Search

Spin-2 ultralight dark matter (ULDM) is a viable dark matter candidate and it can be constrained using gravitational wave (GW) observations. In this paper, we investigate the detectability of spin-2 ULDM by space-based GW interferometers. By considering a direct coupling between spin-2 ULDM and ordinary matter, we derive the corresponding response functions and sensitivity curves for various time-delay interferometry channels and calculate the optimal sensitivity curves for future millihertz GW detectors. Our results demonstrate that the space-based detectors can place stringent constraints on the coupling constant of spin-2 ULDM, reaching $\alpha \sim 10^{-10}$ around a mass of $m \sim 10^{-17} \rm eV$, surpassing current limits from ground-based detectors and pulsar timing arrays. Thus, the space-based GW detectors can serve as powerful tools not only for detecting GWs but also for probing fundamental properties of ultralight dark matter., Comment: 12 pages, 6 figures

To date, over 4,000 pulsars have been detected. In this study, we identify 231 X-ray counterparts of {\it ATNF} pulsars by performing a spatial cross-match across the {\it Chandra}, {\it XMM-Newton} observational catalogs. This dataset represents the largest sample of X-ray counterparts ever compiled, including 98 normal pulsars (NPs) and 133 millisecond pulsars (MSPs). Based on this significantly expanded sample, we re-establish the correlation between X-ray luminosity and spin-down power, given by $L_{\rm X} \propto \dot{E}^{0.85\pm0.05}$ across the whole X-ray band. The strong correlation is also observed in hard X-ray band, while in soft X-ray band there is no significant correlation. Furthermore, $L_{\rm X}$ shows a strong correlation with spin period and characteristic age for NPs. For the first time, we observe a strongly positive correlation between $L_{\rm X}$ and the light cylinder magnetic field ($B_{\rm lc}$) for MSPs, with both NPs and MSPs following the relationship $L_{\rm X} \propto B_{\rm lc}^{1.14}$, consistent with the outer-gap model of pulsars that explains the mechanism of X-ray emission. Additionally, we investigate potential X-ray counterparts for GPPS pulsars, finding a lower likelihood of detection compared to {\it ATNF} pulsars., Comment: 18 pages, 4 figures, 3 tables, accepted by ApJ

Embedding quantum dot circuits into microwave cavities has emerged as a novel platform for controlling photon emission statistics by electrical means. With such a model, we reveal previously undefined quantum phase transitions in electrically driven lasing regimes by breaking the photon gain-loss balance condition. For one-photon interaction, the scaling theory indicates that the system undergoes a continuous phase transition from thermal to coherent photon emissions, consistent with conventional laser physics. Going beyond this, a hidden discontinuous quantum phase transition from superbunched to coherent states in two-photon processes, accompanied by the bistability within a mean-field theory, is predicted. Our prediction, along with its extension to multiphoton processes, represents a key step towards accessing lasing phase transitions.

Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO.

Looped Transformers have shown exceptional neural algorithmic reasoning capability in simulating traditional graph algorithms, but their application to more complex structures like hypergraphs remains underexplored. Hypergraphs generalize graphs by modeling higher-order relationships among multiple entities, enabling richer representations but introducing significant computational challenges. In this work, we extend the Loop Transformer architecture's neural algorithmic reasoning capability to simulate hypergraph algorithms, addressing the gap between neural networks and combinatorial optimization over hypergraphs. Specifically, we propose a novel degradation mechanism for reducing hypergraphs to graph representations, enabling the simulation of graph-based algorithms, such as Dijkstra's shortest path. Furthermore, we introduce a hyperedge-aware encoding scheme to simulate hypergraph-specific algorithms, exemplified by Helly's algorithm. We establish theoretical guarantees for these simulations, demonstrating the feasibility of processing high-dimensional and combinatorial data using Loop Transformers. This work highlights the potential of Transformers as general-purpose algorithmic solvers for structured data.

To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential ($\mu_B$) remains a challenging yet essential goal in the study of QCD matter under extreme conditions. In this study, we develop a deep learning assisted quasi-parton model, which utilizes three deep neural networks, to reconstruct the QCD EoS at zero $\mu_B$ and predict the EoS and transport coefficient $\eta/s$ at finite $\mu_B$. The EoS derived from our quasi-parton model shows excellent agreement with lattice QCD results obtained using Taylor expansion techniques. The minimum value of $\eta/s$ is found to be approximately 175 MeV and decreases with increasing chemical potential within the confidence interval. This model not only provides a robust framework for understanding the properties of the QCD EoS at finite $\mu_B$ but also offers critical input for relativistic hydrodynamic simulations of nuclear matter produced in heavy-ion collisions by the RHIC beam energy scan program., Comment: 7 pages, 8 figures

Implications of general properties of quantum field theory, such as causality, unitarity, and locality include constraints on the couplings of the effective field theory (EFT) coefficients. These constraints follow from the connections between the infrared (IR) and ultraviolet (UV) theory imposed by dispersion relations for four-particle amplitudes which formally allow us to express EFT couplings through the moments of positive-definite functions (imaginary parts of partial wave amplitudes) forming the EFT-hedron geometry. Previous studies of these positivity bounds were mainly focused on the weakly coupled EFTs, limiting the analysis to tree-level amplitudes of the IR theory. In this work, we extend the scope of positivity bounds including one-loop amplitudes, which is essential especially for the loops of massless particles. Examining a single scalar theory we found that the presence of massless loops cannot be reduced only to the running of EFT couplings because loops modify the crossing symmetry relations (null constraints). Our results demonstrate that while for small coupling constants, the one-loop bounds are in good agreement with the tree-level results, the allowed EFT parameter ranges can be significantly modified if a weak coupling assumption is not additionally imposed. In particular, we found that an upper bound on dimension-8 coupling becomes significantly stronger, and the dimension-12 coupling can be slightly negative which was thought to be forbidden by tree-level positivity bounds., Comment: 25 pages, 5 figures

