Search

At least 25 kinds of detector-like devices need to be integrated in Phase I of the High Energy Photon Source (HEPS), and the work needs to be carefully planned to maximise productivity with highly limited human resources. After a systematic analysis on the actual work involved in detector integration, a separation of concerns between collaborating groups of personnel is established to minimise the duplication of efforts. To facilitate software development for detector integration, the ADGenICam library, which abstracts repeated code in EPICS modules for cameras, is extended to support a much wider range of detectors. An increasingly considerable fraction of detectors, both inside and outside HEPS, offer performance that exceed capabilities of the areaDetector framework in EPICS. Given this background, areaDetector's limitations in performance and architecture are analysed, and a QueueIOC -based framework that overcomes these limitations is introduced. A simple, flexible ZeroMQ-based protocol is used for data transport in this framework, while RDMA transport and multi-node readout will be explored for higher data throughputs. By calling C/C++ libraries from within Python, the performance of the former and the expressiveness of the latter can coexist nicely; the expressiveness allows for much higher efficiency in the implementation and use of integration modules functionally comparable to their EPICS counterparts., Comment: 11 pages, 3 figures

We study the next-to-leading order (NLO) electroweak (EW) corrections to the $\gamma \gamma \to \tau^+ \tau^-$ process in Pb-Pb ultraperipheral collision (UPC). We find that the EW correction $\delta \sigma_{\mathrm{EW}}$ decreases the total cross section $\sigma_{\mathrm{NLO}} = \sigma_{\mathrm{LO}} + \delta \sigma_{\mathrm{EW}}$ by -3% at Pb-Pb center-of-mass energy $\sqrt{s_{NN}}=5.02$ TeV. The weak correction plays significant role whose contribution is about -4 times of that of QED. The CMS and ATLAS collaborations use the reaction $\gamma\gamma \to \tau^+ \tau^-$ in Pb-Pb and proton-proton UPC to constrain tau's anomalous magnetic moment $a_\tau$. By parameterizing the $\gamma \tau \tau$ vertex with two form factors $F_{1,2}$, the cross section can be written as $\sigma_{a_\tau} = \sigma_{\mathrm{LO}} + \delta \sigma_{a_\tau}$, where $\delta \sigma_{a_\tau}$ is proportional to $a_\tau$. Under this $F_{1,2}$ parametrization scheme, it is found that there is some deviation between the NLO EW correction $\delta \sigma_{\mathrm{EW}}$ and $\delta \sigma_{a_\tau}$ which is derived either by the CMS constraint range on $a_\tau$ or from the precise SM prediction of $a_\tau$. We also find that various differential distributions of the two ratios $\mathrm{d} \sigma_{\mathrm{NLO}}/ \mathrm{d} \sigma_{\mathrm{LO}}$ and $\mathrm{d} \sigma_{a_\tau}/ \mathrm{d} \sigma_{\mathrm{LO}}$ have different lineshapes. This work is significant to precisely study the interaction of $\gamma \tau \tau$ via $\gamma \gamma \to \tau^+ \tau^-$ process., Comment: 18 pages, 6 figures

Most learned B-frame codecs with hierarchical temporal prediction suffer from the domain shift issue caused by the discrepancy in the Group-of-Pictures (GOP) size used for training and test. As such, the motion estimation network may fail to predict large motion properly. One effective strategy to mitigate this domain shift issue is to downsample video frames for motion estimation. However, finding the optimal downsampling factor involves a time-consuming rate-distortion optimization process. This work introduces lightweight classifiers to determine the downsampling factor. To strike a good rate-distortion-complexity trade-off, our classifiers observe simple state signals, including only the coding and reference frames, to predict the best downsampling factor. We present two variants that adopt binary and multi-class classifiers, respectively. The binary classifier adopts the Focal Loss for training, classifying between motion estimation at high and low resolutions. Our multi-class classifier is trained with novel soft labels incorporating the knowledge of the rate-distortion costs of different downsampling factors. Both variants operate as add-on modules without the need to re-train the B-frame codec. Experimental results confirm that they achieve comparable coding performance to the brute-force search methods while greatly reducing computational complexity.

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.

Wavefield reconstruction inversion (WRI) has been considered a potential solution to the issue of local minima inherent in conventional full waveform inversion (FWI) methods. However, most current WRI research has been confined to 2D problems due to the computational challenges posed by solving augmented systems for optimal data-fitting wavefields. This constraint limits WRI applicability to realistic 3D scenarios. This study introduces a GPU-accelerated 3D source-free adaptive WRI (GPU-SF-AWRI) method that overcomes these computational barriers by adaptively controlling the computational accuracy of wavefield simulation and optimizing GPU utilization, thus enhancing its suitability for 3D applications. The inclusion of an on-the-fly source estimation technique further boosts its performance on realistic problems. Numerical experiments reveal that the proposed GPU-accelerated method achieves a 195-fold speedup compared to CPU-based approaches. By incorporating adaptive accuracy and total variation regularization, we attain a 2-fold speedup while maintaining inversion accuracy. We applied the GPU-SF-AWRI method to numerical and actual Vertical Seismic Profiling (VSP) physical modeling P-wave data, confirming its efficacy in addressing real data challenges and mitigating local minima associated with conventional FWI.

