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The origins of lenticular galaxies (S0s) can be classified into two main categories: ``minor mergers" in low-density environments (LDEs) and ``faded spirals" in high-density environments (HDEs). The transitional phase in the evolution of S0s, namely, star-forming lenticular galaxies (SFS0s), can serve as an important probe for analyzing the complex processes involved in the transformation between different galaxy types and the quenching of star formation (SF). We attempt to find the impact of different environments on the global properties and spatially resolved quantities of SFS0s. We selected 71 SFS0s from the SDSS-IV MaNGA Survey, comprising 23 SFS0s in HDEs (SFS0s$\_$HE) and 48 SFS0s in LDEs (SFS0s$\_$LE). We examined the effects of the environment, by studying the global properties, concentration index, and radial profiles of the derived quantities. The varied environments of SFS0s do not lead to any significant difference in global properties (e.g., S$\acute{\rm e}$rsic index). By calculating $CI_{\rm H_{\alpha}/cont}$, we observe that different environments may cause varying concentrations of SF. Specifically, SFS0s$\_$LE, affected by external gas mergers or inflow, exhibit a more centrally concentrated SF (i.e., larger $CI_{\rm H_{\alpha}/cont}$). This trend is further supported by $CI_{\rm SFR, H_{\alpha}}$, which only considers the gas disk of the galaxy. This observation is aligned with the observed shrinking of gas disks in galaxies affected by ram-pressure stripping in HDEs. Furthermore, their $\Sigma_{\rm SFR}$ or resolved sSFR are comparable. On average, SFS0s$\_$LE display significantly higher values for both quantities. Finally, the observed D$_{\rm n}4000$ and gas-phase metallicity gradient correspond well to their assumed origins. However, we did not find a significantly lower gas-phase metallicity in SFS0s$\_$LE. Abridged, Comment: 15 pages, 7 figures, 4 tables. Accepted for publication in A&A

LiDAR panoptic segmentation, which jointly performs instance and semantic segmentation for things and stuff classes, plays a fundamental role in LiDAR perception tasks. While most existing methods explicitly separate these two segmentation tasks and utilize different branches (i.e., semantic and instance branches), some recent methods have embraced the query-based paradigm to unify LiDAR panoptic segmentation. However, the distinct spatial distribution and inherent characteristics of objects(things) and their surroundings(stuff) in 3D scenes lead to challenges, including the mutual competition of things/stuff and the ambiguity of classification/segmentation. In this paper, we propose decoupling things/stuff queries according to their intrinsic properties for individual decoding and disentangling classification/segmentation to mitigate ambiguity. To this end, we propose a novel framework dubbed DQFormer to implement semantic and instance segmentation in a unified workflow. Specifically, we design a decoupled query generator to propose informative queries with semantics by localizing things/stuff positions and fusing multi-level BEV embeddings. Moreover, a query-oriented mask decoder is introduced to decode corresponding segmentation masks by performing masked cross-attention between queries and mask embeddings. Finally, the decoded masks are combined with the semantics of the queries to produce panoptic results. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our DQFormer framework., Comment: 13 pages, 10 figures

Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major challenge lies in developing an efficient and scalable quantum control system. To address this, we propose a novel Hierarchical MicroArchitecture (HiMA) designed to facilitate qubit scaling and exploit quantum process-level parallelism. This microarchitecture is based on three core elements: (i) discrete qubit-level drive and readout, (ii) a process-based hierarchical trigger mechanism, and (iii) multiprocessing with a staggered triggering technique to enable efficient quantum process-level parallelism. We implement HiMA as a control system for a 72-qubit tunable superconducting quantum processing unit, serving a public quantum cloud computing platform, which is capable of expanding to 6144 qubits through three-layer cascading. In our benchmarking tests, HiMA achieves up to a 4.89x speedup under a 5-process parallel configuration. Consequently, to the best of our knowledge, we have achieved the highest CLOPS (Circuit Layer Operations Per Second), reaching up to 43,680, across all publicly available platforms.

