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Two-dimensional (2D) magnets have attracted significant attentions in recent years due to their importance in the research on both fundamental physics and spintronic applications. Here, we report the discovery of a new ternary compound FePd2Te2. It features a layered quasi-2D crystal structure with one-dimensional Fe zigzag chains extending along the b-axis in the cleavage plane. Single crystals of FePd2Te2 with centimeter-size could be grown. Density functional theory calculations, mechanical exfoliation and atomic force microscopy on these crystals reveal that they are 2D materialsthat can be thinned down to 5 nm. Magnetic characterization shows that FePd2Te2 is an easy-plane ferromagnet with Tc 183 K and strong in-plane uniaxial magnetic anisotropy. Magnetoresistance and anomalous Hall effect demonstrate that ferromagnetism could maintain in FePd2Te2 flakes with large coercivity. A crystal twinning effect is observed by scanning tunneling microscopy which makes the Fe chains right-angle bent in the cleavage plane and creates an intriguing spin texture. Our results show that FePd2Te2 is a correlated anisotropic 2D magnets that may attract multidisciplinary research interests.

Large language models (LLMs) have significantly improved code generation, particularly in one-pass code generation. However, most existing approaches focus solely on generating code in a single programming language, overlooking the potential of leveraging the multi-language capabilities of LLMs. LLMs have varying patterns of errors across different languages, suggesting that a more robust approach could be developed by leveraging these multi-language outputs. In this study, we propose Multi-Programming Language Ensemble (MPLE), a novel ensemble-based method that utilizes code generation across multiple programming languages to enhance overall performance. By treating each language-specific code generation process as an individual "weak expert" and effectively integrating their outputs, our method mitigates language-specific errors and biases. This multi-language ensemble strategy leverages the complementary strengths of different programming languages, enabling the model to produce more accurate and robust code. Our approach can be seamlessly integrated with commonly used techniques such as the reflection algorithm and Monte Carlo tree search to improve code generation quality further. Experimental results show that our framework consistently enhances baseline performance by up to 17.92% on existing benchmarks (HumanEval and HumanEval-plus), with a standout result of 96.25% accuracy on the HumanEval benchmark, achieving new state-of-the-art results across various LLM models. The code will be released at https://github.com/NinjaTech-AI/MPLE, Comment: Code available at https://github.com/NinjaTech-AI/MPLE

First-Person-View (FPV) holds immense potential for revolutionizing the trajectory of Unmanned Aerial Vehicles (UAVs), offering an exhilarating avenue for navigating complex building structures. Yet, traditional Neural Radiance Field (NeRF) methods face challenges such as sampling single points per iteration and requiring an extensive array of views for supervision. UAV videos exacerbate these issues with limited viewpoints and significant spatial scale variations, resulting in inadequate detail rendering across diverse scales. In response, we introduce FPV-NeRF, addressing these challenges through three key facets: (1) Temporal consistency. Leveraging spatio-temporal continuity ensures seamless coherence between frames; (2) Global structure. Incorporating various global features during point sampling preserves space integrity; (3) Local granularity. Employing a comprehensive framework and multi-resolution supervision for multi-scale scene feature representation tackles the intricacies of UAV video spatial scales. Additionally, due to the scarcity of publicly available FPV videos, we introduce an innovative view synthesis method using NeRF to generate FPV perspectives from UAV footage, enhancing spatial perception for drones. Our novel dataset spans diverse trajectories, from outdoor to indoor environments, in the UAV domain, differing significantly from traditional NeRF scenarios. Through extensive experiments encompassing both interior and exterior building structures, FPV-NeRF demonstrates a superior understanding of the UAV flying space, outperforming state-of-the-art methods in our curated UAV dataset. Explore our project page for further insights: https://fpv-nerf.github.io/., Comment: Accepted to ECCV 2024

