pwc-archive/papers-with-abstracts huggingface dataset:
Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition
Authors: Mengxi Liu, Daniel Geißler, Dominique Nshimyimana, Sizhen Bian, Bo Zhou, Paul Lukowicz
Date: 2024-06-16 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Kolmogorov-Arnold Networks

In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activity Recognition (HAR). Where conventional networks perform a parameterized weighted sum of the inputs at each node and then feed the result into a statically defined nonlinearity, KANs perform non-linear computations represented by B-SPLINES on the edges leading to each node and then just sum up the inputs at the node. Instead of learning weights, the system learns the spline parameters. In the original work, such networks have been shown to be able to more efficiently and exactly learn sophisticated real valued functions e.g. in regression or PDE solution. We hypothesize that such an ability is also advantageous for computing low-level features for IMU-based HAR. To this end, we have implemented KAN as the feature extraction architecture for IMU-based human activity recognition tasks, including four architecture variations. We present an initial performance investigation of the KAN feature extractor on four public HAR datasets. It shows that the KAN-based feature extractor outperforms CNN-based extractors on all datasets while being more parameter efficient.
Hyperdimensional Quantum Factorization
Authors: Prathyush Poduval, Zhuowen Zou, Alvaro Velasquez, Mohsen Imani
Date: 2024-06-13 00:00:00
Tasks: Information Retrieval, Retrieval

This paper presents a quantum algorithm for efficiently decoding hypervectors, a crucial process in extracting atomic elements from hypervectors - an essential task in Hyperdimensional Computing (HDC) models for interpretable learning and information retrieval. HDC employs high-dimensional vectors and efficient operators to encode and manipulate information, representing complex objects from atomic concepts. When one attempts to decode a hypervector that is the product (binding) of multiple hypervectors, the factorization becomes prohibitively costly with classical optimization-based methods and specialized recurrent networks, an inherent consequence of the binding operation. We propose HDQF, an innovative quantum computing approach, to address this challenge. By exploiting parallels between HDC and quantum computing and capitalizing on quantum algorithms' speedup capabilities, HDQF encodes potential factors as a quantum superposition using qubit states and bipolar vector representation. This yields a quadratic speedup over classical search methods and effectively mitigates Hypervector Factorization capacity issues.
Optimal Robust Contract Design
Authors: Bo Peng, Zhihao Gavin Tang
Date: 2024-06-17 00:00:00

We consider the robust contract design problem when the principal only has limited information about the actions the agent can take. The principal evaluates a contract according to its worst-case performance caused by the uncertain action space. Carroll (AER 2015) showed that a linear contract is optimal among deterministic contracts. Recently, Kambhampati (JET 2023) showed that the principal's payoff can be strictly increased via randomization over linear contracts. In this paper, we characterize the optimal randomized contract, which remains linear and admits a closed form of its cumulative density function. The advantage of randomized contracts over deterministic contracts can be arbitrarily large even when the principal knows only one non-trivial action of the agent. Furthermore, our result generalizes to the model of contracting with teams, by Dai and Toikka (Econometrica 2022).
A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI
Authors: Haruka Kita, Sotetsu Koyamada, Yotaro Yamaguchi, Shin Ishii
Date: 2024-06-14 00:00:00

Contract bridge, a cooperative game characterized by imperfect information and multi-agent dynamics, poses significant challenges and serves as a critical benchmark in artificial intelligence (AI) research. Success in this domain requires agents to effectively cooperate with their partners. This study demonstrates that an appropriate combination of existing methods can perform surprisingly well in bridge bidding against WBridge5, a leading benchmark in the bridge bidding system and a multiple-time World Computer-Bridge Championship winner. Our approach is notably simple, yet it outperforms the current state-of-the-art methodologies in this field. Furthermore, we have made our code and models publicly available as open-source software. This initiative provides a strong starting foundation for future bridge AI research, facilitating the development and verification of new strategies and advancements in the field.
ESI-GAL: EEG Source Imaging-based Trajectory Estimation for Grasp and Lift Task
Authors: Anant Jain, Lalan Kumar
Date: 2024-06-17 00:00:00
Tasks: Brain Computer Interface, Decoder, EEG, Electroencephalogram (EEG), Prediction, Trajectory Prediction

Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature. In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding. The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with a time lag and window size of 100 ms and 450 ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively. This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain EEG features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.
Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness
Authors: Satyam Kumar, Sai Srujana Buddi, Utkarsh Oggy Sarawgi, Vineet Garg, Shivesh Ranjan, Ognjen, Rudovic, Ahmed Hussen Abdelaziz, Saurabh Adya
Date: 2024-06-12 00:00:00
Tasks: Action Detection, Activity Detection, Speech Enhancement, speech-recognition, Speech Recognition

Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need for effective personalized VAD systems has become paramount. In this paper, we present a comparative analysis of Personalized Voice Activity Detection (PVAD) systems to assess their real-world effectiveness. We introduce a comprehensive approach to assess PVAD systems, incorporating various performance metrics such as frame-level and utterance-level error rates, detection latency and accuracy, alongside user-level analysis. Through extensive experimentation and evaluation, we provide a thorough understanding of the strengths and limitations of various PVAD variants. This paper advances the understanding of PVAD technology by offering insights into its efficacy and viability in practical applications using a comprehensive set of metrics.
Autograding Mathematical Induction Proofs with Natural Language Processing
Authors: Chenyan Zhao, Mariana Silva, Seth Poulsen
Date: 2024-06-11 00:00:00
Tasks: Mathematical Induction, Mathematical Proofs

In mathematical proof education, there remains a need for interventions that help students learn to write mathematical proofs. Research has shown that timely feedback can be very helpful to students learning new skills. While for many years natural language processing models have struggled to perform well on tasks related to mathematical texts, recent developments in natural language processing have created the opportunity to complete the task of giving students instant feedback on their mathematical proofs. In this paper, we present a set of training methods and models capable of autograding freeform mathematical proofs by leveraging existing large language models and other machine learning techniques. The models are trained using proof data collected from four different proof by induction problems. We use four different robust large language models to compare their performances, and all achieve satisfactory performances to various degrees. Additionally, we recruit human graders to grade the same proofs as the training data, and find that the best grading model is also more accurate than most human graders. With the development of these grading models, we create and deploy an autograder for proof by induction problems and perform a user study with students. Results from the study shows that students are able to make significant improvements to their proofs using the feedback from the autograder, but students still do not trust the AI autograders as much as they trust human graders. Future work can improve on the autograder feedback and figure out ways to help students trust AI autograders.
Analytic Models for the Capacity Distribution in MDG-impaired Optical SDM Transmission
Authors: Lucas Alves Zischler, Darli A. A. Mello
Date: 2024-06-18 00:00:00

In coupled space-division multiplexing (SDM) transmission systems, imperfections in optical amplifiers and passive devices introduce mode-dependent loss (MDL) and gain (MDG). These effects render the channel capacity stochastic and result in a decrease in average capacity. Several previous studies employ multi-section simulations to model the capacity of these systems. Additionally, relevant works derive analytically the capacity distribution for a single-mode system with polarization-dependent gain and loss (mode count D = 2). However, to the best of our knowledge, analytic expressions of the capacity distribution for systems with D > 2 have not been presented. In this paper, we provide analytic expressions for the capacity of optical systems with arbitrary mode counts. The expressions rely on Gaussian approximations for the per-mode capacity distributions and for the overall capacity distribution, as well as on fitting parameters for the capacity cross-correlation among different modes. Compared to simulations, the derived analytical expressions exhibit a suitable level of accuracy across a wide range of practical scenarios.
Neural logic programs and neural nets
Authors: Christian Antić
Date: 2024-06-13 00:00:00

Neural-symbolic integration aims to combine the connectionist subsymbolic with the logical symbolic approach to artificial intelligence. In this paper, we first define the answer set semantics of (boolean) neural nets and then introduce from first principles a class of neural logic programs and show that nets and programs are equivalent.
Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph
Authors: Wanqing Zhao, Yuta Nakashima, Haiyuan Chen, Noboru Babaguchi
Date: 2024-06-14 00:00:00
Tasks: Domain Generalization, Fake News Detection

