pwc-archive/papers-with-abstracts huggingface dataset:
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification
Authors: Zhiwen Xiao, Xin Xu, Huanlai Xing, Bowen Zhao, Xinhan Wang, Fuhong Song, Rong Qu, Li Feng
Date: 2024-02-26 00:00:00
Proceeding: IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024 2
Tasks: Knowledge Distillation, Relation Network, Time Series, Time Series Classification, Transfer Learning

This article proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.e., Head_A and Head_B), each containing two squash convolutional neural network (CNN) blocks and one dynamic routing block to extract the local features from the data and mine the connections among them. The GRN consists of two
capsule-based transformer blocks and one dynamic routing block to capture the global patterns of each variable and correlate the useful information of multiple variables. Unfortunately, it is difficult to directly deploy PCapN on mobile devices due to its strict requirement for computing resources. So, this article designs a lightweight capsule network (LCapN) to mimic the cumbersome PCapN. To promote knowledge transfer from PCapN to LCapN, this article proposes a deep transformer capsule mutual (DTCM) distillation method. It is targeted and offline, using one- and two-way operations to supervise the knowledge distillation (KD) process for the dual-network-based student and teacher models. Experimental results show that the proposed PCapN and DTCM achieve excellent performance on University of East Anglia 2018
(UEA2018) datasets regarding top-1 accuracy.
Sidewalk-based bicycle path network design incorporating equity in cycling time
Authors: Yutong Cai, Ghim Ping Ong, Qiang Meng
Date: 2024-05-27 00:00:00
Proceeding: Computer-Aided Civil and Infrastructure Engineering 2024 5

Many cities find it difficult to claim enough land to build dedicated bicycle lanes. In response, this study proposes a novel framework to design a bicycle path network based on the existing sidewalks where selected sidewalk links are converted into eligible bicycle paths. The output will be a subset of the sidewalk links chosen to be converted to eligible bicycle paths with minimum cost such that all origin–destination (O-D) pairs are connected with bicycle paths and cyclists from each O-D pair can enjoy similar degrees of equity. The equity defined here is that cyclists from each O-D pair will not need to travel excessively longer in time in the designed bicycle path network than in the original sidewalk network. A novel decomposition-based dynamic dimensional search is proposed to solve the problem. The numerical experiments of a university campus and Clementi town in Singapore have shown our algorithm with varying equity parameter choices can provide tangible inclusive bicycle path network designs and improve as many as 80% equity in certain O-D pairs with critical inequity issues.
TGCA-PVT: Topic-Guided Context-Aware Pyramid Vision Transformer for Sticker Emotion Recognition
Authors: Jian Chen, Wei Wang, Yuzhu Hu, Junxin Chen, Han Liu, Xiping Hu
Date: 2024-10-28 00:00:00
Proceeding: MM '24: Proceedings of the 32nd ACM International Conference on Multimedia 2024 10
Tasks: Emotion Recognition

Online chatting has become an essential aspect of our daily interactions, with stickers emerging as a prevalent tool for conveying emotions more vividly than plain text. While conventional image emotion recognition focuses on global features, sticker emotion recognition necessitates incorporating both global and local features, along with additional modalities like text. To address this, we introduce a topic ID-guided transformer method to facilitate a more nuanced analysis of the stickers. Considering that each sticker will have a topic, and stickers with the same topic will have the same object, we introduce a topic ID and regard the stickers with the same topic ID as topic context. Our approach encompasses a novel topic-guided context-aware module and a topic-guided attention mechanism, enabling the extraction of comprehensive topic context features from stickers sharing the same topic ID, significantly enhancing emotion recognition accuracy. Moreover, we integrate a frequency linear attention module to leverage frequency domain information to better capture the object information of the stickers and a locally enhanced re-attention mechanism for improved local feature extraction. Extensive experiments and ablation studies on the large-scale sticker emotion dataset SER30k validate the efficacy of our method. Experimental results show that our proposed method obtains the best accuracy on both single-modal and multi-modal sticker emotion recognition.
LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions
Authors: Zhehui Liao, Maria Antoniak, Inyoung Cheong, Evie Yu-Yen Cheng, Ai-Heng Lee, Kyle Lo, Joseph Chee Chang, Amy X. Zhang
Date: 2024-10-30 00:00:00

