pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
Authors: Silin Chen, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Ming Liu
Date: 2024-10-22 00:00:00
Tasks: Management

This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathematical operations, matrix operations, linear algebra, and optimization techniques crucial for training ML and DL models. Advanced subjects like neural networks, optimization algorithms, and frequency domain methods are also explored, along with real-world applications of large language models (LLMs) and artificial intelligence (AI) in big data management. Designed for both beginners and advanced learners, the book emphasizes the critical role of mathematical principles in developing scalable AI solutions. Practical examples and Python code are provided throughout, ensuring readers gain hands-on experience in applying theoretical knowledge to solve complex problems in ML, DL, and big data analytics.
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
Authors: Silong Yong, Yaqi Xie, Simon Stepputtis, Katia Sycara
Date: 2024-10-19 00:00:00
Tasks: Attribute, NeRF

Volume rendering in neural radiance fields is inherently time-consuming due to the large number of MLP calls on the points sampled per ray. Previous works would address this issue by introducing new neural networks or data structures. In this work, We propose GL-NeRF, a new perspective of computing volume rendering with the Gauss-Laguerre quadrature. GL-NeRF significantly reduces the number of MLP calls needed for volume rendering, introducing no additional data structures or neural networks. The simple formulation makes adopting GL-NeRF in any NeRF model possible. In the paper, we first justify the use of the Gauss-Laguerre quadrature and then demonstrate this plug-and-play attribute by implementing it in two different NeRF models. We show that with a minimal drop in performance, GL-NeRF can significantly reduce the number of MLP calls, showing the potential to speed up any NeRF model.
Performance of User-Assisted Nonlinear Energy Harvesting NOMA Network with Alamouti/MRC
Authors: Büşra Demirkol, Oğuz Kucur
Date: 2024-10-28 00:00:00

This paper focuses on evaluating the outage performance of a dual-hop single-phase non-orthogonal multiple-access (NOMA) system. The base station employs the Alamouti space-time block coding technique (Alamouti-STBC), enabling simultaneous communication with two mobile users, and the far user employs a maximal ratio combining (MRC) scheme. In this setup, the near user serves as a full-duplex (FD) (or half-duplex (HD)) energy harvesting (EH) relay, adopting decode-and-forward (DF) protocol for the far user. The study involves the development of a system model and the closed-form equations of exact and asymptotic outage probabilities (OP) over Nakagami-m fading channels with and without direct link considering a threshold-based nonlinear EH relaying model. We verify analytical results by Monte Carlo simulations and show that the presence of a direct link in the system enhances the performance of the far user considerably by mitigating the degradation caused by the self-interference in the near user.
Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array
Authors: Xiao Tang, Yudan Jiang, Jinxin Liu, Qinghe Du, Dusit Niyato, Zhu Han
Date: 2024-10-27 00:00:00
Tasks: Deep Learning

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination.
Dynamic User Grouping based on Location and Heading in 5G NR Systems
Authors: Dino Pjanić, Korkut Emre Arslantürk, Xuesong Cai, Fredrik Tufvesson
Date: 2024-10-22 00:00:00
Tasks: Clustering

User grouping based on geographic location in fifth generation (5G) New Radio (NR) systems has several applications that can significantly improve network performance, user experience, and service delivery. We demonstrate how Sounding Reference Signals channel fingerprints can be used for dynamic user grouping in a 5G NR commercial deployment based on outdoor positions and heading direction employing machine learning methods such as neural networks combined with clustering methods.
Integrating Reasoning Systems for Trustworthy AI, Proceedings of the 4th Workshop on Logic and Practice of Programming (LPOP)
Authors: Anil Nerode, Yanhong A. Liu
Date: 2024-10-01 00:00:00
Tasks: Position

This proceedings contains abstracts and position papers for the work to be presented at the fourth Logic and Practice of Programming (LPOP) Workshop. The workshop is to be held in Dallas, Texas, USA, and as a hybrid event, on October 13, 2024, in conjunction with the 40th International Conference on Logic Programming (ICLP). The focus of this workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints.
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks
Authors: Mohammadreza Doostmohammadian, Sérgio Pequito
Date: 2024-10-27 00:00:00
Tasks: Distributed Optimization, Quantization