The rare physical property of negative thermal expansion (NTE) is intriguing because materials with large NTE over a wide temperature range can serve as high-performance thermal expansion compensators. However, applications of NTE are hindered by the fact that most of the available NTE materials show small magnitudes of NTE, and/or NTE occurs only in a narrow temperature range. Herein, for the first time, we investigated the effect of anion substitution instead of general Pb/Ti-site substitutions on the thermal expansion properties of a typical ferroelectric NTE material, PbTiO3. Intriguingly, the substitution of S for O in PbTiO3 further increases the tetragonality of PbTiO3. Consequently, an unusually enhanced NTE with an average volumetric coefficient of thermal expansion $\bar{\alpha}_V$ = -2.50 $\times$ 10$^{-5}$/K was achieved over a wide temperature range (300 -- 790 K), which is contrasted to that of pristine PbTiO3 ($\bar{\alpha}_V$ = -1.99 $\times$ 10$^{-5}$/K RT -- 763 K). The intensified NTE is attributed to the enhanced hybridization between Pb/Ti and O/S atoms by the substitution of S, as evidenced by our theoretical investigations. We therefore demonstrate a new technique for introducing mixed anions to achieve large NTE over a wide temperature range in PbTiO3-based ferroelectrics., Comment: 14 pages, 4 figures

Quiescent galaxies over $34$, although our measurements are consistent within errors. This technique may be applied to other JWST surveys., Comment: Submitted to The Astrophysical Journal. 19 pages, 7 figures. Code to reproduce Figs. 2 - 4 can be found at https://github.com/alex-delavega/umap_jades_2025. Comments are welcome!

The first-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation. This technique maintains a queue of video frames with progressively increasing noise, continuously producing clean frames at the queue's head while Gaussian noise is enqueued at the tail. However, FIFO-Diffusion often struggles to keep long-range temporal consistency in the generated videos due to the lack of correspondence modeling across frames. In this paper, we propose Ouroboros-Diffusion, a novel video denoising framework designed to enhance structural and content (subject) consistency, enabling the generation of consistent videos of arbitrary length. Specifically, we introduce a new latent sampling technique at the queue tail to improve structural consistency, ensuring perceptually smooth transitions among frames. To enhance subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA) mechanism, which aligns subjects across frames within short segments to achieve better visual coherence. Furthermore, we introduce self-recurrent guidance. This technique leverages information from all previous cleaner frames at the front of the queue to guide the denoising of noisier frames at the end, fostering rich and contextual global information interaction. Extensive experiments of long video generation on the VBench benchmark demonstrate the superiority of our Ouroboros-Diffusion, particularly in terms of subject consistency, motion smoothness, and temporal consistency.

Electron pairing along with phase coherence generates superconductivity below the critical temperature ($T_c$). In underdoped high-$T_c$ cuprates, these two quantum phenomena may occur at separate temperatures, which was lately confirmed in the quasi-two-dimensional (quasi-2D) iron chalcogenide superconductors. Here, we report a systematic investigation on the pre-pairing behavior in a triclinic iron pnictide superconductor (Ca$_{0.85}$La$_{0.15}$)$_{10}$(Pt$_3$As$_8$)(Fe$_2$As$_2$)$_5$ with $T_c \approx $ 30 K, where the superconductivity is quasi-2D manifested by the Berezinskii-Kosterlitz-Thouless behaviors. Inelastic neutron scattering experiments unambiguously reveal a spin resonance peak around $E_R =$ 13 meV in the superconducting state, but its intensity continuously decreases when warming up across $T_c$, accompanied with an anomaly around $T^{*}\approx$ 45 K in spin correlations, and a suppression by an in-plane magnetic field persisting to the same temperature. Below $T^{*}$, a significant Nernst signal and a reduction of density of states at the Fermi level are also observed. These results suggest that the precursor of spin resonance is highly related to the preformed Cooper pairs driven by phase fluctuations, much like the pseudogap case in cuprates., Comment: 12 pages, 8 figures, Accepted by SCIENCE CHINA Physics, Mechanics & Astronomy. https://doi.org/10.1007/s11433-024-2596-6

The gauged $U(1)_{L_\mu-L_\tau}$ model is well-motivated to explain the muon $g-2$ anomaly and dark matter in previous studies. However, the latest NA64$\mu$ experiment has almost excluded all the parameter space for the muon $g-2$, which indicates that the light dark matter benchmark scenarios interpreting muon $g-2$ in previous studies are also not allowed at present. In light of many recent and future experimental results, we revisit the minimal $Z'$ portal dark matter in $U(1)_{L_\mu-L_\tau}$. Focus on the phenomenology of dark matter $\chi$, we first explore the viable parameter space for the light dark matter under various tight constraints. Around the $Z'$ resonance, we find that there is still a large parameter space for $m_\chi\gtrsim10$ MeV via thermal freeze-out. On the other hand, the constraints on $Z'$ above the electroweak scale are quite loose but are less studied for dark matter. We also investigate the heavy dark matter around the TeV scale and corresponding constraints. A large part of the parameter space for dark matter is within the reach of future experiments., Comment: 17 pages, 7 figures

We study the question posed by G. Gundersen and C. C. Yang, in which the following two types of binomial differential equations are investigated, $$ a(z)f'f''-b(z)(f)^{2}=c(z)e^{2d(z)},~~a(z)ff'-b(z)(f'')^{2}=c(z)e^{2d(z)}, $$ where $a(z)$, $b(z)$ and $c(z)$ are polynomials such that $a(z)b(z)c(z)\not\equiv 0$, $d(z)$ is non-constant polynomial. The explicit forms of entire solutions of the above binomial differential equations are obtained by using the Nevanlinna theory, which gives partial solutions to the question of G. Gundersen and C. C. Yang. In addition, some examples are given to illustrate these results.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

Semantic segmentation of remote sensing images is essential for various applications, including vegetation monitoring, disaster management, and urban planning. Previous studies have demonstrated that the self-attention mechanism (SA) is an effective approach for designing segmentation networks that can capture long-range pixel dependencies. SA enables the network to model the global dependencies between the input features, resulting in improved segmentation outcomes. However, the high density of attentional feature maps used in this mechanism causes exponential increases in computational complexity. Additionally, it introduces redundant information that negatively impacts the feature representation. Inspired by traditional threshold segmentation algorithms, we propose a novel threshold attention mechanism (TAM). This mechanism significantly reduces computational effort while also better modeling the correlation between different regions of the feature map. Based on TAM, we present a threshold attention network (TANet) for semantic segmentation. TANet consists of an attentional feature enhancement module (AFEM) for global feature enhancement of shallow features and a threshold attention pyramid pooling module (TAPP) for acquiring feature information at different scales for deep features. We have conducted extensive experiments on the ISPRS Vaihingen and Potsdam datasets. The results demonstrate the validity and superiority of our proposed TANet compared to the most state-of-the-art models.

Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological experiments. Computational methods are increasingly vital for efficient and scalable gene-disease association prediction. Graph-based learning models, which leverage node features and network relationships, are commonly employed for biomolecular predictions. However, existing methods often struggle to effectively integrate node features, heterogeneous structures, and semantic information. To address these challenges, we propose COmprehensive MEtapath-based heterogeneous graph Transformer(COMET) for predicting gene-disease associations. COMET integrates diverse datasets to construct comprehensive heterogeneous networks, initializing node features with BioGPT. We define seven Metapaths and utilize a transformer framework to aggregate Metapath instances, capturing global contexts and long-distance dependencies. Through intra- and inter-metapath aggregation using attention mechanisms, COMET fuses latent vectors from multiple Metapaths to enhance GDA prediction accuracy. Our method demonstrates superior robustness compared to state-of-the-art approaches. Ablation studies and visualizations validate COMET's effectiveness, providing valuable insights for advancing human health research., Comment: 6 pages

Time photonic crystals are media in which their electromagnetic parameters are modulated periodically in time, showing promising applications in non-resonant lasers and particle accelerators, among others. Traditionally utilized to study space photonic crystals, topological band theory has also been translated recently to analyze time photonic crystals with time inversion symmetry, enabling the construction of the temporal version of topological edge states. However, temporal disorder can readily break time inversion symmetry in practice, hence likely destroying the edge states associated with this type of time photonic crystals. To overcome this limitation, here we propose a new class of time photonic crystals presenting chiral symmetry instead, whose edge states exhibit superior robustness over the time-reversal-symmetry-protected counterparts. Our time photonic crystal is equivalent to a temporal version of the Su-Schrieffer-Heeger model, and the chiral symmetry of this type of time photonic crystals quantizes the winding number defined in the Bloch frequency band. Remarkably, random temporal disorders do not impact the eigenfrequencies of these chiral-symmetry-protected edge states, while instead enhancing their temporal localizations. Our findings thus provide a promising paradigm to control field amplification with exceptional robustness as well as being a feasible platform to investigate various topological phases in time-varying media.

Supermassive black holes (SMBHs) might originate from supermassive primordial black holes (PBHs). However, the hypothesis that these PBHs formed through the enhancement of the primordial curvature perturbations has consistently faced significant challenges due to the stringent constraints imposed by $\mu$-distortion in the cosmic microwave background (CMB). In this work, we investigate the impact of non-Gaussianity on $\mu$-distortion constraint in the context of broad power spectra. Our results show that, under the assumption of non-Gaussian curvature perturbations, a broad power spectrum may lead to weaker $\mu$-distortion constraints compared to the Gaussian cases. Our findings highlight the potential of the broad power spectrum to alleviate the $\mu$-distortion constraints on supermassive PBHs under large non-Gaussianity., Comment: 24 pages,7 figues,1 table

We present Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation. The overall Vchitect-2.0 system has several key designs. (1) By introducing a novel Multimodal Diffusion Block, our approach achieves consistent alignment between text descriptions and generated video frames, while maintaining temporal coherence across sequences. (2) To overcome memory and computational bottlenecks, we propose a Memory-efficient Training framework that incorporates hybrid parallelism and other memory reduction techniques, enabling efficient training of long video sequences on distributed systems. (3) Additionally, our enhanced data processing pipeline ensures the creation of Vchitect T2V DataVerse, a high-quality million-scale training dataset through rigorous annotation and aesthetic evaluation. Extensive benchmarking demonstrates that Vchitect-2.0 outperforms existing methods in video quality, training efficiency, and scalability, serving as a suitable base for high-fidelity video generation.

The large apertures of the upcoming generation of Giant Segmented Mirror Telescopes will enable unprecedented angular resolutions that scale as $\propto$ $\lambda$/D and higher sensitivities that scale as $D^4$ for point sources corrected by adaptive optics. However, all will have pupil segmentation caused by mechanical struts holding up the secondary mirror [European Extremely Large Telescope and Thirty Meter Telescope] or intrinsically, by design, as in the Giant Magellan Telescope. These gaps will be separated by more than a typical atmospheric coherence length (Fried Parameter). The pupil fragmentation at scales larger than the typical atmospheric coherence length, combined with wavefront sensors with weak or ambiguous sensitivity to differential piston, can introduce differential piston areas of the wavefront known as "petal modes". Commonly used wavefront sensors, such as a pyramid WFS, also struggle with phase wrapping caused by >$\lambda$/2 differential piston WFE. We have developed the holographic dispersed fringe sensor, a single pupil-plane optic that employs holography to interfere the dispersed light from each segment onto different spatial locations in the focal plane to sense and correct differential piston between the segments. This allows for a very high and linear dynamic piston sensing range of approximately $\pm$10 $\mu$m. We have begun the initial attempts at phasing a segmented pupil utilizing the HDFS on the High Contrast Adaptive optics phasing Testbed and the Extreme Magellan Adaptive Optics instrument (MagAO-X) at the University of Arizona. Additionally, we have demonstrated use of the HDFS as a differential piston sensor on-sky for the first time. We were able to phase each segment to within $\pm\lambda$/11.3 residual piston WFE ($\lambda$ = 800 nm) of a reference segment and achieved ~50 nm RMS residual piston WFE across the aperture in poor seeing conditions., Comment: 34 pages, 26 figures

We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. To maximize computational capacity, we integrate it with Mixture of Experts (MoE), creating a model with 32 experts and 456 billion total parameters, of which 45.9 billion are activated for each token. We develop an optimized parallel strategy and highly efficient computation-communication overlap techniques for MoE and lightning attention. This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens. The context window of MiniMax-Text-01 can reach up to 1 million tokens during training and extrapolate to 4 million tokens during inference at an affordable cost. Our vision-language model, MiniMax-VL-01 is built through continued training with 512 billion vision-language tokens. Experiments on both standard and in-house benchmarks show that our models match the performance of state-of-the-art models like GPT-4o and Claude-3.5-Sonnet while offering 20-32 times longer context window. We publicly release MiniMax-01 at https://github.com/MiniMax-AI., Comment: A technical report from MiniMax. The authors are listed in alphabetical order. We open-sourced our MiniMax-01 at https://github.com/MiniMax-AI

Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we have performed an Ursa Major cluster HI Survey (FUMaS) covering the entire UMa region centered at RA=11$^h$59$^m$28$^s$.3, DEC=49\degr05\arcmin18\arcsec with a radius of 7.5\degr. We have obtained the most complete catalog of HIsources in the UMa cluster, containing 179 HI sources with velocities in the range 625-1213.4 km~s$^{-1}$ and masses in the range 10$^{6.0}$-10$^{10.1}$ M$_{\odot}$ assuming a distance of 17.4 Mpc. Among them, 55 HI sources were detected for the first time. 32 HI sources do not have optical counterparts with known redshifts, and we found 25 possible counterparts in the multicolor optical image, and another 7 may be HI clouds. We also detected HI distributions in some interacting systems, e.g. the overlapping gas disks between NGC 3992 and its three companion galaxies, filaments around NGC 4026 and NGC 4111, and debris-like gas around NGC 3998. We computed the HIMF of the UMa cluster using the 1/V$_\mathrm{max}$ method and fitting it with the non-linear least squares (NLLS) and modified maximum likelihood (MML) method for the parameters: log$_{10}$($\phi_*$/Mpc$^{-3}$) = -0.86 $\pm$ 0.18, $\alpha$ = -1.10 $\pm$ 0.08 and log$_{10}$($M_*$/$M_{\odot}$) = 9.92 $\pm$ 0.23 for the NLLS method, and log$_{10}$($\phi_*$/Mpc$^{-3}$) = -0.78 $\pm$ 0.11, $\alpha$ = -1.10 $\pm$ 0.05 and log$_{10}$($M_*$/$M_{\odot}$) = 9.88 $\pm$ 0.14 for the MML method. This result is similar to that derived with VLA data, but the slope is steeper at the low-mass end because we detected more low-mass galaxies. The slope is flatter than that of the global HIMF, which is in agreement with the theoretical prediction that galaxies in high-density regions are stripped of gas due to interactions., Comment: 23 pages, 14 figures, submitted to ApJS

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include {\gamma}-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy {\gamma}-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy., Comment: 67 pages, 24 figures, accepted for publication in SCIENCE CHINA Physics, Mechanics & Astronomy

We present QSteed, a quantum compilation system that can be deployed on real quantum computing devices and quantum computing clusters. It is designed to meet the challenges of effectively compiling quantum tasks and managing multiple quantum backends. The system integrates two core components: a quantum compiler and a quantum computing resource virtualization manager, both of which provide standardized interfaces. The resource manager models quantum chips into different abstract layers, including the real quantum processing unit (QPU), the standard QPU (StdQPU), the substructure QPU (SubQPU), and the virtual QPU (VQPU), and stores this information in a quantum computing resource virtualization database, thus realizing the unified management of quantum computing devices. The quantum compiler adopts a modular and extensible design, providing a flexible framework for customizing compilation optimization strategies. It provides hardware-aware compilation algorithms that account for quantum gate noise and qubit coupling structures. By selecting the most suitable computing resources from the VQPU library, the compiler maps quantum tasks to the optimal qubit regions of the target device. We validated the effectiveness of the QSteed on the superconducting devices of the Quafu quantum cloud computing cluster. The quantum computing resource virtualization management technology of QSteed and the flexible and extensible design of its compiler make it possible to achieve unified management and task compilation for backend devices of multiple physical systems such as neutral-atom and ion-trap., Comment: White paper for QSteed, with 7 figures and 18 pages

In the paper, we derive the $\xi$-moments $\langle\xi_{2;b_1}^{n;\|}\rangle$ of the longitudinal leading-twist distribution amplitude $\phi_{2;b_1}^{\|}$ for $^1P_1$-state $b_1(1235)$-meson by using the QCD sum rules under the background field theory. Considering the contributions from the vacuum condensates up to dimension-six, its first two non-zero $\xi$-moments at the scale 1 GeV are $\langle\xi_{2;b_1}^{1;\|}\rangle= -0.647^{+0.118}_{-0.113}$ and $\langle\xi_{2;b_1}^{3;\|}\rangle = -0.328^{+0.055}_{-0.052}$, respectively. Using those moments, we then fix the Gegenbauer expansion series of $\phi_{2;b_1}^{\|}$ and apply it to calculate $B\to b_1(1235)$ transition form factors (TFFs) that are derived by using the QCD light-cone sum rules. Those TFFs are then extrapolated to the physically allowable $q^2$-range via the simplified series expansion. As for the branching fractions, we obtain ${\cal B}(\bar B^0 \to b_1^+(1235)e^- \bar\nu_e) = 2.179^{+0.553}_{-0.422}\times 10^{-4}$, ${\cal B}(B^0 \to b_1^-(1235)\mu^+\nu_\mu) = 2.166^{+0.544}_{-0.415}\times 10^{-4}$, ${\cal B}(B^+ \to b_1^0(1235)e^+\nu_e) = 2.353^{+0.597}_{-0.456}\times 10^{-4}$, and ${\cal B}(B^+ \to b_1^0(1235)\mu^+\nu_\mu) = 2.339^{+0.587}_{-0.448}\times 10^{-4}$, respectively., Comment: 11 pages, 5 figures, comments welcome

Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However, incompleteness in views and labels is a common challenge, often resulting from data collection oversights and uncertainties in manual annotation. Furthermore, the task of learning robust multi-view representations that are both view-consistent and view-specific from diverse views still a challenge problem in MvMLC. To address these issues, we propose a novel framework for incomplete multi-view multi-label classification (iMvMLC). Our method factorizes multi-view representations into two independent sets of factors: view-consistent and view-specific, and we correspondingly design a graph disentangling loss to fully reduce redundancy between these representations. Additionally, our framework innovatively decomposes consistent representation learning into three key sub-objectives: (i) how to extract view-shared information across different views, (ii) how to eliminate intra-view redundancy in consistent representations, and (iii) how to preserve task-relevant information. To this end, we design a robust task-relevant consistency learning module that collaboratively learns high-quality consistent representations, leveraging a masked cross-view prediction (MCP) strategy and information theory. Notably, all modules in our framework are developed to function effectively under conditions of incomplete views and labels, making our method adaptable to various multi-view and multi-label datasets. Extensive experiments on five datasets demonstrate that our method outperforms other leading approaches.