Link prediction has become a critical problem in network science and has thus attracted increasing research interest. Popularity and similarity are two primary mechanisms in the formation of real networks. However, the roles of popularity and similarity mechanisms in link prediction across various domain networks remain poorly understood. Accordingly, this study used orbit degrees of graphlets to construct multi-order popularity- and similarity-based network link predictors, demonstrating that traditional popularity- and similarity-based indices can be efficiently represented in terms of orbit degrees. Moreover, we designed a supervised learning model that fuses multiple orbit-degree-based features and validated its link prediction performance. We also evaluated the mean absolute Shapley additive explanations of each feature within this model across 550 real-world networks from six domains. We observed that the homophily mechanism, which is a similarity-based feature, dominated social networks, with its win rate being 91\%. Moreover, a different similarity-based feature was prominent in economic, technological, and information networks. Finally, no single feature dominated the biological and transportation networks. The proposed approach improves the accuracy and interpretability of link prediction, thus facilitating the analysis of complex networks., Comment: 40 pages, 9 figures

IC 10 X-1 is one of close X-ray binaries containing a Wolf-Rayet donor, which can provide an evolutionary link between high-mass X-ray binaries and gravitational wave sources. It is still unclear about the precise nature of the accreting compact object in IC 10 X-1, although it looks more like a black hole than a neutron star. In this work, we use a binary population synthesis method to simulate the formation of IC 10 X-1 like binaries by assuming different common-envelope ejection efficiencies. This work represents a big step forward over previous studies since we adopt new criteria of mass-transfer stability. These criteria allow the formation of IC 10 X-1 like systems without experiencing common envelope evolution. Based on our calculations, we propose that the compact object in IC 10 X-1 is a black hole with mass of $\sim 10-30M_\odot$ and the progenitor evolution of this binary probably just experienced stable mass transfer., Comment: 17 pages, 10 figures, accepted by ApJ

Using inelastic scattering of charmed strange mesons by open-charm mesons in Pb-Pb collisions at the Large Hadron Collider, we study the production of $\psi (4040)$, $\psi (4160)$, and $\psi (4415)$ mesons. Master rate equations with the inelastic scattering are established. The scattering is caused by quark interchange in association with color interactions between all constituent pairs in different mesons. We consider fifty-one reactions between charmed strange mesons and open-charm mesons. Unpolarized cross sections for the reactions are obtained from a temperature-dependent interquark potential. Temperature dependence of the cross sections leads to that contributions of the reactions to the production of $\psi (4040)$, $\psi (4160)$, and $\psi (4415)$ change with decreasing temperature during evolution of hadronic matter. For central Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV it turned out from the master rate equations that the $\psi(4040)$ number density is larger than the $\psi(4160)$ number density which is larger than the $\psi(4415)$ number density., Comment: 57 pages, 29 figures, 9 tables

We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of $^{29-33}\mathrm{Si}$ on a carbon target at $\sim$230~MeV/nucleon. The experimental data covering charge changes of $\Delta Z$ = 1-4 are reproduced well by the isospin-dependent quantum molecular dynamics (IQMD) coupled with the evaporation GEMINI (IQMD+GEMINI) model. We further explore the mechanisms underlying the single-proton removal reaction in this model framework. We conclude that the cross sections from direct proton knockout exhibit a overall weak dependence on the mass number of $\mathrm{Si}$ projectiles. The proton evaporation induced after the projectile excitation significantly affects the cross sections for neutron-deficient $\mathrm{Si}$ isotopes, while neutron evaporation plays a crucial role in the reactions of neutron-rich $\mathrm{Si}$ isotopes. It is presented that the relative magnitude of one-proton and one-neutron separation energies is an essential factor that influences evaporation processes., Comment: 7 pages, 4 figures

Higher-order community detection (HCD) reveals both mesoscale structures and functional characteristics of real-life networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher-order fuzzy community information. This study presents a novel concept of higher-order fuzzy memberships that quantify the membership grades of motifs to crisp higher-order communities, thereby revealing the partial community affiliations. Furthermore, we employ higher-order fuzzy memberships to enhance HCD via a general framework called fuzzy memberships assisted motif-based evolutionary modularity (FMMEM). In FFMEM, on the one hand, a fuzzy membership-based neighbor community modification (FM-NCM) strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging (FM-LCM) strategy is also proposed to combine excessively fragmented communities for enhancing local search ability. Experimental results indicate that the FMMEM framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in the networks with ambiguous and complex structures., Comment: 12 pages, 6 figures

