pwc-archive/papers-with-abstracts huggingface dataset:
Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools
Authors: Yingbo Ma, Yukyeong Song, Jeremy A. Balch, Yuanfang Ren, Divya Vellanki, Zhenhong Hu, Meghan Brennan, Suraj Kolla, Ziyuan Guan, Brooke Armfield, Tezcan Ozrazgat-Baslanti, Parisa Rashidi, Tyler J. Loftus, Azra Bihorac, Benjamin Shickel
Date: 2024-07-10 00:00:00
Tasks: Articles, Ethics

As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce.
The Voice: Lessons on Trustworthy Conversational Agents from "Dune"
Authors: Philip Feldman
Date: 2024-07-10 00:00:00

The potential for untrustworthy conversational agents presents a significant threat for covert social manipulation. Taking inspiration from Frank Herbert's "Dune", where the Bene Gesserit Sisterhood uses the Voice for influence, manipulation, and control of people, we explore how generative AI provides a way to implement individualized influence at industrial scales. Already, these models can manipulate communication across text, image, speech, and most recently video. They are rapidly becoming affordable enough for any organization of even moderate means to train and deploy. If employed by malicious actors, they risk becoming powerful tools for shaping public opinion, sowing discord, and undermining organizations from companies to governments. As researchers and developers, it is crucial to recognize the potential for such weaponization and to explore strategies for prevention, detection, and defense against these emerging forms of sociotechnical manipulation.
Monitoring Time-Varying Changes of Historic Structures Through Photogrammetry-Driven Digital Twinning
Authors: Xiangxiong Kong
Date: 2024-07-09 00:00:00

Historic structures are important for our society but could be prone to structural deterioration due to long service durations and natural impacts. Monitoring the deterioration of historic structures becomes essential for stakeholders to take appropriate interventions. Existing work in the literature primarily focuses on assessing the structural damage at a given moment instead of evaluating the development of deterioration over time. To address this gap, we proposed a novel five-component digital twin framework to monitor time-varying changes in historic structures. A testbed of a casemate in Fort Soledad on the island of Guam was selected to validate our framework. Using this testbed, key implementation steps in our digital twin framework were performed. The findings from this study confirm that our digital twin framework can effectively monitor deterioration over time, which is an urgent need in the cultural heritage preservation community.
AI Companions Reduce Loneliness
Authors: Julian De Freitas, Ahmet K Uguralp, Zeliha O Uguralp, Puntoni Stefano
Date: 2024-07-09 00:00:00

Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness. Behavioral research provides little insight into whether these applications are effective at alleviating loneliness. We address this question by focusing on AI companions applications designed to provide consumers with synthetic interaction partners. Studies 1 and 2 find suggestive evidence that consumers use AI companions to alleviate loneliness, by employing a novel methodology for fine tuning large language models to detect loneliness in conversations and reviews. Study 3 finds that AI companions successfully alleviate loneliness on par only with interacting with another person, and more than other activities such watching YouTube videos. Moreover, consumers underestimate the degree to which AI companions improve their loneliness. Study 4 uses a longitudinal design and finds that an AI companion consistently reduces loneliness over the course of a week. Study 5 provides evidence that both the chatbots' performance and, especially, whether it makes users feel heard, explain reductions in loneliness. Study 6 provides an additional robustness check for the loneliness alleviating benefits of AI companions.
Making LLMs Work for Enterprise Data Tasks
Authors: Çağatay Demiralp, Fabian Wenz, Peter Baile Chen, Moe Kayali, Nesime Tatbul, Michael Stonebraker
Date: 2024-07-22 00:00:00
Tasks: Management, Text to SQL, Text-To-SQL

Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs' performance is tied to their training data, a crucial question is how useful they can be in improving enterprise database management and analysis tasks. To address this, we contribute experimental results on LLMs' performance for text-to-SQL and semantic column-type detection tasks on enterprise datasets. The performance of LLMs on enterprise data is significantly lower than on benchmark datasets commonly used. Informed by our findings and feedback from industry practitioners, we identify three fundamental challenges -- latency, cost, and quality -- and propose potential solutions to use LLMs in enterprise data workflows effectively.
Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks
Authors: Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
Date: 2024-08-05 00:00:00
Tasks: Quantization

Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization problems described over a network of nodes and proposes a computationally efficient algorithm that solves distributed convex problems and optimally finds the solution to locally non-convex objective functions. In contrast to batch gradient optimization in some literature, our algorithm is on a single-time scale with no extra inner consensus loop. It evaluates one gradient entry per node per time. Further, the algorithm addresses link-level nonlinearity representing, for example, logarithmic quantization of the exchanged data or clipping of the exchanged data bits. Leveraging perturbation-based theory and algebraic Laplacian network analysis proves optimal convergence and dynamics stability over time-varying and switching networks. The time-varying network setup might be due to packet drops or link failures. Despite the nonlinear nature of the dynamics, we prove exact convergence in the face of odd sign-preserving sector-bound nonlinear data transmission over the links. Illustrative numerical simulations further highlight our contributions.
LitSearch: A Retrieval Benchmark for Scientific Literature Search
Authors: Anirudh Ajith, Mengzhou Xia, Alexis Chevalier, Tanya Goyal, Danqi Chen, Tianyu Gao
Date: 2024-07-10 00:00:00
Tasks: Articles, Reranking, Retrieval

Literature search questions, such as "Where can I find research on the evaluation of consistency in generated summaries?" pose significant challenges for modern search engines and retrieval systems. These questions often require a deep understanding of research concepts and the ability to reason across entire articles. In this work, we introduce LitSearch, a retrieval benchmark comprising 597 realistic literature search queries about recent ML and NLP papers. LitSearch is constructed using a combination of (1) questions generated by GPT-4 based on paragraphs containing inline citations from research papers and (2) questions manually written by authors about their recently published papers. All LitSearch questions were manually examined or edited by experts to ensure high quality. We extensively benchmark state-of-the-art retrieval models and also evaluate two LLM-based reranking pipelines. We find a significant performance gap between BM25 and state-of-the-art dense retrievers, with a 24.8% absolute difference in recall@5. The LLM-based reranking strategies further improve the best-performing dense retriever by 4.4%. Additionally, commercial search engines and research tools like Google Search perform poorly on LitSearch, lagging behind the best dense retriever by up to 32 recall points. Taken together, these results show that LitSearch is an informative new testbed for retrieval systems while catering to a real-world use case.
Disturbance-based Discretization, Differentiable IDS Channel, and an IDS-Correcting Code for DNA Storage
Authors: Alan J. X. Guo, Mengyi Wei, Yufan Dai, Yali Wei, Pengchen Zhang
Date: 2024-07-10 00:00:00

Insertion, deletion, and substitution (IDS) error-correcting codes have garnered increased attention with recent advancements in DNA storage technology. However, a universal method for designing tailored IDS-correcting codes across varying channel settings remains underexplored. We present an autoencoder-based approach, THEA-code, aimed at efficiently generating IDS-correcting codes for complex IDS channels. In the work, a disturbance-based discretization is proposed to discretize the features of the autoencoder, and a simulated differentiable IDS channel is developed as a differentiable alternative for IDS operations. These innovations facilitate the successful convergence of the autoencoder, producing channel-customized IDS-correcting codes that demonstrate commendable performance across complex IDS channels, particularly in the realistic DNA storage channel.
WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
Authors: Peizhuo Li, Sebastian Starke, Yuting Ye, Olga Sorkine-Hornung
Date: 2024-07-11 00:00:00
Proceeding: SIGGRAPH 2024 7
Tasks: Retrieval

We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we propose a phase manifold consisting of multiple closed curves, each corresponding to a latent amplitude. With our proposed vector quantized periodic autoencoder, we learn a shared phase manifold for multiple characters, such as a human and a dog, without any supervision. This is achieved by exploiting the discrete structure and a shallow network as bottlenecks, such that semantically similar motions are clustered into the same curve of the manifold, and the motions within the same component are aligned temporally by the phase variable. In combination with an improved motion matching framework, we demonstrate the manifold's capability of timing and semantics alignment in several applications, including motion retrieval, transfer and stylization. Code and pre-trained models for this paper are available at https://peizhuoli.github.io/walkthedog.
Centralization in Attester-Proposer Separation
Authors: Mallesh Pai, Max Resnick
Date: 2024-08-06 00:00:00