It is generally believed that electronic correlation, geometric frustration, and topology, \textit{individually}, can facilitate the emergence of various intriguing properties that have attracted a broad audience for both fundamental research and potential applications. Here, we report a systematic investigation on a quasi-kagome Kondo Weyl semimetal candidate Ce$_3$TiSb$_5$. A series of unconventional Hall effects are observed. In the paramagnetic phase, signature of dynamic $c$-$f$ hybridization is revealed by a reduction of anomalous Hall effect and is connected to frustration-promoted incoherent Kondo scattering. A large topological Hall effect exceeding 0.2 $\mu\Omega$ cm is found at low temperatures, which should be ascribed to the noncolinear magnetic structures of the frustrated quasi-kagome lattice. In addition, a peculiar loop-shaped Hall effect with switching chirality is also seen, which is inferred to be associated with magnetic domain walls that pin history-dependent spin chirality and / or Fermi-arc surface states projected from the in-gap Weyl nodes. These exotic results place Ce$_3$TiSb$_5$ in a regime of highly-frustrated antiferromagnetic dense Kondo lattice with a nontrivial topology on an ``extended" global phase diagram, and highlight the interplay among electronic correlation, geometric frustration and topology., Comment: 13+3 pages, 6+3 figures, 2+1 tables

We put forward a conical principle and a degeneracy locating principle of decoupling. The former generalises the Pramanik-Seeger argument used in the proof of decoupling for the light cone. The latter locates the degenerate part of a manifold and effectively reduces the decoupling problem to two extremes: non-degenerate case and totally degenerate case. Both principles aim to provide a new algebraic approach of reducing decoupling for new manifolds to decoupling for known manifolds.

The Janis-Newman-Winicour spacetime corresponds to a static spherically symmetric solution of Einstein equations with the energy momentum tensor of a massless quintessence field. It is understood that the spacetime describes a naked singularity. The solution has two parameters, $b$ and $s$. To our knowledge, the exact physical meaning of the two parameters is still unclear. In this paper, starting from the Janis-Newman-Winicour naked singularity solution, we first obtain a wormhole solution by a complex transformation. Then let the parameter $s$ approaching infinity, we obtain the well-known exponential wormhole solution. After that, we embed both the Janis-Newman-Winicour naked singularity and its wormhole counterpart in the background of de Sitter or anti-de Sitter Universe with the energy momentum tensor of massive quintessence and massive phantom fields, respectively. To our surprise, the resulting quintessence potential is actually the dilaton potential found by one of us. It hints us that, by modulating the parameters in the charged dilaton black hole solutions we can get the Janis-Newman-Winicour solution. Furthermore, a charged wormhole solution is obtained by performing complex transformation on the charged dilaton black hole solutions in the background of de Sitter or anti-de Sitter Universe. We eventually find that $s$ is actually related to the coupling constant of dilaton field to Maxwell field and $b$ is related to a negative mass for the dilaton black holes. A negative black hole mass is physically forbidden. Therefore, we conclude that the Janis-Newman-Winicour naked singularity solution is not physically allowed., Comment: 20 pages,3 figures

Current models for point cloud recognition demonstrate promising performance on synthetic datasets. However, real-world point cloud data inevitably contains noise, impacting model robustness. While recent efforts focus on enhancing robustness through various strategies, there still remains a gap in comprehensive analyzes from the standpoint of network architecture design. Unlike traditional methods that rely on generic techniques, our approach optimizes model robustness to noise corruption through network architecture design. Inspired by the token-mixing technique applied in 2D images, we propose Set-Mixer, a noise-robust aggregation module which facilitates communication among all points to extract geometric shape information and mitigating the influence of individual noise points. A sorting strategy is designed to enable our module to be invariant to point permutation, which also tackles the unordered structure of point cloud and introduces consistent relative spatial information. Experiments conducted on ModelNet40-C indicate that Set-Mixer significantly enhances the model performance on noisy point clouds, underscoring its potential to advance real-world applicability in 3D recognition and perception tasks., Comment: 22 pages, 9 figures