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization, this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies, resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrum analysis.
ScreenWriter: Automatic Screenplay Generation and Movie Summarisation
Authors: Louis Mahon, Mirella Lapata
Date: 2024-10-17 00:00:00

The proliferation of creative video content has driven demand for textual descriptions or summaries that allow users to recall key plot points or get an overview without watching. The volume of movie content and speed of turnover motivates automatic summarisation, which is nevertheless challenging, requiring identifying character intentions and very long-range temporal dependencies. The few existing methods attempting this task rely heavily on textual screenplays as input, greatly limiting their applicability. In this work, we propose the task of automatic screenplay generation, and a method, ScreenWriter, that operates only on video and produces output which includes dialogue, speaker names, scene breaks, and visual descriptions. ScreenWriter introduces a novel algorithm to segment the video into scenes based on the sequence of visual vectors, and a novel method for the challenging problem of determining character names, based on a database of actors' faces. We further demonstrate how these automatic screenplays can be used to generate plot synopses with a hierarchical summarisation method based on scene breaks. We test the quality of the final summaries on the recent MovieSum dataset, which we augment with videos, and show that they are superior to a number of comparison models which assume access to goldstandard screenplays.
Movie Trailer Genre Classification Using Multimodal Pretrained Features
Authors: Serkan Sulun, Paula Viana, Matthew E. P. Davies
Date: 2024-10-11 00:00:00
Tasks: Classification, Genre classification

We introduce a novel method for movie genre classification, capitalizing on a diverse set of readily accessible pretrained models. These models extract high-level features related to visual scenery, objects, characters, text, speech, music, and audio effects. To intelligently fuse these pretrained features, we train small classifier models with low time and memory requirements. Employing the transformer model, our approach utilizes all video and audio frames of movie trailers without performing any temporal pooling, efficiently exploiting the correspondence between all elements, as opposed to the fixed and low number of frames typically used by traditional methods. Our approach fuses features originating from different tasks and modalities, with different dimensionalities, different temporal lengths, and complex dependencies as opposed to current approaches. Our method outperforms state-of-the-art movie genre classification models in terms of precision, recall, and mean average precision (mAP). To foster future research, we make the pretrained features for the entire MovieNet dataset, along with our genre classification code and the trained models, publicly available.
Predicting potato plant vigor from the seed tuber properties
Authors: Elisa Atza, Rob Klooster, Falko Hofstra, Frank van der Werff, Hans van Doorn, Neil Budko
Date: 2024-10-24 00:00:00

The vigor of potato plants, defined as the canopy area at the end of the exponential growth stage, depends on the origin and physiological state of the seed tuber. Experiments carried out with six potato varieties in three test fields over three years show that there is a 73%-90% correlation in the vigor of the plants from the same seedlot grown in different test fields. However, these correlations are not always observed on the level of individual varieties and vanish or become negative when the seed tubers and young plants experience environmental stress. A comprehensive study of the association between the vigor and the seed tuber biochemistry has revealed that, while 50%-70% of the variation in the plant vigor is explained by the tuber data, the vigor is dominated by the potato genotype. Analysis of individual predictors, such as the abundance of a particular metabolite, indicates that the vigor enhancing properties of the seed tubers differ between genotypes. Variety-specific models show that, for some varieties, up to 30% of the vigor variation within the variety is explained by and can be predicted from the tuber biochemistry, whereas, for other varieties, the association between the tuber composition and the vigor is much weaker.
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Authors: Daniel Paulin, Peter A. Whalley, Neil K. Chada, Benedict Leimkuhler
Date: 2024-10-14 00:00:00

