pwc-archive/papers-with-abstracts huggingface dataset:
Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing
Authors: Jiangwei Wang, Lili Su, Songyang Han, Dongjin Song, Fei Miao
Date: 2023-08-23 00:00:00
Tasks: Autonomous Vehicles, Trajectory Prediction

Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes, putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, etc. On the other hand, modern vehicle-to-vehicle communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other. In this paper, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers privacy. To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of 97.3%, average detection delay of 1.2s, and 0 false alarm.
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach
Authors: Tarek Berghout, Mohamed Benbouzid
Date: 2023-10-02 00:00:00
Proceeding: MDPI Applied Sciences 2023 10
Tasks: Denoising, Diagnostic, Prognosis, Transfer Learning

The diagnosis and prognosis of aeronautical-bearing health conditions are essential to proactively ensuring efficient power transmission, safety, and reduced downtime. The rarity of failures in such safety-critical systems drives this process towards data-driven analytics of fault injection and aging experiments, rather than complex physics-based modeling. Nonetheless, data-based condition monitoring is very challenging due to data complexity, unavailability, and drift resulting from distortions generated by harsh operating conditions, scarcity of failure patterns, and rapid data change, respectively. Accordingly, the objective of this work is three-fold. First, to reduce data complexity and improve feature space representation, a robust data engineering scheme, including feature extraction, denoising, outlier removal, filtering, smoothing, scaling, and balancing, is introduced in this work. Second, collaborative selection-based incremental deep transfer learning (CSIDTL) is introduced to overcome the problem of the lack of patterns, incrementing the number of source domains in different training rounds. Third, long short-term memory (LSTM) adaptive learning rules are fully taken into account to combat further data complexity and data change problems. The well-structured methodology is applied on a huge dataset of aeronautical bearings dedicated to both diagnostic and prognosis studies, which perfectly addresses the above challenges in a form of a classification problem with 13 different conditions, 7 operating modes, and 3 stages of damage severity. Conducting CSIDTL following a three-fold cross-validation process allows us to improve classification performance by about 12.15% and 10.87% compared with state-of-the-art methods, reaching classification accuracy rates of 93.63% and 95.65% in diagnosis and prognosis, respectively.
MultiRBP: Multi-task Neural Network for Protein–RNA Binding Prediction
Authors: Jonathan Karin, Hagai Michel, Yaron Orenstein
Date: 2020-08-01 00:00:00
Proceeding: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics 2020 8

Protein–RNA binding plays vital roles in post-transcriptional gene regulation. High-throughput in vitro binding measurements were generated for more than 200 RNA-binding proteins, enabling the development of computational methods to predict binding to any RNA transcript of interest. In recent years, deep learning-based methods have been developed to predict RNA binding in vitro achieving state-of-the-art results. However, all methods train a single model per protein, under-utilizing the similarities in binding preferences shared by multiple RNA-binding proteins. In this work, we developed MultiRBP, a deep learning-based method to predict RNA binding of hundreds of proteins to a given RNA sequence. The innovation of MultiRBP is in its multi-task nature, i.e., predicting binding for hundreds of proteins at the same time. We trained MultiRBP on the RNAcompete dataset, the most comprehensive dataset of in vitro binding measurements. Our method outperformed extant methods in both in vitro and in vivo RNA-binding prediction. Our method achieved an average Pearson correlation of 0.692±0.17 for in vitro binding prediction, and a median AUROC of 0.668±0.09 for in vivo binding prediction. Moreover, by visualizing the learned binding preferences, MultiRBP provided more interpretable visualization than a single-task model. The code is publicly available at github.com/OrensteinLab/MultiRBP.
Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer
Authors: Junggu Choi, Kion Kim, Soohyun Park, Juyoen Hur, Hyunjung Yang, Younghoon Kim, Hakbae Lee, Sanghoon Han
Date: 2023-09-26 00:00:00
Tasks: Decision Making, feature selection

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. College student groups have been frequently selected as the target population in previous studies because they are easily affected by pandemics. In this study, multivariable datasets were collected from 751 college students based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g., decision-making in uncertain situations) were more important in post-pandemic conditions. We believe that our study will serve as a reference for researchers studying similar topics.
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
Authors: Connor T. Jerzak, Adel Daoud
Date: 2023-09-30 00:00:00
Tasks: Causal Inference, Earth Observation

