Search

Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series. To gain a more comprehensive understanding and provide insightful explanations, we utilize symbolic regression techniques to derive explicit expressions for the non-linear dynamics in the evolution of time series variables. However, these techniques face challenges in computational efficiency and generalizability across diverse real-world time series data. To overcome these challenges, we propose \textbf{N}eural-\textbf{E}nhanced \textbf{Mo}nte-Carlo \textbf{T}ree \textbf{S}earch (NEMoTS) for time series. NEMoTS leverages the exploration-exploitation balance of Monte-Carlo Tree Search (MCTS), significantly reducing the search space in symbolic regression and improving expression quality. Furthermore, by integrating neural networks with MCTS, NEMoTS not only capitalizes on their superior fitting capabilities to concentrate on more pertinent operations post-search space reduction, but also replaces the complex and time-consuming simulation process, thereby substantially improving computational efficiency and generalizability in time series analysis. NEMoTS offers an efficient and comprehensive approach to time series analysis. Experiments with three real-world datasets demonstrate NEMoTS's significant superiority in performance, efficiency, reliability, and interpretability, making it well-suited for large-scale real-world time series data.

The shape of the cold interstellar molecular gas is determined by several processes, including self-gravity, tidal force, turbulence, magnetic field, and galactic shear. Based on the 3D dust extinction map derived by Vergely et al., we identify a sample of 550 molecular clouds (MCs) within $3\hspace{0.2em}\rm kpc$ of the solar vicinity in the Galactic disk. Our sample contains clouds whose size ranges from pc to kiloparsec, which enables us to study the effect of Galactic-scale processes, such as shear, on cloud evolution. We find that our sample clouds follow a power-law mass-size relation of $M\propto 32.00\hspace{0.2em}{R_{\rm{max}}}^{1.77}$, $M\propto 20.59\hspace{0.2em}{R_S}^{2.04}$ and $M\propto 14.41\hspace{0.2em}{R_V}^{2.29}$, where $R_{\rm{max}}$ is the major axis-based cloud radius, $R_S$ is the area-based radius, and $R_V$ is the volume-based radius, respectively. These clouds have a mean constant surface density of $\sim 7 \hspace{0.2em} \rm {M_{\odot}pc^{-2}}$, and follow a volume density-size relation of $\rho \propto 2.60\hspace{0.2em}{R_{\rm{max}}}^{-0.55}$. As cloud size increases, their shapes gradually transition from ellipsoidal to disk-like to bar-like structures. Large clouds tend to have a pitch angle of $28^{\circ} - 45^{\circ}$, where the angle is measured concerning the Galactic tangential direction. These giant clouds also tend to stay parallel to the Galactic disk plane and are confined within the Galactic molecular gas disk. Our results show that large molecular clouds in the Milky Way can be shaped by Galactic shear and confined in the vertical direction by gravity., Comment: Accepted by APJ

We present an all-fiber, fully open Fabry-Perot interferometer (FPI) cavity that is suitable for fluidic measurement applications. Fabrication of the FPI involves the alignment and bonding of three optical fiber sections using either ceramic glue or low-temperature melting glass. The fabrication procedure allows the protection of the cleaved optical fiber end faces, which serve as the two mirrors of the FPI, from damage, thus ensuring the high visibility of the FPI sensor. The FPI's complete openness permits the analyte of interest fluids to flow smoothly into the cavity and interact directly with the light, obviating the need for additional assistance. The fabrication experiment demonstrates that the fabrication procedure can readily achieve a visibility of over 20 dB. Refractive index testing indicates that the sensor exhibits a sensitivity of over 1116 nm/RIU within the range of 1.334-1.375. A comparison of temperature investigations indicates that the fully open cavity FPI fabricated by bonding with low-temperature melting glass exhibits relatively lower temperature immunity than that bonded with ceramic glue. Both have a relatively low temperature fluctuation within the temperature range of 40{\deg}C-100{\deg}C, with less than 3 nm and 4.5 nm in the over 60{\deg}C changes, respectively. Our proposed fully open FPI is an economical, robust, and simple-to-fabricate structure with the potential for mass production. This renders it an appealing option for practical optofluidics applications., Comment: 6 pages, 10 figures

