pwc-archive/papers-with-abstracts huggingface dataset:
Tree-Sliced Wasserstein Distance: A Geometric Perspective
Authors: Viet-Hoang Tran, Trang Pham, Tho Tran, Minh Khoi Nguyen Nhat, Thanh Chu, Tam Le, Tan M. Nguyen
Date: 2024-06-19 00:00:00
Tasks: Computational Efficiency, Style Transfer

Many variants of Optimal Transport (OT) have been developed to address its heavy computation. Among them, notably, Sliced Wasserstein (SW) is widely used for application domains by projecting the OT problem onto one-dimensional lines, and leveraging the closed-form expression of the univariate OT to reduce the computational burden. However, projecting measures onto low-dimensional spaces can lead to a loss of topological information. To mitigate this issue, in this work, we propose to replace one-dimensional lines with a more intricate structure, called tree systems. This structure is metrizable by a tree metric, which yields a closed-form expression for OT problems on tree systems. We provide an extensive theoretical analysis to formally define tree systems with their topological properties, introduce the concept of splitting maps, which operate as the projection mechanism onto these structures, then finally propose a novel variant of Radon transform for tree systems and verify its injectivity. This framework leads to an efficient metric between measures, termed Tree-Sliced Wasserstein distance on Systems of Lines (TSW-SL). By conducting a variety of experiments on gradient flows, image style transfer, and generative models, we illustrate that our proposed approach performs favorably compared to SW and its variants.
Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Authors: Xuehao Zhai, Junqi Jiang, Adam Dejl, Antonio Rago, Fangce Guo, Francesca Toni, Aruna Sivakumar
Date: 2024-06-19 00:00:00
Tasks: counterfactual

Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitated the collection of multi-modal mobility data, offering valuable insights into daily activity patterns. Many studies have adopted advanced data-driven techniques to explore the potential of these multi-modal mobility data in land use inference. However, existing studies often process samples independently, ignoring the spatial correlations among neighbouring objects and heterogeneity among different services. Furthermore, the inherently low interpretability of complex deep learning methods poses a significant barrier in urban planning, where transparency and extrapolability are crucial for making long-term policy decisions. To overcome these challenges, we introduce an explainable framework for inferring land use that synergises heterogeneous graph neural networks (HGNs) with Explainable AI techniques, enhancing both accuracy and explainability. The empirical experiments demonstrate that the proposed HGNs significantly outperform baseline graph neural networks for all six land-use indicators, especially in terms of 'office' and 'sustenance'. As explanations, we consider feature attribution and counterfactual explanations. The analysis of feature attribution explanations shows that the symmetrical nature of the `residence' and 'work' categories predicted by the framework aligns well with the commuter's 'work' and 'recreation' activities in London. The analysis of the counterfactual explanations reveals that variations in node features and types are primarily responsible for the differences observed between the predicted land use distribution and the ideal mixed state. These analyses demonstrate that the proposed HGNs can suitably support urban stakeholders in their urban planning and policy-making.
Converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion
Authors: Pranav Janjani, Mayank Palan, Sarvesh Shirude, Ninad Shegokar, Sunny Kumar, Faruk Kazi
Date: 2024-06-19 00:00:00
Tasks: Informativeness

Recent advances in large language models (LLMs) have led to new summarization strategies, offering an extensive toolkit for extracting important information. However, these approaches are frequently limited by their reliance on isolated sources of data. The amount of information that can be gathered is limited and covers a smaller range of themes, which introduces the possibility of falsified content and limited support for multilingual and multimodal data. The paper proposes a novel approach to summarization that tackles such challenges by utilizing the strength of multiple sources to deliver a more exhaustive and informative understanding of intricate topics. The research progresses beyond conventional, unimodal sources such as text documents and integrates a more diverse range of data, including YouTube playlists, pre-prints, and Wikipedia pages. The aforementioned varied sources are then converted into a unified textual representation, enabling a more holistic analysis. This multifaceted approach to summary generation empowers us to extract pertinent information from a wider array of sources. The primary tenet of this approach is to maximize information gain while minimizing information overlap and maintaining a high level of informativeness, which encourages the generation of highly coherent summaries.
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes
Authors: Vansh Nagpal, Siva Likitha Valluru, Kausik Lakkaraju, Biplav Srivastava
Date: 2024-06-19 00:00:00
Tasks: Multi-Armed Bandits, Nutrition

