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Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that focuses on low-inference instructional practices, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that has been demonstrated to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers' utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.

Multi-instance Repetitive Action Counting (MRAC) aims to estimate the number of repetitive actions performed by multiple instances in untrimmed videos, commonly found in human-centric domains like sports and exercise. In this paper, we propose MultiCounter, a fully end-to-end deep learning framework that enables simultaneous detection, tracking, and counting of repetitive actions of multiple human instances. Specifically, MultiCounter incorporates two novel modules: 1) mixed spatiotemporal interaction for efficient context correlation across consecutive frames, and 2) task-specific heads for accurate perception of periodic boundaries and generalization for action-agnostic human instances. We train MultiCounter on a synthetic dataset called MultiRep generated from annotated real-world videos. Experiments on the MultiRep dataset validate the fundamental challenge of MRAC tasks and showcase the superiority of our proposed model. Compared to ByteTrack+RepNet, a solution that combines an advanced tracker with a single repetition counter, MultiCounter substantially improves Period-mAP by 41.0%, reduces AvgMAE by 58.6%, and increases AvgOBO 1.48 times. This sets a new benchmark in the field of MRAC. Moreover, MultiCounter runs in real-time on a commodity GPU server and is insensitive to the number of human instances in a video., Comment: Accepted by ECAI 2024

The China Jinping underground Laboratory (CJPL) is an excellent location for studying solar, terrestrial, and supernova neutrinos due to its 2400-meter vertical rock overburden. Its unparalleled depth gives an opportunity to investigate the cosmic-ray muons with exceptionally high average energy at $\sim360$ GeV. This paper details a study of muon-related backgrounds based on 1178 days of data collected by the 1-ton prototype neutrino detector used for the Jinping Neutrino Experiment (JNE) since 2017. The apparent effects of detectors' finite size on the measured result are first discussed in detail. The analysis of 493 cosmic-ray muon candidates and $13.6\pm5.7$ cosmogenic neutron candidates, along with a thorough evaluation of detection efficiency and uncertainties, gives a muon flux of $(3.56\pm0.16_{\mathrm{stat.}}\pm0.10_{\mathrm{syst.}})\times10^{-10}~\mathrm{cm}^{-2}\mathrm{s^{-1}}$ and a cosmogenic neutron yield of $(3.37\pm 1.41_{\mathrm{stat.}}\pm 0.31_{\mathrm{sys.}}) \times 10^{-4}~\mathrm{\mu}^{-1} \mathrm{g}^{-1} \mathrm{cm}^{2}$ in LAB-based liquid scintillator.

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

The CSR External-target Experiment (CEE) is a large-scale spectrometer under construction at the Heavy Ion Research Facility in Lanzhou (HIRFL) for studying the phase structure of nuclear matter at high baryon density and the equation of states of nuclear matter at supra-saturation densities. One of the key components is a large acceptance dipole magnet with a central field of 0.5 T and the homogeneity of 5% within a 1 m long, 1.2 m wide, and 0.9 m high aperture. Detectors will be installed within this aperture. An innovative design for the superconducting detector magnet is proposed that goes beyond the conventional approach. The magnet is designed as a coil-dominant type, with conductors discretized on a racetrack-shaped cross-section to generate the necessary fields. A warm iron yoke is used to enhance the central field and minimize the stray field. The magnet has overall dimensions of 3.4 meters in length, 2.7 meters in height, and 4.3 meters in width. The coils will be wound using a 19-strand rope cable comprised of 12 NbTi superconducting wires and 7 copper wires. The ratio of copper to superconductor of the cable is 6.9. The keel supports serve as the primary structural support for the coils to withstand the electromagnetic force. The coils will be indirectly cooled by liquid helium within three external helium vessels. To ensure reliable protection of the magnet during a quench, an active protection method combined with quench-back effect is employed. In this paper, we mainly present the detailed design of the magnetic field, structure, quench protection and cryostat for the spectrometer magnet.

Let $(M,g)$ be a closed, connected and orientable Riemannian manifold with nonnegative Ricci curvature. Consider a Lagrangian $L(x,v):TM\to\R$ defined by $L(x,v):=\frac 12g_x(v,v)-\omega(v)+c$, where $c\in\R$ and $\omega$ is a closed 1-form. From the perspective of differential geometry, we estimate the Laplacian of the weak KAM solution $u$ to the associated Hamilton-Jacobi equation $H(x,du)=c[L]$ in the barrier sense. This analysis enables us to prove that each weak KAM solution $u$ is constant if and only if $\omega$ is a harmonic 1-form. Furthermore, we explore several applications to the Mather quotient and Ma\~n\'e's Lagrangian.

The [O III] $\lambda\lambda\ 4960,5008$ emission lines in the optical spectra of galaxies and quasars have been widely used to investigate the possible variation of the fine-structure constant $\alpha$ over cosmic time. In this work, we utilize the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) quasar survey, for the first time, to measure the relative $\alpha$ variation $\Delta\alpha/\alpha$ in time through the [O III] doublet method. From the LAMOST Data Release 9 quasar catalog, we refine a sample of 209 quasar spectra with strong and narrow [O III] emission lines over a redshift range of $0

This paper employs an intra-personal game-theoretic framework to investigate how decreasing impatience influences irreversible investment behaviors in a continuous-time setting. We consider a capacity expansion problem under weighted discount functions, a class of nonexponential functions that exhibit decreasing impatience, including the hyperbolic discount function as a special case. By deriving the Bellman system that characterizes the equilibrium, we establish the framework for analyzing investment behaviors of agents subject to decreasing impatience. From an economic perspective, we demonstrates that decreasing impatience prompts early investment. From a technical standpoint, we warn that decreasing impatience can lead to the failure of the smooth pasting principle.

