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This paper extends the classical network calculus to spatial scenarios, focusing on wireless networks with heterogeneous traffic and varying transmit power levels. Building on spatial network calculus, a prior extension of network calculus to spatial settings, we propose a generalized framework by introducing spatial regulations for stationary marked point processes. The regulations correspond to two key constraints: the total transmit power within a spatial region and the cumulative received power at a receiver. Then we prove the equivalence of ball regulation and shot-noise regulation for stationary marked point processes and establish a universal lower bound on the performance of all network links under these constraints. This framework is applicable to diverse network scenarios, as demonstrated by the analysis of performance guarantees for networks with multi-class users. In addition, we propose an SINR-based power control scheme adapted to user traffic, which ensures differentiated quality of service (QoS) for different user classes. We derive deterministic performance guarantees for all links in complex and heterogeneous wireless networks., Comment: 13 pages, 8 figures

This paper introduces a new stochastic geometry-based model to analyze the Request-to-Send/Clear-to-Send (RTS/CTS) handshake mechanism in wireless local area networks (WLANs). We develop an advanced hard-core point process model, termed the dual-zone hard-core process (DZHCP), which extends traditional hard-core models to capture the spatial interactions and exclusion effects introduced by the RTS/CTS mechanism. This model integrates key parameters accounting for the thinning effects imposed by RTS/CTS, enabling a refined characterization of active transmitters in the network. Analytical expressions are derived for the intensity of the DZHCP, the mean interference, and an approximation of the success probability, providing insight into how network performance depends on critical design parameters. Our results provide better estimates of interference levels and success probability, which could inform strategies for better interference management and improved performance in future WLAN designs., Comment: submitted to IEEE Transactions on Wireless Communications

Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inputs in most methods, often results in a certain loss of global structure information. Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details. To overcome these drawbacks, we introduce PointCFormer, a transformer framework optimized for robust global retention and precise local detail capture in point cloud completion. This framework embraces several key advantages. First, we propose a relation-based local feature extraction method to perceive local delicate geometry characteristics. This approach establishes a fine-grained relationship metric between the target point and its k-nearest neighbors, quantifying each neighboring point's contribution to the target point's local features. Secondly, we introduce a progressive feature extractor that integrates our local feature perception method with self-attention. Starting with a denser sampling of points as input, it iteratively queries long-distance global dependencies and local neighborhood relationships. This extractor maintains enhanced global structure and refined local details, without generating substantial computational overhead. Additionally, we develop a correction module after generating point proxies in the latent space to reintroduce denser information from the input points, enhancing the representation capability of the point proxies. PointCFormer demonstrates state-of-the-art performance on several widely used benchmarks. Our code is available at https://github.com/Zyyyyy0926/PointCFormer_Plus_Pytorch., Comment: 9 pages, 8 figures, AAAI 2025, references added

Statistical downscaling is a technique used in climate modeling to increase the resolution of climate simulations. High-resolution climate information is essential for various high-impact applications, including natural hazard risk assessment. However, simulating climate at high resolution is intractable. Thus, climate simulations are often conducted at a coarse scale and then downscaled to the desired resolution. Existing downscaling techniques are either simulation-based methods with high computational costs, or statistical approaches with limitations in accuracy or application specificity. We introduce Generative Bias Correction and Super-Resolution (GenBCSR), a two-stage probabilistic framework for statistical downscaling that overcomes the limitations of previous methods. GenBCSR employs two transformations to match high-dimensional distributions at different resolutions: (i) the first stage, bias correction, aligns the distributions at coarse scale, (ii) the second stage, statistical super-resolution, lifts the corrected coarse distribution by introducing fine-grained details. Each stage is instantiated by a state-of-the-art generative model, resulting in an efficient and effective computational pipeline for the well-studied distribution matching problem. By framing the downscaling problem as distribution matching, GenBCSR relaxes the constraints of supervised learning, which requires samples to be aligned. Despite not requiring such correspondence, we show that GenBCSR surpasses standard approaches in predictive accuracy of critical impact variables, particularly in predicting the tails (99% percentile) of composite indexes composed of interacting variables, achieving up to 4-5 folds of error reduction.

Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval framework that employs lightweight diffusion models to synthesize diverse seed embeddings from user queries that represent potential directions for music exploration. Unlike deterministic methods that map user query to a single point in embedding space, Diff4Steer provides a statistical prior on the target modality (audio) for retrieval, effectively capturing the uncertainty and multi-faceted nature of user preferences. Furthermore, Diff4Steer can be steered by image or text inputs, enabling more flexible and controllable music discovery combined with nearest neighbor search. Our framework outperforms deterministic regression methods and LLM-based generative retrieval baseline in terms of retrieval and ranking metrics, demonstrating its effectiveness in capturing user preferences, leading to more diverse and relevant recommendations. Listening examples are available at tinyurl.com/diff4steer., Comment: NeurIPS 2024 Creative AI Track

Dirac nodal line semimetals with topologically protected drumhead surface states have attracted intense theoretical and experimental attention over a decade. However, the study of type-II Dirac nodal line semimetals is rare, especially the type-II nodal chain semimetals have not been confirmed by experiment due to the lack of ideal material platform. In this study, based on symmetry analysis and the first-principles electronic structure calculations, we predict that CrB4 is an ideal type-II Dirac nodal chain semimetal protected by the mirror symmetry. Moreover, there are two nodal rings protected by both space-inversion and time-reversal symmetries in CrB4. More importantly,in CrB4 the topologically protected drumhead surface states span the entire Brillouin zone at the Fermi level. On the other hand, the electron-hole compensation and the high mobility of Dirac fermions can result in large magnetoresistance effects in CrB4 according to the two-band model.Considering the fact that CrB4 has been synthesized experimentally and the spin-orbit coupling is very weak, CrB4 provides an ideal material platform for studying the exotic properties of type-II Dirac nodal chain semimetals in experiment.

While the conducting CuO$_2$ planes in cuprate superconductors have been widely recognized as a crucial component in producing high superconducting $T_\text{c}$, recent experimental and theoretical studies on Ba$_{2-x}$Sr$_x$CuO$_{3+}$$_\delta$ have also drawn much attention to the importance of Cu-O chains in one-dimensional (1D) cuprates. To better understand the cuprates containing Cu-O chains, here we have studied the electronic, magnetic, and phonon properties of Sr$_2$CuO$_3$ bulk and films based on the spin-polarized density functional theory calculations. We first reproduced the typical Mott insulator feature of the cuprate parent compound for bulk Sr$_2$CuO$_3$, and then built a Sr$_2$CuO$_3$ thin film with Cu-O chains exposed on the surface to directly investigate their characteristics. Different from the insulating bulk phase, the Sr$_2$CuO$_3$ surface shows interesting metallic properties. Further electronic structure calculations reveal the existence of spin-polarized electron gas between surface Sr atoms that strongly depends on the interchain coupling of Cu spins. Moreover, the phonon modes that involve the vibrations of in-chain and out-of-chain O atoms can induce strong charge and spin fluctuations in the surface layer of Sr$_2$CuO$_3$ film, which suggests significant multiple degree-of-freedom couplings that may be important for the superconductivity in 1D cuprates. Our work provides a comprehensive viewpoint of the properties of Cu-O chains in Sr$_2$CuO$_3$, facilitating a complete understanding of 1D cuprate superconductors., Comment: 10 pages, 8 figures

The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers., Comment: 44 pages, 6 figures, 2 tables

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes' common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multi-scale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals., Comment: 21pages, 7 figures

Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model, facilitating the transfer of knowledge of large models. In contrast to much of the previous work, we scale up the parameters of the student model during training, to benefit from overparameterization without increasing the inference latency. In particular, we propose a tensor decomposition strategy that effectively over-parameterizes the relatively small student model through an efficient and nearly lossless decomposition of its parameter matrices into higher-dimensional tensors. To ensure efficiency, we further introduce a tensor constraint loss to align the high-dimensional tensors between the student and teacher models. Comprehensive experiments validate the significant performance enhancement by our approach in various KD tasks, covering computer vision and natural language processing areas. Our code is available at https://github.com/intell-sci-comput/OPDF., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-body computational toolkit, termed $\texttt{Zen}$, utilizes the Vienna $\textit{ab initio}$ simulation package and the $\texttt{Quantum ESPRESSO}$ code to perform density functional theory calculations and generate band structures for realistic materials. The challenges associated with correlated electron systems are addressed through two distinct yet complementary quantum impurity solvers: the natural orbitals renormalization group solver for zero temperature and the hybridization expansion continuous-time quantum Monte Carlo solver for finite temperature. Additionally, this newly developed toolkit incorporates several valuable post-processing tools, such as $\texttt{ACFlow}$, which employs the maximum entropy method and the stochastic pole expansion method for the analytic continuation of Matsubara Green's functions and self-energy functions. To validate the performance of this toolkit, we examine three representative cases: the correlated metal SrVO$_{3}$, the nickel-based unconventional superconductor La$_{3}$Ni$_{2}$O$_{7}$, and the wide-gap Mott insulator MnO. The results obtained demonstrate strong agreement with experimental findings and previously available theoretical results. Notably, we successfully elucidate the quasiparticle peak and band renormalization in SrVO$_{3}$, the dominance of Hund correlation in La$_{3}$Ni$_{2}$O$_{7}$, and the pressure-driven insulator-metal transition as well as the high-spin to low-spin transition in MnO. These findings suggest that $\texttt{Zen}$ is proficient in accurately describing the electronic structures of $d$-electron correlated materials., Comment: 14 pages, 8 figures, 1 table

Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state-of-the-art method to produce localized future climate information, involves running a regional climate model (RCM) driven by an Earth System Model (ESM), but it is too computationally expensive to apply to large climate projection ensembles. We propose a novel approach combining dynamical downscaling with generative artificial intelligence to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale. This approach leverages the generalizability of physics-based models and the sampling efficiency of diffusion models, enabling the downscaling of large multi-model ensembles. We evaluate our method against dynamically-downscaled climate projections from the CMIP6 ensemble. Our results demonstrate its ability to provide more accurate uncertainty bounds on future regional climate than alternatives such as dynamical downscaling of smaller ensembles, or traditional empirical statistical downscaling methods. We also show that dynamical-generative downscaling results in significantly lower errors than bias correction and spatial disaggregation (BCSD), and captures more accurately the spectra and multivariate correlations of meteorological fields. These characteristics make the dynamical-generative framework a flexible, accurate, and efficient way to downscale large ensembles of climate projections, currently out of reach for pure dynamical downscaling.

This study presents a roadmap towards utilizing a single arbitrary gate for universal quantum computing. Since two decades ago, it has been widely accepted that almost any single arbitrary gate with qubit number $>2$ is universal. Utilizing a single arbitrary gate for compiling is beneficial for systems with limited degrees of freedom, e.g. the scattering based quantum computing schemes. However, how to efficiently compile the wanted gate with a single arbitrary gate, and finally achieve fault-tolerant quantum computing is unknown. In this work, we show almost any target gate can be compiled to precision $\epsilon$ with a circuit depth of approximately $\log(\epsilon^{-1})$ with an improved brute-force compiling method. Under the assumption of reasonable classical resource, we show the gate imperfection can be lowered to $10^{-3}$. By treating the imperfection as coherent error, we show that the error can be further reduced by roughly two orders of magnitude with a measurement-free quantum error correction method.