The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and why. We present the first large-scale survey of 816 verified research article authors to understand how the research community leverages and perceives LLMs as research tools. We examine participants' self-reported LLM usage, finding that 81% of researchers have already incorporated LLMs into different aspects of their research workflow. We also find that traditionally disadvantaged groups in academia (non-White, junior, and non-native English speaking researchers) report higher LLM usage and perceived benefits, suggesting potential for improved research equity. However, women, non-binary, and senior researchers have greater ethical concerns, potentially hindering adoption.
An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection
Authors: Antonio J. Muñoz-Montoro, Pablo Revuelta-Sanz, Damian Martínez-Muñoz, Juan Torre-Cruz, José Ranilla
Date: 2024-11-08 00:00:00
Tasks: Denoising

In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is based on singular value decomposition to avoid dependence on the initial values of the NMF parameters. Additionally, novel update rules to simultaneously address the multichannel denoising while preserving an orthogonal constraint to maximize source separation have been designed. The proposed system has been evaluated for the task of wheezing detection showing a significant improvement over state-of-the-art algorithms when noisy sound sources are present. Moreover, parallel and high-performance techniques have been used to speedup the execution of the proposed system, showing that it is possible to achieve fast execution times, which enables its implementation in real-world scenarios.
Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents
Authors: Aleksei Pastushkov
Date: 2024-11-06 00:00:00

We examine the dynamics of informational efficiency in a market with asymmetrically informed, boundedly rational traders who adaptively learn optimal strategies using simple multiarmed bandit (MAB) algorithms. The strategies available to the traders have two dimensions: on the one hand, the traders must endogenously choose whether to acquire a costly information signal, on the other, they must determine how aggressively they trade by choosing the share of their wealth to be invested in the risky asset. Our study contributes to two strands of literature: the literature comparing the effects of competitive and strategic behavior on asset price efficiency under costly information as well as the actively growing literature on algorithmic tacit collusion and pseudo-collusion in financial markets. We find that for certain market environments (with low information costs) our model reproduces the results of Kyle [1989] in that the ability of traders to trade strategically leads to worse price efficiency compared to the purely competitive case. For other environments (with high information costs), on the other hand, our results show that a market with strategically acting traders can be more efficient than a purely competitive one. Furthermore, we obtain novel results on the ability of independently learning traders to coordinate on a pseudo-collusive behavior, leading to non-competitive pricing. Contrary to some recent contributions (see e.g. [Cartea et al. 2022]), we find that the pseudo-collusive behavior in our model is robust to a large number of agents, demonstrating that even in the setting of financial markets with a large number of independently learning traders non-competitive pricing and pseudo-collusive behavior can frequently arise.
CRC-Assisted Channel Codes for Integrated Passive Sensing and Communications
Authors: Chenghong Bian, Kaitao Meng, Huihui Wu, Yumeng Zhang, Deniz Gunduz
Date: 2024-11-08 00:00:00
Tasks: parameter estimation, regression

We propose a novel coded integrated passive sensing and communication (CIPSAC) system with orthogonal frequency division multiplexing (OFDM), where a multi-antenna base station (BS) passively senses the parameters of the targets and decodes the information bit sequences transmitted by a user. The transmitted signal is comprised of pilot and data OFDM symbols where the data symbols adopt cyclic redundancy check (CRC)-assisted channel codes to facilitate both the decoding and sensing procedures. In the proposed scheme, CRC not only enhances the reliability of communication but also provides guidance to the parameter sensing procedure at the BS. In particular, a novel iterative parameter sensing and channel decoding (IPSCD) algorithm is proposed, where the correctly decoded codewords that pass CRC are utilized for sensing to improve the parameter estimation accuracy, and in return, more accurate parameter estimates lead to a larger number of correctly decoded data symbols. Conventional sensing algorithms rely only on the received pilot signals, while we utilize both the data and pilot signals for sensing. We provide a detailed analysis of the optimal strategy, in which the wrongly decoded data packets are replaced by zero codewords. To further improve the performance, we introduce learning-based near-orthogonal superposition (NOS) codes, which exhibit superior error correction capability especially in the short block length regime. NOS codes are trained using a weighted loss function, where a hyper parameter is introduced to balance the sensing and the communication losses. Simulation results show the effectiveness of the proposed CIPSAC system and the IPSCD algorithm, where both the sensing and decoding performances are significantly improved with a few iterations. We also carry out extensive ablation studies for a comprehensive understanding of the proposed scheme.
Integrated Location Sensing and Communication for Ultra-Massive MIMO With Hybrid-Field Beam-Squint Effect
Authors: Zhen Gao, Xingyu Zhou, Boyu Ning, Yu Su, Tong Qin, Dusit Niyato
Date: 2024-11-08 00:00:00

