pwc-archive/papers-with-abstracts huggingface dataset:
DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction
Authors: Chen Zhu, Liang Du, Hong Chen, Shuang Zhao, Zixun Sun, Xin Wang, Wenwu Zhu
Date: 2023-05-03 00:00:00
Tasks: Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditional methods typically learn fixed feature representations without dynamically refining feature representations according to the context information, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components: (I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context; (II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-art performance among current CTR methods.
A Computational Model For Individual Scholars' Writing Style Dynamics
Authors: Teddy Lazebnik, Ariel Rosenfeld
Date: 2023-05-01 00:00:00

A manuscript's writing style is central in determining its readership, influence, and impact. Past research has shown that, in many cases, scholars present a unique writing style that is manifested in their manuscripts. In this work, we report a comprehensive investigation into how scholars' writing styles evolve throughout their careers focusing on their academic relations with their advisors and peers. Our results show that scholars' writing styles tend to stabilize early on in their careers -- roughly their 13th publication. Around the same time, scholars' departures from their advisors' writing styles seem to converge as well. Last, collaborations involving fewer scholars, scholars from the same gender, or from the same field of study seem to bring about greater change in their co-authors' writing styles with younger scholars being especially influenceable.
Understanding the Impact of Culture in Assessing Helpfulness of Online Reviews
Authors: Khaled Alanezi, Nuha Albadi, Omar Hammad, Maram Kurdi, Shivakant Mishra
Date: 2023-04-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Recommendation Systems

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.
Variation of the subhalo abundance in dark matter halos
Authors: Tomoaki Ishiyama, Toshiyuki Fukushige, Junichiro Makino
Date: 2008-12-03 00:00:00

We analyzed the statistics of subhalo abundance of galaxy-sized and giant-galaxy-sized halos formed in a high-resolution cosmological simulation of a 46.5Mpc cube with the uniform mass resolution of $10^6 M_{\odot}$. We analyzed all halos with mass more than $1.5 \times 10^{12}M_{\odot}$ formed in this simulation box. The total number of halos was 125. We found that the subhalo abundance, measured by the number of subhalos with maximum rotation velocity larger than 10% of that of the parent halo, shows large halo-to-halo variations. The results of recent ultra-high-resolution runs fall within the variation of our samples. We found that the concentration parameter and the radius at the moment of the maximum expansion shows fairly tight correlation with the subhalo abundance. This correlation suggests that the variation of the subhalo abundance is at least partly due to the difference in the formation history. Halos formed earlier have smaller number of subhalos at present.
FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural Network
Authors: Shusen Tang, Zeqing Xia, Zhouhui Lian, Yingmin Tang, Jianguo Xiao
Date: 2019-11-14 00:00:00
Proceeding: Computer Graphics Forum 2019 11

Despite the recent impressive development of deep neural networks, using deep learning based methods to generate large-scale Chinese fonts is still a rather challenging task due to the huge number of intricate Chinese glyphs, e.g., the official standard Chinese charset GB18030-2000 consists of 27,533 Chinese characters. Until now, most existing models for this task adopt Convolutional Neural Networks (CNNs) to generate bitmap images of Chinese characters due to CNN based models' remarkable success in various applications. However, CNN based models focus more on image-level features while usually ignore stroke order information when writing characters. Instead, we treat Chinese characters as sequences of points (i.e., writing trajectories) and propose to handle this task via an effective Recurrent Neural Network (RNN) model with monotonic attention mechanism, which can learn from as few as hundreds of training samples and then synthesize glyphs for remaining thousands of characters in the same style. Experimental results show that our proposed FontRNN can be used for synthesizing large-scale Chinese fonts as well as generating realistic Chinese handwritings efficiently.
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst Tasks
Authors: Bagus Tris Atmaja, Akira Sasou
Date: 2023-05-05 00:00:00
Proceeding: ICASSP 2023 5
Tasks: Automatic Speech Recognition, Cultural Vocal Bursts Intensity Prediction, Self-Supervised Learning, speech-recognition, Speech Recognition, Vocal Bursts Intensity Prediction, Vocal Bursts Type Prediction, Vocal Bursts Valence Prediction

The search for emotional biomarkers within the human voice is a challenging research area. Previous studies focused on predicting affective state from speech; this study explores various tasks on affective vocal bursts. Borrowing the success of self-supervised learning in automatic speech recognition, we extracted acoustic embedding using variants of wav2vec 2.0 for four affective vocal bursts tasks: High, Two, Culture, and Type. Using a similar architecture for all tasks, the evaluation of acoustic embeddings reveals the potential use of wav2vec 2.0 variants over the conventional acoustic features in affective vocal bursts tasks. We evaluated both conventional acoustic features and these acoustic embeddings on the different number of twenty seeds evaluation and reported the maximum and average scores with their standard deviations in the validation set. Three high scores from these validations for all tasks assist the generation of predictions for the test set. We compared the test scores with previous studies and obtained remarkable improvements.
Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network
Authors: Hannah O. Kargbo, Jie Zhang, Anh N. Phan
Date: 2021-08-20 00:00:00
Proceeding: Applied Energy 2021 8

