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Malicious shell commands are linchpins to many cyber-attacks, but may not be easy to understand by security analysts due to complicated and often disguised code structures. Advances in large language models (LLMs) have unlocked the possibility of generating understandable explanations for shell commands. However, existing general-purpose LLMs suffer from a lack of expert knowledge and a tendency to hallucinate in the task of shell command explanation. In this paper, we present Raconteur, a knowledgeable, expressive and portable shell command explainer powered by LLM. Raconteur is infused with professional knowledge to provide comprehensive explanations on shell commands, including not only what the command does (i.e., behavior) but also why the command does it (i.e., purpose). To shed light on the high-level intent of the command, we also translate the natural-language-based explanation into standard technique & tactic defined by MITRE ATT&CK, the worldwide knowledge base of cybersecurity. To enable Raconteur to explain unseen private commands, we further develop a documentation retriever to obtain relevant information from complementary documentations to assist the explanation process. We have created a large-scale dataset for training and conducted extensive experiments to evaluate the capability of Raconteur in shell command explanation. The experiments verify that Raconteur is able to provide high-quality explanations and in-depth insight of the intent of the command., Comment: Accepted by NDSS Symposium 2025. Please cite this paper as "Jiangyi Deng, Xinfeng Li, Yanjiao Chen, Yijie Bai, Haiqin Weng, Yan Liu, Tao Wei, Wenyuan Xu. RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer. In the 32nd Annual Network and Distributed System Security Symposium (NDSS 2025)."

With recent advances in building foundation models for texts and video data, there is a surge of interest in foundation models for time series. A family of models have been developed, utilizing a temporal auto-regressive generative Transformer architecture, whose effectiveness has been proven in Large Language Models. While the empirical results are promising, almost all existing time series foundation models have only been tested on well-curated ``benchmark'' datasets very similar to texts. However, real-world time series exhibit unique challenges, such as variable channel sizes across domains, missing values, and varying signal sampling intervals due to the multi-resolution nature of real-world data. Additionally, the uni-directional nature of temporally auto-regressive decoding limits the incorporation of domain knowledge, such as physical laws expressed as partial differential equations (PDEs). To address these challenges, we introduce the Time Diffusion Transformer (TimeDiT), a general foundation model for time series that employs a denoising diffusion paradigm instead of temporal auto-regressive generation. TimeDiT leverages the Transformer architecture to capture temporal dependencies and employs diffusion processes to generate high-quality candidate samples without imposing stringent assumptions on the target distribution via novel masking schemes and a channel alignment strategy. Furthermore, we propose a finetuning-free model editing strategy that allows the seamless integration of external knowledge during the sampling process without updating any model parameters. Extensive experiments conducted on a varity of tasks such as forecasting, imputation, and anomaly detection, demonstrate the effectiveness of TimeDiT., Comment: 23 Pages, 6 Figures, 11 Tables. First present at ICML 2024 Workshop on Foundation Models in the Wild

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings., Comment: ICML 2024 Carema Ready Version. 20 Pages, 12 Figures, 10 Tables

Recent breakthroughs have ushered the quantum network into a new era, where quantum information can be stored, transferred, and processed across multiple nodes on a metropolitan scale. A key challenge in this new era is enhancing the capabilities of individual nodes, providing precise and robust control over multiple qubits and advanced functionality for scalable quantum networks. Here, we report on precise and complex control in a hybrid quantum node based on a diamond color center. We demonstrate hybrid coherent control by entangling three types of qubits: an electron spin as an interface qubit, a nuclear spin with long memory time, and a flying photonic qubit, with their qubit frequencies spanning three distinct regimes from the optical domain to the rf domain. By incorporating two additional memory qubits, we encode three memory qubits into a logical state using the three-qubit repetition code and entangle this logical qubit with a photonic qubit. Leveraging hybrid qubits and precise control, we repeatedly read out the error syndromes of memory qubits through the electron spin, serving as an auxiliary qubit, then apply a real-time feedback operation to correct bit-flip errors. We execute and verify active error correction for up to twelve rounds and demonstrate the improvement over the uncorrected counterpart. Our results demonstrate the feasibility of several key functionalities for next-generation quantum repeaters, paving the way towards full-fledged metropolitan-scale quantum networks for a wide range of practical applications., Comment: 8 pages, 3 figures

When the effects of relative motion at the solid object interfaces are not negligible, the contact method is required in the smoothed particle hydrodynamics (SPH) method to prevent virtual shear and tensile stresses. However, there is still a lack of a three-dimensional (3D) contact method that can be well applied to various deformation situations, especially for extreme deformation. In this study, we propose a generalized 3D hybrid contact method for SPH. First, an improved high accuracy free-surface particle detection method is developed, including optimization of the detection process to reduce the detection time and consideration of the effect of material compressibility on the filtering parameters to extend the existing semi-geometric method from the incompressible (weakly-compressible) field to the compressible field. Then, a novel 3D local surface reconstruction method is developed based on the free-surface particles and region growing method, including the selection of the initial edge, the principle of triangle expansion, and the evaluation function, followed by the surface-surface contact detection and enforcement of normal penalty and tangential friction forces according to the penalty function method and Coulomb friction law. Finally, the particle-particle contact method is added to deal with cases where surface-surface contact fails, e.g., some particles are unable to reconstruct the local surface when the material undergoes extreme deformations. The proposed method is validated by several numerical tests, and the results show that the proposed method is capable of handling various contact problems with accuracy and stability, including small, large, and extreme deformation problems.

