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GRB 230812B, detected by the Gamma-Ray Integrated Detectors (GRID) constellation mission, is an exceptionally bright gamma-ray burst (GRB) with a duration of only 3 seconds. Sitting near the traditional boundary ($\sim$ 2 s) between long and short GRBs, GRB 230812B is notably associated with a supernova (SN), indicating a massive star progenitor. This makes it a rare example of a short-duration GRB resulting from stellar collapse. Our analysis, using a time-evolving synchrotron model, suggests that the burst has an emission radius of approximately $10^{14.5}$~cm. We propose that the short duration of GRB 230812B is due to the combined effects of the central engine's activity time and the time required for the jet to break through the stellar envelope. Our findings provide another case that challenges the conventional view that short-duration GRBs originate exclusively from compact object mergers, demonstrating that a broader range of durations exists for GRBs arising from the collapse of massive stars., Comment: 10 pages, 3 tables, 11 figures

Subject-driven text-to-image (T2I) customization has drawn significant interest in academia and industry. This task enables pre-trained models to generate novel images based on unique subjects. Existing studies adopt a self-reconstructive perspective, focusing on capturing all details of a single image, which will misconstrue the specific image's irrelevant attributes (e.g., view, pose, and background) as the subject intrinsic attributes. This misconstruction leads to both overfitting or underfitting of irrelevant and intrinsic attributes of the subject, i.e., these attributes are over-represented or under-represented simultaneously, causing a trade-off between similarity and controllability. In this study, we argue an ideal subject representation can be achieved by a cross-differential perspective, i.e., decoupling subject intrinsic attributes from irrelevant attributes via contrastive learning, which allows the model to focus more on intrinsic attributes through intra-consistency (features of the same subject are spatially closer) and inter-distinctiveness (features of different subjects have distinguished differences). Specifically, we propose CustomContrast, a novel framework, which includes a Multilevel Contrastive Learning (MCL) paradigm and a Multimodal Feature Injection (MFI) Encoder. The MCL paradigm is used to extract intrinsic features of subjects from high-level semantics to low-level appearance through crossmodal semantic contrastive learning and multiscale appearance contrastive learning. To facilitate contrastive learning, we introduce the MFI encoder to capture cross-modal representations. Extensive experiments show the effectiveness of CustomContrast in subject similarity and text controllability.

Abundant, well-annotated multimodal data in remote sensing are pivotal for aligning complex visual remote sensing (RS) scenes with human language, enabling the development of specialized vision language models across diverse RS interpretation tasks. However, annotating RS images with rich linguistic semantics at scale demands expertise in RS and substantial human labor, making it costly and often impractical. In this study, we propose a workflow that leverages large language models (LLMs) to generate multimodal datasets with semantically rich captions at scale from plain OpenStreetMap (OSM) data for images sourced from the Google Earth Engine (GEE) platform. This approach facilitates the generation of paired remote sensing data and can be readily scaled up using openly available data. Within this framework, we present RSTeller, a multimodal dataset comprising over 1 million RS images, each accompanied by multiple descriptive captions. Extensive experiments demonstrate that RSTeller enhances the performance of multiple existing vision language models for RS scene understanding through continual pre-training. Our methodology significantly reduces the manual effort and expertise needed for annotating remote sensing imagery while democratizing access to high-quality annotated data. This advancement fosters progress in visual language modeling and encourages broader participation in remote sensing research and applications. The RSTeller dataset is available at https://github.com/SlytherinGe/RSTeller., Comment: Submitted to ISPRS

We address the problem of verifying neural networks against geometric transformations of the input image, including rotation, scaling, shearing, and translation. The proposed method computes provably sound piecewise linear constraints for the pixel values by using sampling and linear approximations in combination with branch-and-bound Lipschitz optimisation. The method obtains provably tighter over-approximations of the perturbation region than the present state-of-the-art. We report results from experiments on a comprehensive set of verification benchmarks on MNIST and CIFAR10. We show that our proposed implementation resolves up to 32% more verification cases than present approaches., Comment: ECAI 2024