The advent of ultra-massive multiple-input-multiple output systems holds great promise for next-generation communications, yet their channels exhibit hybrid far- and near- field beam-squint (HFBS) effect. In this paper, we not only overcome but also harness the HFBS effect to propose an integrated location sensing and communication (ILSC) framework. During the uplink training stage, user terminals (UTs) transmit reference signals for simultaneous channel estimation and location sensing. This stage leverages an elaborately designed hybrid-field projection matrix to overcome the HFBS effect and estimate the channel in compressive manner. Subsequently, the scatterers' locations can be sensed from the spherical wavefront based on the channel estimation results. By treating the sensed scatterers as virtual anchors, we employ a weighted least-squares approach to derive UT' s location. Moreover, we propose an iterative refinement mechanism, which utilizes the accurately estimated time difference of arrival of multipath components to enhance location sensing precision. In the following downlink data transmission stage, we leverage the acquired location information to further optimize the hybrid beamformer, which combines the beam broadening and focusing to mitigate the spectral efficiency degradation resulted from the HFBS effect. Extensive simulation experiments demonstrate that the proposed ILSC scheme has superior location sensing and communication performance than conventional methods.
Enforcing asymptotic behavior with DNNs for approximation and regression in finance
Authors: Hardik Routray, Bernhard Hientzsch
Date: 2024-11-08 00:00:00
Tasks: regression

We propose a simple methodology to approximate functions with given asymptotic behavior by specifically constructed terms and an unconstrained deep neural network (DNN). The methodology we describe extends to various asymptotic behaviors and multiple dimensions and is easy to implement. In this work we demonstrate it for linear asymptotic behavior in one-dimensional examples. We apply it to function approximation and regression problems where we measure approximation of only function values (``Vanilla Machine Learning''-VML) or also approximation of function and derivative values (``Differential Machine Learning''-DML) on several examples. We see that enforcing given asymptotic behavior leads to better approximation and faster convergence.
Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation
Authors: Mu Yang, Bowen Shi, Matthew Le, Wei-Ning Hsu, Andros Tjandra
Date: 2024-11-07 00:00:00
Tasks: Audio Generation, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

This work focuses on improving Text-To-Audio (TTA) generation on zero-shot and few-shot settings (i.e. generating unseen or uncommon audio events). Inspired by the success of Retrieval-Augmented Generation (RAG) in Large Language Models, we propose Audiobox TTA-RAG, a novel retrieval-augmented TTA approach based on Audiobox, a flow-matching audio generation model. Unlike the vanilla Audiobox TTA solution that generates audio conditioned on text only, we extend the TTA process by augmenting the conditioning input with both text and retrieved audio samples. Our retrieval method does not require the external database to have labeled audio, offering more practical use cases. We show that the proposed model can effectively leverage the retrieved audio samples and significantly improve zero-shot and few-shot TTA performance, with large margins on multiple evaluation metrics, while maintaining the ability to generate semantically aligned audio for the in-domain setting.
Frequency stability of grid-forming power-limiting droop control
Authors: Amirhossein Iraniparast, Dominic Groß
Date: 2024-11-08 00:00:00

In this paper, we analyze power-limiting grid-forming droop control used for grid-connected power converters. Compared to conventional grid-forming droop control, power-limiting droop control explicitly accounts for active power limits of the generation (e.g., renewables) interfaced by the converter. While power-limiting droop control has been demonstrated to work well in simulation and experiment, analytical results are not readily available. To address this gap, we first reformulate the dynamics of a power system comprised of converters using power-limiting droop controller as a projected dynamical system in nodal coordinates. Next, we change coordinates from nodal coordinates to edge coordinates to interpret the resulting dynamics as primal-dual dynamics associated with a constrained power flow problem. Leveraging convergence results for primal-dual dynamics, we show that, under mild feasibility assumptions, the frequency dynamics of a power system comprised of converters using power-limiting droop control are globally asymptotically stable with respect to the set of optimizers of its associated constrained power flow problem. Moreover, we show that (i) the converter frequencies synchronize to a common synchronous frequency for each grid-forming converter, and (ii) characterize the synchronous frequency in the case of converters operating at their power limit. Specifically, this result establishes that power-limiting droop control exhibits properties similar to so-called power-sharing in conventional unconstrained droop control.
Bias correction and instrumental variables for direct data-driven model-reference control
Authors: Manas Mejari, Valentina Breschi, Simone Formentin, Dario Piga
Date: 2024-11-08 00:00:00

Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI) systems using noisy state-input data, employing novel noise mitigation techniques. Specifically, we demonstrate that using data-based covariance parameterization of the controller enables bias-correction and instrumental variable techniques within the data-driven optimization, thus reducing measurement noise effects as data volume increases. The number of decision variables remains independent of dataset size, making this method scalable to large datasets. The approach's effectiveness is demonstrated with a numerical example.
A Hybrid Algorithm for Iterative Adaptation of Feedforward Controllers: an Application on Electromechanical Switches
Authors: Eloy Serrano-Seco, Eduardo Moya-Lasheras, Edgar Ramirez-Laboreo
Date: 2024-03-23 00:00:00

Electromechanical switching devices such as relays, solenoid valves, and contactors offer several technical and economic advantages that make them widely used in industry. However, uncontrolled operations result in undesirable impact-related phenomena at the end of the stroke. As a solution, different soft-landing controls have been proposed. Among them, feedforward control with iterative techniques that adapt its parameters is a solution when real-time feedback is not available. However, these techniques typically require a large number of operations to converge or are computationally intensive, which limits a real implementation. In this paper, we present a new algorithm for the iterative adaptation that is able to eventually adapt the search coordinate system and to reduce the search dimensional size in order to accelerate convergence. Moreover, it automatically toggles between a derivative-free and a gradient-based method to balance exploration and exploitation. To demonstrate the high potential of the proposal, each novel part of the algorithm is compared with a state-of-the-art approach via simulation.
Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal
Authors: Yifan Xie, Julian Berberich, Felix Brändle, Frank Allgöwer
Date: 2024-11-08 00:00:00
Tasks: Model Predictive Control, Scheduling

This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min-max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and over all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation.
Time-to-reach Bounds for Verification of Dynamical Systems Using the Koopman Spectrum
Authors: Jianqiang Ding, Shankar A. Deka
Date: 2024-11-08 00:00:00

In this work, we present a novel Koopman spectrum-based reachability verification method for nonlinear systems. Contrary to conventional methods that focus on characterizing all potential states of a dynamical system over a presupposed time span, our approach seeks to verify the reachability by assessing the non-emptiness of estimated time-to-reach intervals without engaging in the explicit computation of reachable set. Based on the spectral analysis of the Koopman operator, we reformulate the problem of verifying existence of reachable trajectories into the problem of determining feasible time-to-reach bounds required for system reachability. By solving linear programming (LP) problems, our algorithm can effectively estimate all potential time intervals during which a dynamical system can enter (and exit) target sets from given initial sets over an unbounded time horizon. Finally, we demonstrate our method in challenging settings, such as verifying the reachability between non-convex or even disconnected sets, as well as backward reachability and multiple entries into target sets. Additionally, we validate its applicability in addressing real-world challenges and scalability to high-dimensional systems through case studies in verifying the reachability of the cart-pole and multi-agent consensus systems.
Time in a bottle. A psychophysics study of human time perception through aging
Authors: Enric Espel Sanchez
Date: 2024-10-24 00:00:00

Time perception is crucial for a coherent human experience. As life progresses, our perception of the passage of time becomes increasingly non-uniform, often feeling as though it accelerates with age. While various causes for this phenomenon have been theorized, a comprehensive mathematical and theoretical framework remains underexplored. This study aims to elucidate the mechanisms behind perceived time dilation by integrating classical and revised psychophysical theorems with a novel mathematical approach. Utilizing Weber-Fechner laws as foundational elements, we develop a model that transitions from exponential to logarithmic functions to represent changes in time perception across the human lifespan. Our results indicate that the perception of time shifts significantly around the age of mental maturity, aligning with a proposed inversion point where sensitivity to temporal stimuli decreases, eventually plateauing out at a constant rate. This model not only explains the underlying causes of time perception changes but also provides analytical values to quantify this acceleration. These findings offer valuable insights into the cognitive and neurological processes influencing how we experience time as we go through life.