pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems
Authors: Amala Sonny, Abhinav Kumar, Linga Reddy Cenkeramaddi
Date: 2024-03-07 00:00:00
Tasks: Activity Recognition, Human Detection, Indoor Localization

Indoor human positioning has become increasingly important for applications such as health monitoring, breath monitoring, human identification, safety and rescue operations, and security surveillance. However, achieving robust indoor human positioning remains challenging due to various constraints. Numerous attempts have been made in the literature to develop efficient indoor positioning systems (IPSs), with a growing focus on machine learning (ML) based techniques. This paper aims to compare and analyze current ML-based wireless techniques and approaches for indoor positioning, providing a comprehensive review of enabling technologies for human detection, positioning, and activity recognition. The study explores different input measurement data, including RSSI, TDOA, etc., for various IPSs. Key positioning techniques such as RSSI-based fingerprinting, Angle-based, and Time-based approaches are examined in conjunction with various ML methods. The survey compares the positioning accuracy, scalability, and algorithm complexity, with the goal of determining the suitable technology in various services. Finally, the paper compares distinct datasets focused on indoor localization, which have been published using diverse technologies. Overall, the paper presents a comprehensive comparison of existing techniques and localization models.
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition
Authors: Hanqi Wang, Tao Chen, Liang Song
Date: 2024-03-06 00:00:00
Tasks: Contrastive Learning, Deep Learning, EEG, EEG Emotion Recognition, Emotion Recognition, Self-Supervised Learning

EEG-based Emotion recognition holds significant promise for applications in human-computer interaction, medicine, and neuroscience. While deep learning has shown potential in this field, current approaches usually rely on large-scale high-quality labeled datasets, limiting the performance of deep learning. Self-supervised learning offers a solution by automatically generating labels, but its inter-subject generalizability remains under-explored. For this reason, our interest lies in offering a self-supervised learning paradigm with better inter-subject generalizability. Inspired by recent efforts in combining low-level and high-level tasks in deep learning, we propose a cascaded self-supervised architecture for EEG emotion recognition. Then, we introduce a low-level task, time-to-frequency reconstruction (TFR). This task leverages the inherent time-frequency relationship in EEG signals. Our architecture integrates it with the high-level contrastive learning modules, performing self-supervised learning for EEG-based emotion recognition. Experiment on DEAP and DREAMER datasets demonstrates superior performance of our method over similar works. The outcome results also highlight the indispensability of the TFR task and the robustness of our method to label scarcity, validating the effectiveness of the proposed method.
Modulation of 2-Point Discrimination Threshold Under Chemically Induced Global Stimulation
Authors: Junjie Hua, Masahiro Furukawa, Taro Maeda
Date: 2024-03-07 00:00:00

The two-point discrimination threshold(2PDT) serves as a critical indicator in the study of tactile acuity, representing the minimal distance at which an individual can differentiate two distinct points of contact on the skin. This measurement is instrumental in exploring the neural mechanisms underlying tactile perception. On the other hand, tactile acuity can be modulated by global stimulation. Prior research indicates that experimental inflammation induced by an application of capsaicin cream increases2PDT. In our study, we applied chemicals(oregano, menthol, and Sichuan pepper) to selectively activate receptors that usually respond to mild physical stimuli to investigate their influence on2PDT without inducing inflammation. The results unveiled a pronounced augmentation of2PDT following any form of global stimulation. Intriguingly, the cumulative effect of the chemical mix on2PDT appeared to be additive. These observations suggest that Wide Dynamic Range(WDR) neurons, functioning as relay nuclei with projections for touch, warmth, and cold sensations, play a pivotal role in this process. In lateral connection structures parallel to afferent nerve transmission pathways with WDR neurons as relay nuclei, global stimulation amplifies excitatory connections over inhibitory ones, thereby elevating the two-point discrimination threshold.
Creating and troubleshooting microscopy analysis workflows: common challenges and common solutions
Authors: Beth A Cimini
Date: 2024-03-06 00:00:00