Large Language Models (LLMs) are revolutionizing the AI industry with their superior capabilities. Training these models requires large-scale GPU clusters and significant computing time, leading to frequent failures that significantly increase training costs. Despite its significance, this field lacks a metric for evaluating reliability. In this work, we introduce a novel reliability metric called \emph{Training Overhead Ratio} (TOR) to evaluate the reliability of fault-tolerant LLM training systems. TOR is defined as the ratio of optimal training time to the observed training time of a system, serving as a practical tool for users to estimate the actual time required to train an LLM on a given system. Furthermore, our investigation identifies the key factor for enhancing reliability and present TOR equations for various types of failures encountered in practice., Comment: To be published in: IEEE International Symposium on Software Reliability Engineering (ISSRE2024) workshop

When a two-dimensional $d$-wave altermagnet is grown on a substrate, the interplay of momentum-dependent spin splittings arising from altermagnetism and Rashba spin-orbit coupling gives rise to a nodal band structure with band degeneracies enforced by a $C_{4z}\mathcal{T}$ symmetry. If we break the $C_{4z}\mathcal{T}$ symmetry by an exchange field, the band degeneracies are found to be immediately lifted, leading to a topological band structure characterized by nontrivial strong and weak topological indices. Remarkably, both the strong topological index and the $Z_{2}$-valued weak topological indices depend sensitively on the direction of the exchange field. As a consequence of the bulk-defect correspondence, we find that the unique dependence of weak topological indices on the exchange field in this system dictates that the presence or absence of topological bound states at lattice dislocations also depends sensitively on the direction of the exchange field. When the substrate is an $s$-wave superconductor, we find that a similar dependence of band topology on the exchange field gives rise to field-sensitive dislocation Majorana zero modes. As topological dislocation bound states are easily detectable by scanning tunneling microscopy, our findings unveil a promising experimental diagnosis of altermagnetic materials among an ever growing list of candidates., Comment: 9 pages, 5 figures

Scaling text-to-speech (TTS) with autoregressive language model (LM) to large-scale datasets by quantizing waveform into discrete speech tokens is making great progress to capture the diversity and expressiveness in human speech, but the speech reconstruction quality from discrete speech token is far from satisfaction depending on the compressed speech token compression ratio. Generative diffusion models trained with score-matching loss and continuous normalized flow trained with flow-matching loss have become prominent in generation of images as well as speech. LM based TTS systems usually quantize speech into discrete tokens and generate these tokens autoregressively, and finally use a diffusion model to up sample coarse-grained speech tokens into fine-grained codec features or mel-spectrograms before reconstructing into waveforms with vocoder, which has a high latency and is not realistic for real time speech applications. In this paper, we systematically investigate varied diffusion models for up sampling stage, which is the main bottleneck for streaming synthesis of LM and diffusion-based architecture, we present the model architecture, objective and subjective metrics to show quality and efficiency improvement.

This survey explores the geometric perspective on policy optimization within the realm of feedback control systems, emphasizing the intrinsic relationship between control design and optimization. By adopting a geometric viewpoint, we aim to provide a nuanced understanding of how various ``complete parameterization'' -- referring to the policy parameters together with its Riemannian geometry -- of control design problems, influence stability and performance of local search algorithms. The paper is structured to address key themes such as policy parameterization, the topology and geometry of stabilizing policies, and their implications for various (non-convex) dynamic performance measures. We focus on a few iconic control design problems, including the Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG) control, and $\mathcal{H}_\infty$ control. In particular, we first discuss the topology and Riemannian geometry of stabilizing policies, distinguishing between their static and dynamic realizations. Expanding on this geometric perspective, we then explore structural properties of the aforementioned performance measures and their interplay with the geometry of stabilizing policies in presence of policy constraints; along the way, we address issues such as spurious stationary points, symmetries of dynamic feedback policies, and (non-)smoothness of the corresponding performance measures. We conclude the survey with algorithmic implications of policy optimization in feedback design.