The time-reversal symmetry is thought to be a necessary condition for realizing valley Hall effect. If the time-reversal symmetry is broken, whether the valley Hall effect can be realized has not been explored. In this letter, based on symmetry analysis and the first-principles electronic structure calculations, we demonstrate that the vally Hall effect without time-reversal symmetry can be realized in two-dimensional altermagnetic materials Fe$_2$WSe$_4$ and Fe$_2$WS$_4$. Due to crystal symmetry required, the vally Hall effect without time-reversal symmetry is called crystal vally Hall effect. In addition, under uniaxial strain, both monolayer Fe$_2$WSe$_4$ and Fe$_2$WS$_4$ can realize piezomagnetic effect. Under biaxial compressive stress, both monolayer Fe$_2$WSe$_4$ and Fe$_2$WS$_4$ will transform from altermagnetic semiconductor phase to bipolarized topological Weyl semimetal phase. Our work not only provides a new direction for exploring the novel valley Hall effect, but also provides a good platform for exploring altermagnetic semiconductors and altermagnetic topological phase transitions., Comment: 6 pages,4 figures

We present a generative AI algorithm for addressing the challenging task of fast, accurate and robust statistical computation of three-dimensional turbulent fluid flows. Our algorithm, termed as GenCFD, is based on a conditional score-based diffusion model. Through extensive numerical experimentation with both incompressible and compressible fluid flows, we demonstrate that GenCFD provides very accurate approximation of statistical quantities of interest such as mean, variance, point pdfs, higher-order moments, while also generating high quality realistic samples of turbulent fluid flows and ensuring excellent spectral resolution. In contrast, ensembles of operator learning baselines which are trained to minimize mean (absolute) square errors regress to the mean flow. We present rigorous theoretical results uncovering the surprising mechanisms through which diffusion models accurately generate fluid flows. These mechanisms are illustrated with solvable toy models that exhibit the relevant features of turbulent fluid flows while being amenable to explicit analytical formulas., Comment: 71 pages, 30 figures

Symmetry protected topology has been studied extensively in the past twenty years, but the topology protected by spin symmetry has just begun to be studied. In this work, based on spin symmetry analysis, we propose that a class of Weyl nodal line semimetals is protected by the spin symmetry. Then, by the first-principles electronic structure calculations, we predict that both altermagnetic $\rm Nb_2FeB_2$ and $\rm Ta_2FeB_2$ are node-network semimetals protected by the spin symmetry. Moreover, both altermagnetic $\rm Nb_2FeB_2$ and $\rm Ta_2FeB_2$ have nodal rings protected by the mirror symmetry and Dirac points protected by nonsymmorphic spin symmetry. Furthermore, both altermagnetic $\rm Nb_2FeB_2$ and $\rm Ta_2FeB_2$ transform node-network semimetal phase into Weyl semimetal phase when considering spin-orbit coupling. Therefore, our work not only enriches the topological phases protected by spin symmetry, but also provides an excellent material platform to investigate the exotic physical properties of multiple altermagnetic topological semimetal phases in experiment., Comment: 6 pages, 4 figures

The discovery of new superconducting materials, particularly those exhibiting high critical temperature ($T_c$), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases. However, the known materials only scratch the surface of the extensive array of possibilities within the realm of materials. Here, we develop InvDesFlow, an AI search engine that integrates deep model pre-training and fine-tuning techniques, diffusion models, and physics-based approaches (e.g., first-principles electronic structure calculation) for the discovery of high-$T_c$ superconductors. Utilizing InvDesFlow, we have obtained 74 dynamically stable materials with critical temperatures predicted by the AI model to be $T_c \geq$ 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset. Furthermore, we analyze trends in our dataset and individual materials including B$_4$CN$_3$ (at 5 GPa) and B$_5$CN$_2$ (at ambient pressure) whose $T_c$s are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-$T_c$ superconductors, outline its potential for accelerating discovery of the materials with targeted properties., Comment: 22 pages, 17 figures, 6 tables

Koebe uniformization is a fundemental problem in complex analysis. In this paper, we use transboundary extremal length to show that every nondegenerate and uncountably connected domain with bounded gap-ratio is conformally homeomorphic to a circle domain.

Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering. However, their study remains a challenge due to the large range scales, and the strong interaction with other, often not fully understood, physics. As a consequence, the spatiotemporal resolution required for accurate simulation of these systems is typically computationally infeasible, particularly for applications of long-term risk assessment, such as the quantification of extreme weather risk due to climate change. While data-driven modeling offers some promise of alleviating these obstacles, the scarcity of high-quality simulations results in limited available data to train such models, which is often compounded by the lack of stability for long-horizon simulations. As such, the computational, algorithmic, and data restrictions generally imply that the probability of rare extreme events is not accurately captured. In this work we present a general strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems. The approach is based on training a post-processing correction operator on under-resolved simulations nudged towards a high-fidelity reference. This enables us to learn the dynamics of the underlying system directly, which allows us to use very little training data, even when the statistics thereof are far from converged. Additionally, through the use of probabilistic network architectures we are able to leverage the uncertainty due to the limited training data to further improve extrapolation capabilities. We apply our framework to severely under-resolved simulations of quasi-geostrophic flow and demonstrate its ability to accurately predict the anisotropic statistics over time horizons more than 30 times longer than the data seen in training.

Low-energy excitations may manifest intricate behaviors of correlated electron systems and provide essential insights into the dynamics of quantum states and phase transitions. We study a two-orbital Hubbard model featuring the so-called holon-doublon low-energy excitations in the Mott insulating narrow band in the orbital-selective Mott phase (OSMP). We employ an improved dynamical mean-field theory (DMFT) technique to calculate the spectral functions at zero temperature. We show that the holon-doublon bound state is not the sole component of the low-energy excitations. Instead, it should be a bound state composed of a Kondo-like state in the wide band and a doublon in the narrow band, named inter-band Kondo-like (IBK) bound states. Notably, as the bandwidths of the two bands approach each other, we find anomalous IBK bound-state excitations in the metallic {\em wide} band., Comment: 5 pages, 4 figures

High-$T_c$ superconductivity (SC) was recently found in the bilayer material La$_3$Ni$_2$O$_7$ (La327) under high pressures. We study the bilayer two-orbital Hubbard model derived from the band structure of the La327. The model is solved by cluster dynamical mean-field theory (CDMFT) with natural orbitals renormalization group (NORG) as impurity solver at zero temperature, considering only normal states. With hole doping, we have observed sequentially the Mott insulator (Mott), pseudogap (PG), non-Fermi liquid (NFL), and Fermi liquid (FL) phases, with quantum correlations decreasing. The ground state of the La327 is in the NFL phase with Hund spin correlation, which transmits the Ni-$3d_{z^2}$ ($z$) orbital inter-layer AFM correlation to the Ni-$3d_{x^2-y^2}$ orbitals. When the $\sigma$-bonding state of the $z$ orbitals ($z+$) is no longer fully filled, the inter-layer antiferromagnetic (AFM) correlations weaken rapidly. At low pressures, the fully filled $z+$ band supports a strong inter-layer AFM correlations, potentially favoring short-range spin density wave (SDW) and suppressing SC. Hole doping at low pressures may achieve a similar effect to high pressures, under which the $z+$ band intersects with the Fermi level, and consequently the spin correlations weaken remarkably, potentially suppressing the possible short-range SDW and favoring SC., Comment: 8 pages, 9 figures

By preforming DFT+DMFT calculations, we systematically investigate the correlated electronic structure in the newly discovered monolayer-trilayer (ML-TL) phase of La$_3$Ni$_2$O$_7$ (1313-La327). Our calculated Fermi surfaces are in good agreement with the result of angle-resolved photoemission spectroscopy. We find that 1313-La327 is a multiorbital correlated metal. An unstable orbital-selective Mott behavior is found in ML. The ML Ni-3$d_{z^2}$ orbital exhibits a Mott behavior, while the ML Ni-3$d_{x^2-y^2}$ orbital is metallic due to self-doping. Quasi-particle peaks appear at the Fermi level due to an interorbital hopping between the localized ML Ni-3$d_{z^2}$ and delocalized Ni-3$d_{x^2-y^2}$ orbitals. We also find a large static local spin susceptibility of ML Ni, suggesting that there is large spin fluctuation in 1313-La327. The TL Ni-$e_g$ orbitals possess similar electronic correlation to those in La$_4$Ni$_3$O$_{10}$. The $e_g$ orbitals of the outer-layer Ni in TL show non-Fermi liquid behaviors. Besides, large weight of high-spin states are found in TL-outer Ni and ML Ni, implying Hundness. Under 16 GPa, a Lifshitz transition is revealed by our calculations and a La-related band crosses the Fermi level. Our work provides a theoretical reference for studying other potential mixed-stacked nickelate superconductors., Comment: 8 pages, 4 figures, 3 tables

