pwc-archive/papers-with-abstracts huggingface dataset:
OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient
Authors: Mohammadjavad Maleki, Sobhan Zahiri
Date: 2024-10-10 00:00:00
Tasks: Drug Discovery, Hyperparameter Optimization

The physicochemical properties of chemical compounds have great importance in several areas, including pharmaceuticals, environmental and separation science. Among these are physicochemical properties such as the octanol-water partition coefficient, which has been considered an important index pointing out lipophilicity and hydrophilicity. It affects drug absorption and membrane permeability. Following Lipinski's rule of five, logP was identified as one of the key determinants of the stability of chemical entities and, as such, needed state-of-the-art methods for measuring lipophilicity. This paper presents a deep-learning model, OWPCP, developed to compute logP using Morgan fingerprints and MACCS keys as input features. It uses the interconnection of such molecular representations with logP values extracted from 26,254 compounds. The dataset was prepared to contain a wide range of chemical structures with differing molecular weights and polar surface area. Hyperparameter optimization was conducted using the Keras Tuner alongside the Hyperband algorithm to enhance the performance. OWPCP demonstrated outstanding performance compared to current computational methods, achieving an MAE=0.247 on the test set and outperforming all previous DL models. Remarkably, while one of the most accurate recent models is based on experimental data on retention time to make predictions, OWPCP manages computing logP efficiently without depending on these factors, being, therefore, very useful during early-stage drug discovery. Our model outperforms the best model, which leverages Retention Time, and our model does not require any experimental data. Further validation of the model performance was done across different functional groups, and it showed very high accuracy, especially for compounds that contain aliphatic OH groups. The results have indicated that OWPCP provides a reliable prediction of logP.
A Methodology for Transformer Ratio Adjustment in Small-Size Rotary Transformers
Authors: Saeed Hajmohammadi, MohammadSadegh KhajueeZadeh, Farid Tootoonchian, Sajjad Mohammadi
Date: 2024-10-23 00:00:00

This study addresses a neglected challenge that has been hidden in the Rotary Transformer (RT) field: the possibility of a discrepancy between transformer ratio and turn number ratio in small-size transformers. Previous investigations have shown that in the geometry design of RTs, as well as their resonant circuit design, the transformer ratio has been regarded as the same as the turn number ratio. This estimation is logical and true when a large-size RT is investigated. However, in small-size RTs, the magnitudes of leakage and magnetization inductances are significantly close, which leads to a difference between transformer ratio and turn number ratio. Accordingly, the absence of an exact methodology for transformer ratio calculation brought us to conduct this investigation. In this regard, a transformer ratio adjustment is suggested after proposing a low-error magnetic model. Its accuracy is high enough to consider different air gaps and subsequently calculate inductance with reference to 3D finite element analysis (3D-FEA). Finally, we take advantage of a test bench to show the exactness and proficiency of the suggested transformer ratio adjustment.
Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
Authors: Gibaeg Kim, SangHun Im, Heung-Seon Oh
Date: 2024-08-13 00:00:00
Proceeding: Findings of the Association for Computational Linguistics ACL 2024 8
Tasks: Hierarchical Multi-label Classification, Multi-Label Classification, text-classification, Text Classification

Hierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance. Recently, the unit-based approach generating unit-based feature representations has outperformed the global approach focusing on a global feature representation. Nevertheless, unit-based models using BCE and ZLPR losses still face static thresholding and label imbalance challenges. Those challenges become more critical in large-scale hierarchies. This paper introduces a novel hierarchy-aware loss function for unit-based HTC models: Hierarchy-aware Biased Bound Margin (HBM) loss. HBM integrates learnable bounds, biases, and a margin to address static thresholding and mitigate label imbalance adaptively. Experimental results on benchmark datasets demonstrate the superior performance of HBM compared to competitive HTC models.
Improved User Identification through Calibrated Monte-Carlo Dropout
Authors: Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlström
Date: 2024-09-29 00:00:00
Proceeding: Knowledge-Based Systems 2024 9
Tasks: Classifier calibration, Uncertainty Quantification, User Identification