Accurate thermometry of laser-cooled ions is crucial for the performance of the trapped-ions quantum computing platform. However, most existing methods face a computational exponential bottleneck. Recently, a thermometry method based on bichromatic driving was theoretically proposed by Ivan Vybornyi et al. to overcome this obstacle, which allows the computational complexity to remain constant with the increase of ion numbers. In this paper, we provide a detailed statistical analysis of this method and prove its robustness to several imperfect experimental conditions using Floquet theory. We then experimentally verify its good performance on a linear segmented surface-electrode ion trap platform for the first time. This method is proven to be effective from near the motional ground state to a few mean phonon numbers. Our theoretical analysis and experimental verification demonstrate that the scheme can accurately and efficiently measure the temperature in ion crystals.

Large language models (LLMs) have shown exceptional performance and vast potential across diverse tasks. However, the deployment of LLMs with high performance in low-resource environments has garnered significant attention in the industry. When GPU hardware resources are limited, we can explore alternative options on CPUs. To mitigate the financial burden and alleviate constraints imposed by hardware resources, optimizing inference performance is necessary. In this paper, we introduce an easily deployable inference performance optimization solution aimed at accelerating LLMs on CPUs. In this solution, we implement an effective way to reduce the KV cache size while ensuring precision. We propose a distributed inference optimization approach and implement it based on oneAPI Collective Communications Library. Furthermore, we propose optimization approaches for LLMs on CPU, and conduct tailored optimizations for the most commonly used models. The code is open-sourced at https://github.com/intel/xFasterTransformer., Comment: 5 pages, 6 figure, ICML 2024 on Foundation Models in the Wild

Fast large-scale network scanning is an important way to understand internet service configurations and security in real time, among which stateless scan is representative. Existing stateless scanners can perform single-packet scans for internet-wide network measurements but are limited to host discovery or port scanning. To obtain further information over TCP, slower stateful scanners must be used in conjunction which spend more time and memory because of connection state maintenance. Through simplifying TCP finite state machine, this paper proposes a novel stateless scanning model, which can establish TCP connections and obtain further responses in a completely stateless manner. Based on this model, we implement ZBanner, an improved modular stateless scanner that utilizes user-defined probes for identifying services and versions, fingerprinting TLS servers, etc. We present unique design of ZBanner and experimentally characterize its feasibility and performance. Experiments show that ZBanner performs better than current state-of-the-art solutions in terms of scan rate and memory usage. ZBanner achieves at least three times faster than current tools for generic ports and over 90 times faster for open ports while keeping a minimum and stable memory usage., Comment: The paper has been submitted and the code will be published later

Attack knowledge graph construction seeks to convert textual cyber threat intelligence (CTI) reports into structured representations, portraying the evolutionary traces of cyber attacks. Even though previous research has proposed various methods to construct attack knowledge graphs, they generally suffer from limited generalization capability to diverse knowledge types as well as requirement of expertise in model design and tuning. Addressing these limitations, we seek to utilize Large Language Models (LLMs), which have achieved enormous success in a broad range of tasks given exceptional capabilities in both language understanding and zero-shot task fulfillment. Thus, we propose a fully automatic LLM-based framework to construct attack knowledge graphs named: AttacKG+. Our framework consists of four consecutive modules: rewriter, parser, identifier, and summarizer, each of which is implemented by instruction prompting and in-context learning empowered by LLMs. Furthermore, we upgrade the existing attack knowledge schema and propose a comprehensive version. We represent a cyber attack as a temporally unfolding event, each temporal step of which encapsulates three layers of representation, including behavior graph, MITRE TTP labels, and state summary. Extensive evaluation demonstrates that: 1) our formulation seamlessly satisfies the information needs in threat event analysis, 2) our construction framework is effective in faithfully and accurately extracting the information defined by AttacKG+, and 3) our attack graph directly benefits downstream security practices such as attack reconstruction. All the code and datasets will be released upon acceptance., Comment: 20 pages, 5 figures