A two-stage gasification has been proven as an effective and robust approach for converting low-valued and/or highly heterogeneous materials i.e. waste, into hydrogen and/or syngas due to its tight control and flexibility in operation. As the gas yield and gas properties depend upon materials and operating conditions, the interactions of operating conditions should not be ignored. However, these have not been able to fully capture experimentally. In this work, an artificial neural network model was developed and validated using experimental data to predict and optimise the gasification process thereby reducing time and costs in developing and testing. The model can predict accurately gas composition and yield corresponding to the variations at the output with a correlation R2 > 0.99. The developed neural network model was then applied for optimising operating conditions of the two-stage gasification for high carbon conversion, high hydrogen yield and low carbon dioxide in nitrogen and carbon dioxide environments. The predicted conditions were tested, and the results agreed well with experimental data. For example, at the optimum operating conditions (900˚C for the 1st stage and 1000 °C for the 2nd stage with a steam/carbon ratio of 3.8 in nitrogen and 5.7 in carbon dioxide environments), the gas yield, hydrogen and carbon dioxide were 96.2 wt%, 70 mol% and 16.4 mol% for nitrogen environment and 97.2 wt%, 66 mol% and 12 mol% for carbon dioxide environment.
A Scoping Review of Internal Migration and Left-behind Children's Wellbeing in China
Authors: Jinkai Li
Date: 2023-05-07 00:00:00

Children's well-being of immigrants is facing several challenges related to physical, mental, and educational risks, which may obstacle human capital accumulation and further development. In rural China, due to the restriction of Hukou registration system, nearly 9 million left-behind children (LBC) are in lack of parental care and supervision in 2020 when their parents internally migrate out for work. Through the systematic scoping review, this study provides a comprehensive literature summary and concludes the overall negative effects of parental migration on LBC's physical, mental (especially for left-behind girls), and educational outcomes (especially for left-behind boys). Noticeably, both parents' and mother's migration may exacerbate LBC's disadvantages. Furthermore, remittance from migrants and more family-level and social support may help mitigate the negative influence. Finally, we put forward theoretical and realistic implications which may shed light on potential research directions. Further studies, especially quantitative studies, are needed to conduct a longitudinal survey, combine the ongoing Hukou reform in China, and simultaneously focus on left-behind children and migrant children.
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
Authors: Grant P. Strimel, Yi Xie, Brian King, Martin Radfar, Ariya Rastrow, Athanasios Mouchtaris
Date: 2023-05-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Streaming speech recognition architectures are employed for low-latency, real-time applications. Such architectures are often characterized by their causality. Causal architectures emit tokens at each frame, relying only on current and past signal, while non-causal models are exposed to a window of future frames at each step to increase predictive accuracy. This dichotomy amounts to a trade-off for real-time Automatic Speech Recognition (ASR) system design: profit from the low-latency benefit of strictly-causal architectures while accepting predictive performance limitations, or realize the modeling benefits of future-context models accompanied by their higher latency penalty. In this work, we relax the constraints of this choice and present the Adaptive Non-Causal Attention Transducer (ANCAT). Our architecture is non-causal in the traditional sense, but executes in a low-latency, streaming manner by dynamically choosing when to rely on future context and to what degree within the audio stream. The resulting mechanism, when coupled with our novel regularization algorithms, delivers comparable accuracy to non-causal configurations while improving significantly upon latency, closing the gap with their causal counterparts. We showcase our design experimentally by reporting comparative ASR task results with measures of accuracy and latency on both publicly accessible and production-scale, voice-assistant datasets.
Well-being policy evaluation methodology based on WE pluralism
Authors: Takeshi Kato
Date: 2023-05-08 00:00:00

Methodologies for evaluating and selecting policies that contribute to the well-being of diverse populations need clarification. To bridge the gap between objective indicators and policies related to well-being, this study shifts from constitutive pluralism based on objective indicators to conceptual pluralism that emphasizes subjective context, develops from subject-object pluralism through individual-group pluralism to WE pluralism, and presents a new policy evaluation method that combines joint fact-finding based on policy plurality. First, to evaluate policies involving diverse stakeholders, I develop from individual subjectivity-objectivity to individual subjectivity and group intersubjectivity, and then move to a narrow-wide WE pluralism in the gradation of I-family-community-municipality-nation-world. Additionally, by referring to some functional forms of well-being, I formulate the dependence of well-being on narrow-wide WE. Finally, given that policies themselves have a plurality of social, ecological, and economic values, I define a set of policies for each of the narrow-wide WE and consider a mapping between the two to provide an evaluation basis. Furthermore, by combining well-being and joint fact-finding on the narrow-wide WE consensus, the policy evaluation method is formulated. The fact-value combined parameter system, combined policy-making approach, and combined impact evaluation are disclosed as examples of implementation. This paper contributes to the realization of a well-being society by bridging philosophical theory and policies based on WE pluralism and presenting a new method of policy evaluation based on subjective context and consensus building.
Consensus analysis of random sub-graphs for distributed filtering with link failures
Authors: Stefano Battilotti, Filippo Cacace, Massimiliano d'Angelo
Date: 2023-05-08 00:00:00

