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Using gauge/gravity duality, we study the exotic hybrid pseudopotentials at finite temperature and chemical potential. The $\Sigma$ hybrid meson can be described by a model including an object called ``defect'' on a string linking the quark and antiquark. It was first proposed by Andreev and perfectly described the $\Sigma_u^-$ hybrid potential at zero temperature and chemical potential. In this paper, we would like to extend this model to finite chemical potential and compare the separate distance and pseudopotentials of $\Sigma_g^+ $ and $\Sigma_u^-$. Unlike $\Sigma_g^+$ ground state, the $\Sigma_u^-$ hybrid pseudopotentials no longer behave as Coulomb-like at short distances. In addition, temperature and chemical potential have a significant impact on the $\Sigma_u^-$ hybrid pseudopotentials. The screen distances and hybrid pseudopotentials of $\Sigma_u^-$ significantly decrease with the increase of temperature and chemical potential. At last, we draw the melting diagram of $\Sigma_g^+ $ and $\Sigma_u^-$ in the $T- \mu$ plane, and confirm that the quark-antiquark pair in $\Sigma_u^-$ excited state is easier to melt than in $\Sigma_g^+$ ground state.

The strength of the force between heavy quarks is studied for heavy quarkonium ($\mathrm{Q\bar{Q}}$) and doubly heavy baryons ($\mathrm{QQq}$) at finite temperature and rapidity using the gauge/gravity duality in this paper. The strength of the interaction is defined as an effective running coupling $\alpha$ from lattice. By considering the $\mathrm{Q\bar{Q}}$ and $\mathrm{QQq}$ moving through the thermal quark-gluon plasma, we find that the interaction of heavy quarks for $\mathrm{Q\bar{Q}}$ is small and remains almost constant when $L<0.2\,\mathrm{fm}$. The strength of the interaction for $\mathrm{QQq}$ is always less than that for $\mathrm{Q\bar{Q}}$ and approaches a constant when $L<0.4\,\mathrm{fm}$. The $\alpha$ and the maximum of effective running coupling $\tilde{\alpha}$ for both $\mathrm{Q\bar{Q}}$ and $\mathrm{QQq}$ decrease with increasing temperature or rapidity. Through comparison, we find that $\mathrm{QQq}$ is more sensitive to changes in temperature and rapidity, and the interaction force between quarks is always less than that of $\mathrm{Q\bar{Q}}$, indicating that $\mathrm{Q\bar{Q}}$ is more stable than $\mathrm{QQq}$ in the presence of temperature and rapidity.

Axion-like particles (ALPs) and dark photons (DPs) are viable dark matter particle candidates. We have searched for possible ALP/DP signals in the PandaX-4T liquid xenon detector using 94.8 days of data. A binned likelihood fit is constructed to search for possible mono-energetic peaks induced by the absorption processes between ALPs/DPs and atomic electrons of xenon. A detailed temporal model of decays associated with xenon isotopes is introduced to constrain the number of background events. No signal excess over background expectations is observed, and we have established the most stringent exclusion limits for most ALP/DP masses ranging from 150 keV/$c^2$ to 1 MeV/$c^2$.

New particles beyond the Standard Model of particle physics, such as axions, can be effectively searched through their interactions with electrons. We use the large liquid xenon detector PandaX-4T to search for novel electronic recoil signals induced by solar axions, neutrinos with anomalous magnetic moment, axion-like particles, dark photons, and light fermionic dark matter. A detailed background model is established with the latest datasets with 1.54 $\rm tonne \cdot year$ exposure. No significant excess above the background has been observed, and we have obtained competitive constraints for axion couplings, neutrino magnetic moment, and fermionic dark matter interactions.

Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system. The deformation factor $w(r)$ in the metric is obtained using the two types of neural network. First, we numerically obtain $w(r)$ using MLP, accurately reproducing the QCD results of the lattice, and calculate the heavy quark potential at finite temperature and the chemical potential. Subsequently, we employ KAN within the Andreev-Zakharov model for validation purpose, which can analytically reconstruct $w(r)$, matching the Andreev-Zakharov model exactly and confirming the validity of MLP. Finally, we construct an analytical holographic model using KAN and study the heavy-quark potential at finite temperature and chemical potential using the KAN-based holographic model. This work demonstrates the potential of KAN to derive analytical expressions for high-energy physics applications.

