pwc-archive/papers-with-abstracts huggingface dataset:
Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides
Authors: Wanling Gao, Jun Zhao, Jianfeng Gui, Zehan Wang, Jie Chen*, Zhenyu Yue*
Date: 2024-12-24 00:00:00
Proceeding: Bioinformatics 2024 12
Tasks: Prediction, Protein Language Model

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models. Here, we provide a comprehensive survey of current BERT-based methods for AMP prediction. An independent benchmark test data set is constructed to evaluate the predictive capabilities of the surveyed tools. Furthermore, we compared the predictive performance of these computational methods based on six different AMP public databases. LM_pred (BFD) outperformed all other surveyed tools due to abundant pretraining data set and the unique vector embedding approach. To avoid the impact of varying training data sets used by different methods on prediction performance, we performed the 5-fold cross-validation experiments using the same data set, involving retraining. Additionally, to explore the applicability and generalization ability of the models, we constructed a short peptide data set and an external data set to test the retrained models. Although these prediction methods based on BERT can achieve good prediction performance, there is still room for improvement in recognition accuracy. With the continuous enhancement of protein language model, we proposed an AMP prediction method based on the ESM-2 pretrained model called iAMP-bert. Experimental results demonstrate that iAMP-bert outperforms other approaches. iAMP-bert is freely accessible to the public at http://iamp.aielab.cc/.
PepMNet: a hybrid deep learning model for predicting peptide properties using hierarchical graph representations
Authors: Daniel Garzon Otero ORCID logo, Omid Akbari and Camille Bilodeau
Date: 2024-12-11 00:00:00
Proceeding: Royal Society of Chemistry 2024 12
Tasks: Drug Design, Property Prediction, Robust classification

Peptides are a powerful class of molecules that can be applied to a range of problems including biomaterials development and drug design. Currently, machine learning-based property prediction models for peptides primarily rely on amino acid sequence, resulting in two key limitations: first, they are not compatible with non-natural peptide features like modified sidechains or staples, and second, they use human-crafted features to describe the relationships between different amino acids, which reduces the model's flexibility and generalizability. To address these challenges, we have developed PepMNet, a deep learning model that integrates atom-level and amino acid-level information through a hierarchical graph approach. The model first learns from an atom-level graph and then generates amino acid representations based on the atomic information captured in the first stage. These amino acid representations are then combined using graph convolutions on an amino acid-level graph to produce a molecular-level representation, which is then passed to a fully connected neural network for property prediction. We evaluated this architecture by predicting two peptide properties: chromatographic retention time (RT) as a regression task and antimicrobial peptide (AMP) activity as a classification task. For the regression task, PepMNet achieved an average R2 of 0.980 across eight datasets, which spanned different dataset sizes and three liquid chromatography (LC) methods. For the classification task, we developed an ensemble of five models to reduce overfitting and ensure robust classification performance, achieving an area under the receiver operating curve (AUC-ROC) of 0.978 and an average precision of 0.981. Overall, our model illustrates the potential for hierarchical deep learning models to learn peptide properties without relying on human engineering amino acid features.
E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides
Authors: Si-Cheng Wang
Date: 2024-05-09 00:00:00
Proceeding: PLOS ONE 2024 5
Tasks: Ensemble Learning

With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier). By considering multiple features, including amino acid composition (AAC) and pseudo amino acid composition (PseAAC) of antimicrobial peptides, we aimed to improve the accuracy and generalization ability of the identification process. To validate the superiority of our model, we employed five-fold cross-validation and compared it with other commonly used methods for antimicrobial peptide identification. In the experimental results on an independent test set, E-CLEAP achieved accuracies of 97.33% and 84% for the AAC and PseAAC features, respectively. The results demonstrated that our model outperformed other methods in all evaluation metrics. The findings of this study highlight the potential of the E-CLEAP model in enhancing the efficiency and accuracy of antimicrobial peptide screening, which holds significant implications for drug development, disease treatment, and biotechnology advancement. Future research can further optimize the model by incorporating additional features and information, as well as validating its reliability on larger datasets and in real-world environments. The source code and all datasets are publicly available at https://github.com/Wangsicheng52/E-CLEAP
The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark
Authors: Qinghua Liu, John Paparrizos
Date: 2024-09-26 00:00:00
Proceeding: NeurIPS D&B 2024 9
Tasks: Anomaly Detection, Benchmarking, Time Series, Time Series Anomaly Detection

