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Database management system (DBMS) configuration debugging, e.g., diagnosing poorly configured DBMS knobs and generating troubleshooting recommendations, is crucial in optimizing DBMS performance. However, the configuration debugging process is tedious and, sometimes challenging, even for seasoned database administrators (DBAs) with sufficient experience in DBMS configurations and good understandings of the DBMS internals (e.g., MySQL or Oracle). To address this difficulty, we propose Andromeda, a framework that utilizes large language models (LLMs) to enable automatic DBMS configuration debugging. Andromeda serves as a natural surrogate of DBAs to answer a wide range of natural language (NL) questions on DBMS configuration issues, and to generate diagnostic suggestions to fix these issues. Nevertheless, directly prompting LLMs with these professional questions may result in overly generic and often unsatisfying answers. To this end, we propose a retrieval-augmented generation (RAG) strategy that effectively provides matched domain-specific contexts for the question from multiple sources. They come from related historical questions, troubleshooting manuals and DBMS telemetries, which significantly improve the performance of configuration debugging. To support the RAG strategy, we develop a document retrieval mechanism addressing heterogeneous documents and design an effective method for telemetry analysis. Extensive experiments on real-world DBMS configuration debugging datasets show that Andromeda significantly outperforms existing solutions.

Modern collider phenomenology requires unprecedented precision for the theoretical predictions, for which slicing techniques provide an essential tool at next-to-next-to-leading order in the strong coupling. The most popular slicing variable is based on the transverse momentum $q_T$ of a color-singlet final state, but its generalization to final states with jets is known to be very difficult. Here we propose two generalizations of $q_T$ that can be used for jet processes, providing proof of concept with an NLO slicing for $pp \to 2$ jets and presenting factorization formulae that pave the way for extending our approach to NNLO. One of these extensions only applies to planar Born processes, such as $pp \to 2$ jets, but offers a dramatic simplification of the soft function. We also discuss how our approach can directly be extended to obtain predictions for the fragmentation of hadrons. This presents a promising path for high-precision QCD calculations with multi-jet final states., Comment: 4 pages + supplemental material, 4 figures

This article is devoted to calculating the form factors of $B_c \to D^{*}$, $B_c \to D$, $B_c \to D_s^{*}$ and $B_c \to D_s$ transitions in the framework of three-point QCD sum rules. At the QCD side, the contributions of $\langle\overline{q}q\rangle$, $\langle\overline{q}g_{s}\sigma Gq\rangle$, $\langle g_{s}^{2}G^{2}\rangle$, $\langle f^{3}G^{3}\rangle$ and $\langle\overline{q}q\rangle \langle g_{s}^{2}G^{2}\rangle$ are taken into account. With the obtained form factors, the decay widths and branching ratios of several two-body nonleptonic decay processes $B_c \to \eta_c D^{*}$, $\eta_c D$, $ J/\psi D^{*}$, $ J/\psi D$, $\eta_c D_s^{*}$, $\eta_c D_s$, $J/\psi D_s^{*}$ and $J/\psi D_s$ are predicted. These results about the form factors and decay properties of $B_c$ meson provide useful information for us to study the heavy-quark dynamics.

We present our recent studies on thermal field theories using quantum algorithms. We first delve into the representation of quantum fields via qubits on general digital quantum computers alongside the quantum algorithms employed to evaluate thermal properties of generic quantum field theories. Then, we show our numerical results of thermal field theories in 1+1 dimensions using quantum simulators. Both fermion and scalar fields will be discussed. These studies aim to understand thermal fixed points for our forthcoming work on studying thermalisation in quantum field theories in real time quantum simulation., Comment: 6 pages, 3 figures; contribution to the proceedings of the 42nd International Conference on High Energy Physics (ICHEP2024), 18-24 July 2024, Prague, Czech Republic

Marmoset, a highly vocalized primate, has become a popular animal model for studying social-communicative behavior and its underlying mechanism comparing with human infant linguistic developments. In the study of vocal communication, it is vital to know the caller identities, call contents, and vocal exchanges. Previous work of a CNN has achieved a joint model for call segmentation, classification, and caller identification for marmoset vocalizations. However, the CNN has limitations in modeling long-range acoustic patterns; the Transformer architecture that has been shown to outperform CNNs, utilizes the self-attention mechanism that efficiently segregates information parallelly over long distances and captures the global structure of marmoset vocalization. We propose using the Transformer to jointly segment and classify the marmoset calls and identify the callers for each vocalization.

Achieving high-resolution, identity consistency, and accurate lip-speech synchronization in face visual dubbing presents significant challenges, particularly for real-time applications like live video streaming. We propose MuseTalk, which generates lip-sync targets in a latent space encoded by a Variational Autoencoder, enabling high-fidelity talking face video generation with efficient inference. Specifically, we project the occluded lower half of the face image and itself as an reference into a low-dimensional latent space and use a multi-scale U-Net to fuse audio and visual features at various levels. We further propose a novel sampling strategy during training, which selects reference images with head poses closely matching the target, allowing the model to focus on precise lip movement by filtering out redundant information. Additionally, we analyze the mechanism of lip-sync loss and reveal its relationship with input information volume. Extensive experiments show that MuseTalk consistently outperforms recent state-of-the-art methods in visual fidelity and achieves comparable lip-sync accuracy. As MuseTalk supports the online generation of face at 256x256 at more than 30 FPS with negligible starting latency, it paves the way for real-time applications., Comment: 15 pages, 4 figures

