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The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from conventional agent-based simulation approaches, this work introduces an innovative approach by employing a large language model (LLM)-driven multi-agent simulation to replicate complex interactions within information ecosystems. We investigate key factors that facilitate news propagation, such as agent personalities and network structures, while also evaluating strategies to combat misinformation. Through simulations across varying network structures, we demonstrate the potential of LLM-based agents in modeling the dynamics of misinformation spread, validating the influence of agent traits on the diffusion process. Our findings emphasize the advantages of LLM-based simulations over traditional techniques, as they uncover underlying causes of information spread -- such as agents promoting discussions -- beyond the predefined rules typically employed in existing agent-based models. Additionally, we evaluate three countermeasure strategies, discovering that brute-force blocking influential agents in the network or announcing news accuracy can effectively mitigate misinformation. However, their effectiveness is influenced by the network structure, highlighting the importance of considering network structure in the development of future misinformation countermeasures.

Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, the training of these models is computationally intensive and susceptible to faults, particularly in the attention mechanism, which is a critical component of transformer-based LLMs. In this paper, we investigate the impact of faults on LLM training, focusing on INF, NaN, and near-INF values in the computation results with systematic fault injection experiments. We observe the propagation patterns of these errors, which can trigger non-trainable states in the model and disrupt training, forcing the procedure to load from checkpoints. To mitigate the impact of these faults, we propose ATTNChecker, the first Algorithm-Based Fault Tolerance (ABFT) technique tailored for the attention mechanism in LLMs. ATTNChecker is designed based on fault propagation patterns of LLM and incorporates performance optimization to adapt to both system reliability and model vulnerability while providing lightweight protection for fast LLM training. Evaluations on four LLMs show that ATTNChecker on average incurs on average 7% overhead on training while detecting and correcting all extreme errors. Compared with the state-of-the-art checkpoint/restore approach, ATTNChecker reduces recovery overhead by up to 49x.

In recent years, Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance and have been pervasively applied and studied in both industry and academia. Nonetheless, LLMs were proved to be highly sensitive to input prompts, with slight differences in the expressions of semantically equivalent programs potentially causing repair failures. Therefore, it is crucial to conduct robustness testing on LAPR techniques before their practical deployment. However, related research is scarce. To this end, we propose MT-LAPR, a Metamorphic Testing framework exclusively for LAPR techniques, which summarizes nine widely-recognized Metamorphic Relations (MRs) by developers across three perturbation levels: token, statement, and block. Afterward, our proposed MRs are applied to buggy codes to generate test cases, which are semantically equivalent yet to affect the inference of LAPR. Experiments are carried out on two extensively examined bug-fixing datasets, i.e., Defect4J and QuixBugs, and four bug-fixing abled LLMs released recently, demonstrating that 34.4% - 48.5% of the test cases expose the instability of LAPR techniques on average, showing the effectiveness of MT-LAPR and uncovering a positive correlation between code readability and the robustness of LAPR techniques. Inspired by the above findings, this paper uses the test cases generated by MT-LAPR as samples to train a CodeT5-based code editing model aiming at improving code readability and then embeds it into the LAPR workflow as a data preprocessing step. Extensive experiments demonstrate that this approach significantly enhances the robustness of LAPR by 49.32% at most.

Periodic soft real-time systems have broad applications in many areas, such as IoT. Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplified to cope with complex situations. The method encodes time series information in the Linux kernel into information that is easy to use for reinforcement learning, allowing the system to generate DVFS policies to adapt system patterns based on the general workload. For encoding, we present two different methods for comparison. Both methods use only one performance counter: system utilization and the kernel only needs minimal information from the userspace. Our method is implemented on Jetson Nano Board (2GB) and is tested with three fixed multitask workloads, which are three, five, and eight tasks in the workload, respectively. For randomness and generalization, we also designed a random workload generator to build different multitask workloads to test. Based on the test results, our method could save 3%-10% power compared to Linux built-in governors.