Magnetism and topology are two major areas of condensed matter physics. The combination of magnetism and topology gives rise to more novel physical effects, which have attracted strongly theoretical and experimental attention. Recently, the concept of altermagnetism has been introduced, characterized by a dual nature: real-space antiferromagnetism and reciprocal-space anisotropic spin polarization. The amalgamation of altermagnetism with topology may lead to the emergence of previously unobserved topological phases and the associated physical effects. In this study, utilizing a four-band lattice model that incorporates altermagnetism and spin group symmetry, we demonstrate that type-I, type-II, and type-III bipolarized Weyl semimetals can exist in altermagnetic systems. Through the first-principles electronic structure calculations, we predict four ideal two-dimensional type-I altermagnetic bipolarized Weyl semimetals Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ (Z=S,Se,Te). More significantly, we introduce the quantum crystal valley Hall effect, a phenomenon achievable in three of these materials namely Fe$_2$WTe$_4$, Fe$_2$MoS$_4$, and Fe$_2$MoTe$_4$, when spin-orbit coupling is considered. Furthermore, these materials have the potential to transition from a quantum crystal valley Hall phase to a Chern insulator phase under strain. In contrast, Fe$_2$MoSe$_4$ remains to be a Weyl semimetal under spin-orbit coupling but is distinguished by possessing only a single pair of Weyl points. Additionally, the position, polarization, and number of Weyl points in Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ can be manipulated by adjusting the direction of the N\'eel vector. Consequently, Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ emerge as promising experimental platforms for investigating the distinctive physical attributes of various altermagnetic topological phases., Comment: 7 pages, 5 figures

This paper works on the structure of infinitely connected Fatou damains of rational maps in terms of Koebe uniformization. Due to the complicated boundary behavior, the existing uniformization results are failed to apply in general. We proved that if the rational map is geometrically finite, then its infinitely connected attracting Fatou damain is conformally homeomorphic to a circle domain., Comment: 13 pages

The tensor network algorithm, a family of prevalent numerical methods for quantum many-body problems, aptly captures the entanglement properties intrinsic to quantum systems, enabling precise representation of quantum states. However, its computational cost is notably high, particularly in calculating physical observables like correlation functions. To surmount the computational challenge and enhance efficiency, we propose integrating the Green's function Monte Carlo (GFMC) method with the projected entangled pair state (PEPS) ansatz. This approach combines the high-efficiency characteristics of Monte Carlo with the sign-free nature of tensor network states and proves effective in addressing the computational bottleneck. To showcase its prowess, we apply this hybrid approach to investigate the antiferromagnetic $J_1$-$J_2$ Heisenberg model on the square lattice, a model notorious for its sign problem in quantum Monte Carlo simulations. Our results reveal a substantial improvement in the accuracy of ground-state energy when utilizing a preliminary PEPS as the guiding wave function for GFMC. By calculating the structure factor and spin-spin correlation functions, we further characterize the phase diagram, identifying a possible columnar valence-bond state phase within the intermediate parameter range of $0.52 < J_2/J_1 < 0.58$. This comprehensive study underscores the efficacy of our combined approach, demonstrating its ability to accurately simulate frustrated quantum spin systems while ensuring computational efficiency., Comment: 11 pages, 15 figures