Let $G\leqslant\mathrm{Sym}(\Omega)$ be a finite transitive permutation group with point stabiliser $H$. We say that a subgroup $K$ of $G$ is a fixer if every element of $K$ has fixed points, and we say that $K$ is large if $|K| \geqslant |H|$. There is a special interest in studying large fixers due to connections with Erd\H{o}s-Ko-Rado type problems. In this paper, we classify up to conjugacy the large fixers of the almost simple primitive groups with socle $\mathrm{PSL}_2(q)$, and we use this result to verify a special case of a conjecture of Spiga on permutation characters. We also present some results on large fixers of almost simple primitive groups with socle an alternating or sporadic group., Comment: 33 pages

The entanglement properties of quantum synchronization, based on a single-ion phonon laser subjected to an external drive, have been studied. It is found that the maximum value of steady-state entanglement between the ion's internal and external states occurs near the noiseless boundary from synchronization to unsynchronization, accompanied by noticeable oscillatory behaviors during the corresponding time evolution of entanglement. In addition, the later time dynamics of entanglement also indicates the occurrence of frequency entrainment, as evidenced by the strong consistency between the bending of the observed frequency and the emergence of Liouvillian exceptional points (LEPs) in the first two eigenvalues of the Liouvillian eigenspectrum. Moreover, the emergence of LEPs, which is intimately associated with frequency entrainment, should be widely observed in quantum synchronization and can be explored in LEPs-based applications., Comment: 12 pages, 6 figures, Supplement 6 pages, 2 figures

In trapped ion system, accurate thermometry of ion is crucial for evaluating the system state and precisely performing quantum operations. However, when the motional state of a single ion is far away from the ground state, the spatial dimension of the phonon state sharply increases, making it difficult to realize accurate and mode-resolved thermometry with existing methods. In this work, we apply deep learning for the first time to the thermometry of trapped ion, providing an efficient and mode-resolved method for accurately estimating large mean phonon numbers. Our trained neural network model can be directly applied to other experimental setups without retraining or post-processing, as long as the related parameters are covered by the model's effective range, and it can also be conveniently extended to other parameter ranges. We have conducted experimental verification based on our surface trap, of which the result has shown the accuracy and efficiency of the method for thermometry of single ion under large mean phonon number, and its mode resolution characteristic can make it better applied to the characterization of system parameters, such as evaluating cooling effectiveness, analyzing surface trap noise.

The production of exotic hadron $\rm X(3872)$ in $pp$ collisions at $\sqrt{s}=2.76\,\mathrm{TeV}$ is investigated by the parton and hadron cascade model PACIAE in this work. In the simulation the final partonic state (quark matter, QM) and the final hadronic state (hadron matter, HM) are continuously processed and recorded. The $\rm X(3872)$ compact tetraquark state and loose molecular state are, respectively, coalesced and recombined in the QM and HM with the quantum statistical mechanics inspired dynamically constrained phase-space coalescence model. The formation time, velocity and temperature of QM (tetraquark state) and HM (molecular state) are proposed as identifying criteria between the two states. Our results in transverse momentum spectrum and rapidity distribution, etc. show a significant discrepancy between the two states and confirm that they are also valuable criteria identifying the $\rm X(3872)$ compact tetraquark state or molecular state., Comment: 6 pages, 3 figures