We show that Execution Tickets and Execution Auctions dramatically increase centralization in the market for block proposals, even without multi-block MEV concerns. Previous analyses have insufficiently or incorrectly modeled the interaction between ahead-of-time auctions and just-in-time (JIT) auctions. We study a model where bidders compete in an execution auction ahead of time, and then the winner holds a JIT auction to resell the proposal rights when the slot arrives. During the execution auction, bidders only know the distribution of their valuations. Bidders then draw values from their distributions and compete in the JIT auction. We show that a bidder who wins the execution auction is substantially advantaged in the JIT auction since they can set a reserve price higher than their own realized value for the slot to increase their revenue. As a result, there is a strong centralizing force in the execution auction, which allows the ex-ante strongest bidder to win the execution auction every time, and similarly gives them the strongest incentive to buy up all the tickets. Similar results trivially apply if the resale market is imperfect, since that only reinforces the advantages of the ex-ante strong buyer. To reiterate, these results do not require the bidders to employ multi-block MEV strategies, although if they did, it would likely amplify the centralizing effects.
Lattice operations for the stable set in substitutable matching markets via re-equilibration dynamics
Authors: Agustin G. Bonifacio, Noelia Juarez, Paola B. Manasero
Date: 2024-07-30 00:00:00

We compute the lattice operations for the (pairwise) stable set in two-sided matching markets where only substitutability on agents' choice functions is imposed. To do this, we use Tarski operators defined on the lattices of worker-quasi-stable and firm-quasi-stable matchings. These operators resemble lay-off and vacancy chain dynamics, respectively. First, we compute the lattice operations in the many-to-one model. Then, we extend these operations to a many-to-many model with substitutable choice functions on one side and responsive preferences on the other, via a morphism that relates many-to-one with many-to-many matchings in a natural way. Finally, we present the lattice operations in the many-to-many model with substitutable choice functions on both sides.
Embracing Fairness in Consumer Electricity Markets using an Automatic Market Maker
Authors: Shaun Sweeney, Chris King, Mark O'Malley, Robert Shorten
Date: 2024-07-30 00:00:00
Tasks: Fairness

As consumer flexibility becomes expected, it is important that the market mechanisms which attain that flexibility are perceived as fair. We set out fairness issues in energy markets today, and propose a market design to address them. Consumption is categorised as either essential or flexible with different prices and reliability levels for each. Prices are generated by an Automatic Market Maker (AMM) based on instantaneous scarcity and resource is allocated using a novel Fair Play algorithm. We empirically show the performance of the system over 1 year for 101 UK households and benchmark its performance against more classical approaches.
A Family of Switching Pursuit Strategies for a Multi-Pursuer Single-Evader Game
Authors: Marco Casini, Andrea Garulli
Date: 2024-07-29 00:00:00

This paper introduces a new family of pursuit strategies for multi-pursuer single-evader games in a planar environment. They leverage conditions under which the minimum-time solution of the game becomes equivalent to that of a suitable two-pursuer single-evader game. This enables the design of strategies in which the pursuers first aim to meet such conditions, and then transition to a two-pursuer game once they are satisfied. As a consequence, naive strategies that are in general unsuccessful, can be turned into winning strategies by switching to the appropriate two-pursuer game. Moreover, it is shown via numerical simulations that the switching mechanism significantly enhances the performance of existing pursuit algorithms, like those based on Voronoi partitions.
Riemannian Geometry-Based EEG Approaches: A Literature Review
Authors: Imad Eddine Tibermacine, Samuele Russo, Ahmed Tibermacine, Abdelaziz Rabehi, Bachir Nail, Kamel Kadri, Christian Napoli
Date: 2024-07-19 00:00:00
Tasks: EEG, Transfer Learning

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements.
Mitigating Cognitive Biases in Multi-Criteria Crowd Assessment
Authors: Shun Ito, Hisashi Kashima
Date: 2024-07-10 00:00:00

Crowdsourcing is an easy, cheap, and fast way to perform large scale quality assessment; however, human judgments are often influenced by cognitive biases, which lowers their credibility. In this study, we focus on cognitive biases associated with a multi-criteria assessment in crowdsourcing; crowdworkers who rate targets with multiple different criteria simultaneously may provide biased responses due to prominence of some criteria or global impressions of the evaluation targets. To identify and mitigate such biases, we first create evaluation datasets using crowdsourcing and investigate the effect of inter-criteria cognitive biases on crowdworker responses. Then, we propose two specific model structures for Bayesian opinion aggregation models that consider inter-criteria relations. Our experiments show that incorporating our proposed structures into the aggregation model is effective to reduce the cognitive biases and help obtain more accurate aggregation results.
The Design of a 3D Character Animation System for Digital Twins in the Metaverse
Authors: Senem Tanberk, Dilek Bilgin Tukel, Kadir Acar
Date: 2024-07-10 00:00:00
Tasks: Solitaire