A study is presented of $B^+ \to K^0_{\rm S} K^- \pi^+ K^-$ and $B^+ \to K^0_{\rm S} K^+ \pi^- K^+$ decays based on the analysis of proton-proton collision data collected with the LHCb detector at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of $9 fb^{-1}$. The $K^0_{\rm S} K \pi$ invariant-mass distributions of both $B^+$ decay modes show, in the $m(K^0_{\rm S} K \pi)<1.85$ GeV mass region, a rich spectrum of light-meson resonances, resolved using an amplitude analysis. A complex mixture of $J^{PC}=0^{-+}, 1^{++}$ and $1^{+-}$ resonances is observed, dominated by $\eta(1405)$, $\eta(1470)$, $\eta(1760)$, $f_1(1285)$, $f_1(1420)$ and $h_1(1405)$ resonances. The $K^0_{\rm S} K \pi$ Dalitz plots are dominated by asymmetric crossing $K^* \bar K$ bands which are different for the two $B^+$ decay modes. This is due to a different interference pattern between the $1^{++}$ and $1^{+-}$ amplitudes in the two channels. Branching fractions are measured for each resonant contribution., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-045.html (LHCb public pages)

We report the X-ray spectral and timing analysis of the high mass X-ray binary EXO 2030+375 during the 2021 type-II outburst based on the Insight-HXMT observations. Pulsations can be detected in the energy band of 1-150 keV. The pulse profile shows energy and luminosity dependence and variability. We observed transitions in the pulse profile shape during the rising and the decaying phase of the outburst. The pulse fraction exhibits an anti-correlation with luminosity and a non-monotonic energy dependence, with a possible dip near 30 keV during the outburst peak. The hardness-intensity diagrams (7-10 keV/4-7 keV) suggest state transitions during the early and late phases of the outburst. These transitions are consistent with the luminosity at which the pulse profile shape changes occur, revealing the source reaching the critical luminosity and transitioning between super-critical and sub-critical accretion regimes. We performed the average and phase-resolved spectral analysis, where the flux-resolved average spectra show a stable spectral evolution with luminosity. The phase-resolved spectral analysis reveals that the dependence of spectral parameters on the pulse phase varies with different luminosities.

The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentric videos are of great interest. However, current datasets for embodied video question answering lack comprehensive and systematic evaluation frameworks. Critical embodied cognitive issues, such as robotic self-cognition, dynamic scene perception, and hallucination, are rarely addressed. To tackle these challenges, we propose ECBench, a high-quality benchmark designed to systematically evaluate the embodied cognitive abilities of LVLMs. ECBench features a diverse range of scene video sources, open and varied question formats, and 30 dimensions of embodied cognition. To ensure quality, balance, and high visual dependence, ECBench uses class-independent meticulous human annotation and multi-round question screening strategies. Additionally, we introduce ECEval, a comprehensive evaluation system that ensures the fairness and rationality of the indicators. Utilizing ECBench, we conduct extensive evaluations of proprietary, open-source, and task-specific LVLMs. ECBench is pivotal in advancing the embodied cognitive capabilities of LVLMs, laying a solid foundation for developing reliable core models for embodied agents. All data and code are available at https://github.com/Rh-Dang/ECBench.

Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emotional image generation relies on discrete emotion categories, making it challenging to capture complex and subtle emotional nuances accurately. Additionally, these methods struggle to control the specific content of generated images based on text prompts. In this work, we introduce the new task of continuous emotional image content generation (C-EICG) and present EmotiCrafter, an emotional image generation model that generates images based on text prompts and Valence-Arousal values. Specifically, we propose a novel emotion-embedding mapping network that embeds Valence-Arousal values into textual features, enabling the capture of specific emotions in alignment with intended input prompts. Additionally, we introduce a loss function to enhance emotion expression. The experimental results show that our method effectively generates images representing specific emotions with the desired content and outperforms existing techniques., Comment: 11 pages, 8 figures

Detecting object-level changes between two images across possibly different views is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from three major limitations: (1) lack of evaluation on image pairs that contain no changes, leading to unreported false positive rates; (2) lack of correspondences (i.e., localizing the regions before and after a change); and (3) poor zero-shot generalization across different domains. To address these issues, we introduce a novel method that leverages change correspondences (a) during training to improve change detection accuracy, and (b) at test time, to minimize false positives. That is, we harness the supervision labels of where an object is added or removed to supervise change detectors, improving their accuracy over previous work by a large margin. Our work is also the first to predict correspondences between pairs of detected changes using estimated homography and the Hungarian algorithm. Our model demonstrates superior performance over existing methods, achieving state-of-the-art results in change detection and change correspondence accuracy across both in-distribution and zero-shot benchmarks.

Video tokenizers are essential for latent video diffusion models, converting raw video data into spatiotemporally compressed latent spaces for efficient training. However, extending state-of-the-art video tokenizers to achieve a temporal compression ratio beyond 4x without increasing channel capacity poses significant challenges. In this work, we propose an alternative approach to enhance temporal compression. We find that the reconstruction quality of temporally subsampled videos from a low-compression encoder surpasses that of high-compression encoders applied to original videos. This indicates that high-compression models can leverage representations from lower-compression models. Building on this insight, we develop a bootstrapped high-temporal-compression model that progressively trains high-compression blocks atop well-trained lower-compression models. Our method includes a cross-level feature-mixing module to retain information from the pretrained low-compression model and guide higher-compression blocks to capture the remaining details from the full video sequence. Evaluation of video benchmarks shows that our method significantly improves reconstruction quality while increasing temporal compression compared to direct extensions of existing video tokenizers. Furthermore, the resulting compact latent space effectively trains a video diffusion model for high-quality video generation with a reduced token budget., Comment: Project website: https://progressive-video-tokenizer.github.io/Pro-MAG/

Continuum instruments are integral to robot-assisted minimally invasive surgery (MIS), with tendon-driven mechanisms being the most common. Real-time tension feedback is crucial for precise articulation but remains a challenge in compact actuation unit designs. Additionally, accurate shape and external force sensing of continuum instruments are essential for advanced control and manipulation. This paper presents a compact and modular actuation unit that integrates a torque cell directly into the pulley module to provide real-time tension feedback. Building on this unit, we propose a novel shape-force sensing framework that incorporates polynomial curvature kinematics to accurately model non-constant curvature. The framework combines pose sensor measurements at the instrument tip and actuation tension feedback at the developed actuation unit. Experimental results demonstrate the improved performance of the proposed shape-force sensing framework in terms of shape reconstruction accuracy and force estimation reliability compared to conventional constant-curvature methods.

