pwc-archive/papers-with-abstracts huggingface dataset:
On Storage Neural Network Augmented Approximate Nearest Neighbor Search
Authors: Taiga Ikeda, Daisuke Miyashita, Jun Deguchi
Date: 2025-01-23 00:00:00

Large-scale approximate nearest neighbor search (ANN) has been gaining attention along with the latest machine learning researches employing ANNs. If the data is too large to fit in memory, it is necessary to search for the most similar vectors to a given query vector from the data stored in storage devices, not from that in memory. The storage device such as NAND flash memory has larger capacity than the memory device such as DRAM, but they also have larger latency to read data. Therefore, ANN methods for storage require completely different approaches from conventional in-memory ANN methods. Since the approximation that the time required for search is determined only by the amount of data fetched from storage holds under reasonable assumptions, our goal is to minimize it while maximizing recall. For partitioning-based ANNs, vectors are partitioned into clusters in the index building phase. In the search phase, some of the clusters are chosen, the vectors in the chosen clusters are fetched from storage, and the nearest vector is retrieved from the fetched vectors. Thus, the key point is to accurately select the clusters containing the ground truth nearest neighbor vectors. We accomplish this by proposing a method to predict the correct clusters by means of a neural network that is gradually refined by alternating supervised learning and duplicated cluster assignment. Compared to state-of-the-art SPANN and an exhaustive method using k-means clustering and linear search, the proposed method achieves 90% recall on SIFT1M with 80% and 58% less data fetched from storage, respectively.
A Method for Multi-Hop Question Answering on Persian Knowledge Graph
Authors: Arash Ghafouri, Mahdi Firouzmandi, Hasan Naderi
Date: 2025-01-18 00:00:00
Tasks: Graph Question Answering, Information Retrieval, Knowledge Graphs, Multi-hop Question Answering, Question Answering, Retrieval

Question answering systems are the latest evolution in information retrieval technology, designed to accept complex queries in natural language and provide accurate answers using both unstructured and structured knowledge sources. Knowledge Graph Question Answering (KGQA) systems fulfill users' information needs by utilizing structured data, representing a vast number of facts as a graph. However, despite significant advancements, major challenges persist in answering multi-hop complex questions, particularly in Persian. One of the main challenges is the accurate understanding and transformation of these multi-hop complex questions into semantically equivalent SPARQL queries, which allows for precise answer retrieval from knowledge graphs. In this study, to address this issue, a dataset of 5,600 Persian multi-hop complex questions was developed, along with their decomposed forms based on the semantic representation of the questions. Following this, Persian language models were trained using this dataset, and an architecture was proposed for answering complex questions using a Persian knowledge graph. Finally, the proposed method was evaluated against similar systems on the PeCoQ dataset. The results demonstrated the superiority of our approach, with an improvement of 12.57% in F1-score and 12.06% in accuracy compared to the best comparable method.
The expressive power of pooling in Graph Neural Networks
Authors: Filippo Maria Bianchi, Veronica Lachi
Date: 2023-04-04 00:00:00
Proceeding: NeurIPS 2023 11

In Graph Neural Networks (GNNs), hierarchical pooling operators generate local summaries of the data by coarsening the graph structure and the vertex features. While considerable attention has been devoted to analyzing the expressive power of message-passing (MP) layers in GNNs, a study on how graph pooling affects the expressiveness of a GNN is still lacking. Additionally, despite the recent advances in the design of pooling operators, there is not a principled criterion to compare them. In this work, we derive sufficient conditions for a pooling operator to fully preserve the expressive power of the MP layers before it. These conditions serve as a universal and theoretically grounded criterion for choosing among existing pooling operators or designing new ones. Based on our theoretical findings, we analyze several existing pooling operators and identify those that fail to satisfy the expressiveness conditions. Finally, we introduce an experimental setup to verify empirically the expressive power of a GNN equipped with pooling layers, in terms of its capability to perform a graph isomorphism test.
Neuroscience and Machine Learning
Authors: QANDIL, HISHAM
Date: 2025-01-28 00:00:00
Proceeding: IOPScience 2025 1

The document explores the intersection of neuroscience and machine learning (ML), highlighting their integration's significance. It covers ML techniques applied in neuroscience, including brain-computer interfaces (BCIs), neural data analysis, and neuroimaging. Key topics include signal processing, feature extraction, and the use of ML models for analyzing neural data. The document also discusses neuroscience-inspired ML algorithms, ethical considerations, and societal implications. Future directions include advancements in real-time BCIs, personalized medicine, and deeper insights into brain function through ML. The integration of ML and neuroscience promises significant advancements in understanding and treating neurological disorders.
AI in Healthcare
Authors: QANDIL, HISHAM
Date: 2025-01-28 00:00:00
Proceeding: IOPScience 2025 1

