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The Carnian Pluvial Episode (CPE) was a major global climate change event in the early Late Triassic that significantly affected marine ecosystems and carbon cycles. One of the most prominent features of the CPE is the coupled multiple negative carbonate-organic carbon isotope excursions. However, at Erguan and Xiashulao from eastern Tethys, a decoupling between carbonate-organic carbon isotope during CPE was observed. At the end of early Carnian (Julian), the carbonate carbon isotope showed a negative excursion of 2-3 per-mille, while the organic carbon isotope exhibited a positive excursion of about 3-4 per-mille. In addition, increased terrestrial inputs is indicated by the rising C/N (3 to 10) and decreasing Y/Ho (42 to 27) that coexist with this decoupling. The coupling of carbon isotope negative excursions is from the shallow shelves and the deep slopes, whereas the decoupling occurs from the deep shelf to the shallow slope. In the deep shelf to the shallow slope, sedimentary organic matter is mainly sourced from pelagic before the CPE as evidenced by low C/N (3) and high Y/Ho (36-42). During the CPE, the increased fresh water flux (Sr/Ba <1) enhanced terrestrial input in organic matter, which may cause positive excursions in the carbon isotope record with elevated TOC content. As a result, the carbonate-organic carbon isotope decoupled. In contrast, organic matter in sediments from the shallow shelf and deep slope are mainly from terrestrial and pelagic sources, respectively. This study reveals the significant impact of terrestrial inputs on marine carbon cycling during the Carnian Pluvial Episode, highlighting the crucial role of climate events in modifying the carbon isotope record., Comment: 49 pages, 10 figures

Deep learning (DL) models are piquing high interest and scaling at an unprecedented rate. To this end, a handful of tiled accelerators have been proposed to support such large-scale training tasks. However, these accelerators often incorporate numerous cores or tiles even extending to wafer-scale, substantial on-chip bandwidth, and distributed memory systems. This results in an exceedingly complex design space. Moreover, conducting actual training experiments to find optimal configurations is impractical due to time constraints. Hence, predicting the optimal mapping of various parallelisms to such tiled system architectures becomes crucial. In this study, leveraging an analysis of existing mainstream DL model training strategies, we introduce a performance simulator named PALM. PALM targets both the training and inference processes for tiled accelerators, aiming to inspire the design of current and future accelerators. Specifically, (i) we establish a scheduling mechanism among tiled accelerators based on an event-driven framework; (ii) we support user-configurable pipeline, tensor, and data parallelism on tiled accelerators, determining the absolute performance throughput under these parallelism strategies; (iii) we model the interaction of on-chip SRAM, NoC, and off-chip DRAM during operator execution. This work is available here: https://github.com/fangjh21/PALM., Comment: 11 pages

In this paper, we propose a novel knowledge transfer framework that introduces continuous normalizing flows for progressive knowledge transformation and leverages multi-step sampling strategies to achieve precision knowledge transfer. We name this framework Knowledge Transfer with Flow Matching (FM-KT), which can be integrated with a metric-based distillation method with any form (\textit{e.g.} vanilla KD, DKD, PKD and DIST) and a meta-encoder with any available architecture (\textit{e.g.} CNN, MLP and Transformer). By introducing stochastic interpolants, FM-KD is readily amenable to arbitrary noise schedules (\textit{e.g.}, VP-ODE, VE-ODE, Rectified flow) for normalized flow path estimation. We theoretically demonstrate that the training objective of FM-KT is equivalent to minimizing the upper bound of the teacher feature map or logit negative log-likelihood. Besides, FM-KT can be viewed as a unique implicit ensemble method that leads to performance gains. By slightly modifying the FM-KT framework, FM-KT can also be transformed into an online distillation framework OFM-KT with desirable performance gains. Through extensive experiments on CIFAR-100, ImageNet-1k, and MS-COCO datasets, we empirically validate the scalability and state-of-the-art performance of our proposed methods among relevant comparison approaches.

We investigate a generalization of topological order from closed systems to open systems, for which the steady states take the place of ground states. We construct typical lattice models with steady-state topological order, and characterize them by complementary approaches based on topological degeneracy of steady states, topological entropy, and dissipative gauge theory. Whereas the (Liouvillian) level splitting between topologically degenerate steady states is exponentially small with respect to the system size, the Liouvillian gap between the steady states and the rest of the spectrum decays algebraically as the system size grows, and closes in the thermodynamic limit. It is shown that steady-state topological order remains definable in the presence of (Liouvillian) gapless modes. The topological phase transition to the trivial phase, where the topological degeneracy is lifted, is accompanied by gapping out the gapless modes. Our work offers a toolbox for investigating open-system topology of steady states., Comment: 33 pages, 17 figures. Joint submission with arXiv:2306.12482

