pwc-archive/papers-with-abstracts huggingface dataset:
Generating synthetic light-adapted electroretinogram waveforms using Artificial Intelligence to improve classification of retinal conditions in under-represented populations
Authors: Mikhail Kulyabin, Aleksei Zhdanov, Andreas Maier, Lynne Loh, Jose J. Estevez, Paul A. Constable
Date: 2024-04-18 00:00:00

Visual electrophysiology is often used clinically to determine functional changes associated with retinal or neurological conditions. The full-field flash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers reference normative data are used to compare with clinical cases that may be rare or underpowered within a specific demographic. To bolster either reference or case datasets the application of synthetic ERG waveforms may offer benefits to disease classification and case-control studies. In this study and as a proof of concept, artificial intelligence (AI) to generate synthetic signals using Generative Adversarial Networks is deployed to up-scale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random Forest Classifiers further improved classification for sex within the group from a balanced accuracy of 0.72 to 0.83 with the added synthetic male waveforms. This is the first study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classification modelling with electroretinogram waveforms.
Modelling infectious disease transmission dynamics in conference environments: An individual-based approach
Authors: Xue Liu, Yue Deng, Jingying Huang, Yuhong Zhang, Jinzhi Lei
Date: 2024-04-17 00:00:00

The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting a computational model designed to simulate transmission processes within a condensed timeframe (one day), beginning with sporadic cases. Our model intricately captures the activities of individual attendees within the conference venue, encompassing meetings, rest intervals, and meal breaks. While meetings entail proximity seating, rest and lunch periods allow attendees to interact with diverse individuals. Moreover, the restroom environment poses an additional avenue for potential infection transmission. Employing an individual-based model, we meticulously replicated the transmission dynamics of infectious diseases, with a specific emphasis on close-contact interactions between infected and susceptible individuals. Through comprehensive analysis of model simulations, we elucidated the intricacies of disease transmission dynamics within conference settings and assessed the efficacy of control strategies to curb disease dissemination. Ultimately, our study proffers a numerical framework for assessing the risk of infectious disease transmission during short-duration conferences, furnishing conference organizers with valuable insights to inform the implementation of targeted prevention and control measures.
How to study the evolution of parametrized games?
Authors: Georgiy Karev
Date: 2024-04-17 00:00:00

I consider a family of games with corresponding payoff matrices, parametrized by the parameter The informal question of interest is: what game is the best? Instead of attempting to suggest a more or less arbitrary definition of the best game, I allow natural selection to find it through honest competition between games. Specifically, I consider a model of a community composed of populations with different payoff matrices and study the natural selection between the populations. The outcome of the community evolution determines the best game. I develop here a mathematical toolbox, that allows the study of evolution of parametrized games. The developed approach is applied to three examples of parametrized games from the literature: local replicator dynamics, pairwise competition and the game of alleles in diploid genomes.
Piercing the Veil of TVL: DeFi Reappraised
Authors: Yichen Luo, Yebo Feng, Jiahua Xu, Paolo Tasca
Date: 2024-04-17 00:00:00
Tasks: Sensitivity

Total value locked (TVL) is widely used to measure the size and popularity of decentralized finance (DeFi). However, TVL can be easily manipulated and inflated through "double counting" activities such as wrapping and leveraging. As existing methodologies addressing double counting are inconsistent and flawed, we propose a new framework, termed "total value redeemable (TVR)", to assess the true underlying value of DeFi. Our formal analysis reveals how DeFi's complex network spreads financial contagion via derivative tokens, increasing TVL's sensitivity to external shocks. To quantify double counting, we construct the DeFi multiplier, which mirrors the money multiplier in traditional finance (TradFi). This measurement reveals substantial double counting in DeFi, finding that the gap between TVL and TVR reached \$139.87 billion during the peak of DeFi activity on December 2, 2021, with a TVL-to-TVR ratio of approximately 2. We conduct sensitivity tests to evaluate the stability of TVL compared to TVR, demonstrating the former's significantly higher level of instability than the latter, especially during market downturns: A 25% decline in the price of Ether (ETH) leads to a \$1 billion greater non-linear decrease in TVL compared to TVR via the liquidations triggered by derivative tokens. We also document that the DeFi money multiplier is positively correlated with crypto market indicators and negatively correlated with macroeconomic indicators. Overall, our findings suggest that TVR is more reliable and stable than TVL.
Beyond the Bid-Ask: Strategic Insights into Spread Prediction and the Global Mid-Price Phenomenon
Authors: Yifan He, Abootaleb Shirvani, Barret Shao, Svetlozar Rachev, Frank Fabozzi
Date: 2024-04-17 00:00:00
Tasks: Navigate, Novel Concepts