We investigate in detail the weak-field gravitational lensing of a relativistic neutral massive particle induced by a regular black-bounce-Schwarzschild black hole proposed by Simpson and Visser. Starting with the calculation of the gravitational deflection of the massive particle up to the third post-Minkowskian order, the Virbhadra-Ellis lens equation is solved perturbatively beyond the weak-deflection limit to achieve the expressions for the lensing observables of the primary and secondary images of a point-like particle source. The main observables contain not only the positions, the flux magnifications, and the gravitational time delays of the individual images, but also the positional relations, the magnification relations (including the total magnification), the magnification centroid, and the differential time delay. We then discuss the velocity-induced effects originated from the deviation of the particle's initial velocity from the speed of light on the black-bounce-Schwarzschild lensing observables of the images of a point-like light source, and the effects induced by the bounce parameter of the spacetime on the measurable image properties of Schwarzschild lensing of the massive particle. As an application of the results, we model the supermassive black hole in the Galactic Center (i.e., Sgr A$^{\ast}$) as the lens, and focus on evaluating the possibilities to detect the new velocity-induced and bounce-induced effects on the practical lensing observables and analyzing the dependence of these effects on the parameters., Comment: Accepted for publication in PRD

An ancient and counterintuitive phenomenon know as the Mpemba effect (water can cool faster when initially heated up) showcases the critical role of initial conditions in relaxation processes. How to realize and utilize this effect for speeding up relaxation is an important but challenging task in purely quantum system till now. Here, we report the first experiment, as far as we know,about the strong Mpemba effect in a single trapped ion system in which an exponentially expedited relaxation in time is observed by preparing an optimal initial state with no excitation of the slowest decaying mode. Also, we find that the condition of realizing such effect coincides with the Liouvillian exceptional point, featuring the coalescence of both the eigenvalues and the eigenmodes of the system. Our work provides an efficient strategy to exponentially accelerate relaxations of quantum system to their stationary state, and suggests a link unexplored yet between the Mpemba effect and the non-Hermitian physics. It could open up the door to engineer a wide range of dissipative quantum systems by utilizing the anomalous Mpemba effect, for applications in quantum simulation and quantum information processing.

The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse numerous existing well-trained models instead of building machine learning models from scratch, with the hope of solving new user tasks even beyond models' original purposes. In this paradigm, developers worldwide can submit their high-performing models spontaneously to the learnware dock system (formerly known as learnware market) without revealing their training data. Once the dock system accepts the model, it assigns a specification and accommodates the model. This specification allows the model to be adequately identified and assembled to reuse according to future users' needs, even if they have no prior knowledge of the model. This paradigm greatly differs from the current big model direction and it is expected that a learnware dock system housing millions or more high-performing models could offer excellent capabilities for both planned tasks where big models are applicable; and unplanned, specialized, data-sensitive scenarios where big models are not present or applicable. This paper describes Beimingwu, the first open-source learnware dock system providing foundational support for future research of learnware paradigm.The system significantly streamlines the model development for new user tasks, thanks to its integrated architecture and engine design, extensive engineering implementations and optimizations, and the integration of various algorithms for learnware identification and reuse. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security. Beimingwu supports the entire process of learnware paradigm. The system lays the foundation for future research in learnware-related algorithms and systems, and prepares the ground for hosting a vast array of learnwares and establishing a learnware ecosystem.

While deep learning models have shown significant performance across various domains, their deployment needs extensive resources and advanced computing infrastructure. As a solution, Machine Learning as a Service (MLaaS) has emerged, lowering the barriers for users to release or productize their deep learning models. However, previous studies have highlighted potential privacy and security concerns associated with MLaaS, and one primary threat is model extraction attacks. To address this, there are many defense solutions but they suffer from unrealistic assumptions and generalization issues, making them less practical for reliable protection. Driven by these limitations, we introduce a novel defense mechanism, SAME, based on the concept of sample reconstruction. This strategy imposes minimal prerequisites on the defender's capabilities, eliminating the need for auxiliary Out-of-Distribution (OOD) datasets, user query history, white-box model access, and additional intervention during model training. It is compatible with existing active defense methods. Our extensive experiments corroborate the superior efficacy of SAME over state-of-the-art solutions. Our code is available at https://github.com/xythink/SAME., Comment: Accepted by AAAI 2024