Expressive speech synthesis aims to generate speech that captures a wide range of para-linguistic features, including emotion and articulation, though current research primarily emphasizes emotional aspects over the nuanced articulatory features mastered by professional voice actors. Inspired by this, we explore expressive speech synthesis through the lens of articulatory phonetics. Specifically, we define a framework with three dimensions: Glottalization, Tenseness, and Resonance (GTR), to guide the synthesis at the voice production level. With this framework, we record a high-quality speech dataset named GTR-Voice, featuring 20 Chinese sentences articulated by a professional voice actor across 125 distinct GTR combinations. We verify the framework and GTR annotations through automatic classification and listening tests, and demonstrate precise controllability along the GTR dimensions on two fine-tuned expressive TTS models. We open-source the dataset and TTS models.

Morphological mapping is a fundamental step in studying the processes that shaped an asteroid surface. Yet, it is challenging and often requires multiple independent assessments by trained experts. Here, we present fast methods to detect and characterize meaningful terrains from the topographic roughness: entropy of information, and local mean surface orientation. We apply our techniques to Didymos and Dimorphos, the target asteroids of NASA's DART mission: first attempt to deflect an asteroid. Our methods reliably identify morphological units at multiple scales. The comparative study reveals various terrain types, signatures of processes that transformed Didymos and Dimorphos. Didymos shows the most heterogeneity and morphology that indicate recent resurfacing events. Dimorphos is comparatively rougher than Didymos, which may result from the formation process of the binary pair and past interaction between the two bodies. Our methods can be readily applied to other bodies and data sets., Comment: submitted to PSJ

In this work, we study the gravitational quasinormal modes of the thick brane in Rastall gravity. Using the asymptotic iteration and direct integration methods, we solve the quasinormal frequencies of the Rastall thick brane. We also obtained the waveforms of these quasinormal modes through numerical evolution. The results indicate that although the Rastall thick brane lacks a bound zero mode, when the Rastall parameter $\lambda\gtrsim0$, a long-lived quasinormal mode appears. This long-lived quasinormal mode may restore the four-dimensional effective Newtonian potential on the brane on a large scale. This may provide a new perspective for the localization of gravity on thick branes, that a thick brane does not necessarily require the gravity to be localized, perhaps quasi-localized is sufficient., Comment: 10 pages, 6 figures, and 1 table

To accommodate real-world dynamics, artificial intelligence systems need to cope with sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) attempting to address catastrophic forgetting with offline training of each task, Online Continual Learning (OCL) is a more challenging yet realistic setting that performs CL in a one-pass data stream. Current OCL methods primarily rely on memory replay of old training samples. However, a notable gap from CL to OCL stems from the additional overfitting-underfitting dilemma associated with the use of rehearsal buffers: the inadequate learning of new training samples (underfitting) and the repeated learning of a few old training samples (overfitting). To this end, we introduce a novel approach, Multi-level Online Sequential Experts (MOSE), which cultivates the model as stacked sub-experts, integrating multi-level supervision and reverse self-distillation. Supervision signals across multiple stages facilitate appropriate convergence of the new task while gathering various strengths from experts by knowledge distillation mitigates the performance decline of old tasks. MOSE demonstrates remarkable efficacy in learning new samples and preserving past knowledge through multi-level experts, thereby significantly advancing OCL performance over state-of-the-art baselines (e.g., up to 7.3% on Split CIFAR-100 and 6.1% on Split Tiny-ImageNet)., Comment: CVPR 2024

A recent experimental study announced the emergence of superconductivity in La$_3$Ni$_2$O$_7$ under pressure, with the highest observed superconducting transition temperature ($T_c$) reaching approximately 80 K beyond 14 GPa. While extensive studies have been devoted to the electronic correlations and potential superconducting pairing mechanisms, there lack investigations into the phonon properties and electron phonon coupling. Using density functional theory in conjunction with Wannier interpolation techniques, we study the phonon properties and electron phonon interactions in La$_3$Ni$_2$O$_7$ under 29.5 GPa. Our findings reveal that the electron phonon coupling is insufficient to solely explain the observed high superconducting $T_c$ $\sim$ 80 K in La$_3$Ni$_2$O$_7$. And the calculated strong Fermi surface nesting may explain the experimental observed charge density wave transition in La$_3$Ni$_2$O$_7$. Our calculations substantiate La$_3$Ni$_2$O$_7$ is an unconventional superconductor., Comment: 5 pages, 4 figures