In this letter, we report the dark matter search results from the commissioning run and the first science run of the PandaX-4T experiment. A blind analysis is carried out on the entire data set. The data processing is improved compared to previous work, unifying the low-level signal reconstruction in a wide energy range up to 120 keV. With a total exposure of 1.54 tonne$\cdot$year, no significant excess of nuclear recoil events is found. The lowest 90% confidence level exclusion on the spin-independent cross section is $1.6 \times 10^{-47} \mathrm{cm}^2$ at a dark matter mass of 40 GeV$/c^2$. Our results represent the most stringent constraint for a dark matter mass above 100 GeV$/c^2$.

The holographic Schwinger effect is investigated in systems with Nf = 0, Nf = 2, and Nf = 2+1 using the Einstein-Maxwell-Dilaton (EMD) model, incorporating equation of state and baryon number susceptibility information from lattice QCD. It is found that the critical electric field is smallest for Nf = 0, indicating that the Schwinger effect is more likely to occur than in systems with Nf = 2 and Nf = 2+ 1. The critical electric field decreases with increasing chemical potential and temperature across all systems. Additionally, potential analysis confirms that the maximum total potential energy increases with the number of flavors, suggesting that existing particles may reduce the probability of particle pair production.

The PandaX-4T liquid xenon detector at the China Jinping Underground Laboratory is used to measure the solar $^8$B neutrino flux by detecting neutrinos through coherent scattering with xenon nuclei. Data samples requiring the coincidence of scintillation and ionization signals (paired), as well as unpaired ionization-only signals (US2), are selected with energy threshold of approximately 1.1 keV (0.33 keV) nuclear recoil energy. Combining the commissioning run and the first science run of PandaX-4T, a total exposure of 1.20 and 1.04 tonne$\cdot$year are collected for the paired and US2, respectively. After unblinding, 3 and 332 events are observed with an expectation of 2.8$\pm$0.5 and 251$\pm$32 background events, for the paired and US2 data, respectively. A combined analysis yields a best-fit $^8$B neutrino signal of 3.5 (75) events from the paired (US2) data sample, with $\sim$37\% uncertainty, and the background-only hypothesis is disfavored at 2.64$\sigma$ significance. This gives a solar $^8$B neutrino flux of ($8.4\pm3.1$)$\times$10$^6$ cm$^{-2}$s$^{-1}$, consistent with the standard solar model prediction. It is also the first indication of solar $^8$B neutrino ``fog'' in a dark matter direct detection experiment., Comment: Accepted by Physical Review Letters

In this paper, we examine the thermodynamics of heavy quarkonium in the spinning black hole background. Specifically, we investigate the effect of angular momentum on the interquark distance, free energy, binding energy, entropy, entropic force, and internal energy of heavy quarkonium from the thermodynamic relationship. Our findings indicate that the angular momentum reduces the maximum value of interquark distance, suggesting that it promotes the dissociation of quarkonium. Additionally, we observe that the angular momentum suppresses free energy. From the results of binding energy, the angular momentum favors the melting of meson into a free quark and antiquark. Moreover, the results show that angular momentum increases the entropy and entropic force, thus accelerates the dissociation of quarkonium. The angular momentum increases the internal energy at large interquark distance. Finally, we find that the angular momentum has a more pronounced effect on quarkonium when the axis of quark pair $Q\overline{Q}$ is transverse to the direction of angular momentum., Comment: 16 pages, 6 figures

Personalized diffusion models (PDMs) have become prominent for adapting pretrained text-to-image models to generate images of specific subjects using minimal training data. However, PDMs are susceptible to minor adversarial perturbations, leading to significant degradation when fine-tuned on corrupted datasets. These vulnerabilities are exploited to create protective perturbations that prevent unauthorized image generation. Existing purification methods attempt to mitigate this issue but often over-purify images, resulting in information loss. In this work, we conduct an in-depth analysis of the fine-tuning process of PDMs through the lens of shortcut learning. We hypothesize and empirically demonstrate that adversarial perturbations induce a latent-space misalignment between images and their text prompts in the CLIP embedding space. This misalignment causes the model to erroneously associate noisy patterns with unique identifiers during fine-tuning, resulting in poor generalization. Based on these insights, we propose a systematic defense framework that includes data purification and contrastive decoupling learning. We first employ off-the-shelf image restoration techniques to realign images with their original semantic meanings in latent space. Then, we introduce contrastive decoupling learning with noise tokens to decouple the learning of personalized concepts from spurious noise patterns. Our study not only uncovers fundamental shortcut learning vulnerabilities in PDMs but also provides a comprehensive evaluation framework for developing stronger protection. Our extensive evaluation demonstrates its superiority over existing purification methods and stronger robustness against adaptive perturbation., Comment: Our code is available at https://github.com/liuyixin-louis/DiffShortcut

