pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information
Authors: Francesco Ardizzon, Roee Diamant, Paolo Casari, Stefano Tomasin
Date: 2022-08-19 00:00:00

We propose a technique to authenticate received packets in underwater acoustic networks based on the physical layer features of the underwater acoustic channel (UWAC). Several sensors a) locally estimate features (e.g., the number of taps or the delay spread) of the UWAC over which the packet is received, b) obtain a compressed feature representation through a neural network (NN), and c) transmit their representations to a central sink node that, using a NN, decides whether the packet has been transmitted by the legitimate node or by an impersonating attacker. Although the purpose of the system is to make a binary decision as to whether a packet is authentic or not, we show the importance of having a rich set of compressed features, while still taking into account transmission rate limits among the nodes. We consider both global training, where all NNs are trained together, and local training, where each NN is trained individually. For the latter scenario, several alternatives for the NN structure and loss function were used for training.
A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems -- Part II: Convergence Properties
Authors: Cem Ates Musluoglu, Charles Hovine, Alexander Bertrand
Date: 2022-08-18 00:00:00

This paper studies the convergence conditions and properties of the distributed adaptive signal fusion (DASF) algorithm, the framework itself having been introduced in a `Part I' companion paper. The DASF algorithm can be used to solve linear signal and feature fusion optimization problems in a distributed fashion, and is in particular well-suited for solving spatial filtering optimization problems encountered in wireless sensor networks. The convergence conditions and results are provided along with rigorous proofs and analyses, as well as various example problems to which they apply. Additionally, we describe procedures that can be added to the DASF algorithm to ensure convergence in specific cases where some of the technical convergence conditions are not satisfied.
Microbiome and metabolome insights into the role of the gastrointestinal-brain axis in neurodegenerative diseases: unveiling potential therapeutic targets
Authors: Helena U. Zacharias, Christoph Kaleta, Francois Cossais, Eva Schaeffer, Henry Berndt, Lena Best, Thomas Dost, Svea Glüsing, Mathieu Groussin, Mathilde Poyet, Sebastian Heinzel, Corinna Bang, Leonard Siebert, Tobias Demetrowitsch, Frank Leypoldt, Rainer Adelung, Thorsten Bartsch, Anja Bosy-Westphal, Karin Schwarz, Daniela Berg
Date: 2022-08-19 00:00:00

Due to the aging of the world population and westernization of lifestyles, the prevalence of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is rapidly rising and is expected to put a strong socioeconomic burden on health systems worldwide. Due to the limited success of clinical trials of therapies against neurodegenerative diseases, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome as well as metabolome studies along the gastrointestinal-brain axis have already revealed important insights into disease pathomechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the association between microbiota, metabolites, and neurodegeneration in light of the gastrointestinal-brain axis. In this context, we also illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude our review with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.
Concepts as Elementary Constituents of Human Consciousness
Authors: Boris Rusakov
Date: 2022-08-18 00:00:00

It is asserted that consciousness functionally is a vision. Biologically it is a sensation of work of a brain that converts external and internal signals into visual output. However, as we well know, human consciousness includes meaning (interpretation), and therefore functionally possesses an additional level of complexity. It consists of concepts which are its minimal components (elementary constituents). A concept is an abstract (i.e. non-existent in nature) common property of different observables, as perceived by humans. It is an irreducible entity since it cannot be divided into any other functional components. At the biological and physical level each concept is a unique sensation encoded in a nervous system as a sensory and visual image. Concepts evolve throughout one's lifetime. We continuously create and acquire new concepts, develop and expand the existing ones, and eliminate and abandon some others. 'I' ('self') is one of the concepts. To acquire a concept one has to learn (to teach one's nervous system) feeling or sensing it, and to connect it to existing concepts, if any. We make a conjecture that both the creation and the acquisition of a concept is a phase transition. The ability to generate and acquire concepts is unique to humans and is the only principal difference between human and animal. We offer scenarios for how this ability could have been acquired by our predecessors, thus making them humans. It is suggested that the animal's brain, being a processor of visual signals that converts them into internal images, has developed this ability as a result of "guessing" or imagining details that are missing in the actual visible picture.
Vine Copula based portfolio level conditional risk measure forecasting
Authors: Emanuel Sommer, Karoline Bax, Claudia Czado
Date: 2022-08-19 00:00:00

