pwc-archive/papers-with-abstracts huggingface dataset:
Transforming Single Photon Camera Images to Color High Dynamic Range Images
Authors: Sumit Sharma, Girish Rongali, Kaushik Mitra
Date: 2024-12-17 00:00:00

Traditional CMOS sensors suffer from restricted dynamic range and sub optimal performance under extreme lighting conditions. They are affected by electronic noise in low light conditions and pixel saturation while capturing high illumination. Recent High Dynamic Range (HDR) Imaging methods, often designed for CMOS Sensors, attempt to address these issues by fusing multiple exposures. However, they frequently introduce artifacts like ghosting and light flickering in dynamic scenarios and non-uniform signal-to-noise ratio in extreme dynamic range conditions. Recently, Single Photon Avalanche Diodes (SPADs), also known as Single Photon Camera (SPC) sensors, have surpassed CMOS sensors due to their capability to capture individual photons with high timing precision. Unlike traditional digital cameras that first convert light energy into analog electrical currents and then digitize them, SPAD sensors perform direct photon detection, making them less susceptible to extreme illumination conditions. Their distinctive non-linear response curve aids in capturing photons across both low-light and high-illumination environments, making them particularly effective for High Dynamic Range Imaging. Despite their advantages, images from SPAD Sensors are often noisy and visually unappealing. To address these challenges, we evaluate state-of-the-art architectures for converting monochromatic SPAD images into Color HDR images at various resolutions. Our evaluation involves both qualitative and quantitative assessments of these architectures, focusing on their effectiveness in each stage of the conversion process.
Expressions of Market-Based Correlations Between Prices and Returns of Two Assets
Authors: Victor Olkhov
Date: 2024-12-17 00:00:00

This paper derives the expressions of correlations between prices of two assets, returns of two assets, and price-return correlations of two assets that depend on statistical moments and correlations of the current values, past values, and volumes of their market trades. The usual frequency-based expressions of correlations of time series of prices and returns describe a partial case of our model when all trade volumes and past trade values are constant. Such an assumptions are rather far from market reality, and its use results in excess losses and wrong forecasts. Traders, banks, and funds that perform multi-million market transactions or manage billion-valued portfolios should consider the impact of large trade volumes on market prices and returns. The use of the market-based correlations of prices and returns of two assets is mandatory for them. The development of macroeconomic models and market forecasts like those being created by BlackRock's Aladdin, JP Morgan, and the U.S. Fed., is impossible without the use of market-based correlations of prices and returns of two assets.
Guiding Generative Protein Language Models with Reinforcement Learning
Authors: Filippo Stocco, Maria Artigues-Lleixa, Andrea Hunklinger, Talal Widatalla, Marc Guell, Noelia Ferruz
Date: 2024-12-17 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autoregressive protein language models (pLMs) have emerged as powerful tools to efficiently design functional proteins with extraordinary diversity, as evidenced by the successful generation of diverse enzyme families, including lysozymes or carbonic anhydrases. However, a fundamental limitation of pLMs is their propensity to sample from dense regions within the training distribution, which constrains their ability to sample from rare, high-value regions of the sequence space. This limitation becomes particularly critical in applications targeting underrepresented distribution tails, such as engineering for enzymatic activity or binding affinity. To address this challenge, we implement DPO_pLM, a reinforcement learning (RL) framework for protein sequence optimization with pLMs. Drawing inspiration from the success of RL in aligning language models to human preferences, we approach protein optimization as an iterative process that fine-tunes pLM weights to maximize a reward provided by an external oracle. Our strategy demonstrates that RL can efficiently optimize for a variety of custom properties without the need for additional data, achieving significant while preserving sequence diversity. We applied DPO_pLM to the design of EGFR binders, successfully identifying nanomolar binders within hours. Our code is publicly available at https://github.com/AI4PDLab/DPO_pLM.
CovNet: Covariance Information-Assisted CSI Feedback for FDD Massive MIMO Systems
Authors: Jialin Zhuang, Xuan He, Yafei Wang, Jiale Liu, Wenjin Wang
Date: 2024-12-17 00:00:00