Large language models (LLMs) excel in various capabilities but also pose safety risks such as generating harmful content and misinformation, even after safety alignment. In this paper, we explore the inner mechanisms of safety alignment from the perspective of mechanistic interpretability, focusing on identifying and analyzing safety neurons within LLMs that are responsible for safety behaviors. We propose generation-time activation contrasting to locate these neurons and dynamic activation patching to evaluate their causal effects. Experiments on multiple recent LLMs show that: (1) Safety neurons are sparse and effective. We can restore $90$% safety performance with intervention only on about $5$% of all the neurons. (2) Safety neurons encode transferrable mechanisms. They exhibit consistent effectiveness on different red-teaming datasets. The finding of safety neurons also interprets "alignment tax". We observe that the identified key neurons for safety and helpfulness significantly overlap, but they require different activation patterns of the shared neurons. Furthermore, we demonstrate an application of safety neurons in detecting unsafe outputs before generation. Our findings may promote further research on understanding LLM alignment. The source codes will be publicly released to facilitate future research.

Both simulations and observations suggest that the disk assembly of galaxies is governed by the interplay between coplanar gas inflow, ex-planar gas outflow and in-situ star formation on the disk, known as the leaky accretion disk. This scenario predicts a strong connection between radial distributions of star formation and chemical abundances. The Milky Way, being the sole galaxy where we can reliably measure star formation histories and the corresponding temporally-resolved chemical abundances with individual stars, provides a unique opportunity to scrutinize this scenario. Based on the recent large spectroscopic and photometric surveys of Milky Way stars, we obtain the radial profiles of magnesium abundance ([Mg/H]) and star formation rate (SFR) surface density at different lookback time. We find the radial profiles of [Mg/H] can be well-reproduced using the leaky accretion disk model with only two free parameters for stars formed within 4 Gyr, as well as the flattening at large radii of metallicity profiles traced by HII regions and Cepheids. Furthermore, the constraint effective yield of the Milky Way and nearby galaxies show broad consistency with the theoretical predictions from stellar chemical evolution model with a mass-loading factor of 0-2. These results support that the recent assembly of the Milky Way adheres to the leaky accretion disk scenario, bridging the disk formation of our home galaxy to the big picture of disk formation in the Universe., Comment: 11 pages, 4 figures, accepted to ApJ

Identifying the active factors that have significant impacts on the output of the complex system is an important but challenging variable selection problem in computer experiments. In this paper, a Bayesian hierarchical Gaussian process model is developed and some latent indicator variables are embedded into this setting for the sake of labelling the important variables. The parameter estimation and variable selection can be processed simultaneously in a full Bayesian framework through an efficient Markov Chain Monte Carlo (MCMC) method -- Metropolis-within-Gibbs sampler. The much better performances of the proposed method compared with the related competitors are evaluated by the analysis of simulated examples and a practical application.

$\textbf{OBJECTIVE}$: Ensuring that machine learning (ML) algorithms are safe and effective within all patient groups, and do not disadvantage particular patients, is essential to clinical decision making and preventing the reinforcement of existing healthcare inequities. The objective of this tutorial is to introduce the medical informatics community to the common notions of fairness within ML, focusing on clinical applications and implementation in practice. $\textbf{TARGET AUDIENCE}$: As gaps in fairness arise in a variety of healthcare applications, this tutorial is designed to provide an understanding of fairness, without assuming prior knowledge, to researchers and clinicians who make use of modern clinical data. $\textbf{SCOPE}$: We describe the fundamental concepts and methods used to define fairness in ML, including an overview of why models in healthcare may be unfair, a summary and comparison of the metrics used to quantify fairness, and a discussion of some ongoing research. We illustrate some of the fairness methods introduced through a case study of mortality prediction in a publicly available electronic health record dataset. Finally, we provide a user-friendly R package for comprehensive group fairness evaluation, enabling researchers and clinicians to assess fairness in their own ML work.