Efficiently training LLMs with long sequences is important yet challenged by the massive computation and memory requirements. Sequence parallelism has been proposed to tackle these problems, but existing methods suffer from scalability or efficiency issues. We propose LoongTrain, a novel system to efficiently train LLMs with long sequences at scale. The core of LoongTrain is the 2D-Attention mechanism, which combines both head-parallel and context-parallel techniques to break the scalability constraints while maintaining efficiency. We introduce Double-Ring-Attention and analyze the performance of device placement strategies to further speed up training. We implement LoongTrain with the hybrid ZeRO and Selective Checkpoint++ techniques. Experiment results show that LoongTrain outperforms state-of-the-art baselines, i.e., DeepSpeed-Ulysses and Megatron Context Parallelism, in both end-to-end training speed and scalability, and improves Model FLOPs Utilization (MFU) by up to 2.88x.

Utilizing an emergent metric developed from deep learning techniques, we determine the complex potential associated with static quarkonium. This study explores the disintegration process of quarkonium by analyzing the real component of this potential, which is crucial for understanding its stability in various conditions. We show that the dissociation length, the critical distance at which a quark and antiquark pair disintegrate, decreases as the temperature increases. Furthermore, our assessment of the imaginary component of the potential indicates an increase in the magnitude of the imaginary potential for quarkonium as temperatures rise. This enhancement contributes to the quarkonium's suppression within the quark-gluon plasma, mirroring the anticipated outcomes from QCD. Our findings not only confirm the theoretical predictions but also demonstrate the efficacy of deep learning methods in advancing our understanding of high-energy particle physics., Comment: 7 pages, 6 figures, 1 table

Within the framework of the Einstein-Maxwell-Dilaton (EMD) model, which incorporates information on the equation of state and baryon number susceptibility from lattice results, we have conducted a comprehensive analysis of the potential energy, running coupling, and dissociation time for heavy quark-antiquark pairs using gauge/gravity duality. This study encompasses various systems, including pure gluon systems, 2 flavor systems, 2+1 flavor systems, and 2+1+1 flavor systems under finite temperature and chemical potential. The results reveal that the linear component of the potential energy diminishes as the flavor increases. It is also found that our results are extremely close to the recent lattice results for 2+1 flavors at finite temperature. Moreover, we have thoroughly investigated the dissociation distance and running coupling constant of quark-antiquark pairs to gain a comprehensive understanding of their behavior across various flavors. Finally, we have examined real-time dynamics of quark dissociation. The findings indicate that the dissociation time of quark-antiquark pairs is dependent on temperature, chemical potential, and flavor of the systems.

PandaX-4T is a xenon-based multi-purpose experiment, focusing on particle and astrophysics research. The data quality monitoring system plays a crucial role in the experiment. This system enables the prompt detection of potential issues during data collection. In order to meet the upgrade requirements of the experiment, we have implemented several updates to improve overall data throughput and provide users with more comprehensive information. As a result, the system is capable of monitoring half of the collected data in future operations of the PandaX-4T experiment. Furthermore, with updated hardware, the system is also well-equipped to meet the requirements of the future multi-ten-tonne level PandaX-xT experiment., Comment: 9 pages, 4 figures and 2 tables

Large Language Models (LLMs) are constrained by outdated information and a tendency to generate incorrect data, commonly referred to as "hallucinations." Retrieval-Augmented Generation (RAG) addresses these limitations by combining the strengths of retrieval-based methods and generative models. This approach involves retrieving relevant information from a large, up-to-date dataset and using it to enhance the generation process, leading to more accurate and contextually appropriate responses. Despite its benefits, RAG introduces a new attack surface for LLMs, particularly because RAG databases are often sourced from public data, such as the web. In this paper, we propose \TrojRAG{} to identify the vulnerabilities and attacks on retrieval parts (RAG database) and their indirect attacks on generative parts (LLMs). Specifically, we identify that poisoning several customized content passages could achieve a retrieval backdoor, where the retrieval works well for clean queries but always returns customized poisoned adversarial queries. Triggers and poisoned passages can be highly customized to implement various attacks. For example, a trigger could be a semantic group like "The Republican Party, Donald Trump, etc." Adversarial passages can be tailored to different contents, not only linked to the triggers but also used to indirectly attack generative LLMs without modifying them. These attacks can include denial-of-service attacks on RAG and semantic steering attacks on LLM generations conditioned by the triggers. Our experiments demonstrate that by just poisoning 10 adversarial passages can induce 98.2\% success rate to retrieve the adversarial passages. Then, these passages can increase the reject ratio of RAG-based GPT-4 from 0.01\% to 74.6\% or increase the rate of negative responses from 0.22\% to 72\% for targeted queries.