In this paper, we introduce a novel nonstationary price staleness factor model allowing for market friction pervasive across assets and possible input covariates. With large panel high-frequency data, we give the maximum likelihood estimators of the regressing coefficients, and the factors and their loading parameters, which recovers the time-varying price stale probability and an integrated functional of the price staleness over two assets. The asymptotic results are obtained when both the dimension $d$ and the sampling frequency $n$ diverge simultaneously. With the local principal component analysis (LPCA) method, we find that the efficient price co-volatilities (systematic and idiosyncratic), are biased downward due to the presence of staleness. Bias corrected estimators of the systematic and idiosyncratic covolatities (spot and integrated) are provided and proved to be consistent. Interestingly, beside their dependence on the dimensionality $d$, the integrated estimates converge with a factor of $n^{-1/2}$ though the local PCA estimates converge with a factor of $n^{-1/4}$, validating the aggregation efficiency after nonlinear nonstationary factor analysis. But the bias correction degrade the convergence rates of the estimated (spot or integrated) systematic covolatilies. Numerical experiments justify our theoretical findings. Empirically, we observe that the staleness correction indeed leads to higher in-sample systematic volatility estimates, but a reduced out-of-sample portfolio risk almost uniformly in tested gross exposure levels.

Large language models (LLMs) have a transformative impact on a variety of scientific tasks across disciplines including biology, chemistry, medicine, and physics. However, ensuring the safety alignment of these models in scientific research remains an underexplored area, with existing benchmarks primarily focusing on textual content and overlooking key scientific representations such as molecular, protein, and genomic languages. Moreover, the safety mechanisms of LLMs in scientific tasks are insufficiently studied. To address these limitations, we introduce SciSafeEval, a comprehensive benchmark designed to evaluate the safety alignment of LLMs across a range of scientific tasks. SciSafeEval spans multiple scientific languages-including textual, molecular, protein, and genomic-and covers a wide range of scientific domains. We evaluate LLMs in zero-shot, few-shot and chain-of-thought settings, and introduce a "jailbreak" enhancement feature that challenges LLMs equipped with safety guardrails, rigorously testing their defenses against malicious intention. Our benchmark surpasses existing safety datasets in both scale and scope, providing a robust platform for assessing the safety and performance of LLMs in scientific contexts. This work aims to facilitate the responsible development and deployment of LLMs, promoting alignment with safety and ethical standards in scientific research.

Understanding propagation mechanisms in complex networks is essential for fields like epidemiology and multi-robot networks. This paper reviews various propagation models, from traditional deterministic frameworks to advanced data-driven and deep learning approaches. We differentiate between static and dynamic networks, noting that static models provide foundational insights, while dynamic models capture real-world temporal changes. Deterministic models like the SIR framework offer clear mathematical insights but often lack adaptability to randomness, whereas stochastic models enhance realism at the cost of interpretability. Behavior-based models focus on individual decision-making, demanding more computational resources. Data-driven approaches improve accuracy in nonlinear scenarios by adapting to evolving networks, using either traditional models or model-free machine learning techniques. We explore supervised and unsupervised learning methods, as well as reinforcement learning, which operates without predefined datasets. The application of graph neural networks (GNNs) is also discussed, highlighting their effectiveness in modeling propagation in complex networks. The paper underscores key applications and challenges associated with each model type, emphasizing the increasing importance of hybrid and machine learning-based solutions in contemporary network propagation issues., Comment: Network Propagation, Machine Learning, De- terministic Model, Model-based Approaches, Model-free Ap- proaches

Inspired by the great progress in the observations of charmonium-like states in recent years, we perform a systematic analysis about the ground states and the first radially excited states of $qc\bar{q}\bar{c}$ ($q$=$u/d$ and $s$) tetraquark systems. Their mass spectra, root mean square (r.m.s.) radii and radial density distributions are predicted within the framework of relativized quark model. By comparing with experimental data, some potential candidates for hidden-charm tetraquark states are suggested. For $qc\bar{q}\bar{c}$ ($q$=$u/d$) system, if $Z_{c}(3900)$ is supposed to be a compact tetraquark state with $J^{PC}=1^{+-}$, $Z(4430)$ can be interpreted as the first radially excited states of $Z_{c}(3900)$. Another broad structure $Z_{c}(4200)$ can also be explained as a partner of $Z_{c}(3900)$, and it arise from a higher state with $J^{PC}=1^{+-}$. In addition, theoretical predictions indicate that the possible assignments for $X(3930)$, $X(4050)$ and $X(4250)$ are low lying $0^{++}$ tetraquark states. As for the $sc\bar{s}\bar{c}$ system, $X(4140)$ and $X(4274)$ structures can be interpreted as this type of tetraquark states with $J^{PC}=1^{++}$, and $X(4350)$ can be described as a $sc\bar{s}\bar{c}$ tetraquark with $J^{PC}=0^{++}$. With regard to $qc\bar{s}\bar{c}$ ($q$=$u/d$) system, we find two potential candidates for this type of tetraquark, which are $Z_{cs}(4000)$ and $Z_{cs}(4220)$ structures. The measured masses of these two structures are in agreement with theoretical predictions for the $1^{+}$ state.