In this paper, a novel movable antenna (MA) empowered secure transmission scheme is designed for cell-free symbiotic radio (SR) systems in the presence of an eavesdropper (Eve). Specifically, multiple distributed access points (APs) equipped with MAs collaboratively transmit confidential information to the primary user (PU), in the meanwhile the backscatter device (BD) transmits its own information to the secondary user (SU) by reflecting incident signals from the APs. The MAs deployed at the APs can adjust their positions flexibly to improve channel conditions between the APs and the PU/SU/BD and suppress the eavesdropping from the Eve on confidential information at the PU. Under this setup, we maximize the secrecy rate of primary transmission through jointly optimizing the APs' transmission beamforming vectors and the positions of the MAs, while adhering to the quality of service constraints at the SU. To address the challenges caused by the non-convexity and find a near-optimal solution, an alternating optimization (AO) framework is proposed, utilizing the successive convex approximation method, the semi-definite relaxation technology and a genetic algorithm modified particle swarm optimization (GA-PSO) algorithm. Numerical results demonstrate the secrecy rate enhancement provided by utilizing the MAs and show the impact of the GA-PSO algorithm for improving the solving accuracy., Comment: 7 pages, 6 figures

This study aims to discover the governing mathematical expressions of car-following dynamics from trajectory data directly using deep learning techniques. We propose an expression exploration framework based on deep symbolic regression (DSR) integrated with a variable intersection selection (VIS) method to find variable combinations that encourage interpretable and parsimonious mathematical expressions. In the exploration learning process, two penalty terms are added to improve the reward function: (i) a complexity penalty to regulate the complexity of the explored expressions to be parsimonious, and (ii) a variable interaction penalty to encourage the expression exploration to focus on variable combinations that can best describe the data. We show the performance of the proposed method to learn several car-following dynamics models and discuss its limitations and future research directions.

The risk of harmful content generated by large language models (LLMs) becomes a critical concern. This paper presents a systematic study on assessing and improving LLMs' capability to perform the task of \textbf{course-correction}, \ie, the model can steer away from generating harmful content autonomously. To start with, we introduce the \textsc{C$^2$-Eval} benchmark for quantitative assessment and analyze 10 popular LLMs, revealing varying proficiency of current safety-tuned LLMs in course-correction. To improve, we propose fine-tuning LLMs with preference learning, emphasizing the preference for timely course-correction. Using an automated pipeline, we create \textsc{C$^2$-Syn}, a synthetic dataset with 750K pairwise preferences, to teach models the concept of timely course-correction through data-driven preference learning. Experiments on 2 LLMs, \textsc{Llama2-Chat 7B} and \textsc{Qwen2 7B}, show that our method effectively enhances course-correction skills without affecting general performance. Additionally, it effectively improves LLMs' safety, particularly in resisting jailbreak attacks., Comment: Dataset and script will be available at https://github.com/pillowsofwind/Course-Correction

While the automated detection of cryptographic API misuses has progressed significantly, its precision diminishes for intricate targets due to the reliance on manually defined patterns. Large Language Models (LLMs), renowned for their contextual understanding, offer a promising avenue to address existing shortcomings. However, applying LLMs in this security-critical domain presents challenges, particularly due to the unreliability stemming from LLMs' stochastic nature and the well-known issue of hallucination. To explore the prevalence of LLMs' unreliable analysis and potential solutions, this paper introduces a systematic evaluation framework to assess LLMs in detecting cryptographic misuses, utilizing a comprehensive dataset encompassing both manually-crafted samples and real-world projects. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. Nevertheless, we demonstrate how a constrained problem scope, coupled with LLMs' self-correction capability, significantly enhances the reliability of the detection. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks. Moreover, we identify the failure patterns that persistently hinder LLMs' reliability, including both cryptographic knowledge deficiency and code semantics misinterpretation. Guided by these insights, we develop an LLM-based workflow to examine open-source repositories, leading to the discovery of 63 real-world cryptographic misuses. Of these, 46 have been acknowledged by the development community, with 23 currently being addressed and 6 resolved. Reflecting on developers' feedback, we offer recommendations for future research and the development of LLM-based security tools.

The commuting origin-destination~(OD) matrix is a critical input for urban planning and transportation, providing crucial information about the population residing in one region and working in another within an interested area. Despite its importance, obtaining and updating the matrix is challenging due to high costs and privacy concerns. This has spurred research into generating commuting OD matrices for areas lacking historical data, utilizing readily available information via computational models. In this regard, existing research is primarily restricted to only a single or few large cities, preventing these models from being applied effectively in other areas with distinct characteristics, particularly in towns and rural areas where such data is urgently needed. To address this, we propose a large-scale dataset comprising commuting OD matrices for 3,233 diverse areas around the U.S. For each area, we provide the commuting OD matrix, combined with regional attributes including demographics and point-of-interests of each region in that area. We believe this comprehensive dataset will facilitate the development of more generalizable commuting OD matrix generation models, which can capture various patterns of distinct areas. Additionally, we use this dataset to benchmark a set of commuting OD generation models, including physical models, element-wise predictive models, and matrix-wise generative models. Surprisingly, we find a new paradigm, which considers the whole area combined with its commuting OD matrix as an attributed directed weighted graph and generates the weighted edges based on the node attributes, can achieve the optimal. This may inspire a new research direction from graph learning in this field., Comment: 16 pages, 9 figures

The mainstream automatic speech recognition (ASR) technology usually requires hundreds to thousands of hours of annotated speech data. Three approaches to low-resourced ASR are phoneme or subword based supervised pre-training, and self-supervised pre-training over multilingual data. The Iu Mien language is the main ethnic language of the Yao ethnic group in China and is low-resourced in the sense that the annotated speech is very limited. With less than 10 hours of transcribed Iu Mien language, this paper investigates and compares the three approaches for Iu Mien speech recognition. Our experiments are based on the recently released, three backbone models pretrained over the 10 languages from the CommonVoice dataset (CV-Lang10), which correspond to the three approaches for low-resourced ASR. It is found that phoneme supervision can achieve better results compared to subword supervision and self-supervision, thereby providing higher data-efficiency. Particularly, the Whistle models, i.e., obtained by the weakly-supervised phoneme-based multilingual pre-training, obtain the most competitive results.