We investigate the asymptotic disconnection time of a large discrete cylinder $(\mathbb{Z}/N\mathbb{Z})^{d}\times \mathbb{Z}$, $d\geq 2$, by simple and biased random walks. For simple random walk, we derive a sharp asymptotic lower bound that matches the upper bound from [Sznitman, Ann. Probab., 2009]. For biased walks, we obtain bounds that asymptotically match in the principal order when the bias is not too strong, which greatly improves non-matching bounds from [Windisch, Ann. Appl. Probab., 2008]. As a crucial tool in the proof, we also obtain a "very strong" coupling between the trace of random walk on the cylinder and random interlacements, which is of independent interest., Comment: 88 pages, 6 tables of symbols

On the trace of a discrete-time simple random walk on $\mathbb{Z}^d$ for $d\geq 2$, we consider the evolution of favorite sites, i.e., sites that achieve the maximal local time at a certain time. For $d=2$, we show that almost surely three favorite sites occur simultaneously infinitely often and eventually there is no simultaneous occurrence of four favorite sites. For $d\geq 3$, we derive sharp asymptotics of the number of favorite sites. This answers an open question of Erd\H{o}s and R\'{e}v\'{e}sz (1987), which was brought up again in Dembo (2005)., Comment: 46 pages, 3 figures

Geometric frustration is known to completely damage kinetic processes of some of the orbitals (and their associated quantum coherence) as to produce flat bands in the non-interacting systems. The impact of introducing additional interaction to the system in such frustrated systems is, however, a highly controversial issue. On the one hand, numerical studies on geometrically frustrated systems of hard-core boson (equivalent to a spin-1/2 systems) typically lead to glass or solid phases containing only local many-body coherence, indicating the persistence of the damage in quantum coherence. On the other, there continues to be noticeable claims of development of superfluidity that implies kinetic flow of particles. To resolve this apparent contradiction of great significance, we present a rigorous proof showing that density-density interaction is incapable of defeating the geometric frustration to allow propagation of those immobile particles, let alone sustaining a superfluidity. Instead, the frustrated systems develop many $\textit{compact}$ many-body localized states as "many-body scars" that do not thermalize, making them good candidates for storing $\textit{robust}$ and $\textit{manipulable}$ quantum information., Comment: 6 pages, 4 figures

In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organize two tracks to handle the argumentative generation tasks in different scenarios, namely, Counter-Argument Generation (Track 1) and Claim-based Argument Generation (Track 2). Each track is equipped with its distinct dataset and baseline model respectively. In total, 32 competing teams register for the challenge, from which we received 11 successful submissions. In this paper, we will present the results of the challenge and a summary of the systems, highlighting commonalities and innovations among participating systems. Datasets and baseline models of the AI-Debater 2023 Challenge have been already released and can be accessed through the official website of the challenge.

In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event cameras output sparse and asynchronous events, providing a potential solution to solve these problems. However, effectively fusing two heterogeneous modalities remains an open issue. In this work, we propose a novel hierarchical feature refinement network for event-frame fusion. The core concept is the design of the coarse-to-fine fusion module, denoted as the cross-modality adaptive feature refinement (CAFR) module. In the initial phase, the bidirectional cross-modality interaction (BCI) part facilitates information bridging from two distinct sources. Subsequently, the features are further refined by aligning the channel-level mean and variance in the two-fold adaptive feature refinement (TAFR) part. We conducted extensive experiments on two benchmarks: the low-resolution PKU-DDD17-Car dataset and the high-resolution DSEC dataset. Experimental results show that our method surpasses the state-of-the-art by an impressive margin of $\textbf{8.0}\%$ on the DSEC dataset. Besides, our method exhibits significantly better robustness (\textbf{69.5}\% versus \textbf{38.7}\%) when introducing 15 different corruption types to the frame images. The code can be found at the link (https://github.com/HuCaoFighting/FRN)., Comment: Accepted by ECCV 2024

Energy measurement of computer devices, which are widely used in the Internet of Things (IoT), is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. A cost-effective solution is to use low-end consumer-grade power meters. However, these low-end power meters cannot provide accurate instantaneous power measurements. In this paper, we propose an easy-to-use approach to derive an instantaneous software-based energy estimation model with only low-end power meters based on data-driven analysis through machine learning. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection method and physical measurement are explored. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92% accuracy can be achieved compared to the long-duration measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in real environment.

Classical max pooling plays a crucial role in reducing data dimensionality among various well-known deep learning models, yet it often leads to the loss of vital information. We proposed a novel hybrid quantum downsampling module (HQD), which is a noise-resilient algorithm. By integrating a substantial number of quantum bits (qubits), our approach ensures the key characteristics of the original image are maximally preserved within the local receptive field. Moreover, HQD provides unique advantages in the context of the noisy intermediate-scale quantum (NISQ) era. We introduce a unique quantum variational circuit in our design, utilizing rotating gates including RX, RY, RZ gates, and the controlled-NOT (CNOT) gate to explore nonlinear characteristics. The results indicate that the network architectures incorporating the HQD module significantly outperform the classical structures with max pooling in CIFAR-10 and CIFAR-100 datasets. The accuracy of all tested models improved by an average of approximately 3%, with a maximum fluctuation of only 0.4% under various quantum noise conditions.

Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm shift in anomaly detection methodologies. This study proposes a Quantum Scoring Module (Qsco), embedding quantum variational circuits into neural networks to enhance the model's processing capabilities in handling uncertainty and unlabeled data. Extensive experiments conducted across eight real-world anomaly detection datasets demonstrate our model's superior performance in detecting anomalies across varied settings and reveal that integrating quantum simulators does not result in prohibitive time complexities. Our study validates the feasibility of quantum-enhanced anomaly detection methods in practical applications.

In the current digital era, the rapid spread of misinformation on online platforms presents significant challenges to societal well-being, public trust, and democratic processes, influencing critical decision making and public opinion. To address these challenges, there is a growing need for automated fake news detection mechanisms. Pre-trained large language models (LLMs) have demonstrated exceptional capabilities across various natural language processing (NLP) tasks, prompting exploration into their potential for verifying news claims. Instead of employing LLMs in a non-agentic way, where LLMs generate responses based on direct prompts in a single shot, our work introduces FactAgent, an agentic approach of utilizing LLMs for fake news detection. FactAgent enables LLMs to emulate human expert behavior in verifying news claims without any model training, following a structured workflow. This workflow breaks down the complex task of news veracity checking into multiple sub-steps, where LLMs complete simple tasks using their internal knowledge or external tools. At the final step of the workflow, LLMs integrate all findings throughout the workflow to determine the news claim's veracity. Compared to manual human verification, FactAgent offers enhanced efficiency. Experimental studies demonstrate the effectiveness of FactAgent in verifying claims without the need for any training process. Moreover, FactAgent provides transparent explanations at each step of the workflow and during final decision-making, offering insights into the reasoning process of fake news detection for end users. FactAgent is highly adaptable, allowing for straightforward updates to its tools that LLMs can leverage within the workflow, as well as updates to the workflow itself using domain knowledge. This adaptability enables FactAgent's application to news verification across various domains.

This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment. Our study involves a combination of between-subject prompt and historic consumption assessments, along with within-subject recommendation scenario evaluations. By examining conversation and survey response data from 160 active users, we find that LLMs offer strong recommendation explainability but lack overall personalization, diversity, and user trust. Our results also indicate that different personalized prompting techniques do not significantly affect user-perceived recommendation quality, but the number of movies a user has watched plays a more significant role. Furthermore, LLMs show a greater ability to recommend lesser-known or niche movies. Through qualitative analysis, we identify key conversational patterns linked to positive and negative user interaction experiences and conclude that providing personal context and examples is crucial for obtaining high-quality recommendations from LLMs.

Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a critical role in Open-Source Software (OSS). Very recently, Large Language Models (LLMs) have demonstrated extensive applicability in diverse code-related task. But few studies systematically explored their effectiveness using LLMs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying LLM to generate high-quality commit messages. Motivated by a pilot analysis, we first clean the most widely-used CMG dataset following practitioners' criteria. Afterward, we re-evaluate diverse state-of-the-art CMG approaches and make comparisons with LLMs, demonstrating the superior performance of LLMs against state-of-the-art CMG approaches. Then, we further propose four manual metrics following the practice of OSS, including Accuracy, Integrity, Applicability, and Readability, and assess various LLMs accordingly. Results reveal that GPT-3.5 performs best overall, but different LLMs carry different advantages. To further boost LLMs' performance in the CMG task, we propose an Efficient Retrieval-based In-Context Learning (ICL) framework, namely ERICommiter, which leverages a two-step filtering to accelerate the retrieval efficiency and introduces semantic/lexical-based retrieval algorithm to construct the ICL examples. Extensive experiments demonstrate the substantial performance improvement of ERICommiter on various LLMs for code diffs of different programming languages. Meanwhile, ERICommiter also significantly reduces the retrieval time while keeping almost the same performance. Our research contributes to the understanding of LLMs' capabilities in the CMG field and provides valuable insights for practitioners seeking to leverage these tools in their workflows.

Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras. Despite advancements, existing datasets fall short in terms of size and diversity, limiting comprehensive evaluation under diverse conditions. This paper presents an in-depth analysis of the VitalVideo dataset, the largest real-world rPPG dataset to date, encompassing 893 subjects and 6 Fitzpatrick skin tones. Our experimentation with six unsupervised methods and three supervised models demonstrates that datasets comprising a few hundred subjects(i.e., 300 for UBFC-rPPG, 500 for PURE, and 700 for MMPD-Simple) are sufficient for effective rPPG model training. Our findings highlight the importance of diversity and consistency in skin tones for precise performance evaluation across different datasets., Comment: 11 pages, 5 figures, CHI24 Workshop PhysioCHI

Reliable numerical computations are central to scientific computing, but the floating-point arithmetic that enables large-scale models is error-prone. Numeric exceptions are a common occurrence and can propagate through code, leading to flawed results. This paper presents FlowFPX, a toolkit for systematically debugging floating-point exceptions by recording their flow, coalescing exception contexts, and fuzzing in select locations. These tools help scientists discover when exceptions happen and track down their origin, smoothing the way to a reliable codebase., Comment: Presented at JuliaCon 2023; to appear in JuliaCon proceedings

In this work, we consider the scaling limit of loop-erased random walk (LERW) in three dimensions and prove that the limiting occupation measure is equivalent to its $\beta$-dimensional Minkowski content, where $\beta \in (1, 5/3]$ is its Hausdorff dimension. In doing this we also establish the existence of the two-point function and provide some sharp estimates on one-point function and ball-hitting probabilities for 3D LERW in any scale, which is a considerable improvement of previous results., Comment: 50 pages

NVIDIA Tensor Cores and AMD Matrix Cores (together called Matrix Accelerators) are of growing interest in high-performance computing and machine learning owing to their high performance. Unfortunately, their numerical behaviors are not publicly documented, including the number of extra precision bits maintained, the accumulation order of addition, and predictable subnormal number handling during computations. This makes it impossible to reliably port codes across these differing accelerators. This paper contributes a collection of {\em Feature Targeted Tests for Numerical Properties} that that help determine these features across five floating-point formats, four rounding modes and additional that highlight the rounding behaviors and preservation of extra precision bits. To show the practical relevance of FTTN, we design a simple matrix-multiplication test designed with insights gathered from our feature-tests. We executed this very simple test on five platforms, producing different answers: V100, A100, and MI250X produced 0, MI100 produced 255.875, and Hopper H100 produced 191.875. Our matrix multiplication tests employ patterns found in iterative refinement-based algorithms, highlighting the need to check for significant result variability when porting code across GPUs.

Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 9 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.

The subway as the most important transportation for daily urban commuting is a typical non-equilibrium complex system, composed of 2 types of basic units with service relationship. One challenge to operate it is passengers be stranded at stations, which arise from the spatiotemporal mismatch of supply scale and demand scale. More seriously, there is a special phenomenon of the propagation of passenger stranding (PPS) by forming stranded stations clusters, which significantly reduces the service efficiency. In this study, Beijing subway as an example is studied to reveal the nature of PPS phenomena from a view point of statistical physics. The simulation results demonstrate phase-transition-like behaviors depending on the ratio of service supply scale and demand scale. The transition point can quantitatively characterize the resilience failure threshold of service. The eigen microstate method is used to extracting the fundamental patterns of PPS phenomena. Moreover, this study offers a theoretical foundation for strategies to improve service, such as topological planning and train timetable optimization. The methodology developed in present work has significant implications for study of other service systems.

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

Deriving atmospheric parameters of a large sample of stars is of vital importance to understand the formation and evolution of the Milky Way. Photometric surveys, especially those with near-ultraviolet filters, can offer accurate measurements of stellar parameters, with the precision comparable to that from low/medium resolution spectroscopy. In this study, we explore the capability of measuring stellar atmospheric parameters from CSST broad-band photometry (particularly the near-ultraviolet bands), based on synthetic colors derived from model spectra. We find that colors from the optical and near-ultraviolet filter systems adopted by CSST show significant sensitivities to the stellar atmospheric parameters, especially the metallicity. According to our mock data tests, the precision of the photometric metallicity is quite high, with typical values of 0.17 dex and 0.20 dex for dwarf and giant stars, respectively. The precision of the effective temperature estimated from broad-band colors are within 50 K., Comment: 16 pages, 18 figures, accepted by Research in Astronomy and Astrophysics

Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS algorithms have been developed to enhance SPH efficiency, the potential efficiency gains from modern computation hardware remain underexplored. This study investigates the impact of GPU parallel architecture, low-precision computing on GPUs, and GPU memory management on NNPS efficiency. Our approach employs a GPU-accelerated mixed-precision SPH framework, utilizing low-precision float-point 16 (FP16) for NNPS while maintaining high precision for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates of particles within background cells. Our testing results show three significant speedup rounds for CPU-based NNPS algorithms. The first comes from parallel GPU computations, with up to a 1000x efficiency gain. The second is achieved through low-precision GPU computing, where the proposed FP16-based RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based approach on GPUs. By optimizing GPU memory bandwidth utilization, the efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as demonstrated in an example with 1 million particles. Our code is released at https://github.com/pnnl/lpNNPS4SPH.

News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information. The fusion of natural language processing (NLP) and RS, spurred by the rise of large language models such as the GPT and T5 series, blurs the boundaries between these domains, making a tendency to treat RS as a language task. ChatGPT, renowned for its user-friendly interface and increasing popularity, has become a prominent choice for a wide range of NLP tasks. While previous studies have explored ChatGPT on recommendation tasks, this study breaks new ground by investigating its fine-tuning capability, particularly within the news domain. In this study, we design two distinct prompts: one designed to treat news RS as the ranking task and another tailored for the rating task. We evaluate ChatGPT's performance in news recommendation by eliciting direct responses through the formulation of these two tasks. More importantly, we unravel the pivotal role of fine-tuning data quality in enhancing ChatGPT's personalized recommendation capabilities, and illustrates its potential in addressing the longstanding challenge of the "cold item" problem in RS. Our experiments, conducted using the Microsoft News dataset (MIND), reveal significant improvements achieved by ChatGPT after fine-tuning, especially in scenarios where a user's topic interests remain consistent, treating news RS as a ranking task. This study illuminates the transformative potential of fine-tuning ChatGPT as a means to advance news RS, offering more effective news consumption experiences.

Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerators such as NVIDIA Tensor Cores, AMD Matrix Cores, and Google TPUs is the support of mixed-precision enabled GEMM. For DNN models, lower-precision FP data formats and computation offer acceptable correctness but significant performance, area, and memory footprint improvement. While promising, the mixed-precision computation on error resilience remains unexplored. To this end, we develop a fault injection framework that systematically injects fault into the mixed-precision computation results. We investigate how the faults affect the accuracy of machine learning applications. Based on the error resilience characteristics, we offer lightweight error detection and correction solutions that significantly improve the overall model accuracy if the models experience hardware faults. The solutions can be efficiently integrated into the accelerator's pipelines.

Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby enhancing the computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle and we propose a global voting method for its estimation. The proposed method sequentially solves three consensus maximization sub-problems, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%.

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment.

We consider the occupation measure of the cut points of a simple random walk on a $d$-dimensional cubic lattice for $d = 2, 3$, and we show that the scaling limit of the occupation measure in weak topology is the natural fractal measure on the Brownian cut points defined via its Minkowski content., Comment: 57 pages, 12 figures

We use 3653 (2661 RRab, 992 RRc) RR Lyrae stars (RRLs) with 7D (3D position, 3D velocity, and metallicity) information selected from SDSS, LAMOST, and Gaia EDR3, and divide the sample into two Oosterhoff groups (Oo I and Oo II) according to their amplitude-period behaviour in the Bailey Diagram. We present a comparative study of these two groups based on chemistry, kinematics, and dynamics. We find that Oo I RRLs are relatively more metal rich, with predominately radially dominated orbits and large eccentricities, while Oo II RRLs are relatively more metal poor, and have mildly radially dominated orbits. The Oosterhoff dichotomy of the Milky Way's halo is more apparent for the inner-halo region than for the outer-halo region. Additionally, we also search for this phenomenon in the halos of the two largest satellite galaxies, the Large and Small Magellanic clouds (LMC, SMC), and compare over different bins in metallicity. We find that the Oosterhoff dichotomy is not immutable, and varies based on position in the Galaxy and from galaxy-to-galaxy. We conclude that the Oosterhoff dichotomy is the result of a combination of stellar and galactic evolution, and that it is much more complex than the dichotomy originally identified in Galactic globular clusters.