pwc-archive/papers-with-abstracts huggingface dataset:
Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study
Authors: Yutan Huang, Chetan Arora, Wen Cheng Houng, Tanjila Kanij, Anuradha Madulgalla, John Grundy
Date: 2025-01-08 00:00:00
Tasks: Information Retrieval

[Context] Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making processes. However, deploying LLMs also presents diverse ethical challenges, and their mitigation strategies remain complex and domain-dependent. [Objective] This paper aims to identify and categorize the key ethical concerns associated with using LLMs, examine existing mitigation strategies, and assess the outstanding challenges in implementing these strategies across various domains. [Method] We conducted a systematic mapping study, reviewing 39 studies that discuss ethical concerns and mitigation strategies related to LLMs. We analyzed these ethical concerns using five ethical dimensions that we extracted based on various existing guidelines, frameworks, and an analysis of the mitigation strategies and implementation challenges. [Results] Our findings reveal that ethical concerns in LLMs are multi-dimensional and context-dependent. While proposed mitigation strategies address some of these concerns, significant challenges still remain. [Conclusion] Our results highlight that ethical issues often hinder the practical implementation of the mitigation strategies, particularly in high-stake areas like healthcare and public governance; existing frameworks often lack adaptability, failing to accommodate evolving societal expectations and diverse contexts.
Behavioural Analytics: Mathematics of the Mind
Authors: Richard Lane, Hannah State-Davey, Claire Taylor, Wendy Holmes, Rachel Boon, Mark Round
Date: 2025-01-07 00:00:00
Tasks: State Estimation, Time Series Analysis

Behavioural analytics provides insights into individual and crowd behaviour, enabling analysis of what previously happened and predictions for how people may be likely to act in the future. In defence and security, this analysis allows organisations to achieve tactical and strategic advantage through influence campaigns, a key counterpart to physical activities. Before action can be taken, online and real-world behaviour must be analysed to determine the level of threat. Huge data volumes mean that automated processes are required to attain an accurate understanding of risk. We describe the mathematical basis of technologies to analyse quotes in multiple languages. These include a Bayesian network to understand behavioural factors, state estimation algorithms for time series analysis, and machine learning algorithms for classification. We present results from studies of quotes in English, French, and Arabic, from anti-violence campaigners, politicians, extremists, and terrorists. The algorithms correctly identify extreme statements; and analysis at individual, group, and population levels detects both trends over time and sharp changes attributed to major geopolitical events. Group analysis shows that additional population characteristics can be determined, such as polarisation over particular issues and large-scale shifts in attitude. Finally, MP voting behaviour and statements from publicly-available records are analysed to determine the level of correlation between what people say and what they do.
TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations
Authors: Dian Tjondronegoro
Date: 2025-01-07 00:00:00

Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors, from healthcare to finance, education, and beyond. However, successfully implementing AI systems remains a complex challenge, requiring a comprehensive and methodologically sound framework. This paper contributes to this challenge by introducing the Trustworthy, Optimized, Adaptable, and Socio-Technologically harmonious (TOAST) framework. It draws on insights from various disciplines to align technical strategy with ethical values, societal responsibilities, and innovation aspirations. The TOAST framework is a novel approach designed to guide the implementation of AI systems, focusing on reliability, accountability, technical advancement, adaptability, and socio-technical harmony. By grounding the TOAST framework in healthcare case studies, this paper provides a robust evaluation of its practicality and theoretical soundness in addressing operational, ethical, and regulatory challenges in high-stakes environments, demonstrating how adaptable AI systems can enhance institutional efficiency, mitigate risks like bias and data privacy, and offer a replicable model for other sectors requiring ethically aligned and efficient AI integration.
Zoning in American Cities: Are Reforms Making a Difference? An AI-based Analysis
Authors: Arianna Salazar-Miranda, Emily Talen
Date: 2025-01-07 00:00:00

Cities are at the forefront of addressing global sustainability challenges, particularly those exacerbated by climate change. Traditional zoning codes, which often segregate land uses, have been linked to increased vehicular dependence, urban sprawl, and social disconnection, undermining broader social and environmental sustainability objectives. This study investigates the adoption and impact of form-based codes (FBCs), which aim to promote sustainable, compact, and mixed-use urban forms as a solution to these issues. Using Natural Language Processing (NLP) techniques, we analyzed zoning documents from over 2000 U.S. census-designated places to identify linguistic patterns indicative of FBC principles. Our findings reveal widespread adoption of FBCs across the country, with notable variations within regions. FBCs are associated with higher floor-to-area ratios, narrower and more consistent street setbacks, and smaller plots. We also find that places with FBCs have improved walkability, shorter commutes, and a higher share of multi-family housing. Our findings highlight the utility of NLP for evaluating zoning codes and underscore the potential benefits of form-based zoning reforms for enhancing urban sustainability.
A Study about Distribution and Acceptance of Conversational Agents for Mental Health in Germany: Keep the Human in the Loop?
Authors: Christina Lukas
Date: 2025-01-05 00:00:00