As microscopy diversifies and becomes ever-more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they've generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by understanding tools and tradeoffs, optimizing data quality, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.
Random Evolutionary Dynamics in Predator-Prey Systems Yields Large, Clustered Ecosystems
Authors: Christian Hamster, Jorik Schaap, Peter van Heijster, Joshua Dijksman
Date: 2024-03-07 00:00:00
Tasks: Clustering

We study the effect of speciation, i.e. the introduction of new species through evolution into communities, in the setting of predator-prey systems. Predator-prey dynamics is classically well modeled by Lotka-Volterra equations, also when multiple predator and prey species co-exist. The consequences of the emergence of new species in such systems are much less well understood. We find that introducing random evolving species leads to robust ecosystems in which large numbers of species coexist. Crucially, in these large ecosystems an emergent clustering of species is observed, tying functional differences to phylogenetic history.
Inverse resolution of spatially varying diffusion coefficient using Physics-Informed neural networks
Authors: Sukirt Thakur, Ehsan Esmaili, Sarah Libring, Luis Solorio, Arezoo M. Ardekani
Date: 2024-03-06 00:00:00

Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally, these systems often have spatial variation in the diffusion coefficient which must be determined, such as for injectable drug-eluting implants into heterogeneous tissues. Unfortunately, obtaining the diffusion coefficient from images in such cases is an inverse problem with only discrete data points. The development of a robust method that can work with such noisy and ill-posed datasets to accurately determine spatially-varying diffusion coefficients is of great value across a large range of disciplines. Here, we developed an inverse solver that uses physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying biological and engineering applications. The residual of the transient diffusion equation for a concentration field is minimized to find the diffusion coefficient. The robustness of the method as an inverse solver was tested using both numerical and experimental datasets. The predictions show good agreement with both the numerical and experimental benchmarks; an error of less than 6.31% was obtained against all numerical benchmarks, while the diffusion coefficient calculated in experimental datasets matches the appropriate ranges of other reported literature values. Our work demonstrates the potential of using PINNs to resolve spatially-varying diffusion coefficients, which may aid a wide-range of applications, such as enabling better-designed drug-eluting implants for regenerative medicine or oncology fields.
Testing Business Cycle Theories: Evidence from the Great Recession
Authors: Bo Li
Date: 2024-03-06 00:00:00

Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-export-growth areas. Most importantly, our unique research design enables us to perform the most comprehensive tests on theories (hypotheses) regarding the business cycle. We show that the following theories (hypotheses) cannot explain the cause of the 1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real business cycle theory, the collateral-driven credit cycle theory, the business uncertainty theory, and the extrapolative expectation theory.
Calibrated rank volatility stabilized models for large equity markets
Authors: David Itkin, Martin Larsson
Date: 2024-03-07 00:00:00

In the framework of stochastic portfolio theory we introduce rank volatility stabilized models for large equity markets over long time horizons. These models are rank-based extensions of the volatility stabilized models introduced by Fernholz & Karatzas in 2005. On the theoretical side we establish global existence of the model and ergodicity of the induced ranked market weights. We also derive explicit expressions for growth-optimal portfolios and show the existence of relative arbitrage with respect to the market portfolio. On the empirical side we calibrate the model to sixteen years of CRSP US equity data matching (i) rank-based volatilities, (ii) stock turnover as measured by market weight collisions, (iii) the average market rate of return and (iv) the capital distribution curve. Assessment of model fit and error analysis is conducted both in and out of sample. To the best of our knowledge this is the first model exhibiting relative arbitrage that has statistically been shown to have a good quantitative fit with the empirical features (i)-(iv). We additionally simulate trajectories of the calibrated model and compare them to historical trajectories, both in and out of sample.
Rethinking the Backward Propagation for Adversarial Transferability
Authors: Xiaosen Wang, Kangheng Tong, Kun He
Date: 2023-06-22 00:00:00
Proceeding: NeurIPS 2023 11