We investigate the effects of rotation on deconfinement and chiral phase transitions in the framework of dynamical holographic QCD model. Instead of transforming to the rotating system by Lorentz boost, we construct an anisotropic gravitational background by incorporating the rotating boundary current. We firstly investigate the pure gluon system under rotation to extract deconfinement phase transition from the Polyakov loop then add 2-flavor probe for chiral restoration phase transition from the chiral condensate. It is observed that at low chemical potentials, the deconfinement phase transition of pure gluon system is of first order and the chiral phase transition of 2-flavor system is of crossover. Both the critical temperatures of deconfinement and chiral phase transitions decrease/increase with imaginary/real angular velocity ($\Omega_I/\Omega$) as $T/T_c\sim 1- C_2 \Omega_I^2$ and $T/T_c\sim 1+ C_2 \Omega^2$, which is consistent with lattice QCD results. In the temperature-chemical potential $T-\mu$ phase diagram, the critical end point (CEP) moves towards regions of higher temperature and chemical potential with real angular velocity.

QCD phase diagram in the $T - \mu$ plane and the equation of state for pure gluon, 2-flavor, 2+1-flavor systems, and 2+1+1-flavor systems have been investigated using the Einstein-Maxwell-Dilaton (EMD) framework at finite temperature and chemical potential. By inputting lattice QCD data for the equation of state and baryon susceptibility at zero chemical potential into holographic model, all the parameters can be determined with the aid of machine learning algorithms. Our findings indicate that the deconfinement phase transition is of first order for the pure gluon system with critical temperature $T_c = 0.265 \rm GeV$ at vanishing chemical potential. The phase transition for the 2-flavor, 2+1-flavor systems, and 2+1+1-flavor systems are crossover at vanishing chemical potential and first-order at high chemical potential, and the critical endpoint(CEP) in the $T - \mu$ plane locates at ($\mu_B^c$=0.46 GeV, $T^c$=0.147 GeV), ($\mu_B^c$ = 0.74 GeV, $T^c$ = 0.094 GeV), and ($\mu_B^c$= 0.87 GeV,$T^c$ = 0.108 GeV), respectively. Additionally, the thermodynamic quantities of the system for different flavors at finite chemical potential are presented in this paper. It is observed that the difference between the 2+1 flavor and 2+1+1 flavor systems is invisible at vanishing chemical potential and low temperature. The location of CEP for 2+1+1 flavor system deviates explicitly from that of the 2+1 flavor system with the increase of chemical potential. Both 2+1 flavor and 2+1+1 flavor systems differ significantly from the 2-flavor system.

Originating from semantic bugs, Entity-Inconsistency Bugs (EIBs) involve misuse of syntactically valid yet incorrect program entities, such as variable identifiers and function names, which often have security implications. Unlike straightforward syntactic vulnerabilities, EIBs are subtle and can remain undetected for years. Traditional detection methods, such as static analysis and dynamic testing, often fall short due to the versatile and context-dependent nature of EIBs. However, with advancements in Large Language Models (LLMs) like GPT-4, we believe LLM-powered automatic EIB detection becomes increasingly feasible through these models' semantics understanding abilities. This research first undertakes a systematic measurement of LLMs' capabilities in detecting EIBs, revealing that GPT-4, while promising, shows limited recall and precision that hinder its practical application. The primary problem lies in the model's tendency to focus on irrelevant code snippets devoid of EIBs. To address this, we introduce a novel, cascaded EIB detection system named WitheredLeaf, which leverages smaller, code-specific language models to filter out most negative cases and mitigate the problem, thereby significantly enhancing the overall precision and recall. We evaluated WitheredLeaf on 154 Python and C GitHub repositories, each with over 1,000 stars, identifying 123 new flaws, 45% of which can be exploited to disrupt the program's normal operations. Out of 69 submitted fixes, 27 have been successfully merged.

