pwc-archive/papers-with-abstracts huggingface dataset:
Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation
Authors: Kang Liu, Zhuoqi Ma, Xiaolu Kang, Zhusi Zhong, Zhicheng Jiao, Grayson Baird, Harrison Bai, Qiguang Miao
Date: 2024-05-23 00:00:00
Tasks: cross-modal alignment, Decoder, Diagnostic, Medical Report Generation, Text Generation

The automated generation of imaging reports proves invaluable in alleviating the workload of radiologists. A clinically applicable reports generation algorithm should demonstrate its effectiveness in producing reports that accurately describe radiology findings and attend to patient-specific indications. In this paper, we introduce a novel method, \textbf{S}tructural \textbf{E}ntities extraction and patient indications \textbf{I}ncorporation (SEI) for chest X-ray report generation. Specifically, we employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports and improve the quality of factual entity sequences. This reduces the noise in the following cross-modal alignment module by aligning X-ray images with factual entity sequences in reports, thereby enhancing the precision of cross-modal alignment and further aiding the model in gradient-free retrieval of similar historical cases. Subsequently, we propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications. This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients. This, in turn, assists in triggering the text decoder to produce high-quality reports. Experiments conducted on MIMIC-CXR validate the superiority of SEI over state-of-the-art approaches on both natural language generation and clinical efficacy metrics.
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
Authors: Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
Date: 2024-05-22 00:00:00
Tasks: In-Context Learning

In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without updating their parameters, significantly boosting their flexibility and generality. ICL possesses many distinct characteristics from conventional machine learning, thereby requiring new approaches to interpret this learning paradigm. Taking the viewpoint of recent works showing that transformers learn in context by formulating an internal optimizer, we propose an influence function-based attribution technique, DETAIL, that addresses the specific characteristics of ICL. We empirically verify the effectiveness of our approach for demonstration attribution while being computationally efficient. Leveraging the results, we then show how DETAIL can help improve model performance in real-world scenarios through demonstration reordering and curation. Finally, we experimentally prove the wide applicability of DETAIL by showing our attribution scores obtained on white-box models are transferable to black-box models in improving model performance.
Comparative Analysis of Four Prominent Ant Colony Optimization Variants: Ant System, Rank-Based Ant System, Max-Min Ant System, and Ant Colony System
Authors: Ahmed Mohamed Abdelmoaty, Ibrahim Ihab Ibrahim
Date: 2024-05-24 00:00:00
Tasks: Traveling Salesman Problem

This research conducts a comparative analysis of four Ant Colony Optimization (ACO) variants -- Ant System (AS), Rank-Based Ant System (ASRank), Max-Min Ant System (MMAS), and Ant Colony System (ACS) -- for solving the Traveling Salesman Problem (TSP). Our findings demonstrate that algorithm performance is significantly influenced by problem scale and instance type. ACS excels in smaller TSP instances due to its rapid convergence, while PACS proves more adaptable for medium-sized problems. MMAS consistently achieves competitive results across all scales, particularly for larger instances, due to its ability to avoid local optima. ASRank, however, struggles to match the performance of the other algorithms. This research provides insights into the strengths and weaknesses of these ACO variants, guiding the selection of the most suitable algorithm for specific TSP applications.
A two-phase-ACO algorithm for solving nonlinear optimization problems subjected to fuzzy relational equations
Authors: Amin Ghodousian, Sara Zal
Date: 2024-05-17 00:00:00

In this paper, we investigate nonlinear optimization problems whose constraints are defined as fuzzy relational equations (FRE) with max-min composition. Since the feasible solution set of the FRE is often a non-convex set and the resolution of the FREs is an NP-hard problem, conventional nonlinear approaches may involve high computational complexity. Based on the theoretical aspects of the problem, an algorithm (called FRE-ACO algorithm) is presented which benefits from the structural properties of the FREs, the ability of discrete ant colony optimization algorithm (denoted by ACO) to tackle combinatorial problems, and that of continuous ant colony optimization algorithm (denoted by ACOR) to solve continuous optimization problems. In the current method, the fundamental ideas underlying ACO and ACOR are combined and form an efficient approach to solve the nonlinear optimization problems constrained with such non-convex regions. Moreover, FRE-ACO algorithm preserves the feasibility of new generated solutions without having to initially find the minimal solutions of the feasible region or check the feasibility after generating the new solutions. FRE-ACO algorithm has been compared with some related works proposed for solving nonlinear optimization problems with respect to maxmin FREs. The obtained results demonstrate that the proposed algorithm has a higher convergence rate and requires a less number of function evaluations compared to other considered algorithms.
From Data Complexity to User Simplicity: A Framework for Linked Open Data Reconciliation and Serendipitous Discovery
Authors: Marco Grasso, Giulia Renda, Marilena Daquino
Date: 2024-05-24 00:00:00

