in searching and inferring network motifs in a class of biological networks that can be represented by vertex-colored graphs. We show the computational complexity for many problems related to colorful topological motifs and present efficient algorithms for special cases. We also present a probabilistic strategy to detect highly frequent motifs in vertex-colored graphs. Experiments on real data sets show that our algorithms are very competitive both in efficiency and in quality of the solutions. △ Less

Submitted 27 May, 2020; originally announced May 2020.

Comments: 28 pages, 9 figures, to be published in Journal of Combinatorial Optimization

ACM Class: F.2.0; G.2.1; G.3

Game Theory based Joint Task Offloading and Resources Allocation Algorithm for Mobile Edge Computing

Authors: Jianen Yan, Ning Li, Zhaoxin Zhang, Alex X. Liu, Jose Fernan Martinez, Xin Yuan

Abstract: Mobile edge computing (MEC) has emerged for reducing energy consumption and latency by allowing mobile users to offload computationally intensive tasks to the MEC server. Due to the spectrum reuse in small cell network, the inter-cell interference has a great effect on MEC performances. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based approach t… ▽ More Mobile edge computing (MEC) has emerged for reducing energy consumption and latency by allowing mobile users to offload computationally intensive tasks to the MEC server. Due to the spectrum reuse in small cell network, the inter-cell interference has a great effect on MEC performances. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based approach to join task offloading decision and resources allocation together in the MEC system. In this algorithm, the offloading decision, the CPU capacity adjustment, the transmission power control, and the network interference management of mobile users are regarded as a game. In this game, based on the best response strategy, each mobile user makes their own utility maximum rather than the utility of the whole system. We prove that this game is an exact potential game and the Nash equilibrium (NE) of this game exists. For reaching the NE, the best response approach is applied. We calculate the best response of these three variables. Moreover, we investigate the properties of this algorithm, including the convergence, the computational complexity, and the Price of anarchy (PoA). The theoretical analysis shows that the inter-cell interference affects on the performances of MEC greatly. The NE of this game is Pareto efficiency. Finally, we evaluate the performances of this algorithm by simulation. The simulation results illustrate that this algorithm is effective in improving the performances of the multi-user MEC system. △ Less

Submitted 16 December, 2019; originally announced December 2019.

Comments: arXiv admin note: substantial text overlap with arXiv:1805.02182

MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility

Authors: Souneil Park, Joan Serra, Enrique Frias Martinez, Nuria Oliver

Abstract: Collective urban mobility embodies the residents' local insights on the city. Mobility practices of the residents are produced from their spatial choices, which involve various considerations such as the atmosphere of destinations, distance, past experiences, and preferences. The advances in mobile computing and the rise of geo-social platforms have provided the means for capturing the mobility pr… ▽ More Collective urban mobility embodies the residents' local insights on the city. Mobility practices of the residents are produced from their spatial choices, which involve various considerations such as the atmosphere of destinations, distance, past experiences, and preferences. The advances in mobile computing and the rise of geo-social platforms have provided the means for capturing the mobility practices; however, interpreting the residents' insights is challenging due to the scale and complexity of an urban environment, and its unique context. In this paper, we present MobInsight, a framework for making localized interpretations of urban mobility that reflect various aspects of the urbanism. MobInsight extracts a rich set of neighborhood features through holistic semantic aggregation, and models the mobility between all-pairs of neighborhoods. We evaluate MobInsight with the mobility data of Barcelona and demonstrate diverse localized and semantically-rich interpretations. △ Less

Submitted 29 September, 2017; originally announced September 2017.

Number of minimal cyclic codes with given length and dimension

Authors: F. E. Brochero Martínez

Abstract: In this article, we count the quantity of minimal cyclic codes of length $n$ and dimension $k$ over a finite field $\mathbb F_q$, in the case when the prime factors of $n$ satisfy a special condition. This problem is equivalent to count the quantity of irreducible factors of $x^n-1\in \mathbb F_q[x]$ of degree $k$. In this article, we count the quantity of minimal cyclic codes of length $n$ and dimension $k$ over a finite field $\mathbb F_q$, in the case when the prime factors of $n$ satisfy a special condition. This problem is equivalent to count the quantity of irreducible factors of $x^n-1\in \mathbb F_q[x]$ of degree $k$. △ Less

Submitted 16 June, 2014; originally announced June 2014.