In this paper, we propose a novel covariance information-assisted channel state information (CSI) feedback scheme for frequency-division duplex (FDD) massive multi-input multi-output (MIMO) systems. Unlike most existing CSI feedback schemes, which rely on instantaneous CSI only, the proposed CovNet leverages CSI covariance information to achieve high-performance CSI reconstruction, primarily consisting of convolutional neural network (CNN) and Transformer architecture. To efficiently utilize covariance information, we propose a covariance information processing procedure and sophisticatedly design the covariance information processing network (CIPN) to further process it. Moreover, the feed-forward network (FFN) in CovNet is designed to jointly leverage the 2D characteristics of the CSI matrix in the angle and delay domains. Simulation results demonstrate that the proposed network effectively leverages covariance information and outperforms the state-of-the-art (SOTA) scheme across the full compression ratio (CR) range.
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive Learning
Authors: Pegah Golchin, Nima Rafiee, Mehrdad Hajizadeh, Ahmad Khalil, Ralf Kundel, Ralf Steinmetz
Date: 2024-06-10 00:00:00
Proceeding: IFIP Networking Conference (IFIP Networking) 2024 6
Tasks: Contrastive Learning, Diversity, Intrusion Detection, Transfer Learning

With the increasing diversity and complexity of cyber attacks on computer networks, there is a growing demand for Intrusion Detection Systems (IDS) that can accurately categorize new unknown network flows. Machine learning- based IDS (ML-IDS) offers a potential solution by learning underlying network traffic characteristics. However, ML-IDS encounters performance degradation in predicting the traffic with a different distribution from its training dataset (i.e., new unseen data), especially for attacks that mimic benign (non- attack) traffic (e.g., multi-stage attacks). Diversity in attack types intensifies the lack of labeled attack traffic, which leads to reduced detection performance and generalization capabilities of ML-IDS. The generalization refers to the model’s capacity to identify new and unseen samples, even in cases where their distribution deviates from the training data used for the ML- IDS. To address these issues, this paper introduces SSCL- IDS, a Self-Supervised Contrastive Learning IDS designed to increase the generalization of ML-IDS. The proposed SSCL- IDS is exclusively trained on benign flows, enabling it to acquire a generic representation of benign traffic patterns and reduce the reliance on annotated network traffic datasets. The proposed SSCL-IDS demonstrates a substantial improvement in detection and generalization across diverse datasets compared to supervised (over 27%) and unsupervised (over 15%) baselines due to its ability to learn a more effective representation of benign flow attributes. Additionally, by leveraging transfer learning with SSCL-IDS as a pretrained model, we achieve AUROC scores surpassing 80% when fine-tuning with less than 20 training samples. Without fine-tuning, the average AUROC score across different datasets resembles random guessing.
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine Learning
Authors: Pegah Golchin, Chengbo Zhou, Pratyush Agnihotri, Mehrdad Hajizadeh, Ralf Kundel, Ralf Steinmetz
Date: 2023-11-28 00:00:00
Proceeding: 19th International Conference on Network and Service Management (CNSM) 2023 11
Tasks: Intrusion Detection