Singing voice conversion (SVC) aims to convert a singer's voice in a given music piece to another singer while keeping the original content. We propose an end-to-end feature disentanglement-based model, which we named SaMoye, to enable zero-shot many-to-many singing voice conversion. SaMoye disentangles the features of the singing voice into content features, timbre features, and pitch features respectively. The content features are enhanced using a GPT-based model to perform cross-prediction with the phoneme of the lyrics. SaMoye can generate the music with converted voice by replacing the timbre features with the target singer. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance. The dataset consists of 1500k pure singing vocal clips containing at least 10,000 singers., Comment: 7 pages, 4 figures

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of Internet of Things (IoT) and Artificial Intelligence (AI) technologies. Although advanced deep learning techniques enhance the efficient data processing and intelligent analysis of complex IoT data, they still suffer from notable challenges when deployed to practical AIoT applications, such as constrained resources, and diverse task requirements. Knowledge transfer is an effective method to enhance learning performance by avoiding the exorbitant costs associated with data recollection and model retraining. Notably, although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances of various knowledge transfer techniques for AIoT field. This survey endeavors to introduce a new concept of knowledge transfer, referred to as Crowd Knowledge Transfer (CrowdTransfer), which aims to transfer prior knowledge learned from a crowd of agents to reduce the training cost and as well as improve the performance of the model in real-world complicated scenarios. Particularly, we present four transfer modes from the perspective of crowd intelligence, including derivation, sharing, evolution and fusion modes. Building upon conventional transfer learning methods, we further delve into advanced crowd knowledge transfer models from three perspectives for various AIoT applications. Furthermore, we explore some applications of AIoT areas, such as human activity recognition, urban computing, multi-robot system, and smart factory. Finally, we discuss the open issues and outline future research directions of knowledge transfer in AIoT community., Comment: This paper has been accepted for publication in IEEE Communications Surveys & Tutorials. Copyright will be transferred without notice, after this version may no longer be accessible

The increasing development of large language models (LLMs) in code generation has drawn significant attention among researchers. To enhance LLM-based code generation ability, current efforts are predominantly directed towards collecting high-quality datasets and leveraging diverse training technologies. However, there is a notable lack of comprehensive studies examining the limitations and boundaries of these existing methods. To bridge this gap, we conducted an extensive empirical study evaluating the performance of three leading closed-source LLMs and four popular open-source LLMs on three commonly used benchmarks. Our investigation, which evaluated the length, cyclomatic complexity and API number of the generated code, revealed that these LLMs face challenges in generating successful code for more complex problems, and tend to produce code that is shorter yet more complicated as compared to canonical solutions. Additionally, we developed a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. Furthermore, to better understand the performance of LLMs in real-world projects, we manually created a real-world benchmark comprising 140 code generation tasks. Our analysis highlights distinct differences in bug distributions between actual scenarios and existing benchmarks. Finally, we propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback. Experimental results demonstrate that our approach can significantly mitigate bugs and increase the passing rate by 29.2% after two iterations, indicating substantial potential for LLMs to handle more complex problems., Comment: 17 pages, 7 figures

Amid the rising intersection of generative AI and human artistic processes, this study probes the critical yet less-explored terrain of alignment in human-centric automatic song composition. We propose a novel task of Colloquial Description-to-Song Generation, which focuses on aligning the generated content with colloquial human expressions. This task is aimed at bridging the gap between colloquial language understanding and auditory expression within an AI model, with the ultimate goal of creating songs that accurately satisfy human auditory expectations and structurally align with musical norms. Current datasets are limited due to their narrow descriptive scope, semantic gaps and inaccuracies. To overcome data scarcity in this domain, we present the Caichong Music Dataset (CaiMD). CaiMD is manually annotated by both professional musicians and amateurs, offering diverse perspectives and a comprehensive understanding of colloquial descriptions. Unlike existing datasets pre-set with expert annotations or auto-generated ones with inherent biases, CaiMD caters more sufficiently to our purpose of aligning AI-generated music with widespread user-desired results. Moreover, we propose an innovative single-stage framework called MuDiT/MuSiT for enabling effective human-machine alignment in song creation. This framework not only achieves cross-modal comprehension between colloquial language and auditory music perceptions but also ensures generated songs align with user-desired results. MuDiT/MuSiT employs one DiT/SiT model for end-to-end generation of musical components like melody, harmony, rhythm, vocals, and instrumentation. The approach ensures harmonious sonic cohesiveness amongst all generated musical components, facilitating better resonance with human auditory expectations., Comment: 19 pages, 5 figures