A common, yet regular, decision made by people, whether healthy or with any health condition, is to decide what to have in meals like breakfast, lunch, and dinner, consisting of a combination of foods for appetizer, main course, side dishes, desserts, and beverages. However, often this decision is seen as a trade-off between nutritious choices (e.g., low salt and sugar) or convenience (e.g., inexpensive, fast to prepare/obtain, taste better). In this preliminary work, we present a data-driven approach for the novel meal recommendation problem that can explore and balance choices for both considerations while also reasoning about a food's constituents and cooking process. Beyond the problem formulation, our contributions also include a goodness measure, a recipe conversion method from text to the recently introduced multimodal rich recipe representation (R3) format, and learning methods using contextual bandits that show promising results.
Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies
Authors: Isaac Sheidlower, Emma Bethel, Douglas Lilly, Reuben M. Aronson, Elaine Schaertl Short
Date: 2024-06-19 00:00:00
Tasks: Reinforcement Learning (RL)

It is crucial that users are empowered to take advantage of the functionality of a robot and use their understanding of that functionality to perform novel and creative tasks. Given a robot trained with Reinforcement Learning (RL), a user may wish to leverage that autonomy along with their familiarity of how they expect the robot to behave to collaborate with the robot. One technique is for the user to take control of some of the robot's action space through teleoperation, allowing the RL policy to simultaneously control the rest. We formalize this type of shared control as Partitioned Control (PC). However, this may not be possible using an out-of-the-box RL policy. For example, a user's control may bring the robot into a failure state from the policy's perspective, causing it to act unexpectedly and hindering the success of the user's desired task. In this work, we formalize this problem and present Imaginary Out-of-Distribution Actions, IODA, an initial algorithm which empowers users to leverage their expectations of a robot's behavior to accomplish new tasks. We deploy IODA in a user study with a real robot and find that IODA leads to both better task performance and a higher degree of alignment between robot behavior and user expectation. We also show that in PC, there is a strong and significant correlation between task performance and the robot's ability to meet user expectations, highlighting the need for approaches like IODA. Code is available at https://github.com/AABL-Lab/ioda_roman_2024
Developing Story: Case Studies of Generative AI's Use in Journalism
Authors: Natalie Grace Brigham, Chongjiu Gao, Tadayoshi Kohno, Franziska Roesner, Niloofar Mireshghallah
Date: 2024-06-19 00:00:00
Tasks: Articles

Journalists are among the many users of large language models (LLMs). To better understand the journalist-AI interactions, we conduct a study of LLM usage by two news agencies through browsing the WildChat dataset, identifying candidate interactions, and verifying them by matching to online published articles. Our analysis uncovers instances where journalists provide sensitive material such as confidential correspondence with sources or articles from other agencies to the LLM as stimuli and prompt it to generate articles, and publish these machine-generated articles with limited intervention (median output-publication ROUGE-L of 0.62). Based on our findings, we call for further research into what constitutes responsible use of AI, and the establishment of clear guidelines and best practices on using LLMs in a journalistic context.
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
Authors: Long Bai, Tong Chen, Qiaozhi Tan, Wan Jun Nah, Yanheng Li, ZhiCheng He, Sishen Yuan, Zhen Chen, Jinlin Wu, Mobarakol Islam, Zhen Li, Hongbin Liu, Hongliang Ren
Date: 2024-06-19 00:00:00
Tasks: Exposure Correction, Image Enhancement, Navigate, Representation Learning