The centralized control plane in Software-Defined Networking (SDN) offers significant advancements in network management capabilities. However, SDN is also susceptible to cybersecurity risks and vulnerabilities. Deploying the Machine Learning (ML) approach in an Intrusion Detection System (IDS) can facilitate early detection of potential vulnerabilities. However, deploying an ML-based IDS solely in either the SDN control plane or the data plane has its benefits and drawbacks. For instance, a high-capacity ML model deployed in the control plane can enhance the detection performance but may increase network latency and the risk of overwhelming the control plane. In contrast, lightweight ML models deployed in the data plane could accelerate intrusion detection with lower detection performance. However, a functional IDS should provide a good detection performance at a line rate. To accomplish these objectives, we introduce a novel method called Collaborative ML-based IDS (CML-IDS), which involves deploying ML models in both the control and data planes to detect network attacks collaboratively. To facilitate this collaboration, we assess the confidence of the classification model, which is flexibly deployed within the programmable data plane. Our evaluation results demonstrate that the CML-IDS enhances the average intrusion detection performance to 93.46% and reduces the misclassification rate by 54.66% when compared to an IDS that solely relies on the ML model deployed in the data plane. Furthermore, CML-IDS effectively reduces network latency caused by forwarding flows to the control plane.
GREGoR: Accelerating Genomics for Rare Diseases
Authors: Moez Dawood, Ben Heavner, Marsha M. Wheeler, Rachel A. Ungar, Jonathan LoTempio, Laurens Wiel, Seth Berger, Jonathan A. Bernstein, Jessica X. Chong, Emmanuèle C. Délot, Evan E. Eichler, Richard A. Gibbs, James R. Lupski, Ali Shojaie, Michael E. Talkowski, Alex H. Wagner, Chia-Lin Wei, Christopher Wellington, Matthew T. Wheeler, GREGoR Partner Members, Claudia M. B. Carvalho, Casey A. Gifford, Susanne May, Danny E. Miller, Heidi L. Rehm, Fritz J. Sedlazeck, Eric Vilain, Anne O'Donnell-Luria, Jennifer E. Posey, Lisa H. Chadwick, Michael J. Bamshad, Stephen B. Montgomery, Genomics Research to Elucidate the Genetics of Rare Diseases, Consortium
Date: 2024-12-18 00:00:00

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.
ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting
Authors: Qi Zheng, Zihao Yao, Yaying Zhang
Date: 2024-12-19 00:00:00
Tasks: Contrastive Learning, Representation Learning, Self-Supervised Learning, Value prediction

Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods; 2) overlooking spatial correlations across variables over time; 3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability.
Preventing Local Pitfalls in Vector Quantization via Optimal Transport
Authors: Borui Zhang, Wenzhao Zheng, Jie zhou, Jiwen Lu
Date: 2024-12-19 00:00:00
Tasks: Image Reconstruction, Quantization

Vector-quantized networks (VQNs) have exhibited remarkable performance across various tasks, yet they are prone to training instability, which complicates the training process due to the necessity for techniques such as subtle initialization and model distillation. In this study, we identify the local minima issue as the primary cause of this instability. To address this, we integrate an optimal transport method in place of the nearest neighbor search to achieve a more globally informed assignment. We introduce OptVQ, a novel vector quantization method that employs the Sinkhorn algorithm to optimize the optimal transport problem, thereby enhancing the stability and efficiency of the training process. To mitigate the influence of diverse data distributions on the Sinkhorn algorithm, we implement a straightforward yet effective normalization strategy. Our comprehensive experiments on image reconstruction tasks demonstrate that OptVQ achieves 100% codebook utilization and surpasses current state-of-the-art VQNs in reconstruction quality.
Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network
Authors: Yunyi Cai, Weiyi Wang, Lanlan Yu, Ruixiang Wang, Gui-Quan Sun, Allisandra G. Kummer, Paulo C. Ventura, Jiancheng Lv, Marco Ajelli, Quan-Hui Liu
Date: 2024-12-18 00:00:00

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%-88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1.
PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
Authors: Yiqun Zhang, Xiaocui Yang, Xiaobai Li, Siyuan Yu, Yi Luan, Shi Feng, Daling Wang, Yifei Zhang
Date: 2024-12-19 00:00:00

Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-risk LBCs is crucial, yet challenging due to the severe shortage of mental health professionals, especially in rural areas. While the House-Tree-Person (HTP) test shows higher child participation rates, its requirement for expert interpretation limits its application in resource-scarce regions. To address this challenge, we propose PsyDraw, a multi-agent system based on Multimodal Large Language Models that assists mental health professionals in analyzing HTP drawings. The system employs specialized agents for feature extraction and psychological interpretation, operating in two stages: comprehensive feature analysis and professional report generation. Evaluation of HTP drawings from 290 primary school students reveals that 71.03% of the analyzes achieved High Consistency with professional evaluations, 26.21% Moderate Consistency and only 2.41% Low Consistency. The system identified 31.03% of cases requiring professional attention, demonstrating its effectiveness as a preliminary screening tool. Currently deployed in pilot schools, \method shows promise in supporting mental health professionals, particularly in resource-limited areas, while maintaining high professional standards in psychological assessment.
Understanding WiFi Signal Frequency Features for Position-Independent Gesture Sensing
Authors: Kai Niu, Fusang Zhang, Xuanzhi Wang, Qin Lv, Haitong Luo, and Daqing Zhang
Date: 2021-03-02 00:00:00
Proceeding: IEEE Transactions on Mobile Computing(Volume: 21, Issue: 11, 01) 2021 3
Tasks: Gesture Recognition, Position, Transfer Learning