We propose a scalable kinetic Langevin dynamics algorithm for sampling parameter spaces of big data and AI applications. Our scheme combines a symmetric forward/backward sweep over minibatches with a symmetric discretization of Langevin dynamics. For a particular Langevin splitting method (UBU), we show that the resulting Symmetric Minibatch Splitting-UBU (SMS-UBU) integrator has bias $O(h^2 d^{1/2})$ in dimension $d>0$ with stepsize $h>0$, despite only using one minibatch per iteration, thus providing excellent control of the sampling bias as a function of the stepsize. We apply the algorithm to explore local modes of the posterior distribution of Bayesian neural networks (BNNs) and evaluate the calibration performance of the posterior predictive probabilities for neural networks with convolutional neural network architectures for classification problems on three different datasets (Fashion-MNIST, Celeb-A and chest X-ray). Our results indicate that BNNs sampled with SMS-UBU can offer significantly better calibration performance compared to standard methods of training and stochastic weight averaging.
Data-Driven Cellular Network Selector for Vehicle Teleoperations
Authors: Barak Gahtan, Reuven Cohen, Alex M. Bronstein, Eli Shapira
Date: 2024-10-15 00:00:00
Tasks: Time Series

Remote control of robotic systems, also known as teleoperation, is crucial for the development of autonomous vehicle (AV) technology. It allows a remote operator to view live video from AVs and, in some cases, to make real-time decisions. The effectiveness of video-based teleoperation systems is heavily influenced by the quality of the cellular network and, in particular, its packet loss rate and latency. To optimize these parameters, an AV can be connected to multiple cellular networks and determine in real time over which cellular network each video packet will be transmitted. We present an algorithm, called Active Network Selector (ANS), which uses a time series machine learning approach for solving this problem. We compare ANS to a baseline non-learning algorithm, which is used today in commercial systems, and show that ANS performs much better, with respect to both packet loss and packet latency.
Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model
Authors: Jinbo Hou, Kehai Qiu, Zitian Zhang, Yong Yu, Kezhi Wang, Stefano Capolongo, Jiliang Zhang, Zeyang Li, Jie Zhang
Date: 2024-10-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Position

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building's wireless performance can be significantly enhanced while ensuring indoor daylight performance.
SEEV: Synthesis with Efficient Exact Verification for ReLU Neural Barrier Functions
Authors: Hongchao Zhang, Zhizhen Qin, Sicun Gao, Andrew Clark
Date: 2024-10-27 00:00:00

Neural Control Barrier Functions (NCBFs) have shown significant promise in enforcing safety constraints on nonlinear autonomous systems. State-of-the-art exact approaches to verifying safety of NCBF-based controllers exploit the piecewise-linear structure of ReLU neural networks, however, such approaches still rely on enumerating all of the activation regions of the network near the safety boundary, thus incurring high computation cost. In this paper, we propose a framework for Synthesis with Efficient Exact Verification (SEEV). Our framework consists of two components, namely (i) an NCBF synthesis algorithm that introduces a novel regularizer to reduce the number of activation regions at the safety boundary, and (ii) a verification algorithm that exploits tight over-approximations of the safety conditions to reduce the cost of verifying each piecewise-linear segment. Our simulations show that SEEV significantly improves verification efficiency while maintaining the CBF quality across various benchmark systems and neural network structures. Our code is available at https://github.com/HongchaoZhang-HZ/SEEV.
Single-word Auditory Attention Decoding Using Deep Learning Model
Authors: Nhan Duc Thanh Nguyen, Huy Phan, Kaare Mikkelsen, Preben Kidmose
Date: 2024-10-15 00:00:00
Tasks: Deep Learning, EEG

Identifying auditory attention by comparing auditory stimuli and corresponding brain responses, is known as auditory attention decoding (AAD). The majority of AAD algorithms utilize the so-called envelope entrainment mechanism, whereby auditory attention is identified by how the envelope of the auditory stream drives variation in the electroencephalography (EEG) signal. However, neural processing can also be decoded based on endogenous cognitive responses, in this case, neural responses evoked by attention to specific words in a speech stream. This approach is largely unexplored in the field of AAD but leads to a single-word auditory attention decoding problem in which an epoch of an EEG signal timed to a specific word is labeled as attended or unattended. This paper presents a deep learning approach, based on EEGNet, to address this challenge. We conducted a subject-independent evaluation on an event-based AAD dataset with three different paradigms: word category oddball, word category with competing speakers, and competing speech streams with targets. The results demonstrate that the adapted model is capable of exploiting cognitive-related spatiotemporal EEG features and achieving at least 58% accuracy on the most realistic competing paradigm for the unseen subjects. To our knowledge, this is the first study dealing with this problem.
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks
Authors: Zohreh Aghababaeyan, Manel Abdellatif, Lionel Briand, Ramesh S
Date: 2024-10-15 00:00:00
Tasks: Diversity, Generative Adversarial Network, Image Generation, Model Selection