The superiority of Multi-Robot Systems (MRS) in various complex environments is unquestionable. However, in complex situations such as search and rescue, environmental monitoring, and automated production, robots are often required to work collaboratively without a central control unit. This necessitates an efficient and robust decentralized control mechanism to process local information and guide the robots' behavior. In this work, we propose a new decentralized controller design method that utilizes the Deep Q-Network (DQN) algorithm from deep reinforcement learning, aimed at improving the integration of local information and robustness of multi-robot systems. The designed controller allows each robot to make decisions independently based on its local observations while enhancing the overall system's collaborative efficiency and adaptability to dynamic environments through a shared learning mechanism. Through testing in simulated environments, we have demonstrated the effectiveness of this controller in improving task execution efficiency, strengthening system fault tolerance, and enhancing adaptability to the environment. Furthermore, we explored the impact of DQN parameter tuning on system performance, providing insights for further optimization of the controller design. Our research not only showcases the potential application of the DQN algorithm in the decentralized control of multi-robot systems but also offers a new perspective on how to enhance the overall performance and robustness of the system through the integration of local information., Comment: Multi-robot system, Reinforcement learning, Information ingrated

With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial intelligence technology with robot hardware is seeing broad application possibilities for MRS. This article surveys the state-of-the-art of robot learning in the context of Multi-Robot Cooperation (MRC) of recent. Commonly adopted robot learning methods (or frameworks) that are inspired by humans and animals are reviewed and their advantages and disadvantages are discussed along with the associated technical challenges. The potential trends of robot learning and MRS integration exploiting the merging of these methods with real-world applications is also discussed at length. Specifically statistical methods are used to quantitatively corroborate the ideas elaborated in the article., Comment: Multi-robot, Cooperation, robot learning

In this study, we obtain specific picture of the phase transitions for the 6-dimensional Gauss-Bonnet Anti-de Sitter (AdS) black hole with triple phases, using the generalized free energy we constructed and Kramers escape rate in stochastic motion. There are six possible phase transition processes between the three different stable states (small, medium, and large black hole states). During these phase transitions, there are two key temperatures. One is the temperature at which the medium black hole state emerges, and the other is the temperature at which the small black hole state annihilates. Meanwhile, two dynamic equilibrium processes are formed. One is a dynamic equilibrium of the transition from the medium black hole state to the large black hole state and the transition from the small black hole state to the medium black hole state. The other is a dynamic equilibrium of the transition from the small black hole state to the medium black hole state and the transition from the medium black hole state to the small black hole state., Comment: 10 pages, 4 figures

The assumption of factorization lies at the core of calculations of medium effects on observables computable in perturbative Quantum Chromodynamics. In this work we examine this assumption, for which we propose a setup to study hard processes and bulk nuclear matter in heavy-ion collisions on the same footing using the Glauber modelling of heavy nuclei. To exemplify this approach, we calculate the leading-order corrections to azimuthal decorrelation in Drell-Yan and boson-jet processes due to cold nuclear matter effects, not considering radiation. At leading order in both the hard momentum scale and the nuclear size, the impact-parameter dependent cross section is found to factorize for both processes. The factorization formula involves a convolution of the hard cross section with the medium-modified parton distributions, and, for boson-jet production, the medium-modified jet function., Comment: 37pages, 6figures, V2 submission to JHEP

A novel thermodynamic phenomenon has been observed in the quantum Ba\~{n}ados-Teitelboim-Zanelli (qBTZ) black hole, utilizing generalized free energy and Kramer escape rate. This phenomenon also reveals the unique property of the quantum black hole. The stochastic thermal motion of various thermodynamic states within the black hole system induces phase transitions, under the influence of generalized free energy which obtained by extending Maxwell's construction. Through the analysis of Kramer escape rate, it is discovered that the qBTZ black hole thermodynamic system exhibits a bounce effect. Furthermore, the overall thermodynamic picture of the qBTZ black hole has been obtained under different quantum backreactions., Comment: 8 pages, 3 figures

Recently, researchers have investigated the capabilities of Large Language Models (LLMs) for generative recommender systems. Existing LLM-based recommender models are trained by adding user and item IDs to a discrete prompt template. However, the disconnect between IDs and natural language makes it difficult for the LLM to learn the relationship between users. To address this issue, we propose a PErsonAlized PrOmpt Distillation (PeaPOD) approach, to distill user preferences as personalized soft prompts. Considering the complexities of user preferences in the real world, we maintain a shared set of learnable prompts that are dynamically weighted based on the user's interests to construct the user-personalized prompt in a compositional manner. Experimental results on three real-world datasets demonstrate the effectiveness of our PeaPOD model on sequential recommendation, top-n recommendation, and explanation generation tasks.

Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses. Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the generated responses are negatively influenced by noise from both external and internal knowledge sources. In this paper, we introduce a novel, training-free decoding method guided by entropy considerations to mitigate this issue. Our approach utilizes entropy-based document-parallel ensemble decoding to prioritize low-entropy distributions from retrieved documents, thereby enhancing the extraction of relevant information of context. Additionally, it incorporates a contrastive decoding mechanism that contrasts the obtained low-entropy ensemble distribution with the high-entropy distribution derived from the model's internal knowledge across layers, which ensures a greater emphasis on reliable external information. Extensive experiments on open-domain question answering datasets demonstrate the superiority of our method.

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance the performance on a wide range of tasks. However, the precise role of user profiles and their effect mechanism on LLMs remains unclear. This study first confirms that the effectiveness of user profiles is primarily due to personalization information rather than semantic information. Furthermore, we investigate how user profiles affect the personalization of LLMs. Within the user profile, we reveal that it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. This discovery unlocks the potential of LLMs to incorporate a greater number of user profiles within the constraints of limited input length. As for the position of user profiles, we observe that user profiles integrated into different positions of the input context do not contribute equally to personalization. Instead, where the user profile that is closer to the beginning affects more on the personalization of LLMs. Our findings reveal the role of user profiles for the personalization of LLMs, and showcase how incorporating user profiles impacts performance providing insight to leverage user profiles effectively.

Deep learning models are well known to be susceptible to backdoor attack, where the attacker only needs to provide a tampered dataset on which the triggers are injected. Models trained on the dataset will passively implant the backdoor, and triggers on the input can mislead the models during testing. Our study shows that the model shows different learning behaviors in clean and poisoned subsets during training. Based on this observation, we propose a general training pipeline to defend against backdoor attacks actively. Benign models can be trained from the unreliable dataset by decoupling the learning process into three stages, i.e., supervised learning, active unlearning, and active semi-supervised fine-tuning. The effectiveness of our approach has been shown in numerous experiments across various backdoor attacks and datasets.

