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Large Language Models (LLMs) demonstrate exceptional capabilities in various scenarios. However, they suffer from much redundant information and tend to be lost in the middle in long context scenarios, leading to inferior performance. To address these challenges, we present Perception Compressor, a training-free prompt compression method. It includes a dual-slope ratio allocator to dynamically assign compression ratios and open-book ratios, a perception retriever that leverages guiding questions and instruction to retrieve the most relevant demonstrations, and a semi-guided iterative compression that retains key information at the token level while removing tokens that distract the LLM. We conduct extensive experiments on long context benchmarks, i.e., NaturalQuestions, LongBench, and MuSiQue. Experiment results show that Perception Compressor outperforms existing methods by a large margin, achieving state-of-the-art performance., Comment: 9 pages, 2 figures

In this paper, we calculate the semileptonic decay $D\to K_0^\ast(1430)\ell\nu_{\ell}$ with $\ell=(e,\mu)$ induced by $c\to s\ell\nu_{\ell}$ transition. For the key component, $D\to K_0^\ast(1430)$ transition form factors (TFFs) $f_{\pm}(q^2)$ are calculated within the framework of QCD light-cone sum rule. Then, we consider two scenarios for $K_0^\ast(1430)$-meson twist-2 distribution amplitude. For the scenario 1 (S1), we take the truncated form based on Gegenbauer polynomial series. Meanwhile, we also consider the scenario 2 (S2) constructed by light-cone harmonic oscillator model, where the model parameters are fixed by the $K_0^\ast(1430)$-meson twist-2 distribution amplitude $10$th-order $\xi$-moments calculated by using the background field theory. For the TFFs at large recoil point, we have $f_+^{\rm (S1)}(0)=0.597^{+0.122}_{-0.121}$ and $f_-^{\rm (S1)}(0)=-0.136^{+0.023}_{-0.035}$, $f_+^{\rm (S2)}(0)= 0.663^{+0.135}_{-0.134}$ and $f_-^{\rm (S2)}(0)=-0.202^{+0.026}_{-0.046}$. After extrapolating TFFs to the whole physical $q^2$-region, we calculate the branching fractions of $D^0\to K_0^{\ast +}(1430)\ell^-\bar\nu_\ell$ and $D^+\to K_0^{\ast 0}(1430)\ell^+\nu_\ell$ which at $10^{-4}$-order level for the S1 and S2 cases. Meanwhile, we predict the CKM matrix $|V_{cs}|^{\rm (S1)}=0.997^{+0.258}_{-0.172}, |V_{cs}|^{\rm (S2)}=0.903^{+0.233}_{-0.155}$, and leptonic flavour universality $\mathcal{R}^{\rm (S1)}_{K_0^*}=0.768^{+0.560}_{-0.368}, \mathcal{R}_{K_0^*}^{\rm (S2)}=0.764^{+0.555}_{-0.365}$. Finally, we discuss the angular observables of forward-backward asymmetries, lepton polarization asymmetries and $q^2$-differential flat terms for this decay., Comment: 10 pages, 5 figures, comments welcome

A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications., Comment: 40 pages, 8 figures

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, \ie, even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at \url{https://github.com/CGCL-codes/UnlearnablePC}, Comment: NeurIPS 2024

Recent advancements in Code Large Language Models (CodeLLMs) have predominantly focused on open-ended code generation tasks, often neglecting the critical aspect of code understanding and comprehension. To bridge this gap, we present CodeMMLU, a comprehensive multiple-choice question-answer benchmark designed to evaluate the depth of software and code understanding in LLMs. CodeMMLU includes over 10,000 questions sourced from diverse domains, encompassing tasks such as code analysis, defect detection, and software engineering principles across multiple programming languages. Unlike traditional benchmarks, CodeMMLU assesses models's ability to reason about code rather than merely generate it, providing deeper insights into their grasp of complex software concepts and systems. Our extensive evaluation reveals that even state-of-the-art models face significant challenges with CodeMMLU, highlighting deficiencies in comprehension beyond code generation. By underscoring the crucial relationship between code understanding and effective generation, CodeMMLU serves as a vital resource for advancing AI-assisted software development, ultimately aiming to create more reliable and capable coding assistants.

Strong spin-orbit coupling and relatively weak hyperfine interactions make germanium hole spin qubits a promising candidate for semiconductor quantum processors. The two-dimensional hole gas structure of strained Ge quantum wells serves as the primary material platform for spin hole qubits.A low disorder material environment is essential for this process. In this work, we fabricated a Ge/SiGe heterojunction with a 60 nm buried quantum well layer on a Si substrate using reduced pressure chemical vapor deposition technology. At a temperature of 16 mK, when the carrier density is 1.87*10^11/cm2, we obtained a mobility as high as 308.64*10^4cm2/Vs. Concurrently, double quantum dot and planar germanium coupling with microwave cavities were also successfully achieved.This fully demonstrates that this structure can be used for the preparation of higher-performance hole spin qubits.

