OpenRaiser/Intern-Atlas huggingface dataset:
Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

This paper investigates multi-objective reinforcement learning (MORL), which focuses on learning Pareto optimal policies in the presence of multiple reward functions. Despite MORL's significant empirical success, there is still a lack of satisfactory understanding of various MORL optimization targets and efficient learning algorithms. Our work offers a systematic analysis of several optimization targets to assess their abilities to find all Pareto optimal policies and controllability over learned policies by the preferences for different objectives. We then identify Tchebycheff scalarization as a favorable scalarization method for MORL. Considering the non-smoothness of Tchebycheff scalarization, we reformulate its minimization problem into a new min-max-max optimization problem. Then, for the stochastic policy class, we propose efficient algorithms using this reformulation to learn Pareto optimal policies. We first propose an online UCB-based algorithm to achieve an $\varepsilon$ learning error with an $\tilde{\mathcal{O}}(\varepsilon^{-2})$ sample complexity for a single given preference. To further reduce the cost of environment exploration under different preferences, we propose a preference-free framework that first explores the environment without pre-defined preferences and then generates solutions for any number of preferences. We prove that it only requires an $\tilde{\mathcal{O}}(\varepsilon^{-2})$ exploration complexity in the exploration phase and demands no additional exploration afterward. Lastly, we analyze the smooth Tchebycheff scalarization, an extension of Tchebycheff scalarization, which is proved to be more advantageous in distinguishing the Pareto optimal policies from other weakly Pareto optimal policies based on entry values of preference vectors. Furthermore, we extend our algorithms and theoretical analysis to accommodate this optimization target.
"My Kind of Woman": Analysing Gender Stereotypes in AI through The Averageness Theory and EU Law
Venue: arXiv cs.CY 2024
Year: 2024

This study delves into gender classification systems, shedding light on the interaction between social stereotypes and algorithmic determinations. Drawing on the "averageness theory," which suggests a relationship between a face's attractiveness and the human ability to ascertain its gender, we explore the potential propagation of human bias into artificial intelligence (AI) systems. Utilising the AI model Stable Diffusion 2.1, we have created a dataset containing various connotations of attractiveness to test whether the correlation between attractiveness and accuracy in gender classification observed in human cognition persists within AI. Our findings indicate that akin to human dynamics, AI systems exhibit variations in gender classification accuracy based on attractiveness, mirroring social prejudices and stereotypes in their algorithmic decisions. This discovery underscores the critical need to consider the impacts of human perceptions on data collection and highlights the necessity for a multidisciplinary and intersectional approach to AI development and AI data training. By incorporating cognitive psychology and feminist legal theory, we examine how data used for AI training can foster gender diversity and fairness under the scope of the AI Act and GDPR, reaffirming how psychological and feminist legal theories can offer valuable insights for ensuring the protection of gender equality and non-discrimination in AI systems.
A Distributed Edge FLISR Solution & Network Simulation Test Platform
Venue: arXiv cs.DC 2024
Year: 2024

The energy sector is experiencing a paradigm shift with the swift adoption of distributed energy sources, renewables, electric vehicles, and an evolving consumer-utility relationship. This necessitates the strategic integration of advanced Information and Communication Technologies (ICT) and the Internet of Things (IoT) to address the emerging challenges. Grid resilience is paramount, as a dependable energy supply is the cornerstone of societal well-being and economic activity. The primary contribution of this research is to investigate the implementation of a novel grid resiliency strategy for the Irish context, employing Fault Location, Isolation and Service Restoration (FLISR) techniques in conjunction with Edge Computing. Through a comprehensive review of existing literature, original research activities, and meticulous data analysis, we aim to develop a solution that bolsters grid resilience and mitigates the impact of service disruptions for both consumers and utilities. Additionally, our work delves into the specific context of the Irish energy grid, including relevant policies and regulations, to ensure the proposed FLISR strategy is not only effective but also readily implementable.
Antenna Model for Safe Human Exposure in Future 6G Smartphones: A Network Perspective
Venue: arXiv cs.NI 2024
Year: 2024

In this article we present the biological effect of antenna topology on a users body. At different values of exposed frequency, the absorbent nature varies in human body. One of the major factors to be taken into consideration for designing 6G mobile antenna is the biological effect and Electromagnetic Field Exposure (EMF).
PatchEX: High-Quality Real-Time Temporal Supersampling through Patch-based Parallel Extrapolation
Venue: arXiv cs.CV 2024
Year: 2024