This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%. Given a macroprogramming framework, the GPT model is capable of writing scripts using high-level functions from the framework to handle possible incidents. Moreover, the GPT model shows efficacy in processing a vast amount of sensor data by offering fast and high-quality responses, which comprise expected results and summarized insights. Overall, the model demonstrates its potential to power a natural language interface. We hope that researchers will find these case studies inspiring to develop further.

The medial axis, a lower-dimensional shape descriptor, plays an important role in the field of digital geometry processing. Despite its importance, robust computation of the medial axis transform from diverse inputs, especially point clouds with defects, remains a significant challenge. In this paper, we tackle the challenge by proposing a new implicit method that diverges from mainstream explicit medial axis computation techniques. Our key technical insight is the difference between the signed distance field (SDF) and the medial field (MF) of a solid shape is the unsigned distance field (UDF) of the shape's medial axis. This allows for formulating medial axis computation as an implicit reconstruction problem. Utilizing a modified double covering method, we extract the medial axis as the zero level-set of the UDF. Extensive experiments show that our method has enhanced accuracy and robustness in learning compact medial axis transform from thorny meshes and point clouds compared to existing methods.

Multiple electroweak phase transitions occurring sequentially in the early universe can give rise to intriguing phenomenology, compared to the typical single-step electroweak phase transition. In this work, we investigate this scenario within the framework of the two-Higgs-doublet model with a pseudoscalar, utilizing the complete one-loop finite-temperature effective potential. After considering relevant experimental and theoretical constraints, we identify four distinct types of phase transitions. In the first case, only the configuration of the CP-even Higgs acquires a non-zero value via a first-order or a cross-over electroweak phase transition, leading to electroweak symmetry breaking. In the remaining three cases, the pseudoscalar fields can obtain vacuum expectation values at different phases of the multi-step phase transition process, leading to spontaneous breaking of the CP symmetry. As the temperature decreases, the phase shifts to the vacuum observed today via first-order electroweak phase transition, at this point, the vacuum expectation value of the pseudoscalar field returns to zero, restoring the CP symmetry. Finally, we compare the transition strength and the stochastic gravitational wave background generated in the four situations along with the projected detection limits., Comment: 24 pages, 7 figures

This paper analyzes the working principle of four-switch cross-shaped reconfigurable intelligent surface (RIS) in detail and reveals the different types of RIS that can be designed based on this structure. Combined with the design examples using this structure in the currently published articles, this paper summarizes and organizes them, and also points out several RIS solutions that have not been designed using this structure. Finally, based on this four-switch cross-shaped structure, this paper proposes a novel RIS design example that can realize the function switching of 1-bit ultra-wideband (UWB) and 2-bit narrowband, and conducts simulation verification. The simulation results show that by optimizing the element structure and controlling the states of the four switches, the 1-bit ultra-wideband function can achieve a frequency band coverage of 10.5GHz-19.8GHz and a 2-bit phase quantization function around 18.12GHz. At the same time, it can realize 60{\deg} two-dimensional beam scanning function. We call this novel design "bit reconfigurable metasurface".

In the new perspective of spatial quantization, this article systematically studies the advantages of reconfigurable reflectarray (RRA) designed with closely spaced elements in terms of sidelobe level (SLL), scanning accuracy, scan loss and beam granularity, including theoretical analysis and simulation verification. This article sequentially studies RRAs with element periods of {\lambda}/2, {\lambda}/4 and {\lambda}/8. Both theoretical and simulation results show that under the condition of the same aperture size, with the number of spatial quantization bits increasing, 1bit RRA using closely spaced structure SLL will have a improvement of about 5dB. The scanning accuracy at 60{\deg} is improved from 54.52{\deg} at {\lambda}/2 to 57.97{\deg} at {\lambda}/8, while the scan loss is improved from 5.02dB at {\lambda}/2 to 2.85dB at {\lambda}/8. In terms of beam granularity, the beam granularity is increased by about 4 times for every 1bit of spatial quantization encryption in the RRA element period. The beam granularity at 0{\deg} of 1bit RRA with unit period of {\lambda}/2 is 0.166{\deg}, {\lambda}/4 is 0.033{\deg}, and {\lambda}/8 is 0.009{\deg}. This study has an important reference value for reconfigurable reflectarray design, communication system and radar design.

Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve translation have typically involved fine-tuning LLMs using parallel corpora. However, vanilla fine-tuning often leads to catastrophic forgetting of the instruction-following capabilities and alignment with human preferences, compromising their broad general abilities and introducing potential security risks. These abilities, which are developed using proprietary and unavailable training data, make existing continual instruction tuning methods ineffective. To overcome this issue, we propose a novel approach called RaDis (Rationale Distillation). RaDis harnesses the strong generative capabilities of LLMs to create rationales for training data, which are then "replayed" to prevent forgetting. These rationales encapsulate general knowledge and safety principles, acting as self-distillation targets to regulate the training process. By jointly training on both reference translations and self-generated rationales, the model can learn new translation skills while preserving its overall general abilities. Extensive experiments demonstrate that our method enhances machine translation performance while maintaining the broader capabilities of LLMs across other tasks. This work presents a pathway for creating more versatile LLMs that excel in specialized tasks without compromising generality and safety.

