pwc-archive/papers-with-abstracts huggingface dataset:
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks
Authors: Ayano Nakai-Kasai, Tadashi Wadayama
Date: 2022-05-06 00:00:00
Tasks: Dimensionality Reduction

Over-the-air computation (AirComp) enables efficient wireless data aggregation in sensor networks by simultaneous processing of calculation and communication. This paper proposes a novel precoder design for AirComp that incorporates statistical properties of sensing data, spatial correlation and heterogeneous data correlation. The proposed design of the precoder requires no iterative processes so that it can be realized with low computational costs. Moreover, this method provides dimensionality reduction of sensing data to reduce communication costs per sensor. We evaluate performance of the proposed method in terms of various system parameters. The results show the superiority of the proposed method to conventional non-iterative methods in cases where there are a large number of sensors and where the number of receive antennas at the aggregator is less than that of the total transmit antennas at the sensors.
Fast and Arbitrary Beam Pattern Design for RIS-Assisted Terahertz Wireless Communication
Authors: Jian Dang, Zaichen Zhang, Yewei Li, Liang Wu, Bingcheng Zhu, Lei Wang
Date: 2022-05-06 00:00:00

Reconfigurable intelligent surface (RIS) can assist terahertz wireless communication to restore the fragile line-of-sight links and facilitate beam steering. Arbitrary reflection beam patterns are desired to meet diverse requirements in different applications. This paper establishes relationship between RIS beam pattern design with two-dimensional finite impulse response filter design and proposes a fast non-iterative algorithm to solve the problem. Simulations show that the proposed method outperforms baseline method. Hence, it represents a promising solution for fast and arbitrary beam pattern design in RIS-assisted terahertz wireless communication.
Age Minimization in Outdoor and Indoor Communications with Relay-aided Dual RIS
Authors: Wanting Lyu, Yue Xiu, Yang Zhao, Chadi Assi, Zhongpei Zhang
Date: 2022-05-06 00:00:00
Tasks: Scheduling

In this paper, we investigate an outdoor and indoor wireless communication network with the assistance of a novel relay-aided double-sided reconfigurable intelligent surface (RIS). A scheduling problem is considered at the outdoor access point (AP) to minimize the sum of age of information (AoI). To serve the indoor users and further enhance the wireless link quality, a novel double-sided RIS with relay is utilized. Since the formulated problem is non-convex with highly-coupled variables, a successive convex approximation (SCA) based alternating optimization (AO) algorithm is proposed to solve it in an iterative manner. Finally, simulation results show the effectiveness and significant performance improvement in terms of AoI of the proposed algorithm compared with other benchmarks.
Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis
Authors: Yun Li, Zhe Liu, Lina Yao, Jessica J. M. Monaghan, David Mcalpine
Date: 2022-05-03 00:00:00
Tasks: Domain Adaptation, EEG, Electroencephalogram (EEG), Unsupervised Domain Adaptation

EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG signals are highly non-stationary, resulting in model's poor generalization to new users, sessions or datasets. Thus, designing a model that can generalize to new datasets is beneficial and indispensable. To mitigate distribution discrepancy across datasets, we propose to achieve Disentangled and Side-aware Unsupervised Domain Adaptation (DSUDA) for cross-dataset tinnitus diagnosis. A disentangled auto-encoder is developed to decouple class-irrelevant information from the EEG signals to improve the classifying ability. The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification. It also align signals of left and right ears to overcome inherent EEG pattern difference. We compare DSUDA with state-of-the-art methods, and our model achieves significant improvements over competitors regarding comprehensive evaluation criteria. The results demonstrate our model can successfully generalize to a new dataset and effectively diagnose tinnitus.
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
Authors: Yun Li, Zhe Liu, Lina Yao, Molly Lucas, Jessica J. M. Monaghan, Yu Zhang
Date: 2022-05-03 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Meta-Learning