Accurately estimating risk measures for financial portfolios is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level and thus fail to capture the complex cross-dependencies between portfolio components. To address this, a new approach is presented that uses vine copulas in combination with univariate ARMA-GARCH models for marginal modelling to compute conditional portfolio-level risk measure estimates by simulating portfolio-level forecasts conditioned on a stress factor. A quantile-based approach is then presented to observe the behaviour of risk measures given a particular state of the conditioning asset(s). In a case study of Spanish equities with different stress factors, the results show that the portfolio is quite robust to a sharp downturn in the American market. At the same time, there is no evidence of this behaviour with respect to the European market.
Automated Detection of Acute Lymphoblastic Leukemia Subtypes from Microscopic Blood Smear Images using Deep Neural Networks
Authors: Md. Taufiqul Haque Khan Tusar, Roban Khan Anik
Date: 2022-07-30 00:00:00
Tasks: All

An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL), which affects people of all age groups, including children and adults. In this study, we propose an automated system to detect various-shaped ALL blast cells from microscopic blood smears images using Deep Neural Networks (DNN). The system can detect multiple subtypes of ALL cells with an accuracy of 98 percent. Moreover, we have developed a telediagnosis software to provide real-time support to diagnose ALL subtypes from microscopic blood smears images.
LMOT: Efficient Light-Weight Detection and Tracking in Crowds
Authors: Rana Mostafa, Hoda Baraka, AbdElMoniem Bayoumi
Date: 2022-08-08 00:00:00
Proceeding: IEEE Access 2022 8
Tasks: 2D Object Detection, Multi-Object Tracking, Object, Object Tracking, Pedestrian Detection

Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model.
Digital Image-Based Stress–Permeability Relationships of Rough Fractures Using Numerical Contact Mechanics and Stokes Equation
Authors: Amanzhol Kubeyev, Nathaniel Forbes Inskip, Tomos Phillips, Yihuai Zhang, Christine Maier, Kevin Bisdom, Andreas Busch, Florian Doster
Date: 2022-01-10 00:00:00
Proceeding: Transport in Porous Media 2022 1
Tasks: Contact mechanics

Flow in fractures is sensitive to their geometrical surface characteristics. The surface can undergo deformation if there is a change in stress. Natural fractures have complex geometries and rough surfaces which complicates the modelling of deformation and fluid flow.
In this paper, we present a computational model that takes a digital image of a rough fracture surface and provides a stress-permeability relationship. The model is based on a first principle contact mechanics approach at the continuum scale. Using this first principle approach we investigate numerically the effect of fracture surface roughness and shifting of surfaces on the permeability evolution under applied stress and compare the results with laboratory experiments. A mudrock core fracture surface was digitalized using an optical microscope, and 2D cross-sections through fracture surface profiles were taken for the modelling. Mechanical deformation is simulated with the contact mechanics based Virtual Element Method solver that we developed within the Matlab Reservoir Simulation Toolbox (MRST) platform. The permeability perpendicular to the fracture cross-section is determined by solving the Stokes equation using the Finite Volume Method. A source of uncertainty in reproducing laboratory results is that the exact anchoring of the two opposite surfaces is difficult to determine while the stress-permeability relationship is sensitive to the exact positioning. We, therefore, investigate the sensitivity to a mismatch in two scenarios: First, we assess the stress-permeability of a fracture created using two opposing matched surfaces from the rock sample, consequently applying relative shear. Second, we assess the stress-permeability of fractures created by randomly selecting opposing surfaces from that sample. We find that a larger shift leads to a smaller drop in permeability due to applied stress, which is in line with a previous laboratory study. We also find that permeability tends to be higher in fractures with higher roughness within the investigated stress range. Finally, we provide empirical stress-permeability relationships for various relative shears and roughnesses for use in hydro-mechanical studies of fractured geological formations.
Genome-wide nucleotide-resolution model of single-strand break site reveals species evolutionary hierarchy
Authors: Sheng Xu, Junkang Wei, Yu Li
Date: 2022-08-21 00:00:00