This research extends the conventional concepts of the bid--ask spread (BAS) and mid-price to include the total market order book bid--ask spread (TMOBBAS) and the global mid-price (GMP). Using high-frequency trading data, we investigate these new constructs, finding that they have heavy tails and significant deviations from normality in the distributions of their log returns, which are confirmed by three different methods. We shift from a static to a dynamic analysis, employing the ARMA(1,1)-GARCH(1,1) model to capture the temporal dependencies in the return time-series, with the normal inverse Gaussian distribution used to capture the heavy tails of the returns. We apply an option pricing model to address the risks associated with the low liquidity indicated by the TMOBBAS and GMP. Additionally, we employ the Rachev ratio to evaluate the risk--return performance at various depths of the limit order book and examine tail risk interdependencies across spread levels. This study provides insights into the dynamics of financial markets, offering tools for trading strategies and systemic risk management.
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding
Authors: George Retsinas, Niki Efthymiou, Petros Maragos
Date: 2024-04-17 00:00:00
Tasks: 3D Pose Estimation, Instance Segmentation, Pose Estimation, Semantic Segmentation

Modern agricultural applications rely more and more on deep learning solutions. However, training well-performing deep networks requires a large amount of annotated data that may not be available and in the case of 3D annotation may not even be feasible for human annotators. In this work, we develop a deep learning approach to segment mushrooms and estimate their pose on 3D data, in the form of point clouds acquired by depth sensors. To circumvent the annotation problem, we create a synthetic dataset of mushroom scenes, where we are fully aware of 3D information, such as the pose of each mushroom. The proposed network has a fully convolutional backbone, that parses sparse 3D data, and predicts pose information that implicitly defines both instance segmentation and pose estimation task. We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors. Code is publicly available at https://github.com/georgeretsi/mushroom-pose.
A Multiagent CyberBattleSim for RL Cyber Operation Agents
Authors: Thomas Kunz, Christian Fisher, James La Novara-Gsell, Christopher Nguyen, Li Li
Date: 2023-04-03 00:00:00
Tasks: CyberBattleSim, Reinforcement Learning (RL)

Hardening cyber physical assets is both crucial and labor-intensive. Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more specifically has shown great promise to automate tasks that otherwise would require significant human insight/intelligence. The development of autonomous RL agents requires a suitable training environment that allows us to quickly evaluate various alternatives, in particular how to arrange training scenarios that pit attackers and defenders against each other. CyberBattleSim is a training environment that supports the training of red agents, i.e., attackers. We added the capability to train blue agents, i.e., defenders. The paper describes our changes and reports on the results we obtained when training blue agents, either in isolation or jointly with red agents. Our results show that training a blue agent does lead to stronger defenses against attacks. In particular, training a blue agent jointly with a red agent increases the blue agent's capability to thwart sophisticated red agents.
Cosmic Growth History and Expansion History
Authors: Eric V. Linder
Date: 2005-07-11 00:00:00

The cosmic expansion history tests the dynamics of the global evolution of the universe and its energy density contents, while the cosmic growth history tests the evolution of the inhomogeneous part of the energy density. Precision comparison of the two histories can distinguish the nature of the physics responsible for the accelerating cosmic expansion: an additional smooth component - dark energy - or a modification of the gravitational field equations. With the aid of a new fitting formula for linear perturbation growth accurate to 0.05-0.2%, we separate out the growth dependence on the expansion history and introduce a new growth index parameter \gamma that quantifies the gravitational modification.
Generating QM1B with PySCF$_{\text{IPU}}$
Authors: Alexander Mathiasen, Hatem Helal, Kerstin Klaser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew Fitzgibbon, Dominic Masters
Date: 2023-11-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: CPU