The advancing maturity of photonic integrated circuit (PIC) fabrication technology enables the high integration of an increasing number of optical components onto a single chip. With the incremental circuit complexity, the calibration of active phase shifters in a large-scale PIC becomes a crucially important issue. The traditional one-by-one calibration techniques encounter significant hurdles with the propagation of calibration errors, and achieving the decoupling of all phase shifters for independent calibration is not straightforward. To address this issue, we propose a machine-learning approach for globally calibrating the large-scale PIC. Our method utilizes a custom network to simultaneously learn the nonlinear phase-current relations for all thermo-optic phase shifters on the PIC by minimizing the negative likelihood of the measurement datasets. Moreover, the reflectivities of all static beamsplitter components can also be synchronizedly extracted using this calibration method. As an example, a quantum walk PIC with a circuit depth of 12 is calibrated, and a programmable discrete-time quantum walk is experimentally demonstrated. These results will greatly benefit the applications of large-scale PICs in photonic quantum information processing., Comment: 9 pages, 5 figures, and comments are welcome

We introduce JuliVQC: a light-weight, yet extremely efficient variational quantum circuit simulator. JuliVQC is part of an effort for classical simulation of the \textit{Zuchongzhi} quantum processors, where it is extensively used to characterize the circuit noises, as a building block in the Schr$\ddot{\text{o}}$dinger-Feynman algorithm for classical verification and performance benchmarking, and for variational optimization of the Fsim gate parameters. The design principle of JuliVQC is three-fold: (1) Transparent implementation of its core algorithms, realized by using the high-performance script language Julia; (2) Efficiency is the focus, with a cache-friendly implementation of each elementary operations and support for shared-memory parallelization; (3) Native support of automatic differentiation for both the noiseless and noisy quantum circuits. We perform extensive numerical experiments on JuliVQC in different application scenarios, including quantum circuits, variational quantum circuits and their noisy counterparts, which show that its performance is among the top of the popular alternatives., Comment: 12 pages, 8 figures

To overcome the restriction of identical distribution assumption, invariant representation learning for unsupervised domain adaptation (UDA) has made significant advances in computer vision and pattern recognition communities. In UDA scenario, the training and test data belong to different domains while the task model is learned to be invariant. Recently, empirical connections between transferability and discriminability have received increasing attention, which is the key to understanding the invariant representations. However, theoretical study of these abilities and in-depth analysis of the learned feature structures are unexplored yet. In this work, we systematically analyze the essentials of transferability and discriminability from the geometric perspective. Our theoretical results provide insights into understanding the co-regularization relation and prove the possibility of learning these abilities. From methodology aspect, the abilities are formulated as geometric properties between domain/cluster subspaces (i.e., orthogonality and equivalence) and characterized as the relation between the norms/ranks of multiple matrices. Two optimization-friendly learning principles are derived, which also ensure some intuitive explanations. Moreover, a feasible range for the co-regularization parameters is deduced to balance the learning of geometric structures. Based on the theoretical results, a geometry-oriented model is proposed for enhancing the transferability and discriminability via nuclear norm optimization. Extensive experiment results validate the effectiveness of the proposed model in empirical applications, and verify that the geometric abilities can be sufficiently learned in the derived feasible range., Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable. Previous work has primarily focused on enhancing decompilation performance by increasing the scale of model parameters or training data for pre-training. Based on the characteristics of the decompilation task, we propose two methods: (1) Without fine-tuning, the Self-Constructed Context Decompilation (sc$^2$dec) method recompiles the LLM's decompilation results to construct pairs for in-context learning, helping the model improve decompilation performance. (2) Fine-grained Alignment Enhancement (FAE), which meticulously aligns assembly code with source code at the statement level by leveraging debugging information, is employed during the fine-tuning phase to achieve further improvements in decompilation. By integrating these two methods, we achieved a Re-Executability performance improvement of approximately 7.35\% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 55.03\%., Comment: Under Review