In this paper we carry out a stability analysis of a distributed consensus algorithm in presence of link failures. The algorithm combines a new broadcast version of a Push-Sum algorithm, specifically designed for handling link failures, with a new recursive consensus filter. The analysis is based on the properties of random Laplacian matrices and random sub-graphs and it may also be relevant for other distributed estimation problems. We characterize the convergence speed, the minimum number of consensus steps needed and the impact of link failures and for both the broadcast Push-Sum and the recursive consensus algorithms. Numerical simulations validate the theoretical analysis.
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches
Authors: Yuanbin Chen, Ying Wang, Xufeng Guo, Zhu Han, Ping Zhang
Date: 2023-05-07 00:00:00
Tasks: Bayesian Inference, Philosophy

In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatialtemporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.
A brief introduction to satellite communications for Non-Terrestrial Networks (NTN)
Authors: Jose Alberto Hernandez, Pedro Reviriego
Date: 2023-05-08 00:00:00

At present (year 2023), approximately 2,500 satellites are currently orbiting the Earth. This number is expected to reach 50,000 satellites (that is, 20 times growth) for the next 10 years, thanks to the recent advances concerning launching satellites at low cost and with high probability of success. In this sense, it is expected that next years the world will witness a massive increase in mobile connectivity thanks to the combination of 5G deployments and satellites, building the so-called Space-Terrestrial Integrated Network (STIN), thanks to the emergence of Non-Terrestrial Networks (NTNs). This document overviews the foundations of satellite communications as a short tutorial for those interested in research and development on Space-Terrestrial Integrated Networks (STIN) and Non-Terrestrial Networks (NTN) for supporting 5G in remote areas.
Accented Text-to-Speech Synthesis with Limited Data
Authors: Xuehao Zhou, Mingyang Zhang, Yi Zhou, Zhizheng Wu, Haizhou Li
Date: 2023-05-08 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference) and prosodic (pitch pattern and phoneme duration) variations. The proposed accented TTS framework consists of two models: an accented front-end for grapheme-to-phoneme (G2P) conversion and an accented acoustic model with integrated pitch and duration predictors for phoneme-to-Mel-spectrogram prediction. The accented front-end directly models the phonetic variation, while the accented acoustic model explicitly controls the prosodic variation. Specifically, both models are first pre-trained on a large amount of data, then only the accent-related layers are fine-tuned on a limited amount of data for the target accent. In the experiments, speech data of three English accents, i.e., General American English, Irish English, and British English Received Pronunciation, are used for pre-training. The pre-trained models are then fine-tuned with Scottish and General Australian English accents, respectively. Both objective and subjective evaluation results show that the accented TTS front-end fine-tuned with a small accented phonetic lexicon (5k words) effectively handles the phonetic variation of accents, while the accented TTS acoustic model fine-tuned with a limited amount of accented speech data (approximately 3 minutes) effectively improves the prosodic rendering including pitch and duration. The overall accent modeling contributes to improved speech quality and accent similarity.
Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions
Authors: Diego Di Carlo, Aditya Arie Nugraha, Mathieu Fontaine, Kazuyoshi Yoshii
Date: 2023-05-08 00:00:00
Tasks: Novel View Synthesis, speech-recognition, Speech Recognition

We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in reducing the resource-intensive measurements required for precise sound source separation and localization, essential as the front-end of speech recognition. Classical approaches to interpolation rely on linear weighting of nearby measurements in space on a fixed, discrete set of frequencies. Drawing inspiration from the success of neural fields for novel view synthesis in computer vision, we introduce the neural steerer, a continuous complex-valued function that takes both frequency and direction as input and produces the corresponding steering vector. Importantly, it incorporates inter-channel phase difference information and a regularization term enforcing filter causality, essential for accurate steering vector modeling. Our experiments, conducted using a dataset of real measured steering vectors, demonstrate the effectiveness of our resolution-free model in interpolating such measurements.
Decentralized Vehicle Coordination and Lane Switching without Switching of Controllers
Authors: Arno Frauenfelder, Adrian Wiltz, Dimos V. Dimarogonas
Date: 2023-05-08 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance

This paper proposes a controller for safe lane change manoeuvres of autonomous vehicles using high-order control barrier and Lyapunov functions. The inputs are calculated using a quadratic program (CLF-CBF-QP) which admits short calculation times. The controller allows for adaptive cruise control, lane following, lane switching and ensures collision avoidance at all times. The novelty of the controller is the decentralized approach to the coordination of vehicles without switching of controllers. In particular, vehicles indicate their manoeuvres which influences their own safe region and that of neighboring vehicles. This is achieved by introducing so-called coordination functions in the design of control barrier functions. In a relevant simulation example, the controller is validated and its effectiveness is demonstrated.