Large Language Models (LLMs) have shown the impressive capability of answering questions in a wide range of scenarios. However, when LLMs face different types of questions, it is worth exploring whether LLMs are aware that some questions have limited answers and need to respond more deterministically but some do not. We refer to this as question awareness of LLMs. The lack of question awareness in LLMs leads to two phenomena that LLMs are: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions. In this paper, we first evaluate the question awareness in LLMs. The experimental results show that LLMs have the issues of lacking awareness of questions in certain domains, e.g. factual knowledge, resulting in hallucinations during the generation. To mitigate these, we propose a method called Question Awareness Temperature Sampling (QuATS). This method enhances the question awareness of LLMs by adaptively adjusting the output distributions based on question features. The automatic adjustment in QuATS eliminates the need for manual temperature tuning in text generation and consistently improves model performance in various benchmarks., Comment: Accepted by EMNLP 2024

In this work, we extend the QCD axion mass mixing in the early Universe and investigate the temperature effects in the mixing. We explore the scenario where two $Z_{\mathcal N}$ QCD axions undergo mass mixing during the QCD phase transition, yielding three distinct mixing scenarios: mixing I, II, and III. These scenarios are realized through fine-tuning of the axion decay constants, the temperature parameters, as well as the value of $\mathcal N$. We conduct a thorough analysis of the level crossing phenomena in these three mixing scenarios, detailing the conditions under which they occur. Notably, in the mixing I and II, the level crossing precedes the critical temperature of the QCD phase transition ($T_{\rm QCD}$), with minimal non-essential discrepancies in the cosmological evolution of the mass eigenvalues at $T_{\rm QCD}$. In contrast, the mixing III exhibits a unique double level crossings, occurring both before and at $T_{\rm QCD}$. Despite superficial similarities in axion evolution between the mixing II and III, we uncover fundamental differences between them. Additionally, we briefly address the transition in energy density between the two axions within our mixing scenarios. This work contributes to a deeper understanding of the role of the QCD axion in the early Universe and its potential implications for dark matter., Comment: 16 pages, 7 figures

The image-based multimodal automatic speech recognition (ASR) model enhances speech recognition performance by incorporating audio-related image. However, some works suggest that introducing image information to model does not help improving ASR performance. In this paper, we propose a novel approach effectively utilizing audio-related image information and set up VHASR, a multimodal speech recognition system that uses vision as hotwords to strengthen the model's speech recognition capability. Our system utilizes a dual-stream architecture, which firstly transcribes the text on the two streams separately, and then combines the outputs. We evaluate the proposed model on four datasets: Flickr8k, ADE20k, COCO, and OpenImages. The experimental results show that VHASR can effectively utilize key information in images to enhance the model's speech recognition ability. Its performance not only surpasses unimodal ASR, but also achieves SOTA among existing image-based multimodal ASR., Comment: 14 pages, 6 figures, accepted by EMNLP 2024

Quantum emitters trapped near photonic crystal waveguides have recently emerged as an exciting platform for realizing novel quantum matter-light interfaces. Here we study tunable photon scattering in a photonic crystal waveguide coupled to a pair of two-level atoms with an arbitrary spatial separation. In the weak-excitation regime, we give the energy levels and their decay rates into the waveguide modes in the dressed basis, which both depend on the distance between the two atoms. We focus on the Bragg case and anti-Bragg case, where subradiant and superradiant states are produced and perfect transmission with a $\pi$ phase shift may occur on resonance. We observe quantum beats in the photon-photon correlation function of the reflected field in the anti-Bragg case. Moreover, the frequencies of quantum beats can be controlled due to the tunability of the bound states via the dispersion engineering of the structure. We also quantify the effects of the system imperfections, including the deviation in the distance between the two atoms and the asymmetry in the atomic decay rates into the waveguide modes. With recent experimental advances in the superconducting microwave transmission lines, our results should soon be realizable., Comment: 11 pages, 7 figures, Comments are welcome

On-body menus present a novel interaction paradigm within Virtual Reality (VR) environments by embedding virtual interfaces directly onto the user's body. Unlike traditional screen-based interfaces, on-body menus enable users to interact with virtual options or icons visually attached to their physical form. In this paper, We investigated the impact of the creation process on the effectiveness of on-body menus, comparing first-person, third-person, and mirror perspectives. Our first study ($N$ = 12) revealed that the mirror perspective led to faster creation times and more accurate recall compared to the other two perspectives. To further explore user preferences, we conducted a second study ($N$ = 18) utilizing a VR system with integrated body tracking. By combining distributions of icons from both studies ($N$ = 30), we confirmed significant preferences in on-body menu placement based on icon category (e.g., Social Media icons were consistently placed on forearms). We also discovered associations between categories, such as Leisure and Social Media icons frequently co-occurring. Our findings highlight the importance of the creation process, uncover user preferences for on-body menu organization, and provide insights to guide the development of intuitive and effective on-body interactions within virtual environments., Comment: 19 pages. PACM HCI: ISS (ACM ISS 2024)

Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by setting up a Retrieval-Augmented Generation (RAG) system, helping models quickly grasp domain knowledge or question-answering patterns without changing model parameters. However, this approach involves trade-offs, such as slower inference speed and increased space occupancy. PEFT assists the model in adapting to tasks through minimal parameter modifications, but the training process still demands high hardware requirements, even with a small number of parameters involved. To address these challenges, we propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning, maintaining low inference costs. RTD constructs a reference datastore from the provided training examples and optimizes the LLM's final vocabulary distribution by flexibly selecting suitable references based on the input, resulting in more trustable responses and enabling the model to adapt to downstream tasks at a low cost. Experimental evaluations on various LLMs using different benchmarks demonstrate that RTD establishes a new paradigm for augmenting models to downstream tasks. Furthermore, our method exhibits strong orthogonality with traditional methods, allowing for concurrent usage.

Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled data while circumventing catastrophic forgetting and preserving prior knowledge from pre-trained backbones. In this work, we introduce a novel method that leverages often-discarded language model heads. By employing these components via a mutual information maximization strategy, our approach helps maintain prior knowledge from the pre-trained backbone and strategically aligns the primary classification head, thereby enhancing model performance. Furthermore, we explore the potential of Large Language Models (LLMs), renowned for their wealth of knowledge, in addressing FCRE challenges. Our comprehensive experimental results underscore the efficacy of the proposed method and offer valuable insights for future work., Comment: Accepted to EMNLP 2024

The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the farthest reaches of the Universe to the lightest white dwarfs in our cosmic backyard, generate a complex and dynamic symphony of gravitational wave signals. Once recorded by gravitational wave detectors, these unique fingerprints have the potential to decipher the birth and growth of cosmic structures over a wide range of scales, from stellar binaries and stellar clusters to galaxies and large-scale structures. The TianQin space-borne gravitational wave mission is scheduled for launch in the 2030s, with an operational lifespan of five years. It will facilitate pivotal insights into the history of our universe. This document presents a concise overview of the detectable sources of TianQin, outlining their characteristics, the challenges they present, and the expected impact of the TianQin observatory on our understanding of them., Comment: TianQin Gravitational Wave Whitepaper, 72 pages, 30 figures

This work is a continuation of the previous one in [{\it Optimization} (2023)], where the existence of optimal solutions and first-order necessary optimality conditions in both Pontryagin's maximum principle form and the variational form were proved for a distributed optimal control problem governed by the three-dimensional viscous Camassa-Holm equations in bounded domains with the cost functional of a quite general form and pointwise control constraints. We will establish the second-order sufficient optimality conditions as well as the Lipschitz stability results of the control system with respect to perturbations of the initial data.

The GD-1 stellar stream exhibits spur and gap structures that may result from a close encounter with a dense substructure. When interpreted as a dark matter subhalo, the perturber is denser than predicted in the standard cold dark matter (CDM) model. In self-interacting dark matter (SIDM), however, a halo could evolve into a phase of gravothermal collapse, resulting in a higher central density than its CDM counterpart. We conduct high-resolution controlled N-body simulations to show that a collapsed SIDM halo could account for the GD-1 perturber's high density. We model a progenitor halo with a mass of $3\times10^8~M_\odot$, motivated by a cosmological simulation of a Milky Way analog, and evolve it in the Milky Way's tidal field. For a cross section per mass of $\sigma/m\approx30-100~{\rm cm^2~g^{-1}}$ at $V_{\rm max }\sim10~{\rm km~s^{-1}}$, the enclosed mass of the SIDM halo within the inner $10~{\rm pc}$ can be increased by more than an order of magnitude compared to its CDM counterpart, leading to a good agreement with the properties of the GD-1 perturber. Our findings indicate that stellar streams provide a novel probe into the self-interacting nature of dark matter., Comment: 10 pages, 4 figures

Large Language Models (LLMs) have shown remarkable capabilities, but their inherent probabilistic nature often leads to inconsistency and inaccuracy in complex problem-solving tasks. This paper introduces DANA (Domain-Aware Neurosymbolic Agent), an architecture that addresses these issues by integrating domain-specific knowledge with neurosymbolic approaches. We begin by analyzing current AI architectures, including AutoGPT, LangChain ReAct and OpenAI's ChatGPT, through a neurosymbolic lens, highlighting how their reliance on probabilistic inference contributes to inconsistent outputs. In response, DANA captures and applies domain expertise in both natural-language and symbolic forms, enabling more deterministic and reliable problem-solving behaviors. We implement a variant of DANA using Hierarchical Task Plans (HTPs) in the open-source OpenSSA framework. This implementation achieves over 90\% accuracy on the FinanceBench financial-analysis benchmark, significantly outperforming current LLM-based systems in both consistency and accuracy. Application of DANA in physical industries such as semiconductor shows that its flexible architecture for incorporating knowledge is effective in mitigating the probabilistic limitations of LLMs and has potential in tackling complex, real-world problems that require reliability and precision.

The challenge in reconfigurable manipulation of sound waves using metasurfaces lies in achieving precise control over acoustic behavior while developing efficient and practical tuning methods for structural configurations. However, most studies on reconfigurable acoustic metasurfaces rely on cumbersome and time-consuming control systems. These approaches often struggle with fabrication techniques, as conventional methods face limitations such as restricted material choices, challenges in achieving complex geometries, and difficulties in incorporating flexible components. This paper proposes a novel approach for developing a reconfigurable metasurface inspired by the Kresling origami pattern, designed for programmable manipulation of acoustic waves at an operating frequency of 2000 Hz. The origami unit cell is fabricated using multi-material 3D printing technology, allowing for the simultaneous printing of two materials with different mechanical properties, thus creating a bistable origami-based structure. Through optimization, two equilibrium states achieve a reflection phase difference of {\pi} through the application of small axial force, F, or torque, T. Various configurations of the metasurface, generated from different combinations of these two equilibria, enable distinct reflective behaviors with switchable and programmable functionalities. The principle of this work simplifies the shaping of acoustic waves through a straightforward mechanical mechanism, eliminating the need for complex control systems and time-consuming adjustments. This innovative approach paves a novel and effective perspective for developing on-demand switchable and tunable devices across diverse fields, including electromagnetics, mechanics, and elastics, leveraging multi-material printing technology.