Proximity ferroelectricity has recently been reported as a new design paradigm for inducing ferroelectricity, where a non-ferroelectric polar material becomes a ferroelectric by interfacing with a thin ferroelectric layer. Strongly polar materials, such as AlN and ZnO, which were previously unswitchable with an external field below their dielectric breakdown fields, can now be switched with practical coercive fields when they are in intimate proximity to a switchable ferroelectric. Here, we develop a general Landau-Ginzburg theory of proximity ferroelectricity in multilayers of non-ferroelectrics and ferroelectrics to analyze their switchability and coercive fields. The theory predicts regimes of both "proximity switching" where the multilayers collectively switch, as well as "proximity suppression" where they collectively do not switch. The mechanism of the proximity ferroelectricity is an internal electric field determined by the polarization of the layers and their relative thickness in a self-consistent manner that renormalizes the double-well ferroelectric potential to lower the steepness of the switching barrier. Further reduction in the coercive field emerges from charged defects in the bulk that act as nucleation centers. The application of the theory to proximity ferroelectricity in Alx-1ScxN/AlN and Zn1-xMgxO/ZnO bilayers is demonstrated. The theory further predicts that multilayers of dielectric/ferroelectric and paraelectric/ferroelectric layers can potentially result in induced ferroelectricity in the dielectric or paraelectric layers, resulting in the entire stack being switched, an exciting avenue for new discoveries. This thawing of "frozen ferroelectrics", paraelectrics and potentially dielectrics, promises a large class of new ferroelectrics with exciting prospects for previously unrealizable domain-patterned optoelectronic and memory technologies., Comment: 42 pages including 7 figures and 4 Appendices. To be submitted to Physical Review

Recent advancements in large language models (LLMs) have led to significant progress in text-based dialogue systems. These systems can now generate high-quality responses that are accurate and coherent across a wide range of topics and tasks. However, spoken dialogue systems still lag behind in terms of naturalness. They tend to produce robotic interactions, with issues such as slow response times, overly generic or cautious replies, and a lack of natural rhythm and fluid turn-taking. This shortcoming is largely due to the over-reliance on the traditional cascaded design, which involve separate, sequential components, as well as the use of text as an intermediate representation. This paper propose a real-time, textless spoken dialogue generation model (RTTL-DG) that aims to overcome these challenges. Our system enables fluid turn-taking and generates responses with minimal delay by processing streaming spoken conversation directly. Additionally, our model incorporates backchannels, filters, laughter, and other paralinguistic signals, which are often absent in cascaded dialogue systems, to create more natural and human-like interactions. The implementations and generated samples are available in our repository: https://github.com/mailong25/rts2s-dg

Efficient neural representations for dynamic video scenes are critical for applications ranging from video compression to interactive simulations. Yet, existing methods often face challenges related to high memory usage, lengthy training times, and temporal consistency. To address these issues, we introduce a novel neural video representation that combines 3D Gaussian splatting with continuous camera motion modeling. By leveraging Neural ODEs, our approach learns smooth camera trajectories while maintaining an explicit 3D scene representation through Gaussians. Additionally, we introduce a spatiotemporal hierarchical learning strategy, progressively refining spatial and temporal features to enhance reconstruction quality and accelerate convergence. This memory-efficient approach achieves high-quality rendering at impressive speeds. Experimental results show that our hierarchical learning, combined with robust camera motion modeling, captures complex dynamic scenes with strong temporal consistency, achieving state-of-the-art performance across diverse video datasets in both high- and low-motion scenarios., Comment: 10 pages, 10 figures

An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers event branches associated with event sequences in the maximum likelihood estimation framework of temporal point processes (TPPs). Specifically, we formulate the inference of event branches as an optimization problem for the event transition matrix under sparse and low-rank constraints, which is embedded in existing TPP models or their learning paradigms. We can implement this optimization problem based on subspace clustering and sparse group-lasso, respectively, and solve it using the Bregman ADMM algorithm, whose unrolling leads to the proposed BADMM module. When learning a classic TPP (e.g., Hawkes process) by the expectation-maximization algorithm, the BADMM module helps derive structured responsibility matrices in the E-step. Similarly, the BADMM module helps derive low-rank and sparse attention maps for the neural TPPs with self-attention layers. The structured responsibility matrices and attention maps, which work as learned event transition matrices, indicate event branches, e.g., inferring isolated events and those key events triggering many subsequent events. Experiments on both synthetic and real-world data show that plugging our BADMM module into existing TPP models and learning paradigms can improve model performance and provide us with interpretable structured event branches. The code is available at \url{https://github.com/qingmeiwangdaily/BADMM_TPP}., Comment: Accepted at AAAI 2025

While both observations and theories demonstrate that protoplanetary disks are not expected to live much longer than $\sim$10 Myr, several examples of prolonged disks have been observed in the past. In this work, we perform a systematic search for aged YSOs still surrounded by protoplanetary disks in the M star catalog from the LAMOST archive. We identify 14 sources older than 10 Myr, still surrounded by protoplanetary disks and with ongoing accretion activities, significantly improving the census of the category known as the Peter Pan disks. The stellar parameters, variability and accretion properties of these objects, as well as their spatial distribution, are investigated. Nearly all of these objects are distributed far away from nearby associations and star forming regions, but show evidence of being members of open clusters. Investigating the correlation between mass accretion rates and stellar masses, we find these long-lived disks accrete at systematically lower levels, compared to their younger counterparts with similar stellar masses. Studying the evolution of mass accretion rates with stellar ages, we find these aged disks follow similar trend as young ones., Comment: 24 pages, 12 figures, 2 tables, Accepted for publication in AJ

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

The Lin-Kernighan-Helsguan (LKH) heuristic is a classic local search algorithm for the Traveling Salesman Problem (TSP). LKH introduces an $\alpha$-value to replace the traditional distance metric for evaluating the edge quality, which leads to a significant improvement. However, we observe that the $\alpha$-value does not make full use of the historical information during the search, and single guiding information often makes LKH hard to escape from some local optima. To address the above issues, we propose a novel way to extract backbone information during the TSP local search process, which is dynamic and can be updated once a local optimal solution is found. We further propose to combine backbone information, $\alpha$-value, and distance to evaluate the edge quality so as to guide the search. Moreover, we abstract their different combinations to arms in a multi-armed bandit (MAB) and use an MAB model to help the algorithm select an appropriate evaluation metric dynamically. Both the backbone information and MAB can provide diverse guiding information and learn from the search history to suggest the best metric. We apply our methods to LKH and LKH-3, which is an extension version of LKH that can be used to solve about 40 variant problems of TSP and Vehicle Routing Problem (VRP). Extensive experiments show the excellent performance and generalization capability of our proposed method, significantly improving LKH for TSP and LKH-3 for two representative TSP and VRP variants, the Colored TSP (CTSP) and Capacitated VRP with Time Windows (CVRPTW).