Large Language Models (LLMs) have been widely used in code completion, and researchers are focusing on scaling up LLMs to improve their accuracy. However, larger LLMs will increase the response time of code completion and decrease the developers' productivity. In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B. Compared to existing LLMs, aiXcoder-7B achieves higher code completion accuracy while having smaller scales (i.e., 7 billion parameters). We attribute the superiority of aiXcoder-7B to three key factors: (1) Multi-objective training. We employ three training objectives, one of which is our proposed Structured Fill-In-the-Middle (SFIM). SFIM considers the syntax structures in code and effectively improves the performance of LLMs for code. (2) Diverse data sampling strategies. They consider inter-file relationships and enhance the capability of LLMs in understanding cross-file contexts. (3) Extensive high-quality data. We establish a rigorous data collection pipeline and consume a total of 1.2 trillion unique tokens for training aiXcoder-7B. This vast volume of data enables aiXcoder-7B to learn a broad distribution of code. We evaluate aiXcoder-7B in five popular code completion benchmarks and a new benchmark collected by this paper. The results show that aiXcoder-7B outperforms the latest six LLMs with similar sizes and even surpasses four larger LLMs (e.g., StarCoder2-15B and CodeLlama-34B), positioning aiXcoder-7B as a lightweight and effective LLM for academia and industry. Finally, we summarize three valuable insights for helping practitioners train the next generations of LLMs for code. aiXcoder-7B has been open-souced and gained significant attention. As of the submission date, aiXcoder-7B has received 2,193 GitHub Stars., Comment: aiXcoder-7B is available at https://github.com/aixcoder-plugin/aiXcoder-7B

A finitely supertranslated Schwarzschild black hole possesses nontrivial super-Lorentz charges compared with the standard one. This may impact the quasinormal modes of the black hole. Since the Einstein's equations are generally covariant, the quasinormal modes of a supertranslated black hole can be obtained by supertranslating the familiar results for a standard black hole. It turns out that the supertranslated quasinormal modes can be obtained by simply shifting the retarded time of the standard modes by an angle-dependent function parameterizing the supertranslation. Therefore, the supertranslated quasinormal modes oscillate at the same frequencies and decay at the same rates as the corresponding standard ones. The supertranslated metric is time translation invariant, but does not explicitly respect spherical symmetries, although it is implicitly rotationally symmetric. So the supertranslated perturbations can still be written as linear combinations of the eigenfunctions of the generalized angular momentum operators for the underlying rotational symmetry. With a suitably defined asymptotic parity transformation, any perturbation can be decomposed into the even and odd parity parts. Then, one may conclude that the isospectrality still holds. To detect such supertranslated quasinormal modes, one has to place multiple gravitational wave interferometers around the supertranslated black hole, and measure the differences in the time shifts between interferometers. Gravitational lensing may also be helpful in the same spirit., Comment: 19 pages, 0 figures, comments very welcomed.Improved discussion on angular momentum operators

We present a method to compute real-time path integrals numerically, by Monte-Carlo sampling on near-Lefschetz thimbles. We present a collection of new tools, which together provide an alternative to existing methods such as the Generalised thimble. These involve a convenient coordinate parameterization of the thimble, direct numerical integration along a radial coordinate into an effective path integral weight and locally deforming the Lefschetz thimble using its Gaussian (non-interacting theory) counterpart in a region about the critical point. We apply this to quantum mechanics, identify possible pitfalls and benefits, and benchmark its efficiency., Comment: 24 pages, 10 figures

Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties in drawing accurate class prototypes from support set samples, primarily due to probable large intra-class differences and small inter-class differences within the task. Recent approaches attempt to incorporate external knowledge or pre-trained language models to augment data, but this requires additional resources and thus does not suit many few-shot scenarios. In this paper, we propose a novel solution to address this issue by adequately leveraging the information within the task itself. Specifically, we utilize label information to construct a task-adaptive metric space, thereby adaptively reducing the intra-class differences and magnifying the inter-class differences. We further employ the optimal transport technique to estimate class prototypes with query set samples together, mitigating the problem of inaccurate and ambiguous support set samples caused by large intra-class differences. We conduct extensive experiments on eight benchmark datasets, and our approach shows obvious advantages over state-of-the-art models across all the tasks on all the datasets. For reproducibility, all the datasets and codes are available at https://github.com/YvoGao/LAQDA., Comment: Accepted by EMNLP 2024 Findings

This study presents an innovative approach to creating a dynamic, AI based emission inventory system for use with the Weather Research and Forecasting model coupled with Chemistry (WRF Chem), designed to simulate vehicular and other anthropogenic emissions at satellite detectable resolution. The methodology leverages state of the art deep learning based computer vision models, primarily employing YOLO (You Only Look Once) architectures (v8 to v10) and T Rex, for high precision object detection. Through extensive data collection, model training, and finetuning, the system achieved significant improvements in detection accuracy, with F1 scores increasing from an initial 0.15 at 0.131 confidence to 0.72 at 0.414 confidence. A custom pipeline converts model outputs into netCDF files storing latitude, longitude, and vehicular count data, enabling real time processing and visualization of emission patterns. The resulting system offers unprecedented temporal and spatial resolution in emission estimates, facilitating more accurate short term air quality forecasts and deeper insights into urban emission dynamics. This research not only enhances WRF Chem simulations but also bridges the gap between AI technologies and atmospheric science methodologies, potentially improving urban air quality management and environmental policymaking. Future work will focus on expanding the system's capabilities to non vehicular sources and further improving detection accuracy in challenging environmental conditions.

