pwc-archive/papers-with-abstracts huggingface dataset:
Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems
Authors: Kenneth B. A. Benício, André L. F. de Almeida, Bruno Sokal, Fazal-E-Asim, Behrooz Makki, Gabor Fodor
Date: 2023-06-21 00:00:00

This paper proposes a tensor-based parametric modeling and estimation framework in multiple-input multiple-output (MIMO) systems assisted by intelligent reflecting surfaces (IRSs). We present two algorithms that exploit the tensor structure of the received pilot signal to estimate the concatenated channel. The first one is an iterative solution based on the alternating least squares algorithm. In contrast, the second method provides closed-form estimates of the involved parameters using the high order single value decomposition. Our numerical results show that our proposed tensor-based methods provide improved performance compared to competing state-of-the-art channel estimation schemes, thanks to the exploitation of the algebraic tensor structure of the combined channel without additional computational complexity.
Reporting existing datasets for automatic epilepsy diagnosis and seizure detection
Authors: Palak Handa, Sakshi Tiwari, Nidhi Goel
Date: 2023-06-21 00:00:00
Tasks: EEG, Seizure Detection

More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.
Blind Asynchronous Goal-Oriented Detection for Massive Connectivity
Authors: Sajad Daei, Saeed Razavikia, Marios Kountouris, Mikael Skoglund, Gabor Fodor, Carlo Fischione
Date: 2023-06-21 00:00:00

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra-reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal-oriented optimization problem is proposed, which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows us to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra-reliable and low-latency communications, making it an important contribution to the emerging 6G networks.
Anti-jamming Method for SAR Using Joint Waveform Modulation and Azimuth Mismatched Filtering
Authors: Zhuan Sun, Zhanyu Zhu
Date: 2023-06-21 00:00:00

High-fidelity deception jamming can seriously mislead Synthetic Aperture Radar (SAR) image interpretation and target detection, which is difficult to identify or eliminate through traditional anti-jamming methods. Based on the Range-Doppler Algorithm (RDA), an anti-jamming method for SAR by using joint waveform modulation and azimuth mismatched filtering is proposed in this paper. The signal model of SAR echo after pulse compression with deception jamming is derived from the radar detection and jamming characteristics. A multi-objective cost function is introduced to suppress the jamming level and keep the real target information, parameterized with the waveform initial phase and the azimuth mismatched filter coefficients, which is optimized using the second-order Taylor expansion approximation and the alternating direction multiplier method (ADMM). The performance of this proposed method is evaluated through the convergence analysis and anti-jamming experiments on the point target and the distributed target scenarios.
Pattern formation in a predator-prey model with Allee effect and hyperbolic mortality on networked and non-networked environments
Authors: Yong Ye, Jiaying Zhou
Date: 2023-06-20 00:00:00

With the development of network science, Turing pattern has been proven to be formed in discrete media such as complex networks, opening up the possibility of exploring it as a generation mechanism in the context of biology, chemistry, and physics. Turing instability in the predator-prey system has been widely studied in recent years. We hope to use the predator-prey interaction relationship in biological populations to explain the influence of network topology on pattern formation. In this paper, we establish a predator-prey model with weak Allee effect, analyze and verify the Turing instability conditions on the large ER (Erd\"{o}s-R\'{e}nyi) random network with the help of Turing stability theory and numerical experiments, and obtain the Turing instability region. The results indicate that diffusion plays a decisive role in the generation of spatial patterns, whether in continuous or discrete media. For spatiotemporal patterns, different initial values can also bring about changes in the pattern. When we analyze the model based on the network framework, we find that the average degree of the network has an important impact on the model, and different average degrees will lead to changes in the distribution pattern of the population.
Protein Discovery with Discrete Walk-Jump Sampling
Authors: Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi
Date: 2023-06-08 00:00:00
Tasks: Denoising

We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our Discrete Walk-Jump Sampling formalism combines the contrastive divergence training of an energy-based model and improved sample quality of a score-based model, while simplifying training and sampling by requiring only a single noise level. We evaluate the robustness of our approach on generative modeling of antibody proteins and introduce the distributional conformity score to benchmark protein generative models. By optimizing and sampling from our models for the proposed distributional conformity score, 97-100% of generated samples are successfully expressed and purified and 70% of functional designs show equal or improved binding affinity compared to known functional antibodies on the first attempt in a single round of laboratory experiments. We also report the first demonstration of long-run fast-mixing MCMC chains where diverse antibody protein classes are visited in a single MCMC chain.
Prognostic Biomarker Identification for Pancreatic Cancer by Analyzing Multiple mRNA Microarray and microRNA Expression Datasets
Authors: Azmain Yakin Srizon
Date: 2023-06-21 00:00:00
Tasks: feature selection

