QMScaler is proposed, a QoS-driven microservice horizontal scaling framework based on Monte Carlo Tree Search (MCTS) that aims to minimize the number of container instances while meeting the QoS requirements of multiple application functions.
Tianyang Zheng, Pengfei Yang, Zhe Xu et al.· Proceedings of the Internati...· 0 citations
A fine-grained GPU parallelization approach that adapts to workload characteristics by assigning set intersection computations within a graph to the most suitable parallelization strategies, and predicts the optimal parallelization approach between this fine-grained and other state-of-the-art GPU kernels with 88% accur...
Atharva Gondhalekar, Wu-Chun Feng· Proceedings of the Internati...· 0 citations
Overall, when evaluating both Chapel and MPI+X implementations of partitioned Jaccard similarity on up to 16 A100-80GB GPUs across four nodes, Chapel achieves comparable performance to MPI+X while also delivering significantly better programmer productivity and agility.
Paul Sathre, Wu-Chun Feng· Proceedings of the Internati...· 0 citations
The Dual-Stage Spiking Swin Transformer (D2S-SwinT), a neuromorphic architecture that integrates the feature representation capability of Swin Transformers with the event-driven efficiency of brain-inspired computation, is proposed, a neuromorphic architecture that integrates the feature representation capability of Sw...
Hong-Jiang Deng, Wen-Hua Hu, Hao-Hui Peng et al.· Proceedings of the Internati...· 0 citations
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Vadar adopts a non-intrusive dynamic interception mechanism to monitor parallel applications with state transition detection to cope with workload change at runtime, and provides comprehensive monitoring for both regular and irregular computational workloads.
Yi-Fan Jia, Shuang Niu, Yu-Hang Hu et al.· Proceedings of the Internati...· 0 citations
TFS is presented, a tile-aware fusion framework that eliminates this intermediate materialization by fusing SpMM and GeMM directly inside the tile registers of Intel’s Advanced Matrix Extensions (AMX).
Xiao Yan, Haodong Bian, Xiao-Ying Wang et al.· Proceedings of the Internati...· 0 citations
Passenger flow in rapidly developing high-speed railway networks varies with temporal fluctuations, spatial interactions among stations, and regional travel dependencies. Accurate forecasts support the management of station operations, resource allocation, and service optimization, yet methods that impose fixed spatial...
The rapid evolution of 6G networks is expected to transform immersive media broadcasting by enabling ultra-low-latency communication, intelligent edge computing, and high-capacity transmission for extended reality, holographic content, and interactive multimedia. However, conventional edge resource management remains l...
Lunatari Sanbella, Chandra Lukita, Anandha Fitriani et al.· Bridging of Emerging AI and...· 0 citations
The reliance on single-point metric estimates (such as a solitary mAP score) to benchmark object detection models frequently masks underlying sample variances, leading to overconfident deployment decisions. This paper presents an automated post-training validation pipeline for YOLO models designed to enforce statistica...
William Steve Rodriguez Villamizar· Zenodo (CERN European Organi...· 0 citations
LUCYD-PISCO training-set sources (v5) Source data needed to regenerate the synthetic training set used to retrain the LUCYD deconvolution network for PISCO (Plankton In Situ Camera Observation) extended-depth-of-field (TAG-lens) images. Code: https://git.geomar.de/team-optics/plankton/underwater-lucyd (sim/gen_trainset...
Veit Dausmann· Zenodo (CERN European Organi...· 0 citations
Mirror, Mirror, in XR: Motion-Capture Dataset, Processing Pipeline, and Analysis Code Description This deposit contains the full data-processing and analysis pipeline underlying a within-subjects VR study of imposed social mirroring. Twelve dyads (24 participants) held structured conversations under three counterbalanc...
Anonymous· Zenodo (CERN European Organi...· 0 citations
Changes in v1.0.7. Two faults in the cavity figure, both found on the page. The near lower corner of the box at the far end was missing, and with it about a sixth of the front lower edge. The two panels of that figure are each wider and taller than the drawing they hold, which is what lets a long flat cavity and a cone...
Tanvir Mahmud Mahim, M. Mosaddequr Rahman, Abu S. M. Mohsin· Zenodo (CERN European Organi...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026