Real-time accurate solutions of large-scale complex dynamical systems are critically needed for control, optimization, uncertainty quantification, and decision-making in practical engineering and science applications, particularly in digital twin contexts. In this work, we develop a model-constrained discontinuous Galerkin Network (DGNet) approach, an extension to our previous work [Model-constrained Tagent Slope Learning Approach for Dynamical Systems], for compressible Euler equations with out-of-distribution generalization. The core of DGNet is the synergy of several key strategies: (i) leveraging time integration schemes to capture temporal correlation and taking advantage of neural network speed for computation time reduction; (ii) employing a model-constrained approach to ensure the learned tangent slope satisfies governing equations; (iii) utilizing a GNN-inspired architecture where edges represent Riemann solver surrogate models and nodes represent volume integration correction surrogate models, enabling capturing discontinuity capacity, aliasing error reduction, and mesh discretization generalizability; (iv) implementing the input normalization technique that allows surrogate models to generalize across different initial conditions, boundary conditions, and solution orders; and (v) incorporating a data randomization technique that not only implicitly promotes agreement between surrogate models and true numerical models up to second-order derivatives, ensuring long-term stability and prediction capacity, but also serves as a data generation engine during training, leading to enhanced generalization on unseen data. To validate the effectiveness, stability, and generalizability of our novel DGNet approach, we present comprehensive numerical results for 1D and 2D compressible Euler equation problems.

Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5-meter telescope at Observatorio Sierra Nevada, the 2.5-meter Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical re-brightening around $10^3-10^4$ s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multi-wavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion-driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fall-back accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines., Comment: Resubmitted to ApJL after addressing the referee's comments; comments are welcome

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM., Comment: This paper has been accepted by the 38th Annual Conference on Neural Information Processing Systems (NeurIPS'24)

Measurements remain as an interesting topic of research since the formulation of quantum theory. Attempts to model quantum measurements by unitary processes are prone to various foundational issues. Here, it is proposed that measurement devices can be modelled to have an open decoherence dynamics that is faster than any other relevant timescale, which is referred to as the ultradecoherence limit. In this limit, it is shown that the clicking rate of measurement devices can be derived from its underlying parameters, not only for the von Neumann ideal measurement devices but also for photon detectors in equal footing. This study offers a glimpse into the intriguing physics of measurement processes in quantum mechanics, with many aspects open for further investigation., Comment: 7 pages, 0 figures

Axions, initially proposed to solve the strong CP problem, have recently gained attention in condensed matter physics, particularly in topological insulators. However, detecting axion dynamics has proven challenging, with no experimental confirmations to date. In this study, we identify the surface of magnetic topological insulators as an ideal platform for observing axion dynamics. The vanishing bulk gap at the surface allows for order $O(1)$ variations in the axion field, making the detection of axion-like phenomena more feasible. In contrast, these phenomena are strongly suppressed in the bulk due to the small magnetic exchange gap. We investigate two-photon decay as a signature of axion dynamics and calculate the branching ratio using a perturbative approach. Our findings reveal that the photon flux emitted from the surface is in-plane and orders of magnitude larger than that from the bulk, making it detectable with modern microwave technology. We also discuss potential material platforms for detecting axion two-photon decay and strategies to enhance the signal-to-noise ratio., Comment: 5+7 pages, 3+1 figures

Developing efficient traffic models is essential for optimizing transportation systems, yet current approaches remain time-intensive and susceptible to human errors due to their reliance on manual processes. Traditional workflows involve exhaustive literature reviews, formula optimization, and iterative testing, leading to inefficiencies in research. In response, we introduce the Traffic Research Agent (TR-Agent), an AI-driven system designed to autonomously develop and refine traffic models through an iterative, closed-loop process. Specifically, we divide the research pipeline into four key stages: idea generation, theory formulation, theory evaluation, and iterative optimization; and construct TR-Agent with four corresponding modules: Idea Generator, Code Generator, Evaluator, and Analyzer. Working in synergy, these modules retrieve knowledge from external resources, generate novel ideas, implement and debug models, and finally assess them on the evaluation datasets. Furthermore, the system continuously refines these models based on iterative feedback, enhancing research efficiency and model performance. Experimental results demonstrate that TR-Agent achieves significant performance improvements across multiple traffic models, including the Intelligent Driver Model (IDM) for car following, the MOBIL lane-changing model, and the Lighthill-Whitham-Richards (LWR) traffic flow model. Additionally, TR-Agent provides detailed explanations for its optimizations, allowing researchers to verify and build upon its improvements easily. This flexibility makes the framework a powerful tool for researchers in transportation and beyond. To further support research and collaboration, we have open-sourced both the code and data used in our experiments, facilitating broader access and enabling continued advancements in the field., Comment: 19 pages, 10 figures