Recently, deep neural networks have been shown to be vulnerable to backdoor attacks. A backdoor is inserted into neural networks via this attack paradigm, thus compromising the integrity of the network. As soon as an attacker presents a trigger during the testing phase, the backdoor in the model is activated, allowing the network to make specific wrong predictions. It is extremely important to defend against backdoor attacks since they are very stealthy and dangerous. In this paper, we propose a novel defense mechanism, Neural Behavioral Alignment (NBA), for backdoor removal. NBA optimizes the distillation process in terms of knowledge form and distillation samples to improve defense performance according to the characteristics of backdoor defense. NBA builds high-level representations of neural behavior within networks in order to facilitate the transfer of knowledge. Additionally, NBA crafts pseudo samples to induce student models exhibit backdoor neural behavior. By aligning the backdoor neural behavior from the student network with the benign neural behavior from the teacher network, NBA enables the proactive removal of backdoors. Extensive experiments show that NBA can effectively defend against six different backdoor attacks and outperform five state-of-the-art defenses.

The form factors of $B_c\rightarrow\eta_c$ and $B_c\rightarrow J/\psi$ are analyzed in the framework of three-point QCD sum rules. In these analyses, the contributions of the vacuum condensate terms $\langle g_{s}^{2}GG\rangle$ and $\langle g_{s}^{3}GGGf\rangle$ are considered. In addition, the decay widths and branching ratios of several decay channels are obtained by using the calculated form factors. These decay processes include the nonleptonic decays of $B_c^- \to \eta_c \pi^-$, $\eta_c K^-$, $\eta_c \rho^-$, $\eta_c K^{*-}$, $B_c^- \to J/\psi \pi^-$, $J/\psi K^-$, $J/\psi \rho^-$, $J/\psi K^{*-}$, and the semileptonic decays of $B_c^- \to \eta_c \mathcal{l} \bar{\nu}$, $B_c^- \to J/\psi \mathcal{l} \bar{\nu}$. These results about the form factors and decay properties of $B_c$ meson provide useful information for us to study the heavy-quark dynamics and find new physics(NP) beyond Standard Model(SM).

Instruction tuning plays a crucial role in shaping the outputs of language models (LMs) to desired styles. In this work, we propose a simple yet effective method, Instruction Modelling (IM), which trains LMs by applying a loss function to the instruction and prompt part rather than solely to the output part. Through experiments across 21 diverse benchmarks, we show that, in many scenarios, IM can effectively improve the LM performance on both NLP tasks (e.g., MMLU, TruthfulQA, and HumanEval) and open-ended generation benchmarks (e.g., MT-Bench and AlpacaEval). Remarkably, in the most advantageous case, IM boosts model performance on AlpacaEval 1.0 by over 100%. We identify two key factors influencing the effectiveness of IM: (1) The ratio between instruction length and output length in the training data; and (2) The number of training examples. We observe that IM is especially beneficial when trained on datasets with lengthy instructions paired with brief outputs, or under the Superficial Alignment Hypothesis (SAH) where a small amount of training examples are used for instruction tuning. Further analysis substantiates our hypothesis that our improvement can be attributed to reduced overfitting to instruction tuning datasets. It is worth noting that we are not proposing \ours as a replacement for current fine-tuning processes. Instead, our work aims to provide practical guidance for instruction tuning LMs, especially in low-resource scenarios., Comment: NeurIPS 2024. Code is available at https://github.com/ZhengxiangShi/InstructionModelling

Uni-directional social interactions are ubiquitous in real social networks whereas undirected interactions are intensively studied. We establish a voter model in a dynamical directed network. We analytically obtain the degree distribution of the evolving network at any given time. Furthermore, we find that the average degree is captured by an emergent game. On the other hand, we find that the fate of opinions is captured by another emergent game. Beyond expectation, the two emergent games are typically different due to the unidirectionality of the evolving networks. The Nash equilibrium analysis of the two games facilitates us to give the criterion under which the minority opinion with few disciples initially takes over the population eventually for in-group bias. Our work fosters the understanding of opinion dynamics ranging from methodology to research content., Comment: arXiv admin note: substantial text overlap with arXiv:2307.05511

Ground-based laser interferometric gravitational wave detectors consist of complex multiple optical cavity systems. An arm-length stabilization (ALS) system has played an important role in bringing such complex detector into operational state and enhance the duty cycle. The sensitivity of these detectors can be improved if the thermal noise of their test mass mirror coatings is reduced. Crystalline AlGaAs coatings are a promising candidate for this. However, traditional ALS system with frequency-doubled 532 nm light is no longer an option with AlGaAs coatings due to the narrow bandgap of GaAs, thus alternative locking schemes must be developed. In this letter, we describe an experimental demonstration of a novel ALS scheme which is compatible with AlGaAs coatings. This ALS scheme will enable the use of AlGaAs coatings and contribute to improved sensitivity of future detectors.