The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imagery into the study of cause and effect. One set of functions enables image-based causal inference analyses. For example, one key function decomposes treatment effect heterogeneity by images using an interpretable Bayesian framework. This allows for determining which types of images or image sequences are most responsive to interventions. A second modeling function allows researchers to control for confounding using images. The package also allows investigators to produce embeddings that serve as vector summaries of the image or video content. Finally, infrastructural functions are also provided, such as tools for writing large-scale image and image sequence data as sequentialized byte strings for more rapid image analysis. causalimages therefore opens new capabilities for causal inference in R, letting researchers use informative imagery in substantive analyses in a fast and accessible manner.
SeisT: A foundational deep learning model for earthquake monitoring tasks
Authors: Sen Li, Xu Yang, Anye Cao, Changbin Wang, Yaoqi Liu, Yapeng Liu, Qiang Niu
Date: 2023-10-02 00:00:00
Tasks: Deep Learning, Out-of-Distribution Generalization

Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective earthquake monitoring capabilities. This paper introduces a foundational deep learning model, the Seismogram Transformer (SeisT), designed for a variety of earthquake monitoring tasks. SeisT combines multiple modules tailored to different tasks and exhibits impressive out-of-distribution generalization performance, outperforming or matching state-of-the-art models in tasks like earthquake detection, seismic phase picking, first-motion polarity classification, magnitude estimation, back-azimuth estimation, and epicentral distance estimation. The performance scores on the tasks are 0.96, 0.96, 0.68, 0.95, 0.86, 0.55, and 0.81, respectively. The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1.7%, 9.5%, and 8.0%, respectively. Our study, through rigorous experiments and evaluations, suggests that SeisT has the potential to contribute to the advancement of seismic signal processing and earthquake research.
Linear attention is (maybe) all you need (to understand transformer optimization)
Authors: Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
Date: 2023-10-02 00:00:00
Tasks: All

Transformer training is notoriously difficult, requiring a careful design of optimizers and use of various heuristics. We make progress towards understanding the subtleties of training Transformers by carefully studying a simple yet canonical linearized shallow Transformer model. Specifically, we train linear Transformers to solve regression tasks, inspired by J.~von Oswald et al.~(ICML 2023), and K.~Ahn et al.~(NeurIPS 2023). Most importantly, we observe that our proposed linearized models can reproduce several prominent aspects of Transformer training dynamics. Consequently, the results obtained in this paper suggest that a simple linearized Transformer model could actually be a valuable, realistic abstraction for understanding Transformer optimization.
Powertrain Hybridization for Autonomous Vehicles
Authors: Shima Nazari, Norma Gowans, Mohammad Abtahi
Date: 2023-09-29 00:00:00
Tasks: Autonomous Vehicles, energy management, Management

The powertrains of today's hybrid electric vehicles (HEVs) are developed for human drivers and, therefore, may not be the optimum choice for future Autonomous vehicles (AVs), given that AVs can accurately manipulate their velocity profile to avoid unnecessary energy loss. In this work, we closely examine the necessary degree of hybridization for AVs compared to human drivers by deploying real-world urban driving profiles and generating equivalent AV drive cycles in a mixed autonomy scenario. We solve the optimal energy management problem for HEVs with various motor sizes from the automotive market, and demonstrate that while human drivers typically require a motor size of around 30 kW to fully benefit from hybridization, AVs can achieve similar gains with only a 12 kW motor. This greater benefit from a smaller motor size can be attributed to a more optimal torque request, allowing for higher gains from regenerative braking and a more efficient engine operation. Furthermore, We investigate the benefits of velocity smoothing for both traditional cars and HEVs and explore the role of different mechanisms contributing to fuel consumption reduction. Our analysis reveals that velocity smoothing provides greater benefits to HEVs equipped with small motors compared to non-hybrid vehicles and HEVs with larger motors.
Safe Non-Stochastic Control of Control-Affine Systems: An Online Convex Optimization Approach
Authors: HongYu Zhou, Yichen Song, Vasileios Tzoumas
Date: 2023-09-28 00:00:00
Tasks: Collision Avoidance