This article introduces a novel software solution to create a Web portal to align Linked Open Data sources and provide user-friendly interfaces for serendipitous discovery. We present the Polifonia Web portal as a motivating scenario and case study to address research problems such as data reconciliation and serving generous interfaces in the music heritage domain.
Shopping Queries Image Dataset (SQID): An Image-Enriched ESCI Dataset for Exploring Multimodal Learning in Product Search
Authors: Marie Al Ghossein, Ching-Wei Chen, Jason Tang
Date: 2024-05-24 00:00:00
Tasks: Information Retrieval, Retrieval

Recent advances in the fields of Information Retrieval and Machine Learning have focused on improving the performance of search engines to enhance the user experience, especially in the world of online shopping. The focus has thus been on leveraging cutting-edge learning techniques and relying on large enriched datasets. This paper introduces the Shopping Queries Image Dataset (SQID), an extension of the Amazon Shopping Queries Dataset enriched with image information associated with 190,000 products. By integrating visual information, SQID facilitates research around multimodal learning techniques that can take into account both textual and visual information for improving product search and ranking. We also provide experimental results leveraging SQID and pretrained models, showing the value of using multimodal data for search and ranking. SQID is available at: https://github.com/Crossing-Minds/shopping-queries-image-dataset.
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
Authors: Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, Maarten de Rijke
Date: 2024-05-24 00:00:00
Tasks: Attribute, Recommendation Systems

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools that may help address these challenges. However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space of items and the behavior space of users, or overly simplistic control strategies (e.g., whether to rank or directly present existing results). To bridge these gap, we introduce ToolRec, a framework for LLM-empowered recommendations via tool learning that uses LLMs as surrogate users, thereby guiding the recommendation process and invoking external tools to generate a recommendation list that aligns closely with users' nuanced preferences. We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. The process factors in the nuances of the context and user preferences. The LLM then invokes external tools based on a user's attribute instructions and probes different segments of the item pool. We consider two types of attribute-oriented tools: rank tools and retrieval tools. Through the integration of LLMs, ToolRec enables conventional recommender systems to become external tools with a natural language interface. Extensive experiments verify the effectiveness of ToolRec, particularly in scenarios that are rich in semantic content.
Revisiting Day-ahead Electricity Price: Simple Model Save Millions
Authors: Linian Wang, Jianghong Liu, Huibin Zhang, Leye Wang
Date: 2024-05-20 00:00:00
Tasks: Decision Making, Time Series

Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveraging this prior, we propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year compared to existing methods. Our findings underscore the value of suitably integrating time series modeling with economic prior for enhanced electricity price forecasting accuracy.
Analog or Digital In-memory Computing? Benchmarking through Quantitative Modeling
Authors: Jiacong Sun, Pouya Houshmand, Marian Verhelst
Date: 2024-05-23 00:00:00
Tasks: Benchmarking

In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient, throughput-efficient and area-efficient machine learning at the edge. However, the differences in hardware architectures, array dimensions, and fabrication technologies among published IMC realizations have made it difficult to grasp their relative strengths. Moreover, previous studies have primarily focused on exploring and benchmarking the peak performance of a single IMC macro rather than full system performance on real workloads. This paper aims to address the lack of a quantitative comparison of Analog In-Memory Computing (AIMC) and Digital In-Memory Computing (DIMC) processor architectures. We propose an analytical IMC performance model that is validated against published implementations and integrated into a system-level exploration framework for comprehensive performance assessments on different workloads with varying IMC configurations. Our experiments show that while DIMC generally has higher computational density than AIMC, AIMC with large macro sizes may have better energy efficiency than DIMC on convolutional-layers and pointwise-layers, which can exploit high spatial unrolling. On the other hand, DIMC with small macro size outperforms AIMC on depthwise-layers, which feature limited spatial unrolling opportunities inside a macro.
Advanced Language Model-based Translator for English-Vietnamese Translation
Authors: Hoai Nam Nguyen, Thanh Trong Nguyen, Quoc Bao Nguyen, Vu Anh Tran
Date: 2024-05-27 00:00:00
Proceeding: 2024 2024 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