In the context of Industry 4.0, digital twin technology has emerged with rapid advancements as a powerful tool for visualizing and analyzing industrial assets. This technology has attracted considerable interest from researchers across diverse domains such as manufacturing, security, transportation, and gaming. The metaverse has emerged as a significant enabler in these domains, facilitating the integration of various technologies to create virtual replicas of physical assets. The utilization of 3D character animation, often referred to as avatars, is crucial for implementing the metaverse. Traditionally, costly motion capture technologies are employed for creating a realistic avatar system. To meet the needs of this evolving landscape, we have developed a modular framework tailored for asset digital twins as a more affordable alternative. This framework offers flexibility for the independent customization of individual system components. To validate our approach, we employ the English peg solitaire game as a use case, generating a solution tree using the breadth-first search algorithm. The results encompass both qualitative and quantitative findings of a data-driven 3D animation system utilizing motion primitives. The presented methodologies and infrastructure are adaptable and modular, making them applicable to asset digital twins across diverse business contexts. This case study lays the groundwork for pilot applications and can be tailored for education, health, or Industry 4.0 material development.
Joint Transmission and Compression Optimization for Networked Sensing with Limited-Capacity Fronthaul Links
Authors: Weifeng Zhu, Shuowen Zhang, Liang Liu
Date: 2024-08-06 00:00:00

This paper considers networked sensing in cellular network, where multiple base stations (BSs) first compress their received echo signals from multiple targets and then forward the quantized signals to the central unit (CU) via limited-capacity fronthaul links, such that the CU can leverage all useful echo signals to perform high-resolution localization. Under this setup, we manage to characterize the posterior Cramer-Rao Bound (PCRB) for localizing all the targets with random positions as a function of the transmit covariance matrix and the compression noise covariance matrix of each BS. Then, a PCRB minimization problem subject to the transmit power constraints and the fronthaul capacity constraints is formulated to jointly design the BSs' transmission and compression strategies. We propose an efficient algorithm to solve this problem based on the alternating optimization technique. Specifically, it is shown that when either the transmit covariance matrices or the compression noise covariance matrices are fixed, the successive convex approximation (SCA) technique can be leveraged to optimize the other type of covariance matrices locally optimally. Moreover, we also propose a novel estimate-then-beamform-then-compress strategy for the massive receive antenna scenario, under which each BS first estimates targets' angle-of-arrivals (AOAs) locally, then beamforms its high-dimension received signals into low-dimension ones based on the estimated AOAs, and last compresses the beamformed signals for fronthaul transmission. An efficient beamforming and compression design method is devised under this strategy. Numerical results are provided to verify the effectiveness of our proposed algorithms.
Generalized Reduced-WMMSE Approach for Cell-Free Massive MIMO With Per-AP Power Constraints
Authors: Wonsik Yoo, DaeSung Yu, Hoon Lee, Seok-Hwan Park
Date: 2024-08-02 00:00:00

The optimization of cooperative beamforming vectors in cell-free massive MIMO (mMIMO) systems is presented where multi-antenna access points (APs) support downlink data transmission of multiple users. Albeit the successes of the weighted minimum mean squared error (WMMSE) algorithm and their variants, they lack careful investigations about computational complexity that scales with the number of antennas and APs. We propose a generalized and reduced WMMSE (G-R-WMMSE) approach whose complexity is significantly lower than conventional WMMSE. We partition the set of beamforming coefficients into subvectors, with each subvector corresponding to a specific AP. Such a partitioning approach decomposes the original WMMSE problem across individual APs. By leveraging the Lagrange duality analysis, a closed-form solution can be derived for each subproblem, which substantially reduces the computation burden. Additionally, we present a parallel execution of the proposed G-R-WMMSE with adaptive step sizes, aiming at further reducing the time complexity. Numerical results validate that the proposed G-R-WMMSE schemes achieve over 99% complexity savings compared to the conventional WMMSE scheme while maintaining almost the same performance.