Recent years have witnessed rapid development in the research area of WiFi sensing, which senses human activities in a
contactless and non-intrusive manner. One major issue that hinders real-world deployment of these systems is position dependence, i.e., once the human target changes location and orientation, the sensing performance degrades significantly. Existing machine learning based methods aim to solve this problem by either generating high-dimensional features or transfer learning the environment knowledge. However, these methods require significant training effort and yet acquire limited improvement. In this paper, we start by understanding and analyzing the Doppler frequency shift in WiFi sensing. We then develop a WiFi frequency model to quantify the relationship between signal frequency and target position, motion direction and speed for human activities. Based on this theoretical model, we prove that the commonly-used movement speed and motion direction features are position dependent, and further identify movement fragments and relative motion
direction changes as two position-independent features. Building upon the frequency model and the position-independent features, we design a suite of position-independent gestures and develop the gesture recognition system accordingly. Evaluation results show that under various conditions (i.e., different locations, orientations, environments, and persons), our system achieves more than 96 percent recognition accuracy without any training, significantly outperforming state-of-the-art machine learning based solutions.
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Authors: Yuxuan Liang, Wentao Zhang, Zeang Sheng, Ling Yang, Quanqing Xu, Jiawei Jiang, Yunhai Tong, Bin Cu
Date: 2024-12-19 00:00:00
Tasks: Graph Mining

In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high computational and storage costs of repeated feature propagation and non-linear transformation during training. One commonly employed approach to address this challenge is model-simplification, which only executes the Propagation (P) once in the pre-processing, and Combine (C) these receptive fields in different ways and then feed them into a simple model for better performance. Despite their high predictive performance and scalability, these methods still face two limitations. First, existing approaches mainly focus on exploring different C methods from the model perspective, neglecting the crucial problem of performance degradation with increasing P depth from the data-centric perspective, known as the over-smoothing problem. Second, pre-processing overhead takes up most of the end-to-end processing time, especially for large-scale graphs. To address these limitations, we present random walk with noise masking (RMask), a plug-and-play module compatible with the existing model-simplification works. This module enables the exploration of deeper GNNs while preserving their scalability. Unlike the previous model-simplification works, we focus on continuous P and found that the noise existing inside each P is the cause of the over-smoothing issue, and use the efficient masking mechanism to eliminate them. Experimental results on six real-world datasets demonstrate that model-simplification works equipped with RMask yield superior performance compared to their original version and can make a good trade-off between accuracy and efficiency.
Do Large Language Models Defend Inferentialist Semantics?: On the Logical Expressivism and Anti-Representationalism of LLMs
Authors: Yuzuki Arai, Sho Tsugawa
Date: 2024-12-19 00:00:00
Tasks: Philosophy

The philosophy of language, which has historically been developed through an anthropocentric lens, is now being forced to move towards post-anthropocentrism due to the advent of large language models (LLMs) like ChatGPT (OpenAI), Claude (Anthropic), which are considered to possess linguistic abilities comparable to those of humans. Traditionally, LLMs have been explained through distributional semantics as their foundational semantics. However, recent research is exploring alternative foundational semantics beyond distributional semantics. This paper proposes Robert Brandom's inferentialist semantics as an suitable foundational semantics for LLMs, specifically focusing on the issue of linguistic representationalism within this post-anthropocentric trend. Here, we show that the anti-representationalism and logical expressivism of inferential semantics, as well as quasi-compositionality, are useful in interpreting the characteristics and behaviors of LLMs. Further, we propose a \emph{consensus theory of truths} for LLMs. This paper argues that the characteristics of LLMs challenge mainstream assumptions in philosophy of language, such as semantic externalism and compositionality. We believe the argument in this paper leads to a re-evaluation of anti\hyphen{}representationalist views of language, potentially leading to new developments in the philosophy of language.