Deep Neural Networks (DNNs) are increasingly deployed across applications. However, ensuring their reliability remains a challenge, and in many situations, alternative models with similar functionality and accuracy are available. Traditional accuracy-based evaluations often fail to capture behavioral differences between models, especially with limited test datasets, making it difficult to select or combine models effectively. Differential testing addresses this by generating test inputs that expose discrepancies in DNN model behavior. However, existing approaches face significant limitations: many rely on model internals or are constrained by available seed inputs. To address these challenges, we propose DiffGAN, a black-box test image generation approach for differential testing of DNN models. DiffGAN leverages a Generative Adversarial Network (GAN) and the Non-dominated Sorting Genetic Algorithm II to generate diverse and valid triggering inputs that reveal behavioral discrepancies between models. DiffGAN employs two custom fitness functions, focusing on diversity and divergence, to guide the exploration of the GAN input space and identify discrepancies between models' outputs. By strategically searching this space, DiffGAN generates inputs with specific features that trigger differences in model behavior. DiffGAN is black-box, making it applicable in more situations. We evaluate DiffGAN on eight DNN model pairs trained on widely used image datasets. Our results show DiffGAN significantly outperforms a SOTA baseline, generating four times more triggering inputs, with greater diversity and validity, within the same budget. Additionally, the generated inputs improve the accuracy of a machine learning-based model selection mechanism, which selects the best-performing model based on input characteristics and can serve as a smart output voting mechanism when using alternative models.
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction
Authors: Hongru Wang, Rui Wang, Boyang Xue, Heming Xia, Jingtao Cao, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong
Date: 2024-10-10 00:00:00
Tasks: In-Context Learning

Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Previous research primarily focuses on APIs with limited arguments from a single source or overlooks the complex dependency relationship between different APIs. However, it is essential to utilize multiple APIs collaboratively from various sources (e.g., different Apps in the iPhone), especially for complex user instructions. In this paper, we introduce \texttt{AppBench}, the first benchmark to evaluate LLMs' ability to plan and execute multiple APIs from various sources in order to complete the user's task. Specifically, we consider two significant challenges in multiple APIs: \textit{1) graph structures:} some APIs can be executed independently while others need to be executed one by one, resulting in graph-like execution order; and \textit{2) permission constraints:} which source is authorized to execute the API call. We have experimental results on 9 distinct LLMs; e.g., GPT-4o achieves only a 2.0\% success rate at the most complex instruction, revealing that the existing state-of-the-art LLMs still cannot perform well in this situation even with the help of in-context learning and finetuning. Our code and data are publicly available at https://github.com/ruleGreen/AppBench.
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
Authors: Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, Kijung Shin
Date: 2024-10-27 00:00:00
Tasks: Anomaly Detection

Graph autoencoders (Graph-AEs) learn representations of given graphs by aiming to accurately reconstruct them. A notable application of Graph-AEs is graph-level anomaly detection (GLAD), whose objective is to identify graphs with anomalous topological structures and/or node features compared to the majority of the graph population. Graph-AEs for GLAD regard a graph with a high mean reconstruction error (i.e. mean of errors from all node pairs and/or nodes) as anomalies. Namely, the methods rest on the assumption that they would better reconstruct graphs with similar characteristics to the majority. We, however, report non-trivial counter-examples, a phenomenon we call reconstruction flip, and highlight the limitations of the existing Graph-AE-based GLAD methods. Specifically, we empirically and theoretically investigate when this assumption holds and when it fails. Through our analyses, we further argue that, while the reconstruction errors for a given graph are effective features for GLAD, leveraging the multifaceted summaries of the reconstruction errors, beyond just mean, can further strengthen the features. Thus, we propose a novel and simple GLAD method, named MUSE. The key innovation of MUSE involves taking multifaceted summaries of reconstruction errors as graph features for GLAD. This surprisingly simple method obtains SOTA performance in GLAD, performing best overall among 14 methods across 10 datasets.