OpenRaiser/Intern-Atlas huggingface dataset:
Personality testing of Large Language Models: Limited temporal stability, but highlighted prosociality
Venue: arXiv cs.AI 2024
Year: 2024

As Large Language Models (LLMs) continue to gain popularity due to their human-like traits and the intimacy they offer to users, their societal impact inevitably expands. This leads to the rising necessity for comprehensive studies to fully understand LLMs and reveal their potential opportunities, drawbacks, and overall societal impact. With that in mind, this research conducted an extensive investigation into seven LLM's, aiming to assess the temporal stability and inter-rater agreement on their responses on personality instruments in two time points. In addition, LLMs personality profile was analyzed and compared to human normative data. The findings revealed varying levels of inter-rater agreement in the LLMs responses over a short time, with some LLMs showing higher agreement (e.g., LIama3 and GPT-4o) compared to others (e.g., GPT-4 and Gemini). Furthermore, agreement depended on used instruments as well as on domain or trait. This implies the variable robustness in LLMs' ability to reliably simulate stable personality characteristics. In the case of scales which showed at least fair agreement, LLMs displayed mostly a socially desirable profile in both agentic and communal domains, as well as a prosocial personality profile reflected in higher agreeableness and conscientiousness and lower Machiavellianism. Exhibiting temporal stability and coherent responses on personality traits is crucial for AI systems due to their societal impact and AI safety concerns.
Last Week with ChatGPT: A Weibo Study on Social Perspective Regarding ChatGPT for Education and Beyond
Venue: arXiv cs.CY 2024
Year: 2024

The application of AI-powered tools has piqued the interest of many fields, particularly in the academic community. This study uses ChatGPT, currently the most powerful and popular AI tool, as a representative example to analyze how the Chinese public perceives the potential of large language models (LLMs) for educational and general purposes. Although facing accessibility challenges, we found that the number of discussions on ChatGPT per month is 16 times that of Ernie Bot developed by Baidu, the most popular alternative product to ChatGPT in the mainland, making ChatGPT a more suitable subject for our analysis. The study also serves as the first effort to investigate the changes in public opinion as AI technologies become more advanced and intelligent. The analysis reveals that, upon first encounters with advanced AI that was not yet highly capable, some social media users believed that AI advancements would benefit education and society, while others feared that advanced AI, like ChatGPT, would make humans feel inferior and lead to problems such as cheating and a decline in moral principles. The majority of users remained neutral. Interestingly, with the rapid development and improvement of AI capabilities, public attitudes have tended to shift in a positive direction. We present a thorough analysis of the trending shift and a roadmap to ensure the ethical application of ChatGPT-like models in education and beyond.
Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?
Venue: arXiv cs.LG 2024
Year: 2024

As Transformer-based models have achieved impressive performance on various time series tasks, Long-Term Series Forecasting (LTSF) tasks have also received extensive attention in recent years. However, due to the inherent computational complexity and long sequences demanding of Transformer-based methods, its application on LTSF tasks still has two major issues that need to be further investigated: 1) Whether the sparse attention mechanism designed by these methods actually reduce the running time on real devices; 2) Whether these models need extra long input sequences to guarantee their performance? The answers given in this paper are negative. Therefore, to better copy with these two issues, we design a lightweight Period-Attention mechanism (Periodformer), which renovates the aggregation of long-term subseries via explicit periodicity and short-term subseries via built-in proximity. Meanwhile, a gating mechanism is embedded into Periodformer to regulate the influence of the attention module on the prediction results. Furthermore, to take full advantage of GPUs for fast hyperparameter optimization (e.g., finding the suitable input length), a Multi-GPU Asynchronous parallel algorithm based on Bayesian Optimization (MABO) is presented. MABO allocates a process to each GPU via a queue mechanism, and then creates multiple trials at a time for asynchronous parallel search, which greatly reduces the search time. Compared with the state-of-the-art methods, the prediction error of Periodformer reduced by 13% and 26% for multivariate and univariate forecasting, respectively. In addition, MABO reduces the average search time by 46% while finding better hyperparameters. As a conclusion, this paper indicates that LTSF may not need complex attention and extra long input sequences. The code has been open sourced on Github.
Steering No-Regret Learners to a Desired Equilibrium
Venue: arXiv cs.GT 2024
Year: 2024

A mediator observes no-regret learners playing an extensive-form game repeatedly across $T$ rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pay the players to play desirable actions. We study two bounds on the mediator's payments: a total budget and a per-round budget. If the mediator's total budget does not grow with $T$, we show that steering is impossible. However, we show that it is enough for the total budget to grow sublinearly with $T$, that is, for the average payment to vanish. When players' full strategies are observed at each round, we show that constant per-round budgets permit steering. In the more challenging setting where only trajectories through the game tree are observable, we show that steering is impossible with constant per-round budgets in general extensive-form games, but possible in normal-form games or if the per-round budget may itself depend on $T$. We also show how our results can be generalized to the case when the equilibrium is being computed online while steering is happening. We supplement our theoretical positive results with experiments highlighting the efficacy of steering in large games.
When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems
Venue: arXiv cs.CR 2024
Year: 2024