Fake news detection in social media has become increasingly important due to the rapid proliferation of personal media channels and the consequential dissemination of misleading information. Existing methods, which primarily rely on multimodal features and graph-based techniques, have shown promising performance in detecting fake news. However, they still face a limitation, i.e., sparsity in graph connections, which hinders capturing possible interactions among tweets. This challenge has motivated us to explore a novel method that densifies the graph's connectivity to capture denser interaction better. Our method constructs a cross-modal tweet graph using CLIP, which encodes images and text into a unified space, allowing us to extract potential connections based on similarities in text and images. We then design a Feature Contextualization Network with Label Propagation (FCN-LP) to model the interaction among tweets as well as positive or negative correlations between predicted labels of connected tweets. The propagated labels from the graph are weighted and aggregated for the final detection. To enhance the model's generalization ability to unseen events, we introduce a domain generalization loss that ensures consistent features between tweets on seen and unseen events. We use three publicly available fake news datasets, Twitter, PHEME, and Weibo, for evaluation. Our method consistently improves the performance over the state-of-the-art methods on all benchmark datasets and effectively demonstrates its aptitude for generalizing fake news detection in social media.
12 Labours tools for developing Functional Tissue Units
Authors: Jagir R. Hussan
Date: 2024-06-13 00:00:00

A brief introduction of the technical approach to model FTUs as an aggregate of cells, whose state transition dynamics are mathematically represented as port-hamiltonians or Differential Algebraic equations is presented. A python library and browser based tool to enable modellers to compose the FTU graph, specify the cellular equations and the interconnection between the cells at the level of physical quantities they exchange consistent with the technical approach is discussed.
The Mathematics of Dots and Pixels: On the Theoretical Foundations of Image Halftoning
Authors: Felix Krahmer, Anna Veselovska
Date: 2024-06-18 00:00:00

The evolution of image halftoning, from its analog roots to contemporary digital methodologies, encapsulates a fascinating journey marked by technological advancements and creative innovations. Yet the theoretical understanding of halftoning is much more recent. In this article, we explore various approaches towards shedding light on the design of halftoning approaches and why they work. We discuss both halftoning in a continuous domain and on a pixel grid. We start by reviewing the mathematical foundation of the so-called electrostatic halftoning method, which departed from the heuristic of considering the back dots of the halftoned image as charged particles attracted by the grey values of the image in combination with mutual repulsion. Such an attraction-repulsion model can be mathematically represented via an energy functional in a reproducing kernel Hilbert space allowing for a rigorous analysis of the resulting optimization problem as well as a convergence analysis in a suitable topology. A second class of methods that we discuss in detail is the class of error diffusion schemes, arguably among the most popular halftoning techniques due to their ability to work directly on a pixel grid and their ease of application. The main idea of these schemes is to choose the locations of the black pixels via a recurrence relation designed to agree with the image in terms of the local averages. We discuss some recent mathematical understanding of these methods that is based on a connection to Sigma-Delta quantizers, a popular class of algorithms for analog-to-digital conversion.
Comment on paper: Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
Authors: Yimeng Min
Date: 2024-06-11 00:00:00
Tasks: All, Position

We identify two major issues in the SoftDist paper (Xia et al.): (1) the failure to run all steps of different baselines on the same hardware environment, and (2) the use of inconsistent time measurements when comparing to other baselines. These issues lead to flawed conclusions. When all steps are executed in the same hardware environment, the primary claim made in SoftDist is no longer supported.
Data Petri Nets meet Probabilistic Programming (Extended version)
Authors: Martin Kuhn, Joscha Grüger, Christoph Matheja, Andrey Rivkin
Date: 2024-06-12 00:00:00
Tasks: Probabilistic Programming, Translation

Probabilistic programming (PP) is a programming paradigm that allows for writing statistical models like ordinary programs, performing simulations by running those programs, and analyzing and refining their statistical behavior using powerful inference engines. This paper takes a step towards leveraging PP for reasoning about data-aware processes. To this end, we present a systematic translation of Data Petri Nets (DPNs) into a model written in a PP language whose features are supported by most PP systems. We show that our translation is sound and provides statistical guarantees for simulating DPNs. Furthermore, we discuss how PP can be used for process mining tasks and report on a prototype implementation of our translation. We also discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.
Large Language Model-empowered multimodal strain sensory system for shape recognition, monitoring, and human interaction of tensegrity
Authors: Zebing Mao, Ryota Kobayashi, Hiroyuki Nabae, Koichi Suzumori
Date: 2024-06-11 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model