Employing graph neural networks (GNNs) to learn cohesive and discriminative node representations for clustering has shown promising results in deep graph clustering. However, existing methods disregard the reciprocal relationship between representation learning and structure augmentation. This study suggests that enhancing embedding and structure synergistically becomes imperative for GNNs to unleash their potential in deep graph clustering. A reliable structure promotes obtaining more cohesive node representations, while high-quality node representations can guide the augmentation of the structure, enhancing structural reliability in return. Moreover, the generalization ability of existing GNNs-based models is relatively poor. While they perform well on graphs with high homogeneity, they perform poorly on graphs with low homogeneity. To this end, we propose a graph clustering framework named Synergistic Deep Graph Clustering Network (SynC). In our approach, we design a Transform Input Graph Auto-Encoder (TIGAE) to obtain high-quality embeddings for guiding structure augmentation. Then, we re-capture neighborhood representations on the augmented graph to obtain clustering-friendly embeddings and conduct self-supervised clustering. Notably, representation learning and structure augmentation share weights, significantly reducing the number of model parameters. Additionally, we introduce a structure fine-tuning strategy to improve the model's generalization. Extensive experiments on benchmark datasets demonstrate the superiority and effectiveness of our method. The code is released on GitHub and Code Ocean.

Context. Recent astrometric and spectroscopic surveys of OB stars have revealed a few stellar-mass black holes (BHs) with orbital periods as low as 10 days. No X-ray counterpart has been detected, due to the absence of a radiatively efficient accretion disk around the BH. Yet, dissipative processes in the hot, dilute and strongly magnetized plasma around the BH (so-called BH corona) can still lead to non-thermal X-ray emission (e.g. synchrotron). Aims. We determine the X-ray luminosity distribution from BH+OB star binaries up to orbital periods of a few thousand days. Methods. We use detailed binary evolution models computed with MESA for initial primary masses of 10-90 $M_{\odot}$ and orbital periods from 1-3000 d. The X-ray luminosity is computed for a broad range of radiative efficiencies. Results. We show that particle acceleration through magnetic reconnection can heat the BH corona. A substantial fraction of the gravitational potential energy from the accreted plasma is converted into non-thermal X-ray emission. Our population synthesis analysis predicts at least 28 (up to 72) BH+OB star binaries in the Large Magellanic Cloud (LMC) to produce X-ray luminosity above 10$^{31}$ erg$\,$s$^{-1}$, observable through focused Chandra observations. We identify a population of SB1 systems in the LMC and HD96670 in the Milky Way comprising O stars with unseen companions of masses above 2.3 $M_{\odot}$ that aligns well with our predictions. The predicted luminosities of the OB companions to these X-ray-emitting BHs are 10$^{4.5-5.5}$ $L_{\odot}$. Conclusions. These results make the case for long-time exposure in X-rays of the stellar-mass BH candidates identified around OB stars. It will constrain the underlying population of X-ray-faint BHs, the evolution from single to double degenerate binaries, and the progenitors of gravitational wave mergers. (Abridged), Comment: Accepted for publication in A&A on July 27, 2024

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science., Comment: 31 pages, 10 figures

With an extremely high dimensionality, the spatial degree of freedom of entangled photons is a key tool for quantum foundation and applied quantum techniques. To fully utilize the feature, the essential task is to experimentally characterize the multiphoton spatial wave function including the entangled amplitude and phase information at different evolutionary stages. However, there is no effective method to measure it. Quantum state tomography is costly, and quantum holography requires additional references. Here we introduce quantum Shack-Hartmann wavefront sensing to perform efficient and reference-free measurement of the biphoton spatial wave function. The joint probability distribution of photon pairs at the back focal plane of a microlens array is measured and used for amplitude extraction and phase reconstruction. In the experiment, we observe that the biphoton amplitude correlation becomes weak while phase correlation shows up during free-space propagation. Our work is a crucial step in quantum physical and adaptive optics and paves the way for characterizing quantum optical fields with high-order correlations or topological patterns., Comment: Main text: 6 pages, 4 figures; Supplemental Material: 13 pages, 11 figures. (c) 2024 American Physical Society

Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge., Comment: 19 pages, 5 figures

Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field.

Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reasoning, and (3) Domain missing, thereby hindering the development of MCoT. Motivated by this, we introduce a novel benchmark (M$^3$CoT) to address the above challenges, advancing the multi-domain, multi-step, and multi-modal CoT. Additionally, we conduct a thorough evaluation involving abundant MCoT approaches on Vision Large Language Models (VLLMs). In addition, we highlight that the current VLLMs still struggle to correctly reason in M$^3$CoT and there remains a large gap between existing VLLMs and human performance in M$^3$CoT, despite their superior results on previous MCoT benchmarks. To our knowledge, we take the first meaningful step toward the multi-domain, multi-step, and multi-modal scenario in MCoT. We hope that M$^3$CoT can serve as a valuable resource, providing a pioneering foundation in multi-domain, multi-step, multi-modal chain-of-thought research., Comment: Accepted at ACL2024 Main Conference

In this paper, we firstly provide the correspondence between the FRT formalism and the Drindeld-Jimbo presentation for $U_{r, s}(\mathfrak{so}_{2n+1})$, using the theory of finite dimensional weight modules. In the affine case $U_{r, s}(\widehat{\mathfrak{so}_{2n+1}}),$ we give its RLL realization via the Gauss decomposition. Thus we establish the correspondence between this realization and the Drinfeld realization.

We obtain the basic $R$-matrix of the two-parameter Quantum group $U=U_{r,s}\mathcal(\mathfrak{so}_{2n})$ via its weight representation theory and determine its $R$-matrix with spectral parameters for the two-parameter quantum affine algebra $U=U_{r,s}\mathcal(\widehat{\mathfrak{so}_{2n}})$. Using the Gauss decomposition of the $R$-matrix realization of $U=U_{r,s}\mathcal(\mathfrak{so}_{2n})$, we study the commutation relations of the Gaussian generators and finally arrive at its $RLL$-formalism of the Drinfeld realization of two-parameter quantum affine algebra $U=U_{r,s}\mathcal(\widehat{\mathfrak{so}_{2n}})$., Comment: 32 pages

In this work, we study the inverse spectral problems for the Sturm-Liouville operators on [0,1] with complex coefficients and a discontinuity at $x=a\in(0,1)$. Assume that the potential on (a,1) and some parameters in the discontinuity and boundary conditions are given. We recover the potential on (0,a) and the other parameters from the eigenvalues. This is the so-called partial inverse problem. The local solvability and stability of the partial inverse problems are obtained for $a\in(0,1)$, in which the error caused by the given partial potential is considered. As a by-product, we also obtain two new uniqueness theorems for the partial inverse problem., Comment: 26 pages

The growing database of gravitational-wave (GW) detections with the binary black holes (BHs) merging in the distant Universe contains subtle insights into their formation scenarios. One of the puzzling properties of detected GW sources is the possible (anti)correlation between mass ratio q of BH-BH binaries and their effective spin. We use rapid binary evolution models to demonstrate that the isolated binary evolution followed by efficient tidal spin-up of stripped helium core produces a similar pattern in Xeff vs q distributions of BH-BH mergers. In our models, the progenitors of unequal BH-BH systems in the stable mass transfer formation scenario are more likely to efficiently shrink their orbits during the second Roche-lobe overflow than the binaries that evolve into nearly equal-mass component systems. This makes it easier for unequal-mass progenitors to enter the tidal spin-up regime and later merge due to GW emission. Our results are, however, sensitive to some input assumptions, especially, the stability of mass transfer and the angular momentum loss during non-conservative mass transfer. We note that mass transfer prescriptions widely adopted in rapid codes favor the formation of BH-BH merger progenitors with unequal masses and moderate separations. We compare our results with detailed stellar model grids and find reasonable agreement after appropriate calibration of the physics models. We anticipate that future detections of unequal-mass BH-BH mergers could provide valuable constraints on the role of the stable mass transfer formation channel. A significant fraction of BH-BH detections with mass ratio q in range (0.4 - 0.7) would be consistent with the mass ratio reversal scenario during the first, relatively conservative mass transfer, and a non-enhanced angular momentum loss during the second, highly non-conservative mass transfer phase., Comment: Version accepted for publication in A&A