The emergence of foundation models in Computer Vision and Natural Language Processing have resulted in immense progress on downstream tasks. This progress was enabled by datasets with billions of training examples. Similar benefits are yet to be unlocked for quantum chemistry, where the potential of deep learning is constrained by comparatively small datasets with 100k to 20M training examples. These datasets are limited in size because the labels are computed using the accurate (but computationally demanding) predictions of Density Functional Theory (DFT). Notably, prior DFT datasets were created using CPU supercomputers without leveraging hardware acceleration. In this paper, we take a first step towards utilising hardware accelerators by introducing the data generator PySCF$_{\text{IPU}}$ using Intelligence Processing Units (IPUs). This allowed us to create the dataset QM1B with one billion training examples containing 9-11 heavy atoms. We demonstrate that a simple baseline neural network (SchNet 9M) improves its performance by simply increasing the amount of training data without additional inductive biases. To encourage future researchers to use QM1B responsibly, we highlight several limitations of QM1B and emphasise the low-resolution of our DFT options, which also serves as motivation for even larger, more accurate datasets. Code and dataset are available on Github: http://github.com/graphcore-research/pyscf-ipu
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Authors: Junkun Yuan, Xinyu Zhang, Hao Zhou, Jian Wang, Zhongwei Qiu, Zhiyin Shao, Shaofeng Zhang, Sifan Long, Kun Kuang, Kun Yao, Junyu Han, Errui Ding, Lanfen Lin, Fei Wu, Jingdong Wang
Date: 2023-10-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 2D Pose Estimation, Attribute, Pedestrian Attribute Recognition, Person Re-Identification, Pose Estimation

Model pre-training is essential in human-centric perception. In this paper, we first introduce masked image modeling (MIM) as a pre-training approach for this task. Upon revisiting the MIM training strategy, we reveal that human structure priors offer significant potential. Motivated by this insight, we further incorporate an intuitive human structure prior - human parts - into pre-training. Specifically, we employ this prior to guide the mask sampling process. Image patches, corresponding to human part regions, have high priority to be masked out. This encourages the model to concentrate more on body structure information during pre-training, yielding substantial benefits across a range of human-centric perception tasks. To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image. We term the entire method as HAP. HAP simply uses a plain ViT as the encoder yet establishes new state-of-the-art performance on 11 human-centric benchmarks, and on-par result on one dataset. For example, HAP achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Authors: Jia Guo, Shuai Lu, Lize Jia, Weihang Zhang, Huiqi Li
Date: 2023-06-05 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Anomaly Detection, Contrastive Learning, Decoder, Defect Detection, Unsupervised Anomaly Detection

Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. However, the features extracted from the encoders that are borrowed from natural image domains coincide little with the features required in the target UAD domain, such as industrial inspection and medical imaging. In this paper, we propose a novel epistemic UAD method, namely ReContrast, which optimizes the entire network to reduce biases towards the pre-trained image domain and orients the network in the target domain. We start with a feature reconstruction approach that detects anomalies from errors. Essentially, the elements of contrastive learning are elegantly embedded in feature reconstruction to prevent the network from training instability, pattern collapse, and identical shortcut, while simultaneously optimizing both the encoder and decoder on the target domain. To demonstrate our transfer ability on various image domains, we conduct extensive experiments across two popular industrial defect detection benchmarks and three medical image UAD tasks, which shows our superiority over current state-of-the-art methods.
Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
Authors: Wenlve Zhou and Zhiheng Zhou
Date: 2024-04-19 00:00:00
Proceeding: journal 2024 4
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

This paper addresses two vital challenges in Unsupervised Domain Adaptation (UDA) with a focus on harnessing the power of Vision-Language Pre-training (VLP) models. Firstly, UDA has primarily relied on ImageNet pre-trained models. However, the potential of VLP models in UDA remains largely unexplored. The rich representation of VLP models holds significant promise for enhancing UDA tasks. To address this, we propose a novel method called Cross-Modal Knowledge Distillation (CMKD), leveraging VLP models as teacher models to guide the learning process in the target domain, resulting in state-of-the-art performance. Secondly, current UDA paradigms involve training separate models for each task, leading to significant storage overhead and impractical model deployment as the number of transfer tasks grows. To overcome this challenge, we introduce Residual Sparse Training (RST) exploiting the benefits conferred by VLP's extensive pre-training, a technique that requires minimal adjustment (approximately 0.1%~0.5%) of VLP model parameters to achieve performance comparable to fine-tuning. Combining CMKD and RST, we present a comprehensive solution that effectively leverages VLP models for UDA tasks while reducing storage overhead for model deployment. Furthermore, CMKD can serve as a baseline in conjunction with other methods like FixMatch, enhancing the performance of UDA. Our proposed method outperforms existing techniques on standard benchmarks. Our code will be available at: https://github.com/Wenlve-Zhou/VLP-UDA.
Source Camera Identification with Multi-Scale Feature Fusion Network
Authors: Chen Hui, Feng Jiang, Shaohui Liu, Debin Zhao
Date: 2022-08-26 00:00:00
Proceeding: 2022 IEEE International Conference on Multimedia and Expo (ICME) 2022 8
Tasks: Decoder, Denoising

