pwc-archive/papers-with-abstracts huggingface dataset:
Embedding-Aligned Language Models
Authors: Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Ethan Liang, Craig Boutilier
Date: 2024-05-24 00:00:00
Tasks: Reinforcement Learning (RL), Text Generation

We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space. Our method leverages reinforcement learning (RL), treating a pre-trained LLM as an environment. Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. We demonstrate the effectiveness of the EAGLE agent using the MovieLens 25M and Amazon Review datasets to surface content gaps that satisfy latent user demand. We also demonstrate the benefit of using an optimal design of a state-dependent action set to improve EAGLE's efficiency. Our work paves the way for controlled and grounded text generation using LLMs, ensuring consistency with domain-specific knowledge and data representations.
Reward Machines for Deep RL in Noisy and Uncertain Environments
Authors: Andrew C. Li, Zizhao Chen, Toryn Q. Klassen, Pashootan Vaezipoor, Rodrigo Toro Icarte, Sheila A. McIlraith
Date: 2024-05-31 00:00:00
Tasks: counterfactual, Decision Making, Sequential Decision Making

Reward Machines provide an automaton-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing the underlying structure of a reward function, they enable the decomposition of an RL task, leading to impressive gains in sample efficiency. Although Reward Machines and similar formal specifications have a rich history of application towards sequential decision-making problems, they critically rely on a ground-truth interpretation of the domain-specific vocabulary that forms the building blocks of the reward function--such ground-truth interpretations are elusive in the real world due in part to partial observability and noisy sensing. In this work, we explore the use of Reward Machines for Deep RL in noisy and uncertain environments. We characterize this problem as a POMDP and propose a suite of RL algorithms that exploit task structure under uncertain interpretation of the domain-specific vocabulary. Through theory and experiments, we expose pitfalls in naive approaches to this problem while simultaneously demonstrating how task structure can be successfully leveraged under noisy interpretations of the vocabulary.
Generating 3D Terrain with 2D Cellular Automata
Authors: Nuno Fachada, António R. Rodrigues, Diogo de Andrade, Phil Lopes
Date: 2024-06-01 00:00:00

This paper presents an initial exploration on the use of 2D cellular automata (CA) for generating 3D terrains through a simple yet effective additive approach. By experimenting with multiple CA transition rules, this preliminary investigation yielded aesthetically interesting landscapes, hinting at the technique's potential applicability for real-time terrain generation in games.
Robust Biharmonic Skinning Using Geometric Fields
Authors: Ana Dodik, Vincent Sitzmann, Justin Solomon, Oded Stein
Date: 2024-06-01 00:00:00

Skinning is a popular way to rig and deform characters for animation, to compute reduced-order simulations, and to define features for geometry processing. Methods built on skinning rely on weight functions that distribute the influence of each degree of freedom across the mesh. Automatic skinning methods generate these weight functions with minimal user input, usually by solving a variational problem on a mesh whose boundary is the skinned surface. This formulation necessitates tetrahedralizing the volume bounded by the surface, which brings with it meshing artifacts, the possibility of tetrahedralization failure, and the impossibility of generating weights for surfaces that are not closed. We introduce a mesh-free and robust automatic skinning method that generates high-quality skinning weights comparable to the current state of the art without volumetric meshes. Our method reliably works even on open surfaces and triangle soups where current methods fail. We achieve this through the use of a Lagrangian representation for skinning weights, which circumvents the need for finite elements while optimizing the biharmonic energy.
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data
Authors: Zeyi Sun, Tong Wu, Pan Zhang, Yuhang Zang, Xiaoyi Dong, Yuanjun Xiong, Dahua Lin, Jiaqi Wang
Date: 2024-05-31 00:00:00
Tasks: Denoising, Descriptive

Recent years have witnessed remarkable progress in multi-view diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D objects with detailed captions. To address this challenge, we propose Bootstrap3D, a novel framework that automatically generates an arbitrary quantity of multi-view images to assist in training multi-view diffusion models. Specifically, we introduce a data generation pipeline that employs (1) 2D and video diffusion models to generate multi-view images based on constructed text prompts, and (2) our fine-tuned 3D-aware MV-LLaVA for filtering high-quality data and rewriting inaccurate captions. Leveraging this pipeline, we have generated 1 million high-quality synthetic multi-view images with dense descriptive captions to address the shortage of high-quality 3D data. Furthermore, we present a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original 2D diffusion prior. Extensive experiments demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment, and maintained view consistency.
Strategic Linear Contextual Bandits
Authors: Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu
Date: 2024-06-01 00:00:00
Tasks: Multi-Armed Bandits, Recommendation Systems