We introduce a transformative approach to English-Vietnamese translation, leveraging the cutting edge capabilities of the Gemma-7B-IT (Gemma Team et al. 2024) model. Enhanced by the Advanced Language Model-based Translator (ALMA) (Xu et al. 2023) methodology, our system significantly advances beyond the conventional Transformer models in handling complex linguistic contexts. This research details our robust training framework, experimental validations, and the rigorous evaluation processes that establish a new state-of-the-art for Vietnamese translation tasks. Our results emphatically surpass those of well-known systems such as VinAI Translate (Nguyen et al. 2022) and Google Translate (Google 2024b), demonstrating an improvement of over 12 BLEU scores against the previously top-performing systems. These achievements highlight the superior flexibility and contextual understanding capabilities of Large Language Models (LLMs) (Zhao et al. 2023) integrated within our ALMA framework, which excel in adapting to varied translation nuances and complexities. Capitalizing on these remarkable advancements, we have also introduced a user-centric translation product, available at https://www.doctranslate.io (Doctranslate 2023). This tool embodies our commitment to merging technological innovation with practical utility, offering users a seamless and high-quality translation experience.
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the Edge
Authors: Thomas Rüegg, Marco Giordano, Michele Magno
Date: 2023-07-07 00:00:00
Proceeding: AICAS 2023 7
Tasks: Homography Estimation, Keypoint Detection, Quantization, Simultaneous Localization and Mapping

Detection and description of keypoints in images is a fundamental component of a wide range of tasks such as Simultaneous Localization And Mapping (SLAM), image alignment and structure from motion (SfM). Efficient computation of these features is crucial for real-time applications and has been addressed by multiple handcrafted algorithms and, recently, by deep neural network-based detectors. Learned detectors achieve high detection performance, but pose high computational requirements, making them slow and impractical for low-power resource constraint platforms. This paper presents a quantized neural keypoint detector and descriptor optimized for edge devices exploiting two recent AI platforms such as MAX78000 by Analog Devices and the Coral AI USB accelerator from Google. To accommodate the diverse constraints and requirements of various applications, we propose and evaluate two model architectures (KP2DtinySmall and KP2DtinyFast) and deploy them on the aforementioned platforms using full 8-bit integer quantization. Furthermore, we extensively evaluate these models in terms of power, latency and accuracy, reporting results on three image sizes (88x88, 320x240 and 640x480), evaluating both quantized and non-quantized models. Fully quantized, KP2DtinySmall reduces network size by a factor of 54x while improving homographic estimation accuracy on 88x88 images on the most stringent threshold (Correctness d1) by 32.4% (0.550) and on 320x240 images by 10.7% (0.648) compared to the KeypointNet architecture by Yang You et. al. This result is achieved by designing a new network with low power platforms in mind, particularly addressing the lower resolution by increasing the density of detectable features. Deployed on the MAX78000 MCU, inference of low-resolution images is run at 59 FPS, consuming 1.1 mJ per image. On the Coral usb accelerator, KP2DtinyFast runs inference on low-resolution images at 527 FPS consuming 3.1 mJ, on high resolution it achieves 70 FPS at 19.9 mJ per inference.
Activator: GLU Activation Function as the Core Component of a Vision Transformer
Authors: Abdullah Nazhat Abdullah, Tarkan Aydin
Date: 2024-05-24 00:00:00