This paper addresses the open problem of conducting change-point analysis for interval-valued time series data using the maximum likelihood estimation (MLE) framework. Motivated by financial time series, we analyze data that includes daily opening (O), up (U), low (L), and closing (C) values, rather than just a closing value as traditionally used. To tackle this, we propose a fundamental model based on stochastic differential equations, which also serves as a transformation of other widely used models, such as the log-transformed geometric Brownian motion model. We derive the joint distribution for these interval-valued observations using the reflection principle and Girsanov's theorem. The MLE is obtained by optimizing the log-likelihood function through first and second-order derivative calculations, utilizing the Newton-Raphson algorithm. We further propose a novel parametric bootstrap method to compute confidence intervals, addressing challenges related to temporal dependency and interval-based data relationships. The performance of the model is evaluated through extensive simulations and real data analysis using S&P500 returns during the 2022 Russo-Ukrainian War. The results demonstrate that the proposed OULC model consistently outperforms the traditional OC model, offering more accurate and reliable change-point detection and parameter estimates.

This paper focuses on distributive uninorms, which induce structures of commutative ordered semirings. We will show that the second uninorm must be locally internal on $A(e)$, and will present a complete characterization of the structure of such uninorms.

Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such as English and Chinese, leaving many other languages underrepresented. This imbalance, while limiting broader applications, generates a natural preference ranking between languages, offering an opportunity to bootstrap the multilingual capabilities of LLM in a self-improving manner. Thus, we propose $\textit{Language Imbalance Driven Rewarding}$, where the inherent imbalance between dominant and non-dominant languages within LLMs is leveraged as a reward signal. Iterative DPO training demonstrates that this approach not only enhances LLM performance in non-dominant languages but also improves the dominant language's capacity, thereby yielding an iterative reward signal. Fine-tuning Meta-Llama-3-8B-Instruct over two iterations of this approach results in continuous improvements in multilingual performance across instruction-following and arithmetic reasoning tasks, evidenced by an average improvement of 7.46% win rate on the X-AlpacaEval leaderboard and 13.9% accuracy on the MGSM benchmark. This work serves as an initial exploration, paving the way for multilingual self-improvement of LLMs., Comment: Work in progress

Kagome materials have been found to exhibit exotic physical properties such as spin frustration, charge density waves, and unconventional superconductivity. However, the number of materials with kagome lattice-related properties discovered so far is relatively small, limiting the exploration of the physical phenomena associated with kagome materials. Due to the weaker interlayer coupling in two-dimensional kagome materials, they are more likely to exhibit kagome lattice-related physical properties. Therefore, the search for potential two-dimensional kagome materials is crucial for understanding the underlying physics of kagome lattices. In this work, we performed high-throughput workflow to discover thermodynamically stable kagome transition metal oxide monolayers based on "1+3" strategy. Starting from a pool of 349 candidate materials, we identified 12 globally stable kagome monolayers, including both magnetic and non-magnetic structures. These monolayers were classified into four categories based on their electronic structures, lattice types, symmetry, band gaps, and magnetic properties. A detailed analysis was performed on kagome structures exhibiting band features near the Fermi level. This study demonstrates the feasibility of the "1+3" strategy in constructing kagome lattices, providing a pathway for further theoretical and experimental exploration of kagome materials and their potential quantum phenomena.

Diverse synchronization dynamics within the grid-following (GFL)/grid-forming (GFM) converters-interlinked system are prone to induce oscillatory instabilities. To quantify their stability influences, frequency-domain modal analysis (FMA) method based on the impedance network can serve as a good reference. However, since the adopted impedance network only retains electrical nodes, oscillation information provided by the FMA method is mainly concerned with circuits (e.g., participation of nodes), which is not convenient for an intuitive probe of sync loops' participations. To address this issue, this paper proposes an extended admittance modeling method for FMA, the basis of which is the explicit characterization of GFL/GFM sync loops. First, a four-port extended impedance model (EIM) of converter with one virtual sync node is proposed. Its resulting extended impedance network (EIN) is formed for the converters-interlinked system. Then, the FMA method can be directly applied to those virtual sync nodes/branches, so as to realize an intuitive evaluation of sync dynamics' effects on oscillations. The effectiveness of the proposed method is validated by the frequency scanning and time domain simulations in a typical point-to-point HVDC system., Comment: This paper is first submitted to IEEE Transactions on Power System on 2022.09.19 and being rejected. Then, this paper is submitted to IEEE Transactions on Power Delivery on 2023.07.23 and being rejected again

The absence of global magnetic fields is often cited to explain why Mars lacks a dense atmosphere. This line of thought is based on a prevailing theory that magnetic fields can shield the atmosphere from solar wind erosion. However, we present observations here to demonstrate a counterintuitive understanding: unlike the global intrinsic magnetic field, the remnant crustal magnetic fields can enhance atmosphere loss when considering loss induced by plasma wave-particle interactions. An analysis of MAVEN data, combined with observation-based simulations, reveals that the bulk of O+ ions would be in resonance with ultra-low frequency (ULF) waves when the latter were present. This interaction then results in significant particle energization, thus enhancing ion escaping. A more detailed analysis attributes the occurrence of the resonance to the presence of Mars' crustal magnetic fields, which cause the majority of nearby ions to gyrate at a frequency matching the resonant condition ({\omega}-k_{\parallel} v_{\parallel}={\Omega}_i) of the waves. The ULF waves, fundamental drivers of this entire process, are excited and propelled by the upstream solar wind. Consequently, our findings offer a plausible explanation for the mysterious changes in Mars' climate, suggesting that the ancient solar wind imparted substantially more energy., Comment: 16 pages & 5 figures & Supplementary Material