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

Building on a previous work~\cite{Han:2024min}, we illustrate that the leading-twist light-cone distribution amplitudes (LCDAs) defined in heavy-quark effective theory (HQET) can be determined through lattice simulations of quasi-distribution amplitudes (quasi-DAs) with a large momentum component $P^z$. Exploiting heavy-quark spin symmetry, we show that the LCDAs for a heavy pseudoscalar and vector meson in the context of HQET exhibit degeneracy, and the degeneracy allows for the utilization of quasi DAs for both pseudoscalar and vector mesons on the lattice. We then derive the relevant short-distance coefficients for the matching between LCDAs defined with QCD fields and HQET LCDAs at the one-loop level. The incorporation of these three quasi DAs can not only confirm the methodology introduced in Ref.~\cite{Han:2024min} but also provides possible insight into power corrections. Discrepancies between the corresponding results offer a valuable perspective for estimating power corrections within the system which are imperative for precise investigations into heavy-meson LCDAs in the future particularly in the context of lattice QCD., Comment: 10 pages, 1 figure

Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May we learn from multiple modalities only during training to avoid multi-modal input in the inference phase? To answer it, we propose \textbf{YOLOO}, a novel multi-modal 3D MOT paradigm: You Only Learn from Others Once. YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once. Leveraging this UTR, YOLOO achieves efficient tracking solely using the point cloud encoder without compromising its performance, fundamentally obviating the need for computationally intensive DNNs. Specifically, YOLOO includes two core components: a unified tri-modal encoder (UTEnc) and a flexible geometric constraint (F-GC) module. UTEnc integrates a point cloud encoder with image and text encoders adapted from pre-trained CLIP. It seamlessly fuses point cloud information with rich visual-textual knowledge from CLIP into the point cloud encoder, yielding highly discriminative UTRs that facilitate the association between trajectories and detections. Additionally, F-GC filters out mismatched associations with similar representations but significant positional discrepancies. It further enhances the robustness of UTRs without requiring any scene-specific tuning, addressing a key limitation of customized geometric constraints (e.g., 3D IoU). Lastly, high-quality 3D trajectories are generated by a traditional data association component. By integrating these advancements into a multi-modal 3D MOT scheme, our YOLOO achieves substantial gains in both robustness and efficiency.

Over the past three decades, multiple reverberation mapping (RM) campaigns conducted for the quasar PG 2130+099 have exhibited inconsistent findings with time delays ranging from $\sim$10 to $\sim$200 days. To achieve a comprehensive understanding of the geometry and dynamics of the broad-line region (BLR) in PG 2130+099, we continued an ongoing high-cadence RM monitoring campaign using the Calar Alto Observatory 2.2m optical telescope for an extra four years from 2019 to 2022. We measured the time lags of several broad emission lines (including He II, He I, H$\beta$, and Fe II) with respect to the 5100 {\AA} continuum, and their time lags continuously vary through the years. Especially, the H$\beta$ time lags exhibited approximately a factor of two increase in the last two years. Additionally, the velocity-resolved time delays of the broad H$\beta$ emission line reveal a back-and-forth change between signs of virial motion and inflow in the BLR. The combination of negligible ($\sim$10%) continuum change and substantial time-lag variation (over two times) results in significant scatter in the intrinsic $R_{\rm H\beta}-L_{\rm 5100}$ relationship for PG 2130+099. Taking into account the consistent changes in the continuum variability time scale and the size of the BLR, we tentatively propose that the changes in the measurement of the BLR size may be affected by 'geometric dilution'., Comment: 21 pages, 13 figures, 7 tables; accepted for publication in ApJ

Precise direct cosmic-ray (CR) measurements provide an important probe to study the energetic particle sources in our Galaxy, and the interstellar environment through which these particles propagate. Uncertainties on hadronic models, ion-nucleon cross sections in particular, are currently the limiting factor towards obtaining more accurate CR ion flux measurements with calorimetric space-based experiments. We present an energy-dependent measurement of the inelastic cross section of protons and helium-4 nuclei (alpha particles) on a Bi$_4$Ge$_3$O$_{12}$ target, using 88 months of data collected by the DAMPE space mission. The kinetic energy range per nucleon of the measurement points ranges from 18 GeV to 9 TeV for protons, and from 5 GeV/n to 3 TeV/n for helium-4 nuclei. Our results lead to a significant improvement of the CR flux normalisation. In the case of helium-4, these results correspond to the first cross section measurements on a heavy target material at energies above 10 GeV/n., Comment: 17 pages, submitted to PRD