Time-series anomaly detection is a fundamental task across scientific fields and industries. However, the field has long faced the ``elephant in the room:'' critical issues including flawed datasets, biased evaluation measures, and inconsistent benchmarking practices that have remained largely ignored and unaddressed. We introduce the TSB-AD to systematically tackle these issues in the following three aspects: (i) Dataset Integrity: with 1070 high-quality time series from a diverse collection of 40 datasets (doubling the size of the largest collection and four times the number of existing curated datasets), we provide the first large-scale, heterogeneous, meticulously curated dataset that combines the effort of human perception and model interpretation; (ii) Measure Reliability: by revealing issues and biases in evaluation measures, we identify the most reliable and accurate measure, namely, VUS-PR for anomaly detection in time series to address concerns from the community; and (iii) Comprehensive Benchmarking: with a broad spectrum of 40 detection algorithms, from statistical methods to the latest foundation models, we perform a comprehensive evaluation that includes a thorough hyperparameter tuning and a unified setup for a fair and reproducible comparison. Our findings challenge the conventional wisdom regarding the superiority of advanced neural network architectures, revealing that simpler architectures and statistical methods often yield better performance. The promising performance of neural networks on multivariate cases and foundation models on point anomalies highlights the need for further advancements in these methods. We open-source the benchmark at https://github.com/TheDatumOrg/TSB-AD to promote further research.
Towards adversarial robustness verification of no-reference image-and video-quality metrics
Authors: Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin
Date: 2023-12-30 00:00:00
Proceeding: Computer Vision and Image Understanding 2023 12
Tasks: Adversarial Attack, Adversarial Robustness, No-Reference Image Quality Assessment

In this paper, we propose a new method of analysing the stability of modern deep image- and video-quality metrics to different adversarial attacks. The stability analysis of quality metrics is becoming important because nowadays the majority of metrics employ neural networks. Unlike traditional quality metrics based on nature scene statistics or other hand-crafter features, learning-based methods are more vulnerable to adversarial attacks. The usage of such unstable metrics in benchmarks may lead to being exploited by the developers of image and video processing algorithms to achieve higher positions in leaderboards. The majority of known adversarial attacks on images designed for computer vision tasks are not fast enough to be used within real-time video processing algorithms. We propose four fast attacks on metrics suitable for real-life scenarios. The proposed methods are based on creating perturbations that increase metrics scores and can be applied frame-by-frame to attack videos. We analyse the stability of seven widely used no-reference image- and video-quality metrics to proposed attacks. The results showed that only three metrics are stable against our real-life attacks. This research yields insights to further aid in designing stable neural-network-based no-reference quality metrics. Proposed attacks can serve as an additional verification of metrics’ reliability.
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
Authors: Jinxiang Lai, Wenlong Wu, Jiawei Zhan, Jian Li, Bin-Bin Gao, Jun Liu, Jie Zhang, Song Guo
Date: 2025-04-07 00:00:00
Tasks: Box-supervised Instance Segmentation, Instance Segmentation, Semantic Segmentation

Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-student framework. Building upon this foundation, we propose a BoxSeg framework involving two novel and general modules named the Quality-Aware Module (QAM) and the Peer-assisted Copy-paste (PC). The QAM obtains high-quality pseudo masks and better measures the mask quality to help reduce the effect of noisy masks, by leveraging the quality-aware multi-mask complementation mechanism. The PC imitates Peer-Assisted Learning to further improve the quality of the low-quality masks with the guidance of the obtained high-quality pseudo masks. Theoretical and experimental analyses demonstrate the proposed QAM and PC are effective. Extensive experimental results show the superiority of our BoxSeg over the state-of-the-art methods, and illustrate the QAM and PC can be applied to improve other models.
Uniform Convergence of Lipschitz Functions with Dependent Gaussian Samples
Authors: Mina Sadat Mahmoudi, Saeed Foroutan, Seyed Abolfazl Motahari, Babak Khalaj
Date: 2025-04-11 00:00:00
Proceeding: ICASSP 2025 4

In many practical learning problems, training samples are not i.i.d., and there is an intrinsic dependency among samples. Therefore, theoretical study of learning with dependent data has recently gained attention. In this paper, we provide a uniform convergence bound for the class of Lipschitz functions with bounded values at zero, under the assumption that the data samples are scalar and have a possibly dependent joint Gaussian distribution. Since other than Lipschitzness, there is no heavy assumption such as convexity or boundedness on the function class, the results are applicable for many practical models including neural networks. We showcase the strength and applicability of our theorems by numerical simulation and real-data analysis.
Not All Data Are Unlearned Equally
Authors: Aravind Krishnan, Siva Reddy, Marius Mosbach
Date: 2025-04-07 00:00:00
Tasks: All, Machine Unlearning

Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context of large language models (LLMs), unlearning has recently received increased attention, particularly for removing knowledge about named entities from models for privacy purposes. While various approaches have been proposed to address the unlearning problem, most existing approaches treat all data points to be unlearned equally, i.e., unlearning that Montreal is a city in Canada is treated exactly the same as unlearning the phone number of the first author of this paper. In this work, we show that this all data is equal assumption does not hold for LLM unlearning. We study how the success of unlearning depends on the frequency of the knowledge we want to unlearn in the pre-training data of a model and find that frequency strongly affects unlearning, i.e., more frequent knowledge is harder to unlearn. Additionally, we uncover a misalignment between probability and generation-based evaluations of unlearning and show that this problem worsens as models become larger. Overall, our experiments highlight the need for better evaluation practices and novel methods for LLM unlearning that take the training data of models into account.
Resurrecting Socrates in the Age of AI: A Study Protocol for Evaluating a Socratic Tutor to Support Research Question Development in Higher Education
Authors: Ben Degen
Date: 2025-04-05 00:00:00
Tasks: Chatbot, Experimental Design, Learning Theory

Formulating research questions is a foundational yet challenging academic skill, one that generative AI systems often oversimplify by offering instant answers at the expense of student reflection. This protocol lays out a study grounded in constructivist learning theory to evaluate a novel AI-based Socratic Tutor, designed to foster cognitive engagement and scaffold research question development in higher education. Anchored in dialogic pedagogy, the tutor engages students through iterative, reflective questioning, aiming to promote System 2 thinking and counteract overreliance on AI-generated outputs. In a quasi-experimental design, approximately 80 German pre-service biology teacher students will be randomly assigned to one of two groups: an AI Socratic Tutor condition and an uninstructed chatbot control. Across multiple cycles, students are expected to formulate research questions based on background texts, with quality assessed through double-blind expert review. The study also examines transfer of skills to novel phenomena and captures student perceptions through mixed-methods analysis, including surveys, interviews and reflective journals. This study aims to advance the understanding of how generative AI can be pedagogically aligned to support, not replace, human cognition and offers design principles for human-AI collaboration in education.
Demo Abstract: Capuchin: A Neural Network Model Generator for 16-bit Microcontrollers
Authors: Le Zhang, Yubo Luo, Shahriar Nirjon
Date: 2022-05-04 00:00:00
Proceeding: ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2022 5
Tasks: Deep Learning

Resource-optimized deep neural networks (DNNs) nowadays run on microcontrollers to perform a wide variety of audio, image and sensor data classification tasks. Despite comprehensive support for deep learning tools for 32-bit microcontrollers, performing deep learning inferences on 16-bit microcontrollers still remains a chal-lenge. Although there are some tools for implementing neural net-works on 16-bit systems, generally, there is a large gap in efficiency between the development tools for 16-bit microcontrollers and 32-bit (or higher) systems. There is also a steep learning curve that discourages beginners inexperienced with microcontrollers and programming in C to develop efficient and effective deep learning models for 16-bit microcontrollers. To fill this gap, we have created a neural network model generator that (1) automatically transfers parameters of a pre-trained DNN or CNN model from commonly used frameworks to a 16-bit microcontroller, and (2) automatically implements the model on the microcontroller to perform on-device inference. The optimization of data transfer saves time and mini-mizes chances of error, and the automatic implementation reduces the complexity to implement DNNs and CNNs on ultra-low-power microcontrollers.
Dr Web: a modern, query-based web data retrieval engine
Authors: Ylli Prifti, Alessandro Provetti, Pasquale De Meo
Date: 2025-02-18 00:00:00
Tasks: Retrieval

This article introduces the Data Retrieval Web Engine (also referred to as doctor web), a flexible and modular tool for extracting structured data from web pages using a simple query language. We discuss the engineering challenges addressed during its development, such as dynamic content handling and messy data extraction. Furthermore, we cover the steps for making the DR Web Engine public, highlighting its open source potential.
Modular Control of Discrete Event System for Modeling and Mitigating Power System Cascading Failures
Authors: Wasseem Al-Rousan, Caisheng Wang, Feng Lin
Date: 2025-04-10 00:00:00