We explore the critical properties of the localization transition in the non-Hermitian Aubry-Andre-Stark (AAS) model with quasiperiodic and Stark potentials, where the non-Hermiticity comes from the nonreciprocal hopping. The localization length, the inverse participation ratio and the energy gap are adopted as the characteristic quantities. We perform the scaling analysis to derive the scaling functions of the three quantities with critical exponents in several critical regions, with respect to the quasiperiodic and Stark potentials and the nonreciprocal strength. We numerically verify the finite-size scaling forms and extract the critical exponents in different situations. Two groups of new critical exponents for the non-Hermitian AAS model and its pure Stark limit are obtained, which are distinct to those for the non-Hermitian Aubry-Andre model and their Hermitian counterparts. Our results indicate that the Hermitian and non-Hermitian AAS, Aubry-Andre, and Stark models belong to different universality classes. We demonstrate that these critical exponents are independent of the nonreciprocal strength, and remain the same in different critical regions and boundary conditions. Furthermore, we establish a hybrid scaling function with a hybrid exponent in the overlap region between the critical regions for the non-Hermitian AAS and Stark models., Comment: 10 pages, 8 figures

Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cause domain shifts as large as, or even larger than, those between centers. To address this, we propose the "one image as one domain" (OIOD) hypothesis, which treats each image as a unique domain, enabling flexible and robust domain generalization. Based on this hypothesis, we develop a unified disentanglement-based domain generalization (UniDDG) framework, which simultaneously handles both multi-source and single-source domain generalization without requiring explicit domain labels. This approach simplifies training with a fixed architecture, independent of the number of source domains, reducing complexity and enhancing scalability. We decouple each input image into content representation and style code, then exchange and combine these within the batch for segmentation, reconstruction, and further disentanglement. By maintaining distinct style codes for each image, our model ensures thorough decoupling of content representations and style codes, improving domain invariance of the content representations. Additionally, we enhance generalization with expansion mask attention (EMA) for boundary preservation and style augmentation (SA) to simulate diverse image styles, improving robustness to domain shifts. Extensive experiments show that our method achieves Dice scores of 84.43% and 88.91% for multi-source to single-center and single-center generalization in optic disc and optic cup segmentation, respectively, and 86.96% and 88.56% for prostate segmentation, outperforming current state-of-the-art domain generalization methods, offering superior performance and adaptability across clinical settings.

In this paper, we develop a unified machine learning (ML) approach to predict high-quality solutions for single-machine scheduling problems with a non-decreasing min-sum objective function with or without release times. Our ML approach is novel in three major aspects. First, our approach is developed for the entire class of the aforementioned problems. To achieve this, we exploit the fact that the entire class of the problems considered can be formulated as a time-indexed formulation in a unified manner. We develop a deep neural network (DNN) which uses the cost parameters in the time-indexed formulation as the inputs to effectively predict a continuous solution to this formulation, based on which a feasible discrete solution is easily constructed. The second novel aspect of our approach lies in how the DNN model is trained. In view of the NP-hard nature of the problems, labels (i.e., optimal solutions) are hard to generate for training. To overcome this difficulty, we generate and utilize a set of special instances, for which optimal solutions can be found with little computational effort, to train the ML model offline. The third novel idea we employ in our approach is that we develop an online single-instance learning approach to fine tune the parameters in the DNN for a given online instance, with the goal of generating an improved solution for the given instance. To this end, we develop a feasibility surrogate that approximates the objective value of a given instance as a continuous function of the outputs of the DNN, which then enables us to derive gradients and update the learnable parameters in the DNN. Numerical results show that our approach can efficiently generate high-quality solutions for a variety of single-machine scheduling min-sum problems with up to 1000 jobs.

Reorienting polyatomic ions such as NH4+ and NO3- exhibit weak magnetic fields because the ions at the extremities trace out current loops; if the periodic reorientations become long-range ordered (i.e. gearing of neighboring NO3-), then the magnetic susceptibility should exhibit a unique signature along the different crystallographic axes. For the case of ammonium nitrate NH4NO3, we report the presence of two successive sharp steps in the molar magnetic susceptibility along the a- and b-axes upon crossing its order-disorder phase transition (from phase IV to phase II). We suggest the first step pertains to the NO3- planes shifting away from facing only along the b-axis and onto the a-axis by 45{\deg}. The second step is attributed to the disordering (ungearing) of the NH4+ and NO3-. In contrast, only one step was observed in the magnetic susceptibility along the c-axis and its large magnitude suggest the NO3- remain weakly correlated even in phase I at 400 K. We also find evidence that the NH4+ become magnetically ordered (geared) along the c-axis only until phase V. The approach employed in this work can be extended to experimentally study the lattice dynamics of other solids possessing planar ions such as amphidynamic crystals., Comment: 13 pages (single column), 4 figures

Topological phases are generally characterized by topological invariants denoted by integer numbers. However, different topological systems often require different topological invariants to measure, and theses definition usually fail at critical points. Therefore, it's challenging to predict what would occur during the transformation between two different topological phases. To address these issues, we propose a general definition based on fidelity and trace distance from quantum information theory: manifold distance (MD). This definition does not rely on the berry connection but rather on the information of the two manifolds - their ground state wave functions. Thus, it can measure different topological systems (including traditional band topology models, non-Hermitian systems, and gapless systems, etc.) and exhibit some universal laws during the transformation between two topological phases. Our research demonstrates for different topological manifolds, the change rate (first-order derivative) or susceptibility (second-order derivative) of MD exhibit various divergent behaviors near the critical points. Compared to the strange correlator, which could be used as a diagnosis for short-range entangled states in 1D and 2D, MD is more universal and could be applied to non-Hermitian systems and long-range entangled states. For subsequent studies, we expect the method to be generalized to real-space or non-lattice models, in order to facilitate the study of a wider range of physical platforms such as open systems and many-body localization., Comment: arXiv admin note: substantial text overlap with arXiv:2405.03323

Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory receptors in the central nervous system, could enhance their inhibitory action, potentially reducing side effects while improving the potency of anesthetics. In this study, we introduce a proteomic learning of GABA receptor-mediated anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins in protein-protein interaction (PPI) networks and over 1.5 millions known binding compounds. We develop a corresponding drug-target interaction network to identify potential lead compounds for novel anesthetic design. To ensure robust proteomic learning predictions, we curated a dataset comprising 136 targets from a pool of 980 targets within the PPI networks. We employed three machine learning algorithms, integrating advanced natural language processing (NLP) models such as pretrained transformer and autoencoder embeddings. Through a comprehensive screening process, we evaluated the side effects and repurposing potential of over 180,000 drug candidates targeting the GABRA5 receptor. Additionally, we assessed the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify those with near-optimal characteristics. This approach also involved optimizing the structures of existing anesthetics. Our work presents an innovative strategy for the development of new anesthetic drugs, optimization of anesthetic use, and deeper understanding of potential anesthesia-related side effects.