Transfer-based attacks generate adversarial examples on the surrogate model, which can mislead other black-box models without access, making it promising to attack real-world applications. Recently, several works have been proposed to boost adversarial transferability, in which the surrogate model is usually overlooked. In this work, we identify that non-linear layers (e.g., ReLU, max-pooling, etc.) truncate the gradient during backward propagation, making the gradient w.r.t. input image imprecise to the loss function. We hypothesize and empirically validate that such truncation undermines the transferability of adversarial examples. Based on these findings, we propose a novel method called Backward Propagation Attack (BPA) to increase the relevance between the gradient w.r.t. input image and loss function so as to generate adversarial examples with higher transferability. Specifically, BPA adopts a non-monotonic function as the derivative of ReLU and incorporates softmax with temperature to smooth the derivative of max-pooling, thereby mitigating the information loss during the backward propagation of gradients. Empirical results on the ImageNet dataset demonstrate that not only does our method substantially boost the adversarial transferability, but it is also general to existing transfer-based attacks. Code is available at https://github.com/Trustworthy-AI-Group/RPA.
OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Authors: Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh
Date: 2023-06-21 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: MMR total

Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELICS, we train vision and language models of 9 and 80 billion parameters named IDEFICS, and obtain competitive performance on different multimodal benchmarks. We release our dataset, models and code.
Reconfigurable Intelligent Surface Aided Full Duplex Networking Systems
Authors: Yuncong Li∗;Yingyang Chen∗;Miaowen Wen†; Duoying Zhang∗;  Bingli Jiao‡; Zhiguo Ding§; Theodoros A. Tsiftsis¶; H. Vincent Poor¶
Date: 2023-10-23 00:00:00
Proceeding: ICC 2023 - IEEE International Conference on Communications 2023 10

In this paper, we propose a multi-cell full-duplex (FD) networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. We aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and the phase shift matrix at RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between SR and minimum mean square error. The optimal solutions of TPC matrices are obtained in closed form, while a successive convex approximation-based algorithm is developed to resolve the phase shift matrix suboptimally. Simulation results show that introducing an RIS into the FD networking system can improve the overall SR significantly. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for self-interference cancellation (SIC) and the number of BS antennas effectively, especially with enough reflecting elements. As a result, the utilization of RIS enables the originally cumbersome FD networking system to become efficient and practical.
Identification of algal blooms in lakes in the Baltic states using Sentinel-2 data and artificial neural networks
Authors: Dalia Grendaite, Linas Petkevicius
Date: 2024-02-14 00:00:00
Proceeding: IEEE Access 2024 2

Algal blooms are a common problem in inland waters, which raise growing awareness on monitoring lakes’ conditions. The on site monitoring is expensive and requires large human resources efforts. This work proposes remote monitoring techniques using satellite images and machine learning algorithms to predict chlorophyll α concentration in water bodies and identify algal blooms. The training and test dataset used in this study includes diverse range of lakes in Baltic countries. The lake spectral features obtained from Sentinel-2 satellite images are used as predictors for proposed deep neural network models. The prediction of chlorophyll α concentration with MAE 7.97 mg/ m3 and bloom vs. non-bloom classification with 71.6 % accuracy was achieved. The use of Bèzier curves for smoothing the point-wise prediction is proposed for identification of algal bloom characteristics: the bloom start date, end date, and duration. The results showed lake type impact on the blooming time. The experimental data and code are released with paper.
Leveraging pre-trained language models for code generation
Authors: Ahmed Soliman, Samir Shaheen, Mayada Hadhoud
Date: 2024-02-29 00:00:00
Proceeding: Complex & Intelligent Systems 2024 2
Tasks: Code Generation, Language Modelling