How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such as hero combinations in multiplayer online battle arena (MOBA) games or decks in card games-is essential for enhancing gameplay and achieving balance. We have developed two advanced measures that extend beyond the simplistic win rate to quantify balance in zero-sum competitive scenarios. These measures are derived from win value estimations, which employ strength rating approximations via the Bradley-Terry model and counter relationship approximations via vector quantization, significantly reducing the computational complexity associated with traditional win value estimations. Throughout the learning process of these models, we identify useful categories of compositions and pinpoint their counter relationships, aligning with the experiences of human players without requiring specific game knowledge. Our methodology hinges on a simple technique to enhance codebook utilization in discrete representation with a deterministic vector quantization process for an extremely small state space. Our framework has been validated in popular online games, including Age of Empires II, Hearthstone, Brawl Stars, and League of Legends. The accuracy of the observed strength relations in these games is comparable to traditional pairwise win value predictions, while also offering a more manageable complexity for analysis. Ultimately, our findings contribute to a deeper understanding of PvP game dynamics and present a methodology that significantly improves game balance evaluation and design., Comment: TMLR 09/2024 https://openreview.net/forum?id=2D36otXvBE

Image restoration methods like super-resolution and image synthesis have been successfully used in commercial cloud gaming products like NVIDIA's DLSS. However, restoration over gaming content is not well studied by the general public. The discrepancy is mainly caused by the lack of ground-truth gaming training data that match the test cases. Due to the unique characteristics of gaming content, the common approach of generating pseudo training data by degrading the original HR images results in inferior restoration performance. In this work, we develop GameIR, a large-scale high-quality computer-synthesized ground-truth dataset to fill in the blanks, targeting at two different applications. The first is super-resolution with deferred rendering, to support the gaming solution of rendering and transferring LR images only and restoring HR images on the client side. We provide 19200 LR-HR paired ground-truth frames coming from 640 videos rendered at 720p and 1440p for this task. The second is novel view synthesis (NVS), to support the multiview gaming solution of rendering and transferring part of the multiview frames and generating the remaining frames on the client side. This task has 57,600 HR frames from 960 videos of 160 scenes with 6 camera views. In addition to the RGB frames, the GBuffers during the deferred rendering stage are also provided, which can be used to help restoration. Furthermore, we evaluate several SOTA super-resolution algorithms and NeRF-based NVS algorithms over our dataset, which demonstrates the effectiveness of our ground-truth GameIR data in improving restoration performance for gaming content. Also, we test the method of incorporating the GBuffers as additional input information for helping super-resolution and NVS. We release our dataset and models to the general public to facilitate research on restoration methods over gaming content.

Flexible photovoltaics with a lightweight and adaptable nature that allows for deployment on curved surfaces and in building facades have always been a goal vigorously pursued by researchers in thin-film solar cell technology. The recent strides made in improving the sunlight-to-electricity conversion efficiency of kesterite Cu$_{2}$ZnSn(S, Se)$_{4}$ (CZTSSe) suggest it to be a perfect candidate. However, making use of rare Mo foil in CZTSSe solar cells causes severe problems in thermal expansion matching, uneven grain growth, and severe problems at the back contact of the devices. Herein, a strategy utilizing single-crystal graphene to modify the back interface of flexible CZTSSe solar cells is proposed. It will be shown that the insertion of graphene at the Mo foil/CZTSSe interface provides strong physical support for the subsequent deposition of the CZTSSe absorber layer, improving the adhesion between the absorber layer and the Mo foil substrate. Additionally, the graphene passivates the rough sites on the surface of the Mo foil, enhancing the chemical homogeneity of the substrate, and resulting in a more crystalline and homogeneous CZTSSe absorber layer on the Mo foil substrate. The detrimental reaction between Mo and CZTSSe has also been eliminated. Through an analysis of the electrical properties, it is found that the introduction of graphene at the back interface promotes the formation of a quasi-ohmic contact at the back contact, decreasing the back contact barrier of the solar cell, and leading to efficient collection of charges at the back interface. This investigation demonstrates that solution-based CZTSSe photovoltaic devices could form the basis of cheap and flexible solar cells.

The Hodge decomposition is a fundamental result in differential geometry and algebraic topology, particularly in the study of differential forms on a Riemannian manifold. Despite extensive research in the past few decades, topology-preserving Hodge decomposition of scalar and vector fields on manifolds with boundaries in the Eulerian representation remains a challenge due to the implicit incorporation of appropriate topology-preserving boundary conditions. In this work, we introduce a comprehensive 5-component topology-preserving Hodge decomposition that unifies normal and tangential components in the Cartesian representation. Implicit representations of planar and volumetric regions defined by level-set functions have been developed. Numerical experiments on various objects, including single-cell RNA velocity, validate the effectiveness of our approach, confirming the expected rigorous $L^2$-orthogonality and the accurate cohomology.