We study the half-indices of $\mathcal{N}=4$ super Yang-Mills theories of orthogonal and symplectic gauge groups. We find precise matching pairs of the half-indices as strong evidence of dualities of the half-BPS boundary conditions and interfaces. The half-indices encode the spectra of excitations on the holographically dual orbifold ETW brane and giant gravitons. The exact form of the index for the orbifold ETW giant gravitons is obtained from the giant graviton expansion of the half-index., Comment: 52 pages, 8 figures

We analyze the clean cusp, seen in the $\eta \pi$ mass distribution with high precision of the $\chi_{c1} \to \eta \pi^+ \pi^-$ reaction in the BESIII experiment, with the aim of making a precise determination of the scattering length $a$ and effective range $r_0$ of $K^+ \bar{K}^{0}$. For that, we follow a previous theoretical work that gave a good reproduction of these data using the chiral unitary approach for the meson-meson interaction, and allow some flexibility in the input to carry a better fit to the data. The important task of determining the uncertainties in the scattering parameters is done using the resampling method and an accuracy in $a$ and $r_0$ is obtained better than $20\%$. The effective range is determined for the first time with this analysis., Comment: 7 pages, 4 figures, 3 tables; V2: New fit with background included

We report the discovery of three faint and ultra-faint dwarf galaxies -- Sculptor A, Sculptor B and Sculptor C -- in the direction of NGC 300 (D=2.0 Mpc), a Large Magellanic Cloud-mass galaxy. Deep ground-based imaging with Gemini/GMOS resolves all three dwarf galaxies into stars, each displaying a red giant branch indicative of an old, metal-poor stellar population. No young stars or HI gas are apparent, and the lack of a GALEX UV detection suggests that all three systems are quenched. Sculptor C (D=2.04$^{+0.10}_{-0.13}$ Mpc; $M_V$=$-$9.1$\pm$0.1 mag or $L_V$=(3.7$^{+0.4}_{-0.3}$)$\times$10$^5$ $L_{\odot}$) is consistent with being a satellite of NGC 300. Sculptor A (D=1.35$^{+0.22}_{-0.08}$ Mpc; $M_V$=$-$6.9$\pm$0.3 mag or $L_V$=(5$^{+1}_{-1}$)$\times$10$^4$ $L_{\odot}$) is likely in the foreground of NGC 300 and at the extreme edge of the Local Group, analogous to the recently discovered ultra-faint Tucana B in terms of its physical properties and environment. Sculptor B (D=2.48$^{+0.21}_{-0.24}$ Mpc; $M_V$=$-$8.1$\pm$0.3 mag or $L_V$=(1.5$^{+0.5}_{-0.4}$)$\times$10$^5$ $L_{\odot}$) is likely in the background, but future distance measurements are necessary to solidify this statement. It is also of interest due to its quiescent state and low stellar mass. Both Sculptor A and B are $\gtrsim$2-4 $r_{vir}$ from NGC 300 itself. The discovery of three dwarf galaxies in isolated or low-density environments offers an opportunity to study the varying effects of ram pressure stripping, reionization and internal feedback in influencing the star formation history of the faintest stellar systems., Comment: 15 pages, 5 figures. ApJ Letters submitted

The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased complexity and difficulty in the operation and control of power systems. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) has been widely implemented in recent years to handle the uncertainty in power systems. However, in critical infrastructures such as power systems, safety issues always receive top priority, while DRL may not always meet the safety requirements of power system operators. The concept of safe reinforcement learning (safe RL) is emerging as a potential solution to overcome the shortcomings of conventional DRL in the operation and control of power systems. This study provides a rigorous review of the latest research efforts focused on safe RL to derive power system control policies while accounting for the unique safety requirements of power grids. Furthermore, this study highlights various safe RL algorithms applied in diverse applications within the power system sector, from single grid-connected power converters, residential smart homes, and buildings to large power distribution networks. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in the operation and control of power system applications is also presented. This review aims to support research in the area of safe RL algorithms, embracing smart power system operation with safety constraints amid high uncertainty from DERs., Comment: 16 pages, 7 figures, 9 tables

Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges within ITS. In this paper, we present a comprehensive survey of diffusion models for ITS, covering both theoretical and practical aspects. First, we introduce the theoretical foundations of diffusion models and their key variants, including conditional diffusion models and latent diffusion models, highlighting their suitability for modeling complex, multi-modal traffic data and enabling controllable generation. Second, we outline the primary challenges in ITS and the corresponding advantages of diffusion models, providing readers with a deeper understanding of the intersection between ITS and diffusion models. Third, we offer a multi-perspective investigation of current applications of diffusion models in ITS domains, including autonomous driving, traffic simulation, trajectory prediction, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key ITS research directions that warrant further investigation. Through this structured overview, we aim to provide researchers with a comprehensive understanding of diffusion models for ITS, thereby advancing their future applications in the transportation domain., Comment: 7 figures