High-refresh rate displays have become very popular in recent years due to the need for superior visual quality in gaming, professional displays and specialized applications like medical imaging. However, high-refresh rate displays alone do not guarantee a superior visual experience; the GPU needs to render frames at a matching rate. Otherwise, we observe disconcerting visual artifacts such as screen tearing and stuttering. Temporal supersampling is an effective technique to increase frame rates by predicting new frames from other rendered frames. There are two methods in this space: interpolation and extrapolation. Interpolation-based methods provide good image quality at the cost of a higher latency because they also require the next rendered frame. On the other hand, extrapolation methods are much faster at the cost of quality. This paper introduces PatchEX, a novel frame extrapolation method that aims to provide the quality of interpolation at the speed of extrapolation. It smartly partitions the extrapolation task into sub-tasks and executes them in parallel to improve both quality and latency. It then uses a patch-based inpainting method and a custom shadow prediction approach to fuse the generated sub-frames. This approach significantly reduces the overall latency while maintaining the quality of the output. Our results demonstrate that PatchEX achieves a 65.29% and 48.46% improvement in PSNR over the latest extrapolation methods ExtraNet and ExtraSS, respectively, while being 6x and 2x faster, respectively.
MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
Venue: arXiv cs.RO 2024
Year: 2024

This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms. The proposed method trains an RL agent equipped with a memory unit to imitate reference motions using a small set of procedurally generated quadruped robots. Through comprehensive simulation and real-world hardware experiments, we demonstrate the efficacy of our approach in achieving locomotion across various robots without requiring robot-specific fine-tuning. Furthermore, we highlight the critical role of the memory unit in enabling generalization, facilitating rapid adaptation to changes in the robot properties, and improving sample efficiency.
Quality Diversity for Robot Learning: Limitations and Future Directions
Venue: arXiv cs.RO 2024
Year: 2024

Quality Diversity (QD) has shown great success in discovering high-performing, diverse policies for robot skill learning. While current benchmarks have led to the development of powerful QD methods, we argue that new paradigms must be developed to facilitate open-ended search and generalizability. In particular, many methods focus on learning diverse agents that each move to a different xy position in MAP-Elites-style bounded archives. Here, we show that such tasks can be accomplished with a single, goal-conditioned policy paired with a classical planner, achieving O(1) space complexity w.r.t. the number of policies and generalization to task variants. We hypothesize that this approach is successful because it extracts task-invariant structural knowledge by modeling a relational graph between adjacent cells in the archive. We motivate this view with emerging evidence from computational neuroscience and explore connections between QD and models of cognitive maps in human and other animal brains. We conclude with a discussion exploring the relationships between QD and cognitive maps, and propose future research directions inspired by cognitive maps towards future generalizable algorithms capable of truly open-ended search.
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
Venue: arXiv cs.LG 2024
Year: 2024

Large pre-trained models have exhibited remarkable achievements across various domains. The substantial training costs associated with these models have led to wide studies of fine-tuning for effectively harnessing their capabilities in solving downstream tasks. Yet, conventional fine-tuning approaches become infeasible when the model lacks access to downstream data due to privacy concerns. Naively integrating fine-tuning approaches with the emerging federated learning frameworks incurs substantial communication overhead and exerts high demand on local computing resources, making it impractical for common resource-limited devices. In this paper, we introduce SFPrompt, an innovative privacy-preserving fine-tuning method tailored for the federated setting where direct uploading of raw data is prohibited and local devices are resource-constrained to run a complete pre-trained model. In essence, SFPrompt judiciously combines split learning with federated learning to handle these challenges. Specifically, the pre-trained model is first partitioned into client and server components, thereby streamlining the client-side model and substantially alleviating computational demands on local resources. SFPrompt then introduces soft prompts into the federated model to enhance the fine-tuning performance. To further reduce communication costs, a novel dataset pruning algorithm and a local-loss update strategy are devised during the fine-tuning process. Extensive experiments demonstrate that SFPrompt delivers competitive performance as the federated full fine-tuning approach while consuming a mere 0.46% of local computing resources and incurring 53% less communication cost.
On Approximately Strategy-Proof Tournament Rules for Collusions of Size at Least Three
Venue: arXiv cs.GT 2024
Year: 2024