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders from different data collection processes. Owing to the superiority of meta-learning, our method does not rely on large-scale datasets like conventional deep learning models. Moreover, we design a subject-specific training process to assist the model in fitting the data pattern of different patients or healthy people. Our method achieves a high accuracy of 73.8\% in the cross-dataset classification. We conduct an extensive analysis to show the effectiveness of side information of ears in enhancing model performance and side-aware meta-learning in improving the quality of the learned features.
Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification
Authors: Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi, Michele Maiella, Sonia Bonni, Marilena Minei, Martina Assogna, Bogdan Ionescu, Giacomo Koch, Emiliano Santarnecchi
Date: 2022-04-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from healthy controls was investigated. A dataset of TMS-EEG recordings from 17 AD and 17 healthy controls (HC) was used in our analysis. A Random Forest classification algorithm was trained on the extracted TEP metrics and its performance is evaluated in a leave-one-subject-out cross-validation. The created model showed promising results in identifying AD patients from HC with an accuracy, sensitivity and specificity of 69.32%, 72.23% and 66.41%, respectively.
Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures
Authors: Yankun Xu, Jie Yang, Mohamad Sawan
Date: 2022-04-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Generative Adversarial Network, Prediction

Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for accurate epileptic seizure (ES) prediction, thereby benefiting patients suffering from epilepsy. To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms. However, the scarcity of seizure onsets leads to significant insufficiency of data for training the DL algorithms. To overcome this data insufficiency, in this paper, we propose a preictal artificial signal synthesis algorithm based on a generative adversarial network to generate synthetic multichannel EEG preictal samples. A high-quality single-channel architecture, determined by visual and statistical evaluations, is used to train the generators of multichannel samples. The effectiveness of the synthetic samples is evaluated by comparing the ES prediction performances without and with synthetic preictal sample augmentation. The leave-one-seizure-out cross validation ES prediction accuracy and corresponding area under the receiver operating characteristic curve evaluation improve from 73.0\% and 0.676 to 78.0\% and 0.704 by 10$\times$ synthetic sample augmentation, respectively. The obtained results indicate that synthetic preictal samples are effective for enhancing ES prediction performance.
Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems
Authors: Christopher Kolloff, Simon Olsson
Date: 2022-05-06 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond. Its success has also led to several synergies with molecular dynamics (MD) simulations, which we use to identify and characterize the major metastable states of molecular systems. Typically, we aim to determine the relative stabilities of these states and how rapidly they interchange. This information allows mechanistic descriptions of molecular mechanisms, enables a quantitative comparison with experiments, and facilitates their rational design. ML impacts all aspects of MD simulations -- from analyzing the data and accelerating sampling to defining more efficient or more accurate simulation models.
Bacteriophage effect on parasitism resistance
Authors: Gabriel R. Palma, Renato M. Coutinho, Wesley A. C. Godoy, Fernando L. Cônsoli, Roberto A. Kraenkel
Date: 2022-05-06 00:00:00
Tasks: Open-Ended Question Answering

Many studies have shown that the protection of the host $\it{Acyrthosiphon~pisum}$ (Hemiptera, Aphididae) against the parasitoid $\it{Aphidius~ervi}$ (Hymenoptera, Braconidae) is conferred by the interaction between the secondary endosymbiont $\it{Hamiltonella~defensa}$ and the bacteriophage $\it{APSE}$ ($\it{Acyrthosiphon~pisum}$ secondary endosymbiont). This interaction consists of the production of toxins by the endosymbiont's molecular machinery, which is encoded by the inserted $\it{APSE}$ genes. The toxins prevent the development of the parasitoid's egg, conferring protection for the host. However, the effects of this microscopic interaction on host-parasitoid dynamics are still an open question. We presented a new mathematical model based on the bacteriophage effect on parasitism resistance. We identified that the vertical transmission of the bacteriophage and the host survival after the parasitoid attack are potential drivers of coexistence. Also, we showed that the vertical transmission of $\it{H.~defensa}$ is proportional to the time that the protected population became extinct. Our results showed that the protected and unprotected hosts' survival after the parasitoid attack is fundamental to understanding the equilibrium of long host-parasitoid dynamics. Finally, we illustrated our model considering its parameters based on experiments performed with $\it{A.~pisum}$ biotypes $\it{Genista~tinctoria}$ and $\it{Medicago~sativa}$.
Financial Markets and the Real Economy: A Statistical Field Perspective on Capital Allocation and Accumulation
Authors: Pierre Gosselin, Aïleen Lotz, Marc Wambst
Date: 2022-05-06 00:00:00