Good mental health enables individuals to cope with the normal stresses of life. In Germany, approximately one-quarter of the adult population is affected by mental illnesses. Teletherapy and digital health applications are available to bridge gaps in care and relieve healthcare professionals. The acceptance of these tools is a strongly influencing factor for their effectiveness, which also needs to be evaluated for AI-based conversational agents (CAs) (e. g. ChatGPT, Siri) to assess the risks and potential for integration into therapeutic practice. This study investigates the perspectives of both the general population and healthcare professionals with the following questions: 1. How frequently are CAs used for mental health? 2. How high is the acceptance of CAs in the field of mental health? 3. To what extent is the use of CAs in counselling, diagnosis, and treatment acceptable? To address these questions, two quantitative online surveys were conducted with 444 participants from the general population and 351 healthcare professionals. Statistical analyses show that 27 % of the surveyed population already confide their concerns to CAs. Not only experience with this technology but also experience with telemedicine shows a higher acceptance among both groups for using CAs for mental health. Additionally, participants from the general population were more likely to support CAs as companions controlled by healthcare professionals rather than as additional experts for the professionals. CAs have the potential to support mental health, particularly in counselling. Future research should examine the influence of different communication media and further possibilities of augmented intelligence. With the right balance between technology and human care, integration into patient-professional interaction can be achieved.
Defending Compute Thresholds Against Legal Loopholes
Authors: Matteo Pistillo, Pablo Villalobos
Date: 2025-01-03 00:00:00

Existing legal frameworks on AI rely on training compute thresholds as a proxy to identify potentially-dangerous AI models and trigger increased regulatory attention. In the United States, Section 4.2(a) of Executive Order 14110 instructs the Secretary of Commerce to require extensive reporting from developers of AI models above a certain training compute threshold. In the European Union, Article 51 of the AI Act establishes a presumption that AI models above a certain compute threshold have high impact capabilities and hence pose systemic risk, thus subjecting their developers to several obligations including capability evaluations, reporting, and incident monitoring. In this paper, we examine some enhancement techniques that are capable of decreasing training compute usage while preserving, or even increasing, model capabilities. Since training compute thresholds rely on training compute as a metric and trigger for increased regulatory attention, these capability-enhancing and compute-saving techniques could constitute a legal loophole to existing training compute thresholds. In particular, we concentrate on four illustrative techniques (fine-tuning, model reuse, model expansion, and above compute-optimal inference compute) with the goal of furthering the conversation about their implications on training compute thresholds as a legal mechanism and advancing policy recommendations that could address the relevant legal loopholes.
The Causal-Effect Score in Data Management
Authors: Felipe Azua, Leopoldo Bertossi
Date: 2025-02-04 00:00:00
Tasks: Management

The Causal Effect (CE) is a numerical measure of causal influence of variables on observed results. Despite being widely used in many areas, only preliminary attempts have been made to use CE as an attribution score in data management, to measure the causal strength of tuples for query answering in databases. In this work, we introduce, generalize and investigate the so-called Causal-Effect Score in the context of classical and probabilistic databases.
Aspects of Artificial Intelligence: Transforming Machine Learning Systems Naturally
Authors: Xiuzhan Guo
Date: 2025-02-03 00:00:00

In this paper, we study the machine learning elements which we are interested in together as a machine learning system, consisting of a collection of machine learning elements and a collection of relations between the elements. The relations we concern are algebraic operations, binary relations, and binary relations with composition that can be reasoned categorically. A machine learning system transformation between two systems is a map between the systems, which preserves the relations we concern. The system transformations given by quotient or clustering, representable functor, and Yoneda embedding are highlighted and discussed by machine learning examples. An adjunction between machine learning systems, a special machine learning system transformation loop, provides the optimal way of solving problems. Machine learning system transformations are linked and compared by their maps at 2-cell, natural transformations. New insights and structures can be obtained from universal properties and algebraic structures given by monads, which are generated from adjunctions.
K Nearest Neighbor-Guided Trajectory Similarity Learning
Authors: Yanchuan Chang, Xu Cai, Christian S. Jensen, Jianzhong Qi
Date: 2025-02-01 00:00:00

Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting similarity signals in the training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism capable of learning multi-granularity trajectory patterns and a k nearest neighbor-based loss that guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these two innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini can outperform the state-of-the-art models by 22% in accuracy on average when learning trajectory similarity measures.
Querying Databases with Function Calling
Authors: Connor Shorten, Charles Pierse, Thomas Benjamin Smith, Karel D'Oosterlinck, Tuana Celik, Erika Cardenas, Leonie Monigatti, Mohd Shukri Hasan, Edward Schmuhl, Daniel Williams, Aravind Kesiraju, Bob van Luijt
Date: 2025-01-23 00:00:00

The capabilities of Large Language Models (LLMs) are rapidly accelerating largely thanks to their integration with external tools. Querying databases is among the most effective of these integrations, enabling LLMs to access private or continually updating data. While Function Calling is the most common method for interfacing external tools to LLMs, its application to database querying as a tool has been underexplored. We propose a tool definition for database querying that unifies accessing data with search queries, filters, or a combination both, as well as transforming results with aggregation and groupby operators. To evaluate its effectiveness, we conduct a study with 8 LLMs spanning 5 model families. We present a novel pipeline adapting the Gorilla LLM framework to create synthetic database schemas and queries. We primarily evaluate the models with the Exact Match of predicted and ground truth query APIs. Among the models tested, Claude 3.5 Sonnet achieves the highest performance with an Exact Match score of 74.3%, followed by GPT-4o mini at 73.7%, and GPT-4o at 71.8%. We further breakdown these results per API component utilized and across synthetic use cases. We find that LLMs are highly effective at utilizing operators on boolean properties, but struggle with text property filters. Across use cases we find robust results with the higher performing models such as GPT-4o, but significant performance variance across use cases from lower performing models. We additionally conduct ablation studies exploring the impact of parallel tool calling, adding a rationale as an argument of the tool call, using a separate tool per database collection, and tool calling with structured outputs. Our findings demonstrate the effectiveness of enabling LLMs to query databases with Function Calling. We have open-sourced our experimental code and results at github.com/weaviate/gorilla.
LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
Authors: Tzu-Tao Chang, Shivaram Venkataraman
Date: 2025-02-04 00:00:00
Tasks: GPU, Video Understanding

Cross-attention is commonly adopted in multimodal large language models (MLLMs) for integrating visual information into the language backbone. However, in applications with large visual inputs, such as video understanding, processing a large number of visual tokens in cross-attention layers leads to high memory demands and often necessitates distributed computation across multiple GPUs. Existing distributed attention mechanisms face significant communication overheads, making cross-attention layers a critical bottleneck for efficient training and inference of MLLMs. To address this, we propose LV-XAttn, a distributed, exact cross-attention mechanism with minimal communication overhead. We observe that in applications involving large visual inputs, the size of the query block is typically much smaller than that of the key-value blocks. Thus, in LV-XAttn we keep the large key-value blocks locally on each GPU and exchange smaller query blocks across GPUs. We also introduce an efficient activation recomputation technique to support longer visual context. We theoretically analyze the communication benefits of LV-XAttn and show that it can achieve speedups for a wide range of models. Our evaluations with Llama 3-V, mPLUG-Owl3 and OpenFlamingo models find that LV-XAttn achieves up to 10.62$\times$ end-to-end speedup compared to existing approaches.
Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb
Authors: Fotis I. Giasemis, Vladimir Lončar, Bertrand Granado, Vladimir Vava Gligorov
Date: 2025-02-04 00:00:00
Tasks: GPU, Graph Neural Network

In high-energy physics, the increasing luminosity and detector granularity at the Large Hadron Collider are driving the need for more efficient data processing solutions. Machine Learning has emerged as a promising tool for reconstructing charged particle tracks, due to its potentially linear computational scaling with detector hits. The recent implementation of a graph neural network-based track reconstruction pipeline in the first level trigger of the LHCb experiment on GPUs serves as a platform for comparative studies between computational architectures in the context of high-energy physics. This paper presents a novel comparison of the throughput of ML model inference between FPGAs and GPUs, focusing on the first step of the track reconstruction pipeline$\unicode{x2013}$an implementation of a multilayer perceptron. Using HLS4ML for FPGA deployment, we benchmark its performance against the GPU implementation and demonstrate the potential of FPGAs for high-throughput, low-latency inference without the need for an expertise in FPGA development and while consuming significantly less power.
Ilargi: a GPU Compatible Factorized ML Model Training Framework
Authors: Wenbo Sun, Rihan Hai
Date: 2025-02-04 00:00:00
Tasks: Computational Efficiency, CPU, Data Integration, GPU