Motivated by the phenomenon of strategic agents gaming a recommender system to maximize the number of times they are recommended to users, we study a strategic variant of the linear contextual bandit problem, where the arms can strategically misreport privately observed contexts to the learner. We treat the algorithm design problem as one of mechanism design under uncertainty and propose the Optimistic Grim Trigger Mechanism (OptGTM) that incentivizes the agents (i.e., arms) to report their contexts truthfully while simultaneously minimizing regret. We also show that failing to account for the strategic nature of the agents results in linear regret. However, a trade-off between mechanism design and regret minimization appears to be unavoidable. More broadly, this work aims to provide insight into the intersection of online learning and mechanism design.
Fair Allocation in Dynamic Mechanism Design
Authors: Alireza Fallah, Michael I. Jordan, Annie Ulichney
Date: 2024-05-31 00:00:00
Tasks: Fairness

We consider a dynamic mechanism design problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group. We begin by studying the static case ($T=1$) and establish that the optimal mechanism involves two types of subsidization: one that increases the overall probability of allocation to all buyers, and another that favors the groups which otherwise have a lower probability of winning the item. We then extend our results to the dynamic case by characterizing a set of recursive functions that determine the optimal allocation and payments in each round. Notably, our results establish that in the dynamic case, the seller, on the one hand, commits to a participation bonus to incentivize truth-telling, and on the other hand, charges an entry fee for every round. Moreover, the optimal allocation once more involves subsidization, which its extent depends on the difference in future utilities for both the seller and buyers when allocating the item to one group versus the others. Finally, we present an approximation scheme to solve the recursive equations and determine an approximately optimal and fair allocation efficiently.
$ε$-Optimally Solving Zero-Sum POSGs
Authors: Erwan Escudie, Matthia Sabatelli, Jilles Dibangoye
Date: 2024-05-29 00:00:00

A recent method for solving zero-sum partially observable stochastic games (zs-POSGs) embeds the original game into a new one called the occupancy Markov game. This reformulation allows applying Bellman's principle of optimality to solve zs-POSGs. However, improving a current solution requires solving a linear program with exponentially many potential constraints, which significantly restricts the scalability of this approach. This paper exploits the optimal value function's novel uniform continuity properties to overcome this limitation. We first construct a new operator that is computationally more efficient than the state-of-the-art update rules without compromising optimality. In particular, improving a current solution now involves a linear program with an exponential drop in constraints. We then also show that point-based value iteration algorithms utilizing our findings improve the scalability of existing methods while maintaining guarantees in various domains.
Evidence for five types of fixation during a random saccade eye tracking task: Implications for the study of oculomotor fatigue
Authors: Lee Friedman, Oleg V. Komogortsev
Date: 2024-06-03 00:00:00

Our interest was to evaluate changes in fixation duration as a function of time-on-task (TOT) during a random saccade task. We employed a large, publicly available dataset. The frequency histogram of fixation durations was multimodal and modelled as a Gaussian mixture. We found five fixation types. The ``ideal'' response would be a single accurate saccade after each target movement, with a typical saccade latency of 200-250 msec, followed by a long fixation (> 800 msec) until the next target jump. We found fixations like this, but they comprised only 10% of all fixations and were the first fixation after target movement only 23.4% of the time. More frequently (57.4% of the time), the first fixation after target movement was short (117.7 msec mean) and was commonly followed by a corrective saccade. Across the entire 100 sec of the task, median total fixation duration decreased. This decrease was approximated with a power law fit with R^2=0.94. A detailed examination of the frequency of each of our five fixation types over time on task (TOT) revealed that the three shortest duration fixation types became more and more frequent with TOT whereas the two longest fixations became less and less frequent. In all cases, the changes over TOT followed power law relationships, with R^2 values between 0.73 and 0.93. We concluded that, over the 100 second duration of our task, long fixations are common in the first 15 to 22 seconds but become less common after that. Short fixations are relatively uncommon in the first 15 to 22 seconds but become more and more common as the task progressed. Apparently. the ability to produce an ideal response, although somewhat likely in the first 22 seconds, rapidly declines. This might be related to a noted decline in saccade accuracy over time.
Detection of Acetone as a Gas Biomarker for Diabetes Based on Gas Sensor Technology
Authors: Jiaming Wei, Tong Liu, Jipeng Huang, Xiaowei Li, Yurui Qi, Gangyin Luo
Date: 2024-06-03 00:00:00
Tasks: Diagnostic

