pwc-archive/papers-with-abstracts huggingface dataset:
Parapred: antibody paratope prediction using convolutional and recurrent neural networks
Authors: Edgar Liberis1, Petar Velickovic, Pietro Sormanni, Michele Vendruscolo, Pietro Lio
Date: 2018-04-16 00:00:00
Proceeding: Bioinformatics 2018 4
Tasks: Antibody-antigen binding prediction

Motivation: Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). Results: In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. Availability and implementation: The Parapred method is freely available as a webserver at http:// www-mvsoftware.ch.cam.ac.uk/and for download at https://github.com/eliberis/parapred. Contact: el398@cam.ac.uk or ps589@cam.ac.uk Supplementary information: Supplementary information is available at Bioinformatics online.
Learning context-aware structural representations to predict antigen and antibody binding interfaces
Authors: Srivamshi Pittala, Chris Bailey-Kellogg
Date: 2020-04-22 00:00:00
Proceeding: Bioinformatics 2020 4
Tasks: Antibody-antigen binding prediction, Transfer Learning

Motivation
Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the ‘ground truth’ of antibody–antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens.

Results
Our framework leverages three key aspects of antibody–antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody–antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein–protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody–antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.

Availability and implementation
The source code is freely available on github at https://github.com/vamships/PECAN.git.
Semantic Learning for Molecular Communication in Internet of Bio-Nano Things
Authors: Hanlin Cai, Ozgur B. Akan
Date: 2025-02-12 00:00:00
Tasks: Decoder, Diagnostic

Molecular communication (MC) provides a foundational framework for information transmission in the Internet of Bio-Nano Things (IoBNT), where efficiency and reliability are crucial. However, the inherent limitations of molecular channels, such as low transmission rates, noise, and intersymbol interference (ISI), limit their ability to support complex data transmission. This paper proposes an end-to-end semantic learning framework designed to optimize task-oriented molecular communication, with a focus on biomedical diagnostic tasks under resource-constrained conditions. The proposed framework employs a deep encoder-decoder architecture to efficiently extract, quantize, and decode semantic features, prioritizing taskrelevant semantic information to enhance diagnostic classification performance. Additionally, a probabilistic channel network is introduced to approximate molecular propagation dynamics, enabling gradient-based optimization for end-to-end learning. Experimental results demonstrate that the proposed semantic framework improves diagnostic accuracy by at least 25% compared to conventional JPEG compression with LDPC coding methods under resource-constrained communication scenarios.
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
Authors: Lucien F. Krapp, Luciano A. Abriata, Fabio Cortés Rodriguez, Matteo Dal Peraro
Date: 2023-04-18 00:00:00
Proceeding: Nature Communications 2023 4
Tasks: Antibody-antigen binding prediction

Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names. The resulting model—the Protein Structure Transformer, PeSTo—surpasses the current state of the art in predicting protein-protein interfaces and can also predict and differentiate between interfaces involving nucleic acids, lipids, ions, and small molecules with high confidence. Its low computational cost enables processing high volumes of structural data, such as molecular dynamics ensembles allowing for the discovery of interfaces that remain otherwise inconspicuous in static experimentally solved structures. Moreover, the growing foldome provided by de novo structural predictions can be easily analyzed, providing new opportunities to uncover unexplored biology.
Antibody interface prediction with 3D Zernike descriptors and SVM
Authors: Sebastian Daberdaku, Carlo Ferrari
Date: 2018-10-05 00:00:00
Proceeding: Bioinformatics 2018 10
Tasks: Antibody-antigen binding prediction, Binary Classification, Diagnostic, Prediction

Motivation
Antibodies are a class of proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. This binding malleability makes them the most valuable category of biopharmaceuticals for both diagnostic and therapeutic applications. The correct identification of the antigen-binding residues in the antibody is crucial for all antibody design and engineering techniques and could also help to understand the complex antigen binding mechanisms. However, the antibody-binding interface prediction field appears to be still rather underdeveloped.

Results
We present a novel method for antibody interface prediction from their experimentally solved structures based on 3D Zernike Descriptors. Roto-translationally invariant descriptors are computed from circular patches of the antibody surface enriched with a chosen subset of physico-chemical properties from the AAindex1 amino acid index set, and are used as samples for a binary classification problem. An SVM classifier is used to distinguish interface surface patches from non-interface ones. The proposed method was shown to outperform other antigen-binding interface prediction software.

