Stream Processing Systems
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Related Articles from SNS
BlobShuffle: Cost-Effective Repartitioning in Stream Processing Systems via Object Storage Exemplified with Kafka Streams
arXiv:2606.03364v1 Announce Type: new Abstract: Shuffling or repartitioning data streams is an essential operation of state-of-the-art stream processing frameworks to support stateful workloads in a large-scale, distributed setting. In today's cloud deployments, however, shuffling can become a major cost driver due to substantial network traffic across multiple availability zones (AZs) as well as an operational burden when operating a high-throughput, strongly consistent messaging backbone...
Content-Adaptive Rate-Quality Curve Prediction Model in Media Processing System
Announce Type: replace Abstract: In streaming media services, video transcoding is a common practice to alleviate bandwidth demands. Unfortunately, traditional methods employing a uniform rate factor (RF) across all videos often result in significant inefficiencies. Content-adaptive encoding (CAE) techniques address this by dynamically adjusting encoding parameters based on video content characteristics.
AXLE: Coordinated Offloading with Asynchronous Back-Streaming in Computational Memory Systems
arXiv:2512.04449v2 Announce Type: replace Abstract: CXL-based Computational Memory (CCM) enables near-memory processing within expanded remote memory, offering opportunities to address data movement costs in disaggregated memory systems and to accelerate overall performance. However, existing offloading mechanisms do not fully leverage the trade-offs of different offload models based on different CXL protocols. This work first examines these tradeoffs and their impact on end-to-end...
Harnessing Streaming Video in the Wild
Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly required to process unbounded video streams in applications such as video-call assistants, live commentary, and embodied robots. An ideal streaming system should support proactive interaction, long-horizon memory, and real-time processing, while resting on a VLM backbone capable of handling diverse in-the-wild streaming tasks. However, existing VLMs excel at offline video understanding but fall short in streaming...
MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
arXiv:2605.00242v2 Announce Type: replace Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely on pre-extracted intermediate representations such as sparse point clouds or spectrogram images, where the rich spatiotemporal information naturally present in radar video streams is discarded for model learning, while such signal processing adds system complexity. In addition, existing...
How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation
arXiv:2605.31186v1 Announce Type: new Abstract: Data streams are nowadays among the most frequently analyzed data structures, with the concept drift posing a major challenge encountered by processing systems. Despite the proposition of numerous solutions to counteract the accuracy degeneration due to concept drift, the scientific community has not yet established a unified framework for evaluating the concept drift detection task. Existing research often relies on classification quality...
Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model
Announce Type: cross Abstract: Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems. Recent topographic models reproduce aspects of this structure but remain unimodal and spatially constrain each layer separately, yielding fragmented maps that capture neither the contiguity of cortical processing streams nor their integration across modalities.
Lossy Microwave Linear Analog Computer (MiLAC) for Future MIMO: Learning-based Architecture Designs for Spectral and Energy Efficiency Maximization
arXiv:2606.02369v1 Announce Type: cross Abstract: Microwave linear analog computers (MiLACs) offer a transformative paradigm for future multiple-input multiple-output (MIMO) systems by shifting complex signal processing into the analog domain, thereby significantly reducing computational complexity, radio-frequency chains, and analog-digital converters, while speeding up computation. However, the practical deployment of MiLACs is severely constrained by the inherent hardware losses of the...
Building iOS Apps with Doom Emacs
I shipped SPEEM, my first iOS app, from Doom Emacs. I don’t mean I just edited a few files in Emacs and switched back when it was time to build. I mean the whole loop: write Swift, build, boot a simulator, install the app, launch it, stream logs, restart LSP, scaffold new projects.
Your Autoregressive Model Already Reveals the Causal Graph
Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We exploit this observation to perform scalable causal discovery from a single observed sequence of discrete events -- without any task-specific retraining. Such single-stream settings arise naturally in vehicle diagnostics, manufacturing systems, and patient trajectories, yet they remain largely unsolved: the...