This paper presents an enhanced approach to user identification using smartphone and wearable sensor data. Our methodology involves segmenting input data and independently analyzing subsequences with CNNs. During testing, we apply calibrated Monte-Carlo Dropout to measure prediction uncertainty. By leveraging the weights obtained from uncertainty quantification, we integrate the results through weighted averaging, thereby improving overall identification accuracy. The main motivation behind this paper is the need to calibrate the CNN for improved weighted averaging. It has been observed that incorrect predictions often receive high confidence, while correct predictions are assigned lower confidence. To tackle this issue, we have implemented the Ensemble of Near Isotonic Regression (ENIR) as an advanced calibration technique. This ensures that certainty scores more accurately reflect the true likelihood of correctness. Furthermore, our experiment shows that calibrating CNN reduces the need for Monte Carlo samples in uncertainty quantification, thereby reducing computational costs. Our thorough evaluation and comparison of different calibration methods have shown improved accuracy in user identification across multiple datasets. Our results showed notable performance improvements when compared to the latest models available. In particular, our approach achieved better results than DB2 by 1.12% and HAR by 0.3% in accuracy.
Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design
Authors: Henrik Ebel, Jan van Delden, Timo Lüddecke, Aditya Borse, Rutwik Gulakala, Marcus Stoffel, Manish Yadav, Merten Stender, Leon Schindler, Kristin Miriam de Payrebrune, Maximilian Raff, C. David Remy, Benedict Röder, Rohit Raj, Tobias Rentschler, Alexander Tismer, Stefan Riedelbauch, Peter Eberhard
Date: 2024-10-07 00:00:00

Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., in areas such as data-driven modeling, control and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems, e.g., in mechanics and dynamics, where design so far requires a lot of specialized knowledge. However, research-wise, compared to established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Possible ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice. The analysis, discussions, and examples are based on the research experience made in a priority program of the German research foundation focusing on research on artificially intelligent design assistants in mechanics and dynamics.
Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges
Authors: Handi Chen, Weipeng Deng, Shuo Yang, Jinfeng Xu, Zhihan Jiang, Edith C. H. Ngai, Jiangchuan Liu, Xue Liu
Date: 2024-10-16 00:00:00
Tasks: Survey

Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by leveraging the computational capabilities of edge networks. The integration of Large Language Models (LLMs) empowers EI to evolve into the next stage: Edge General Intelligence (EGI), enabling more adaptive and versatile applications that require advanced understanding and reasoning capabilities. However, systematic exploration in this area remains insufficient. This survey delineates the distinctions between EGI and traditional EI, categorizing LLM-empowered EGI into three conceptual systems: centralized, hybrid, and decentralized. For each system, we detail the framework designs and review existing implementations. Furthermore, we evaluate the performance and throughput of various Small Language Models (SLMs) that are more suitable for development on edge devices. This survey provides researchers with a comprehensive vision of EGI, offering insights into its vast potential and establishing a foundation for future advancements in this rapidly evolving field.
Efficient Adaptive Federated Optimization
Authors: Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer, Tian Li
Date: 2024-10-10 00:00:00
Tasks: Federated Learning

Adaptive optimization is critical in federated learning, where enabling adaptivity on both the server and client sides has proven essential for achieving optimal performance. However, the scalability of such jointly adaptive systems is often hindered by resource limitations in communication and memory. In this paper, we introduce a class of efficient adaptive algorithms, named $FedAda^2$ and its enhanced version $FedAda^2$++, designed specifically for large-scale, cross-device federated environments. $FedAda^2$ optimizes communication efficiency by avoiding the transfer of preconditioners between the server and clients. Additionally, $FedAda^2$++ extends this approach by incorporating memory-efficient adaptive optimizers on the client side, further reducing on-device memory usage. Theoretically, we demonstrate that $FedAda^2$ and $FedAda^2$++ achieve the same convergence rates for general, non-convex objectives as its more resource-intensive counterparts that directly integrate joint adaptivity. Extensive empirical evaluations on image and text datasets demonstrate both the advantages of joint adaptivity and the effectiveness of $FedAda^2$/$FedAda^2$++.
Scalable Co-Clustering for Large-Scale Data through Dynamic Partitioning and Hierarchical Merging
Authors: Zihan Wu, Zhaoke Huang, Hong Yan
Date: 2024-10-09 00:00:00
Tasks: Clustering, Computational Efficiency