Availability and implementation
Linux binaries and Python scripts are available at https://github.com/sebastiandaberdaku/AntibodyInterfacePrediction. The datasets generated and/or analyzed during the current study are available at https://doi.org/10.6084/m9.figshare.5442229.

Supplementary information
Supplementary data are available at Bioinformatics online.
Exploring the Role of Node Diversity in Directed Graph Representation Learning
Authors: Jincheng Huang, Yujie Mo, Ping Hu, Xiaoshuang Shi, Shangbo Yuan, Zeyu  Zhang, Xiaofeng Zhu
Date: 2024-07-25 00:00:00
Proceeding: IJCAI 2024 7
Tasks: Diversity, Graph Representation Learning, Link Prediction, Representation Learning

Manymethods of Directed Graph Neural Networks
 (DGNNs) are designed to equally treat nodes in
 the same neighbor set (i.e., out-neighbor set and
 in-neighbor set) for every node, without consider
ing the node diversity in directed graphs, so they
 are often unavailable to adaptively acquire suitable
 information from neighbors of different directions.
 To alleviate this issue, in this paper, we investigate
 a new way to first consider node diversity for rep
resentation learning on directed graphs, i.e., neigh
bor diversity and degree diversity, and then propose
 a new NDDGNN framework to adaptively assign
 weights to both outgoing information and incom
ing information at the node level. Extensive ex
periments on seven real-world datasets validate the
 superior performance of our method compared to
 state-of-the-art methods in terms of both node clas
sification and link prediction tasks.
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation
Authors: Lingkun Luo, Shiqiang Hu, Liming Chen
Date: 2025-02-10 00:00:00
Tasks: Decision Making, Domain Adaptation, image-classification, Image Classification

In domain adaptation (DA), the effectiveness of deep learning-based models is often constrained by batch learning strategies that fail to fully apprehend the global statistical and geometric characteristics of data distributions. Addressing this gap, we introduce 'Global Awareness Enhanced Domain Adaptation' (GAN-DA), a novel approach that transcends traditional batch-based limitations. GAN-DA integrates a unique predefined feature representation (PFR) to facilitate the alignment of cross-domain distributions, thereby achieving a comprehensive global statistical awareness. This representation is innovatively expanded to encompass orthogonal and common feature aspects, which enhances the unification of global manifold structures and refines decision boundaries for more effective DA. Our extensive experiments, encompassing 27 diverse cross-domain image classification tasks, demonstrate GAN-DA's remarkable superiority, outperforming 24 established DA methods by a significant margin. Furthermore, our in-depth analyses shed light on the decision-making processes, revealing insights into the adaptability and efficiency of GAN-DA. This approach not only addresses the limitations of existing DA methodologies but also sets a new benchmark in the realm of domain adaptation, offering broad implications for future research and applications in this field.
A Real-Time Human Pose Measurement System for Human-In-The-Loop Dynamic Simulators
Authors: Giulietti N., Todesca D., Carnevale M., Giberti H.
Date: 2025-02-04 00:00:00
Proceeding: IEEE Access 2025 2
Tasks: 3D Human Pose Estimation, 3D Human Pose Tracking, 3D Reconstruction, Pose Estimation

The possibility of achieving real-time evaluation of human pose enables the ability to control robotic platforms based on user’s pose (or even on user’s inertial properties) in Human-In-The-Loop simulators, for sports and rehabilitation as an example. This study presents a vision-based, marker-less measurement system for real-time 3D human pose estimation. The system exploits pre-trained 2D human pose detection models and integrates an α-β-γ filter to reduce fluctuations in detected key points. It also introduces a novel Weighted Direct Linear Triangulation method, enhancing 3D reconstruction accuracy by assigning higher weights to key points consistent across current and previous frames. The method’s accuracy and execution time are assessed using the public Human3.6M dataset, evaluating different model configurations, formats, camera setups, and acquisition modes for real-time applications. The YOLOv8x-pose model with four cameras achieves the highest accuracy, with a Mean-Per-Joint Position Error of 18.2 mm and an execution time of 15 ms, outperforming state-of-the-art methods. Converting models to the TensorRT framework reduces execution time by 4.2 ms without significant accuracy loss. The system is integrated into a clinical rehabilitation device, a three-degree-of-freedom parallel kinematic machine, to facilitate patient participation in exergames. The proposed human-pose estimation method achieves real-time performance, enabling the motion platform to be controlled dynamically based on the patient’s actual standing pose.
TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation
Authors: Alex Jinpeng Wang, Dongxing Mao, Jiawei Zhang, Weiming Han, Zhuobai Dong, Linjie Li, Yiqi Lin, Zhengyuan Yang, Libo Qin, Fuwei Zhang, Lijuan Wang, Min Li
Date: 2025-02-11 00:00:00
Tasks: Image Generation