Many optimal and robust control problems are nonconvex and potentially nonsmooth in their policy optimization forms. In Part II of this paper, we introduce a new and unified Extended Convex Lifting (ECL) framework to reveal hidden convexity in classical optimal and robust control problems from a modern optimization perspective. Our ECL offers a bridge between nonconvex policy optimization and convex reformulations, enabling convex analysis for nonconvex problems. Despite non-convexity and non-smoothness, the existence of an ECL not only reveals that minimizing the original function is equivalent to a convex problem but also certifies a class of first-order non-degenerate stationary points to be globally optimal. Therefore, no spurious stationarity exists in the set of non-degenerate policies. This ECL framework can cover many benchmark control problems, including state feedback linear quadratic regulator (LQR), dynamic output feedback linear quadratic Gaussian (LQG) control, and $\mathcal{H}_\infty$ robust control. ECL can also handle a class of distributed control problems when the notion of quadratic invariance (QI) holds. We further show that all static stabilizing policies are non-degenerate for state feedback LQR and $\mathcal{H}_\infty$ control under standard assumptions. We believe that the new ECL framework may be of independent interest for analyzing nonconvex problems beyond control.

In quantum computing, the connectivity of qubits placed on two-dimensional chips limits the scalability and functionality of solid-state quantum computers. This paper presents two approaches to constructing complex quantum networks from simple qubit arrays, specifically grid lattices. The first approach utilizes a subset of qubits as tunable couplers, effectively yielding a range of non-trivial graph-based Hamiltonians. The second approach employs dynamic graph engineering by periodically activating and deactivating couplers, enabling the creation of effective quantum walks with longer-range couplings. Numerical simulations verify the effective dynamics of these approaches. In terms of these two approaches, we explore implementing various graphs, including cubes and fullerenes, etc, on two-dimensional lattices. These techniques facilitate the realization of analog quantum simulation, particularly continuous-time quantum walks discussed in detail in this manuscript, for different computational tasks on superconducting quantum chips despite their inherent low dimensional simple architecture., Comment: 9 pages, 12 figures

In the circuit quantum electrodynamics architectures, to realize a long-range quantum network mediated by flying photon, it is necessary to shape the temporal profile of emitted photons to achieve high transfer efficiency between two quantum nodes. In this work, we demonstrate a new single-rail and dual-rail time-bin shaped photon generator without additional flux-tunable elements, which can act as a quantum interface of a point-to-point quantum network. In our approach, we adopt a qubit-resonator-transmission line configuration, and the effective coupling strength between the qubit and the resonator can be varied by parametrically modulating the qubit frequency. In this way, the coupling is directly proportional to the parametric modulation amplitude and covers a broad tunable range beyond 20 MHz for the sample we used. Additionally, when emitting shaped photons, we find that the spurious frequency shift (-0.4 MHz) due to parametric modulation is small and can be readily calibrated through chirping. We develop an efficient photon field measurement setup based on the data stream processing of GPU. Utilizing this system, we perform photon temporal profile measurement, quantum state tomography of photon field, and quantum process tomography of single-rail quantum state transfer based on a heterodyne measurement scheme. The single-rail encoding state transfer fidelity of shaped photon emission is 90.32%, and that for unshaped photon is 97.20%, respectively. We believe that the fidelity of shaped photon emission is mainly limited by the qubit coherence time. The results demonstrate that our method is hardware efficient, simple to implement, and scalable. It could become a viable tool in a high-quality quantum network utilizing both single-rail and dual-rail time-bin encoding.