Based on the holographic model, which incorporates the equation of state (EoS) and baryon number susceptibility for different flavors, we calculate the drag force, jet quenching parameter, and diffusion coefficient of the heavy quark at finite temperature and chemical potential. The holographic results for the diffusion coefficient align with lattice data for $N_f = 0$ and $N_f = 2+1$, falling within their error margins. The holographic diffusion coefficient for heavy quark in the systems of different flavors agrees with the fitting results of ALICE data well. The jet quenching parameter in our model demonstrates strong consistency with the results from both RHIC and LHC for different flavors. By comparing the experimental data with other models, we can confirm the validity of our holographic model calculation. The work reinforces the potential of bottom-up holographic model in advancing our understanding of transport properties of hot and dense quark-gluon plasma.

In this work, we discuss the description of neutral $\Sigma$ baryons with $I(J^P)=1(1/2^+)$ and $I(J^P)=1(3/2^+)$ using two bottom-up approaches: the deformed background and the static dilaton. In both models, we consider a non-linear Regge trajectory extension motivated by the \emph{strange} nature that $\Sigma$ baryons have. We found that both models describe these systems with an RMS error smaller than 10 $\%$. We also perform a configurational entropy calculation in both models to discuss hadronic stability.

We study the QCD topology and axion properties at finite temperature and chemical potential in the framework of the two-flavor Nambu$-$Jona-Lasinio model. We find that the behaviors of the two lowest cumulants of the QCD topological charge distribution and axion properties are highly sensitive to the critical behavior of the chiral phase transition. In particular, the topological susceptibility and the axion mass follow the response of the chiral condensate to temperature and chemical potential, showing that both quantities decrease monotonically with the increment of temperature and/or chemical potential. However, it is important to note that the normalized fourth cumulant behaves differently depending on the temperature. At low temperatures, it is a non-monotonic function of the chemical potential, while at high temperatures, it monotonically decreases. Additionally, its value invariably approaches the asymptotic value of $b_2^{\text {inst }}=-1/12$, predicted by the dilute instanton gas model. We also observe that with the increase in chemical potential at relatively low temperatures, the axion self-coupling constant exhibits a sharp peak around the critical point, which can even be more than twice its vacuum value. After that, the self-coupling drops sharply to a much lower value than its vacuum value, eventually approaching zero in the high chemical potential limit. The finding that the axion self-coupling constant is significantly enhanced in high-density environments near the chiral phase transition could lead to the creation or enhancement of an axion Bose-Einstein condensate in compact astrophysical objects., Comment: 10 pages, 10 figures

Direct laser acceleration of electrons in near-critical-density (NCD) carbon nanotube foams (CNFs) has its advantages in the high-efficiency generation of relativistic electrons and broadband X-rays. Here, we report the first simultaneous measurement on the spectra of laser-driven electrons and X-rays from CNFs at moderately relativistic intensities of around 5\times{10}^{19}\ W/cm^2.\ The density and thickness of the CNFs were scanned in the experiments, indicating the optimized electrons temperature of 5.5 MeV and X-ray critical energy of 5 keV. Two-dimensional (2D) particle-in-cell (PIC) simulations confirm that the electrons, with a temperature significantly higher than the pondermotive scale, are directly accelerated by the laser along the NCD plasma channel, while the bright X-rays are emitted by these electrons through betatron radiation or Thomson backscattering inside the channel. The simultaneously generated electrons and X-rays, automatically synchronized with the femtosecond laser driver, are suitable for applications such as bi-modal radiography., Comment: arXiv admin note: text overlap with arXiv:2010.05702

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.

Quantum communication networks are crucial for both secure communication and cryptographic networked tasks. Building quantum communication networks in a scalable and cost-effective way is essential for their widespread adoption, among which a stable and miniaturized high-quality quantum light source is a key component. Here, we establish a complete polarization entanglement-based fully connected network, which features an ultrabright integrated Bragg reflection waveguide quantum source, managed by an untrusted service provider, and a streamlined polarization analysis module, which requires only one single-photon detector for each end user. We perform a continuously working quantum entanglement distribution and create correlated bit strings between users. Within the framework of one-time universal hashing, we provide the first experimental implementation of source-independent quantum digital signatures using imperfect keys circumventing the necessity for private amplification. More importantly, we further beat the 1/3 fault-tolerance bound in Byzantine agreement, achieving unconditional security without relying on sophisticated techniques. Our results offer an affordable and practical route for addressing consensus challenges within the emerging quantum network landscape.