Artificial Intelligence (AI) has significantly transformed various industries, with healthcare being one of the most profoundly affected. AI technologies have greatly enhanced the capabilities of medical professionals, enabling more precise diagnostics, personalized treatment plans, and improved patient outcomes. This article delves into the impact of AI on healthcare, focusing on early disease detection, personalized medicine, and robotic surgery. The integration of AI into healthcare has ushered in a new era of medical innovation and efficiency. By leveraging vast amounts of data, AI algorithms can identify patterns and correlations that often elude human comprehension, leading to groundbreaking advancements in various aspects of healthcare. For instance, early disease detection has seen remarkable improvements through AI-powered imaging and predictive analytics. Machine learning models can now analyze medical images with exceptional precision, detecting anomalies and potential health issues at their earliest stages, thereby enabling timely intervention and better prognoses. Furthermore, personalized medicine has become a reality, thanks to AI's ability to process and analyze individual patient data, including genetic information, lifestyle factors, and medical history. This approach allows for tailored treatment plans that are more effective and less prone to adverse effects. AI-driven tools can predict patient responses to specific medications, optimize treatment protocols, and minimize the trial-and-error approach often associated with conventional medicine. Robotic surgery, another notable application of AI, has revolutionized surgical procedures by enhancing precision, reducing recovery times, and minimizing risks. AI-powered surgical robots assist surgeons in performing complex operations with unparalleled accuracy, leading to better patient outcomes and shorter hospital stays. These advancements not only improve the quality of care but also reduce healthcare costs, making advanced medical treatments more accessible to a broader population. In summary, the integration of AI in healthcare has led to significant improvements in early disease detection, personalized medicine, and robotic surgery. By harnessing the power of AI, medical professionals can deliver more accurate diagnoses, develop tailored treatment plans, and perform complex surgeries with greater precision. As AI continues to evolve, its impact on healthcare is set to grow, promising a future where medical care is more efficient, effective, and accessible.
A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario
Authors: Luca Zedda, Andrea Loddo, Cecilia Di Ruberto
Date: 2025-01-26 00:00:00
Proceeding: Computers in Biology and Medicine 2025 1
Tasks: Diagnostic, Malaria Falciparum Detection, Malaria Malariae Detection, Malaria Ovale Detection, Malaria Vivax Detection, Medical Diagnosis, Medical Image Analysis, medical image detection, Object Detection

Background: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is
responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial
for effective treatment, yet conventional microscopy faces limitations in variability and efficiency.
Methods: We propose a novel computer-aided detection framework based on deep learning and attention
mechanisms, extending the YOLO-SPAM and YOLO-PAM models. Our approach facilitates the detection and
classification of malaria parasites across all infection stages and supports multi-species identification.
Results: The framework was evaluated on three publicly available datasets, demonstrating high accuracy
in detecting four distinct malaria species and their life stages. Comparative analysis against state-of-the-art
methodologies indicates significant improvements in both detection rates and diagnostic utility.
Conclusion: This study presents a robust solution for automated malaria detection, offering valuable support
for pathologists and enhancing diagnostic practices in real-world scenarios.
FuzzyClass: A family of Fuzzy and Non-Fuzzy probabilistic-based classifiers
Authors: Jodavid A. Ferreira, Ronei M. Moraes
Date: 2023-08-26 00:00:00
Proceeding: Journal of Open Source Software 2023 8

This paper presents a package written in the language R for classifiers based on Naive Bayes
and Fuzzy Naive Bayes named FuzzyClass. This R package implements eight fuzzy classifiers,
with option for using the classical ones too. An example in which the Fuzzy Gaussian Naive
Bayes method is presented.
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding
Authors: Axel De Nardin, Silvia Zottin, Claudio Piciarelli, Emanuela Colombi, Gian Luca Foresti
Date: 2023-08-10 00:00:00
Proceeding: International Journal of Neural Systems (IJNS) 2023 8
Tasks: Few-Shot Learning, Segmentation, Semantic Segmentation