We study how to safely control nonlinear control-affine systems that are corrupted with bounded non-stochastic noise, i.e., noise that is unknown a priori and that is not necessarily governed by a stochastic model. We focus on safety constraints that take the form of time-varying convex constraints such as collision-avoidance and control-effort constraints. We provide an algorithm with bounded dynamic regret, i.e., bounded suboptimality against an optimal clairvoyant controller that knows the realization of the noise a prior. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite real-world unpredictable disturbances such as wind gusts. To develop the algorithm, we capture our problem as a sequential game between a controller and an adversary, where the controller plays first, choosing the control input, whereas the adversary plays second, choosing the noise's realization. The controller aims to minimize its cumulative tracking error despite being unable to know the noise's realization a prior. We validate our algorithm in simulated scenarios of (i) an inverted pendulum aiming to stay upright, and (ii) a quadrotor aiming to fly to a goal location through an unknown cluttered environment.
Toward Universal Speech Enhancement for Diverse Input Conditions
Authors: Wangyou Zhang, Kohei Saijo, Zhong-Qiu Wang, Shinji Watanabe, Yanmin Qian
Date: 2023-09-29 00:00:00
Tasks: Denoising, Speech Enhancement

The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performance in some common datasets, most of them are designed only for a single condition (e.g., single-channel, multi-channel, or a fixed sampling frequency) or only consider a single task (e.g., denoising or dereverberation). Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model. In this paper, we make the first attempt to investigate this line of research. First, we devise a single SE model that is independent of microphone channels, signal lengths, and sampling frequencies. Second, we design a universal SE benchmark by combining existing public corpora with multiple conditions. Our experiments on a wide range of datasets show that the proposed single model can successfully handle diverse conditions with strong performance.
LRPD: Large Replay Parallel Dataset
Authors: Ivan Yakovlev, Mikhail Melnikov, Nikita Bukhal, Rostislav Makarov, Alexander Alenin, Nikita Torgashov, Anton Okhotnikov
Date: 2023-09-29 00:00:00
Tasks: Voice Anti-spoofing

The latest research in the field of voice anti-spoofing (VAS) shows that deep neural networks (DNN) outperform classic approaches like GMM in the task of presentation attack detection. However, DNNs require a lot of data to converge, and still lack generalization ability. In order to foster the progress of neural network systems, we introduce a Large Replay Parallel Dataset (LRPD) aimed for a detection of replay attacks. LRPD contains more than 1M utterances collected by 19 recording devices in 17 various environments. We also provide an example training pipeline in PyTorch [1] and a baseline system, that achieves 0.28% Equal Error Rate (EER) on evaluation subset of LRPD and 11.91% EER on publicly available ASVpoof 2017 [2] eval set. These results show that model trained with LRPD dataset has a consistent performance on the fully unknown conditions. Our dataset is free for research purposes and hosted on GDrive. Baseline code and pre-trained models are available at GitHub.
Low-Resource Self-Supervised Learning with SSL-Enhanced TTS
Authors: Po-chun Hsu, Ali Elkahky, Wei-Ning Hsu, Yossi Adi, Tu Anh Nguyen, Jade Copet, Emmanuel Dupoux, Hung-Yi Lee, Abdelrahman Mohamed
Date: 2023-09-29 00:00:00
Tasks: Self-Supervised Learning, text-to-speech, Text to Speech

Self-supervised learning (SSL) techniques have achieved remarkable results in various speech processing tasks. Nonetheless, a significant challenge remains in reducing the reliance on vast amounts of speech data for pre-training. This paper proposes to address this challenge by leveraging synthetic speech to augment a low-resource pre-training corpus. We construct a high-quality text-to-speech (TTS) system with limited resources using SSL features and generate a large synthetic corpus for pre-training. Experimental results demonstrate that our proposed approach effectively reduces the demand for speech data by 90% with only slight performance degradation. To the best of our knowledge, this is the first work aiming to enhance low-resource self-supervised learning in speech processing.
Synthetic Speech Detection Based on Temporal Consistency and Distribution of Speaker Features
Authors: Yuxiang Zhang, Zhuo Li, Jingze Lu, Wenchao Wang, Pengyuan Zhang
Date: 2023-09-29 00:00:00
Tasks: Synthetic Speech Detection, text-to-speech, Text to Speech