Millisecond pulsars can serve as effective probes to investigate the presence of Intermediate-mass Black Holes (IMBHs) within Galactic globular clusters (GCs). Based on the standard structure models for GCs, we conduct simulations to analyze the distributions of pulsar accelerations within the central region of NGC 6517. By comparing the measured accelerations of pulsars obtained from their period derivatives $\dot P$ to the simulated distribution profiles, we demonstrate that a central excess of dark mass is required to account for the measured accelerations. Our analysis, which relies on existing pulsar timing observations, is currently unable to differentiate between two possible scenarios: an IMBH precisely situated at the core of the cluster with mass $\gtrsim 9000^{+4000}_{-3000}~M_{\odot}$, or a central concentration of stellar mass dark remnants with a comparable total mass. However, with additional acceleration measurements from a few more pulsars in the cluster, it will be possible to differentiate the source of the nonluminous matter., Comment: 6 pages, 3 figures, 1 table. Accepted for publication in MNRAS

With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This has sparked significant interest in applying LLMs to enhance various aspects of healthcare, ranging from medical education to clinical decision support. However, medicine involves multifaceted data modalities and nuanced reasoning skills, presenting challenges for integrating LLMs. This paper provides a comprehensive review on the applications and implications of LLMs in medicine. It begins by examining the fundamental applications of general-purpose and specialized LLMs, demonstrating their utilities in knowledge retrieval, research support, clinical workflow automation, and diagnostic assistance. Recognizing the inherent multimodality of medicine, the review then focuses on multimodal LLMs, investigating their ability to process diverse data types like medical imaging and EHRs to augment diagnostic accuracy. To address LLMs' limitations regarding personalization and complex clinical reasoning, the paper explores the emerging development of LLM-powered autonomous agents for healthcare. Furthermore, it summarizes the evaluation methodologies for assessing LLMs' reliability and safety in medical contexts. Overall, this review offers an extensive analysis on the transformative potential of LLMs in modern medicine. It also highlights the pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice. Visit https://github.com/mingze-yuan/Awesome-LLM-Healthcare for an accompanying GitHub repository containing latest papers., Comment: 24 pages, 1 figure, 3 tables

Watanabe disproved the 4-dimensional Smale conjecture by constructing topologically trivial $D^{4}$-bundles over spheres and showing that they are smoothly nontrivial using configuration space integrals. In this paper, we define a new version of configuration space integrals that only relies on a formal smooth structure on the $D^{4}$-bundle (i.e., a vector bundle structure on the vertical tangent microbundle). It coincides with Watanabe's definition when the $D^{4}$-bundle is smooth. We obtain several applications. First, we give a lower bound (in terms of the graph homology) on the dimension of the rational homotopy and homology groups of $\textrm{Top}(4)$ and $\textrm{Homeo}(S^4)$ (the homeomorphism group of $\mathbb{R}^4$ and $S^4$). In particular, this implies that $\textrm{Top}(4)$ and $\textrm{Homeo}(S^4)$ are not rationally equivalent to any finite-dimensional CW complexes. Second, we discover a generalized Miller-Morita-Mumford class $\kappa_{\theta}(\pi)\in H^{3}(B;\mathbf{Q})$, which is defined for any topological 4-manifold bundle $X\to E\to B$. This class obstructs the existence of a formal smooth structure on the bundle. Third, we show that for any compact, orientable, smooth 4-manifold $X$ (possibly with boundary), the inclusion map from its diffeomorphism group to its homeomorphism group is not rationally $2$-connected (hence not a weak homotopy equivalence). This implies that the space of smooth structures on $X$ has a nontrivial rational homotopy group in dimension 2., Comment: 79 pages, comments welcome