Co-clustering simultaneously clusters rows and columns, revealing more fine-grained groups. However, existing co-clustering methods suffer from poor scalability and cannot handle large-scale data. This paper presents a novel and scalable co-clustering method designed to uncover intricate patterns in high-dimensional, large-scale datasets. Specifically, we first propose a large matrix partitioning algorithm that partitions a large matrix into smaller submatrices, enabling parallel co-clustering. This method employs a probabilistic model to optimize the configuration of submatrices, balancing the computational efficiency and depth of analysis. Additionally, we propose a hierarchical co-cluster merging algorithm that efficiently identifies and merges co-clusters from these submatrices, enhancing the robustness and reliability of the process. Extensive evaluations validate the effectiveness and efficiency of our method. Experimental results demonstrate a significant reduction in computation time, with an approximate 83% decrease for dense matrices and up to 30% for sparse matrices.
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers
Authors: Diogo Reis Santos, Albert Sund Aillet, Antonio Boiano, Usevalad Milasheuski, Lorenzo Giusti, Marco Di Gennaro, Sanaz Kianoush, Luca Barbieri, Monica Nicoli, Michele Carminati, Alessandro E. C. Redondi, Stefano Savazzi, Luigi Serio
Date: 2024-10-02 00:00:00
Tasks: Decision Making, Federated Learning, Management

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.
Gesture2Text: A Generalizable Decoder for Word-Gesture Keyboards in XR Through Trajectory Coarse Discretization and Pre-training
Authors: Junxiao Shen, Khadija Khaldi, Enmin Zhou, Hemant Bhaskar Surale, Amy Karlson
Date: 2024-10-08 00:00:00
Tasks: Decoder, Quantization, Template Matching

Text entry with word-gesture keyboards (WGK) is emerging as a popular method and becoming a key interaction for Extended Reality (XR). However, the diversity of interaction modes, keyboard sizes, and visual feedback in these environments introduces divergent word-gesture trajectory data patterns, thus leading to complexity in decoding trajectories into text. Template-matching decoding methods, such as SHARK^2, are commonly used for these WGK systems because they are easy to implement and configure. However, these methods are susceptible to decoding inaccuracies for noisy trajectories. While conventional neural-network-based decoders (neural decoders) trained on word-gesture trajectory data have been proposed to improve accuracy, they have their own limitations: they require extensive data for training and deep-learning expertise for implementation. To address these challenges, we propose a novel solution that combines ease of implementation with high decoding accuracy: a generalizable neural decoder enabled by pre-training on large-scale coarsely discretized word-gesture trajectories. This approach produces a ready-to-use WGK decoder that is generalizable across mid-air and on-surface WGK systems in augmented reality (AR) and virtual reality (VR), which is evident by a robust average Top-4 accuracy of 90.4% on four diverse datasets. It significantly outperforms SHARK^2 with a 37.2% enhancement and surpasses the conventional neural decoder by 7.4%. Moreover, the Pre-trained Neural Decoder's size is only 4 MB after quantization, without sacrificing accuracy, and it can operate in real-time, executing in just 97 milliseconds on Quest 3.
TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices
Authors: Rahatara Ferdousi, M. Anwar Hossain, Abdulmotaleb El Saddik
Date: 2024-10-07 00:00:00
Tasks: Image Generation, Texture Synthesis

Texture image generation has been studied for various applications, including gaming and entertainment. However, context-specific realistic texture generation for industrial applications, such as generating defect textures on railway components, remains unexplored. A mobile-friendly, LLM-based tool that generates fine-grained defect characteristics offers a solution to the challenge of understanding the impact of defects from actual occurrences. We introduce TextureMeDefect, an innovative tool leveraging an LLM-based AI-Inferencing engine. The tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. We conducted a multifaceted evaluation to assess the relevance of the generated texture, time, and cost in using this tool on iOS and Android platforms. We also analyzed the software usability score (SUS) across three scenarios. TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster, showcasing the potential of AI-driven mobile applications on consumer-grade devices.
Sliding DFT-based Signal Recovery for Modulo ADC with 1-bit Folding Information
Authors: Neil Irwin Bernardo
Date: 2024-10-24 00:00:00
Tasks: Quantization