Thermal properties of quantum fields at finite temperature are crucial to understanding strongly interacting matter and recent development in quantum computing has provided an alternative and promising avenue of study. In this work, we study thermal field theories involving only fermions using quantum algorithms. We first delve into the presentations of fermion fields via qubits on digital quantum computers alongside the quantum algorithms such as quantum imaginary time evolutions employed to evaluate thermal properties of generic quantum field theories. Specifically, we show numerical results such as the thermal distribution and the energy density of thermal field theories for Majorana fermions in 1+1 dimensions using quantum simulators. In addition to free field theory, we also study the effects of interactions resulting from coupling with a spatially homogeneous Majorana field. In both cases, we show analytically that thermal properties of the system can be described using phase-space distributions, and the quantum simulation results agree with analytical and semiclassical expectations. Our work is an important step to understand thermal fixed points, preparing for quantum simulation of thermalization in real time., Comment: 30 pages, 6 figures

In this paper, we study discrete Carleman estimates for space semi-discrete approximations of one-dimensional stochastic parabolic equation. As applications of these discrete Carleman estimates, we apply them to study two inverse problems for the spatial semi-discrete stochastic parabolic equations, including a discrete inverse random source problem and a discrete Cauchy problem. We firstly establish two Carleman estimates for a one-dimensional semi-discrete stochastic parabolic equation, one for homogeneous boundary and the other for non-homogeneous boundary. Then we apply these two estimates separately to derive two stability results. The first one is the Lipschitz stability for the discrete inverse random source problem. The second one is the H\"{o}lder stability for the discrete Cauchy problem.

Opinion dynamics is of paramount importance as it provides insights into the complex dynamics of opinion propagation and social relationship adjustment. It is assumed in most of the previous works that social relationships evolve much faster than opinions. This is not always true in reality. We propose an analytical approximation to study this issue for arbitrary time scales between opinion adjustment and network evolution. To this end, the coefficient of determination in statistics is introduced and a one-dimensional stable manifold is analytically found, i.e., the most likely trajectory. With the aid of the stable manifold, we further obtain the fate of opinions and the consensus time, i.e., fixation probability and fixation time. We find that for in-group bias, the more likely individuals are to adopt the popular opinion, the less likely the majority opinion takes over the population, i.e., conformity inhibits the domination of popular opinions. This counter-intuitive result can be interpreted from a game perspective, in which in-group bias refers to a coordination game and rewiring probability refers to a rescaling of the selection intensity. Our work proposes an efficient approximation method to foster the understanding of opinion dynamics in dynamical networks., Comment: 13 pages, 7 figures. arXiv admin note: text overlap with arXiv:2312.05041

Given the importance of ancient Chinese in capturing the essence of rich historical and cultural heritage, the rapid advancements in Large Language Models (LLMs) necessitate benchmarks that can effectively evaluate their understanding of ancient contexts. To meet this need, we present AC-EVAL, an innovative benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese. AC-EVAL is structured across three levels of difficulty reflecting different facets of language comprehension: general historical knowledge, short text understanding, and long text comprehension. The benchmark comprises 13 tasks, spanning historical facts, geography, social customs, art, philosophy, classical poetry and prose, providing a comprehensive assessment framework. Our extensive evaluation of top-performing LLMs, tailored for both English and Chinese, reveals a substantial potential for enhancing ancient text comprehension. By highlighting the strengths and weaknesses of LLMs, AC-EVAL aims to promote their development and application forward in the realms of ancient Chinese language education and scholarly research. The AC-EVAL data and evaluation code are available at https://github.com/yuting-wei/AC-EVAL.

The phase transition has always been a major focus in the study of black hole thermodynamics. This study employs the Kramer escape rate from stochastic processes to investigate the first-order phase transition strength between the large and small black hole states. The results indicate that the phase transition of the third-order Lovelock black holes exhibits significant asymmetric characteristics in diverse dimensions both in the hyperbolic and spherical topology, with an overall trend of the transition from large black holes to small black holes. Especially for the spherical topology, when the dimension is higher than seven, there exists a certain temperature beyond which a dynamic equilibrium is established for the phase transition. This study provides valuable insights into the first-order phase transition rate of black holes and enriches the understanding of black hole phase transitions., Comment: v1: 11 pages, 8 figures

In this article, we analyze the electromagnetic form factors of the vector heavy-light mesons to the pseudoscalar heavy-light mesons in the framework of three-point QCD sum rules, where the contributions of vacuum condensate terms $\langle\overline{q}q\rangle$, $\langle\overline{q}g_{s}\sigma Gq\rangle$, $\langle g_{s}^{2}G^{2}\rangle$, $\langle f^{3}G^{3}\rangle$ and $\langle\overline{q}q\rangle\langle g_{s}^{2}G^{2}\rangle$ are considered. With these results, we also obtain the radiative decay widths of the vector heavy-light mesons and then compare our results with those of other collaboration's. The final results about the radiative decay widths are $\Gamma(D^{*0}\to D^{0}\gamma)=1.74^{+0.40}_{-0.37}$ keV, $\Gamma(D^{*+}\to D^{+}\gamma)=0.17^{+0.08}_{-0.07}$ keV, $\Gamma(D_{s}^{*}\to D_{s}\gamma)=0.029^{+0.009}_{-0.008}$ keV, $\Gamma(B^{*0}\to B^{0}\gamma)=0.018^{+0.006}_{-0.005}$ keV, $\Gamma(B^{*+}\to B^{+}\gamma)=0.015^{+0.007}_{-0.007}$ keV and $\Gamma(B^{*}_{s}\to B_{s}\gamma)=0.016^{+0.003}_{-0.005}$ keV.

