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In large-scale distributed scenarios, increasingly complex tasks demand more intelligent collaboration across networks, requiring the joint extraction of structural representations from data samples. However, conventional task-specific approaches often result in nonstructural embeddings, leading to collapsed variability among data samples within the same class, particularly in classification tasks. To address this issue and fully leverage the intrinsic structure of data for downstream applications, we propose a novel distributed learning framework that ensures both diverse and discriminative representations. For independent […]

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Claude AI has developed into one of the most reliable business-oriented AI systems that operate in the current market. Claude serves as the perfect AI solution for enterprises because Anthropic designed it to maintain safety, dependability, and deliver optimal performance. The transition from Claude 3 to Claude 4 represents a substantial improvement in reasoning, scalability, and real-world application. Enterprises now prefer Claude because it has developed capabilities to process long contexts and generate structured outputs. Why Claude AI? […]

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Large language model (LLM) inference in multi-tenant clouds is becoming an increasingly important contributor to data-center carbon emissions, yet existing carbon-aware scheduling techniques target long-running training jobs and are ill-suited for the short, bursty, SLO-sensitive nature of online serving. We propose CAPS (Carbon–Aware Prompt Scheduling), an online bi-objective scheduler that jointly optimizes goodput and per-request carbon cost for multi-tenant LLM inference. CAPS first employs a lightweight prompt complexity predictor to estimate token generation cost and latency risk for […]

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Megacity mobility research has long relied on aggregate statistical indicators — composite traffic-planning indices and accessibility surfaces — that capture the system at rest but are disconnected from the operational decisions shaping mobility minute to minute. In parallel, Agentic AI, articulated in the Web of Agents narrative of [28], offers a reference paradigm of semantically interoperable autonomous agents that negotiate and coordinate across open networks. We propose a unifying Agentic AI reference architecture for urban transportation that maps […]

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This position paper argues that in software-defined dry-lab AI research, the one-person laboratory (OPL) is the relevant minimum accountable unit under compressed coordination and should be treated as a first-class unit of evaluation wherever bounded verification and public contestability hold. We develop three propositions. P1 (descriptive): public research-agent systems and laboratory-shaped benchmarks suggest that the minimum efficient research unit is moving downward in parts of AI research. P2 (causal, conditional): the relevant gains are narrower than common “AI […]

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We present a self-contained exposition of the Lyons carabiner, focusing on the triple obstruction mechanism arising in Section~ref{sec:triple}. Three independent numerical obstructions—phantom coupling $pi$, holonomy defect $rho$, and braid deficit $beta$—together form the triple $(pi,rho,beta)=(3,4,9)$, which uniquely recovers the MDS code $[6,4,3]_5$ over $mathbb{F}_5$. This identification is not a postulate but a provable consequence of the weight distribution and the complement involution. We then develop the Pontryagin–Heegner bridge: phantom weights are “silent frequencies” in the Pontryagin dual of […]

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I saw many discussions about TMLR and other journals lately and how their review processes are considered fairer and less random. My question is, how much does it hurt one’s chance much of getting interviewed/hired as a ML research scientist if they choose to publish at only journals like TMLR, JMLR, or Neurocomputing, instead of conferences? Edit: just to clarify, I mean corporate research scientist positions instead of academic positions. submitted by /u/dontknowwhattoplay [link] [comments]

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Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neural networks for both tabular data and generic tensors (e.g., images) for classification and regression. The package implements predefined architectures, and torch models can easily be converted to mlr3 learners. It also allows users […]

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Rabies remains a major public health concern across many African and Asian countries, where accurate diagnosis is critical for effective epidemiological surveillance. The gold standard diagnostic methods rely heavily on fluorescence microscopy, necessitating skilled laboratory personnel for the accurate interpretation of results. Such expertise is often scarce, particularly in regions with low annual sample volumes. This paper presents an automated, AI-driven diagnostic system designed to address these challenges. We developed a robust pipeline utilizing fluorescent image analysis through […]

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