pwc-archive/papers-with-abstracts huggingface dataset:
DRS: Deep Question Reformulation With Structured Output
Authors: Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai, Nanyun Peng, Kai-Wei Chang
Date: 2024-11-27 00:00:00
Tasks: Question Answering

Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of the underlying information. Recent studies reveal that while LLMs can detect unanswerable questions, they struggle to assist users in reformulating these questions. Even advanced models like GPT-3.5 demonstrate limited effectiveness in this regard. To address this limitation, we propose DRS: Deep Question Reformulation with Structured Output, a novel zero-shot method aimed at enhancing LLMs ability to assist users in reformulating questions to extract relevant information from new documents. DRS combines the strengths of LLMs with a DFS-based algorithm to iteratively explore potential entity combinations and constrain outputs using predefined entities. This structured approach significantly enhances the reformulation capabilities of LLMs. Comprehensive experimental evaluations demonstrate that DRS improves the reformulation accuracy of GPT-3.5 from 23.03% to 70.42%, while also enhancing the performance of open-source models, such as Gemma2-9B, from 26.35% to 56.75%.
QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative Data Extraction from Online Health Discussions
Authors: Ramez Kouzy, Roxanna Attar-Olyaee, Michael K. Rooney, Comron J. Hassanzadeh, Junyi Jessy Li, Osama Mohamad
Date: 2024-11-27 00:00:00
Tasks: Prompt Engineering

Health-related discussions on social media like Reddit offer valuable insights, but extracting quantitative data from unstructured text is challenging. In this work, we present an adapted framework from QuaLLM into QuaLLM-Health for extracting clinically relevant quantitative data from Reddit discussions about glucagon-like peptide-1 (GLP-1) receptor agonists using large language models (LLMs). We collected 410k posts and comments from five GLP-1-related communities using the Reddit API in July 2024. After filtering for cancer-related discussions, 2,059 unique entries remained. We developed annotation guidelines to manually extract variables such as cancer survivorship, family cancer history, cancer types mentioned, risk perceptions, and discussions with physicians. Two domain-experts independently annotated a random sample of 100 entries to create a gold-standard dataset. We then employed iterative prompt engineering with OpenAI's "GPT-4o-mini" on the gold-standard dataset to build an optimized pipeline that allowed us to extract variables from the large dataset. The optimized LLM achieved accuracies above 0.85 for all variables, with precision, recall and F1 score macro averaged > 0.90, indicating balanced performance. Stability testing showed a 95% match rate across runs, confirming consistency. Applying the framework to the full dataset enabled efficient extraction of variables necessary for downstream analysis, costing under $3 and completing in approximately one hour. QuaLLM-Health demonstrates that LLMs can effectively and efficiently extract clinically relevant quantitative data from unstructured social media content. Incorporating human expertise and iterative prompt refinement ensures accuracy and reliability. This methodology can be adapted for large-scale analysis of patient-generated data across various health domains, facilitating valuable insights for healthcare research.
Preserving Information: How does Topological Data Analysis improve Neural Network performance?
Authors: A. Stolarek, W. Jaworek
Date: 2024-11-27 00:00:00
Tasks: Topological Data Analysis

Artificial Neural Networks (ANNs) require significant amounts of data and computational resources to achieve high effectiveness in performing the tasks for which they are trained. To reduce resource demands, various techniques, such as Neuron Pruning, are applied. Due to the complex structure of ANNs, interpreting the behavior of hidden layers and the features they recognize in the data is challenging. A lack of comprehensive understanding of which information is utilized during inference can lead to inefficient use of available data, thereby lowering the overall performance of the models. In this paper, we introduce a method for integrating Topological Data Analysis (TDA) with Convolutional Neural Networks (CNN) in the context of image recognition. This method significantly enhances the performance of neural networks by leveraging a broader range of information present in the data, enabling the model to make more informed and accurate predictions. Our approach, further referred to as Vector Stitching, involves combining raw image data with additional topological information derived through TDA methods. This approach enables the neural network to train on an enriched dataset, incorporating topological features that might otherwise remain unexploited or not captured by the network's inherent mechanisms. The results of our experiments highlight the potential of incorporating results of additional data analysis into the network's inference process, resulting in enhanced performance in pattern recognition tasks in digital images, particularly when using limited datasets. This work contributes to the development of methods for integrating TDA with deep learning and explores how concepts from Information Theory can explain the performance of such hybrid methods in practical implementation environments.
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware
Authors: Shubham Pande, Sai Sukruth Bezugam, Tinish Bhattacharya, Ewelina Wlazlak, Anjan Chakaravorty, Bhaswar Chakrabarti, Dmitri Strukov
Date: 2024-11-27 00:00:00
Tasks: Reinforcement Learning (RL)

Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity. Herein, a novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated to meet this demand. This synapse features two distinct state variables: a neuron coupling weight and an "eligibility trace" that dictates synaptic weight updates. The coupling weight is encoded in the ReRAM conductance, while the "eligibility trace" is encoded in the local temperature of the ReRAM and is modulated by applying voltage pulses to a physically co-located resistive heating element. The utility of the proposed synapse has been investigated using two representative tasks: first, temporal signal classification using Recurrent Spiking Neural Networks (RSNNs) employing the e-prop algorithm, and second, Reinforcement Learning (RL) for path planning tasks in feedforward networks using a modified version of the same learning rule. System-level simulations, accounting for various device and system-level non-idealities, confirm that these synapses offer a robust solution for the fast, compact, and energy-efficient implementation of advanced learning rules in neuromorphic hardware.
An Improved Dung Beetle Optimizer for Random Forest Optimization
Authors: Lianghao Tan, Xiaoyi Liu, Dong Liu, Shubing Liu, Weixi Wu, Huangqi Jiang
Date: 2024-11-24 00:00:00
Tasks: Binary Classification, Diversity, Position

To improve the convergence speed and optimization accuracy of the Dung Beetle Optimizer (DBO), this paper proposes an improved algorithm based on circle mapping and longitudinal-horizontal crossover strategy (CICRDBO). First, the Circle method is used to map the initial population to increase diversity. Second, the longitudinal-horizontal crossover strategy is applied to enhance the global search ability by ensuring the position updates of the dung beetle. Simulations were conducted on 10 benchmark test functions, and the results demonstrate that the improved algorithm performs well in both convergence speed and optimization accuracy. The improved algorithm is further applied to the hyperparameter selection of the Random Forest classification algorithm for binary classification prediction in the retail industry. Various combination comparisons prove the practicality of the improved algorithm, followed by SHapley Additive exPlanations (SHAP) analysis.
EQNN: Enhanced Quantum Neural Network
Authors: Abel C. H. Chen
Date: 2024-11-21 00:00:00

With the maturation of quantum computing technology, research has gradually shifted towards exploring its applications. Alongside the rise of artificial intelligence, various machine learning methods have been developed into quantum circuits and algorithms. Among them, Quantum Neural Networks (QNNs) can map inputs to quantum circuits through Feature Maps (FMs) and adjust parameter values via variational models, making them applicable in regression and classification tasks. However, designing a FM that is suitable for a given application problem is a significant challenge. In light of this, this study proposes an Enhanced Quantum Neural Network (EQNN), which includes an Enhanced Feature Map (EFM) designed in this research. This EFM effectively maps input variables to a value range more suitable for quantum computing, serving as the input to the variational model to improve accuracy. In the experimental environment, this study uses mobile data usage prediction as a case study, recommending appropriate rate plans based on users' mobile data usage. The proposed EQNN is compared with current mainstream QNNs, and experimental results show that the EQNN achieves higher accuracy with fewer quantum logic gates and converges to the optimal solution faster under different optimization algorithms.
A Flexible Defense Against the Winner's Curse
Authors: Tijana Zrnic, William Fithian
Date: 2024-11-27 00:00:00
Tasks: Selection bias, valid

Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly, in machine learning, practitioners are often interested in the population performance of the model that performs best empirically. However, cherry-picking the best candidate leads to the winner's curse: the observed performance for the winner is biased upwards, rendering conclusions based on standard measures of uncertainty invalid. We introduce the zoom correction, a novel approach for valid inference on the winner. Our method is flexible: it can be employed in both parametric and nonparametric settings, can handle arbitrary dependencies between candidates, and automatically adapts to the level of selection bias. The method easily extends to important related problems, such as inference on the top k winners, inference on the value and identity of the population winner, and inference on "near-winners."
Probabilistic size-and-shape functional mixed models
Authors: Fangyi Wang, Karthik Bharath, Oksana Chkrebtii, Sebastian Kurtek
Date: 2024-11-27 00:00:00
Tasks: Uncertainty Quantification