In Retrieval-Augmented Generation (RAG) tasks using Large Language Models (LLMs), the quality of retrieved information is critical to the final output. This paper introduces the IRSC benchmark for evaluating the performance of embedding models in multilingual RAG tasks. The benchmark encompasses five retrieval tasks: query retrieval, title retrieval, part-of-paragraph retrieval, keyword retrieval, and summary retrieval. Our research addresses the current lack of comprehensive testing and effective comparison methods for embedding models in RAG scenarios. We introduced new metrics: the Similarity of Semantic Comprehension Index (SSCI) and the Retrieval Capability Contest Index (RCCI), and evaluated models such as Snowflake-Arctic, BGE, GTE, and M3E. Our contributions include: 1) the IRSC benchmark, 2) the SSCI and RCCI metrics, and 3) insights into the cross-lingual limitations of embedding models. The IRSC benchmark aims to enhance the understanding and development of accurate retrieval systems in RAG tasks. All code and datasets are available at: https://github.com/Jasaxion/IRSC_Benchmark

In this study, we present a detailed investigation of kagome superconductors CsV3Sb5 single crystal and its Ta-doped variant, Cs(V0.86Ta0.14)3Sb5, through specific heat measurements. Our results show a clear suppression of the charge density wave (CDW) and notable increase in the superconducting transition temperature (Tc) from 2.8 K to 4.6 K upon Ta doping. The electronic specific heat of the pristine CsV3Sb5 sample can be fitted with a model comprising an s-wave gap and a highly anisotropic extended s-wave gap, where the ratio 2{\Delta}/k_B T_c is smaller than the weak coupling limit of 3.5. For the doped sample Cs(V0.86Ta0.14)3Sb5, it exhibits two isotropic s-wave gaps, yielding the larger gap of 2{\Delta}/k_B T_c=5.04, which indicates a significant enhancement in superconducting coupling. This evolution is attributed to the increased density of states (DOS) near the Fermi level released through the suppression of the CDW gap. Our results demonstrate enhanced superconducting coupling and variation of gap structure in CsV3Sb5 due to Ta doping.

Nonadiabatic holonomic quantum computation (NHQC) is one of the promising approaches to realizing fault-tolerant quantum computation. However, due to the imperfect control in the experimental environments, the holonomic gate still needs to be further improved. Here, we propose a dynamically optimized NHQC (OPNHQC) scheme based on dynamically corrected gate technique. The scheme is implemented by carefully designing a sequence of elementary pulses to fulfill cyclic evolution, while the dynamical phase is not accumulated. In this way, the constructed holonomic gate is immune to the error. It is found that our scheme can correct the $X$ error up to fourth order. In addition, combining with the DFS encoding our scheme can be immune to both the $X$ and $Z$ errors. Therefore, our proposed scheme offers a prospective way to the realization of scalable fault-tolerant holonomic quantum computation., Comment: 9 pages, 7 figures

Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the Noisy Intermediate-Scale Quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era., Comment: This vision paper is crafted to be accessible to a broad audience. We welcome any questions, feedback, or suggestions. Please feel free to reach out to us

The operation and management of the metro system in urban areas rely on accurate predictions of future passenger flow. While using all the available information can potentially improve on the accuracy of the flow prediction, there has been little attention to the hierarchical relationship between the type of tickets collected from the passengers entering/exiting a station and its resulting passenger flow. To this end, we propose a novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN). The proposed framework consists of three components: initial prediction, task judgment and hierarchical coordination modules. Using the Wuxi, China metro network as an example, we study two prediction approaches (i) traditional prediction approach where the model directly predicts passenger flow at the station, and (ii) hierarchical prediction approach where the prediction of ticket type and station passenger flow are performed simultaneously considering the hierarchical constraints (i.e., the sum of predicted passenger flow per ticket type equals the predicted station aggregated passenger flow). Experimental results indicate that in the traditional prediction approach, our IPF-HMGNN can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model by 49.56% and 53.88%, respectively. In the hierarchical prediction approach, IPF-HMGNN can achieve a maximum reduction of 35.32% in MAE and 36.18% in RMSE, while satisfying the hierarchical constraint.

Learned Image Compression (LIC) models have achieved superior rate-distortion performance than traditional codecs. Existing LIC models use CNN, Transformer, or Mixed CNN-Transformer as basic blocks. However, limited by the shifted window attention, Swin-Transformer-based LIC exhibits a restricted growth of receptive fields, affecting the ability to model large objects in the image. To address this issue, we incorporate window partition into channel attention for the first time to obtain large receptive fields and capture more global information. Since channel attention hinders local information learning, it is important to extend existing attention mechanisms in Transformer codecs to the space-channel attention to establish multiple receptive fields, being able to capture global correlations with large receptive fields while maintaining detailed characterization of local correlations with small receptive fields. We also incorporate the discrete wavelet transform into our Spatial-Channel Hybrid (SCH) framework for efficient frequency-dependent down-sampling and further enlarging receptive fields. Experiment results demonstrate that our method achieves state-of-the-art performances, reducing BD-rate by 18.54%, 23.98%, 22.33%, and 24.71% on four standard datasets compared to VTM-23.1., Comment: ACCV2024 accepted; reviewed version

Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Project page: \href{https://zju3dv.github.io/splatloc}{https://zju3dv.github.io/splatloc}.

The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based robotic semantic communications (SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevant knowledge paths (KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot's observations for objects recognition and feasible KP identification. Next, to support ultra-low-latency feature transmission (ULL-FT), we propose a novel transmission approach that exploits classifier's robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture model, we derive the relation between BEP and classification margin, which sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using DNNs as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.