We propose two novel two-dimensional (2D) topological materials, (PtN$_4$)$_3$C$_{32}$ and (MnN$_4$)$_3$C$_{32}$, with a special geometry that we named as kagome-in-honeycomb (KIH) lattice structure, to illustrate the coexistence of the paradigmatic states of kagome physics, Dirac fermions and flat bands, that are difficult to be simultaneously observed in three-dimensional realistic systems. In such system, MN$_4$(M=Pt or Mn) moieties are embedded in honeycomb graphene sheet according to kagome lattice structure, thereby resulting in a KIH lattice. Using the first-principles calculations, we have systemically studied the structural, electronic, and topological properties of these two materials. In the absence of spin-orbit coupling (SOC), they both exhibit the coexistence of Dirac/quadratic-crossing cone and flat band near the Fermi level. When SOC is included, a sizable topological gap is opened at the Dirac/quadratic-crossing nodal point. For nonmagnetic (PtN$_4$)$_3$C$_{32}$, the system is converted into a $\mathbb{Z}_2$ topological quantum spin Hall insulator defined on a curved Fermi level, while for ferromagnetic (MnN$_4$)$_3$C$_{32}$, the material is changed from a half-semi-metal to a quantum anomalous Hall insulator with nonzero Chern number and nontrivial chiral edge states. Our findings not only predict a new family of 2D quantum materials, but also provide an experimentally feasible platform to explore the emergent kagome physics, topological quantum Hall physics, strongly correlated phenomena, and theirs fascinating applications., Comment: 6 figures. arXiv admin note: text overlap with arXiv:2207.03703

Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics. However, many of these systems exhibit ergodicity and an attractor: a compact and highly complex manifold, to which trajectories converge in finite-time, that supports an invariant measure, i.e., a probability distribution that is invariant under the action of the dynamics, which dictates the long-term statistical behavior of the system. In this work, we leverage this structure to propose a new framework that targets learning the invariant measure as well as the dynamics, in contrast with typical methods that only target the misfit between trajectories, which often leads to divergence as the trajectories' length increases. We use our framework to propose a tractable and sample efficient objective that can be used with any existing learning objectives. Our Dynamics Stable Learning by Invariant Measure (DySLIM) objective enables model training that achieves better point-wise tracking and long-term statistical accuracy relative to other learning objectives. By targeting the distribution with a scalable regularization term, we hope that this approach can be extended to more complex systems exhibiting slowly-variant distributions, such as weather and climate models., Comment: ICML 2024; Code to reproduce our experiments is available at https://github.com/google-research/swirl-dynamics/tree/main/swirl_dynamics/projects/ergodic

High temperature superconductivity was recently found in the bilayer nickelate $\rm{La}_3 \rm{Ni}_2 \rm{O}_7$ (La327), followed by the discovery of superconductivity in the trilayer $\rm{La}_4 \rm{Ni}_3 \rm{O}_{10}$ (La4310), under high pressure. Through studying the electronic correlation of La4310 with DFT+DMFT, and further comparing it with that of La327, we find that the $e_g$ orbitals of the outer-layer Ni cations in La4310 have a similar (but slightly weaker) electronic correlation to those in La327, in which the electrons behave as a non-Fermi liquid with Hund correlation and linear-in-temperature scattering rate. Our results suggest that the experimentally observed ``strange metal'' behavior may be explained by the Hund spin correlation featuring high spin states and spin-orbital separation. In contrast, the electrons in the inner-layer Ni cations in La4310 behave as a Fermi liquid. The weaker electronic correlation in La4310 is attributed to more hole-doping, which may explain its lower superconducting transition temperature., Comment: 6 pages, 4 figures