We first assemble a full set of the Boltzmann Equation in Diffusion Approximation (BEDA) for studying thermalization/hydrodynamization and quark production in out of equilibrium systems. We then discuss thermalization and the production of three flavors of massless quarks in spatially homogeneous systems initially filled only with gluons. A complete parametric understanding for thermalization and quark production is obtained for both initially very dense or dilute systems, which are complemented by detailed numerical simulations. For initial distributions more relevant for heavy-ion collisions, the complete thermal equilibration is found to be significantly delayed by considering quark production., Comment: 4 pages, 3 figures, contribution to the proceedings of the 30th International Conference on Ultra-relativistic Nucleus-Nucleus Collisions (Quark Matter 2023)

Opinion dynamics is crucial for unraveling the complexities of human interaction in the information age. How to speed up consensus without disturbing the fate of the system is key for opinion dynamics. We propose a voter model on adaptive networks, which resembles the coevolutionary process between opinions and social relationships. We prove the existence of a one-dimensional stable manifold for the system, which facilitates us to study both the fate of the system and the consensus time it takes. Surprisingly, we find the adjustment of social relations speeds up consensus but does not affect the fate of the system. For echo-chamber-like networks which consist of two homogeneous subnetworks connected by few sparse links, a small probability of adaptive edge dynamics is sufficient to accelerate consensus formation, which is counterintuitive. If the network structure makes consensus much slower than that of the regular networks, minor random rewiring makes a discontinuous drop in consensus time. Our work opens up an avenue for speeding up consensus without disturbing the fate of the system. It can be insightful for crowd control., Comment: 7 pages, 3 figures

Due to the lack of large-scale text-3D correspondence data, recent text-to-3D generation works mainly rely on utilizing 2D diffusion models for synthesizing 3D data. Since diffusion-based methods typically require significant optimization time for both training and inference, the use of GAN-based models would still be desirable for fast 3D generation. In this work, we propose Triplane Attention for text-guided 3D generation (TPA3D), an end-to-end trainable GAN-based deep learning model for fast text-to-3D generation. With only 3D shape data and their rendered 2D images observed during training, our TPA3D is designed to retrieve detailed visual descriptions for synthesizing the corresponding 3D mesh data. This is achieved by the proposed attention mechanisms on the extracted sentence and word-level text features. In our experiments, we show that TPA3D generates high-quality 3D textured shapes aligned with fine-grained descriptions, while impressive computation efficiency can be observed., Comment: ECCV2024, Project Page: https://redxouls.github.io/TPA3D/

We first assemble a full set of the Boltzmann Equation in Diffusion Approximation (BEDA) for studying thermalization/hydrodynamization as well as the production of massless quarks and antiquarks in out of equilibrium systems. In the BEDA, the time evolution of a generic system is characterized by the following space-time dependent quantities: the jet quenching parameter, the effective temperature, and two more for each quark flavor that describe the conversion between gluons and quarks/antiquarks via the $2\leftrightarrow2$ processes. Out of the latter two quantities, an effective net quark chemical potential is defined, which equals the net quark chemical potential after thermal equilibration. We then study thermalization and the production of three flavors of massless quarks and antiquarks in spatially homogeneous systems initially filled only with gluons. A parametric understanding of thermalization and quark production is obtained for either initially very dense or dilute systems, which are complemented by detailed numerical simulations for intermediate values of initial gluon occupancy $f_0$. For a wide range of $f_0$, the final equilibration time is determined to be about one order of magnitude longer than that in the corresponding pure gluon systems. Moreover, during the final stage of the thermalization process for $f_0\geq 10^{-4}$, gluons are found to thermalize earlier than quarks and antiquarks, undergoing the top-down thermalization., Comment: 49 pages, 17 figures

The detection of low-frequency gravitational waves on Earth requires the reduction of displacement noise, which dominates the low-frequency band. One method to cancel test mass displacement noise is a neutron displacement-noise-free interferometer (DFI). This paper proposes a new neutron DFI configuration, a Sagnac-type neutron DFI, which uses a Sagnac interferometer in place of the Mach-Zehnder interferometer. We demonstrate that a sensitivity of the Sagnac-type neutron DFI is higher than that of a conventional neutron DFI with the same interferometer scale. This configuration is particularly significant for neutron DFIs with limited space for construction and limited flux from available neutron sources., Comment: 17 pages, 6figures

An investigation of the influence of the hydrofoil on load reduction performance during an amphibious aircraft landing on still and wavy water is conducted by solving the Unsteady Reynolds-Averaged Navier-Stokes equations coupled with the standard $k-\omega$ turbulence model in this paper. During the simulations, the numerical wave tank is realized by using the velocity-inlet boundary wave maker coupled with damping wave elimination technique on the outlet, while the volume of fluid model is employed to track the water-air interface. Subsequently, the effects of geometric parameters of hydrofoil have been first discussed on still water, which indicates the primary factor influencing the load reduction is the static load coefficient of hydrofoil. Furthermore, the effects of descent velocity, wave length and wave height on load reduction are comprehensively investigated. The results show that the vertical load reduces more than 55$\%$ at the early stage of landing on the still water through assembling the hydrofoil for different descent velocity cases. Meanwhile, for the amphibious aircraft with high forward velocity, the bottom of the fuselage will come into close contact with the first wave when landing on crest position, and then the forebody will impact the next wave surface with extreme force. In this circumstance, the load reduction rate decreases to around 30$\%$, which will entail a further decline with the increase of wave length or wave height.