In this paper, we mainly consider the arithmetic properties of the cyclotomic matrix $B_p(k)=\left[J_p(\chi^{ki},\chi^{kj})^{-1}\right]_{1\le i,j\le (p-1-k)/k}$, where $p$ is an odd prime, $1\le k

One of the most celebrated accomplishments of modern physics is the description of fundamental principles of nature in the language of geometry. As the motion of celestial bodies is governed by the geometry of spacetime, the motion of electrons in condensed matter can be characterized by the geometry of the Hilbert space of their wave functions. Such quantum geometry, comprising of Berry curvature and quantum metric, can thus exert profound influences on various properties of materials. The dipoles of both Berry curvature and quantum metric produce nonlinear transport. The quantum metric plays an important role in flat-band superconductors by enhancing the transition temperature. The uniformly distributed momentum-space quantum geometry stabilizes the fractional Chern insulators and results in the fractional quantum anomalous Hall effect. We here review in detail quantum geometry in condensed matter, paying close attention to its effects on nonlinear transport, superconductivity, and topological properties. Possible future research directions in this field are also envisaged., Comment: Review article accepted by National Science Review, unproofread; 17 pages, 8 figures, 1 table

Relic galaxies, the oldest ultra-compact massive galaxies (UCMGs), contain almost exclusively "pristine" stars formed during an intense star formation (SF) burst at high redshift. As such, they allow us to study in detail the early mechanism of galaxy assembly in the Universe. Using the largest catalogue of spectroscopically confirmed UCMGs for which a degree of relicness (DoR) had been estimated, the INSPIRE catalogue, we investigate whether or not relics prefer dense environments. The objective of this study is to determine if the DoR, which measures how extreme the SF history was, and the surrounding environment are correlated. In order to achieve this goal, we employ the AMICO galaxy cluster catalogue to compute the probability for a galaxy to be a member of a cluster, and measure the local density around each UCMG using machine learning-based photometric redshifts. We find that UCMGs can reside both in clusters and in the field, but objects with very low DoR (< 0.3, i.e., a relatively extended SF history) prefer under-dense environments. We additionally report a correlation between the DoR and the distance from the cluster centre: more extreme relics, when located in clusters, tend to occupy the more central regions of them. We finally outline potential evolution scenarios for UCMGs at different DoR to reconcile their presence in both clusters and field environments, Comment: Accepted for publication on MNRAS, 11 pages, 8 figure, 1 table

The strong electron-electron interaction in transition metal dichalcogenides (TMDs) gives rise to phenomena such as strong exciton and trion binding and excitonic condensation, as well as large negative exchange and correlation contributions to the electron energies, resulting in negative electronic compressibility. Here we use angle-resolved photoemission spectroscopy to demonstrate a striking effect of negative electronic compressibility in semiconducting TMD MoS2 on the charge transfer to and from a partial overlayer of monolayer semimetallic WTe2. We track the changes in binding energy of the valence bands of both WTe2 and MoS2 as a function of surface transfer doping with donor (K) and acceptor (F4-TCNQ) species. Donor doping increases the binding energy of the MoS2 valence band, as expected, while counterintuitively reducing the binding energy of the WTe2 valence bands and core levels. The inverse effect is observed for acceptor doping, where a typical reduction in the MoS2 binding energies is accopanied by an unexpected increase in those of WTe2. The observations imply a reversal of the expected charge transfer; donor (acceptor) deposition decreases (increases) the carrier density in the WTe2 adlayer. The charge transfer reversal is a direct consequence of the negative electronic compressibility of the MoS2 surface layer, for which addition (subtraction) of charge leads to attraction (repulsion) of further charge from neighbouring layers. These findings highlight the importance of many-body interactions for the electrons in transition metal dichalcogenides and underscore the potential for exploring strongly correlated quantum states in two-dimensional semiconductors., Comment: 4 pages, 5 figures + supplementary information

LLMs are an integral part of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the quality of end-to-end RAG systems, there is a lack of research on understanding the appropriateness of an LLM for the RAG task. Thus, we introduce a new metric, Trust-Score, that provides a holistic evaluation of the trustworthiness of LLMs in an RAG framework. We show that various prompting methods, such as in-context learning, fail to adapt LLMs effectively to the RAG task. Thus, we propose Trust-Align, a framework to align LLMs for higher Trust-Score. LLaMA-3-8b, aligned with our method, significantly outperforms open-source LLMs of comparable sizes on ASQA (up 10.7), QAMPARI (up 29.2) and ELI5 (up 14.9). We release our code at: https://github.com/declare-lab/trust-align.

We demonstrate chiral propagation of plasmon polaritons and show it is more efficient and easier to control than the recently observed chiral shear phonon polaritons. We consider plasmon polaritons created in an anisotropic two-dimensional (2D) material, twisted with respect to an anisotropic substrate, to best exploit the competition between anisotropic electron-electron interactions and the anisotropic electronic structure of the host material. Gate voltage and twist angle are then used for precise control of the chiral plasmon polaritons, overcoming the existing restrictions with chiral phonon polaritons. These findings open up feasible opportunities for efficient and tunable plasmon-based nanophotonics and compact high-performance on-chip optical devices.