We introduce ''Berry-dipole semimetals'', whose band degeneracies are characterized by quantized Berry dipoles. Through a two-band model constructed by Hopf map, we reveal that the Berry-dipole semimetals display a multitude of salient properties distinct from other topological semimetals. On the boundary, we find that the first-order Berry-dipole semimetal harbors anomalous paired Fermi arcs with the same spin polarization, even though the layer Chern number is zero, and the second-order Berry-dipole semimetal hosts dispersionless hinge arcs. In the bulk, we find that the low-energy Berry-dipole Hamiltonian near the band node has a quadratic energy dispersion and peculiar Berry curvature, which give rise to rather unique characteristics in the intrinsic anomalous Hall effect, orbital magnetization and Landau levels. Our study shows that Berry-dipole semimetals are a class of topological gapless phases supporting rich intriguing physics., Comment: 7+12 pages, 7 figures

Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to reconstruct clothed humans. We introduce PhysAvatar, a novel framework that combines inverse rendering with inverse physics to automatically estimate the shape and appearance of a human from multi-view video data along with the physical parameters of the fabric of their clothes. For this purpose, we adopt a mesh-aligned 4D Gaussian technique for spatio-temporal mesh tracking as well as a physically based inverse renderer to estimate the intrinsic material properties. PhysAvatar integrates a physics simulator to estimate the physical parameters of the garments using gradient-based optimization in a principled manner. These novel capabilities enable PhysAvatar to create high-quality novel-view renderings of avatars dressed in loose-fitting clothes under motions and lighting conditions not seen in the training data. This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop. Our project website is at: https://qingqing-zhao.github.io/PhysAvatar, Comment: Project Page: https://qingqing-zhao.github.io/PhysAvatar

This paper presents our approach for the VA (Valence-Arousal) estimation task in the ABAW6 competition. We devised a comprehensive model by preprocessing video frames and audio segments to extract visual and audio features. Through the utilization of Temporal Convolutional Network (TCN) modules, we effectively captured the temporal and spatial correlations between these features. Subsequently, we employed a Transformer encoder structure to learn long-range dependencies, thereby enhancing the model's performance and generalization ability. Our method leverages a multimodal data fusion approach, integrating pre-trained audio and video backbones for feature extraction, followed by TCN-based spatiotemporal encoding and Transformer-based temporal information capture. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in VA estimation on the AffWild2 dataset., Comment: 8 pages,3 figures

Frustration, that is, the impossibility to satisfy the energetic preferences between all spin pairs simultaneously, underlies the complexity of many fundamental properties in spin systems, including the computational hardness to determine their ground states. Coherent Ising machines (CIM) have been proposed as a promising analog computational approach to efficiently find different degenerate ground states of large and complex Ising models. However, CIMs also face challenges in solving frustrated Ising models: Frustration not only reduces the probability to find good solutions, but it also prohibits to leverage quantum effects in doing so. To circumvent these detrimental effects of frustration, we show how frustrated Ising models can be mapped to frustration-free CIM configurations by including ancillary modes and modifying the coupling protocol used in current CIM designs. In our proposal, degenerate optical parametric oscillator (DOPO) modes encode the ground state candidates of the studied Ising model, while the ancillary modes enable the autonomous transformation to a frustration-free Ising model that preserves the ground states encoded in the DOPO modes. Such frustration elimination may empower current CIMs to improve precision and to benefit from quantum effects in dealing with frustrated Ising models., Comment: 6 pages, 3 figures

Two-dimensional systems with honeycomb lattice are known to be a paradigmatic platform to explore the various types of Hall effects, owing to that the interplay of lattice geometry, spin-orbit coupling and magnetism can give rise to very rich features in the quantum geometry of wave functions. In this work, we consider honeycomb topological antiferromagets that are effectively described by a $\mathcal{PT}$-symmetric antiferromagnetic Kane-Mele model, and explore the evolution of its nonlinear Hall response with respect to the change of lattice anisotropy, chemical potential, and the direction of the N\'{e}el vector. Due to the $\mathcal{PT}$-symmetry, the leading-order Hall effect of quantum geometric origin is the intrinsic nonlinear Hall effect, which is a second-order effect of electric fields and is independent of the scattering time. We investigate the behavior of the intrinsic nonlinear Hall conductivity tensor across topological phase transitions driven by antiferromagnetic exchange field and lattice anisotropy and find that its components do not change sign, which is different from the extrinsic nonlinear Hall effect. In the weakly doped regime, we find that the intrinsic nonlinear Hall effect is valley-polarized. By varying the chemical potential, we find that the nonlinear Hall conductivity tensors exhibit kinks when the Fermi surface undergoes Lifshitz transitions. Furthermore, we find that the existence of spin-orbit coupling to lift the spin-rotation symmetry is decisive for the use of intrinsic nonlinear Hall effect to detect the direction of the N\'{e}el vector. Our work shows that the two-dimensional honeycomb topological antiferromagnets are an ideal class of material systems with rich properties for the study of intrinsic nonlinear Hall effect., Comment: 9 pages, 7 figures