A tournament organizer must select one of $n$ possible teams as the winner of a competition after observing all $\binom{n}{2}$ matches between them. The organizer would like to find a tournament rule that simultaneously satisfies the following desiderata. It must be Condorcet-consistent (henceforth, CC), meaning it selects as the winner the unique team that beats all other teams (if one exists). It must also be strongly non-manipulable for groups of size $k$ at probability $α$ (henceforth, k-SNM-$α$), meaning that no subset of $\leq k$ teams can fix the matches among themselves in order to increase the chances any of it's members being selected by more than $α$. Our contributions are threefold. First, wee consider a natural generalization of the Randomized Single Elimination Bracket rule from [Schneider et al. 2017] to $d$-ary trees and provide upper bounds to its manipulability. Then, we propose a novel tournament rule that is CC and 3-SNM-1/2, a strict improvement upon the concurrent work of [Dinev and Weinberg, 2022] who proposed a CC and 3-SNM-31/60 rule. Finally, we initiate the study of reductions among tournament rules.
Big5PersonalityEssays: Introducing a Novel Synthetic Generated Dataset Consisting of Short State-of-Consciousness Essays Annotated Based on the Five Factor Model of Personality
Venue: arXiv cs.OH 2024
Year: 2024

Given the high advances of large language models (LLM) it is of vital importance to study their behaviors and apply their utility in all kinds of scientific fields. Psychology has been, in recent years, poorly approached using novel computational tools. One of the reasons is the high complexity of the data required for a proper analysis. Moreover, psychology, with a focus on psychometry, has few datasets available for analysis and artificial intelligence usage. Because of these facts, this study introduces a synthethic database of short essays labeled based on the five factor model (FFM) of personality traits.
Pretraining a Neural Operator in Lower Dimensions
Venue: arXiv cs.LG 2024
Year: 2024

There has recently been increasing attention towards developing foundational neural Partial Differential Equation (PDE) solvers and neural operators through large-scale pretraining. However, unlike vision and language models that make use of abundant and inexpensive (unlabeled) data for pretraining, these neural solvers usually rely on simulated PDE data, which can be costly to obtain, especially for high-dimensional PDEs. In this work, we aim to Pretrain neural PDE solvers on Lower Dimensional PDEs (PreLowD) where data collection is the least expensive. We evaluated the effectiveness of this pretraining strategy in similar PDEs in higher dimensions. We use the Factorized Fourier Neural Operator (FFNO) due to having the necessary flexibility to be applied to PDE data of arbitrary spatial dimensions and reuse trained parameters in lower dimensions. In addition, our work sheds light on the effect of the fine-tuning configuration to make the most of this pretraining strategy. Code is available at https://github.com/BaratiLab/PreLowD.
SAfEPaTh: A System-Level Approach for Efficient Power and Thermal Estimation of Convolutional Neural Network Accelerator
Venue: arXiv cs.LG 2024
Year: 2024

The design of energy-efficient, high-performance, and reliable Convolutional Neural Network (CNN) accelerators involves significant challenges due to complex power and thermal management issues. This paper introduces SAfEPaTh, a novel system-level approach for accurately estimating power and temperature in tile-based CNN accelerators. By addressing both steady-state and transient-state scenarios, SAfEPaTh effectively captures the dynamic effects of pipeline bubbles in interlayer pipelines, utilizing real CNN workloads for comprehensive evaluation. Unlike traditional methods, it eliminates the need for circuit-level simulations or on-chip measurements. Our methodology leverages TANIA, a cutting-edge hybrid digital-analog tile-based accelerator featuring analog-in-memory computing cores alongside digital cores. Through rigorous simulation results using the ResNet18 model, we demonstrate SAfEPaTh's capability to accurately estimate power and temperature within 500 seconds, encompassing CNN model accelerator mapping exploration and detailed power and thermal estimations. This efficiency and accuracy make SAfEPaTh an invaluable tool for designers, enabling them to optimize performance while adhering to stringent power and thermal constraints. Furthermore, SAfEPaTh's adaptability extends its utility across various CNN models and accelerator architectures, underscoring its broad applicability in the field. This study contributes significantly to the advancement of energy-efficient and reliable CNN accelerator designs, addressing critical challenges in dynamic power and thermal management.
Revising the Problem of Partial Labels from the Perspective of CNNs' Robustness
Venue: arXiv cs.CV 2024
Year: 2024