We propose two distinct crosscap states for the two-dimensional (2D) Ising field theory. These two crosscap states, identifying Ising spins or dual spins (domain walls) at antipodal points, are shown to be related via the Kramers-Wannier duality transformation. We derive their Majorana free field representations and extend bosonization techniques to calculate correlation functions of the 2D Ising conformal field theory (CFT) with different crosscap boundaries. We further develop a conformal perturbation theory to calculate the Klein bottle entropy as a universal scaling function [Phys. Rev. Lett. 130, 151602 (2023)] in the 2D Ising field theory. The formalism developed in this work is applicable to many other 2D CFTs perturbed by relevant operators., Comment: 6+30 pages, 1+2 figures

Latent diffusion models have shown promising results in text-to-audio (T2A) generation tasks, yet previous models have encountered difficulties in generation quality, computational cost, diffusion sampling, and data preparation. In this paper, we introduce EzAudio, a transformer-based T2A diffusion model, to handle these challenges. Our approach includes several key innovations: (1) We build the T2A model on the latent space of a 1D waveform Variational Autoencoder (VAE), avoiding the complexities of handling 2D spectrogram representations and using an additional neural vocoder. (2) We design an optimized diffusion transformer architecture specifically tailored for audio latent representations and diffusion modeling, which enhances convergence speed, training stability, and memory usage, making the training process easier and more efficient. (3) To tackle data scarcity, we adopt a data-efficient training strategy that leverages unlabeled data for learning acoustic dependencies, audio caption data annotated by audio-language models for text-to-audio alignment learning, and human-labeled data for fine-tuning. (4) We introduce a classifier-free guidance (CFG) rescaling method that simplifies EzAudio by achieving strong prompt alignment while preserving great audio quality when using larger CFG scores, eliminating the need to struggle with finding the optimal CFG score to balance this trade-off. EzAudio surpasses existing open-source models in both objective metrics and subjective evaluations, delivering realistic listening experiences while maintaining a streamlined model structure, low training costs, and an easy-to-follow training pipeline. Code, data, and pre-trained models are released at: https://haidog-yaqub.github.io/EzAudio-Page/., Comment: submitted to ICASSP 2025

We study the size and shape statistics of ground state fuzzy spheres when projected onto the transverse plane, utilizing the regularized SU(N=2) matrix model in D=(1+3)-dimensional spacetime. We show that they appear as ellipses, from matrix/membrane correspondence. With our numerical and analytical approximation for the ground state wavefunction, we provide estimations for their expected surface areas, perimeters, eccentricities, and shape-parameters. These geometric constants of quantum membranes deviate drastically from classical mechanics.

Scaling up quantum dots to two-dimensional (2D) arrays is a crucial step for advancing semiconductor quantum computation. However, maintaining excellent tunability of quantum dot parameters, including both nearest-neighbor and next-nearest-neighbor couplings, during 2D scaling is challenging, particularly for silicon quantum dots due to their relatively small size. Here, we present a highly controllable and interconnected 2D quantum dot array in planar silicon, demonstrating independent control over electron fillings and the tunnel couplings of nearest-neighbor dots. More importantly, we also demonstrate the wide tuning of tunnel couplings between next-nearest-neighbor dots,which plays a crucial role in 2D quantum dot arrays. This excellent tunability enables us to alter the coupling configuration of the array as needed. These results open up the possibility of utilizing silicon quantum dot arrays as versatile platforms for quantum computing and quantum simulation.

Electron spin qubits in silicon are a promising platform for fault-tolerant quantum computing. Low-frequency noise, including nuclear spin fluctuations and charge noise, is a primary factor limiting gate fidelities. Suppressing this noise is crucial for high-fidelity qubit operations. Here, we report on a two-qubit quantum device in natural silicon with universal qubit control, designed to investigate the upper limits of gate fidelities in a non-purified Si/SiGe quantum dot device. By employing advanced device structures, qubit manipulation techniques, and optimization methods, we have achieved single-qubit gate fidelities exceeding 99% and a two-qubit Controlled-Z (CZ) gate fidelity of 91%. Decoupled CZ gates are used to prepare Bell states with a fidelity of 91%, typically exceeding previously reported values in natural silicon devices. These results underscore that even natural silicon has the potential to achieve high-fidelity gate operations, particularly with further optimization methods to suppress low-frequency noise.

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11\%, increasing the final user utility function value by 0.24\%, and decreasing the variance of results by 95.45\%.

We present an analysis of lepton-jet azimuthal decorrelation in deep-inelastic scattering (DIS) at next-to-next-to-next-to-leading logarithmic (N$^{3}$LL) accuracy, combined with fixed-order corrections at $\mathcal{O}(\alpha_s^2)$. In this study, jets are defined in the lab frame using the anti-$k_T$ clustering algorithm and the winner-take-all recombination scheme. The N$^{3}$LL resummation results are derived from the transverse-momentum dependent factorization formula within the soft-collinear effective theory, while the $\mathcal{O}(\alpha_s^2)$ fixed-order matching distribution is calculated using the {\tt NLOJET++} event generator. The azimuthal decorrelation between the jet and electron serves as a critical probe of the three-dimensional structure of the nucleon. Our numerical predictions provide a robust framework for precision studies of QCD and the nucleon's internal structure through jet observables in DIS. These results are particularly significant for analyses involving jets in HERA data and the forthcoming electron-ion collider experiments., Comment: 27 pages, 5 figures