A tensegrity-based system is a promising approach for dynamic exploration of uneven and unpredictable environments, particularly, space exploration. However, implementing such systems presents challenges in terms of intelligent aspects: state recognition, wireless monitoring, human interaction, and smart analyzing and advising function. Here, we introduce a 6-strut tensegrity integrate with 24 multimodal strain sensors by leveraging both deep learning model and large language models to realize smart tensegrity. Using conductive flexible tendons assisted by long short-term memory model, the tensegrity achieves the self-shape reconstruction without extern sensors. Through integrating the flask server and gpt-3.5-turbo model, the tensegrity autonomously enables to send data to iPhone for wireless monitoring and provides data analysis, explanation, prediction, and suggestions to human for decision making. Finally, human interaction system of the tensegrity helps human obtain necessary information of tensegrity from the aspect of human language. Overall, this intelligent tensegrity-based system with self-sensing tendons showcases potential for future exploration, making it a versatile tool for real-world applications.
Binaural Selective Attention Model for Target Speaker Extraction
Authors: Hanyu Meng, Qiquan Zhang, Xiangyu Zhang, Vidhyasaharan Sethu, Eliathamby Ambikairajah
Date: 2024-06-18 00:00:00
Tasks: model, Target Speaker Extraction

The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing. In this paper, we present a binaural time-domain Target Speaker Extraction model based on the Filter-and-Sum Network (FaSNet). Inspired by human selective hearing, our proposed model introduces target speaker embedding into separators using a multi-head attention-based selective attention block. We also compared two binaural interaction approaches -- the cosine similarity of time-domain signals and inter-channel correlation in learned spectral representations. Our experimental results show that our proposed model outperforms monaural configurations and state-of-the-art multi-channel target speaker extraction models, achieving best-in-class performance with 18.52 dB SI-SDR, 19.12 dB SDR, and 3.05 PESQ scores under anechoic two-speaker test configurations.
Universal Score-based Speech Enhancement with High Content Preservation
Authors: Robin Scheibler, Yusuke Fujita, Yuma Shirahata, Tatsuya Komatsu
Date: 2024-06-18 00:00:00
Tasks: Speech Enhancement

We propose UNIVERSE++, a universal speech enhancement method based on score-based diffusion and adversarial training. Specifically, we improve the existing UNIVERSE model that decouples clean speech feature extraction and diffusion. Our contributions are three-fold. First, we make several modifications to the network architecture, improving training stability and final performance. Second, we introduce an adversarial loss to promote learning high quality speech features. Third, we propose a low-rank adaptation scheme with a phoneme fidelity loss to improve content preservation in the enhanced speech. In the experiments, we train a universal enhancement model on a large scale dataset of speech degraded by noise, reverberation, and various distortions. The results on multiple public benchmark datasets demonstrate that UNIVERSE++ compares favorably to both discriminative and generative baselines for a wide range of qualitative and intelligibility metrics.
Sound event detection based on auxiliary decoder and maximum probability aggregation for DCASE Challenge 2024 Task 4
Authors: Sang Won Son, Jongyeon Park, Hong Kook Kim, Sulaiman Vesal, Jeong Eun Lim
Date: 2024-06-17 00:00:00
Tasks: Decoder, Event Detection, Sound Event Detection

In this report, we propose three novel methods for developing a sound event detection (SED) model for the DCASE 2024 Challenge Task 4. First, we propose an auxiliary decoder attached to the final convolutional block to improve feature extraction capabilities while reducing dependency on embeddings from pre-trained large models. The proposed auxiliary decoder operates independently from the main decoder, enhancing performance of the convolutional block during the initial training stages by assigning a different weight strategy between main and auxiliary decoder losses. Next, to address the time interval issue between the DESED and MAESTRO datasets, we propose maximum probability aggregation (MPA) during the training step. The proposed MPA method enables the model's output to be aligned with soft labels of 1 s in the MAESTRO dataset. Finally, we propose a multi-channel input feature that employs various versions of logmel and MFCC features to generate time-frequency pattern. The experimental results demonstrate the efficacy of these proposed methods in a view of improving SED performance by achieving a balanced enhancement across different datasets and label types. Ultimately, this approach presents a significant step forward in developing more robust and flexible SED models