Solid-state quantum emitters are essential sources of single photons, and enhancing their emission rates is of paramount importance for applications in quantum communications, computing, and metrology. One approach is to couple quantum emitters with resonant photonic nanostructures, where the emission rate is enhanced due to the Purcell effect. Dielectric nanoantennas are promising as they provide strong emission enhancement compared to plasmonic ones, which suffer from high Ohmic loss. Here, we designed and fabricated a dielectric Fano resonator based on a pair of silicon (Si) ellipses and a disk, which supports the mode hybridization between quasi-bound-states-in-the-continuum (quasi-BIC) and Mie resonance. We demonstrated the performance of the developed resonant system by interfacing it with single photon emitters (SPEs) based on nitrogen-vacancy (NV-) centers in nanodiamonds (NDs). We observed that the interfaced emitters have a Purcell enhancement factor of ~10, with sub-ns emission lifetime and a polarization contrast of 9. Our results indicate a promising method for developing efficient and compact single-photon sources for integrated quantum photonics applications., Comment: 20 pages, 4 figures

The inclusive production of pseudoscalar heavy quarkoniua ($\eta_c,~\eta_b$ and $B_c$) via photon-photon fusion in heavy ion ultraperipheral collision (UPC) are calculated to QCD next-to-leading order in the framework of non-relativistic QCD (NRQCD). The total cross section of $\eta_c$ produced in Pb-Pb UPC is 194 $\mathrm{nb}^{-1}$ and 1052 $\mathrm{nb}^{-1}$ at nucleon-nucleon c.m. energies $\sqrt{S_{\mathrm{NN}}}=$ 5.52 TeV and 39.4 TeV, respectively. The cross sections for $\eta_b$ and $B_c$ mesons are more than two to three orders of magnitude smaller. We make a detailed phenomenological analysis on the $\eta_c$ production; the uncertainties caused by the renormalization scale and the charm quark mass, the cross sections in other ultraperipheral nucleon-nucleon colliding systems, and the transverse momentum distribution are discussed. At the coming HL-LHC and future FCC, the heavy ion UPC opens another door of the study on the production of heavy quarkonium., Comment: 16 pages, 1 figures, 4 tables

Real-world application models are commonly deployed in dynamic environments, where the target domain distribution undergoes temporal changes. Continual Test-Time Adaptation (CTTA) has recently emerged as a promising technique to gradually adapt a source-trained model to continually changing target domains. Despite recent advancements in addressing CTTA, two critical issues remain: 1) Fixed thresholds for pseudo-labeling in existing methodologies generate low-quality pseudo-labels, as model confidence varies across categories and domains; 2) Stochastic parameter restoration methods for mitigating catastrophic forgetting fail to effectively preserve critical information due to their intrinsic randomness. To tackle these challenges for detection models in CTTA scenarios, we present CTAOD, featuring three core components. Firstly, the object-level contrastive learning module extracts object-level features for contrastive learning to refine the feature representation in the target domain. Secondly, the adaptive monitoring module dynamically skips unnecessary adaptation and updates the category-specific threshold based on predicted confidence scores to enable efficiency and improve the quality of pseudo-labels. Lastly, the data-driven stochastic restoration mechanism selectively reset inactive parameters with higher possibilities, ensuring the retention of essential knowledge. We demonstrate the effectiveness of CTAOD on four CTTA object detection tasks, where CTAOD outperforms existing methods, especially achieving a 3.2 mAP improvement and a 20% increase in efficiency on the Cityscapes-to-Cityscapes-C CTTA task. The code will be released.

This paper aims to balance performance and cost in a two-hop wireless cooperative communication network where the source and relays have contradictory optimization goals and make decisions in a distributed manner. This differs from most existing works that have typically assumed that source and relay nodes follow a schedule created implicitly by a central controller. We propose that the relays form an alliance in an attempt to maximize the benefit of relaying while the source aims to increase the channel capacity cost-effectively. To this end, we establish the trade problem as a Stackelberg game, and prove the existence of its equilibrium. Another important aspect is that we use multi-agent reinforcement learning (MARL) to approach the equilibrium in a situation where the instantaneous channel state information (CSI) is unavailable, and the source and relays do not have knowledge of each other's goal. A multi-agent deep deterministic policy gradient-based framework is designed, where the relay alliance and the source act as agents. Experiments demonstrate that the proposed method can obtain an acceptable performance that is close to the game-theoretic equilibrium for all players under time-invariant environments, which considerably outperforms its potential alternatives and is only about 2.9% away from the optimal solution.

Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases, which may cause negative social impacts or even bring catastrophic results in application. Previous works on this problem mainly focused on using black-box methods such as probing to detect and quantify social biases in PLMs by observing model outputs. As a result, previous debiasing methods mainly finetune or even pre-train language models on newly constructed anti-stereotypical datasets, which are high-cost. In this work, we try to unveil the mystery of social bias inside language models by introducing the concept of {\sc Social Bias Neurons}. Specifically, we propose {\sc Integrated Gap Gradients (IG$^2$)} to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. By formalizing undesirable behavior as a distributional property of language, we employ sentiment-bearing prompts to elicit classes of sensitive words (demographics) correlated with such sentiments. Our IG$^2$ thus attributes the uneven distribution for different demographics to specific Social Bias Neurons, which track the trail of unwanted behavior inside PLM units to achieve interoperability. Moreover, derived from our interpretable technique, {\sc Bias Neuron Suppression (BNS)} is further proposed to mitigate social biases. By studying BERT, RoBERTa, and their attributable differences from debiased FairBERTa, IG$^2$ allows us to locate and suppress identified neurons, and further mitigate undesired behaviors. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost.