Quantum locking using optical spring and homodyne detection has been devised to reduce quantum noise that limits the sensitivity of DECIGO, a space-based gravitational wave antenna in the frequency band around 0.1 Hz for detection of primordial gravitational waves. The reduction in the upper limit of energy density ${\Omega}_{\mathrm{GW}}$ from $2{\times}10^{-15}$ to $1{\times}10^{-16}$, as inferred from recent observations, necessitates improved sensitivity in DECIGO to meet its primary science goals. To accurately evaluate the effectiveness of this method, this paper considers a detection mechanism that takes into account the influence of vacuum fluctuations on homodyne detection. In addition, an advanced signal processing method is devised to efficiently utilize signals from each photodetector, and design parameters for this configuration are optimized for the quantum noise. Our results show that this method is effective in reducing quantum noise, despite the detrimental impact of vacuum fluctuations on its sensitivity., Comment: 12 pages, 5 figures

The surge in video and social media content underscores the need for a deeper understanding of multimedia data. Most of the existing mature video understanding techniques perform well with short formats and content that requires only shallow understanding, but do not perform well with long format videos that require deep understanding and reasoning. Deep Video Understanding (DVU) Challenge aims to push the boundaries of multimodal extraction, fusion, and analytics to address the problem of holistically analyzing long videos and extract useful knowledge to solve different types of queries. This paper introduces a query-aware method for long video localization and relation discrimination, leveraging an imagelanguage pretrained model. This model adeptly selects frames pertinent to queries, obviating the need for a complete movie-level knowledge graph. Our approach achieved first and fourth positions for two groups of movie-level queries. Sufficient experiments and final rankings demonstrate its effectiveness and robustness., Comment: ACM MM 2023 Grand Challenge

In this work, we analyze the strong vertices $\Sigma_{c}\Delta D^{*}$ and $\Sigma_{b}\Delta B^{*}$ using the three-point QCD sum rules under the tensor structures $i\epsilon^{\rho\tau\alpha\beta}p_{\alpha}p'_{\beta}$, $p^{\rho}p'^{\tau}$ and $p^{\rho}p^{\tau}$. We firstly calculate the momentum dependent strong coupling constants $g(Q^{2})$ by considering contributions of the perturbative part and the condensate terms $\langle\overline{q}q\rangle$, $\langle g_{s}^{2}GG \rangle$, $\langle\overline{q}g_{s}\sigma Gq\rangle$ and $\langle\overline{q}q\rangle^{2}$. By fitting these coupling constants into analytical functions and extrapolating them into time-like regions, we then obtain the on-shell values of strong coupling constants for these vertices. The results are $g_{1\Sigma_{c}\Delta D^{*}}=5.13^{+0.39}_{-0.49}$ GeV$^{-1}$, $g_{2\Sigma_{c}\Delta D^{*}}=-3.03^{+0.27}_{-0.35}$ GeV$^{-2}$, $g_{3\Sigma_{c}\Delta D^{*}}=17.64^{+1.51}_{-1.95}$ GeV$^{-2}$, $g_{1\Sigma_{b}\Delta B^{*}}=20.97^{+2.15}_{-2.39}$ GeV$^{-1}$, $g_{2\Sigma_{b}\Delta B^{*}}=-11.42^{+1.17}_{-1.28}$ GeV$^{-2}$ and $g_{3\Sigma_{b}\Delta B^{*}}=24.87^{+2.57}_{-2.82}$ GeV$^{-2}$. These strong coupling constants are important parameters which can help us to understand the strong decay behaviors of hadrons.

In this article, the strong coupling constants of vertices $BB\Upsilon$, $BB^{*}\Upsilon$, $B^{*}B^{*}\Upsilon$, $BB^{*}\eta_{b}$ and $B^{*}B^{*}\eta_{b}$ are analyzed in the framework of QCD sum rules. In this work, all possible off-shell cases and the contributions of vacuum condensate terms including $\langle\overline{q}q\rangle$, $\langle\overline{q}g_{s}\sigma Gq\rangle$, $\langle g_{s}^{2}G^{2}\rangle$, $\langle f^{3}G^{3}\rangle$ and $\langle\overline{q}q\rangle\langle g_{s}^{2}G^{2}\rangle$ are considered. The momentum dependent strong coupling constants are first calculated and then are fitted into analytical functions $g(Q^{2})$ which are used to extrapolate into time-like regions to obtain the final values of strong coupling constants. The final results are $g_{BB\Upsilon}=40.67^{+7.55}_{-4.20}$, $g_{BB^{*}\Upsilon}=11.58^{+2.19}_{-1.09}$ GeV$^{-1}$, $g_{B^{*}B^{*}\Upsilon}=57.02^{+5.32}_{-5.31}$, $g_{BB^{*}\eta_{b}}=23.39^{+4.74}_{-2.30}$ and $g_{B^{*}B^{*}\eta_{b}}=12.49^{+2.12}_{-1.35}$ GeV$^{-1}$. These strong coupling constants are important input parameters which reflect the dynamic properties of the interactions among the mesons and quarkonia., Comment: arXiv admin note: text overlap with arXiv:2304.13969

The voter model on networks is crucial to understand opinion formation. Uni-directional social interactions are ubiquitous in real social networks whereas undirected interactions are intensively studied. We establish a voter model on a dynamical directed network. We show that the opinion invasion is captured by a replicator equation of an emergent four-player two-strategy game, and the average in(out)-degree for the two opinions is fully captured by an emergent three-player two-strategy game. Interestingly, it is shown that the difference between the two emergent games arises from the uni-directionality of the network. The difference implies that the opinion with a small number of disciples can take over the population for in-group bias, provided that the network is directed. Our work makes an explicit connection between opinion dynamics and evolutionary games.