We report the first search for the elastic scatterings between cosmic-ray boosted sub-MeV dark matter and electrons in the PandaX-4T liquid xenon experiment. Sub-MeV dark matter particles can be accelerated by scattering with electrons in the cosmic rays and produce detectable electron recoil signals in the detector. Using the commissioning data from PandaX-4T of 0.63~tonne$\cdot$year exposure, we set new constraints on DM-electron scattering cross sections for DM masses ranging from 10~eV/$c^2$ to 3~keV/$c^2$., Comment: 6 pages, 3 figures

In this work, we use the AdS/CFT correspondence to study the behavior of a triply heavy baryon within anisotropic backgrounds. Beginning with the total action of the three quarks, we derive the balance equation for the three-quark system and compute the separation distance and potential energy. Our results reveal a consistent decrease in both the separation distance and potential energy for the A configuration and the B configuration as the anisotropy coefficient $a$ increases. This suggests that the presence of an anisotropic background promotes the dissolution of the three-quark system. Additionally, we compare the potential energies of the A and B configurations and observe that the A configuration has a slightly smaller potential energy, suggesting greater stability compared to the B configuration.

Neutrinos from core-collapse supernovae are essential for the understanding of neutrino physics and stellar evolution. The dual-phase xenon dark matter detectors can provide a way to track explosions of galactic supernovae by detecting neutrinos through coherent elastic neutrino-nucleus scatterings. In this study, a variation of progenitor masses as well as explosion models are assumed to predict the neutrino fluxes and spectra, which result in the number of expected neutrino events ranging from 6.6 to 13.7 at a distance of 10 kpc over a 10-second duration with negligible backgrounds at PandaX-4T. Two specialized triggering alarms for monitoring supernova burst neutrinos are built. The efficiency of detecting supernova explosions at various distances in the Milky Way is estimated. These alarms will be implemented in the real-time supernova monitoring system at PandaX-4T in the near future, providing the astronomical communities with supernova early warnings., Comment: 9 pages,6 figures

PandaX-4T experiment is a deep-underground dark matter direct search experiment that employs a dual-phase time projection chamber with a sensitive volume containing 3.7 tonne of liquid xenon. The detector of PandaX-4T is capable of simultaneously collecting the primary scintillation and ionization signals, utilizing their ratio to discriminate dark matter signals from background sources such as gamma rays and beta particles. The signal response model plays a crucial role in interpreting the data obtained by PandaX-4T. It describes the conversion from the deposited energy by dark matter interactions to the detectable signals within the detector. The signal response model is utilized in various PandaX-4T results. This work provides a comprehensive description of the procedures involved in constructing and parameter-fitting the signal response model for the energy range of approximately 1 keV to 25 keV for electronic recoils and 6 keV to 90 keV for nuclear recoils. It also covers the signal reconstruction, selection, and correction methods, which are crucial components integrated into the signal response model.

Deep learning techniques have demonstrated great potential for accurately estimating brain age by analyzing Magnetic Resonance Imaging (MRI) data from healthy individuals. However, current methods for brain age estimation often directly utilize whole input images, overlooking two important considerations: 1) the heterogeneous nature of brain aging, where different brain regions may degenerate at different rates, and 2) the existence of age-independent redundancies in brain structure. To overcome these limitations, we propose a Dual Graph Attention based Disentanglement Multi-instance Learning (DGA-DMIL) framework for improving brain age estimation. Specifically, the 3D MRI data, treated as a bag of instances, is fed into a 2D convolutional neural network backbone, to capture the unique aging patterns in MRI. A dual graph attention aggregator is then proposed to learn the backbone features by exploiting the intra- and inter-instance relationships. Furthermore, a disentanglement branch is introduced to separate age-related features from age-independent structural representations to ameliorate the interference of redundant information on age prediction. To verify the effectiveness of the proposed framework, we evaluate it on two datasets, UK Biobank and ADNI, containing a total of 35,388 healthy individuals. Our proposed model demonstrates exceptional accuracy in estimating brain age, achieving a remarkable mean absolute error of 2.12 years in the UK Biobank. The results establish our approach as state-of-the-art compared to other competing brain age estimation models. In addition, the instance contribution scores identify the varied importance of brain areas for aging prediction, which provides deeper insights into the understanding of brain aging., Comment: 12 pages, 9 figures