The study of strongly correlated fermionic systems, crucial for understanding condensed matter physics, has been significantly advanced by numerical computational methods. Among these, the Determinant Quantum Monte Carlo (DQMC) method stands out for its ability to provide exact numerical solutions. However, the computational complexity of DQMC, particularly in dealing with large system sizes and the notorious sign problem, limits its applicability. We introduce an innovative approach to enhance DQMC efficiency through the implementation of submatrix updates. Building upon the foundational work of conventional fast updates and delay updates, our method leverages a generalized submatrix update algorithm to address challenges in simulating strongly correlated fermionic systems with both onsite and extended interactions at both finite and zero temperatures. We demonstrate the method's superiority by comparing it with previous update methods in terms of computational complexity and efficiency. Specifically, our submatrix update method significantly reduces the computational overhead, enabling the simulation of system sizes up to 8,000 sites without pushing hard. This advancement allows for a more accurate determination of the finite temperature phase diagram of the 3D Hubbard model at half-filling. Our findings not only shed light on the phase transitions within these complex systems but also pave the way for more effective simulations of strongly correlated electrons, potentially guiding experimental efforts in cold atom simulations of the 3D Hubbard model., Comment: 18 pages, 12 figures, 3 tables, note added

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model., Comment: This paper has been accepted by China Communications.The source code has been released at:https://github.com/giongwu86/By-AFLDDPG

With the rapid development of Kesterite Cu2ZnSn(S, Se)4 solar cells in the past few years, how to achieve higher cost-performance ratio has become an important topic in the future development and industrialization of this technology. Herein, we demonstrate an all-solution route for the cell fabrication, in particular targeting at the solution processed window layer comprised of ZnO nanoparticles/Ag nanowires. A multi-interface engineering strategy assisted by organic polymers and molecules is explored to synergistically improve the film deposition, passivate the surface defects and facilitate the charge transfer. These efforts help us achieve high-performance and robust Kesterite solar cells at extremely low time and energy costs, with efficiency records of 14.37% and 13.12% being realized in rigid and flexible Kesterite solar cells, respectively. Our strategy here is also promising to be transplanted into other solar cells with similar geometric and energy band structures, helping reduce production costs and shorten the production cycle (i.e. increasing production capacity) of these photovoltaic industries.

Atomic disorder, a widespread problem in compound crystalline materials, is a imperative affecting the performance of multi-chalcogenide Cu2ZnSn(S, Se)4 (CZTSSe) photovoltaic device known for its low cost and environmental friendliness. Cu-Zn disorder is particularly abundantly present in CZTSSe due to its extraordinarily low formation energy, having induced high-concentration deep defects and severe charge loss, while its regulation remains challenging due to the contradiction between disorder-order phase transition thermodynamics and atom-interchange kinetics. Herein, through introducing more vacancies in the CZTSSe surface, we explored a vacancy-assisted strategy to reduce the atom-interchange barrier limit to facilitate the Cu-Zn ordering kinetic process. The improvement in the Cu-Zn order degree has significantly reduced the charge loss in the device and helped us realize 15.4% (certified at 14.9%) and 13.5% efficiency (certified at 13.3%) in 0.27 cm2 and 1.1 cm2-area CZTSSe solar cells, respectively, thus bringing substantial advancement for emerging inorganic thin-film photovoltaics.