Source camera identification (SCI) technology has attracted increasing attentions over the past few years. However, the existing methods suppress image content with denoising filters that are largely agnostic to the specific sensor pattern noise (SPN) signal of interest. Such practices may potentially degrade the performance of SPN-based SCI due to un-reliable SPNs, especially when forensic images are transmitted through social networking platforms. In this paper, we address the problem of SPN-based device identification and propose a multi-scale feature fusion network (MSFFN) to boost the sensor-based source camera identification attribution. Specifically, several image patches of different scales are selected and input into the MSFFN to extract the SPN. The MSFFN is a multi-scale encoder-decoder structure, which is used to suppress image content and improve source attribution. Subsequently, the content-independent SPN features of different scales are fused. At last, the fused features are used for image source identification. Experimental results compared with the state-of-the-art demonstrate that the proposed scheme achieves significant improvements, especially in the accuracy of social networking image source identification.
BCGS: Blockchain-assisted privacy-preserving cross-domain authentication for VANETs
Authors: Biwen Chen, Zhongming Wang, Tao Xiang, Jiyun Yang, Debiao He, Kim-Kwang Raymond Choo
Date: 2023-03-15 00:00:00
Proceeding: Vehicular Communications 2023 3
Tasks: Privacy Preserving

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Secure authentication among vehicles in VANETs is an important requirement, but there are various limitations in many existing authentication protocols, for example, in terms of privacy protection, malicious entity tracking, and cross-domain authentication. In response, we first reconstruct a secure and effective short group signature scheme for realizing anonymous authentication and traceable identity within a domain. We then propose a secure blockchain-based privacy-preserving cross-domain authentication protocol for VANETs, namely BCGS, by integrating both blockchain and group signature. In BCGS, blockchain is leveraged to facilitate trusted information sharing and consequently supports cross-domain authentication among vehicles, and the group signature scheme is used to provide conditional privacy protection. Our comprehensive security and performance evaluations demonstrate that BCGS outperforms other similar approaches in terms of security, computation, and storage costs.
ArSen-20: A New Benchmark for Arabic Sentiment Detection
Authors: Yang Fang, Cheng Xu
Date: 2024-04-11 00:00:00
Proceeding: 5th Workshop on African Natural Language Processing 2024 4
Tasks: Arabic Sentiment Analysis, Sentiment Analysis

Sentiment detection remains a pivotal task in natural language processing, yet its development in Arabic lags due to a scarcity of training materials compared to English. Addressing this gap, we present ArSen-20, a benchmark dataset tailored to propel Arabic sentiment detection forward. ArSen-20 comprises 20,000 professionally labeled tweets sourced from Twitter, focusing on the theme of COVID-19 and spanning the period from 2020 to 2023. Beyond tweet content, the dataset incorporates metadata associated with the user, enriching the contextual understanding. ArSen-20 offers a comprehensive resource to foster advancements in Arabic sentiment analysis and facilitate research in this critical domain.
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Authors: Tianfu Wang, Qilin Fan, Chao Wang, Long Yang, Leilei Ding, Nicholas Jing Yuan, Hui Xiong
Date: 2024-04-19 00:00:00
Tasks: Decoder, Network Embedding, Reinforcement Learning (RL), Scheduling

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at GitHub (https://github.com/GeminiLight/flag-vne).
Dynamic Temperature Knowledge Distillation
Authors: Yukang Wei, Yu Bai
Date: 2024-04-19 00:00:00
Tasks: Knowledge Distillation

Temperature plays a pivotal role in moderating label softness in the realm of knowledge distillation (KD). Traditional approaches often employ a static temperature throughout the KD process, which fails to address the nuanced complexities of samples with varying levels of difficulty and overlooks the distinct capabilities of different teacher-student pairings. This leads to a less-than-ideal transfer of knowledge. To improve the process of knowledge propagation, we proposed Dynamic Temperature Knowledge Distillation (DTKD) which introduces a dynamic, cooperative temperature control for both teacher and student models simultaneously within each training iterafion. In particular, we proposed "\textbf{sharpness}" as a metric to quantify the smoothness of a model's output distribution. By minimizing the sharpness difference between the teacher and the student, we can derive sample-specific temperatures for them respectively. Extensive experiments on CIFAR-100 and ImageNet-2012 demonstrate that DTKD performs comparably to leading KD techniques, with added robustness in Target Class KD and None-target Class KD scenarios.The code is available at https://github.com/JinYu1998/DTKD.