Text-conditioned image generation has gained significant attention in recent years and are processing increasingly longer and comprehensive text prompt. In everyday life, dense and intricate text appears in contexts like advertisements, infographics, and signage, where the integration of both text and visuals is essential for conveying complex information. However, despite these advances, the generation of images containing long-form text remains a persistent challenge, largely due to the limitations of existing datasets, which often focus on shorter and simpler text. To address this gap, we introduce TextAtlas5M, a novel dataset specifically designed to evaluate long-text rendering in text-conditioned image generation. Our dataset consists of 5 million long-text generated and collected images across diverse data types, enabling comprehensive evaluation of large-scale generative models on long-text image generation. We further curate 3000 human-improved test set TextAtlasEval across 3 data domains, establishing one of the most extensive benchmarks for text-conditioned generation. Evaluations suggest that the TextAtlasEval benchmarks present significant challenges even for the most advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source counterparts show an even larger performance gap. These evidences position TextAtlas5M as a valuable dataset for training and evaluating future-generation text-conditioned image generation models.
Heterogeneity in Sectoral Production and the Macro Effect of Sectoral Shocks
Authors: Jacob Toner Gosselin
Date: 2025-02-11 00:00:00

The effect of a negative sectoral shock on GDP depends on how important the shocked sector is as a direct and indirect supplier and how easily sectors can substitute inputs. Past estimates of the parameters that determine these qualities in the US have been restrictive: they have not been allowed to vary across industries or across time. This paper uses a novel empirical strategy to relax those restrictions, by exploiting variation in input expenditure share shifts within industries rather than across industries. The resulting estimates exhibit significant sectoral and temporal heterogeneity, and are dynamically correlated with weighted patents. In a calibrated GE model of multi-sector production, this heterogeneity (1) raises[lowers] the GDP effect of negative shocks to sectors whose customers are less[more] able to substitute inputs (e.g. the GDP effect of "Chemical products" shocks rises), (2) raises[lowers] the GDP effect of negative sectoral shocks in years where sectors are less[more] able to substitute inputs, and (3) raises[lowers] the GDP effect of negative shocks to sectors as they become more[less] central input suppliers (e.g. between 1997 and 2023 the GDP effect of "Paper products" shocks fell and the GDP effect of "Computer and electronic products" shocks rose due to changes in their importance as input suppliers).
Do AI assistants help students write formal specifications? A study with ChatGPT and the B-Method
Authors: Alfredo Capozucca, Daniil Yampolskyi, Alexander Goldberg, Maximiliano Cristiá
Date: 2025-01-20 00:00:00

This paper investigates the role of AI assistants, specifically OpenAI's ChatGPT, in teaching formal methods (FM) to undergraduate students, using the B-method as a formal specification technique. While existing studies demonstrate the effectiveness of AI in coding tasks, no study reports on its impact on formal specifications. We examine whether ChatGPT provides an advantage when writing B-specifications and analyse student trust in its outputs. Our findings indicate that the AI does not help students to enhance the correctness of their specifications, with low trust correlating to better outcomes. Additionally, we identify a behavioural pattern with which to interact with ChatGPT which may influence the correctness of B-specifications.
neuro2voc: Decoding Vocalizations from Neural Activity
Authors: Fei Gao
Date: 2025-02-02 00:00:00
Tasks: Contrastive Learning