Machine Learning (ML) optimization frameworks have gained attention for their ability to accelerate the optimization of large-scale Quadratically Constrained Quadratic Programs (QCQPs) by learning shared problem structures. However, existing ML frameworks often rely heavily on strong problem assumptions and large-scale solvers. This paper introduces NeuralQP, a general hypergraph-based framework for large-scale QCQPs. NeuralQP features two main components: Hypergraph-based Neural Prediction, which generates embeddings and predicted solutions for QCQPs without problem assumptions, and Parallel Neighborhood Optimization, which employs a McCormick relaxation-based repair strategy to identify and correct illegal variables, iteratively improving the solution with a small-scale solver. We further prove that our framework UniEGNN with our hypergraph representation is equivalent to the Interior-Point Method (IPM) for quadratic programming. Experiments on two benchmark problems and large-scale real-world instances from QPLIB demonstrate that NeuralQP outperforms state-of-the-art solvers (e.g., Gurobi and SCIP) in both solution quality and time efficiency, further validating the efficiency of ML optimization frameworks for QCQPs.

Field-of-View (FoV) adaptive streaming significantly reduces bandwidth requirement of immersive point cloud video (PCV) by only transmitting visible points in a viewer's FoV. The traditional approaches often focus on trajectory-based 6 degree-of-freedom (6DoF) FoV predictions. The predicted FoV is then used to calculate point visibility. Such approaches do not explicitly consider video content's impact on viewer attention, and the conversion from FoV to point visibility is often error-prone and time-consuming. We reformulate the PCV FoV prediction problem from the cell visibility perspective, allowing for precise decision-making regarding the transmission of 3D data at the cell level based on the predicted visibility distribution. We develop a novel spatial visibility and object-aware graph model that leverages the historical 3D visibility data and incorporates spatial perception, neighboring cell correlation, and occlusion information to predict the cell visibility in the future. Our model significantly improves the long-term cell visibility prediction, reducing the prediction MSE loss by up to 50% compared to the state-of-the-art models while maintaining real-time performance (more than 30fps) for point cloud videos with over 1 million points.

Active learning (AL) has achieved great success by selecting the most valuable examples from unlabeled data. However, they usually deteriorate in real scenarios where open-set noise gets involved, which is studied as open-set annotation (OSA). In this paper, we owe the deterioration to the unreliable predictions arising from softmax-based translation invariance and propose a Dirichlet-based Coarse-to-Fine Example Selection (DCFS) strategy accordingly. Our method introduces simplex-based evidential deep learning (EDL) to break translation invariance and distinguish known and unknown classes by considering evidence-based data and distribution uncertainty simultaneously. Furthermore, hard known-class examples are identified by model discrepancy generated from two classifier heads, where we amplify and alleviate the model discrepancy respectively for unknown and known classes. Finally, we combine the discrepancy with uncertainties to form a two-stage strategy, selecting the most informative examples from known classes. Extensive experiments on various openness ratio datasets demonstrate that DCFS achieves state-of-art performance.

Physics-based simulation is essential for developing and evaluating robot manipulation policies, particularly in scenarios involving deformable objects and complex contact interactions. However, existing simulators often struggle to balance computational efficiency with numerical accuracy, especially when modeling deformable materials with frictional contact constraints. We introduce an efficient subspace representation for the Incremental Potential Contact (IPC) method, leveraging model reduction to decrease the number of degrees of freedom. Our approach decouples simulation complexity from the resolution of the input model by representing elasticity in a low-resolution subspace while maintaining collision constraints on an embedded high-resolution surface. Our barrier formulation ensures intersection-free trajectories and configurations regardless of material stiffness, time step size, or contact severity. We validate our simulator through quantitative experiments with a soft bubble gripper grasping and qualitative demonstrations of placing a plate on a dish rack. The results demonstrate our simulator's efficiency, physical accuracy, computational stability, and robust handling of frictional contact, making it well-suited for generating demonstration data and evaluating downstream robot training applications.

Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key aspects: understanding, reasoning, and multilinguality. Specifically, our evaluation encompasses 6 tasks using data from 37 medical datasets, including two newly constructed and more challenging question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. These datasets offer greater clinical relevance compared to standard medical QA benchmarks such as MedQA, translating more effectively into real-world clinical utility. Our analysis of o1 suggests that the enhanced reasoning ability of LLMs may (significantly) benefit their capability to understand various medical instructions and reason through complex clinical scenarios. Notably, o1 surpasses the previous GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two newly created complex QA scenarios. But meanwhile, we identify several weaknesses in both the model capability and the existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. We release our raw data and model outputs at https://ucsc-vlaa.github.io/o1_medicine/ for future research., Comment: The first four authors contributed equally, project page available at https://ucsc-vlaa.github.io/o1_medicine/