This paper provides a general method to directly translate a classical economic framework with a large number of agents into a field-formalism model. This type of formalism allows the analytical treatment of economic models with an arbitrary number of agents, while preserving the system's interactions and microeconomic features of the individual level.We apply this methodology to model the interactions between financial markets and the real economy, described in a classical framework of a large number of heterogeneous agents, investors and firms. Firms are spread among sectors but may shift between sectors to improve their returns. They compete by producing differentiated goods and reward their investors by paying dividends and through their stocks' valuation. Investors invest in firms and move along sectors based on firms' expected long-run returns.The field-formalism model derived from this framework allows for collective states to emerge. We show that the number of firms in each sector depends on the aggregate financial capital invested in the sector and its firms' expected long-term returns. Capital accumulation in each sector depends both on short-term returns and expected long-term returns relative to neighbouring sectors.For each sector, three patterns of accumulation emerge. In the first pattern, the dividend component of short-term returns is determinant for sectors with small number of firms and low capital. In the second pattern, both short and long-term returns in the sector drive intermediate-to-high capital. In the third pattern, higher expectations of long-term returns drive massive inputs of capital.Instability in capital accumulation may arise among and within sectors. We therefore widen our approach and study the dynamics of the collective configurations, in particular interactions between average capital and expected long-term returns, and show that overall stability crucially depends on the expectations' formation process.Expectations that are highly reactive to capital variations stabilize high capital configurations, and drive low-to-moderate capital sectors towards zero or a higher level of capital, depending on their initial capital. Inversely, low-to moderate capital configurations are stabilized by expectations moderately reactive to capital variations, and drive high capital sectors towards more moderate level of capital equilibria.Eventually, the combination of expectations both highly sensitive to exogenous conditions and highly reactive to variations in capital imply that large fluctuations of capital in the system, at the possible expense of the real economy.
Far from Asymptopia
Authors: Michael C. Abbott, Benjamin B. Machta
Date: 2022-05-06 00:00:00

Inference from limited data requires a notion of measure on parameter space, most explicit in the Bayesian framework as a prior. Here we demonstrate that Jeffreys prior, the best-known uninformative choice, introduces enormous bias when applied to typical scientific models. Such models have a relevant effective dimensionality much smaller than the number of microscopic parameters. Because Jeffreys prior treats all microscopic parameters equally, it is from uniform when projected onto the sub-space of relevant parameters, due to variations in the local co-volume of irrelevant directions. We present results on a principled choice of measure which avoids this issue, leading to unbiased inference in complex models. This optimal prior depends on the quantity of data to be gathered, and approaches Jeffreys prior in the asymptotic limit. However, this limit cannot be justified without an impossibly large amount of data, exponential in the number of microscopic parameters.
A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions
Authors: Sheetal Kusal, Shruti Patil, Jyoti Choudrie, Ketan Kotecha, Deepali Vora, Ilias Pappas
Date: 2022-04-26 00:00:00
Tasks: Systematic Literature Review

Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards
Authors: Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low
Date: 2021-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Data Valuation, Fairness