Accurate decoding of neural spike trains and relating them to motor output is a challenging task due to the inherent sparsity and length in neural spikes and the complexity of brain circuits. This master project investigates experimental methods for decoding zebra finch motor outputs (in both discrete syllables and continuous spectrograms), from invasive neural recordings obtained from Neuropixels. There are three major achievements: (1) XGBoost with SHAP analysis trained on spike rates revealed neuronal interaction patterns crucial for syllable classification. (2) Novel method (tokenizing neural data with GPT2) and architecture (Mamba2) demonstrated potential for decoding of syllables using spikes. (3) A combined contrastive learning-VAE framework successfully generated spectrograms from binned neural data. This work establishes a promising foundation for neural decoding of complex motor outputs and offers several novel methodological approaches for processing sparse neural data.
An Applied Analysis of Securing 5G/6G Core Networks with Post-Quantum Key Encapsulation Methods
Authors: Paul Scalise, Robert Garcia, Matthew Boeding, Michael Hempel, Hamid Sharif
Date: 2024-10-30 00:00:00
Proceeding: MDPI Electronics 2024 10

Fifth Generation (5G) cellular networks have been adopted worldwide since the rollout began around 2019. It brought with it many innovations and new services, such as Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low-Latency Communications (URLLC), and Massive Internet of Things (mIoT). Furthermore, 5G introduced a more scalable approach to network operations using fully software-based Virtualized Network Functions (VNF) in Core Networks (CN) rather than the prior hardware-based approach. However, while this shift towards a fully software-based system design provides numerous significant benefits, such as increased interoperability, scalability, and cost-effectiveness, it also brings with it an increased cybersecurity risk. Security is crucial to maintaining trust between vendors, operators, and consumers. Cyberattacks are rapidly increasing in number and sophistication, and we are seeing a shift towards zero-trust approaches. This means that even communications between VNFs inside a 5G core must be scrutinized and hardened against attacks, especially with the advent of quantum computers. The National Institute of Standards and Technology (NIST), over the past 10 years, has led efforts to standardize post-quantum cryptography (PQC) to protect against quantum attacks. This paper covers a custom implementation of the open-source free5GC CN, to expand its HTTPS capabilities for VNFs by introducing PQC Key Encapsulation Methods (KEM) for Transport Layer Security (TLS) v1.3. This paper provides the details of this integration with a focus on the latency of different PQC KEMs in initial handshakes between VNFs, on packet size, and the implications in a 5G environment. This work also conducts a security comparison between the PQC-equipped free5GC and other open-source 5G CNs. The presented results indicate a negligible increase in UE connection setup duration and a small increase in connection setup data requirements, strongly indicating that PQC KEM’s benefits far outweigh any downsides when integrated into 5G and 6G core services. To the best of our knowledge, this is the first work incorporating PQC into an open-source 5G core. Furthermore, the results from this effort demonstrate that employing PQC ciphers for securing VNF communications results in only a negligible impact on latency and bandwidth usage, thus demonstrating significant benefits to 5G cybersecurity.
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
Authors: Tommaso Baldi, Javier Campos, Olivia Weng, Caleb Geniesse, Nhan Tran, Ryan Kastner, Alessandro Biondi
Date: 2025-02-12 00:00:00
Tasks: Quantization

In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems.
Hallucination, Monofacts, and Miscalibration: An Empirical Investigation
Authors: Miranda Muqing Miao, Michael Kearns
Date: 2025-02-11 00:00:00
Tasks: Decoder, Hallucination, In-Context Learning, Text Generation

Hallucinated facts in large language models (LLMs) have recently been shown to obey a statistical lower bound determined by the monofact rate (related to the classical Good-Turing missing mass estimator) minus model miscalibration (Kalai & Vempala, 2024). We present the first empirical investigation of this three-way relationship in classical n-gram models and fine-tuned encoder-decoder Transformers. By generating training data from Pareto distributions with varying shape parameters, we systematically control the monofact rates and establish its positive relationship with hallucination. To bridge theory and practice, we derive an empirical analog of the hallucination bound by replacing the population miscalibration term (Section 2.1) with an empirical bin-wise KL divergence and confirm its practical viability. We then introduce selective upweighting -- a simple yet effective technique that strategically repeats as little as 5% of training examples -- to deliberately inject miscalibration into the model. This intervention reduces hallucination by up to 40%, challenging universal deduplication policies. Our experiments reveal a critical trade-off: selective upweighting maintains pre-injection levels of accuracy while substantially reducing hallucination, whereas standard training gradually improves accuracy but fails to address persistently high hallucination, indicating an inherent tension in optimization objectives.