Single-strand breaks (SSBs) are the major DNA damage in the genome arising spontaneously as the outcome of genotoxins and intermediates of DNA transactions. SSBs play a crucial role in various biological processes and show a non-random distribution in the genome. Several SSB detection approaches such as S1 END-seq and SSiNGLe-ILM emerged to characterize the genomic landscape of SSB with nucleotide resolution. However, these sequencing-based methods are costly and unfeasible for large-scale analysis of diverse species. Thus, we proposed the first computational approach, SSBlazer, which is an explainable and scalable deep learning framework for genome-wide nucleotide-resolution SSB site prediction. We demonstrated that SSBlazer can accurately predict SSB sites and sufficiently alleviate false positives by constructing an imbalanced dataset to simulate the realistic SSB distribution. The model interpretation analysis reveals that SSBlazer captures the pattern of individual CpG in genomic context and the motif of TGCC in the center region as critical features. Besides, SSBlazer is a lightweight model with robust cross-species generalization ability in the cross-species evaluation, which enables the large-scale genome-wide application in diverse species. Strikingly, the putative SSB genomic landscapes of 216 vertebrates reveal a negative correlation between SSB frequency and evolutionary hierarchy, suggesting that the genome tends to be integrity during evolution.
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
Authors: Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi
Date: 2022-08-21 00:00:00
Proceeding: CVPR 2023 1
Tasks: Medical Image Analysis, Semantic Segmentation, Semi-supervised Change Detection, Semi-supervised Medical Image Segmentation, Semi-Supervised Semantic Segmentation

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version. Intriguingly, we observe that such a simple pipeline already achieves competitive results against recent advanced works, when transferred to our segmentation scenario. Its success heavily relies on the manual design of strong data augmentations, however, which may be limited and inadequate to explore a broader perturbation space. Motivated by this, we propose an auxiliary feature perturbation stream as a supplement, leading to an expanded perturbation space. On the other, to sufficiently probe original image-level augmentations, we present a dual-stream perturbation technique, enabling two strong views to be simultaneously guided by a common weak view. Consequently, our overall Unified Dual-Stream Perturbations approach (UniMatch) surpasses all existing methods significantly across all evaluation protocols on the Pascal, Cityscapes, and COCO benchmarks. Its superiority is also demonstrated in remote sensing interpretation and medical image analysis. We hope our reproduced FixMatch and our results can inspire more future works. Code and logs are available at https://github.com/LiheYoung/UniMatch.
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning
Authors: Siyi Du, Ben Hers, Nourhan Bayasi, Ghassan Hamarneh, Rafeef Garbi
Date: 2022-08-22 00:00:00
Tasks: Attribute, Contrastive Learning, Deep Learning, Disentanglement, Fairness, Lesion Classification, Skin Lesion Classification

Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions, where lesions on darker skin types are usually underrepresented and have lower diagnosis accuracy, receives little attention. In this paper, we propose FairDisCo, a disentanglement deep learning framework with contrastive learning that utilizes an additional network branch to remove sensitive attributes, i.e. skin-type information from representations for fairness and another contrastive branch to enhance feature extraction. We compare FairDisCo to three fairness methods, namely, resampling, reweighting, and attribute-aware, on two newly released skin lesion datasets with different skin types: Fitzpatrick17k and Diverse Dermatology Images (DDI). We adapt two fairness-based metrics DPM and EOM for our multiple classes and sensitive attributes task, highlighting the skin-type bias in skin lesion classification. Extensive experimental evaluation demonstrates the effectiveness of FairDisCo, with fairer and superior performance on skin lesion classification tasks.
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
Authors: Tingting Wu, Xiao Ding, Hao Zhang, Jinglong Gao, Li Du, Bing Qin, Ting Liu
Date: 2022-08-21 00:00:00
Tasks: image-classification, Image Classification, regression

Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e.g., easy to hard) sequence. Previous work takes incorrect samples as generic hard ones without discriminating between hard samples (i.e., hard samples in correct data) and incorrect samples. Indeed, a model should learn from hard samples to promote generalization rather than overfit to incorrect ones. In this paper, we address this problem by appending a novel loss function DiscrimLoss, on top of the existing task loss. Its main effect is to automatically and stably estimate the importance of easy samples and difficult samples (including hard and incorrect samples) at the early stages of training to improve the model performance. Then, during the following stages, DiscrimLoss is dedicated to discriminating between hard and incorrect samples to improve the model generalization. Such a training strategy can be formulated dynamically in a self-supervised manner, effectively mimicking the main principle of curriculum learning. Experiments on image classification, image regression, text sequence regression, and event relation reasoning demonstrate the versatility and effectiveness of our method, particularly in the presence of diversified noise levels.
Rethinking Knowledge Distillation via Cross-Entropy
Authors: Zhendong Yang, Zhe Li, Yuan Gong, Tianke Zhang, Shanshan Lao, Chun Yuan, Yu Li
Date: 2022-08-22 00:00:00
Tasks: Knowledge Distillation