Materials with chiral electronic structures are of great interest. We report a triazatruxene, TAT, molecular semiconductor with chiral alkyl side chains that crystallises from solution to form chirally-stacked columns with a helical pitch of 6 TATs (2.3 nm). These crystals show strong circularly polarised, CP, green photoluminescence, with dissymmetry of 24%. Electronic structure calculations using the full crystal structure, show that this chiral stacking associates angular momentum to the valence and conduction states and thus gives rise to the observed CP luminescence. Free-standing crystals are not useful for active semiconductor devices, but we have discovered that co-sublimation of TAT as the guest in a structurally mismatched host enables the fabrication of thin films where the chiral crystallization is achieved in-situ by thermally-triggered nano-phase segregation of dopant and host whilst preserving the integrity of the film. This enables fabrication of bright (green) organic light-emitting diodes with unexpectedly high external quantum efficiencies of up to 16% and electroluminescence dissymmetries above 10%. These materials and this process method offer significant application potential in spintronics, optical displays and multidimensional optoelectronics.

Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and advanced mathematics plays a key role in designing these features. Persistent homology theory, which equips topological invariants with persistence, provides valuable insights into molecular structures. The calculation of Betti numbers is based on differential that typically satisfy \(d^2 = 0\). Our recent work has extended this concept by employing Mayer homology with a generalized differential that satisfies \(d^N = 0\) for \(N \geq 2\), leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales. In this study, we utilize PMH to create a novel multiscale topological vectorization for molecular representation, offering valuable tools for descriptive and predictive analysis in molecular data and machine learning prediction. Specifically, benchmark tests on established protein-ligand datasets, including PDBbind-2007, PDBbind-2013, and PDBbind-2016, demonstrate the superior performance of our Mayer homology models in predicting protein-ligand binding affinities.

The Regeneration Cavity (RC) is a critical component of the Any Light Particle Search II (ALPS II) experiment. It increases the signal from possible axions and axion-like particles in the experiment by nearly four orders of magnitude. The total round-trip optical losses of the power circulating in the cavity must be minimized in order to maximize the resonant enhancement of the cavity, which is an important figure of merit for ALPS II. Lower optical losses also increase the cavity storage time and with the 123 meter long ALPS II RC we have demonstrated the longest storage time of a two-mirror optical cavity. We measured a storage time of $7.17 \pm 0.01$ ms, equivalent to a linewidth of 44.4 Hz and a finesse of 27,500 at a wavelength of 1064 nm., Comment: 16 pages, 8 figures, 1 table

Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus achieving efficiency in both storage and computation. Additionally, prompting modifies only the LM's inputs and harnesses the generative capabilities of language models to address various downstream tasks in a unified manner. This significantly reduces the need for human labor in designing task-specific models. These advantages become even more evident as the number of tasks served by the LM scales up. Motivated by the strengths of prompting, we are the first to explore the potential of prompting speech LMs in the domain of speech processing. Recently, there has been a growing interest in converting speech into discrete units for language modeling. Our pioneer research demonstrates that these quantized speech units are highly versatile within our unified prompting framework. Not only can they serve as class labels, but they also contain rich phonetic information that can be re-synthesized back into speech signals for speech generation tasks. Specifically, we reformulate speech processing tasks into speech-to-unit generation tasks. As a result, we can seamlessly integrate tasks such as speech classification, sequence generation, and speech generation within a single, unified prompting framework. The experiment results show that the prompting method can achieve competitive performance compared to the strong fine-tuning method based on self-supervised learning models with a similar number of trainable parameters. The prompting method also shows promising results in the few-shot setting. Moreover, with the advanced speech LMs coming into the stage, the proposed prompting framework attains great potential., Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP)

This work presents ParGo, a novel Partial-Global projector designed to connect the vision and language modalities for Multimodal Large Language Models (MLLMs). Unlike previous works that rely on global attention-based projectors, our ParGo bridges the representation gap between the separately pre-trained vision encoders and the LLMs by integrating global and partial views, which alleviates the overemphasis on prominent regions. To facilitate the effective training of ParGo, we collect a large-scale detail-captioned image-text dataset named ParGoCap-1M-PT, consisting of 1 million images paired with high-quality captions. Extensive experiments on several MLLM benchmarks demonstrate the effectiveness of our ParGo, highlighting its superiority in aligning vision and language modalities. Compared to conventional Q-Former projector, our ParGo achieves an improvement of 259.96 in MME benchmark. Furthermore, our experiments reveal that ParGo significantly outperforms other projectors, particularly in tasks that emphasize detail perception ability.

In this study, we conducted simulations to find the geometric aberrations expected for images taken by the Main Survey Camera (MSC) of the Chinese Space Station Telescope (CSST) due to its motion. As anticipated by previous work, our findings indicate that the geometric distortion of light impacts the focal plane's apparent scale, with a more pronounced influence as the size of the focal plane increases. Our models suggest that the effect consistently influences the pixel scale in both the vertical and parallel directions. The apparent scale variation follows a sinusoidal distribution throughout one orbit period. Simulations reveal that the effect is particularly pronounced in the center of the Galaxy and gradually diminishes along the direction of ecliptic latitude. At low ecliptic latitudes, the total aberration leads to about 0.94 pixels offset (a 20-minute exposure) and 0.26 pixels offset (a 300-second exposure) at the edge of the field of view, respectively. Appropriate processings for the geometric effect during the CSST pre- and post-observation phases are presented., Comment: 13 pages, 8 figures; accepted by RAA

Quantum computing has tremendous potential to overcome some of the fundamental limitations present in classical information processing. Yet, today's technological limitations in the quality and scaling prevent exploiting its full potential. Quantum computing based on superconducting quantum processing units (QPUs) is among the most promising approaches towards practical quantum advantage. In this article the basic technological approach of IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. In particular, the focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We also present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.