With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath analysis, characterized by its speed, convenience, and non-invasive nature, is leading the trend in diagnostic development. Studies have shown that the acetone levels in the breath of diabetes patients are higher than normal, making acetone a basis for diabetes breath analysis. This provides a more readily accepted method for early diabetes prevention and monitoring. Addressing issues such as the invasive nature, disease transmission risks, and complexity of diabetes testing, this study aims to design a diabetes gas biomarker acetone detection system centered around a sensor array using gas sensors and pattern recognition algorithms. The research covers sensor selection, sensor preparation, circuit design, data acquisition and processing, and detection model establishment to accurately identify acetone. Titanium dioxide was chosen as the nano gas-sensitive material to prepare the acetone gas sensor, with data collection conducted using STM32. Filtering was applied to process the raw sensor data, followed by feature extraction using principal component analysis. A recognition model based on support vector machine algorithm was used for qualitative identification of gas samples, while a recognition model based on backpropagation neural network was employed for quantitative detection of gas sample concentrations. Experimental results demonstrated recognition accuracies of 96% and 97.5% for acetone-ethanol and acetone-methanol mixed gases, and 90% for ternary acetone, ethanol, and methanol mixed gases.
Advancing Ear Biometrics: Enhancing Accuracy and Robustness through Deep Learning
Authors: Youssef Mohamed, Zeyad Youssef, Ahmed Heakl, Ahmed Zaky
Date: 2024-05-31 00:00:00
Tasks: Deep Learning, Face Recognition

Biometric identification is a reliable method to verify individuals based on their unique physical or behavioral traits, offering a secure alternative to traditional methods like passwords or PINs. This study focuses on ear biometric identification, exploiting its distinctive features for enhanced accuracy, reliability, and usability. While past studies typically investigate face recognition and fingerprint analysis, our research demonstrates the effectiveness of ear biometrics in overcoming limitations such as variations in facial expressions and lighting conditions. We utilized two datasets: AMI (700 images from 100 individuals) and EarNV1.0 (28,412 images from 164 individuals). To improve the accuracy and robustness of our ear biometric identification system, we applied various techniques including data preprocessing and augmentation. Our models achieved a testing accuracy of 99.35% on the AMI Dataset and 98.1% on the EarNV1.0 dataset, showcasing the effectiveness of our approach in precisely identifying individuals based on ear biometric characteristics.
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning
Authors: Eura Nofshin, Esther Brown, Brian Lim, Weiwei Pan, Finale Doshi-Velez
Date: 2024-05-31 00:00:00
Tasks: Interpretable Machine Learning

Explanations of an AI's function can assist human decision-makers, but the most useful explanation depends on the decision's context, referred to as the downstream task. User studies are necessary to determine the best explanations for each task. Unfortunately, testing every explanation and task combination is impractical, especially considering the many factors influencing human+AI collaboration beyond the explanation's content. This work leverages two insights to streamline finding the most effective explanation. First, explanations can be characterized by properties, such as faithfulness or complexity, which indicate if they contain the right information for the task. Second, we introduce XAIsim2real, a pipeline for running synthetic user studies. In our validation study, XAIsim2real accurately predicts user preferences across three tasks, making it a valuable tool for refining explanation choices before full studies. Additionally, it uncovers nuanced relationships, like how cognitive budget limits a user's engagement with complex explanations -- a trend confirmed with real users.
The Impossibility of Fair LLMs
Authors: Jacy Anthis, Kristian Lum, Michael Ekstrand, Avi Feller, Chenhao Tan
Date: 2024-05-28 00:00:00
Tasks: Fairness

The rise of general-purpose artificial intelligence (AI) systems, particularly large language models (LLMs), has raised pressing moral questions about how to reduce bias and ensure fairness at scale. Researchers have documented a sort of "bias" in the significant correlations between demographics (e.g., race, gender) in LLM prompts and responses, but it remains unclear how LLM fairness could be evaluated with more rigorous definitions, such as group fairness or fair representations. We analyze a variety of technical fairness frameworks and find inherent challenges in each that make the development of a fair LLM intractable. We show that each framework either does not logically extend to the general-purpose AI context or is infeasible in practice, primarily due to the large amounts of unstructured training data and the many potential combinations of human populations, use cases, and sensitive attributes. These inherent challenges would persist for general-purpose AI, including LLMs, even if empirical challenges, such as limited participatory input and limited measurement methods, were overcome. Nonetheless, fairness will remain an important type of model evaluation, and there are still promising research directions, particularly the development of standards for the responsibility of LLM developers, context-specific evaluations, and methods of iterative, participatory, and AI-assisted evaluation that could scale fairness across the diverse contexts of modern human-AI interaction.
The Curse of Beam-Squint in ISAC: Causes, Implications, and Mitigation Strategies
Authors: Ahmet M. Elbir, Kumar Vijay Mishra, Abdulkadir Celik, Ahmed M. Eltawil
Date: 2024-06-05 00:00:00
Tasks: ISAC, parameter estimation

Integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize spectrum and thereby save cost and power. At the higher end of the spectrum, ISAC systems operate at wideband using large antenna arrays to meet the stringent demands for high-resolution sensing and enhanced communications capacity. However, the wideband implementation entails beam-squint, that is, deviations in the generated beam directions because of the narrowband assumption in the analog components. This causes significant degradation in the communications capacity, target detection, and parameter estimation. This article presents the design challenges caused by beam-squint and its mitigation in ISAC systems. In this context, we also discuss several ISAC design perspectives including far-/near-field beamforming, channel/direction estimation, sparse array design, and index modulation. There are also several research opportunities in waveform design, beam training, and array processing to adequately address beam-squint in ISAC.
Performance Analysis of Hybrid Cellular and Cell-free MIMO Network
Authors: Zhuoyin Dai, Jingran Xu, Xiaoli Xu, Ruoguang Li, Yong Zeng
Date: 2024-06-04 00:00:00

Cell-free wireless communication is envisioned as one of the most promising network architectures, which can achieve stable and uniform communication performance while improving the system energy and spectrum efficiency. The deployment of cell-free networks is envisioned to be a longterm evolutionary process, in which cell-free access points (APs) will be gradually introduced into the communication network and collaborate with the existing cellular base stations (BSs). To further explore the performance limits of hybrid cellular and cell-free networks, this paper develops a hybrid network model based on stochastic geometric toolkits, which reveals the coupling of the signal and interference from both the cellular and cell-free networks. Specifically, the conjugate beamforming is applied in hybrid cellular and cell-free networks, which enables user equipment (UE) to benefit from both cellular BSs and cell-free APs. The aggregate signal received from the hybrid network is approximated via moment matching, and coverage probability is characterized by deriving the Laplace transform of the interference. The analysis of signal strength and coverage probability is verified by extensive simulations.
Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access
Authors: Shengsong Luo, Junjie Ma, Chongbin Xu, Xin Wang
Date: 2024-06-03 00:00:00
Tasks: Action Detection, Activity Detection

We consider the identifiability issue of maximum likelihood based activity detection in massive MIMO based grant-free random access. A prior work by Chen et al. indicates that the identifiability undergoes a phase transition for commonly-used random signatures. In this paper, we provide an analytical characterization of the boundary of the phase transition curve. Our theoretical results agree well with the numerical experiments.
Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection
Authors: Andrzej Cichocki, Sergio Cruces, Auxiliadora Sarmiento, Toshihisa Tanaka
Date: 2024-06-02 00:00:00

This paper introduces a novel family of generalized exponentiated gradient (EG) updates derived from an Alpha-Beta divergence regularization function. Collectively referred to as EGAB, the proposed updates belong to the category of multiplicative gradient algorithms for positive data and demonstrate considerable flexibility by controlling iteration behavior and performance through three hyperparameters: $\alpha$, $\beta$, and the learning rate $\eta$. To enforce a unit $l_1$ norm constraint for nonnegative weight vectors within generalized EGAB algorithms, we develop two slightly distinct approaches. One method exploits scale-invariant loss functions, while the other relies on gradient projections onto the feasible domain. As an illustration of their applicability, we evaluate the proposed updates in addressing the online portfolio selection problem (OLPS) using gradient-based methods. Here, they not only offer a unified perspective on the search directions of various OLPS algorithms (including the standard exponentiated gradient and diverse mean-reversion strategies), but also facilitate smooth interpolation and extension of these updates due to the flexibility in hyperparameter selection. Simulation results confirm that the adaptability of these generalized gradient updates can effectively enhance the performance for some portfolios, particularly in scenarios involving transaction costs.
Conformal Transformation of Kernels: A Geometric Perspective on Text Classification
Authors: Ioana Rădulescu, Alexandra Băicoianu, Adela Mihai
Date: 2024-06-01 00:00:00
Tasks: Binary Classification, text-classification, Text Classification

In this article we investigate the effects of conformal transformations on kernel functions used in Support Vector Machines. Our focus lies in the task of text document categorization, which involves assigning each document to a particular category. We introduce a new Gaussian Cosine kernel alongside two conformal transformations. Building upon previous studies that demonstrated the efficacy of conformal transformations in increasing class separability on synthetic and low-dimensional datasets, we extend this analysis to the high-dimensional domain of text data. Our experiments, conducted on the Reuters dataset on two types of binary classification tasks, compare the performance of Linear, Gaussian, and Gaussian Cosine kernels against their conformally transformed counterparts. The findings indicate that conformal transformations can significantly improve kernel performance, particularly for sub-optimal kernels. Specifically, improvements were observed in 60% of the tested scenarios for the Linear kernel, 84% for the Gaussian kernel, and 80% for the Gaussian Cosine kernel. In light of these findings, it becomes clear that conformal transformations play a pivotal role in enhancing kernel performance, offering substantial benefits.