Knowledge Distillation (KD) has developed extensively and boosted various tasks. The classical KD method adds the KD loss to the original cross-entropy (CE) loss. We try to decompose the KD loss to explore its relation with the CE loss. Surprisingly, we find it can be regarded as a combination of the CE loss and an extra loss which has the identical form as the CE loss. However, we notice the extra loss forces the student's relative probability to learn the teacher's absolute probability. Moreover, the sum of the two probabilities is different, making it hard to optimize. To address this issue, we revise the formulation and propose a distributed loss. In addition, we utilize teachers' target output as the soft target, proposing the soft loss. Combining the soft loss and the distributed loss, we propose a new KD loss (NKD). Furthermore, we smooth students' target output to treat it as the soft target for training without teachers and propose a teacher-free new KD loss (tf-NKD). Our method achieves state-of-the-art performance on CIFAR-100 and ImageNet. For example, with ResNet-34 as the teacher, we boost the ImageNet Top-1 accuracy of ResNet18 from 69.90% to 71.96%. In training without teachers, MobileNet, ResNet-18 and SwinTransformer-Tiny achieve 70.04%, 70.76%, and 81.48%, which are 0.83%, 0.86%, and 0.30% higher than the baseline, respectively. The code is available at https://github.com/yzd-v/cls_KD.
LTE4G: Long-Tail Experts for Graph Neural Networks
Authors: Sukwon Yun, Kibum Kim, Kanghoon Yoon, Chanyoung Park
Date: 2022-08-22 00:00:00
Tasks: Knowledge Distillation, Node Classification

Existing Graph Neural Networks (GNNs) usually assume a balanced situation where both the class distribution and the node degree distribution are balanced. However, in real-world situations, we often encounter cases where a few classes (i.e., head class) dominate other classes (i.e., tail class) as well as in the node degree perspective, and thus naively applying existing GNNs eventually fall short of generalizing to the tail cases. Although recent studies proposed methods to handle long-tail situations on graphs, they only focus on either the class long-tailedness or the degree long-tailedness. In this paper, we propose a novel framework for training GNNs, called Long-Tail Experts for Graphs (LTE4G), which jointly considers the class long-tailedness, and the degree long-tailedness for node classification. The core idea is to assign an expert GNN model to each subset of nodes that are split in a balanced manner considering both the class and degree long-tailedness. After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i.e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively. We demonstrate that LTE4G outperforms a wide range of state-of-the-art methods in node classification evaluated on both manual and natural imbalanced graphs. The source code of LTE4G can be found at https://github.com/SukwonYun/LTE4G.
Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias
Authors: Samuel E. Armstrong
Date: 2022-08-22 00:00:00
Tasks: Survey

Many works in biomedical computer science research use machine learning techniques to give accurate results. However, these techniques may not be feasible for real-time analysis of data pulled from live hospital feeds. In this project, different machine learning techniques are compared from various sources to find one that provides not only high accuracy but also low latency and memory overhead to be used in real-world health care systems.
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
Authors: Zhihui Lin, Tianyu Yang, Maomao Li, Ziyu Wang, Chun Yuan, Wenhao Jiang, Wei Liu
Date: 2022-08-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS). However, continuously growing and redundant template features lead to an inefficient inference. To alleviate this, we propose a novel Sequential Weighted Expectation-Maximization (SWEM) network to greatly reduce the redundancy of memory features. Different from the previous methods which only detect feature redundancy between frames, SWEM merges both intra-frame and inter-frame similar features by leveraging the sequential weighted EM algorithm. Further, adaptive weights for frame features endow SWEM with the flexibility to represent hard samples, improving the discrimination of templates. Besides, the proposed method maintains a fixed number of template features in memory, which ensures the stable inference complexity of the VOS system. Extensive experiments on commonly used DAVIS and YouTube-VOS datasets verify the high efficiency (36 FPS) and high performance (84.3\% $\mathcal{J}\&\mathcal{F}$ on DAVIS 2017 validation dataset) of SWEM. Code is available at: https://github.com/lmm077/SWEM.