In the past two decades, the exploration of quantumness within Schwarzschild spacetime has garnered significant interest, particularly regarding the Hawking radiation's impact on quantum correlations and quantum coherence. Building on this foundation, we investigate how Hawking radiation influences maximal steered coherence (MSC)-a crucial measure for gauging the ability to generate coherence through steering. We find that as the Hawking temperature increases, the physically accessible MSC degrade while the unaccessible MSC increase. This observation is attributed to a redistribution of the initial quantum correlations, previously acknowledged by inertial observers, across all bipartite modes. In particular, we find that in limit case that the Hawking temperature tends to infinity, the accessible MSC equals to 1/\sqrt{2} of its initial value, and the unaccessible MSC also equals to the same value. Our findings illuminate the intricate dynamics of quantum information in the vicinity of black holes, suggesting that Hawking radiation plays a pivotal role in reshaping the landscape of quantum coherence and entanglement in curved spacetime. This study not only advances our theoretical understanding of black hole thermodynamics but also opens new avenues for investigating the interface between quantum mechanics and general relativity., Comment: 4 pages, 1 figure

Recent studies have increasingly focused on the effect of relativistic motion on quantum coherence. Prior research predominantly examined the influence of relative motion on basis-dependent quantum coherence, underscoring its susceptibility to decoherence under accelerated conditions. Yet, the effect of relativistic motion on basis-independent quantum coherence, which is critical for understanding the intrinsic quantum features of a system, remains an interesting open question. This paper addresses this question by examining how total, collective, and localized coherence are affected by acceleration and coupling strength. Our analysis reveals that both total and collective coherence significantly decrease with increasing acceleration and coupling strength, ultimately vanishing at high levels of acceleration. This underscores the profound impact of Unruh thermal noise. Conversely, localized coherence exhibits relative stability, decreasing to zero only under the extreme condition of infinite acceleration. Moreover, we demonstrate that collective, localized, and basis-independent coherence collectively satisfy the triangle inequality. These findings are crucial for enhancing our understanding of quantum information dynamics in environments subjected to high acceleration and offer valuable insights on the behavior of quantum coherence under relativistic conditions., Comment: 7 pages, 3 figures

Over-the-air computation (AirComp) integrates analog communication with task-oriented computation, serving as a key enabling technique for communication-efficient federated learning (FL) over wireless networks. However, AirComp-enabled FL (AirFL) with a single global consensus model fails to address the data heterogeneity in real-life FL scenarios with non-independent and identically distributed local datasets. In this paper, we introduce reconfigurable intelligent surface (RIS) technology to enable efficient personalized AirFL, mitigating the data heterogeneity issue. First, we achieve statistical interference elimination across different clusters in the personalized AirFL framework via RIS phase shift configuration. Then, we propose two personalized aggregation schemes involving power control and denoising factor design from the perspectives of first- and second-order moments, respectively, to enhance the FL convergence. Numerical results validate the superior performance of our proposed schemes over existing baselines., Comment: Accepted by SCIENCE CHINA Information Sciences

Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency. Taking the image classification task as a starting point, ADC leverages LLMs for the detailed class design and code generation to collect relevant samples via search engines, significantly reducing the need for manual annotation and speeding up the data generation process. Despite these advantages, ADC also encounters real-world challenges such as label errors (label noise) and imbalanced data distributions (label bias). We provide open-source software that incorporates existing methods for label error detection, robust learning under noisy and biased data, ensuring a higher-quality training data and more robust model training procedure. Furthermore, we design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning. These datasets are vital because there are few existing datasets specifically for label noise detection, despite its importance. Finally, we evaluate the performance of existing popular methods on these datasets, thereby facilitating further research in the field.

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

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

Efficiently detecting target weld seams while ensuring sub-millimeter accuracy has always been an important challenge in autonomous welding, which has significant application in industrial practice. Previous works mostly focused on recognizing and localizing welding seams one by one, leading to inferior efficiency in modeling the workpiece. This paper proposes a novel framework capable of multiple weld seams extraction using both RGB images and 3D point clouds. The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth. Our method is further accelerated by using a pre-trained deep learning model to ensure both efficiency and generalization ability. The performance of the proposed method has been comprehensively tested on various workpieces featuring both linear and curved weld seams and in physical experiment systems. The results showcase considerable potential for real-world industrial applications, emphasizing the method's efficiency and effectiveness. Videos of the real-world experiments can be found at https://youtu.be/pq162HSP2D4.

Manipulating quantum states is at the heart of quantum information processing and quantum metrology. Landau-Zener Rabi oscillation (LZRO), which arises from a quantum two-level system swept repeatedly across the avoided crossing point in the time domain, has been suggested for widespread use in manipulating quantum states. Cold atom is one of the most prominent platforms for quantum computing and precision measurement. However, LZRO has never been observed in cold atoms due to its stringent requirements. By compensating for the linear drift of the clock laser and optimizing experimental parameters, we successfully measured LZRO on the strontium atomic optical clock platform under both fast and slow passage limits within $4$ to $6$ driving periods. Compared to previous results on other platforms, the duration of the plateau is $10^4$ times longer in the optical lattice clock. The experimental data also suggest that destructive Landau-Zener interference can effectively suppress dephasing effects in the optical lattice clock, paving the way for manipulating quantum states against various environmental effects in cold atomic systems.