Transformer architecture currently represents the main driver behind many successes in a variety of tasks addressed by deep learning, especially the recent advances in natural language processing (NLP) culminating with large language models (LLM). In addition, transformer architecture has found a wide spread of interest from computer vision (CV) researchers and practitioners, allowing for many advancements in vision-related tasks and opening the door for multi-task and multi-modal deep learning architectures that share the same principle of operation. One drawback to these architectures is their reliance on the scaled dot product attention mechanism with the softmax activation function, which is computationally expensive and requires large compute capabilities both for training and inference. This paper investigates substituting the attention mechanism usually adopted for transformer architecture with an architecture incorporating gated linear unit (GLU) activation within a multi-layer perceptron (MLP) structure in conjunction with the default MLP incorporated in the traditional transformer design. Another step forward taken by this paper is to eliminate the second non-gated MLP to further reduce the computational cost. Experimental assessments conducted by this research show that both proposed modifications and reductions offer competitive performance in relation to baseline architectures, in support of the aims of this work in establishing a more efficient yet capable alternative to the traditional attention mechanism as the core component in designing transformer architectures.
Development of an open education resources (OER) system: a comparative analysis and implementation approach
Authors: Nimol Thuon, Wangrui Zhang
Date: 2024-05-26 00:00:00
Tasks: Management

Several institutions are collaborating on the development of a new web-based Open Education Resources (OER) system designed exclusively for non-commercial educational purposes. This initiative is underpinned by meticulous research aimed at constructing an OER system that optimizes user experiences across diverse user profiles. A significant emphasis is placed on utilizing open-source tools, frameworks, and technologies. The project includes a comparative analysis of the top five open-source Learning Management Systems (LMS), providing critical insights to inform the development process. The primary objective is to create a web-based system that facilitates the sharing of educational resources for non-commercial users, leveraging information and communication technologies. The project is structured around two key teams: a research team and a development team. This comprehensive approach is intended to establish a robust, user-centric OER system, informed by insights from existing platforms and the latest advancements in open education resource development.
Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier
Authors: Antonio Quintero-Rincón, Jorge Prendes, Valeria Muro, Carlos D'Giano
Date: 2024-05-21 00:00:00
Tasks: Classification, EEG, Seizure Detection, Sensitivity, Specificity

Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity, and specificity.
Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
Authors: Hao Zeng, Wei Zhong, Xingbai Xu
Date: 2024-05-20 00:00:00
Tasks: Transfer Learning

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR. Classical SAR model estimation often loses accuracy with small target data samples. Our framework enhances estimation and prediction by leveraging information from similar source data. We introduce a two-stage algorithm, consisting of a transferring stage and a debiasing stage, to estimate parameters and establish theoretical convergence rates for the estimators. Additionally, if the informative source data are unknown, we propose a transferable source detection algorithm using spatial residual bootstrap to maintain spatial dependence and derive its detection consistency. Simulation studies show our algorithm substantially improves the classical two-stage least squares estimator. We demonstrate our method's effectiveness in predicting outcomes in U.S. presidential swing states, where it outperforms traditional methods. In addition, our tranSAR model predicts that the Democratic party will win the 2024 U.S. presidential election.
Towards Completeness-Oriented Tool Retrieval for Large Language Models
Authors: Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Ji-Rong Wen
Date: 2024-05-25 00:00:00
Tasks: Retrieval

Recently, integrating external tools with Large Language Models (LLMs) has gained significant attention as an effective strategy to mitigate the limitations inherent in their pre-training data. However, real-world systems often incorporate a wide array of tools, making it impractical to input all tools into LLMs due to length limitations and latency constraints. Therefore, to fully exploit the potential of tool-augmented LLMs, it is crucial to develop an effective tool retrieval system. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions, frequently leading to the retrieval of redundant, similar tools. Consequently, these methods fail to provide a complete set of diverse tools necessary for addressing the multifaceted problems encountered by LLMs. In this paper, we propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools. Specifically, we first fine-tune the PLM-based retrieval models to capture the semantic relationships between queries and tools in the semantic learning stage. Subsequently, we construct three bipartite graphs among queries, scenes, and tools and introduce a dual-view graph collaborative learning framework to capture the intricate collaborative relationships among tools during the collaborative learning stage. Extensive experiments on both the open benchmark and the newly introduced ToolLens dataset show that COLT achieves superior performance. Notably, the performance of BERT-mini (11M) with our proposed model framework outperforms BERT-large (340M), which has 30 times more parameters. Furthermore, we will release ToolLens publicly to facilitate future research on tool retrieval.