The reliable recovery and uncertainty quantification of a fixed effect function $\mu$ in a functional mixed model, for modelling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variations along the $x$ and $y$ axes are confounded with additive measurement error, and cannot in general be disentangled. The question then as to what properties of $\mu$ may be reliably recovered becomes important. We demonstrate that it is possible to recover the size-and-shape of a square-integrable $\mu$ under a Bayesian functional mixed model. The size-and-shape of $\mu$ is a geometric property invariant to a family of space-time unitary transformations, viewed as rotations of the Hilbert space, that jointly transform the $x$ and $y$ axes. A random object-level unitary transformation then captures size-and-shape \emph{preserving} deviations of $\mu$ from an individual function, while a random linear term and measurement error capture size-and-shape \emph{altering} deviations. The model is regularized by appropriate priors on the unitary transformations, posterior summaries of which may then be suitably interpreted as optimal data-driven rotations of a fixed orthonormal basis for the Hilbert space. Our numerical experiments demonstrate utility of the proposed model, and superiority over the current state-of-the-art.
Ridge Regression for Manifold-valued Time-Series with Application to Meteorological Forecast
Authors: Esfandiar Nava-Yazdani
Date: 2024-11-27 00:00:00
Tasks: regression, Time Series, Time Series Prediction

We propose a natural intrinsic extension of the ridge regression from Euclidean spaces to general manifolds, which relies on Riemannian least-squares fitting, empirical covariance, and Mahalanobis distance. We utilize it for time-series prediction and apply the approach to forecast hurricane tracks and their wind speeds.
Analytic Continuation by Feature Learning
Authors: Zhe Zhao, Jingping Xu, Ce Wang, Yaping Yang
Date: 2024-11-22 00:00:00

Analytic continuation aims to reconstruct real-time spectral functions from imaginary-time Green's functions; however, this process is notoriously ill-posed and challenging to solve. We propose a novel neural network architecture, named the Feature Learning Network (FL-net), to enhance the prediction accuracy of spectral functions, achieving an improvement of at least $20\%$ over traditional methods, such as the Maximum Entropy Method (MEM), and previous neural network approaches. Furthermore, we develop an analytical method to evaluate the robustness of the proposed network. Using this method, we demonstrate that increasing the hidden dimensionality of FL-net, while leading to lower loss, results in decreased robustness. Overall, our model provides valuable insights into effectively addressing the complex challenges associated with analytic continuation.
Delineating Feminist Studies through bibliometric analysis
Authors: Natsumi S. Shokida, Diego Kozlowski, Vincent Larivière
Date: 2024-11-27 00:00:00

The multidisciplinary and socially anchored nature of Feminist Studies presents unique challenges for bibliometric analysis, as this research area transcends traditional disciplinary boundaries and reflects discussions from feminist and LGBTQIA+ social movements. This paper proposes a novel approach for identifying gender/sex related publications scattered across diverse scientific disciplines. Using the Dimensions database, we employ bibliometric techniques, natural language processing (NLP) and manual curation to compile a dataset of scientific publications that allows for the analysis of Gender Studies and its influence across different disciplines. This is achieved through a methodology that combines a core of specialized journals with a comprehensive keyword search over titles. These keywords are obtained by applying Topic Modeling (BERTopic) to the corpus of titles and abstracts from the core. This methodological strategy, divided into two stages, reflects the dynamic interaction between Gender Studies and its dialogue with different disciplines. This hybrid system surpasses basic keyword search by mitigating potential biases introduced through manual keyword enumeration. The resulting dataset comprises over 1.9 million scientific documents published between 1668 and 2023, spanning four languages. This dataset enables a characterization of Gender Studies in terms of addressed topics, citation and collaboration dynamics, and institutional and regional participation. By addressing the methodological challenges of studying "more-than-disciplinary" research areas, this approach could also be adapted to delineate other conversations where disciplinary boundaries are difficult to disentangle.
The Rn-index: a more accurate variant of the Rk-index
Authors: Alonso Rodriguez-Navarro
Date: 2024-11-27 00:00:00

The contribution to pushing the boundaries of knowledge is a critical metric for evaluating the research performance of countries and institutions, which in many cases is not revealed by common bibliometric indicators. The Rk-index was specifically designed to assess such contributions, and the Rn-index is a variant that corrects the weakness of the Rk-index, particularly in the evaluation of countries that produce a high proportion of global advancements. This is the case of the USA and China in many technological fields. Additionally, the Rn-index is simple to calculate and understand, as it involves only summing the ratios between the local and global ranks of papers, ordered by their citation count. Moreover, the Rn-index may also be fractionally counted.
Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track
Authors: Deepak Gupta, Dina Demner-Fushman, William Hersh, Steven Bedrick, Kirk Roberts
Date: 2024-11-27 00:00:00
Tasks: Medical Question Answering, Question Answering, Retrieval