The size of a galaxy is one of the fundamental parameters that reflects its growth and assembly history. Traditionally, the size of the Milky Way has been characterized by the scale length of the disk, based on the assumption of an exponential density profile. Earlier scale length measurements suggest the Milky Way is an overly compact galaxy, compared to similar galaxies of its mass. These size measurements, however, ignore the presence of the bulge, and the assumption of a single-exponential disk profile faces growing challenges from the recent observations. The half-light radius is an alternative size measurement that is independent of the galaxy density profile and has been widely used to quantify the size of external galaxies. Here we report the half-light radius of the Milky Way, derived from a new measurement of the age-resolved Galactic surface brightness profile in an unprecedentedly wide radial range from ${\rm R=0}$ to 17~kpc. We find a broken surface brightness profile with a nearly flat distribution between 3.5 and 7.5 kpc, which results in a half-light radius of 5.75$\pm$0.38 kpc, significantly larger than the scale-length inferred from the canonical single-exponential disk profile but in good consistency with local disk galaxies of similar mass. Because our density profile can be decomposed by stellar age and extrapolated backwards in time, we can also confirm that the size history of the Milky Way is broadly consistent with high-redshift galaxies but with systematically smaller size at each look back time. Our results suggest that the Milky Way is a typical disk galaxy regarding its size and has likely experienced inefficient secular size growth., Comment: 30 pages, 4figures, published online in Nature Astronomy on 27 June 2024, https://rdcu.be/dL3z5. Here is the version prior to the peer review

We show with Gaia XP spectroscopy that extremely metal-rich stars in the Milky Way (EMR; $[M/H]_{XP} > 0.5$) - but only those - are largely confined to a tight "knot" at the center of the Galaxy. This EMR knot is round in projection, has a fairly abrupt edge near $\sim 1.5$kpc, and is a dynamically hot system. This central knot also contains very metal-rich (VMR; $+0.2\le [M/H]_{XP} \le +0.4$) stars. However, in contrast to EMR stars, the bulk of VMR stars form an extended, highly flattened distribution in the inner Galaxy ($R_{\mathrm{GC}}\lesssim 5$ kpc). We draw on TNG50 simulations of Milky Way analogs for context and find that compact, metal-rich knots confined to $<1.5$kpc are a universal feature. In typical simulated analogs, the top 5-10% most metal-rich stars are confined to a central knot; however, in our Milky Way data this fraction is only 0.1%. Dust-penetrating wide-area near-infrared spectroscopy, such as SDSS-V, will be needed for a rigorous estimate of the fraction of stars in the Galactic EMR knot. Why in our Milky Way only EMR giants are confined to such a central knot remains to be explained. Remarkably, the central few kiloparsecs of the Milky Way harbor both the highest concentration of metal-poor stars (the `poor old heart') and almost all EMR stars. This highlights the stellar population diversity at the bottom of galactic potential wells., Comment: 11 pages, 7 figures, submitted to ApJ

Code Community Question Answering (CCQA) seeks to tackle programming-related issues, thereby boosting productivity in both software engineering and academic research. Recent advancements in Reinforcement Learning from Human Feedback (RLHF) have transformed the fine-tuning process of Large Language Models (LLMs) to produce responses that closely mimic human behavior. Leveraging LLMs with RLHF for practical CCQA applications has thus emerged as a promising area of study. Unlike standard code question-answering tasks, CCQA involves multiple possible answers, with varying user preferences for each response. Additionally, code communities often show a preference for new APIs. These challenges prevent LLMs from generating responses that cater to the diverse preferences of users in CCQA tasks. To address these issues, we propose a novel framework called Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering (ALMupQA) to create user-focused responses. Our approach starts with Multi-perspective Preference Ranking Alignment (MPRA), which synthesizes varied user preferences based on the characteristics of answers from code communities. We then introduce a Retrieval-augmented In-context Learning (RIL) module to mitigate the problem of outdated answers by retrieving responses to similar questions from a question bank. Due to the limited availability of high-quality, multi-answer CCQA datasets, we also developed a dataset named StaCCQA from real code communities. Extensive experiments demonstrated the effectiveness of the ALMupQA framework in terms of accuracy and user preference. Compared to the base model, ALMupQA showed nearly an 11% improvement in BLEU, with increases of 20% and 17.5% in BERTScore and CodeBERTScore, respectively.