Over the years, the humanities community has increasingly requested the creation of artificial intelligence
frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at
identifying the different structural components of a document page, is a particularly interesting task
connected to this trend, specifically when it comes to handwritten texts. While there are many effective
approaches to this problem, they all rely on large amounts of data for the training of the underlying
models, which is rarely possible in a real-world scenario, as the process of producing the ground truth
segmentation task with the required precision to the pixel level is a very time-consuming task and often
requires a certain degree of domain knowledge regarding the documents at hand. For this reason, in the
present paper, we propose an effective few-shot learning framework for document layout segmentation
relying on two novel components, namely a dynamic instance generation and a segmentation refinement
module. This approach is able of achieving performances comparable to the current state of the art on
the popular Diva-HisDB dataset, while relying on just a fraction of the available data.
ICDAR 2024 Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts (SAM)
Authors: Silvia Zottin, Axel De Nardin, Gian Luca Foresti, Emanuela Colombi, Claudio Piciarelli
Date: 2024-09-11 00:00:00
Proceeding: International Conference on Document Analysis and Recognition (ICDAR) 2024 9
Tasks: Diversity, Document Layout Analysis, Few-Shot Learning, Optical Character Recognition

Layout analysis is a critical aspect of Document Image Analysis, particularly when it comes to ancient manuscripts. It serves as a foundational step in streamlining subsequent tasks such as optical character recognition and automated transcription. However, one key challenge in this context is represented by the lack of available ground truths as they are extremely time-consuming to produce. Nevertheless, numerous approaches addressing this challenge heavily lean towards a fully supervised learning paradigm, which represents a rare scenario in a real-world setting. For this reason, with this competition, we propose the challenge of addressing this task with a few-shot learning approach, involving the use of only three images for training. The competition dataset, called U-DIADS-Bib, comprises four distinct ancient manuscripts, presenting heterogeneous layout structures, levels of degradation, and languages used. This diversity adds intrigue and complexity to the challenge. In addition, we have also allowed participating in the competition with traditional many-shot learning approaches, for which the whole training set of U-DIADS-Bib was made available.
Articulating the role of nuclear energy in the circular economy of China: A machine learning approach
Authors: Yiting Qiu, Adnan Khan, Danish
Date: 2025-01-28 00:00:00

Nuclear energy is increasingly recognized as a critical component of circular economy frameworks due to its capacity to provide a stable, low-carbon energy source. Reducing dependency on fossil fuels promotes sustainable practices and aligns with circular economy goals such as resource efficiency, pollution reduction, and waste minimization. The existing literature has primarily focused on the contribution of nuclear energy to decarbonization, whereas the potential of nuclear energy in facilitating a circular economy has been largely neglected. In light of this context, this paper explores the impact of nuclear energy on the circular economy, thereby offering strong econometric evidence. The study used the advanced econometric tool Dynamic Auto-Regressive Distributive Lag (DYNARDL) method for empirical estimation to obtain long- and short-run estimates. The regression estimates, derived from a sample of China spanning 1990 to 2017, support the hypothesis that nuclear energy negatively impacts the circular economy in both the long- and short-run. Advanced econometric tests confirm the stability of the models, homoscedasticity, and the absence of serial correlation, ensuring the reliability of our findings. The study emphasizes the importance of policy strategies, including expanding nuclear energy adoption, advancing environmental technologies, and the effective use of nuclear energy by integrating comprehensive datasets and methodologies; this paper provides a foundation for scalable and equitable solutions as China moves toward a greener and more sustainable future.
Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
Authors: Haoyang Zheng, Hengrong Du, Ruqi Zhang, Guang Lin
Date: 2025-01-28 00:00:00

Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration, we introduce the Discrete Replica EXchangE Langevin (DREXEL) sampler and its variant with Adjusted Metropolis (DREAM). These samplers use two GDSs at different temperatures and step sizes: one focuses on local exploitation, while the other explores broader energy landscapes. When energy differences are significant, sample swaps occur, which are determined by a mechanism tailored for discrete sampling to ensure detailed balance. Theoretically, we prove that the proposed samplers satisfy detailed balance and converge to the target distribution under mild conditions. Experiments across 2d synthetic simulations, sampling from Ising models and restricted Boltzmann machines, and training deep energy-based models further confirm their efficiency in exploring non-convex discrete energy landscapes.
US AISI and UK AISI Joint Pre-Deployment Test: Anthropic’s Claude 3.5 Sonnet (October 2024 Release)
Authors: US AI Safety Institute, UK AI Safety Institute
Date: 2024-11-19 00:00:00
Proceeding: NIST 2024 11

This technical report details a pre-deployment evaluation of Anthropic’s upgraded version of Claude 3.5 Sonnet, released October 22, 2024 (hereafter referred to as Sonnet 3.5 (new)). This evaluation was conducted jointly by the United States Artificial Intelligence Safety Institute (US AISI) and the United Kingdom Artificial Intelligence Safety Institute (UK AISI), and this report describes in detail its technical methodology and findings. For general background and a summary of this report, see the corresponding blog post. US AISI and UK AISI’s joint pre-deployment evaluation assessed four domains: biological capabilities, cyber capabilities, software and AI development capabilities, and safeguard effectiveness. US AISI and UK AISI each ran independent tests on Sonnet 3.5 (new), working together to inform and improve methodology and interpretation of findings. US AISI and UK AISI shared their initial findings with Anthropic prior to the model’s release. The following sections introduce each evaluation domain jointly and present specific technical descriptions, methodologies, and findings in each domain as specific to either US AISI or UK AISI, as appropriate.
Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
Authors: Zijie Liu, Xinyu Zhao, Jie Peng, Zhuangdi Zhu, Qingyu Chen, Xia Hu, Tianlong Chen
Date: 2025-01-29 00:00:00
Tasks: Diagnostic

Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks. These tuning methods and benchmarks overlook critical aspects like evidence-based reasoning and handling distracting information. To bridge this gap, we introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards. Moreover, we explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes. Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64\%$ in multi-round reasoning scenarios and $6.18\%$ in accuracy in a noisy environment. Our findings highlight dialogue tuning as a promising approach for advancing clinically aligned and robust medical AI systems.
Improving Your Model Ranking on Chatbot Arena by Vote Rigging
Authors: Rui Min, Tianyu Pang, Chao Du, Qian Liu, Minhao Cheng, Min Lin
Date: 2025-01-29 00:00:00
Tasks: Chatbot

Chatbot Arena is a popular platform for evaluating LLMs by pairwise battles, where users vote for their preferred response from two randomly sampled anonymous models. While Chatbot Arena is widely regarded as a reliable LLM ranking leaderboard, we show that crowdsourced voting can be rigged to improve (or decrease) the ranking of a target model $m_{t}$. We first introduce a straightforward target-only rigging strategy that focuses on new battles involving $m_{t}$, identifying it via watermarking or a binary classifier, and exclusively voting for $m_{t}$ wins. However, this strategy is practically inefficient because there are over $190$ models on Chatbot Arena and on average only about $1\%$ of new battles will involve $m_{t}$. To overcome this, we propose omnipresent rigging strategies, exploiting the Elo rating mechanism of Chatbot Arena that any new vote on a battle can influence the ranking of the target model $m_{t}$, even if $m_{t}$ is not directly involved in the battle. We conduct experiments on around $1.7$ million historical votes from the Chatbot Arena Notebook, showing that omnipresent rigging strategies can improve model rankings by rigging only hundreds of new votes. While we have evaluated several defense mechanisms, our findings highlight the importance of continued efforts to prevent vote rigging. Our code is available at https://github.com/sail-sg/Rigging-ChatbotArena.
GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings
Authors: Ziang Liu, Yuanchen Ju, Yu Da, Tom Silver, Pranav N. Thakkar, Jenna Li, Justin Guo, Katherine Dimitropoulou, Tapomayukh Bhattacharjee
Date: 2025-01-29 00:00:00

Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical tasks such as handover, bathing, dressing, and rehabilitation, a key aspect of this diversity is the functional range of motion (fROM), which can vary significantly between individuals. In this work, we learn to predict personalized fROM as a way to generalize robot decision-making in a wide range of caregiving tasks. We propose a novel data-driven method for predicting personalized fROM using functional assessment scores from occupational therapy. We develop a neural model that learns to embed functional assessment scores into a latent representation of the user's physical function. The model is trained using motion capture data collected from users with emulated mobility limitations. After training, the model predicts personalized fROM for new users without motion capture. Through simulated experiments and a real-robot user study, we show that the personalized fROM predictions from our model enable the robot to provide personalized and effective assistance while improving the user's agency in action. See our website for more visualizations: https://emprise.cs.cornell.edu/grace/.
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning
Authors: Junseok Park, Hyeonseo Yang, Min Whoo Lee, Won-Seok Choi, Minsu Lee, Byoung-Tak Zhang
Date: 2025-01-29 00:00:00
Tasks: Navigate, Reinforcement Learning (RL)

Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biological systems, such as human toddlers, naturally navigate this balance by transitioning from free exploration with sparse rewards to goal-directed behavior guided by increasingly dense rewards. Inspired by this natural progression, we investigate the Toddler-Inspired Reward Transition in goal-oriented RL tasks. Our study focuses on transitioning from sparse to potential-based dense (S2D) rewards while preserving optimal strategies. Through experiments on dynamic robotic arm manipulation and egocentric 3D navigation tasks, we demonstrate that effective S2D reward transitions significantly enhance learning performance and sample efficiency. Additionally, using a Cross-Density Visualizer, we show that S2D transitions smooth the policy loss landscape, resulting in wider minima that improve generalization in RL models. In addition, we reinterpret Tolman's maze experiments, underscoring the critical role of early free exploratory learning in the context of S2D rewards.