Cascading failures in power systems caused by sequential tripping of components are a serious concern as they can lead to complete or partial shutdowns, disrupting vital services and causing damage and inconvenience. In prior work, we developed a new approach for identifying and preventing cascading failures in power systems. The approach uses supervisory control technique of discrete event systems (DES) by incorporating both on-line lookahead control and forcible events. In this paper, we use modular supervisory control of DES to reduce computation complexity and increase the robustness and reliability of control. Modular supervisory control allows us to predict and mitigate cascading failures in power systems more effectively. We implemented the proposed control technique on a simulation platform developed in MATLAB and applied the proposed DES controller. The calculations of modular supervisory control of DES are performed using an external tool and imported into the MATLAB platform. We conduct simulation studies for the IEEE 30-bus, 118-bus and 300-bus systems, and the results demonstrate the effectiveness of our proposed approach.
Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications
Authors: Adithya Krishna, Sohan Debnath, André van Schaik, Mahesh Mehendale, Chetan Singh Thakur
Date: 2025-04-09 00:00:00
Tasks: Edge-computing

High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces (BCIs) utilizing high-density intracortical recordings with hundreds or thousands of electrodes. However, transmitting raw neural data presents significant challenges due to limited communication bandwidth and resultant excessive heating. To address this challenge, we propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs). The CAE encoder section is implemented on RAMAN, an energy-efficient tinyML accelerator designed for edge computing, and subsequently deployed on an Efinix Ti60 FPGA with 37.3k LUTs and 8.6k register utilization. RAMAN leverages sparsity in activation and weights through zero skipping, gating, and weight compression techniques. Additionally, we employ hardware-software co-optimization by pruning CAE encoder model parameters using a hardware-aware balanced stochastic pruning strategy, resolving workload imbalance issues and eliminating indexing overhead to reduce parameter storage requirements by up to 32.4%. Using the proposed compact depthwise separable convolutional autoencoder (DS-CAE) model, the compressed neural data from RAMAN is reconstructed offline with superior signal-to-noise and distortion ratios (SNDR) of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings.
Beyond Moore's Law: Harnessing the Redshift of Generative AI with Effective Hardware-Software Co-Design
Authors: Amir Yazdanbakhsh
Date: 2025-04-09 00:00:00
Tasks: Philosophy

For decades, Moore's Law has served as a steadfast pillar in computer architecture and system design, promoting a clear abstraction between hardware and software. This traditional Moore's computing paradigm has deepened the rift between the two, enabling software developers to achieve near-exponential performance gains often without needing to delve deeply into hardware-specific optimizations. Yet today, Moore's Law -- with its once relentless performance gains now diminished to incremental improvements -- faces inevitable physical barriers. This stagnation necessitates a reevaluation of the conventional system design philosophy. The traditional decoupled system design philosophy, which maintains strict abstractions between hardware and software, is increasingly obsolete. The once-clear boundary between software and hardware is rapidly dissolving, replaced by co-design. It is imperative for the computing community to intensify its commitment to hardware-software co-design, elevating system abstractions to first-class citizens and reimagining design principles to satisfy the insatiable appetite of modern computing. Hardware-software co-design is not a recent innovation. To illustrate its historical evolution, I classify its development into five relatively distinct ``epochs''. This post also highlights the growing influence of the architecture community in interdisciplinary teams -- particularly alongside ML researchers -- and explores why current co-design paradigms are struggling in today's computing landscape. Additionally, I will examine the concept of the ``hardware lottery'' and explore directions to mitigate its constraining influence on the next era of computing innovation.
Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection
Authors: Jon Gutiérrez Zaballa, Koldo Basterretxea, Javier Echanobe
Date: 2025-04-07 00:00:00
Tasks: Autonomous Driving, Decoder, Quantization, Semantic Segmentation

Machine learning-based embedded systems for safety-critical applications, such as aerospace and autonomous driving, must be robust to perturbations caused by soft errors. As transistor geometries shrink and voltages decrease, modern electronic devices become more susceptible to background radiation, increasing the concern about failures produced by soft errors. The resilience of deep neural networks (DNNs) to these errors depends not only on target device technology but also on model structure and the numerical representation and arithmetic precision of their parameters. Compression techniques like pruning and quantization, used to reduce memory footprint and computational complexity, alter both model structure and representation, affecting soft error robustness. In this regard, although often overlooked, the choice of activation functions (AFs) impacts not only accuracy and trainability but also compressibility and error resilience. This paper explores the use of bounded AFs to enhance robustness against parameter perturbations, while evaluating their effects on model accuracy, compressibility, and computational load with a technology-agnostic approach. We focus on encoder-decoder convolutional models developed for semantic segmentation of hyperspectral images with application to autonomous driving systems. Experiments are conducted on an AMD-Xilinx's KV260 SoM.