Polarization and wavelength multiplexing are the two most widely employed techniques to improve the capacity in the metasurfaces. Existing works have pushed each technique to its individual limits. For example, the polarization multiplexing channels working at a single wavelength have been significantly increased by using noise engineering. However, it is still challenging to achieve the multiplexing limits of wavelength and polarization simultaneously. Besides, such multiplexing methods suffer from computational inefficiencies, hindering their application in tasks like image recognition that require extensive training computation. In this work, we introduce a gradient-based optimization algorithm using deep neural network (DNN) to achieve the limits of both polarization and wavelength multiplexing with high computational efficiency. We experimentally demonstrate this capability, achieving a record-breaking capacity of 15 holographic images across five wavelengths and the maximum of three independent polarization channels, as well as 18 holographic images across three wavelengths and six corelated polarization channels. Moreover, leveraging the high computational efficiency of our DNN-based method, which is well-suited for processing large datasets, we implement large-scale image recognition tasks across 36 classes encoded in a record of nine multiplexed channels (three wavelengths * three polarizations), achieving 96% classification accuracy in calculations and 91.5% in experiments. This work sets a new benchmark for high-capacity multiplexing with metasurfaces and demonstrates the power of gradient-based inverse design for realizing multi-functional optical elements.

Massive off-chip accesses in GPUs are the main performance bottleneck, and we divided these accesses into three types: (1) Write, (2) Data-Read, and (3) Read-Only. Besides, We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the inter-dup and inter-dup lines. As for the inter-dup detection, we reduce the extra read requests caused by the traditional read-verify hash process. Besides, we design several techniques to manage duplicate blocks. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 37.79%. Besides, CMD can improve the performance of memory-intensive workloads by 50.18%.

Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditional models only rely on the semantic congruence between queries and items to ascertain relevance. However, this approach represents merely one aspect of the relevance judgement, and is insufficient in isolation. Even powerful Large Language Models (LLMs) still cannot accurately judge the relevance of a query and an item from a semantic perspective. To augment LLMs-driven relevance modeling, this study proposes leveraging user interactions recorded in search logs to yield insights into users' implicit search intentions. The challenge lies in the effective prompting of LLMs to capture dynamic search intentions, which poses several obstacles in real-world relevance scenarios, i.e., the absence of domain-specific knowledge, the inadequacy of an isolated prompt, and the prohibitive costs associated with deploying LLMs. In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively. Specifically, we perform the user-driven behavior neighbors retrieval from the daily search logs to obtain domain-specific knowledge in time, retrieving candidates that users consider to meet their expectations. Then, we guide LLMs for relevance modeling by employing advanced prompting techniques that progressively improve the outputs of the LLMs, followed by a progressive aggregation with comprehensive consideration of diverse aspects. For online serving, we have developed an industrial application framework tailored for the deployment of LLMs in relevance modeling. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance.

Software development is a repetitive task, as developers usually reuse or get inspiration from existing implementations. Code search, which refers to the retrieval of relevant code snippets from a codebase according to the developer's intent that has been expressed as a query, has become increasingly important in the software development process. Due to the success of deep learning in various applications, a great number of deep learning based code search approaches have sprung up and achieved promising results. However, developers may not follow the same naming conventions and the same variable may have different variable names in different implementations, bringing a challenge to deep learning based code search methods that rely on explicit variable correspondences to understand source code. To overcome this challenge, we propose a naming-agnostic code search method (NACS) based on contrastive multi-view code representation learning. NACS strips information bound to variable names from Abstract Syntax Tree (AST), the representation of the abstract syntactic structure of source code, and focuses on capturing intrinsic properties solely from AST structures. We use semantic-level and syntax-level augmentation techniques to prepare realistically rational data and adopt contrastive learning to design a graph-view modeling component in NACS to enhance the understanding of code snippets. We further model ASTs in a path view to strengthen the graph-view modeling component through multi-view learning. Extensive experiments show that NACS provides superior code search performance compared to baselines and NACS can be adapted to help existing code search methods overcome the impact of different naming conventions.

Ambient backscatter communications (AmBC) are a promising technology for addressing the energy consumption challenge in wireless communications through the reflection or absorption of surrounding radio frequency (RF) signals. However, it grapples with the intricacies of ambient RF signal and the round-trip path loss. For traditional detectors, the incorporation of pilot sequences results in a reduction in spectral efficiency. Furthermore, traditional energy-based detectors are inherently susceptible to a notable error floor issue, attributed to the co-channel direct link interference (DLI). Consequently, this paper proposes a blind symbol detector without the prior knowledge of the channel state information, signal variance, and noise variance. By leveraging the intra-symbol differential amplitude shift keying (IDASK) scheme, this detector effectively redirects the majority of the DLI energy towards the largest eigenvalue of the received sample covariance matrix, thereby utilizing the second largest eigenvalue for efficient symbol detection. In addition, this paper conducts theoretical performance analyses of the proposed detector in terms of the false alarm probability, missed detection probability, and the bit-error rate (BER) lower bound. Simulation results demonstrate that the proposed blind detector exhibits a significant enhancement in symbol detection performance compared to its traditional counterparts.