The accelerating demand for wireless communication necessitates wideband, energy-efficient photonic sub-terahertz (sub-THz) sources to enable ultra-fast data transfer. However, as critical components for THz photonic mixing, photodiodes (PDs) face a fundamental trade-off between quantum efficiency and bandwidth, presenting a major obstacle to achieving high-speed performance with high optoelectronic conversion efficiency. Here, we overcome this challenge by demonstrating an InP-based, waveguide-integrated modified uni-traveling carrier photodiode (MUTC-PD) with a terahertz bandwidth exceeding 200 GHz and a bandwidth-efficiency product (BEP) surpassing 130 GHz * 100%. Through the integration of a spot-size converter (SSC) to enhance external responsivity, alongside optimized electric field distribution, balanced carrier transport, and minimized parasitic capacitance, the device achieves a 3-dB bandwidth of 206 GHz and an external responsivity of 0.8 A/W, setting a new benchmark for BEP. Packaged with WR-5.1 waveguide output, it delivers radio-frequency (RF) power exceeding -5 dBm across the 127-185 GHz frequency range. As a proof of concept, we achieved a wireless transmission of 54 meters with a single-line rate of up to 120 Gbps, leveraging photonics-aided technology without requiring a low-noise amplifier (LNA). This work establishes a pathway to significantly enhance optical power budgets and reduce energy consumption, presenting a transformative step toward high-bandwidth, high-efficiency sub-THz communication systems and next-generation wireless networks.

Recent JWST observations have revealed the prevalence of spiral structures at $z > 1$. Unlike in the local Universe, the origin and the consequence of spirals at this epoch remain unexplored. We use public JWST/NIRCam data from the COSMOS-Web survey to map spiral structures in eight massive ($> 10^{10.5}\,\rm M_{\odot}$) star-forming galaxies at $z_{\rm spec} \sim 1.5$. We present a method for systematically quantifying spiral arms at $z>1$, enabling direct measurements of flux distributions. Using rest-frame near-IR images, we construct morphological models accurately tracing spiral arms. We detect offsets ($\sim 0.2 - 0.8\,\rm kpc$) between the rest-frame optical and near-IR flux distributions across most arms. Drawing parallels to the local Universe, we conclude that these offsets reflect the presence of density waves. For nine out of eighteen arms, the offsets indicate spiral shocks triggered by density waves. Five arms have offsets in the opposite direction and are likely associated with tidal interactions. For the remaining cases with no detected offsets, we suggest that stochastic 'clumpy' star formation is the primary driver of their formation. In conclusion, we find a multi-faceted nature of spiral arms at $z > 1$, similar to that in the local Universe., Comment: Accepted for publication in ApJ Letters; 12 pages, 6 figures

In this paper, we fit the $B\to D$ vector transition form factor (TFF) by using the data measured by BABAR and Belle Collaborations within Monte Carlo (MC) method. Meanwhile, the $B\to D$ TFF is also calculated by using the QCD light-cone sum rules approach (LCSRs) within right-handed chiral current correlation function. In which, the $D$-meson leading-twist light-cone distribution amplitude (LCDA) serves as crucial input parameter is reconstructed with light-cone harmonic oscillator model where its longitudinal behavior primarily determined by the model-free parameter $B_{2;D}$. After matching the TFF with two scenarios from MC and LCSRs, we have $B_{2;D}=0.17$. Then, we present the curve of $D$-meson leading-twist LCDA in comparison with other theoretical approaches. Subsequently, the $B\to D$ TFF $f_{+}^{BD}(q^2)$ at the large recoil region is $f_{+}^{BD}(0)=0.625^{+0.087}_{-0.113}$, which is compared in detail with theoretical estimates and experimental measurements. Furthermore, we calculate the decay width and branching ratio of the Cabibbo-favored semileptonic decays $B\to D\ell \bar{\nu}_{\ell}$, which lead to the results $\mathcal{B}(B^0\to D^-\ell ^+\nu _{\ell}) =(1.96_{-0.55}^{+0.51})\times 10^{-2}$ and $\mathcal{B}(B^+\to \bar{D}^0\ell ^+\nu _{\ell}) =(2.12_{-0.59}^{+0.55})\times 10^{-2}$. Finally, we predict the CKM matrix element with two scenarios $|V_{cb}|_{\rm SR}=42.97_{-2.57}^{+2.42}\times 10^{-3}$ and $|V_{cb} |_{\rm MC}=42.82_{-1.29}^{+1.07}\times 10^{-3}$ from $B^0\to D^-\ell^+\nu_{\ell}$, $|V_{cb}|_{\rm SR}=41.93_{-1.05}^{+1.03}\times 10^{-3}$ and $|V_{cb} |_{\rm MC}=41.82_{-0.25}^{+0.23}\times 10^{-3}$ from $B^+\to \bar{D}^0\ell^+\nu_{\ell}$ which are in good agreement with theoretical and experimental predictions., Comment: 12 pages, 7 figures, comments welcome

Hybridizable \(H(\textrm{div})\)-conforming finite elements for symmetric tensors on simplices with barycentric refinement are developed in this work for arbitrary dimensions and any polynomial order. By employing barycentric refinement and an intrinsic tangential-normal (\(t\)-\(n\)) decomposition, novel basis functions are constructed to redistribute degrees of freedom while preserving \(H(\textrm{div})\)-conformity and symmetry, and ensuring inf-sup stability. These hybridizable elements enhance computational flexibility and efficiency, with applications to mixed finite element methods for linear elasticity., Comment: 25 pages, 2 figures