The modulo analog-to-digital converter (ADC) is a promising solution to resolve the limited dynamic range (DR) issue of conventional ADCs and achieve an enhanced digital resolution given a fixed quantization bit budget. However, a modulo ADC requires an unfolding scheme to correct the nonlinear distortion introduced by the modulo operation. This paper presents a sliding discrete Fourier Transform (DFT)-based method for fast signal reconstruction given the modulo ADC output sequence and a 1-bit folding information sequence. In contrast to existing DFT-based signal recovery techniques for modulo ADCs, our proposed sliding DFT method reduces the required observation time and minimizes the spectral leakage effects via proper choice of window function parameters. A mean squared error (MSE) performance guarantee is established for the proposed signal recovery algorithm. More precisely, we derive sufficient conditions for the oversampling factor ($\mathrm{OF}$) and the number of quantization bits ($b$) to obtain a specific MSE performance. Our numerical results demonstrate that modulo ADCs equipped with our proposed recovery method can outperform conventional ADCs without modulo for $\mathrm{OF} \geq 4$ and $b \geq 4$. The impact of spectral leakage on the MSE performance of the proposed sliding DFT recovery method is also quantified.
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles
Authors: Rui Du, Kai Zhao, Jinlong Hou, Qiang Zhang, Peter Zhang
Date: 2024-10-09 00:00:00
Tasks: Autonomous Vehicles, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Coordination among connected and autonomous vehicles (CAVs) is advancing due to developments in control and communication technologies. However, much of the current work is based on oversimplified and unrealistic task-specific assumptions, which may introduce vulnerabilities. This is critical because CAVs not only interact with their environment but are also integral parts of it. Insufficient exploration can result in policies that carry latent risks, highlighting the need for methods that explore the environment both extensively and efficiently. This work introduces OPTIMA, a novel distributed reinforcement learning framework for cooperative autonomous vehicle tasks. OPTIMA alternates between thorough data sampling from environmental interactions and multi-agent reinforcement learning algorithms to optimize CAV cooperation, emphasizing both safety and efficiency. Our goal is to improve the generality and performance of CAVs in highly complex and crowded scenarios. Furthermore, the industrial-scale distributed training system easily adapts to different algorithms, reward functions, and strategies.
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception
Authors: Ahmad M. Nazar, Abdulkadir Celik, Mohamed Y. Selim, Asmaa Abdallah, Daji Qiao, Ahmed M. Eltawil
Date: 2024-10-08 00:00:00
Tasks: RAG, Retrieval-augmented Generation

Large language models (LLMs) hold significant promise in advancing network management and orchestration in 6G and beyond networks. However, existing LLMs are limited in domain-specific knowledge and their ability to handle multi-modal sensory data, which is critical for real-time situational awareness in dynamic wireless environments. This paper addresses this gap by introducing ENWAR, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework. ENWAR seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness. ENWAR is evaluated on the GPS, LiDAR, and camera modality combinations of DeepSense6G dataset with state-of-the-art LLMs such as Mistral-7b/8x7b and LLaMa3.1-8/70/405b. Compared to general and often superficial environmental descriptions of these vanilla LLMs, ENWAR delivers richer spatial analysis, accurately identifies positions, analyzes obstacles, and assesses line-of-sight between vehicles. Results show that ENWAR achieves key performance indicators of up to 70% relevancy, 55% context recall, 80% correctness, and 86% faithfulness, demonstrating its efficacy in multi-modal perception and interpretation.
A frequency-domain approach for estimating continuous-time diffusively coupled linear networks
Authors: Desen Liang, E. M. M., Kivits, Maarten Schoukens, Paul M. J. Van den Hof
Date: 2024-10-24 00:00:00

This paper addresses the problem of consistently estimating a continuous-time (CT) diffusively coupled network (DCN) to identify physical components in a physical network. We develop a three-step frequency-domain identification method for linear CT DCNs that allows to accurately recover all the physical component values of the network while exploiting the particular symmetric structure in a DCN model. This method uses the estimated noise covariance as a non-parametric noise model to minimize variance of the parameter estimates, obviating the need to select a parametric noise model. Moreover, this method is extended to subnetworks identification, which enables identifying the local dynamics in DCNs on the basis of partial measurements. The method is illustrated with an application from In-Circuit Testing of printed circuit boards. Experimental results highlight the method's ability to consistently estimate component values in a complex network with only a single excitation.