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

We present a multi-frequency polarimetric study for the quasar 1604+159. The source was observed at the $L$ band with the American Very Long Baseline Array (VLBA) and the $L$, $X$, and $U$ bands with the Very Large Array (VLA). These observations provide different resolutions from mas to arcsec, enabling us to probe the morphology and magnetic field from tens of parsec to hundreds of kilo-parsec scale. We detect a symmetrical Fanaroff-Riley-Class-I-like structure. The source has several lobes and bulges, forming a cocoon shape. The polarization is normal to the edges of the structure with high fractional polarization up to $\sim 60\%$. Two hotspots are observed at the eastern and western sides of the source, located symmetrically relative to the core. The flux density ratio ($>1.5$) between the two hotspots suggests the Doppler beaming effect exists at a large scale. The polarized emission in the hotspots also shows a symmetrical structure with an oblique direction from the jet direction. In general, the jet propagates in a collimating structure with several bends. Polarization is also detected perpendicular to the local jet from $\sim$100 mas to $\sim$ 1 arcsec. The jet shows strong polarized intensity and high fractional polarization at the bending edges. We discuss the possible origins of the observed structure and magnetic field., Comment: 17 pages, accepted for publication in ApJ

Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing need for models capable of integrating multimodal information in the chemical domain. In this paper, we introduce \textbf{ChemVLM}, an open-source chemical multimodal large language model specifically designed for chemical applications. ChemVLM is trained on a carefully curated bilingual multimodal dataset that enhances its ability to understand both textual and visual chemical information, including molecular structures, reactions, and chemistry examination questions. We develop three datasets for comprehensive evaluation, tailored to Chemical Optical Character Recognition (OCR), Multimodal Chemical Reasoning (MMCR), and Multimodal Molecule Understanding tasks. We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks. Experimental results demonstrate that ChemVLM achieves competitive performance across all evaluated tasks. Our model can be found at https://huggingface.co/AI4Chem/ChemVLM-26B., Comment: 11 pages, updated version

Cyber Threat Intelligence (CTI) summarization task requires the system to generate concise and accurate highlights from raw intelligence data, which plays an important role in providing decision-makers with crucial information to quickly detect and respond to cyber threats in the cybersecurity domain. However, efficient techniques for summarizing CTI reports, including facts, analytical insights, attack processes, etc., have largely been unexplored, primarily due to the lack of available dataset. To this end, we present CTISum, a new benchmark for CTI summarization task. Considering the importance of attack process, a novel fine-grained subtask of attack process summarization is proposed to enable defenders to assess risk, identify security gaps, vulnerabilities, and so on. Specifically, we first design a multi-stage annotation pipeline to gather and annotate the CTI data, and then benchmark the CTISum with a collection of extractive and abstractive summarization methods. Experimental results show that current state-of-the-art models exhibit limitations when applied to CTISum, underscoring the fact that automatically producing concise summaries of CTI reports remains an open research challenge.

Hybrid Reinforcement Learning (RL), where an agent learns from both an offline dataset and online explorations in an unknown environment, has garnered significant recent interest. A crucial question posed by Xie et al. (2022) is whether hybrid RL can improve upon the existing lower bounds established in purely offline and purely online RL without relying on the single-policy concentrability assumption. While Li et al. (2023) provided an affirmative answer to this question in the tabular PAC RL case, the question remains unsettled for both the regret-minimizing RL case and the non-tabular case. In this work, building upon recent advancements in offline RL and reward-agnostic exploration, we develop computationally efficient algorithms for both PAC and regret-minimizing RL with linear function approximation, without single-policy concentrability. We demonstrate that these algorithms achieve sharper error or regret bounds that are no worse than, and can improve on, the optimal sample complexity in offline RL (the first algorithm, for PAC RL) and online RL (the second algorithm, for regret-minimizing RL) in linear Markov decision processes (MDPs), regardless of the quality of the behavior policy. To our knowledge, this work establishes the tightest theoretical guarantees currently available for hybrid RL in linear MDPs.

The kagome spin ice can host frustrated magnetic excitations by flipping its local spin. Under an inelastic tunneling condition, the tip in a scanning tunneling microscope can flip the local spin, and we apply this technique to kagome metal HoAgGe with a long-range ordered spin ice ground state. Away from defects, we discover a pair of pronounced dips in the local tunneling spectrum at symmetrical bias voltages with negative intensity values, serving as a striking inelastic tunneling signal. This signal disappears above the spin ice formation temperature and has a dependence on the magnetic fields, demonstrating its intimate relation with the spin ice magnetism. We provide a two-level spin-flip model to explain the tunneling dips considering the spin ice magnetism under spin-orbit coupling. Our results uncover a local emergent excitation of spin ice magnetism in a kagome metal, suggesting that local electrical field induced spin flip climbs over a barrier caused by spin-orbital locking.