In a mixed traffic with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) coexisting, data-driven predictive control of CAVs promises system-wide traffic performance improvements. Yet, most existing approaches focus on a centralized setup, which is not computationally scalable while failing to protect data privacy. The robustness against unknown disturbances has not been well addressed either, causing safety concerns. In this paper, we propose a decentralized robust DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) approach for CAVs to smooth mixed traffic flow. In particular, each CAV computes its control input based on locally available data from its involved subsystem. Meanwhile, the interaction between neighboring subsystems is modeled as a bounded disturbance, for which appropriate estimation methods are proposed. Then, we formulate a robust optimization problem and present its tractable computational solutions. Compared with the centralized formulation, our method greatly reduces computation burden with better safety performance, while naturally preserving data privacy. Extensive traffic simulations validate its wave-dampening ability, safety performance, and computational benefits.

Given a Polish group $G$, let $E(G)$ be the right coset equivalence relation $G^\omega/c(G)$, where $c(G)$ is the group of all convergent sequences in $G$. We first established two results: (1) Let $G,H$ be two Polish groups. If $H$ is TSI but $G$ is not, then $E(G)\not\le_BE(H)$. (2) Let $G$ be a Polish group. Then the following are equivalent: (a) $G$ is TSI non-archimedean; (b)$E(G)\leq_B E_0^\omega$; and (c) $E(G)\leq_B{\mathbb R}^\omega/c_0$. In particular, $E(G)\sim_B E_0^\omega$ iff $G$ is TSI uncountable non-archimedean. A critical theorem presented in this article is as follows: Let $G$ be a TSI Polish group, and let $H$ be a closed subgroup of the product of a sequence of TSI strongly NSS Polish groups. If $E(G)\le_BE(H)$, then there exists a continuous homomorphism $S:G_0\rightarrow H$ such that $\ker(S)$ is non-archimedean, where $G_0$ is the connected component of the identity of $G$. The converse holds if $G$ is connected, $S(G)$ is closed in $H$, and the interval $[0,1]$ can be embedded into $H$. As its applications, we prove several Rigid theorems for TSI Lie groups, locally compact Polish groups, separable Banach spaces, and separable Fr\'echet spaces, respectively., Comment: 38 pages

Thouless pumping, a dynamical version of the integer quantum Hall effect, represents the quantized charge pumped during an adiabatic cyclic evolution. Here we report experimental observations of nontrivial topological pumping that is induced by disorder even during a topologically trivial pumping trajectory. With a 41-qubit superconducting quantum processor, we develop a Floquet engineering technique to realize cycles of adiabatic pumping by simultaneously varying the on-site potentials and the hopping couplings. We demonstrate Thouless pumping in the presence of disorder and show its breakdown as the strength of disorder increases. Moreover, we observe two types of topological pumping that are induced by on-site potential disorder and hopping disorder, respectively. Especially, an intrinsic topological pump that is induced by quasi-periodic hopping disorder has never been experimentally realized before. Our highly controllable system provides a valuable quantum simulating platform for studying various aspects of topological physics in the presence of disorder.