There is an increasing trend to mine vulnerabilities from software repositories and use machine learning techniques to automatically detect software vulnerabilities. A fundamental but unresolved research question is: how do different factors in the mining and learning process impact the accuracy of identifying vulnerabilities in software projects of varying characteristics? Substantial research has been dedicated in this area, including source code static analysis, software repository mining, and NLP-based machine learning. However, practitioners lack experience regarding the key factors for building a baseline model of the state-of-the-art. In addition, there lacks of experience regarding the transferability of the vulnerability signatures from project to project. This study investigates how the combination of different vulnerability features and three representative machine learning models impact the accuracy of vulnerability detection in 17 real-world projects. We examine two types of vulnerability representations: 1) code features extracted through NLP with varying tokenization strategies and three different embedding techniques (bag-of-words, word2vec, and fastText) and 2) a set of eight architectural metrics that capture the abstract design of the software systems. The three machine learning algorithms include a random forest model, a support vector machines model, and a residual neural network model. The analysis shows a recommended baseline model with signatures extracted through bag-of-words embedding, combined with the random forest, consistently increases the detection accuracy by about 4% compared to other combinations in all 17 projects. Furthermore, we observe the limitation of transferring vulnerability signatures across domains based on our experiments.

The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. To address these challenges, we propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM incorporates a multiscale graph neural network to discern hierarchical nonlinear temporal dependencies across various time scales. Besides, it also integrates a recurrent learning component and a residual fusion mechanism, enhancing its capability to accurately capture spatial and temporal variations in charging patterns. The effectiveness of the proposed MSTEM has been validated through comparative analysis with six baseline models using three evaluation metrics. The case studies utilize real-world datasets for both fast and slow charging loads at EVCS in Perth, UK. The experimental results demonstrate the superiority of MSTEM in short-term continuous load forecasting for EVCS., Comment: 5 pages, 1 figure, AEEES 2024

Optoacoustic (OA) imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Through carefully-designed illumination schedules of ON and OFF laser pulses, the resulting OA signal is a multiplex of different reporter species and the background. By exploiting their distinct inherent photo-physical properties which govern the rate of switching, one can recover the concentration maps of protein reporters from the temporally entangled OA images. In this paper, we propose a quantitative unmixing approach in the form of a regularized inversion algorithm based on a mathematical model of the temporal decay of the signal measuring the underlying protein reporters. We validate three types of reporters on simulated and experimental datasets and show successful unmixing results.

Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise. With recent advances of abstractive summarization, we propose our \emph{Gist Detector} to leverage the gist detection ability of a summarization model and integrate the extracted gist into downstream models to enhance their long text understanding ability. Specifically, Gist Detector first learns the gist detection knowledge distilled from a summarization model, and then produces gist-aware representations to augment downstream models. We evaluate our method on three different tasks: long document classification, distantly supervised open-domain question answering, and non-parallel text style transfer. The experimental results show that our method can significantly improve the performance of baseline models on all tasks., Comment: arXiv admin note: text overlap with arXiv:2110.04741

In the circuit quantum electrodynamics architectures, to realize a long-range quantum network mediated by flying photon, it is necessary to shape the temporal profile of emitted photons to achieve high transfer efficiency between two quantum nodes. In this work, we demonstrate a new single-rail and dual-rail time-bin shaped photon generator without additional flux-tunable elements, which can act as a quantum interface of a point-to-point quantum network. In our approach, we adopt a qubit-resonator-transmission line configuration, and the effective coupling strength between the qubit and the resonator can be varied by parametrically modulating the qubit frequency. In this way, the coupling is directly proportional to the parametric modulation amplitude and covers a broad tunable range beyond 20 MHz for the sample we used. Additionally, when emitting shaped photons, we find that the spurious frequency shift (-0.4 MHz) due to parametric modulation is small and can be readily calibrated through chirping. We develop an efficient photon field measurement setup based on the data stream processing of GPU. Utilizing this system, we perform photon temporal profile measurement, quantum state tomography of photon field, and quantum process tomography of single-rail quantum state transfer based on a heterodyne measurement scheme. The single-rail encoding state transfer fidelity of shaped photon emission is 90.32%, and that for unshaped photon is 97.20%, respectively. We believe that the fidelity of shaped photon emission is mainly limited by the qubit coherence time. The results demonstrate that our method is hardware efficient, simple to implement, and scalable. It could become a viable tool in a high-quality quantum network utilizing both single-rail and dual-rail time-bin encoding.

The success of Reinforcement Learning from Human Feedback (RLHF) in language model alignment is critically dependent on the capability of the reward model (RM). However, as the training process progresses, the output distribution of the policy model shifts, leading to the RM's reduced ability to distinguish between responses. This issue is further compounded when the RM, trained on a specific data distribution, struggles to generalize to examples outside of that distribution. These two issues can be united as a challenge posed by the shifted distribution of the environment. To surmount this challenge, we introduce MetaRM, a method leveraging meta-learning to align the RM with the shifted environment distribution. MetaRM is designed to train the RM by minimizing data loss, particularly for data that can improve the differentiation ability to examples of the shifted target distribution. Extensive experiments demonstrate that MetaRM significantly improves the RM's distinguishing ability in iterative RLHF optimization, and also provides the capacity to identify subtle differences in out-of-distribution samples., Comment: 11 pages, 6 figures. arXiv admin note: text overlap with arXiv:2401.06080