Wireless Capsule Endoscopy (WCE) is highly valued for its non-invasive and painless approach, though its effectiveness is compromised by uneven illumination from hardware constraints and complex internal dynamics, leading to overexposed or underexposed images. While researchers have discussed the challenges of low-light enhancement in WCE, the issue of correcting for different exposure levels remains underexplored. To tackle this, we introduce EndoUIC, a WCE unified illumination correction solution using an end-to-end promptable diffusion transformer (DiT) model. In our work, the illumination prompt module shall navigate the model to adapt to different exposure levels and perform targeted image enhancement, in which the Adaptive Prompt Integration (API) and Global Prompt Scanner (GPS) modules shall further boost the concurrent representation learning between the prompt parameters and features. Besides, the U-shaped restoration DiT model shall capture the long-range dependencies and contextual information for unified illumination restoration. Moreover, we present a novel Capsule-endoscopy Exposure Correction (CEC) dataset, including ground-truth and corrupted image pairs annotated by expert photographers. Extensive experiments against a variety of state-of-the-art (SOTA) methods on four datasets showcase the effectiveness of our proposed method and components in WCE illumination restoration, and the additional downstream experiments further demonstrate its utility for clinical diagnosis and surgical assistance.
Multilingual De-Duplication Strategies: Applying scalable similarity search with monolingual & multilingual embedding models
Authors: Stefan Pasch, Dimitirios Petridis, Jannic Cutura
Date: 2024-06-19 00:00:00
Tasks: Computational Efficiency, Translation

This paper addresses the deduplication of multilingual textual data using advanced NLP tools. We compare a two-step method involving translation to English followed by embedding with mpnet, and a multilingual embedding model (distiluse). The two-step approach achieved a higher F1 score (82% vs. 60%), particularly with less widely used languages, which can be increased up to 89% by leveraging expert rules based on domain knowledge. We also highlight limitations related to token length constraints and computational efficiency. Our methodology suggests improvements for future multilingual deduplication tasks.
An Embedded Intelligent System for Attendance Monitoring
Authors: Touzene Abderraouf, Abed Abdeljalil Wassim, Slimane Larabi
Date: 2024-06-19 00:00:00
Tasks: Management

In this paper, we propose an intelligent embedded system for monitoring class attendance and sending the attendance list to a remote computer. The proposed system consists of two parts : an embedded device (Raspberry with PI camera) for facial recognition and a web application for attendance management. The proposed solution take into account the different challenges: the limited resources of the Raspberry Pi, the need to adapt the facial recognition model and achieving acceptable performance using images provided by the Raspberry Pi camera.
From Single Agent to Multi-Agent: Improving Traffic Signal Control
Authors: Maksim Tislenko, Dmitrii Kisilev
Date: 2024-06-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Traffic Signal Control

Due to accelerating urbanization, the importance of solving the signal control problem increases. This paper analyzes various existing methods and suggests options for increasing the number of agents to reduce the average travel time. Experiments were carried out with 2 datasets. The results show that in some cases, the implementation of multiple agents can improve existing methods. For a fine-tuned large language model approach there is small enhancement on all metrics.
Development of a Dual-Input Neural Model for Detecting AI-Generated Imagery
Authors: Jonathan Gallagher, William Pugsley
Date: 2024-06-19 00:00:00
Tasks: Misinformation

Over the past years, images generated by artificial intelligence have become more prevalent and more realistic. Their advent raises ethical questions relating to misinformation, artistic expression, and identity theft, among others. The crux of many of these moral questions is the difficulty in distinguishing between real and fake images. It is important to develop tools that are able to detect AI-generated images, especially when these images are too realistic-looking for the human eye to identify as fake. This paper proposes a dual-branch neural network architecture that takes both images and their Fourier frequency decomposition as inputs. We use standard CNN-based methods for both branches as described in Stuchi et al. [7], followed by fully-connected layers. Our proposed model achieves an accuracy of 94% on the CIFAKE dataset, which significantly outperforms classic ML methods and CNNs, achieving performance comparable to some state-of-the-art architectures, such as ResNet.
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning
Authors: Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi, Dinesh Manocha
Date: 2024-06-19 00:00:00
Tasks: Attribute, Few-Shot Learning, Prompt Engineering

