Hasil untuk "Computer Science"

Victoria J. Hodge, J. Austin

K. Bogart

Elena Kakoulli

Edge artificial intelligence (AI) workloads generate bursty, heterogeneous traffic on Networks-on-Chip (NoCs) under tight energy and latency constraints. Hybrid NoCs that overlay electronic meshes with silicon photonic express links can reduce long-path latency via wavelength-division multiplexing, but thermal drift and intermittent optical availability complicate routing. This study introduces a decentralized, photonic-aware controller based on Deep Reinforcement Learning (DRL) with Proximal Policy Optimization (PPO). The policy uses router-local observables—per-port buffer occupancy with short histories, hop distance, a local injection estimate, and a per-cycle optical validity signal—and applies action masking so chosen outputs are always feasible; the controller is co-designed with the router pipeline to retain single-cycle decisions and a modest memory footprint. Cycle-accurate simulations with synthetic traffic and benchmark-derived traces evaluate mean packet latency, throughput, and energy per delivered bit against deterministic, adaptive, and recent DRL baselines; ablation studies isolate the roles of optical validity cues and locality. The results show consistent improvements in congestion-forming regimes and on long electronic paths bridged by photonic links, with robustness across mesh sizes and wavelength concurrency. Overall, the evidence indicates that photonic-aware PPO provides a practical, thermally robust control plane for hybrid NoCs and a scalable routing solution for AI-centric manycore and edge systems.

Shu Abe, Yuya Kodama, Hiroyoshi Yamada et al.

Can Cao, Sheikh Ifatur Rahman, Chris Chae et al.

We show AlN/GaN high electron mobility transistors with in situ SiN for passivation and regrown n ^+ GaN ohmic contacts using a selective etching process that is more suitable for device scaling. The regrown ohmic contacts have a clean and sharp edge definition with a contact resistance of 0.25 Ω·mm. The interfacial resistance between the regrown n ^+ GaN and the 2DEG at AlN/GaN interface is 0.058 Ω·mm, close to the theoretical quantum conductance limit. The fabricated devices with a gate length of 0.7 μm exhibit a maximum current density of 1.57 A mm ^−1 and on-resistance of 1.85 Ω·mm at a gate bias of 1 V.

Xuehui Li, Pan Tang, Xinping Pang et al.

Abstract Alzheimer's disease (AD) is a neurodegenerative disorder characterized primarily by cognitive impairment. The motivation of this paper is to explore the impact of the visual information transmission pathway (V–H pathway) on AD, and the following feature were observed: Hemoglobin expression on the V–H pathway becomes dysregulated as AD occurs so as to the pathway becomes dysfunctional. According to the feature, the following conclusion was proposed: As AD occurs, abnormal tau proteins penetrate bloodstream and arrive at the brain regions of the pathway. Then the tau proteins or other toxic substances attack hemoglobin molecules. Under the attack, hemoglobin expression becomes more dysregulated. The dysfunction of V–H pathway has an impact on early symptoms of AD, such as spatial recognition disorder and face recognition disorder.

N. M. Kilany, Lobna H. El-Refai

Abstract Besides achieving high quality products, statistical techniques are applied in many fields associated with health such as medicine, biology and etc. Adhering to the quality performance of an item to the desired level is a very important issue in various fields. Process capability indices play a vital role in evaluating the performance of an item. In this paper, the larger-the-better process capability index for the three-parameter Omega model based on progressive type-II censoring sample is calculated. On the basis of progressive type-II censoring the statistical inference about process capability index is carried out through the maximum likelihood. Also, the confidence interval is proposed and the hypothesis test for estimating the lifetime performance of products. Gibbs within Metropolis–Hasting samplers procedure is used for performing Markov Chain Monte Carlo (MCMC) technique to achieve Bayes estimation for unknown parameters. Simulation study is calculated to show that Omega distribution's performance is more effective. At the end of this paper, there are two real-life applications, one of them is about high-performance liquid chromatography (HPLC) data of blood samples from organ transplant recipients. The other application is about real-life data of ball bearing data. These applications are used to illustrate the importance of Omega distribution in lifetime data analysis.

Zhiqiang Zhao, Peihong Ma, Meng Jia et al.

Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglecting the importance of shallow features for crowd counting. In order to surmount this issue, a dilated convolutional-neural-network-based cross-level contextual information extraction network is proposed in this work, which is abbreviated as CL-DCNN. Specifically, a dilated contextual module (DCM) is constructed by importing cross-level connection between different feature maps. It can effectively integrate contextual information while conserving the local details of crowd scenes. Extensive experiments show that the proposed approach outperforms state-of-the-art approaches using five public datasets, i.e., ShanghaiTech part A, ShanghaiTech part B, Mall, UCF_CC_50 and UCF-QNRF, achieving MAE 52.6, 8.1, 1.55, 181.8, and 96.4, respectively.

Meijia Zhang, Junzheng Li, Xiyuan Zheng

Abstract Hashing has been extensively utilized in cross-modal retrieval due to its high efficiency in handling large-scale, high-dimensional data. However, most existing cross-modal hashing methods operate as offline learning models, which learn hash codes in a batch-based manner and prove to be inefficient for streaming data. Recently, several online cross-modal hashing methods have been proposed to address the streaming data scenario. Nevertheless, these methods fail to fully leverage the semantic information and accurately optimize hashing in a discrete fashion. As a result, both the accuracy and efficiency of online cross-modal hashing methods are not ideal. To address these issues, this paper introduces the Semantic Embedding-based Online Cross-modal Hashing (SEOCH) method, which integrates semantic information exploitation and online learning into a unified framework. To exploit the semantic information, we map the semantic labels to a latent semantic space and construct a semantic similarity matrix to preserve the similarity between new data and existing data in the Hamming space. Moreover, we employ a discrete optimization strategy to enhance the efficiency of cross-modal retrieval for online hashing. Through extensive experiments on two publicly available multi-label datasets, we demonstrate the superiority of the SEOCH method.

Yu Miao, Alexandre Yokochi, Goran Jovanovic et al.

Non-thermal plasma as a tool in chemical reaction engineering has been studied for many years. The temperature of electrons in non-thermal plasma far exceeds other particles, which leads to its high efficiency. Besides the well-studied destruction of volatile organic compounds (VOCs), the reaction environment generated by non-thermal plasma is also suitable for the activation of many significant gas-phase chemical reactions, e.g., as methane coupling, reduction of carbon dioxide, ammonia synthesis, nitrogen fixation, as well as some liquid phase chemical reactions such as the treatment of contaminated water. Material synthesis is another target field of non-thermal plasma. Plasma in micro scale with several enhanced properties makes it an even more promising tool for plasma-chemical processing. This work summarizes different types of non-thermal plasmas and their performance in commonly studied chemical reactions. The advantages gained by generating non-thermal plasma in micro scale with constricted spaces, reduced timescales, and micro-/nano-structured electrodes are also discussed.

Philippe Mathieu, Juan Manuel Corchado, Alfonso González-Briones et al.

<b>Introduction</b>  [...]

Andrey V. Kuznetsov

The aim of this paper is to establish the bounds of applicability of the single-domain numerical approach for computations of convection in composite porous/ fluid domains. The large number of papers that have utilized this numerical approach motivates this research. The popularity of this approach is due to the simplicity of its numerical formulation. Since the utilization of the single-domain numerical approach does not require the explicit imposing of any boundary conditions at the porous/ fluid interface, the aim of the this research is to investigate whether this method always produces accurate numerical solutions.

Honghua Rao, Heming Jia, Di Wu et al.

The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results.

Jian HUANG, Xin LI, Fang CHEN et al.

The traditional high-level remote landslide recognition efficiency which relies on the artificial discrimination of geological experts is low. In this paper, an automatic landslide terrain recognition model based on deep learning is developed to improve the efficiency of the screening of potential landslide hazard in a large area. The model takes remote sensing images, DEM data, geological zones, river system and other geological observation data of the target area as input. For the huge difference of different types of observation data, a feature branch network is designed and constructed to accurately extract the corresponding landslide features: Among them, deep network architecture is used to extract complex features from optical image data, and shallow network architecture is used to extract features from structured data such as altitude, geological composition, river and fault zone distribution. Subsequently, a feature fusion module was designed to fuse the extraction results of the two networks to obtain a comprehensive landslide hazard feature. The model performs semantic segmentation of the landslide area based on the extracted landslide features, and achieves accurate pixel-level landslide terrain classification and positioning. The experimental results show that the recognition accuracy(ACC) of the model reaches 0.85, which can provide technical support for automatic landslide identification.