With the development of the open source community, the code is often copied, spread, and evolved in multiple software systems, which brings uncertainty and risk to the software system (e.g., bug propagation and copyright infringement). Therefore, it is important to conduct code clone detection to discover similar code pairs. Many approaches have been proposed to detect code clones where token-based tools can scale to big code. However, due to the lack of program details, they cannot handle more complicated code clones, i.e., semantic code clones. In this paper, we introduce CC2Vec, a novel code encoding method designed to swiftly identify simple code clones while also enhancing the capability for semantic code clone detection. To retain the program details between tokens, CC2Vec divides them into different categories (i.e., typed tokens) according to the syntactic types and then applies two self-attention mechanism layers to encode them. To resist changes in the code structure of semantic code clones, CC2Vec performs contrastive learning to reduce the differences introduced by different code implementations. We evaluate CC2Vec on two widely used datasets (i.e., BigCloneBench and Google Code Jam) and the results report that our method can effectively detect simple code clones. In addition, CC2Vec not only attains comparable performance to widely used semantic code clone detection systems such as ASTNN, SCDetector, and FCCA by simply fine-tuning, but also significantly surpasses these methods in both detection efficiency., Comment: 21 pages, 7 figures

This paper proposes a new gradient-based XAI method called Guided AbsoluteGrad for saliency map explanations. We utilize both positive and negative gradient magnitudes and employ gradient variance to distinguish the important areas for noise deduction. We also introduce a novel evaluation metric named ReCover And Predict (RCAP), which considers the Localization and Visual Noise Level objectives of the explanations. We propose two propositions for these two objectives and prove the necessity of evaluating them. We evaluate Guided AbsoluteGrad with seven gradient-based XAI methods using the RCAP metric and other SOTA metrics in three case studies: (1) ImageNet dataset with ResNet50 model; (2) International Skin Imaging Collaboration (ISIC) dataset with EfficientNet model; (3) the Places365 dataset with DenseNet161 model. Our method surpasses other gradient-based approaches, showcasing the quality of enhanced saliency map explanations through gradient magnitude., Comment: CAI2024 Camera-ready Submission

We present a multimode theory of squeezed state generation in resonant systems valid for arbitrary pump power and including pump depletion. The Hamiltonian is written in terms of asymptotic-in and -out fields from scattering theory, capable of describing a general interaction. As an example we consider the lossy generation of a highly squeezed state by an effective second-order interaction in a silicon nitride ring resonator point-coupled to a waveguide. We calculate the photon number, Schmidt number, and the second-order correlation function of the generated state in the waveguide. The treatment we present provides a path forward to study the deterministic generation of non-Gaussian states in resonant systems., Comment: 14 pages, 4 figures

For the first time, the fatigue behavior involving external oxygen in highly Sc-doped AlN ferroelectric film was observed using transmission electron microscope techniques. Despite increasing the Sc composition in AlScN film contributes to reducing the device operation voltage, the inherent affinity of Sc for oxygen introduces instability in device performance. In this study, oxygen incorporation at top electrode edges and grain boundaries accompanied with an increase in current leakage and the disappearance of ferroelectric properties, was observed in nanoscale after long-term field cycling. This observation indicates the emergence of non-ferroelectric and even amorphous states. This presented work revealed solid experimental evidence of an oxygen-involved fatigue mechanism, providing valuable insights into the physical nature of the ferroelectric properties of AlScN films., Comment: 2 Pages,7 figures

We exploit holographic duality to study the system of a one-dimensional interface contacting two semi-infinite two-dimensional CFTs. Central to our investigation is the introduction of a dynamical scalar field located on the bulk interface brane which breaks the scaling symmetry of the dual interface field theory, along with its consequential backreaction on the system. We define an interface entropy from holographic entanglement entropy. At zero temperature we construct several illustrative examples and observe that the $g$-theorem is always satisfied. These examples also reveal distinct features of the interface entropy that are intricately linked to the scalar potential profiles. At finite temperature we find that the dynamical scalar field enables the bulk theory to have new configurations which would be infeasible solely with a tension term on the interface brane., Comment: 64 pages, many figures; v2: minor improvements, references added

Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards. The development of NGSE methods has raised an important question: how accurately can QI methods be ranked without ground truth. To answer this question, we propose a Cramer-Rao bound (CRB)-based framework that quantifies the upper bound in ranking QI methods without any ground truth. We present the application of this framework in guiding the use of a well-known NGSE technique, namely the regression-without-truth (RWT) technique. Our results show the utility of this framework in quantifying the performance of this NGSE technique for different patient numbers. These results provide motivation towards studying other applications of this upper bound.

In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability., Comment: Accepted at the ICSE'24 conference, NIER track

To thoroughly assess the mathematical reasoning abilities of Large Language Models (LLMs), we need to carefully curate evaluation datasets covering diverse mathematical concepts and mathematical problems at different difficulty levels. In pursuit of this objective, we propose FineMath in this paper, a fine-grained mathematical evaluation benchmark dataset for assessing Chinese LLMs. FineMath is created to cover the major key mathematical concepts taught in elementary school math, which are further divided into 17 categories of math word problems, enabling in-depth analysis of mathematical reasoning abilities of LLMs. All the 17 categories of math word problems are manually annotated with their difficulty levels according to the number of reasoning steps required to solve these problems. We conduct extensive experiments on a wide range of LLMs on FineMath and find that there is still considerable room for improvements in terms of mathematical reasoning capability of Chinese LLMs. We also carry out an in-depth analysis on the evaluation process and methods that have been overlooked previously. These two factors significantly influence the model results and our understanding of their mathematical reasoning capabilities. The dataset will be publicly available soon.