Code assistance refers to the utilization of various tools, techniques, and models to help developers in the process of software development. As coding tasks become increasingly complex, code assistant plays a pivotal role in enhancing developer productivity, reducing errors, and facilitating a more efficient coding workflow. This assistance can manifest in various forms, including code autocompletion, error detection and correction, code generation, documentation support, and context-aware suggestions. Language models have emerged as integral components of code assistance, offering developers the capability to receive intelligent suggestions, generate code snippets, and enhance overall coding proficiency. In this paper, we propose new hybrid models for code generation by leveraging pre-trained language models BERT, RoBERTa, ELECTRA, and LUKE with the Marian Causal Language Model. Selecting these models based on their strong performance in various natural language processing tasks. We evaluate the performance of these models on two datasets CoNaLa and DJANGO and compare them to existing state-of-the-art models. We aim to investigate the potential of pre-trained transformer language models to revolutionize code generation, offering improved precision and efficiency in navigating complex coding scenarios. Additionally, conducting error analysis and refining the generated code. Our results show that these models, when combined with the Marian Decoder, significantly improve code generation accuracy and efficiency. Notably, the RoBERTaMarian model achieved a maximum BLEU score of 35.74 and an exact match accuracy of 13.8% on CoNaLa, while LUKE-Marian attained a BLEU score of 89.34 and an exact match accuracy of 78.50% on DJANGO. Implementation of this work is available at https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation.
Certifiably Robust Graph Contrastive Learning
Authors: Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang
Date: 2023-10-05 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning

Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attacks on both the graph structure and node attributes. Although empirical approaches have been proposed to enhance the robustness of GCL, the certifiable robustness of GCL is still remain unexplored. In this paper, we develop the first certifiably robust framework in GCL. Specifically, we first propose a unified criteria to evaluate and certify the robustness of GCL. We then introduce a novel technique, RES (Randomized Edgedrop Smoothing), to ensure certifiable robustness for any GCL model, and this certified robustness can be provably preserved in downstream tasks. Furthermore, an effective training method is proposed for robust GCL. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model. The source code of RES is available at https://github.com/ventr1c/RES-GCL.
An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection
Authors: Ga Xiang, Chen Shi, Yangsen Zhang
Date: 2023-08-04 00:00:00
Proceeding: Electronics 2023 8
Tasks: Event Extraction

Advanced Persistent Threat (APT) seriously threatens a nation’s cyberspace security. Current defense technologies are typically unable to detect it effectively since APT attack is complex and the signatures for detection are not clear. To enhance the understanding of APT attacks, in this paper,a novel approach for extracting APT attack events from web texts is proposed. First, the APT event types and event schema are defined. Secondly, an APT attack event extraction dataset in Chinese is constructed. Finally, an APT attack event extraction model based on the BERT‑BiGRU‑CRF architecture is proposed. Comparative experiments are conducted with ERNIE, BERT, and BERT‑BiGRU-CRF models, and the results show that the APT attack event extraction model based on BERT‑BiGRU-CRF achieves the highest F1 value, indicating the best extraction performance. Currently, there is seldom APT event extraction research, the work in this paper contributes a new method to Cyber Threat Intelligence (CTI) analysis. By considering the multi‑stages, complexity of APT attacks, and the data source from huge credible web texts, the APT event extraction method enhances the understanding of APT attacks and is helpful to improve APT attack detection capabilities.
Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios
Authors: Jiangfeng Nan, Weiwen Deng, Member, IEEE, Ruzheng Zhang, Ying Wang, Rui Zhao, Juan Ding
Date: 2023-06-26 00:00:00
Proceeding: journal 2023 6
Tasks: Autonomous Driving, Decision Making

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow. This, as a result, may impose serious safety risks, and often cause traffic jam with autonomous driving. Therefore, human-like autonomous driving becomes important, yet imperative. This paper presents an interaction-aware decisionmaking and planning method for human-like autonomous driving in merge scenarios. Rather than directly mimicking human behavior, deep inverse reinforcement learning is employed to learn the human-used reward function for decisionmaking and planning from naturalistic driving data to enhance interpretability and generalizability. To consider the interaction factor, the reward function for planning is utilized to evaluate the joint trajectories of the autonomous driving vehicle (ADV) and traffic vehicles. In contrast to predicting trajectories of traffic vehicles with the fixed trajectory of ADV given by the upstream prediction model, the trajectories of traffic vehicles are predicted by responding to the ADV’s behavior in this paper. Additionally, the decision-making module is employed to reduce the solution space of planning via the selection of a proper gap for merging. Both the decision-making and planning algorithms follow a “sampling, evaluation, and selection” framework. After beingverified through experiments, the results indicate that the planned trajectories with the presented method are highly similar to those of human drivers. Moreover, compared to the interaction-unaware planning method, the interaction-aware planning method behaves closer to human drivers.