Many research papers have recently focused on behavioral-based driver authentication systems in vehicles. Pushed by Artificial Intelligence (AI) advancements, these works propose powerful models to identify drivers through their unique biometric behavior. However, these models have never been scrutinized from a security point of view, rather focusing on the performance of the AI algorithms. Several limitations and oversights make implementing the state-of-the-art impractical, such as their secure connection to the vehicle's network and the management of security alerts. Furthermore, due to the extensive use of AI, these systems may be vulnerable to adversarial attacks. However, there is currently no discussion on the feasibility and impact of such attacks in this scenario. Driven by the significant gap between research and practical application, this paper seeks to connect these two domains. We propose the first security-aware system model for behavioral-based driver authentication. We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures designed for our constrained environments. We formalize a realistic system and threat model reflecting a real-world vehicle's network for their implementation. When evaluated on real driving data, our models outclass the state-of-the-art with an accuracy of up to 0.999 in identification and authentication. Moreover, we are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. We show how attackers can still exploit these systems with a perfect attack success rate (up to 1.000). Finally, we discuss requirements for deploying driver authentication systems securely. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
Federated Learning for Medical Image Analysis: A Survey
Venue: arXiv cs.CV 2024
Year: 2024

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. In this survey, we first introduce the background knowledge of federated learning for dealing with privacy protection and collaborative learning issues in medical imaging. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.
Exploration of improved, roller-based spreading strategies for cohesive powders in additive manufacturing via coupled DEM-FEM simulations
Venue: arXiv cs.CE 2024
Year: 2024

Spreading of fine (D50 <=20um) powders into thin layers typically requires a mechanism such as a roller to overcome the cohesive forces between particles. Roller-based spreading requires careful optimization and can result in low density and/or inconsistent layers depending on the characteristics of the powder feedstock. Here, we explore improved, roller-based spreading strategies for highly cohesive powders using an integrated discrete element-finite element (DEM-FEM) framework. Powder characteristics are emulated using a self-similarity approach based on experimental calibration for a Ti-6Al-4V 0-20um powder. We find that optimal roller-based spreading relies on a combination of surface friction of the roller and roller kinematics that impart sufficient kinetic energy to break cohesive bonds between powder particles. However, excess rotation can impart excessive kinetic energy, causing ejection of particles and a non-uniform layer. Interestingly, the identified optimal surface velocities for counter-rotation as well as rotational oscillation are very similar, suggesting this quantity as the critical kinematic parameter. When these conditions are chosen appropriately, layers with packing fractions beyond 50% are predicted for layer thicknesses as small as ~2 times D90 of the exemplary powder, and the layer quality is robust with respect to substrate adhesion over a 10-fold range. The latter is an important consideration given the spatially varying substrate conditions in AM due to the combination of fused/bound and bare powder regions. As compared to counter-rotation, the proposed rotational oscillation is particularly attractive because it can overcome practical issues with mechanical runout of roller mechanisms. In particular, the application to rubber-coated rollers, which promises to reduce the risk of tool damage and particle streaking, is recommended for future investigation.
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits. We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking. Additionally, we provide generic frameworks for learning on both static and dynamic graphs. Our extensive experiments lead to several key observations. Notably, using correlation vectors as node features, incorporating larger number of regions of interest, and employing sparser graphs lead to improved performance. To foster further advancements in graph-based data driven neuroimaging analysis, we offer a comprehensive open-source Python package that includes the benchmark datasets, baseline implementations, model training, and standard evaluation.
Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks
Venue: arXiv cs.LG 2024
Year: 2024

Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI solutions for specific, limited conditions, hindering models from learning and adapting to generic situations, such as those met across radio communication systems. This paper emphasizes the pivotal role of achieving model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. Implementing these principles can decrease the number of models deployed and increase adaptability in diverse radio communication environments. To address the challenges of model generalization in communication systems, we propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation. We illustrate these concepts by designing a generalized link adaptation algorithm, demonstrating the benefits of our proposed approach.
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values
Venue: arXiv cs.LG 2024
Year: 2024

Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 for 14 types of cancer, and an AUC greater than 0.9 for 2 types of cancer. We also developed CL-based Cox (CLCox) models for predicting cancer prognosis. Our CLCox models trained with the TCGA data outperformed existing methods significantly in predicting the prognosis of 19 types of cancer under consideration. The performance of CLCox models and CL-based classifiers trained with TCGA lung and prostate cancer data were validated using the data from two independent cohorts. We also show that the CLCox model trained with the whole transcriptome significantly outperforms the Cox model trained with the 21 genes of Oncotype DX that is in clinical use for breast cancer patients. CL-based classifiers and CLCox models for 19 types of cancer are publicly available and can be used to predict cancer prognosis using the RNA-seq transcriptome of an individual tumor. Python codes for model training and testing are also publicly accessible, and can be applied to train new CL-based models using gene expression data of tumors.
Probabilistic Visibility-Aware Trajectory Planning for Target Tracking in Cluttered Environments
Venue: arXiv cs.RO 2024
Year: 2024