Possessing the five-year durability rate of nearly 5%, currently, the fourth leading cause for cancer-related deaths is pancreatic cancer. Previously, several works have resolved that early diagnosis performs a meaningful function in enhancing the durability rate and diverse online tools have been utilized to distinguish prognostic biomarker which is a lengthy process. We believe that the statistical feature selection method can produce a better and faster result here. To authenticate our statement, we picked three different mRNA microarray (GSE15471, GSE28735, and GSE16515) and a microRNA (GSE41372) dataset for identification of differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs). By adopting some feature selecting methods, 178 DEGs and 16 DEMs were elected. After identifying target genes of DEMs, we selected two DEGs (ECT2 and NRP2) which were also identified among DEMs target genes. Moreover, overall durability report established that ECT2 and NRP2 were associated with poor overall survival. Hence, we concluded that for pancreatic cancer, statistical feature selection approaches certainly perform better for biomarker identification than pre-defined online programs, and here, ECT2 and NRP2 can act as possible prognostic biomarkers. All the resources, programs and snippets of our literature can be discovered at https://github.com/Srizon143005/PancreaticCancerBiomarkers.
Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
Authors: Qinglong Cao, Yuntian Chen, Chao Ma, Xiaokang Yang
Date: 2023-05-29 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support. Conventional matching methods without consideration of varying object orientations can fail to activate same-category objects with different orientations. Moreover, conventional algorithms can lead to false recognition of lower-scored rotated semantic objects. In response to these challenges, the authors propose a novel few-shot rotation-invariant aerial semantic segmentation network (FRINet). FRINet matches each query feature rotation-adaptively with orientation-varying yet category-consistent support information. The segmentation predictions from different orientations are supervised by the same label, and the backbones are pre-trained in the base category to boost segmentation performance. Experimental results demonstrate that FRINet achieves state-of-the-art performance in few-shot aerial semantic segmentation benchmark.
VitalVideos-Europe: A dataset of face videos with PPG and blood pressure ground truths
Authors: Pieter-Jan Toye
Date: 2023-06-02 00:00:00

We collected a large dataset consisting of 850 unique participants. For every participant we recorded two 30 second uncompressed videos, synchronized PPG waveforms and a single blood pressure measurement. Gender, age and skin color were also registered for every participant. The dataset includes roughly equal numbers of males and females, as well as participants of all ages. While the skin color distribution could have been more balanced, the dataset contains individuals from every skin color. The data was collected in a diverse set of locations to ensure a wide variety of backgrounds and lighting conditions. In an effort to assist in the research and development of remote vital sign measurement we are now opening up access to this dataset. vitalvideos.org for all datasets.
Natural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using Transformers
Authors: Mahmud Hasan, Labiba Islam, Ismat Jahan, Sabrina Mannan Meem, Rashedur M. Rahman
Date: 2023-05-04 00:00:00
Proceeding: Vietnam Journal of Computer Science 2023 5
Tasks: Hyperparameter Optimization, Sentiment Analysis, text-classification, Text Classification

The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks such as sentiment analysis (SA) in Bangla is relatively low compared to SA in the English language. It is because there are not enough high-quality publically available datasets for training language models for text classification tasks in Bangla. In this paper, we propose a Bangla annotated dataset for sentiment analysis on the ongoing Ukraine–Russia war. The dataset was developed by collecting Bangla comments from various videos of three prominent YouTube TV news channels of Bangladesh covering their report on the ongoing conflict. A total of 10,861 Bangla comments were collected and labeled with three polarity sentiments, namely Neutral, Pro-Ukraine (Positive), and Pro-Russia (Negative). A benchmark classifier was developed by experimenting with several transformer-based language models all pre-trained on unlabeled Bangla corpus. The models were fine-tuned using our procured dataset. Hyperparameter optimization was performed on all 5 transformer language models which include: BanglaBERT, XLM-RoBERTa-base, XLM-RoBERTa-large, Distil-mBERT and mBERT. Each model was evaluated and analyzed using several evaluation metrics which include: F1 score, accuracy, and AIC (Akaike Information Criterion). The best-performing model achieved the highest accuracy of 86% with 0.82 F1 score. Based on accuracy, F1 score and AIC, BanglaBERT outperforms baseline and all the other transformer-based classifiers.
C3PO: A Lightweight Copying Mechanism for Translating Pseudocode to Code
Authors: Vishruth Veerendranath, Vibha Masti, Prajwal Anagani, Mamatha Hr
Date: 2022-11-20 00:00:00
Proceeding: AACL-IJCNLP 2022 11
Tasks: Code Translation, Text-to-Code Generation

Writing computer programs is a skill that remains inaccessible to most due to the barrier of programming language (PL) syntax. While large language models (LLMs) have been proposed to translate natural language pseudocode to PL code, they are costly in terms of data and compute. We propose a lightweight alternative to LLMs that exploits the property of code wherein most tokens can be simply copied from the pseudocode. We divide the problem into three phases: Copy, Generate, and Combine. In the Copy Phase, a binary classifier is employed to determine and mask the pseudocode tokens that can be directly copied into the code. In the Generate Phase, a Sequence-to-Sequence model is used to generate the masked PL code equivalent. In the Combine Phase, the generated sequence is combined with the tokens that the Copy Phase had masked. We show that our C3PO models achieve similar performance to non-C3PO models while reducing the computational cost of training as well as the vocabulary sizes.
Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference
Authors: Boyan Li, Luziwei Leng, Shuaijie Shen, Kaixuan Zhang, JianGuo Zhang, Jianxing Liao, Ran Cheng
Date: 2023-06-21 00:00:00
Tasks: image-classification, Image Classification