Nowadays, artificial intelligence (AI) technology with large models plays an increasingly important role in both academia and industry. It also brings a rapidly increasing demand for the computing power of the hardware. As the computing demand for AI continues to grow, the growth of hardware computing power has failed to keep up. This has become a significant factor restricting the development of AI. The augmentation of hardware computing power is mainly propelled by the escalation of transistor density and chip area. However, the former is impeded by the termination of the Moore's Law and Dennard scaling, and the latter is significantly restricted by the challenge of disrupting the legacy fabrication equipment and process. In recent years, advanced packaging technologies that have gradually matured are increasingly used to implement bigger chips that integrate multiple chiplets, while still providing interconnections with chip-level density and bandwidth. Compared to conventional high-performance computing paradigms such as multi-accelerator and datacenter-scale computing, Wafer-scale Computing shows remarkable advantages in communication bandwidth, integration density, and programmability potential. Not surprisingly, disruptive Wafer-scale Computing also brings unprecedented design challenges for hardware architecture, design-system-technology co-optimization, power and cooling systems, and compiler tool chain. At present, there are no comprehensive surveys summarizing the current state and design insights of Wafer-scale Computing. This paper aims to take the first step to help academia and industry review existing wafer-scale chips and essential technologies in a one-stop manner. So that people can conveniently grasp the basic knowledge and key points, understand the achievements and shortcomings of existing research, and contribute to this promising research direction.

We present the construction of a solvable Lindblad model in arbitrary dimensions, wherein the Liouvillian can be mapped to a BCS-Hubbard model featuring an imaginary interaction. The Hilbert space of the system can be divided into multiple sectors, each characterized by an onsite invariant configuration. The model exhibits bistable steady states in all spatial dimensions, which is guaranteed by the fermion-number parity. Notably, the Liouvillian gap exhibits a Zeno transition, below which the Liouvillian gap is linear with respect to the dissipation. We also uncover a generic dimension-dependent gap behavior: In one dimension, the gap originates from multiple sectors with spectral crossing; in higher dimensions, a single sector determines the gap., Comment: 7 pages, 2 figures

The interplay between dissipation and correlation can lead to new emergent phenomena. Here we study non-equilibrium phases of matter with robust topological degeneracy of steady states, which is a generalization of the ground-state topological degeneracy of closed systems. Specifically, we construct two representative Lindbladians using engineered dissipation, and exactly solve the steady states with topological degeneracy. We find that while the degeneracy is fragile under noise in two dimensions, it is stable in three dimensions, where a genuine many-body phase with topological degeneracy is realized. We identify universal features of dissipative topological physics such as the deconfined emergent gauge field and slow relaxation dynamics of topological defects. The transition from a topologically ordered phase to a trivial phase is also investigated via numerical simulation. Our work highlights the essential difference between ground-state topological order in closed systems and steady-state topological order in open systems., Comment: 6+9 pages, 3+2 figures

Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves performing a large number of sampling steps, which impedes the possibility of real-time sample synthesis. Traditional accelerated sampling algorithms via knowledge distillation rely on pre-trained model weights and discrete time step scenarios, necessitating additional training sessions to achieve their goals. To address these issues, we propose the Catch-Up Distillation (CUD), which encourages the current moment output of the velocity estimation model ``catch up'' with its previous moment output. Specifically, CUD adjusts the original Ordinary Differential Equation (ODE) training objective to align the current moment output with both the ground truth label and the previous moment output, utilizing Runge-Kutta-based multi-step alignment distillation for precise ODE estimation while preventing asynchronous updates. Furthermore, we investigate the design space for CUDs under continuous time-step scenarios and analyze how to determine the suitable strategies. To demonstrate CUD's effectiveness, we conduct thorough ablation and comparison experiments on CIFAR-10, MNIST, and ImageNet-64. On CIFAR-10, we obtain a FID of 2.80 by sampling in 15 steps under one-session training and the new state-of-the-art FID of 3.37 by sampling in one step with additional training. This latter result necessitated only 620k iterations with a batch size of 128, in contrast to Consistency Distillation, which demanded 2100k iterations with a larger batch size of 256. Our code is released at https://anonymous.4open.science/r/Catch-Up-Distillation-E31F.

Mn-doped \b{eta}-Ga2O3 (GMO) films with room-temperature ferromagnetism (RTFM) are synthesized by polymer-assisted deposition and the effects of annealing atmosphere (air or pure O2 gas) on their structures and physical properties are investigated. The characterizations show that the concentrations of vacancy defects and Mn dopants in various valence states and lattice constants of the samples are all modulated by the annealing atmosphere. Notably, the samples annealed in air (GMO-air) exhibit a saturation magnetization as strong as 170% times that of the samples annealed in pure O2 gas (GMO-O2), which can be quantitatively explained by oxygen vacancy (VO) controlled ferromagnetism due to bound magnetic polarons established between delocalized hydrogenic electrons of VOs and local magnetic moments of Mn2+, Mn3+, and Mn4+ ions in the samples. Our results provide insights into mechanism-based tuning of RTFM in Ga2O3 and may be useful for design, fabrication, and application of related spintronic materials.