Convolutional neural networks (CNNs) have gained increasing popularity and versatility in recent decades, finding applications in diverse domains. These remarkable achievements are greatly attributed to the support of extensive datasets with precise labels. However, annotating image datasets is intricate and complex, particularly in the case of multi-label datasets. Hence, the concept of partial-label setting has been proposed to reduce annotation costs, and numerous corresponding solutions have been introduced. The evaluation methods for these existing solutions have been primarily based on accuracy. That is, their performance is assessed by their predictive accuracy on the test set. However, we insist that such an evaluation is insufficient and one-sided. On one hand, since the quality of the test set has not been evaluated, the assessment results are unreliable. On the other hand, the partial-label problem may also be raised by undergoing adversarial attacks. Therefore, incorporating robustness into the evaluation system is crucial. For this purpose, we first propose two attack models to generate multiple partial-label datasets with varying degrees of label missing rates. Subsequently, we introduce a lightweight partial-label solution using pseudo-labeling techniques and a designed loss function. Then, we employ D-Score to analyze both the proposed and existing methods to determine whether they can enhance robustness while improving accuracy. Extensive experimental results demonstrate that while certain methods may improve accuracy, the enhancement in robustness is not significant, and in some cases, it even diminishes.
Time Matters: Examine Temporal Effects on Biomedical Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Time roots in applying language models for biomedical applications: models are trained on historical data and will be deployed for new or future data, which may vary from training data. While increasing biomedical tasks have employed state-of-the-art language models, there are very few studies have examined temporal effects on biomedical models when data usually shifts across development and deployment. This study fills the gap by statistically probing relations between language model performance and data shifts across three biomedical tasks. We deploy diverse metrics to evaluate model performance, distance methods to measure data drifts, and statistical methods to quantify temporal effects on biomedical language models. Our study shows that time matters for deploying biomedical language models, while the degree of performance degradation varies by biomedical tasks and statistical quantification approaches. We believe this study can establish a solid benchmark to evaluate and assess temporal effects on deploying biomedical language models.
PARS3: Parallel Sparse Skew-Symmetric Matrix-Vector Multiplication with Reverse Cuthill-McKee Reordering
Venue: arXiv cs.DC 2024
Year: 2024

Sparse matrices, as prevalent primitive of various scientific computing algorithms, persist as a bottleneck in processing. A skew-symmetric matrix flips signs of symmetric pairs in a symmetric matrix. Our work, Parallel 3-Way Banded Skew-Symmetric Sparse Matrix-Vector Multiplication, equally improves parallel symmetric SpMV kernels with a different perspective than the common literature trends, by manipulating the form of matrix in a preprocessing step to accelerate the repeated computations of iterative solvers. We effectively use Reverse Cuthill-McKee (RCM) reordering algorithm to transform a sparse skew-symmetrix matrix into a band matrix, then efficiently parallelize it by splitting the band structure into 3 different parts by considering its local sparsity. Our proposed method with RCM is novel in the sense that it is the first implementation of parallel skew-symmetric SpMV kernels. Our enhancements in SpMV and findings are valuable with significant strong scalings of up to 19x over the serial compressed SpMV implementation. We overperform a heuristic-based graph-coloring approach with synchronization phases in implementing parallel symmetric SpMVs. Our approach also naturally applies to parallel sparse symmetric SpMVs, that can inspire widespread SpMV solutions to adapt presented optimizations in this paper.
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
Venue: arXiv cs.CR 2024
Year: 2024

Predictive machine learning models are becoming increasingly deployed in high-stakes contexts involving sensitive personal data; in these contexts, there is a trade-off between model explainability and data privacy. In this work, we push the boundaries of this trade-off: with a focus on foundation models for image classification fine-tuning, we reveal unforeseen privacy risks of post-hoc model explanations and subsequently offer mitigation strategies for such risks. First, we construct VAR-LRT and L1/L2-LRT, two new membership inference attacks based on feature attribution explanations that are significantly more successful than existing explanation-leveraging attacks, particularly in the low false-positive rate regime that allows an adversary to identify specific training set members with confidence. Second, we find empirically that optimized differentially private fine-tuning substantially diminishes the success of the aforementioned attacks, while maintaining high model accuracy. We carry out a systematic empirical investigation of our 2 new attacks with 5 vision transformer architectures, 5 benchmark datasets, 4 state-of-the-art post-hoc explanation methods, and 4 privacy strength settings.
SDLNet: Statistical Deep Learning Network for Co-Occurring Object Detection and Identification
Venue: arXiv cs.CV 2024
Year: 2024

With the growing advances in deep learning based technologies the detection and identification of co-occurring objects is a challenging task which has many applications in areas such as, security and surveillance. In this paper, we propose a novel framework called SDLNet- Statistical analysis with Deep Learning Network that identifies co-occurring objects in conjunction with base objects in multilabel object categories. The pipeline of proposed work is implemented in two stages: in the first stage of SDLNet we deal with multilabel detectors for discovering labels, and in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we learn co-occurrence statistics by setting base classes and frequently occurring classes, following this we build association rules and generate frequent patterns. The crucial part of SDLNet is recognizing base classes and making consideration for co-occurring classes. Finally, the generated co-occurrence matrix based on frequent patterns will show base classes and their corresponding co-occurring classes. SDLNet is evaluated on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on these benchmark datasets are reported in Sec 4.