Sulfide Kesterite Cu2ZnSnS4 (CZTS), a nontoxic and low-cost photovoltaic material, has always being facing severe charge recombination and poor carrier transport, resulting in the cell efficiency record stagnating around 11% for years. Gradient bandgap is a promising approach to relieve these issues, however, has not been effectively realized in Kesterite solar cells due to the challenges in controlling the gradient distribution of alloying elements at high temperatures. Herein, targeting at the Cd alloyed CZTS, we propose a pre-crystallization strategy to reduce the intense vertical mass transport and Cd rapid diffusion in the film growth process, thereby realizing front Cd-gradient CZTS absorber. The Cd-gradient CZTS absorber, exhibiting downward bending conduction band structure, has significantly enhanced the minority carrier transport and additionally improved band alignment and interface property of CZTS/CdS heterojunction. Ultimately, we have achieved a champion total-area efficiency of 13.5% (active-area efficiency: 14.1%) in the cell and in particular a high open-circuit voltage of >800 mV. We have also achieved a certified total-area cell efficiency of 13.16%, realizing a substantial step forward for the pure sulfide Kesterite solar cell.

We report the physical properties of ThRu$_3$Si$_2$ featured with distorted Ru kagome lattice. The combined experiments of resistivity, magnetization and specific heat reveal bulk superconductivity with $T_{\rm{c}}$ = 3.8 K. The specific heat jump and calculated electron-phonon coupling indicate a moderate coupled BCS superconductor. In comparison with LaRu$_3$Si$_2$, the calculated electronic structure in ThRu$_3$Si$_2$ shows an electron-doping effect with electron filling lifted from 100 meV below flat bands to 300 meV above it. This explains the lower superconducting transition temperature and weaker electron correlations observed in ThRu$_3$Si$_2$. Our work suggests the $T_{\rm{c}}$ and electronic correlations in kagome superconductor could have intimate connection with the flat bands., Comment: 7 pages, 5 figures

Neutron star$-$white dwarf (NSWD) binaries are one of the most abundant sources of gravitational waves (GW) in the Milky Way. These GW sources are the evolutionary products of primordial binaries that experienced many processes of binary interaction. We employ a binary population synthesis method to investigate the properties of Galactic NSWD binaries detectable by the Laser Interferometer Space Antenna (LISA). In this paper, only the NSWD systems with a COWD or ONeWD component are included. We consider various models related to mass transfer efficiencies during primordial binary evolution, supernova explosion mechanisms at NS formation, common envelope ejection efficiencies, and critical WD masses that determining the stability of mass transfer between WDs and NSs. Based on our calculations, we estimate that tens to hundreds of LISA NSWD binaries exist in the Milky Way. We find that the detection of LISA NSWD binaries is able to provide profound insights into mass transfer efficiencies during the evolution of primordial binaries and critical WD masses during mass transfer from a WD to an NS., Comment: 11 pages, 5 figures, 1 table, accepted to MNRAS

We implement an experiment on a photonic quantum processor establishing efficacy of an elementary quantum system in classical information storage. The advantage is established by considering a class of simple bipartite games played with the communication resource qubit and classical bit (c-bit), respectively. Conventional wisdom, as articulated by the no-go theorems of Holevo and Frenkel-Weiner, suggests that such a quantum advantage is unattainable in scenarios wherein sender and receiver possess shared randomness or classical correlation between them. Notably, the advantage we report is demonstrated in a scenario where participating players lack any form of shared randomness. Our experiment involves the development of a variational triangular polarimeter, enabling the realization of positive operator value measurements crucial for establishing the targeted quantum advantage. In addition to demonstrating a robust communication advantage of a single qubit our experiment also opens avenues for immediate applications in near-term quantum technologies. Furthermore, it constitutes a semi-device-independent non-classicality certification scheme for the quantum encoding-decoding apparatus, underscoring the broader implications of our work beyond its immediate technological applications.

Residual entropy, indicative of the degrees of freedom in a system at absolute zero, is a cornerstone for understanding quantum and classical ground states. Despite its critical role in elucidating low-temperature phenomena and ground state degeneracy, accurately quantifying residual entropy remains a formidable challenge due to significant computational hurdles. In this Letter, we introduce the Temperature Incremental Monte Carlo (TIMC) method, our novel solution crafted to surmount these challenges. The TIMC method incrementally calculates the partition function ratio of neighboring temperatures within Monte Carlo simulations, enabling precise entropy calculations and providing insights into a spectrum of other temperature-dependent observables in a single computational sweep of temperatures. We have rigorously applied TIMC to a variety of complex systems, such as the frustrated antiferromagnetic Ising model on both C60 and 2D triangular lattices, the Newman-Moore spin glass model, and a 2D quantum transverse field Ising model. Notably, our method surmounts the traditional obstacles encountered in partition function measurements when mapping $d$-dimensional quantum models to $d+1$-dimensional classical counterparts. The TIMC method's finesse in detailing entropy across the entire temperature range enriches our comprehension of critical phenomena in condensed matter physics. This includes insights into spin glasses, phases exhibiting spontaneous symmetry breaking, topological states of matter and fracton phases. Our approach not only advances the methodology for probing the entropic landscape of such systems but also paves the way for exploring their broader thermodynamic and quantum mechanical properties., Comment: 5 pages, 5 figures

The classification of one-parameter small quantum groups is an interesting open question. The current paper reveals a new phenomenon that there exist abundant exotic (about 5 times the standard) small quantum groups beyond the Lusztig small quantum groups (with double grouplikes) , which arise from the two-parameter setting. In particular, when the order $\ell$ of one-parameter $q$ satisfies $(\ell, 210)\not=1$, the isoclasses of explicit representatives (most of them have Drinfeld doubles) are new finite dimensional pointed Hopf algebras, which complement to the work under the assumption $(\ell, 210)=1$

Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile information, without considering their interrelationships or excluding irrelevant and conflicting information within them. To address the above issues, we introduce a Heterogeneous Graph Attention Network to perform reasoning across multiple Profile information, called Pro-HAN. Specifically, we design three types of edges, denoted as intra-Pro, inter-Pro, and utterance-Pro, to capture interrelationships among multiple Pros. We establish a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics. Further analysis experiments also confirm the effectiveness of our method in modeling multi-source profile information., Comment: Accepted at ICASSP 2024

Starting from more than 11,200 short-period (less than 0.5 days) EW-type eclipsing binary candidates with the All-Sky Automated Survey for Supernovae (ASAS-SN) V-band light curves, we use MCMC and neural networks (NNs) to obtain the mass ratio ($q$), orbital inclination ($incl$), fill-out factor ($f$) and temperature ratio ($T_s/T_p$). After cross-matching with the Gaia DR3 database, the final sample contains parameters of 2,399 A-type and 8,712 W-type contact binaries (CBs). We present the distributions of parameters of these 11,111 short-period CBs. The mass ratio ($q$) and fill-out factor ($f$) are found to obey log-normal distributions, and the remaining parameters obey normal distributions. There is a significant period-temperature correlation of these CBs. Additionally, the temperature ratio (${T_s}$/${T_p}$) tends to increase as the orbital period decreases for W-type CBs. There is no significant correlation between them for A-type CBs. The mass ratio and fill-out factor ($q-f$) diagram suggest there is no significant correlation between these two parameters. A clear correlation exists between the mass ratio and radius ratio. The radius ratio increases with the mass ratio. Moreover, the deep fill-out CBs tend to fall on the upper boundary of the $q$$-$${R_s}$/${R_p}$ distribution, while the shallow fill-out CBs fall on the lower boundary., Comment: 15 pages, 8 figures, 1 table.Accepted for publication in ApJS. The full dataset will be available with the ApJS article

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impact the communication efficiency. Furthermore, existing works often resort to basic mechanisms to integrate observed and received information, impairing the learning process. To tackle these difficulties, we propose Targeted and Trusted Multi-Agent Communication (T2MAC), a straightforward yet effective method that enables agents to learn selective engagement and evidence-driven integration. With T2MAC, agents have the capability to craft individualized messages, pinpoint ideal communication windows, and engage with reliable partners, thereby refining communication efficiency. Following the reception of messages, the agents integrate information observed and received from different sources at an evidence level. This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors. We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the state-of-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization., Comment: AAAI24