Accurate node localization is vital for mobile ad hoc networks (MANETs). Current methods like Time of Arrival (TOA) can estimate node positions using imprecise baseplates and achieve the Cram\'er-Rao lower bound (CRLB) accuracy. In multi-hop MANETs, some nodes lack direct links to base anchors, depending on neighbor nodes as dynamic anchors for chain localization. However, the dynamic nature of MANETs challenges TOA's robustness due to the availability and accuracy of base anchors, coupled with ranging errors. To address the issue of cascading positioning error divergence, we first derive the CRLB for any primary node in MANETs as a metric to tackle localization error in cascading scenarios. Second, we propose an advanced two-step TOA method based on CRLB which is able to approximate target node's CRLB with only local neighbor information. Finally, simulation results confirm the robustness of our algorithm, achieving CRLB-level accuracy for small ranging errors and maintaining precision for larger errors compared to existing TOA methods.

Many practical optimization problems lack strong convexity. Fortunately, recent studies have revealed that first-order algorithms also enjoy linear convergences under various weaker regularity conditions. While the relationship among different conditions for convex and smooth functions is well-understood, it is not the case for the nonsmooth setting. In this paper, we go beyond convexity and smoothness, and clarify the connections among common regularity conditions in the class of weakly convex functions, including $\textit{strong convexity}$, $\textit{restricted secant inequality}$, $\textit{subdifferential error bound}$, $\textit{Polyak-{\L}ojasiewicz inequality}$, and $\textit{quadratic growth}$. In addition, using these regularity conditions, we present a simple and modular proof for the linear convergence of the proximal point method (PPM) for convex and weakly convex optimization problems. The linear convergence also holds when the subproblems of PPM are solved inexactly with a proper control of inexactness., Comment: 29 pages, 3 figures, and 1 table

The Willems' fundamental lemma, which characterizes linear time-invariant (LTI) systems using input and output trajectories, has found many successful applications. Combining this with receding horizon control leads to a popular Data-EnablEd Predictive Control (DeePC) scheme. DeePC is first established for LTI systems and has been extended and applied for practical systems beyond LTI settings. However, the relationship between different DeePC variants, involving regularization and dimension reduction, remains unclear. In this paper, we first introduce a new bi-level optimization formulation that combines a data pre-processing step as an inner problem (system identification) and predictive control as an outer problem (online control). We next discuss a series of convex approximations by relaxing some hard constraints in the bi-level optimization as suitable regularization terms, accounting for an implicit identification. These include some existing DeePC variants as well as two new variants, for which we establish their equivalence under appropriate settings. Notably, our analysis reveals a novel variant, called DeePC-SVD-Iter, which has remarkable empirical performance of direct methods on systems beyond deterministic LTI settings.

Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control, which reveals elegant nonconvex geometry in various benchmark problems, especially in fully observable state-feedback cases. This paper considers two fundamental optimal and robust control problems with partial observability: the Linear Quadratic Gaussian (LQG) control with stochastic noises, and $\mathcal{H}_\infty$ robust control with adversarial noises. In the policy space, the former problem is smooth but nonconvex, while the latter one is nonsmooth and nonconvex. We highlight some interesting and surprising ``discontinuity'' of LQG and $\mathcal{H}_\infty$ cost functions around the boundary of their domains. Despite the lack of convexity (and possibly smoothness), our main results show that for a class of non-degenerate policies, all Clarke stationary points are globally optimal and there is no spurious local minimum for both LQG and $\mathcal{H}_\infty$ control. Our proof techniques rely on a new and unified framework of Extended Convex Lifting (ECL), which reconciles the gap between nonconvex policy optimization and convex reformulations. This ECL framework is of independent interest, and we will discuss its details in Part II of this paper., Comment: 79 pages, 12 figures