We report the observation of both resonant quantum tunneling of magnetization (RQTM) and resonant quantum magnetodielectric (RQMD) effect in the perovskite multiferroic metal-organic framework [CH3NH3]Co(HCOO)3. An intrinsic magnetic phase separation emerges at low temperatures due to hydrogen-bond-modified long range super-exchange interaction, leading to the coexistence of canted antiferromagnetic order and single-ion magnet. Subsequently, a stair-shaped magnetic hysteresis loop along the [101] direction characterizing the RQTM appears below the magnetic blocking temperature. More interestingly, the magnetic field dependence of dielectric permittivity exhibits pronounced negative peaks at the critical fields corresponding to the RQTM, a phenomenon termed the RQMD effect which enables electrical detection of the RQTM. These intriguing properties make the multiferroic metal-organic framework a promising candidate for solid-state quantum computing., Comment: 13 pages, 4 figures

In the framework of the theory of general relativity, in order to obtain stable traversable wormholes, matter needs to violate the null energy condition. It is well known that the violation of the energy condition (EC) of matter leads to various physical problems. To address this issue, researchers have turned their attention to exploring modified theories of gravity, aiming to avoid the violation of ECs by introducing geometric terms. In this paper, within the framework of the $f(Q)$ modified gravitational theory, we investigate the effectiveness of ECs for matter in traversable wormholes. We examine the compliance of four types of energy conditions (weak energy condition, null energy condition, dominant energy condition, and strong energy condition) in the model by selecting a power-law model for $f(Q)$ and considering different shape functions $b(r)$. Our study reveals that for traversable wormholes realized through the $f(Q)$ modified gravity theory using the power-law model $f(Q)=a(-Q)^n$, all four types of ECs for matter can be satisfied. There is no need to introduce exotic matter (violating the null energy condition) or special matter (violating other energy conditions) artificially in the physics of wormholes., Comment: 18 pages, 5 figures

Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relies on multi-instance learning and pseudo-labeling. This paper introduces WeakSAM and solves the weakly-supervised object detection (WSOD) and segmentation by utilizing the pre-learned world knowledge contained in a vision foundation model, i.e., the Segment Anything Model (SAM). WeakSAM addresses two critical limitations in traditional WSOD retraining, i.e., pseudo ground truth (PGT) incompleteness and noisy PGT instances, through adaptive PGT generation and Region of Interest (RoI) drop regularization. It also addresses the SAM's problems of requiring prompts and category unawareness for automatic object detection and segmentation. Our results indicate that WeakSAM significantly surpasses previous state-of-the-art methods in WSOD and WSIS benchmarks with large margins, i.e. average improvements of 7.4% and 8.5%, respectively. The code is available at \url{https://github.com/hustvl/WeakSAM}., Comment: Accepted by ACM MM 2024. Code is available at https://github.com/hustvl/WeakSAM

We study the electronic orders at the lower van Hove filling in the kagome lattice. In the weak limit of the Hubbard interaction $U$ versus the hopping parameter $t$, we find that the system develops itinerant ferromagnetism; In the intermediate range of $U$, we find the system develops noncollinear magnetic order with orthogonal spin moments on nearest-neighbor bonds. This is in fact a Chern insulator supporting quantized anomalous Hall conductance; In the strong $U$ limit, we map the Hubbard model to the $t$-$J$ model with $J = 4t^2/U$. For moderate values of $J$ we recover the noncollinear magnetic order obtained in the Hubbard model. However, in the limit of $J\to 0$ (or $U\to \infty$) we find the ferromagnetic order revives. The results are obtained by combination of the random-phase approximation and functional renormalization group in the weak to moderate limit of $U$, and the variational quantum Monte Carlo for the $t$-$J$ model in the strong coupling limit. The phase diagram is distinctly different to that at the higher van Hove filling studied earlier, and the difference can be attributed to the lack of particle-hole symmetry in the band structure with respect to the Dirac point.

The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due to semantic gaps between Music Information Retrieval (MIR) algorithms and human understanding, discrepancies between professionals and the public, and low precision of annotations, existing music description datasets cannot serve as benchmarks. To this end, we present MuChin, the first open-source music description benchmark in Chinese colloquial language, designed to evaluate the performance of multimodal LLMs in understanding and describing music. We established the Caichong Music Annotation Platform (CaiMAP) that employs an innovative multi-person, multi-stage assurance method, and recruited both amateurs and professionals to ensure the precision of annotations and alignment with popular semantics. Utilizing this method, we built a dataset with multi-dimensional, high-precision music annotations, the Caichong Music Dataset (CaiMD), and carefully selected 1,000 high-quality entries to serve as the test set for MuChin. Based on MuChin, we analyzed the discrepancies between professionals and amateurs in terms of music description, and empirically demonstrated the effectiveness of annotated data for fine-tuning LLMs. Ultimately, we employed MuChin to evaluate existing music understanding models on their ability to provide colloquial descriptions of music. All data related to the benchmark, along with the scoring code and detailed appendices, have been open-sourced (https://github.com/CarlWangChina/MuChin/)., Comment: Accepted by International Joint Conference on Artificial Intelligence 2024 (IJCAI 2024)

Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications. However, when handling tasks involving repetitive sub-tasks and/or deceptive contents, such as arithmetic calculation and article-level fake news detection, simple instructional prompts suffer from inaccurate responses. Existing works show that more complicated prompting strategies, such as Chain-of-Thoughts and Least-to-Most, can unlock LLM's powerful capacity in diverse areas. Recent researches reveal that simple divide-and-conquer prompting strategy, i.e. simply dividing the input sequence to multiple sub-inputs, can also substantially improve LLM's performance in some specific tasks such as misinformation detection. In this paper, we aim at examining the utility of divide-and-conquer prompting strategy and answer on which kind of tasks this strategy gets advantages. Specifically, we provide a theoretic analysis to divide-and-conquer prompting strategy and help us identify the specific tasks where DaC prompting can bring performance boost with theoretic guarantee. We then present two cases (large integer arithmetic and fact verification) where experimental results aligns with our theoretic analysis., Comment: Preprint

