BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

Interaction induced decay of a heteronuclear two-atom system

Two-atom systems in small traps are of fundamental interest, first of all for understanding the role of interactions in degenerate cold gases and for the creation of quantum gates in quantum information processing with single-atom traps. One of the key quantities is the inelastic relaxation (decay) time when one of the atoms or both are in a higher hyperfine state. Here we measure this quantity in a heteronuclear system of $^{87}$Rb and $^{85}$Rb in a micro optical trap and demonstrate experimentally and theoretically the presence of both fast and slow relaxation processes, depending on the choice of the initial hyperfine states. The developed experimental method allows us to single out a particular relaxation process and, in this sense, our experiment is a "superclean platform" for collisional physics studies. Our results have also implications for engineering of quantum states via controlled collisions and creation of two-qubit quantum gates.Comment: 8 pages, 3 figure

Distributed online constrained convex optimization with event-triggered communication

This paper focuses on the distributed online convex optimization problem with time-varying inequality constraints over a network of agents, where each agent collaborates with its neighboring agents to minimize the cumulative network-wide loss over time. To reduce communication overhead between the agents, we propose a distributed event-triggered online primal-dual algorithm over a time-varying directed graph. With several classes of appropriately chose decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. Finally, a numerical simulation example is provided to verify the theoretical results.Comment: 12 pages, 3 figure

MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation

In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, Multimodal Large Language Model (MLLM), we train MoMA to serve a dual role as both a feature extractor and a generator. This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model. To better leverage the generated features, we further introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model, improving the resemblance of the target object in generated images. Remarkably, as a tuning-free plug-and-play module, our model requires only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation and prompt faithfulness. Our work is open-source, thereby providing universal access to these advancements

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality

RecycleGPT: An Autoregressive Language Model with Recyclable Module

Existing large language models have to run K times to generate a sequence of K tokens. In this paper, we present RecycleGPT, a generative language model with fast decoding speed by recycling pre-generated model states without running the whole model in multiple steps. Our approach relies on the observation that adjacent tokens in a sequence usually have strong correlations and the next token in a sequence can be reasonably guessed or inferred based on the preceding ones. Experiments and analysis demonstrate the effectiveness of our approach in lowering inference latency, achieving up to 1.4x speedup while preserving high performance.Comment: Technical Repor

The efficacy of repetitive transcranial magnetic stimulation in postherpetic neuralgia: a meta-analysis of randomized controlled trials

PurposeThis systematic review and meta-analysis aimed to evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) in postherpetic neuralgia (PHN).MethodsThrough an extensive search in four databases until October 2023, we selected five randomized controlled trials adhering to our specific criteria, involving 257 patients in total. For continuous outcomes, the standardized mean difference (SMD) was calculated. Heterogeneity among the studies was assessed using Cochran’s I2 and Q statistics, adopting a random-effects model for I2 values over 50%. For assessing potential publication bias, we utilized both funnel plot and Egger’s test.ResultsOur analysis found that rTMS reduced the overall visual analogue scale (VAS) (SMD: −1.52, 95% CI: −2.81 to −0.23, p = 0.02), VAS at 1 month post-treatment (SMD: −2.21, 95% CI: −4.31 to −0.10, p = 0.04), VAS at 3 months post-treatment (SMD: −1.51, 95% CI: −2.81 to −0.22, p = 0.02), as well as patients’ global impression of change scale (PGIC) (SMD: −1.48, 95% CI: −2.87 to −0.09, p = 0.04) and short-form McGill pain questionnaire (SF-MPQ) (SMD: −1.25, 95% CI: −2.41 to −0.09, p = 0.03) compared to the sham-rTMS group.ConclusionOur study suggests that rTMS might have a potential alleviating effect on PHN symptoms. However, due to the limited number of studies and variations in rTMS parameters, larger sample studies involving more diverse populations, as well as further clarification of the most appropriate stimulation protocol, are still needed.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, Identifier ID: CRD42023488420

Calculation and system of support resistance of shield for contugous-multiple coal seams with coordinated mining

Under the coordinated mining mode of close-multiple coal seams, due to the mining influence between coal seams, the roof structural characteristics are different after each coal seam extraction, so the calculation methods of support resistance shield of each coal seam are also different. In order to provide ideas for the setting load of shield determination in each coal seam, the calculation methods of the support capacity of shield in each coal seam is given by comprehensive use of theoretical analysis, system development and field measurement. The results show that: ①The voussoir beam, given load of loose body and voussoir beam with given load of loose body balance roof structure models after each coal seam extraction are established. Voussoir beam balance roof structure model is applicable to the coal seams extractions that are not affected by mining or are less affected by mining. Given load of loose body balance roof structure model is applicable to the coal seams extractions with a single roof stratum and is affected by the upper coal seam extraction. Voussoir beam with given load of loose body balance roof structure model is applicable to the coal seams extractions with multi-rock strata and within has a thick and hard lithology. At the same time, affected by the extraction of the upper coal seam, the rock stratum can still maintain continuity and integrity. ②The “overburden breaking and load evaluation system for close-multiple coal seams extraction” suitable for Kailuan Group is developed, and the recommended selection results of setting load of shield in each coal seam are put forward. Through the field measurement of support capacity of shield, the load utilization rate of shield in each coal seam is generally low and the load margin of shield is too large after using the empirical selection results of the setting load of shield. After adopting the recommended selection results of the setting load of shield in each coal seam, the load utilization rate of shield in each coal seam is significantly improved and the load margin of shield is significantly reduced

High-Speed Train Traction System Reliability Analysis

As the core power unit of high-speed train (HST), the diagnosis of faults in traction motor system has a significant importance on both safety and reliability, which can avoid HST crashes. According to the current problems such as early fault characteristics are not obvious and tight coupling, the reliable model with fault severity analysis and the required diagnosis accuracy cannot be achieved by current techniques. Therefore, it is crucial to evaluate HST reliability through resilience enhancement strategies to ensure it can operate with higher resilience. This chapter proposes a method for evaluating the overall reliability of HST traction motors associated with the idea of system modeling and machine learning techniques. First, a novel fault severity model is proposed suitable for the normal and fault conditions. Then electromagnetic torque energy entropy coding is utilized to extract fault features and construct different feature matrixes. Resilience enhancement strategies with support vector machine models are generated from a novel gray wolf optimizer algorithm. The performance of the proposed work is validated through simulation and experimentation on a fault-testing verification platform for the HST traction system