With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summarization of the biomedical literature, clinical note summarization, etc. However, hallucinations or confabulations remain one of the key challenges when using LLMs in the biomedical and other domains. Inaccuracies may be particularly harmful in high-risk situations, such as medical question answering, making clinical decisions, or appraising biomedical research. Studies on the evaluation of the LLMs abilities to ground generated statements in verifiable sources have shown that models perform significantly worse on lay-user-generated questions, and often fail to reference relevant sources. This can be problematic when those seeking information want evidence from studies to back up the claims from LLMs. Unsupported statements are a major barrier to using LLMs in any applications that may affect health. Methods for grounding generated statements in reliable sources along with practical evaluation approaches are needed to overcome this barrier. Towards this, in our pilot task organized at TREC 2024, we introduced the task of reference attribution as a means to mitigate the generation of false statements by LLMs answering biomedical questions.
SlideSpawn: An Automatic Slides Generation System for Research Publications
Authors: Keshav Kumar, Ravindranath Chowdary
Date: 2024-11-20 00:00:00
Tasks: Sentence

Research papers are well structured documents. They have text, figures, equations, tables etc., to covey their ideas and findings. They are divided into sections like Introduction, Model, Experiments etc., which deal with different aspects of research. Characteristics like these set research papers apart from ordinary documents and allows us to significantly improve their summarization. In this paper, we propose a novel system, SlideSpwan, that takes PDF of a research document as an input and generates a quality presentation providing it's summary in a visual and concise fashion. The system first converts the PDF of the paper to an XML document that has the structural information about various elements. Then a machine learning model, trained on PS5K dataset and Aminer 9.5K Insights dataset (that we introduce), is used to predict salience of each sentence in the paper. Sentences for slides are selected using ILP and clustered based on their similarity with each cluster being given a suitable title. Finally a slide is generated by placing any graphical element referenced in the selected sentences next to them. Experiments on a test set of 650 pairs of papers and slides demonstrate that our system generates presentations with better quality.
Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction
Authors: Mohamed Rashad
Date: 2024-11-19 00:00:00
Tasks: document understanding, Optical Character Recognition (OCR)

We present Arabic-Nougat, a suite of OCR models for converting Arabic book pages into structured Markdown text. Based on Meta's Nougat architecture, Arabic-Nougat includes three specialized models: arabic-small-nougat, arabic-base-nougat, and arabic-large-nougat. These models are fine-tuned on a synthetic dataset, arabic-img2md, comprising 13.7k pairs of Arabic book pages and their Markdown representations. Key contributions include the Aranizer-PBE-86k tokenizer, designed for efficient tokenization, and the use of torch.bfloat16 precision with Flash Attention 2 for optimized training and inference. Our models achieve state-of-the-art performance, with arabic-large-nougat delivering the highest Markdown Structure Accuracy and the lowest Character Error Rate. Additionally, we release a large-scale dataset containing 1.1 billion Arabic tokens extracted from over 8,500 books using our best-performing model, providing a valuable resource for Arabic OCR research. All models, datasets, and code are open-sourced and available at https://github.com/MohamedAliRashad/arabic-nougat.
TED-Net: Dispersal Attention for Perceiving Interaction Region in Indirectly-Contact HOI Detection
Authors: Yuxiao Wang, Qi Liu, Yu Lei
Date: 2024-01-26 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2024 1
Tasks: Human-Object Interaction Detection, object-detection, Object Detection

Human-Object Interaction (HOI) detection is a fertile research ground that merits further investigation in computer vision, and plays an important role in image high-level semantic information understanding. To achieve superior object detection
performance, existing HOI models predominantly concentrate on the corresponding bounding box information of humans and objects, respectively, and ignore their surrounding information, thus it results in imprecise inference of instance interaction, which is severe for indirectly-contact interaction images (Intersectionover-Union = 0). To address that, a novel Triple stream Enhanced encoder-decoder Dispersal Network (TED-Net), equipped with human, object, and instance interaction decoding streams, is proposed to decouple instances’ relationships. Meanwhile, we design a dispersal attention mechanism to capture indirectly-contact interaction information and an auxiliary discrimination mechanism to improve the ability of instance interaction decoding stream for action category recognition. Experimental results show that the proposed TED-Net achieves the best performance among HOI models using the ResNet-50 backbone on the (big) HICODet dataset and comes third on the (small) V-COCO dataset in leaderboard. Additionally, two indirectly-contact interaction datasets, namely, HICO-Det-IC and V-COCO-IC, are constructed to demonstrate the usefulness and effectiveness of our TED-Net in
interacting between indirectly-contact instances, with an average of +3.80 mAP on HICO-Det-IC and +5.46 mAP on V-COCO-IC. Code is available at https://drliuqi.github.io/.