Large language models (LLMs) are great at processing multiple natural language processing tasks, but their abilities are constrained by inferior performance with long context, slow inference speed, and the high cost of computing the results. Deploying LLMs with precise and informative context helps users process large-scale datasets more effectively and cost-efficiently. Existing works rely on compressing long prompt contexts into soft prompts. However, soft prompt compression encounters limitations in transferability across different LLMs, especially API-based LLMs. To this end, this work aims to compress lengthy prompts in the form of natural language with LLM transferability. This poses two challenges: (i) Natural Language (NL) prompts are incompatible with back-propagation, and (ii) NL prompts lack flexibility in imposing length constraints. In this work, we propose a Natural Language Prompt Encapsulation (Nano-Capsulator) framework compressing original prompts into NL formatted Capsule Prompt while maintaining the prompt utility and transferability. Specifically, to tackle the first challenge, the Nano-Capsulator is optimized by a reward function that interacts with the proposed semantics preserving loss. To address the second question, the Nano-Capsulator is optimized by a reward function featuring length constraints. Experimental results demonstrate that the Capsule Prompt can reduce 81.4% of the original length, decrease inference latency up to 4.5x, and save 80.1% of budget overheads while providing transferability across diverse LLMs and different datasets.

We propose a major upgrade to the existing PandaX-4T experiment in the China Jinping Underground Laboratory. The new experiment, PandaX-xT, will be a multi-ten-tonne liquid xenon, ultra-low background, and general-purpose observatory. The full-scaled PandaX-xT contains a 43-tonne liquid xenon active target. Such an experiment will significantly advance our fundamental understanding of particle physics and astrophysics. The sensitivity of dark matter direct detection will be improved by nearly two orders of magnitude compared to the current best limits, approaching the so-called "neutrino floor" for a dark matter mass above 10 GeV/$c^2$, providing a decisive test to the Weakly Interacting Massive Particle paradigm. By searching for the neutrinoless double beta decay of $^{136}$Xe isotope in the detector, the effective Majorana neutrino mass can be measured to a [10 -- 41] meV/$c^2$ sensitivity, providing a key test to the Dirac/Majorana nature of neutrino s. Astrophysical neutrinos and other ultra-rare interactions can also be measured and searched for with an unprecedented background level, opening up new windows of discovery. Depending on the findings, PandaX-xT will seek the next stage upgrade utilizing isotopic separation on natural xenon.

Differential cross sections of the elastic pion-proton scattering are investigated at very small momentum transfer in a holographic QCD model, considering both the strong and Coulomb interaction in the Regge regime. The strong interaction is described by the Pomeron and Reggeon exchange, and the Coulomb interaction is characterized by the one photon exchange. The two interactions are linked through an interference term and we only need to determine a single adjustable parameter involved in this term. As to the parameters for the strong interaction, we can utilize the values determined in the previous studies. The differential cross sections can be predicted without any additional parameters, and it is shown that our predictions are consistent with the experimental data. We explicitly show the momentum transfer dependence for the interference effect. The energy dependence of the contribution ratios for each component is also discussed., Comment: 24 pages, 5 figures, version accepted for publication in Phys. Rev. D

Large language models (LLMs) with long sequences begin to power more and more fundamentally new applications we use every day. Existing methods for long-sequence LLM training are neither efficient nor compatible with commonly-used training algorithms such as FlashAttention. We design InternEvo to address these issues. InternEvo decouples all of the sharding dimensions into a new hierarchical space, and systematically analyzes the memory and communication cost of LLM training. Then, it generates an effective hybrid parallelism strategy. We design a new selective overlap mechanism to mitigate the communication overhead introduced by the hybrid parallelism. We also implement memory management techniques to reduce GPU memory fragmentation. Evaluation results show that InternEvo generates parallelization strategies that match or outperform existing methods in model FLOPs utilization.

The proton-proton ($pp$) fusion chain dominates the neutrino production from the Sun. The uncertainty of the predicted $pp$ neutrino flux is at the sub-percent level, whereas that of the best measurement is $\mathcal{O}(10\%)$. In this paper, we present the first result to measure the solar $pp$ neutrinos in the electron recoil energy range from 24 to 144 keV, using the PandaX-4T commissioning data with 0.63 tonne$\times$year exposure. The $pp$ neutrino flux is determined to be $(8.0 \pm 3.9 \,{\rm{(stat)}} \pm 10.0 \,{\rm{(syst)}} )\times 10^{10}\, $$\rm{s}^{-1} \rm{cm}^{-2}$, consistent with Standard Solar Model and existing measurements, corresponding to a flux upper limit of $23.3\times 10^{10}\, $$\rm{s}^{-1} \rm{cm}^{-2}$ at 90\% C.L.., Comment: 6 pages, 5 figures

Based on lattice QCD results of equation of state (EOS) and baryon number susceptibility at zero baryon chemical potential, and supplemented by machine learning techniques, we construct the analytic form of the holographic black hole metric in the Einstein-Maxwell-Dilaton (EMD) framework for pure gluon, 2-flavor, and 2+1-flavor systems, respectively. The dilaton potentials solved from Einstein equations are in good agreement with the extended non-conformal DeWolfe-Gubser-Rosen (DGR) type dilaton potentials fixed by lattice QCD EOS, which indicates the robustness of the EMD framework. The predicted critical endpoint (CEP) in the 2+1-flavor system is located at $(T^c$=0.094GeV, $\mu^c_B$=0.74GeV), which is close to the results from the realistic Polyakov-Nambu-Jona-Lasinio(PNJL) model, the functional renormalization group, and the holographic model with extended DeWolfe-Gubser-Rosen dilaton potential.