The transition metal dichalcogenides (TMDCs) with a 1T' structural phase are predicted to be two-dimensional topological insulators at zero temperature. Although the quantized edge conductance of 1T'-WTe$_2$ has been confirmed to survive up to 100 K, this temperature is still relatively low for industrial applications. Addressing the limited studies on temperature effects in 1T'-TMDCs, our research focuses on the electronic and crystal properties of the epitaxial 1T'-WSe$_2$ monolayers grown on bilayer graphene (BLG) and SrTiO$_3$(100) substrates at various temperatures. For the 1T'-WSe$_2$ grown on BLG, we observed a significant thermal expansion effect on its band structures with a thermal expansion coefficient of $\sim$60$\times$10$^{-6}$ K$^{-1}$. In contrast, the 1T'-WSe$_2$ grown on SrTiO$_3$(100) exhibits minimal changes with varied temperatures due to the enhanced strain exerted by the substrate. Besides, A significant Coulomb gap (CG) was observed pinned at the Fermi level in the angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling spectroscopy (STS). The CG was founded to decrease with increasing temperatures, and can persist up to 200 K for 1T'-WSe$_2$/BLG, consistent with our Monte Carlo simulations. The robustness of the CG and the positive fundamental gap endow the epitaxial 1T'-WSe$_2$ monolayers with huge potential for realizing the quantum spin Hall devices.

The significant discrepancy in the difference of squared nuclear charge radii $\Delta R^2$ of $^{3,4}$He obtained from electronic-atom or muonic-atom energy levels is a puzzle. In this paper, we show that the tension is resolved by including off-diagonal mixing effects due to the hyperfine interaction. Our findings indicate that the hyperfine mixing effect from the $n\,^3\!S$ and $n\,^1\!S$ states ($n>2$) of $^3$He leads to a $-1.37$ kHz adjustment in the isotope shift of the $2\,^1\!S-2\,^3\!S$ transition, surpassing the current uncertainty by a factor of $7$. This results in a change of $-0.0064~\rm{fm}^2$ in $\Delta R^2$, shifting from $1.0757(15)~\mathrm{fm}^2$ to $1.0693(15)~\mathrm{fm}^2$ as determined by Werf {\it et al.}, significantly reducing the discrepancy with the value of $1.0636(31)~\mathrm{fm}^2$ determined by $\mu\rm{He}^+$, and aligning with the result of $1.069(3)$ $\mathrm{fm}^2$ obtained from the $2\,^3\!S-2\,^3\!P$ transition. This adjustment will result in a noticeable change in the absolute nuclear charge radius of $^{3}$He by $-0.0017~\rm{fm}$, aligning the revised value of $1.9715(11)~\mathrm{fm}$ with the value of $1.97007(94)~\mathrm{fm}$ determined by $\mu^3\rm{He}^+$ within $1\sigma$. Our results offer crucial insights into resolving discrepancy in $\Delta R^2$ for $^{3,4}$He and determining the charge radius of $^3$He.

Genuine multipartite non-locality is not only of fundamental interest but also serves as an important resource for quantum information theory. We consider the $N$-partite scenario and provide an analytical upper bound on the maximal expectation value of the generalized Svetlichny inequality achieved by an arbitrary $N$-qubit system. Furthermore, the constraints on quantum states for which the upper bound is tight are also presented and illustrated by noisy generalized Greenberger-Horne-Zeilinger (GHZ) states. Especially, the new techniques proposed to derive the upper bound allow more insights into the structure of the generalized Svetlichny operator and enable us to systematically investigate the relevant properties. As an operational approach, the variation of the correlation matrix we defined makes it more convenient to search for suitable unit vectors that satisfy the tightness conditions. Finally, our results give feasible experimental implementations in detecting the genuine multipartite non-locality and can potentially be applied to other quantum information processing tasks., Comment: 13 pages, 1 figure

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a "volumetric core," obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology-preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.

An axion-like spectator during inflation can trigger a tachyonic instability which amplifies the modes of one of the helicities of the gauge field, resulting in the production of parity-violating gravitational waves (GWs). In this paper we investigate the impact of the coupling $RFF$ of the gauge field to gravity on the production of GWs. We find that such a coupling introduces a multiplicative factor to the tachyonic mass, which effectively enhances the amplitude of the gauge field modes. Produced GWs are expected to be observed by future space-based GW detectors. Additionally, we find that the strong backreaction due to particle production leads to multiple peaks in the energy spectrum of GWs., Comment: 8 pages, 3 figures

In this paper, we establish the parahoric reduction theory of formal connections (or Higgs fields) on a formal principal bundle with parahoric structures, which generalizes Babbitt-Varadarajan's result for the case without parahoric structures [5] and Boalch's result for the case of regular singularity [9]. As applications, we prove the equivalence between extrinsic definition and intrinsic definition of regular singularity and provide a criterion of relative regularity for formal connections, and also demonstrate a parahoric version of Frenkel-Zhu's Borel reduction theorem of formal connections [23]., Comment: 24 pages, comments are welcome!

The mass transfer process is prevalent during the inspiral phase of compact binary systems. Our study focuses on systems comprising low-mass white dwarfs, particularly in neutron star-white dwarf binaries and double white dwarf binaries, where a stable mass transfer process occurs at low frequencies. By analyzing the evolution of gravitational wave frequencies in the presence of mass transfer within quasi-circular orbits, we derive an analytical expression for the time-dependent frequency across different frequency bands and the waveforms emitted by compact binaries. Considering gravitational waves emitted by compact binaries in the $1\thicksim10$ mHz band, based on the Fisher analysis, we find that the mass transfer rate can be measured as accurately as $10^{-7} M_\odot/\text{year}$ by space-based gravitational-wave detectors with a signal-to-noise ratio of the order of $10^3$. Including the mass transfer effect in the waveforms provides a new possibility to measure the individual masses of double white dwarf binaries. The relative error of measured white dwarf masses can be down to the order of $0.01$.