Current synthetic speech detection (SSD) methods perform well on certain datasets but still face issues of robustness and interpretability. A possible reason is that these methods do not analyze the deficiencies of synthetic speech. In this paper, the flaws of the speaker features inherent in the text-to-speech (TTS) process are analyzed. Differences in the temporal consistency of intra-utterance speaker features arise due to the lack of fine-grained control over speaker features in TTS. Since the speaker representations in TTS are based on speaker embeddings extracted by encoders, the distribution of inter-utterance speaker features differs between synthetic and bonafide speech. Based on these analyzes, an SSD method based on temporal consistency and distribution of speaker features is proposed. On one hand, modeling the temporal consistency of intra-utterance speaker features can aid speech anti-spoofing. On the other hand, distribution differences in inter-utterance speaker features can be utilized for SSD. The proposed method offers low computational complexity and performs well in both cross-dataset and silence trimming scenarios.
Enhancing Code-switching Speech Recognition with Interactive Language Biases
Authors: Hexin Liu, Leibny Paola Garcia, Xiangyu Zhang, Andy W. H. Khong, Sanjeev Khudanpur
Date: 2023-09-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Languages usually switch within a multilingual speech signal, especially in a bilingual society. This phenomenon is referred to as code-switching (CS), making automatic speech recognition (ASR) challenging under a multilingual scenario. We propose to improve CS-ASR by biasing the hybrid CTC/attention ASR model with multi-level language information comprising frame- and token-level language posteriors. The interaction between various resolutions of language biases is subsequently explored in this work. We conducted experiments on datasets from the ASRU 2019 code-switching challenge. Compared to the baseline, the proposed interactive language biases (ILB) method achieves higher performance and ablation studies highlight the effects of different language biases and their interactions. In addition, the results presented indicate that language bias implicitly enhances internal language modeling, leading to performance degradation after employing an external language model.
Towards High Resolution Weather Monitoring with Sound Data
Authors: Enis Berk Çoban, Megan Perra, Michael I. Mandel
Date: 2023-09-28 00:00:00

Across various research domains, remotely-sensed weather products are valuable for answering many scientific questions; however, their temporal and spatial resolutions are often too coarse to answer many questions. For instance, in wildlife research, it's crucial to have fine-scaled, highly localized weather observations when studying animal movement and behavior. This paper harnesses acoustic data to identify variations in rain, wind and air temperature at different thresholds, with rain being the most successfully predicted. Training a model solely on acoustic data yields optimal results, but it demands labor-intensive sample labeling. Meanwhile, hourly satellite data from the MERRA-2 system, though sufficient for certain tasks, produced predictions that were notably less accurate in predict these acoustic labels. We find that acoustic classifiers can be trained from the MERRA-2 data that are more accurate than the raw MERRA-2 data itself. By using MERRA-2 to roughly identify rain in the acoustic data, we were able to produce a functional model without using human-validated labels. Since MERRA-2 has global coverage, our method offers a practical way to train rain models using acoustic datasets around the world.
Rationality and connectivity in stochastic learning for networked coordination games
Authors: Yifei Zhang, Marcos M. Vasconcelos
Date: 2023-09-29 00:00:00

Coordination is a desirable feature in many multi-agent systems such as robotic and socioeconomic networks. We consider a task allocation problem as a binary networked coordination game over an undirected regular graph. Each agent in the graph has bounded rationality, and uses a distributed stochastic learning algorithm to update its action choice conditioned on the actions currently played by its neighbors. After establishing that our framework leads to a potential game, we analyze the regime of bounded rationality, where the agents are allowed to make sub-optimal decisions with some probability. Our analysis shows that there is a relationship between the connectivity of the network, and the rationality of the agents. In particular, we show that in some scenarios, an agent can afford to be less rational and still converge to a near optimal collective strategy, provided that its connectivity degree increases. Such phenomenon is akin to the wisdom of crowds.
Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks
Authors: Yi Zhang, Yichao Wang, Junbo Zhao, Shan Zuo
Date: 2023-09-29 00:00:00

In this letter, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an extended discrete state observer to enhance the robustness against unknown dynamics and an attack compensation mechanism to eliminate the adverse effects of DoS attacks, we finally propose a distributed resilient model-free adaptive control algorithm to solve the URABC problem. A numerical example validates the proposed results.