The machine learning (ML) training over disparate data sources traditionally involves materialization, which can impose substantial time and space overhead due to data movement and replication. Factorized learning, which leverages direct computation on disparate sources through linear algebra (LA) rewriting, has emerged as a viable alternative to improve computational efficiency. However, the adaptation of factorized learning to leverage the full capabilities of modern LA-friendly hardware like GPUs has been limited, often requiring manual intervention for algorithm compatibility. This paper introduces Ilargi, a novel factorized learning framework that utilizes matrix-represented data integration (DI) metadata to facilitate automatic factorization across CPU and GPU environments without the need for costly relational joins. Ilargi incorporates an ML-based cost estimator to intelligently selects between factorization and materialization based on data properties, algorithm complexity, hardware environments, and their interactions. This strategy ensures up to 8.9x speedups on GPUs and achieves over 20% acceleration in batch ML training workloads, thereby enhancing the practicability of ML training across diverse data integration scenarios and hardware platforms. To our knowledge, this work is the very first effort in GPU-compatible factorized learning.
Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
Authors: Shubham Malhotra, Fnu Yashu, Muhammad Saqib, Dipkumar Mehta, Jagdish Jangid, Sachin Dixit
Date: 2025-02-03 00:00:00
Tasks: Deep Reinforcement Learning, rllib, Scheduling

This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods.
ModServe: Scalable and Resource-Efficient Large Multimodal Model Serving
Authors: Haoran Qiu, Anish Biswas, Zihan Zhao, Jayashree Mohan, Alind Khare, Esha Choukse, Íñigo Goiri, Zeyu Zhang, Haiying Shen, Chetan Bansal, Ramachandran Ramjee, Rodrigo Fonseca
Date: 2025-02-02 00:00:00
Tasks: Decoder, GPU, model, Scheduling

Large multimodal models (LMMs) demonstrate impressive capabilities in understanding images, videos, and audio beyond text. However, efficiently serving LMMs in production environments poses significant challenges due to their complex architectures and heterogeneous characteristics across their multi-stage inference pipelines. We present the first comprehensive systems analysis of two prominent LMM architectures, decoder-only and cross-attention, across six representative open-source models, revealing key systems design implications. We also present an in-depth analysis of production LMM inference traces, uncovering unique workload characteristics, including variable, heavy-tailed request distributions and bursty traffic patterns. Based on these insights, we propose ModServe, a modular LMM serving system that decouples stages for independent optimization and adaptive scaling. ModServe dynamically reconfigures stages and handles bursty traffic with modality-aware scheduling and autoscaling to meet tail latency SLOs while minimizing costs. ModServe achieves 3.3-5.5x higher throughput (leading to 25-41.3% cost saving) while meeting SLOs on a 128-GPU cluster with production traces.
FedRIR: Rethinking Information Representation in Federated Learning
Authors: Yongqiang Huang, Zerui Shao, Ziyuan Yang, Zexin Lu, Yi Zhang
Date: 2025-02-02 00:00:00
Tasks: Federated Learning

Mobile and Web-of-Things (WoT) devices at the network edge generate vast amounts of data for machine learning applications, yet privacy concerns hinder centralized model training. Federated Learning (FL) allows clients (devices) to collaboratively train a shared model coordinated by a central server without transfer private data, but inherent statistical heterogeneity among clients presents challenges, often leading to a dilemma between clients' needs for personalized local models and the server's goal of building a generalized global model. Existing FL methods typically prioritize either global generalization or local personalization, resulting in a trade-off between these two objectives and limiting the full potential of diverse client data. To address this challenge, we propose a novel framework that simultaneously enhances global generalization and local personalization by Rethinking Information Representation in the Federated learning process (FedRIR). Specifically, we introduce Masked Client-Specific Learning (MCSL), which isolates and extracts fine-grained client-specific features tailored to each client's unique data characteristics, thereby enhancing personalization. Concurrently, the Information Distillation Module (IDM) refines the global shared features by filtering out redundant client-specific information, resulting in a purer and more robust global representation that enhances generalization. By integrating the refined global features with the isolated client-specific features, we construct enriched representations that effectively capture both global patterns and local nuances, thereby improving the performance of downstream tasks on the client. The code is available at https://github.com/Deep-Imaging-Group/FedRIR.