Image-text contrastive models such as CLIP learn transferable and robust representations for zero-shot transfer to a variety of downstream tasks. However, to obtain strong downstream performances, prompts need to be carefully curated, which can be a tedious engineering task. To address the issue of manual prompt engineering, prompt-tuning is used where a set of contextual vectors are learned by leveraging information from the training data. Despite their effectiveness, existing prompt-tuning frameworks often lack interpretability, thus limiting their ability to understand the compositional nature of images. In this work, we first identify that incorporating compositional attributes (e.g., a "green" tree frog) in the design of manual prompts can significantly enhance image-text alignment scores. Building upon this observation, we propose a novel and interpretable prompt-tuning method named IntCoOp, which learns to jointly align attribute-level inductive biases and class embeddings during prompt-tuning. To assess the effectiveness of our approach, we evaluate IntCoOp across two representative tasks in a few-shot learning setup: generalization to novel classes, and unseen domain shifts. Through extensive experiments across 10 downstream datasets on CLIP, we find that introducing attribute-level inductive biases leads to superior performance against state-of-the-art prompt tuning frameworks. Notably, in a 16-shot setup, IntCoOp improves CoOp by 7.35% in average performance across 10 diverse datasets.
Root-KGD: A Novel Framework for Root Cause Diagnosis Based on Knowledge Graph and Industrial Data
Authors: Jiyu Chen, Jinchuan Qian, Xinmin Zhang, Zhihuan Song
Date: 2024-06-19 00:00:00
Tasks: Fault Diagnosis

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
Authors: Jirui Qi, Gabriele Sarti, Raquel Fernández, Arianna Bisazza
Date: 2024-06-19 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain. Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers. However, self-citing LLMs often struggle to match the required format, refer to non-existent sources, and fail to faithfully reflect LLMs' context usage throughout the generation. In this work, we present MIRAGE --Model Internals-based RAG Explanations -- a plug-and-play approach using model internals for faithful answer attribution in RAG applications. MIRAGE detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction via saliency methods. We evaluate our proposed approach on a multilingual extractive QA dataset, finding high agreement with human answer attribution. On open-ended QA, MIRAGE achieves citation quality and efficiency comparable to self-citation while also allowing for a finer-grained control of attribution parameters. Our qualitative evaluation highlights the faithfulness of MIRAGE's attributions and underscores the promising application of model internals for RAG answer attribution.
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training
Authors: Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
Date: 2024-06-19 00:00:00

Generative models of language exhibit impressive capabilities but still place non-negligible probability mass over undesirable outputs. In this work, we address the task of updating a model to avoid unwanted outputs while minimally changing model behavior otherwise, a challenge we refer to as a minimal targeted update. We first formalize the notion of a minimal targeted update and propose a method to achieve such updates using negative examples from a model's generations. Our proposed Targeted Negative Training (TNT) results in updates that keep the new distribution close to the original, unlike existing losses for negative signal which push down probability but do not control what the updated distribution will be. In experiments, we demonstrate that TNT yields a better trade-off between reducing unwanted behavior and maintaining model generation behavior than baselines, paving the way towards a modeling paradigm based on iterative training updates that constrain models from generating undesirable outputs while preserving their impressive capabilities.
Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health
Authors: Bo Wen, Raquel Norel, Julia Liu, Thaddeus Stappenbeck, Farhana Zulkernine, Huamin Chen
Date: 2024-06-19 00:00:00
Tasks: Chatbot

The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity.
Improving GFlowNets with Monte Carlo Tree Search
Authors: Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry Vetrov
Date: 2024-06-19 00:00:00

Generative Flow Networks (GFlowNets) treat sampling from distributions over compositional discrete spaces as a sequential decision-making problem, training a stochastic policy to construct objects step by step. Recent studies have revealed strong connections between GFlowNets and entropy-regularized reinforcement learning. Building on these insights, we propose to enhance planning capabilities of GFlowNets by applying Monte Carlo Tree Search (MCTS). Specifically, we show how the MENTS algorithm (Xiao et al., 2019) can be adapted for GFlowNets and used during both training and inference. Our experiments demonstrate that this approach improves the sample efficiency of GFlowNet training and the generation fidelity of pre-trained GFlowNet models.