Phase transition is important for understanding the nature and evolution of the black hole thermodynamic system. In this study, the connection between the phase transition of a black hole and the winding number derived by the complex analysis is used to predict the type of the black hole phase transition. For the third-order Lovelock black holes, at the hyperbolic topology in any dimensions and the spherical topology in $7$ dimensions, we arrive at the winding numbers both are $W=3$ which predicts that the system will undergo both the first-order and second-order phase transitions. For the spherical topology in $7

Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction. First, we construct a Molecule-Property relation Graph (MPG): molecule and properties are nodes, while property labels decide edges. Then, to utilize the topological information of MPG, we reformulate an episode in meta-learning as a subgraph of the MPG, containing a target property node, molecule nodes, and auxiliary property nodes. Third, as episodes in the form of subgraphs are no longer independent of each other, we propose to schedule the subgraph sampling process with a contrastive loss function, which considers the consistency and discrimination of subgraphs. Extensive experiments on 5 commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-art methods by 5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposed module. Our code is available at https://github.com/HICAI-ZJU/GS-Meta., Comment: Accepted by IJCAI 2023

The spatially precise integration of arrays of micro-patterned two-dimensional (2D) crystals onto three-dimensionally structured Si/SiO$_2$ substrates represents an attractive strategy towards the low-cost system-on-chip integration of extended functions in silicon microelectronics. However, the reliable integration of the arrays of 2D materials on non-flat surfaces has thus far proved extremely challenging due to their poor adhesion to underlying substrates as ruled by weak van der Waals interactions. Here we report on a novel fabrication method based on nano-subsidence which enables the precise and reliable integration of the micro-patterned 2D materials/silicon photodiode arrays exhibiting high uniformity. Our devices display peak sensitivity as high as 0.35 A/W and external quantum efficiency (EQE) of ca. 90%, outperforming most commercial photodiodes. The nano-subsidence technique opens a viable path to on-chip integrate 2D crystals onto silicon for beyond-silicon microelectronics., Comment: 41 pages, 5 figures, with SI

Maxwell equal area law is an important and traditional analytical tool in thermodynamic phase transition, especially in the calculation of gas-liquid phase transition, which reconciles the theoretical calculation with the experimental results. Undoubtedly, its importance is also self-evident for the black hole thermodynamic system. In this study, we construct a generalized Maxwell equal area law, which allows different states of thermodynamic systems to be within the generalized free energy. The black hole thermodynamic characteristics are spontaneously emerged in the free energy landscape. Furthermore, by analytic continuation, we utilize the properties of analytical functions to investigate some universal characteristics of thermodynamic phase transitions in black holes, and preliminarily establish the counterpart of thermodynamic phase transitions in the complex domain., Comment: v1:6 pages, 3 figures; v2: add References and some comments; v3: title changed and clarifications added; v4: to be published in PLB

We study transverse momentum anisotropies, in particular, the elliptic flow $v_2$ due to the interference effect sourced by valence quarks in high-energy hadron-hadron collisions. Our main formula is derived as the high-energy (eikonal) limit of the impact-parameter dependent cross section in quantum field theory, which agrees with that in terms of the impact parameter in the classical picture. As a quantitative assessment of the interference effect, we calculate $v_2$ in the azimuthal distribution of gluons at a comprehensive coverage of the impact parameter and the transverse momentum in high-energy pion-pion collisions. In a broad range of the impact parameter, a sizable amount of $v_2$, comparable with that produced due to saturated dense gluons or final-state interactions, is found to develop. In our calculations, the valence sector of the pion wave function is obtained numerically from the Basis Light-Front Quantization, a non-perturbative light-front Hamiltonian approach. And our formalism is generic and can be applied to other small collision systems like proton-proton collisions.

Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and forecasting. However, existing decomposition methods rely on batch processing with a time complexity of O(W), where W is the number of data points within a time window. Therefore, they cannot always efficiently support real-time analysis that demands low processing delay. To address this challenge, we propose OneShotSTL, an efficient and accurate algorithm that can decompose time series online with an update time complexity of O(1). OneShotSTL is more than $1,000$ times faster than the batch methods, with accuracy comparable to the best counterparts. Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1,000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy., Comment: PVLDB 2023

DECi-hertz Interferometer Gravitational Wave Observatory (DECIGO) is a future mission for a space-borne laser interferometer. DECIGO has 1,000-km-long arm cavities mainly to detect the primordial gravitational waves (PGW) at lower frequencies around 0.1 Hz. Observations in the electromagnetic spectrum have lowered the bounds on the upper limit of PGW energy density ($\Omega_{\rm gw} \sim 10^{-15} \to 10^{-16}$). As a result, DECIGO's target sensitivity, which is mainly limited by quantum noise, needs further improvement. To maximize the feasibility of detection while constrained by DECIGO's large diffraction loss, a quantum locking technique with an optical spring was theoretically proposed to improve the signal-to-noise ratio of the PGW. In this paper, we experimentally verify one key element of the optical-spring quantum locking: sensitivity optimization by completing the square of multiple detector outputs. This experiment is operated on a simplified tabletop optical setup with classical noise simulating quantum noise. We succeed in getting the best of the sensitivities with two different laser powers by the square completion method., Comment: 10 pages, 14 figures

Phase transition is a core content of black hole thermodynamics. This study adopted the Kramer's escape rate method for describing the Brownian motion of particles in an external field to investigate the intensity of the phase transition between small and large black hole states. Some existing studies mostly focused on the formal analysis of the thermodynamic phase transition of black holes, but they neglected the detailed description of the phase transition process. Our results show that the phase transition between small and large black holes for charged anti-de Sitter (AdS) black holes presents serious asymmetric features, and the overall process is dominated by the transition from a small black hole to a large black hole. This study filled a research gap of a stochastic process analysis on the issue of the first-order phase transition rate in the AdS black hole., Comment: 8 pages, 3 figures, match the published version

For further gravitational wave (GW) detections, it is significant to invent a technique to reduce all kinds of mirror displacement noise dominant at low frequencies for ground-based detectors. The neutron displacement-noise-free interferometer (DFI) is one of the tools to reduce all the mirror displacement noise at lower frequencies. In this paper, we describe a further simplified configuration of a neutron DFI in terms of neutron incidence direction. In the new configuration, neutrons enter the interferometer with unidirectional incidence at four speeds as opposed to two bidirectional incidences of opposite directions at two speeds as reported previously. This simplification of the neutron DFI is significant for proof-of-principle experiments.