The influence of hydrated cation-{\pi} interaction forces on the adsorption and filtration capabilities of graphene-based membrane materials is significant. However, the lack of interaction potential between hydrated Cs+ and graphene limits the scope of adsorption studies. Here, it is developed that a deep neural network potential function model to predict the interaction force between hydrated Cs+ and graphene. The deep potential has DFT-level accuracy, enabling accurate property prediction. This deep potential is employed to investigate the properties of the graphene surface solution, including the density distribution, mean square displacement, and vibrational power spectrum of water. Furthermore, calculations of the molecular orbital electron distributions indicate the presence of electron migration in the molecular orbitals of graphene and hydrated Cs+, resulting in a strong electrostatic interaction force. The method provides a powerful tool to study the adsorption behavior of hydrated cations on graphene surfaces and offers a new solution for handling radionuclides.

In this paper, a fixed-time disturbance observerbased model predictive control algorithm is proposed for trajectory tracking of quadrotor in the presence of disturbances. First, a novel multivariable fixed-time disturbance observer is proposed to estimate the lumped disturbances. The bi-limit homogeneity and Lyapunov techniques are employed to ensure the convergence of estimation error within a fixed convergence time, independent of the initial estimation error. Then, an observerbased model predictive control strategy is formulated to achieve robust trajectory tracking of quadrotor, attenuating the lumped disturbances and model uncertainties. Finally, simulations and real-world experiments are provided to illustrate the effectiveness of the proposed method.

The study of the gauge field is an everlasting topic in modern physics. Spin-orbit coupling is a powerful tool in ultracold atomic systems, resulting in an artificial gauge field that can be easily manipulated and observed in a tabletop environment. Combining optical lattices and atom-atom interaction, the artificial gauge field can be made density-dependent. In this work, we investigate a one-dimensional Bose-Hubbard model with spin-orbit coupling, where a density-dependent gauge field emerges spontaneously in low-energy physics. First, we focus on the two-body quantum walk dynamics and give an interpretation of the phenomena with resonant tunneling. Then, we calculate the mean-field phase diagram using the two-site Gutzwiller ansatz. Two types of superfluid phase and a Mott insulator phase are found. Finally, we discuss the experimental realization protocol with Raman lattices., Comment: 11 pages, 8 figures

Close white dwarf binaries (CWDBs) are considered to be progenitors of several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic variables). These violent events are broadly used in studies of general relativity and cosmology. However, obtaining precise stellar parameter measurements for both components of CWDBs is a challenging task given their low luminosities, swift time variation, and complex orbits. High-resolution spectra (R$> 20 000$) are preferred but expensive, resulting in a sample size that is insufficient for robust population study. To release the full potential of the less expensive low-resolution spectroscopic surveys, and thus greatly expand the CWDB sample size, it is necessary to develop a robust pipeline for spectra decomposition and analysis. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. The best-fit stellar parameters were obtained by finding the least $\chi^2$ solution to these feature lines and the continuum simultaneously. We demonstrate the reliability of our code with two well-studied CWDBs, WD 1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly identified CWDB candidates, most of which are fitted with double component models for the first time. Our estimates agree with previous results for the common stars and follow the statistical distribution in the literature. The application of our code to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring., Comment: 14pages, 12 figures, 2 tables.Accepted by A&A

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual locating a found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or a found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution of the found peaks, which helps explore more peaks. The experiments are conducted on 20 widely-used benchmark MMOPs. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performed algorithms proposed recently and four winner algorithms in the IEEE CEC competitions for multimodal optimization., Comment: under review

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

Dialogical Argument Mining(DialAM) is an important branch of Argument Mining(AM). DialAM-2024 is a shared task focusing on dialogical argument mining, which requires us to identify argumentative relations and illocutionary relations among proposition nodes and locution nodes. To accomplish this, we propose a two-stage pipeline, which includes the Two-Step S-Node Prediction Model in Stage 1 and the YA-Node Prediction Model in Stage 2. We also augment the training data in both stages and introduce context in Stage 2. We successfully completed the task and achieved good results. Our team Pokemon ranked 1st in the ARI Focused score and 4th in the Global Focused score., Comment: Published on the 11th Workshop on Argument Mining

Suppressing high-frequency laser phase noise, particularly at frequencies near and beyond typical feedback bandwidths of a few MHz, is a critical yet challenging task in many advanced applications. Feedforward-based methods generally outperform feedback in high-frequency range, but their performances are more susceptible to perturbations. In this work, we focus on the Pound-Drever-Hall (PDH)-feedforward method we demonstrated recently [Yu-Xin Chao et al., Optica 11(7), 945-950 (2024)] and analyze the factors that affect its long-term stability. By constructing a simple circuit allowing for adaptive control of the feedforward gain in response to power fluctuations of cavity transmission, we demonstrate a robust $\geq 40$~dB suppression of laser phase noise around 2~MHz and a noise suppression bandwidth up to 50~MHz. In comparison, when using normal PDH feedback, robust noise suppression of over 40 dB can only occur for frequencies below tens of kHz in most setups. Our findings may pave the way for general usage of PDH feedforward and allow for simple construction of low-noise lasers for precise quantum controls and precision metrology.