Target tracking has numerous significant civilian and military applications, and maintaining the visibility of the target plays a vital role in ensuring the success of the tracking task. Existing visibility-aware planners primarily focus on keeping the target within the limited field of view of an onboard sensor and avoiding obstacle occlusion. However, the negative impact of system uncertainty is often neglected, rendering the planners delicate to uncertainties in practice. To bridge the gap, this work proposes a real-time, non-myopic trajectory planner for visibility-aware and safe target tracking in the presence of system uncertainty. For more accurate target motion prediction, we introduce the concept of belief-space probability of detection (BPOD) to measure the predictive visibility of the target under stochastic robot and target states. An Extended Kalman Filter variant incorporating BPOD is developed to predict target belief state under uncertain visibility within the planning horizon. To reach real-time trajectory planning, we propose a computationally efficient algorithm to uniformly calculate both BPOD and the chance-constrained collision risk by utilizing linearized signed distance function (SDF), and then design a two-stage strategy for lightweight calculation of SDF in sequential convex programming. Extensive simulation results with benchmark comparisons show the capacity of the proposed approach to robustly maintain the visibility of the target under high system uncertainty. The practicality of the proposed trajectory planner is validated by real-world experiments.
Optimized Gradient Tracking for Decentralized Online Learning
Venue: arXiv cs.LG 2024
Year: 2024

This work considers the problem of decentralized online learning, where the goal is to track the optimum of the sum of time-varying functions, distributed across several nodes in a network. The local availability of the functions and their gradients necessitates coordination and consensus among the nodes. We put forth the Generalized Gradient Tracking (GGT) framework that unifies a number of existing approaches, including the state-of-the-art ones. The performance of the proposed GGT algorithm is theoretically analyzed using a novel semidefinite programming-based analysis that yields the desired regret bounds under very general conditions and without requiring the gradient boundedness assumption. The results are applicable to the special cases of GGT, which include various state-of-the-art algorithms as well as new dynamic versions of various classical decentralized algorithms. To further minimize the regret, we consider a condensed version of GGT with only four free parameters. A procedure for offline tuning of these parameters using only the problem parameters is also detailed. The resulting optimized GGT (oGGT) algorithm not only achieves improved dynamic regret bounds, but also outperforms all state-of-the-art algorithms on both synthetic and real-world datasets.
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Venue: arXiv cs.LG 2024
Year: 2024

Vision Transformers (ViTs) have shown impressive performance and have become a unified backbone for multiple vision tasks. However, both the attention mechanism and multi-layer perceptrons (MLPs) in ViTs are not sufficiently efficient due to dense multiplications, leading to costly training and inference. To this end, we propose to reparameterize pre-trained ViTs with a mixture of multiplication primitives, e.g., bitwise shifts and additions, towards a new type of multiplication-reduced model, dubbed $\textbf{ShiftAddViT}$, which aims to achieve end-to-end inference speedups on GPUs without requiring training from scratch. Specifically, all $\texttt{MatMuls}$ among queries, keys, and values are reparameterized using additive kernels, after mapping queries and keys to binary codes in Hamming space. The remaining MLPs or linear layers are then reparameterized with shift kernels. We utilize TVM to implement and optimize those customized kernels for practical hardware deployment on GPUs. We find that such a reparameterization on attention maintains model accuracy, while inevitably leading to accuracy drops when being applied to MLPs. To marry the best of both worlds, we further propose a new mixture of experts (MoE) framework to reparameterize MLPs by taking multiplication or its primitives as experts, e.g., multiplication and shift, and designing a new latency-aware load-balancing loss. Such a loss helps to train a generic router for assigning a dynamic amount of input tokens to different experts according to their latency. Extensive experiments on various 2D/3D Transformer-based vision tasks consistently validate the effectiveness of our proposed ShiftAddViT, achieving up to $\textbf{5.18$\times$}$ latency reductions on GPUs and $\textbf{42.9}$% energy savings, while maintaining a comparable accuracy as original or efficient ViTs.
Rate-Splitting Multiple Access for Simultaneous Multi-User Communication and Multi-Target Sensing
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we initiate the study of rate-splitting multiple access (RSMA) for a mono-static integrated sensing and communication (ISAC) system, where the dual-functional base station (BS) simultaneously communicates with multiple users and detects multiple moving targets. We aim at optimizing the ISAC waveform to jointly maximize the max-min fairness (MMF) rate of the communication users and minimize the largest eigenvalue of the Cramér-Rao bound (CRB) matrix for unbiased estimation. The CRB matrix considered in this work is general as it involves the estimation of angular direction, complex reflection coefficient, and Doppler frequency for multiple moving targets. Simulation results demonstrate that RSMA maintains a larger communication and sensing trade-off than conventional space-division multiple access (SDMA) and it is capable of detecting multiple targets with a high detection accuracy. The finding highlights the potential of RSMA as an effective and powerful strategy for interference management in the general multi-user multi-target ISAC systems.