We study recovering fluid density and velocity from sparse multiview videos. Existing neural dynamic reconstruction methods predominantly rely on optical flows; therefore, they cannot accurately estimate the density and uncover the underlying velocity due to the inherent visual ambiguities of fluid velocity, as fluids are often shapeless and lack stable visual features. The challenge is further pronounced by the turbulent nature of fluid flows, which calls for properly designed fluid velocity representations. To address these challenges, we propose hybrid neural fluid fields (HyFluid), a neural approach to jointly infer fluid density and velocity fields. Specifically, to deal with visual ambiguities of fluid velocity, we introduce a set of physics-based losses that enforce inferring a physically plausible velocity field, which is divergence-free and drives the transport of density. To deal with the turbulent nature of fluid velocity, we design a hybrid neural velocity representation that includes a base neural velocity field that captures most irrotational energy and a vortex particle-based velocity that models residual turbulent velocity. We show that our method enables recovering vortical flow details. Our approach opens up possibilities for various learning and reconstruction applications centered around 3D incompressible flow, including fluid re-simulation and editing, future prediction, and neural dynamic scene composition. Project website: https://kovenyu.com/HyFluid/, Comment: NeurIPS 2023. Project website: https://kovenyu.com/HyFluid/ The first two authors contribute equally

To efficiently scale large model (LM) training, researchers transition from data parallelism (DP) to hybrid parallelism (HP) on GPU clusters, which frequently experience hardware and software failures. Existing works introduce in-memory checkpointing optimizations that snapshot parameters to device memory for rapid failure recovery. However, these methods introduce severe resource competition between checkpointing and training, which can work under DP but can hardly scale under resource-intensive HP. To ensure low checkpointing overhead for hybrid-parallel training, this paper introduces a distributed in-memory checkpointing system with near-zero in-memory saving overhead. It strives from two aspects to mitigate the on-host resource competition caused by in-memory checkpointing: (1) It introduces Hierarchical Asynchronous Snapshotting Coordination in the checkpoint saving stage. This approach uses three-level asynchronous on-device scheduling to enhance parallelism between snapshotting and training, thereby minimizing snapshotting overhead. (2) It proposes Hybrid In-memory Checkpoint Protection to enhance checkpoint completeness during hardware failures. Unlike methods that require inter-node communications, which may block training under HP, it creates intra-node redundancy with efficient resource utilization, protecting training against hardware failures with minimal overhead. With these methods, this work enables fast restart for failed HP training with Distributed In-memory Checkpoint Loading, bypassing inefficiencies in NFS reads. In our evaluation, we achieve zero in-memory checkpoint saving overhead on Frontier while training Llama-2-34B on 256 MI250X devices (512 GPUs)., Comment: Fault Tolerance, Checkpoint Optimization, Large Language Model, Foundation Model, Hybrid parallelism

Characterizing the nature of hydrodynamical transport properties in quantum dynamics provides valuable insights into the fundamental understanding of exotic non-equilibrium phases of matter. Experimentally simulating infinite-temperature transport on large-scale complex quantum systems is of considerable interest. Here, using a controllable and coherent superconducting quantum simulator, we experimentally realize the analog quantum circuit, which can efficiently prepare the Haar-random states, and probe spin transport at infinite temperature. We observe diffusive spin transport during the unitary evolution of the ladder-type quantum simulator with ergodic dynamics. Moreover, we explore the transport properties of the systems subjected to strong disorder or a tilted potential, revealing signatures of anomalous subdiffusion in accompany with the breakdown of thermalization. Our work demonstrates a scalable method of probing infinite-temperature spin transport on analog quantum simulators, which paves the way to study other intriguing out-of-equilibrium phenomena from the perspective of transport., Comment: Main text: 13 pages, 5 figures; Supplementary: 17 pages, 16 figures, 1 table

The recently developed DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) method has shown promising performance for data-driven predictive control of Connected and Autonomous Vehicles (CAVs) in mixed traffic. However, its simplistic zero assumption of the future velocity errors for the head vehicle may pose safety concerns and limit its performance of smoothing traffic flow. In this paper, we propose a robust DeeP-LCC method to control CAVs in mixed traffic with enhanced safety performance. In particular, we first present a robust formulation that enforces a safety constraint for a range of potential velocity error trajectories, and then estimate all potential velocity errors based on the past data from the head vehicle. We also provide efficient computational approaches to solve the robust optimization for online predictive control. Nonlinear traffic simulations show that our robust DeeP-LCC can provide better traffic efficiency and stronger safety performance while requiring less offline data.