This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e.g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions. Distributing synthetic data as rewards (instead of trained models or money) offers task- and model-agnostic benefits for downstream learning tasks and is less likely to violate data privacy regulation. To realize the framework, we firstly propose a data valuation function using maximum mean discrepancy (MMD) that values data based on its quantity and quality in terms of its closeness to the true data distribution and provide theoretical results guiding the kernel choice in our MMD-based data valuation function. Then, we formulate the reward scheme as a linear optimization problem that when solved, guarantees certain incentives such as fairness in the CGM framework. We devise a weighted sampling algorithm for generating synthetic data to be distributed to each party as reward such that the value of its data and the synthetic data combined matches its assigned reward value by the reward scheme. We empirically show using simulated and real-world datasets that the parties' synthetic data rewards are commensurate to their contributions.
Vietnamese Automatic Speech Recognition using Wav2vec 2.0
Authors: Le Duy Khanh
Date: 2022-05-08 00:00:00
Proceeding: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h 2022 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We fine-tuned the Wav2vec2-based model on about 160 hours of Vietnamese speech dataset from different resources, including VIOS, COMMON VOICE, FOSD, and VLSP (100h) using Connectionist Temporal Classification (CTC). As a result, we gain 10.78% and 15.05% (without Language Model) WER on COMMON VOICE and VIOS datasets, respectively.
Self-Supervised Contrastive Learning for Singing Voices
Authors: Hiromu Yakura, Kento Watanabe, Masataka Goto
Date: 2022-04-26 00:00:00
Proceeding: IEEE/ACM Transactions on Audio, Speech, and Language Processing 2022 4
Tasks: Contrastive Learning, Singer Identification, Vocal technique classification

This study introduces self-supervised contrastive learning to acquire feature representations of singing voices. To acquire robust representations in an unsupervised manner, regular self-supervised contrastive learning trains neural networks to make the feature representation of a sample close to those of its computationally transformed versions. Similarly, we employ two transformations—pitch shifting and time stretching—considering the nature of singing voices. Nevertheless, we use them reversely: we train networks to push away representations of the transformed versions. The networks then attempt to discriminate changes in vocal timbres introduced by pitch shifting without time stretching and those in singing expressions introduced by time stretching without pitch shifting. Consequently, the acquired representations become attentive to vocal timbre and singing expression. This was confirmed through a singer identification task, where we trained a classifier to learn the relationship between the feature representations to the corresponding singer labels of 500 singers. As a result, the employed transformations helped the classifier improve the classification accuracy by 9.12% (top-1 accuracy: 63.08%) compared with the case where the feature representations fed to the classifier were acquired without the transformations (top-1 accuracy: 53.96%). Furthermore, the proposed approach can be extended to acquire feature representations attentive to either vocal timbre or singing expression but not to the other by changing how the transformations are incorporated. We particularly explored the characteristics of such vocal timbre- or singing expression-oriented feature representations against song genre, singer gender, and vocal technique, and confirmed that they successfully capture different aspects of singing voices.
VocalSet: A Singing Voice Dataset
Authors: Julia Wilkins, Prem Seetharaman, Alison Wahl, Bryan Pardo
Date: 2018-09-25 00:00:00
Proceeding: International Society for Music Information Retrieval Conference 2018 9
Tasks: Singer Identification, Vocal technique classification

We present VocalSet, a singing voice dataset of a capella singing. Existing singing voice datasets either do not capture a large range of vocal techniques, have very few singers, or are single-pitch and devoid of musical context. VocalSet captures not only a range of vowels, but also a diverse set of voices on many different vocal techniques, sung in contexts of scales, arpeggios, long tones, and excerpts. VocalSet has recordings of 10.1 hours of 20 professional singers (11 male, 9 female) performing 17 different different vocal techniques. This data will facilitate the development of new machine learning models for singer identification, vocal technique identification, singing generation and other related applications. To illustrate this, we establish baseline results on vocal technique classification and singer identification by training convolutional network classifiers on VocalSet to perform these tasks.
Context-Aware Abbreviation Expansion Using Large Language Models
Authors: Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan, Meredith Ringel Morris, Michael P. Brenner
Date: 2022-05-08 00:00:00
Proceeding: NAACL 2022 7

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters. Our approach is to expand the abbreviations into full-phrase options by leveraging conversation context with the power of pretrained large language models (LLMs). Through zero-shot, few-shot, and fine-tuning experiments on four public conversation datasets, we show that for replies to the initial turn of a dialog, an LLM with 64B parameters is able to exactly expand over 70% of phrases with abbreviation length up to 10, leading to an effective keystroke saving rate of up to about 77% on these exact expansions. Including a small amount of context in the form of a single conversation turn more than doubles abbreviation expansion accuracies compared to having no context, an effect that is more pronounced for longer phrases. Additionally, the robustness of models against typo noise can be enhanced through fine-tuning on noisy data.