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness., Comment: This work is still under work and we welcome your contribution

$^{134}$Xe is a candidate isotope for neutrinoless double beta decay~($0\nu\beta\beta$) search. In addition, the two-neutrino case ($2\nu\beta\beta$) allowed by the Standard Model of particle physics has not yet been observed. Utilizing the 10.4% of $^{134}$Xe in the natural xenon in the PandaX-4T detector and its first 94.9-day exposure, we have established the most stringent constraints on $2\nu\beta\beta$ and $0\nu\beta\beta$ of $^{134}$Xe half-lives, with limits of $2.8\times10^{22}$ yr and $3.0\times10^{23}$ yr at 90% confidence level, respectively. The $2\nu\beta\beta$ ($0\nu\beta\beta$) limit surpasses the previously reported best result by a factor of 32 (2.7), highlighting the potential of large monolithic natural xenon detectors.

Signal reconstruction through software processing is a crucial component of the background and signal models in the PandaX-4T experiment, which is a multi-tonne dark matter direct search experiment. The accuracy of signal reconstruction is influenced by various detector artifacts, including noise, dark count of photomultiplier, impurity photoionization in the detector, and other relevant considerations. In this study, we present a detailed description of a semi-data-driven approach designed to simulate the signal waveform. This work provides a reliable model for the efficiency and bias of the signal reconstruction in the data analysis of PandaX-4T. By comparing critical variables which relate to the temporal shape and hit pattern of the signals, we demonstrate a good agreement between the simulation and data.

Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. However, the security issues, e.g., Trojan attacks, of prompt tuning on a few data samples are not well-studied. Transferring established data poisoning attacks directly to few-shot prompt tuning presents multiple challenges. One significant issue is the \textit{poisoned imbalance issue}, where non-target class samples are added to the target class, resulting in a greater number of target-class samples compared to non-target class. While this issue is not critical in regular tuning, it significantly hampers the few-shot prompt tuning, making it difficult to simultaneously achieve a high attack success rate (ASR) and maintain clean data accuracy (CDA). Additionally, few-shot prompting is prone to overfitting in terms of both ASR and CDA. In this paper, we introduce \textit{TrojFSP}, a method designed to address the challenges. To solve the poisoned imbalance issue, we develop a \textit{Target-Class Shrink (TC-Shrink)} technique, which aims to equalize the number of poisoning samples. To combat overfitting, we employ a \textit{Selective Token Poisoning} technique to boost attack performance. Furthermore, we introduce a \textit{Trojan-Trigger Attention} objective function to amplify the attention of the poisoned trojan prompt on triggers. Experiments show that our TrojFSP achieves an ASR of over 99\% while maintaining negligible decreases in CDA across various PLMs and datasets., Comment: 9 pages, 2 figures

The emergence of Artificial Intelligence (AI)-driven audio attacks has revealed new security vulnerabilities in voice control systems. While researchers have introduced a multitude of attack strategies targeting voice control systems (VCS), the continual advancements of VCS have diminished the impact of many such attacks. Recognizing this dynamic landscape, our study endeavors to comprehensively assess the resilience of commercial voice control systems against a spectrum of malicious audio attacks. Through extensive experimentation, we evaluate six prominent attack techniques across a collection of voice control interfaces and devices. Contrary to prevailing narratives, our results suggest that commercial voice control systems exhibit enhanced resistance to existing threats. Particularly, our research highlights the ineffectiveness of white-box attacks in black-box scenarios. Furthermore, the adversaries encounter substantial obstacles in obtaining precise gradient estimations during query-based interactions with commercial systems, such as Apple Siri and Samsung Bixby. Meanwhile, we find that current defense strategies are not completely immune to advanced attacks. Our findings contribute valuable insights for enhancing defense mechanisms in VCS. Through this survey, we aim to raise awareness within the academic community about the security concerns of VCS and advocate for continued research in this crucial area., Comment: 14 pages