We present early-phase good-cadence (hour-to-day) simultaneous multiband ($ugi$ and $vrz$ bands) imaging of the nearby supernova SN~2024ggi, which exploded in the nearby galaxy, NGC 3621. A quick follow-up was conducted within less than a day after the explosion and continued $\sim$23 days. The $uvg$ band light curves display a rapid rise ($\sim$1.4 mag day$^{-1}$) to maximum in $\sim$4 days and absolute magnitude $M_{g}\sim$--17.75 mag. The post-peak decay rate in redder bands is $\sim$0.01 mag day$^{-1}$. Different colors (e.g., $u-g$ and $v-r$) of SN~2024ggi are slightly redder than SN 2023ixf. A significant rise ($\sim$12.5 kK) in black-body temperature (optical) was noticed within $\sim$2 days after the explosion, which successively decreased, indicating shock break out inside a dense circumstellar medium (CSM) surrounding the progenitor. Using semianalytical modeling, the ejecta mass and progenitor radius were estimated as 1.2 $M_\odot$ and $\sim$550 $R_\odot$. The archival deep images ($g,r,i and z$ bands) from the Dark Energy Camera Legacy Survey were examined, and a possible progenitor was detected in each band ($\sim$22--22.5 mag) and had a mass range of 14--17 $M_\odot$., Comment: Pages 12, Table 1, Figures 7

We find that the Berry curvature splits the edge plasmons propagating along the opposite directions in quantum anomalous Hall insulators even with vanishing Chern number. When the bulk is insulating, only one unidirectional edge plasmon mode survives whose direction can be changed by external fields. The unidirectional edge plasmon in the long-wavelength limit is acoustic and essentially determined by the anomalous Hall conductivity. The group velocity of the chiral edge plasmon would change its sign for a large wave vector, which originates from the k-quadratic correction to the effective mass. The impacts of the Fermi level and the wave vector on the bulk and edge plasmons are discussed. Our work provides a well quantitative explanation of the recent observation of the chiral edge plasmon in quantum anomalous Hall insulators and some insight into the application of realistic topological materials in chiral plasmonics., Comment: 6 pages, 4 figures

This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQChallenge-CVPR-NTIRE2024., Comment: Accepted by CVPR2024 Workshop. The challenge report for CVPR NTIRE2024 Short-form UGC Video Quality Assessment Challenge

We obtain $L^p$ estimates of the maximal Schr\"odinger operator in $\mathbb R^n$ using polynomial partitioning, bilinear refined Strichartz estimates, and weighted restriction estimates., Comment: 18 pages

We obtain some sharp $L^p$ weighted Fourier restriction estimates of the form $\|Ef\|_{L^p(B^{n+1}(0,R),Hdx)} \lessapprox R^{\beta}\|f\|_2$, where $E$ is the Fourier extension operator over the truncated paraboloid, and $H$ is a weight function on $\mathbb R^{n+1}$ which is $n$-dimensional up to scale $\sqrt R$., Comment: 15 pages, 2 figures

Foot contact is an important cue for human motion capture, understanding, and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However, these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion, as well as accurate and dense foot-contact annotation. To fill this gap, we propose a Multimodal MoCap Dataset with Vision and Pressure sensors, named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations, which is especially useful for both plausible shape estimation, robust pose fitting without foot drifting, and accurate global translation tracking. To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://metaverse-ai-lab-thu.github.io/MMVP-Dataset/., Comment: CVPR2024

We theoretically propose Ac$_3$Ni$_2$O$_7$, La$_2$BaNi$_2$O$_6$F, and La$_2$SrNi$_2$O$_6$F compounds to be benchmark materials for bilayer nickelate superconductivity. The stable phase of Ac$_3$Ni$_2$O$_7$ and La$_2$BaNi$_2$O$_6$F are found to be $I4/mmm$ without the lattice distortion caused by octahedra rotation at ambient pressure, where as the lattice distortion in La$_2$SrNi$_2$O$_6$F can be suppressed with relatively small external pressure of 4 GPa. The magnetism, electronic structure and spin susceptibilities of Ac$_3$Ni$_2$O$_7$ are extremely close to those of La$_3$Ni$_2$O$_7$ at 30 GPa. The ground state of La$_2$BaNi$_2$O$_6$F and La$_2$SrNi$_2$O$_6$F are antiferromagnetically coupled checkerboard bilayer with sizable magnetic moment on Ni. In addition, the inter-layer coupling $J_{\perp}$ between Ni-bilayers in La$_2$BaNi$_2$O$_6$F or La$_2$SrNi$_2$O$_6$F is only $\sim$ 1/10 of that in Ac$_3$Ni$_2$O$_7$ or La$_3$Ni$_2$O$_7$ at 30 GPa. We argue that these compounds may serve as superconducting candidates at ambient pressure and can be employed to testify theoretical proposals for bilayer nickelate superconductivity.

Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are substantially shaped by multi-modal data, such as pictures and textual descriptions. These modalities not only offer intuitive guidance but also cater to personalized user preferences. However, the predominant personalization approaches mainly focus on the ID or text-based recommendation problem, failing to comprehend the information spanning various tasks or modalities. In this paper, our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP), which effectively leverages multi-modal data while eliminating the complexities associated with task- and modality-specific customization. We argue that the advancements in foundational generative modeling have provided the flexibility and effectiveness necessary to achieve the objective. In light of this, we develop a generic and extensible personalization generative framework, that can handle a wide range of personalized needs including item recommendation, product search, preference prediction, explanation generation, and further user-guided image generation. Our methodology enhances the capabilities of foundational language models for personalized tasks by seamlessly ingesting interleaved cross-modal user history information, ensuring a more precise and customized experience for users. To train and evaluate the proposed multi-modal personalized tasks, we also introduce a novel and comprehensive benchmark covering a variety of user requirements. Our experiments on the real-world benchmark showcase the model's potential, outperforming competitive methods specialized for each task., Comment: ICLR 2024

This paper presents a novel application of explainable AI (XAI) for root-causing performance degradation in machine learning models that learn continuously from user engagement data. In such systems a single feature corruption can cause cascading feature, label and concept drifts. We have successfully applied this technique to improve the reliability of models used in personalized advertising. Performance degradation in such systems manifest as prediction anomalies in the models. These models are typically trained continuously using features that are produced by hundreds of real time data processing pipelines or derived from other upstream models. A failure in any of these pipelines or an instability in any of the upstream models can cause feature corruption, causing the model's predicted output to deviate from the actual output and the training data to become corrupted. The causal relationship between the features and the predicted output is complex, and root-causing is challenging due to the scale and dynamism of the system. We demonstrate how temporal shifts in the global feature importance distribution can effectively isolate the cause of a prediction anomaly, with better recall than model-to-feature correlation methods. The technique appears to be effective even when approximating the local feature importance using a simple perturbation-based method, and aggregating over a few thousand examples. We have found this technique to be a model-agnostic, cheap and effective way to monitor complex data pipelines in production and have deployed a system for continuously analyzing the global feature importance distribution of continuously trained models., Comment: Submitted to The 2nd World Conference on eXplainable Artificial Intelligence, 17-19 July, 2024, Malta, Valletta

In this paper, we utilize the effective corrections of the $\bar{\mu}$-scheme in loop quantum black holes to obtain a 4-dimensional spherically symmetric metric with a cosmological constant. By imposing the areal gauge on the components of Ashtekar variables in the classical theory and applying the holonomy corrections, we derive the equations of motion, which can be solved to obtain the expression for the effective metric in the Painlev\'{e}-Gullstrand coordinates. Compared to the classical dS (AdS) spacetime, the LQG correction sets an upper bound on the cosmological constant as $\Lambda<\frac{3}{\gamma^2\Delta}$. The thermodynamic properties of black holes have also been calculated. We interestingly found that for a small black hole, the temperature of the LQG black hole decreases as the mass decreases, which is quite different with the classical scenario. Moreover, our result shows that a logarithmic term appeared as the leading order correction to the Beikenstein-Hawking entropy. Furthermore, the LQG corrections also introduce an extra phase transition in the black hole's heat capacity at smaller radius., Comment: 10 pages, 4 figures; V2, 14 pages, 9 figures

Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no effective way to verify whether they can meet the requirements for real-world deployment. Firstly, current algorithms typically train a specific model for each time series. Maintaining such many models is impractical in a large-scale system with tens of thousands of curves. The performance of using merely one unified model to detect anomalies remains unknown. Secondly, most TSAD models are trained on the historical part of a time series and are tested on its future segment. In distributed systems, however, there are frequent system deployments and upgrades, with new, previously unseen time series emerging daily. The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown. Lastly, the assumptions of the evaluation metrics in existing benchmarks are far from practical demands. To solve the above-mentioned problems, we propose an industrial-grade benchmark TimeSeriesBench. We assess the performance of existing algorithms across more than 168 evaluation settings and provide comprehensive analysis for the future design of anomaly detection algorithms. An industrial dataset is also released along with TimeSeriesBench., Comment: Accepted by ISSRE'24

Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation. However, the substantial size and parameter volume of these LLMs require massive GPU memory. This memory demand increases with the length of the input text, leading to an urgent need for more efficient methods of information storage and processing. This study introduces Anchor-based LLMs (AnLLMs), which utilize an innovative anchor-based self-attention network (AnSAN) and also an anchor-based inference strategy. This approach enables LLMs to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. Experiments on question-answering benchmarks reveal that AnLLMs maintain similar accuracy levels while achieving up to 99% keys/values cache reduction and up to 3.5 times faster inference. Despite a minor compromise in accuracy, the substantial enhancements of AnLLMs employing the AnSAN technique in resource utilization and computational efficiency underscore their potential for practical LLM applications., Comment: The paper has been accepted by the ACL2024 conference. Work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed "target attention" mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models., Comment: WWW 2024

Fourfold anisotropic magnetoresistance (AMR) have been widely observed in quantum materials, but the underlying mechanisms remain poorly understood. Here we find, in a variety of three-dimensional Dirac materials that can be unifiedly described by the massive Dirac equation, the intrinsic orbital magnetic moment of electrons vary synchronously with the magnetic field and give rise to a {\pi} periodic correction to its velocity, further leading to unusual fourfold AMR, dubbed intrinsic orbital fourfold AMR. Our theory not only explains the observation of fourfold AMR in bismuth but also uncovers the nature of the dominant fourfold AMR in thin films of antiferromagnetic topological insulator MnBi2Te4, which arises from the near cancellation of the twofold AMR from the surface states and bulk states due to distinct spin-momentum lockings. Our work provides a new mechanism for creation and manipulation of intrinsic fourfold AMR in both conventional conductors and various topological insulators., Comment: 6 pages and 2 figures. Comments are welcome

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evaluation platforms, such as the widely recognized HuggingFace open LLM leaderboard, neglect a crucial aspect -- uncertainty, which is vital for thoroughly assessing LLMs. To bridge this gap, we introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our examination involves eight LLMs (LLM series) spanning five representative natural language processing tasks. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs. These results underscore the significance of incorporating uncertainty in the evaluation of LLMs., Comment: 25 pages, preprints

This paper is a write-up of the ideas that were presented, developed and discussed at the fourth International Workshop on QCD Challenges from pp to AA, which took place in February 2023 in Padua, Italy. The goal of the workshop was to focus on some of the open questions in the field of high-energy heavy-ion physics and to stimulate the formulation of concrete suggestions for making progresses on both the experimental and theoretical sides. The paper gives a brief introduction to each topic and then summarizes the primary results.

In the present work, considering critical gravity as a gravity model, an electrically charged topological Anti-de Sitter black hole with a matter source characterized by a nonlinear electrodynamics framework is obtained. This configuration is defined by an integration constant, three key structural constants, and a constant that represents the topology of the event horizon. Additionally, based on the Wald formalism, we probe that this configuration enjoys non-trivial thermodynamic quantities, establishing the corresponding first law of black hole thermodynamics, as well as local stability under thermal and electrical fluctuations. Additionally, via the Gibbs free energy we note that the topology of the base manifold allows us to compare this charged configuration with respect to the thermal AdS space-time, allowing us to obtain a first-order phase transition. The quasinormal modes and the greybody factor are also calculated by considering the spherical situation. We found that the quasinormal modes exhibit a straightforward change for variations of one of the structural constants., Comment: 15 pages, 17 figures; V2, 18 pages, 18 figures