The Universe with the cosmic anisotropy will have a preferred direction of expansion. Therefore, reconstructing the expansion history by Gaussian Process (GP) can be used to probe the cosmic anisotropy model-independently. In this paper, for the luminosity distance $d_L(z)$ reconstruction, we turn to the inverse distance ladder where the type Ia supernova (SNIa) from the Pantheon+ sample determine the relative distances and the strongly gravitationally lensed quasars from H0LiCOW sample anchor these relative distances with some absolute distance measurements. By isolating the anisotropic information maybe carried by the Hubble constant $H_0$ and obtaining the constraint on the intrinsic parameter of SNIa, the absolute magnitude $M=-19.2522^{+0.0270}_{-0.0279}$ (at $68\%$ CL), we find that $d_L(z)$ reconstructions from samples located in different region of the Galactic coordinate system are almost consistent with each other and only a very weak preference for the cosmic anisotropy is found., Comment: 8 page, 5 figures

We present the detailed analysis of a new hybrid (p- and g-mode) sdB pulsator, TIC441725813 (TYC 4427-1021-1), discovered and monitored by TESS over 670 days. The TESS light curves available for this star were analysed using prewhitening techniques to extract mode frequencies accurately. The pulsation spectrum is then interpreted through methods that include asymptotic period spacing relationships and rotational multiplets identification. We also exploited a high signal-to-noise ratio (S/N), low-resolution spectrum of TIC441725813 using grids of non-local thermodynamic equilibrium (NLTE) model atmospheres to derive its atmospheric parameters. Interestingly, several frequency multiplets interpreted as rotational splittings of deep-probing g-modes indicate a slow rotation period of at least $85.3 \pm 3.6$ day, while splittings of mostly envelope-probing p-modes suggest a significantly shorter rotation period of $17.9 \pm 0.7$ day, which implies the core (mainly the helium mantle with possibly the deeper partially-mixed helium-burning core that it surrounds) rotates at least ~4.7 times slower than the envelope. The radial velocity curves indicate that TIC441725813 is in a close binary system with a low-luminosity companion, possibly a white dwarf. While elusive in the available TESS photometry, a low-frequency signal that would correspond to a period of $\sim 6.7$ h is found, albeit at low S/N. TIC441725813 is a particularly interesting sdB star whose envelope rotates faster than the core. We hypothesise that this might be caused by the effects of tidal interaction with a companion, although in the present case, the presence of such a companion will have to be further investigated. This analysis paves the way toward a more detailed seismic probing of TIC441725813 using optimisation techniques, which will be presented in a second paper., Comment: 14 pages, 14 figures

In 1694, Gregory and Newton proposed the problem to determine the kissing number of a rigid material ball. This problem and its higher dimensional generalization have been studied by many mathematicians, including Minkowski, van der Waerden, Hadwiger, Swinnerton-Dyer, Watson, Levenshtein, Odlyzko, Sloane and Musin. In this paper, we introduce and study a further generalization of the kissing numbers for convex bodies and obtain some exact results, in particular for balls in dimensions three, four and eight., Comment: 26 pages, 3 figures

Realistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propose Emotional Intensity-aware Network (EINet), dynamically adjusting intonation and rhythm by incorporating controllable emotional intensity. To better capture nuances in emotional intensity, we go beyond mere distance measurements among acoustic features. Instead, an emotion evaluator is utilized to precisely quantify speaker's emotional state. By employing an intensity mapper, intensity pseudo-labels are obtained to bridge the gap between emotional speech intensity modeling and run-time conversion. To ensure high speech quality while retaining controllability, an emotion renderer is used for combining linguistic features smoothly with manipulated emotional features at frame level. Furthermore, we employ a duration predictor to facilitate adaptive prediction of rhythm changes condition on specifying intensity value. Experimental results show EINet's superior performance in naturalness and diversity of emotional expression compared to state-of-the-art EVC methods., Comment: Accepted to INTERSPEECH2024

Precise information on the Higgs boson self-couplings provides the foundation for unveiling the electroweak symmetry breaking mechanism. Due to the scarcity of Higgs boson pair events at the LHC, only loose limits have been obtained. This is based on the assumption that the cross section is a quadratic function of the trilinear Higgs self-coupling in the $\kappa$ framework. However, if higher-order corrections of virtual Higgs bosons are included, the function form would dramatically change. In particular, new quartic and cubic power dependence on the trilinear Higgs self-coupling would appear. To get this new function form, we have performed a specialized renormalization procedure suitable for tracking all the Higgs self-couplings in each calculation step. Moreover, we introduce renormalization of the scaling parameter in the $\kappa$ framework to ensure the cancellation of all ultraviolet divergences. With the new function forms of the cross sections in both the gluon-gluon fusion and vector boson fusion channels, the upper limit of $\kappa_{\lambda_3}=\lambda_{\rm 3H}/\lambda_{\rm 3H}^{\rm SM}$ by the ATLAS (CMS) collaboration is reduced from 6.6 (6.49) to 5.4 (5.37). However, it is still hard to extract a meaningful constraint on the quartic Higgs self-coupling $\lambda_{\rm 4H}$ from Higgs boson pair production data. We also present the invariant mass distributions of the Higgs boson pair at different values of $\kappa_{\lambda}$, which could help to set optimal cuts in the experimental analysis., Comment: 12 pages, 4 figures