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposing limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introducing a spiking patch encoding layer to enhance local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells. Source codes are publicly accessible at https://github.com/EMI-Group/mixer-snn.
Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons
Authors: Sidi Yaya Arnaud Yarga, Sean U. N. Wood
Date: 2023-06-22 00:00:00
Tasks: GPU

Spiking neural networks (SNN) are able to learn spatiotemporal features while using less energy, especially on neuromorphic hardware. The most widely used spiking neuron in deep learning is the Leaky Integrate and Fire (LIF) neuron. LIF neurons operate sequentially, however, since the computation of state at time t relies on the state at time t-1 being computed. This limitation is shared with Recurrent Neural Networks (RNN) and results in slow training on Graphics Processing Units (GPU). In this paper, we propose the Stochastic Parallelizable Spiking Neuron (SPSN) to overcome the sequential training limitation of LIF neurons. By separating the linear integration component from the non-linear spiking function, SPSN can be run in parallel over time. The proposed approach results in performance comparable with the state-of-the-art for feedforward neural networks on the Spiking Heidelberg Digits (SHD) dataset, outperforming LIF networks while training 10 times faster and outperforming non-spiking networks with the same network architecture. For longer input sequences of 10000 time-steps, we show that the proposed approach results in 4000 times faster training, thus demonstrating the potential of the proposed approach to accelerate SNN training for very large datasets.
Class-Incremental Learning based on Label Generation
Authors: Yijia Shao, Yiduo Guo, Dongyan Zhao, Bing Liu
Date: 2023-06-22 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.
Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study
Authors: Yue Xing
Date: 2023-06-21 00:00:00
Tasks: regression

In recent years, studies such as \cite{carmon2019unlabeled,gowal2021improving,xing2022artificial} have demonstrated that incorporating additional real or generated data with pseudo-labels can enhance adversarial training through a two-stage training approach. In this paper, we perform a theoretical analysis of the asymptotic behavior of this method in high-dimensional linear regression. While a double-descent phenomenon can be observed in ridgeless training, with an appropriate $\mathcal{L}_2$ regularization, the two-stage adversarial training achieves a better performance. Finally, we derive a shortcut cross-validation formula specifically tailored for the two-stage training method.
Hierarchical Neural Simulation-Based Inference Over Event Ensembles
Authors: Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer
Date: 2023-06-21 00:00:00

When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where "local" parameters impact individual events and "global" parameters influence the entire dataset. We introduce practical approaches for frequentist and Bayesian dataset-wide probabilistic inference in cases where the likelihood is intractable, but simulations can be realized via a hierarchical forward model. We construct neural estimators for the likelihood(-ratio) or posterior and show that explicitly accounting for the model's hierarchical structure can lead to significantly tighter parameter constraints. We ground our discussion using case studies from the physical sciences, focusing on examples from particle physics and cosmology.
Fitted Value Iteration Methods for Bicausal Optimal Transport
Authors: Erhan Bayraktar, Bingyan Han
Date: 2023-06-22 00:00:00

We develop a fitted value iteration (FVI) method to compute bicausal optimal transport (OT) where couplings have an adapted structure. Based on the dynamic programming formulation, FVI adopts a function class to approximate the value functions in bicausal OT. Under the concentrability condition and approximate completeness assumption, we prove the sample complexity using (local) Rademacher complexity. Furthermore, we demonstrate that multilayer neural networks with appropriate structures satisfy the crucial assumptions required in sample complexity proofs. Numerical experiments reveal that FVI outperforms linear programming and adapted Sinkhorn methods in scalability as the time horizon increases, while still maintaining acceptable accuracy.
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications
Authors: Hiroyuki Hanada, Noriaki Hashimoto, Kouichi Taji, Ichiro Takeuchi
Date: 2023-06-22 00:00:00
Tasks: feature selection, Model Selection

In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of ML methods known as linear estimators, there exists an efficient model update framework called the low-rank update that can effectively handle changes in a small number of rows and columns within the data matrix. However, for ML methods beyond linear estimators, there is currently no comprehensive framework available to obtain knowledge about the updated solution within a specific computational complexity. In light of this, our study introduces a method called the Generalized Low-Rank Update (GLRU) which extends the low-rank update framework of linear estimators to ML methods formulated as a certain class of regularized empirical risk minimization, including commonly used methods such as SVM and logistic regression. The proposed GLRU method not only expands the range of its applicability but also provides information about the updated solutions with a computational complexity proportional to the amount of dataset changes. To demonstrate the effectiveness of the GLRU method, we conduct experiments showcasing its efficiency in performing cross-validation and feature selection compared to other baseline methods.