The rapid advancement of the thin-film lithium niobate platform has established it as a premier choice for high-performance photonics integration. High-speed optical coherent receivers are essential for supporting the large communication capacities required by data center interconnects. Although high-speed photodiodes have been demonstrated on the thin-film LiNbO3 platform, the development of an ultrahigh-speed optical coherent receiver on this platform has not yet been realized. Here, we propose and experimentally demonstrate an ultra-wideband PD and ultrahigh-speed optical coherent receiver on an InP-LiNbO3 wafer-level heterogeneous integration platform. The fabricated single PD exhibits a record-high bandwidth of 140 GHz and successfully receives a high-quality 100-Gbaud pulse amplitude modulation (PAM4) signal. Furthermore, a thin-film LiNbO3 optical coherent receiver, featuring a large balanced detection bandwidth of 60 GHz, a large common mode rejection ratio (CMRR) exceeding 20 dB, and a low energy consumption of 9.6 fJ per bit, enables an ultrahigh-speed coherent reception with advanced modulation formats. The single-polarization I-Q coherent receiver, incorporating a compact 2x4 90 optical hybrid and a balanced photodetector array, achieves a receiving capacity of 600 Gbps per channel with 100-Gbaud 64 quadrature amplitude modulation (QAM) signal and 512 Gbps per channel with 128-Gbaud 16 QAM signal. Additionally, we demonstrate a long-distance reception of 100 Gbaud quadrature phase-shift keying (QPSK) and 16 QAM signals over transmission distances of 1040 km and 25 km. A seven-channel single-polarization I-Q coherent receiving chip achieves a total receiving capacity of 3.584 Tbps. This heterogeneous-integrated thin-film LiNbO3 optical coherent receiver shows the potential for Pbps-scale applications in future hyperscale data center interconnects.

With experimental data of DIS involving transversely polarized proton, we have calculated the 3-D charge density inside the polarized proton, which is found to have a significant non-spherical symmetry. Then we have calculated the property of electromagnetic field (E-M field) generated by a single transversely polarized proton ($p^{\uparrow}$). Based on them, the E-M field generated in small collision system $p^{\uparrow}+A$ are studied. We find that the orientation of this E-M field has a significant dependence on the polarization direction of the proton, and the correlator ($\Delta\gamma$ ) has also significant dependence on the angle between reaction plane and polarization direction. This finding provides us a new method for probing the chiral magnetic effect (CME)., Comment: 5 pages, 8 figures

Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video grounding datasets merely focus on simple events and are either limited to shorter videos or brief sentences, which hinders the model from evolving toward stronger multimodal understanding capabilities. To address these limitations, we present a large-scale video grounding dataset named SynopGround, in which more than 2800 hours of videos are sourced from popular TV dramas and are paired with accurately localized human-written synopses. Each paragraph in the synopsis serves as a language query and is manually annotated with precise temporal boundaries in the long video. These paragraph queries are tightly correlated to each other and contain a wealth of abstract expressions summarizing video storylines and specific descriptions portraying event details, which enables the model to learn multimodal perception on more intricate concepts over longer context dependencies. Based on the dataset, we further introduce a more complex setting of video grounding dubbed Multi-Paragraph Video Grounding (MPVG), which takes as input multiple paragraphs and a long video for grounding each paragraph query to its temporal interval. In addition, we propose a novel Local-Global Multimodal Reasoner (LGMR) to explicitly model the local-global structures of long-term multimodal inputs for MPVG. Our method provides an effective baseline solution to the multi-paragraph video grounding problem. Extensive experiments verify the proposed model's effectiveness as well as its superiority in long-term multi-paragraph video grounding over prior state-of-the-arts. Dataset and code are publicly available. Project page: https://synopground.github.io/., Comment: Accepted to ACM MM 2024. Project page: https://synopground.github.io/

Light detection and ranging (LiDAR) has emerged as an indispensable tool in autonomous technology. Among its various techniques, frequency modulated continuous wave (FMCW) LiDAR stands out due to its capability to operate with ultralow return power, immunity to unwanted light, and simultaneous acquisition of distance and velocity. However, achieving a rapid update rate with sub-micron precision remains a challenge for FMCW LiDARs. Here, we present such a LiDAR with a sub-10 nm precision and a 24.6 kHz update rate by combining a broadband Fourier domain mode-locked (FDML) laser with a silicon nitride soliton microcomb. An ultrahigh frequency chirp rate up to 320 PHz/s is linearized by a 50 GHz microcomb to reach this performance. Our theoretical analysis also contributes to resolving the challenge of FMCW velocity measurements with nonlinear frequency sweeps and enables us to realize velocity measurement with an uncertainty below 0.4 mm/s. Our work shows how nanophotonic microcombs can unlock the potential of ultrafast frequency sweeping lasers for applications including LiDAR, optical coherence tomography and sensing.

This paper focuses on the optimal coverage problem (OCP) for multi-agent systems with decentralized optimization. A game based distributed decision approach for the the multi-agent OCP is proposed. The equivalence between the equilibrium of the game and the extreme value of the global performance objective is strictly proved. Then, a distributed algorithm only using local information to obtain the global near-optimal coverage is developed, and its convergence is proved. Finally, the proposed method is applied to maximize the covering time of a satellite constellation for a target. The simulation results under different scenarios show our method costs much less computation time under some level index than traditional centralized optimization., Comment: 11 pages,11 figures