A Data-Driven Approach for Discovering the Most Probable Transition Pathway for a Stochastic Carbon Cycle System

Many natural systems exhibit tipping points where changing environmental conditions spark a sudden shift to a new and sometimes quite different state. Global climate change is often associated with the stability of marine carbon stocks. We consider a stochastic carbonate system of the upper ocean to capture such transition phenomena. Based on the Onsager-Machlup action functional theory, we calculate the most probable transition pathway between the metastable and oscillatory states via a neural shooting method, and further explore the effects of external random carbon input rates on the most probable transition pathway, which provides a basis to recognize naturally occurring tipping points. Particularly, we investigate the effect of the transition time on the transition pathway and further compute the optimal transition time using physics informed neural network, towards the maximum carbonate concentration state in the oscillatory regimes. This work offers some insights on the effects of random carbon input on climate transition in a simple model. Key words: Onsager-Machlup action functional, the most probable transition pathway, neural shooting method, stochastic carbon cycle system

Most Probable Transitions from Metastable to Oscillatory Regimes in a Carbon Cycle System

Global climate changes are related to the ocean's store of carbon. We study a carbonate system of the upper ocean, which has metastable and oscillatory regimes, under small random fluctuations. We calculate the most probable transition path via a geometric minimum action method in the context of the large deviations theory. By examining the most probable transition paths from metastable to oscillatory regimes for various external carbon input rates, we find two different transition patterns, which gives us an early warning sign for the dramatic change in the carbonate state of the ocean

Study of the Optimization of Structural Designs in Residential Buildings

The building structural design which has met both functional and quality assurances could no longer meet the demands of human life and spirit, especially with the continuous improvement in living standards and increasing in public needs. Therefore, there is necessary to optimize the design of building structures. This study will give a brief introduction to the optimization theory of structural design, present some optimization measures and theoretical reference for future structural design optimization

The Most Likely Transition Path for a Class of Distribution-Dependent Stochastic Systems

Distribution-dependent stochastic dynamical systems arise widely in engineering and science. We consider a class of such systems which model the limit behaviors of interacting particles moving in a vector field with random fluctuations. We aim to examine the most likely transition path between equilibrium stable states of the vector field. In the small noise regime, we find that the rate function (or action functional) does not involve with the solution of the skeleton equation, which describes unperturbed deterministic flow of the vector field shifted by the interaction at zero distance. As a result, we are led to study the most likely transition path for a stochastic differential equation without distribution-dependency. This enables the computation of the most likely transition path for these distribution-dependent stochastic dynamical systems by the adaptive minimum action method and we illustrate our approach in two examples.Comment: 10 pages, 2 figure

Performance analysis of a polling model with BMAP and across-queue state-dependent service discipline

As various video services become popular, video streaming will dominate the mobile data traffic. The H.264 standard has been widely used for video compression. As the successor to H.264, H.265 can compress video streaming better, hence it is gradually gaining market share. However, in the short term H.264 will not be completely replaced, and will co-exist with H.265. Using H.264 and H.265 standards, three types of frames are generated, and among different types of frames exist dependencies. Since the radio resources are limited, using dependencies and quantities of frames in buffers, an appropriate time division transmission policy can be applied to transmit different types of frames sequentially, in order to avoid the occurrence of video carton or decoding failure. Polling models with batch Markovian arrival process (BMAP) and across-queue state-dependent service discipline are considered to be effective means in the design and optimization of appropriate time division transmission policies. However, the BMAP and across-queue state-dependent service discipline of the polling models lead to the large state space and several coupled state transition processes, which complicate the performance analysis. There have been very few researches in this regard. In this paper, a polling model of this type is analyzed. By constructing a supplementary embedded Markov chain and applying the matrix-analytic method based on the semi-regenerative process, the expressions of important performance measures including the joint queue length distribution, the customer blocking probability and the customer mean waiting time are obtained. The analysis will provide inspiration for analyzing the polling models with BMAP and across-queue state-dependent service discipline, to guide the design and optimization of time division transmission policies for transmitting the video compressed by H.264 and H.265

A Comprehensive Study of Black Phosphorus-Graphite Composite Anodes and HEMM Synthesis Conditions for Improved Cycle Stability

Black phosphorus (BP) is a high capacity anode material and has been synthesized with different carbon materials to mitigate volume changes during lithiation/delithiation. There is a large discrepancy in cycle stability of phosphorus-carbon materials in the literature, and factors affecting cycle performance are not well elucidated. In this study, the electrochemical performance of a black phosphorus-graphite (BP-G) composite anode material with regards to (1) material composition, (2) electrolyte additive, (3) ballmilling synthesis conditions, and (4) electrode loading is thoroughly investigated. In particular, this study reveals how ballmilling synthesis conditions correlate to electrochemical performance. Results show that the main contributors to cycle stability of BP-G composites are material composition and electrode loading, while first cycle efficiency and reversible capacity are strongly dependent on ballmilling synthetic conditions. Composition control is the most effective way to mitigate the volume change-induced mechanical degradation of BP-G composites, while ballmilling processing optimization is the main contributor to BP activation in BP-G composites, improving reversible capacity and first cycle efficiency. We thereby propose an optimized, HEMM-based synthetic route for improved BP-G materials. This study provides a comprehensive understanding of BP-G electrochemical performance and the correlation to HEMM synthesis conditions

Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Large language models (LLMs) based on transformers have made significant strides in recent years, the success of which is driven by scaling up their model size. Despite their high algorithmic performance, the computational and memory requirements of LLMs present unprecedented challenges. To tackle the high compute requirements of LLMs, the Mixture-of-Experts (MoE) architecture was introduced which is able to scale its model size without proportionally scaling up its computational requirements. Unfortunately, MoE's high memory demands and dynamic activation of sparse experts restrict its applicability to real-world problems. Previous solutions that offload MoE's memory-hungry expert parameters to CPU memory fall short because the latency to migrate activated experts from CPU to GPU incurs high performance overhead. Our proposed Pre-gated MoE system effectively tackles the compute and memory challenges of conventional MoE architectures using our algorithm-system co-design. Pre-gated MoE employs our novel pre-gating function which alleviates the dynamic nature of sparse expert activation, allowing our proposed system to address the large memory footprint of MoEs while also achieving high performance. We demonstrate that Pre-gated MoE is able to improve performance, reduce GPU memory consumption, while also maintaining the same level of model quality. These features allow our Pre-gated MoE system to cost-effectively deploy large-scale LLMs using just a single GPU with high performance

How many eyes are spying on your shared folders?

Today peer-to-peer (P2P) file sharing networks help tens of millions of users to share contents on the Internet. However, users' private files in their shared folders might become accessible to everybody inadvertently. In this paper, we investigate this kind of user privacy exposures in Kad, one of the biggest P2P file sharing networks, and try to answer two questions: Q1. Whether and to what extent does this problem exist in current systems? Q2. Are attackers aware of this privacy vulnerability and are they abusing obtained private information? We build a monitoring system called Dragonfly based on the eclipse mechanism to passively monitor sharing and downloading events in Kad. We also use the Honeyfile approach to share forged private information to observe attackers' behaviors. Based on Dragonfly and Honeyfiles, we give affirmative answers to the above two questions. Within two weeks, more than five thousand private files related to ten sensitive keywords were shared by Kad users, and over half of them come from Italy and Spain. Within one month, each honey file was downloaded for about 40 times in average, and its inner password information was exploited for 25 times. These results show that this privacy problem has become a serious threat for P2P users. Finally, we design and implement Numen, a plug-in for eMule, which can effectively protect user private files from being shared without notice. Copyright 2012 ACM.EI