MSC Class: 20C05 (primary) and 16S34(secondary)

Weight enumerator of some irreducible cyclic codes

Authors: F. E. Brochero Martínez, C. R. Giraldo Vergara

Abstract: In this article, we show explicitly all possible weight enumerators for every irreducible cyclic code of length $n$ over a finite field $\mathbb F_q$, in the case which each prime divisor of $n$ is also a divisor of $q-1$. In this article, we show explicitly all possible weight enumerators for every irreducible cyclic code of length $n$ over a finite field $\mathbb F_q$, in the case which each prime divisor of $n$ is also a divisor of $q-1$. △ Less

Submitted 8 May, 2014; v1 submitted 27 April, 2014; originally announced April 2014.

Comments: Submitted to Designs, Codes and Cryptography, 8 pages

MSC Class: 12E05(primary) and 94B05(secondary)

Explicit factorization of $x^n-1\in \mathbb F_q[x]$

Authors: F. E. Brochero Martínez, C. R. Giraldo Vergara, L. Batista de Oliveira

Abstract: Let $\mathbb F_q$ be a finite field and $n$ a positive integer. In this article, we prove that, under some conditions on $q$ and $n$, the polynomial $x^n-1$ can be split into irreducible binomials $x^t-a$ and an explicit factorization into irreducible factors is given. Finally, weakening one of our hypothesis, we also obtain factors of the form $x^{2t}-ax^t+b$ and explicit splitting of $x^n-1$ i… ▽ More Let $\mathbb F_q$ be a finite field and $n$ a positive integer. In this article, we prove that, under some conditions on $q$ and $n$, the polynomial $x^n-1$ can be split into irreducible binomials $x^t-a$ and an explicit factorization into irreducible factors is given. Finally, weakening one of our hypothesis, we also obtain factors of the form $x^{2t}-ax^t+b$ and explicit splitting of $x^n-1$ into irreducible factors is given. △ Less

Submitted 18 May, 2014; v1 submitted 24 April, 2014; originally announced April 2014.

Comments: Submitted to Designs, Codes and Cryptography. 9 pages

MSC Class: 12E05(primary) and 94B05(secondary)

IACTalks: an on-line archive of astronomy-related seminars

Authors: Johan H. Knapen, Jorge A. Pérez Prieto, Tariq Shahbaz, Anna Ferré-Mateu, Nicola Caon, Cristina Ramos Almeida, Brandon Tingley, Valentina Luridiana, Inés Flores-Cacho, Orlagh Creevey, Arturo Manchado Torres, Ignacio Trujillo, Maria Rosa Zapatero Osorio, Francisco Sánchez Martínez, Francisco López Molina, Gabriel Pérez Díaz, Miguel Briganti, Inés Bonet

Abstract: We present IACTalks, a free and open access seminars archive (http://iactalks.iac.es) aimed at promoting astronomy and the exchange of ideas by providing high-quality scientific seminars to the astronomical community. The archive of seminars and talks given at the Instituto de Astrofiísica de Canarias goes back to 2008. Over 360 talks and seminars are now freely available by streaming over the int… ▽ More We present IACTalks, a free and open access seminars archive (http://iactalks.iac.es) aimed at promoting astronomy and the exchange of ideas by providing high-quality scientific seminars to the astronomical community. The archive of seminars and talks given at the Instituto de Astrofiísica de Canarias goes back to 2008. Over 360 talks and seminars are now freely available by streaming over the internet. We describe the user interface, which includes two video streams, one showing the speaker, the other the presentation. A search function is available, and seminars are indexed by keywords and in some cases by series, such as special training courses or the 2011 Winter School of Astrophysics, on secular evolution of galaxies. The archive is made available as an open resource, to be used by scientists and the public. △ Less

Submitted 27 June, 2012; originally announced June 2012.

Comments: 2 pages, 2 figures