We systematically investigate the mass spectrum and two-body open-charm strong decays of charmonium states in a coupled-channel model where the $^3P_0$ quark-antiquark pair creation mechanism is employed. The results of masses, mass shifts, proportions of the $c\bar{c}$ component, and open-charm decay widths are provided. The $S$-$D$ wave mixing angles and di-electric decay widths for vector mesons are also presented. Based on our results, we find that the $\psi(3770)$, $\psi(4040)$, $\psi(4160)$, $\psi(4360)$, and $\psi(4415)$ can be assigned as the $1^3D_1$-, $3^3S_1$-, $2^3D_1$-, $4^3S_1$-, and $3^3D_1$-dominated charmonium states, respectively. The $\psi_3(3842)$ is a good candidate of the $\psi_3(1D)$ charmonium state. The calculated mass and strong decay width of $\chi_{c1}(2P)$ with significant continuum contribution ($\sim$57\%) favor the charmonium interpretation for the mysterious $\chi_{c1}(3872)$. When considering the large uncertainty in the observed decay width, the possibility to assign the $\chi_{c0}(3860)$ as the $\chi_{c0}(2P)$ charmonium state cannot be ruled out. One may describe well the properties of $\chi_{c2}(3930)$ with the $\chi_{c2}(2P)$ charmonium. The predictions on properties of other $c\bar{c}$ states can be tested by future experiments., Comment: 19 pages,1 figure, 12 tables

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models. Although VFL enables collaborative machine learning without sharing raw data, it is still susceptible to various privacy threats. In this paper, we conduct the first comprehensive survey of the state-of-the-art in privacy attacks and defenses in VFL. We provide taxonomies for both attacks and defenses, based on their characterizations, and discuss open challenges and future research directions. Specifically, our discussion is structured around the model's life cycle, by delving into the privacy threats encountered during different stages of machine learning and their corresponding countermeasures. This survey not only serves as a resource for the research community but also offers clear guidance and actionable insights for practitioners to safeguard data privacy throughout the model's life cycle.

The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks. Our dataset APPS+ and StepCoder are available online., Comment: 13 pages, 5 figures

In this paper, we explore the multiplayer General Lotto Blotto game over a single battlefield, a notable variant of the Colonel Blotto game. In this version, each player employs a probability distribution for resource allocation, ensuring that the expected expenditure does not surpass their budget. We first establish the existence of a Nash equilibrium for a modified version of this game, in which there is a common threshold that no player's bid can exceed. We next extend our findings to demonstrate the existence of a Nash equilibrium in the original game, which does not incorporate this threshold. Moreover, we provide detailed characterizations of the Nash equilibrium for both the original game and its modified version. In the Nash equilibrium of the unmodified game, we observe that the upper endpoints of the supports of players' equilibrium strategies coincide, and the minimum value of a player's support above zero inversely correlates with their budget. Specifically, we present closed-form solutions for the Nash equilibrium with threshold for two players.

The opacity of AI models necessitates both validation and evaluation before their integration into services. To investigate these models, explainable AI (XAI) employs methods that elucidate the relationship between input features and output predictions. The operations of XAI extend beyond the execution of a single algorithm, involving a series of activities that include preprocessing data, adjusting XAI to align with model parameters, invoking the model to generate predictions, and summarizing the XAI results. Adversarial attacks are well-known threats that aim to mislead AI models. The assessment complexity, especially for XAI, increases when open-source AI models are subject to adversarial attacks, due to various combinations. To automate the numerous entities and tasks involved in XAI-based assessments, we propose a cloud-based service framework that encapsulates computing components as microservices and organizes assessment tasks into pipelines. The current XAI tools are not inherently service-oriented. This framework also integrates open XAI tool libraries as part of the pipeline composition. We demonstrate the application of XAI services for assessing five quality attributes of AI models: (1) computational cost, (2) performance, (3) robustness, (4) explanation deviation, and (5) explanation resilience across computer vision and tabular cases. The service framework generates aggregated analysis that showcases the quality attributes for more than a hundred combination scenarios., Comment: Accepted by IEEE International Conference on Software Services Engineering (SSE) 2024

We report a measurement of the radiative lifetime of the $5s5p \; {}^{\rm{3}}P^{\rm{o}}_{\rm{0}}$ metastable state in ${}^{87}$Sr, which is coupled to the 5$s^{\rm{2}} \;$ ${}^{\rm{1}}S_{\rm{0}}$ ground state via a hyperfine-induced electric dipole transition. The radiative lifetime is determined to be 151.4(48) s, in good agreement with theoretical results. Our approach relies on accurate measurements of laser intensity and free-space Rabi frequency, enabling lifetime measurements of any excited state and particularly suitable for long-lived states.

Propagating chiral magnetic waves (CMW) are expected to exist in chiral plasmas due to the interplay between the chiral magnetic and chiral separation effects induced by the presence of a chiral anomaly. Unfortunately, it was pointed out that, because of the effects of electric conductivity and dissipation, CMW are overdamped and therefore their signatures are unlikely to be seen in heavy-ion collision experiments and in the quark gluon plasma. Nonetheless, the chiral anomaly plays a fundamental role in Weyl semimetals and their anomalous transport properties as well. Hence, CMW could be potentially observed in topological semimetals using table-top experiments. By using a holographic model for strongly coupled Weyl semimetals, we investigate in detail the nature of CMW in presence of Coulomb interactions and axial charge relaxation and estimate whether, and in which regimes, CMW could be observed as underdamped collective excitations in topological materials., Comment: 39 pages, 11 figures, published version

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization.