In-network computing (INC) offers orders-of-magnitude performance gains for various applications. However, the existing pipeline-based switch architecture, though well-suited for stateless packet forwarding, imposes three constraints on stateful INC applications: (i) control-plane table management, (ii) scarce memory resources under per-stage layout, and (iii) a strict memory access model. To address these constraints, this paper provides a full-stack solution, covering a programmable chipset Solar-NP, a programming language NPC, and a complete toolchain XuanWu. First, Solar-NP adopts a run-to-completion-based (RTC-based) architecture with three new hardware features: data-plane table management, a hierarchical memory pool, and built-in data structures with atomic access guarantees. Second, to fully exploit the hardware features, our language NPC introduces a new abstraction, namely operation-action table (OAT), that allows table manipulations in the data plane. Finally, our XuanWu toolchain provides various utility software covering the complete workflow of developing, testing, debugging, and deployment. To show the power of our solution, we implement three types of INC cases, including in-network control, in-network telemetry, and in-network storage. Experimental results demonstrate that our solution allows more operations to be offloaded and achieves higher performance than today's pipeline-based solutions.

Emotional image generation aims to create images that effectively reflect target emotions. A fundamental challenge in this task is the affective gap, which refers to the discrepancy between visual content and emotional states perceived by users. Existing methods generally assume strong and explicit associations between target emotions and specific objects (e.g., “monster” and “fear”), which limits their generalization ability when encountering uncommon emotion-object pairs. This limitation stems from two main factors: 1) Most existing approaches primarily focus on semantic alignment without explicitly modeling how emotions influence visual attributes such as brightness and colorfulness; 2) diffusion-based image generation methods have limited capability in handling diverse sentiment-semantic pairs. To address these challenges, we propose EmoSENSE, a novel hierarchical fuzzy reinforcement learning framework for the emotional image generation task. EmoSENSE consists of a high-level module and a low-level module, working collaboratively in a hierarchical structure to inject sentiment-semantic knowledge into emotional images. The high-level module quantifies sentiment-semantic correlations within a unified emotional space, connecting emotions to visual attributes. The low-level module refines this connection by optimizing a fuzzy-logic-based mapping between emotions and visual attributes through reinforcement learning, enabling flexible adaptation to diverse emotion-object pairs. Extensive qualitative and quantitative experiments on public dataset demonstrate that EmoSENSE significantly enhances both the visual quality and emotional expression ability of the generated images, achieving a 12.21% higher EmoAccuracy-8 classes than the previous state-of-the-art methods.https://github.com/forever3600/EmoSENSE.

Cross-modal retrieval is essential for interpreting cultural heritage data, but its effectiveness is often limited by incomplete or inconsistent textual descriptions, caused by historical data loss and the high cost of expert annotation.While large language models (LLMs) offer a promising solution by enriching textual descriptions, their outputs frequently suffer from hallucinations or miss visually grounded details.To address these challenges, we propose C 3 , a data augmentation framework that enhances cross-modal retrieval performance by improving the completeness and consistency of LLM-generated descriptions.C 3 introduces a completeness evaluation module to assess semantic coverage using both visual cues and language-model outputs.Furthermore, to mitigate factual inconsistencies, we formulate a Markov Decision Process to supervise Chain-of-Thought reasoning, guiding consistency evaluation through adaptive query control.Experiments on the cultural heritage datasets CulTi and TimeTravel, as well as on general benchmarks MSCOCO and Flickr30K, demonstrate that C 3 achieves state-of-the-art performance in both fine-tuned and zero-shot settings.The code of this paper is available at https://github.com/JianZhang24/C-3.

Diffusion-based garment synthesis tasks primarily focus on the design phase in the fashion domain, while the garment production process remains largely underexplored. To bridge this gap, we introduce a new task: Flat Sketch to Realistic Garment Image (FS2RG), which generates realistic garment images by integrating flat sketches and textual guidance. FS2RG presents two key challenges: 1) fabric characteristics are solely guided by textual prompts, providing insufficient visual supervision for diffusion-based models, which limits their ability to capture fine-grained fabric details; 2) flat sketches and textual guidance may provide conflicting information, requiring the model to selectively preserve or modify garment attributes while maintaining structural coherence. To tackle this task, we propose HiGarment, a novel framework that comprises two core components: i) a multi-modal semantic enhancement mechanism that enhances fabric representation across textual and visual modalities, and ii) a harmonized cross-attention mechanism that dynamically balances information from flat sketches and text prompts, allowing controllable synthesis by generating either sketch-aligned (image-biased) or text-guided (text-biased) outputs. Furthermore, we collect Multi-modal Detailed Garment, the largest open-source dataset for garment generation. Experimental results and user studies demonstrate the effectiveness of HiGarment in garment synthesis. The code and dataset are available at https://github.com/Maple498/HiGarment.

Instruction-based garment editing enables precise image modifications via natural language, with broad applications in fashion design and customization. Unlike general editing tasks, it requires understanding garment-specific semantics and attribute dependencies. However, progress is limited by the scarcity of high-quality instruction-image pairs, as manual annotation is costly and hard to scale. While MLLMs have shown promise in automated data synthesis, their application to garment editing is constrained by imprecise instruction modeling and a lack of fashion-specific supervisory signals. To address these challenges, we present an automated pipeline for constructing a garment editing dataset. We first define six editing instruction categories aligned with real-world fashion workflows to guide the generation of balanced and diverse instruction-image triplets. Second, we introduce Fashion Edit Score, a semantic-aware evaluation metric that captures semantic dependencies between garment attributes and provides reliable supervision during construction. Using this pipeline, we construct a total of 52,257 candidate triplets and retain 20,596 high-quality triplets to build EditGarment, the first instruction-based dataset tailored to standalone garment editing. The project page is https://yindq99.github.io/EditGarment-project/.

Ankit Srivastava et al. (Srivastava et al. 2020) proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with CentOS-compatible Oracle Linux. Our experiments, covering the same three algorithms of the original ramBLe article (Srivastava et al. 2020), evaluate the strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions from the original article and propose our explanation of the differences obtained in our results.Author: Please confirm or add details for any funding or financial support for the research of this article. ?>

In data center networks (DCNs), host-side packet processing accounts for a large portion of the end-to-end latency of TCP flows. Thus, the profiling of host-side latency anomalies has been considered as a crucial part in DCN performance diagnosis and troubleshooting. In particular, such profiling requires full coverage (i.e., profiling every TCP packet handled by end-hosts) and low overhead (i.e., profiling should avoid high CPU consumption in end-hosts). However, existing solutions fully rely on end-hosts to implement host-side latency profiling, leading to low coverage or high overhead. In this paper, we propose Torp, a framework that offers full-coverage and low-overhead profiling of host-side latency. Our key idea is to offload profiling operations to top-of-rack (ToR) switches, which inherently offer full coverage and line-rate packet processing performance. Specifically, Torp selectively offloads profiling operations to the ToR switch based on switch limitations. It efficiently coordinates the ToR switch and end-hosts to execute the entire latency profiling task. We have implemented Torp on 32×100Gbps Tofino switches. Testbed experiments indicate that Torp achieves full coverage and orders of magnitude lower host-side overhead compared to other solutions.

Knowledge of interannual mass variations of the Antarctic ice sheet (AIS) associated with its surface mass change is important for correctly interpreting the long-term mass trend and evaluating the fidelity of surface mass balance from regional climate models. Here, we revisit the interannual anomalies of mass change from Gravity Recovery and Climate Experiment and elevation change from ENVISAT over the AIS during 2003-2009, with the objective of obtaining higher resolution interannual mass anomalies based on ENVISAT data. High positive correlations (>0.6) between the two interannual anomalies are primarily found over the west AIS and coastal regions in the east AIS, occupying more than 40% of the AIS. By combining the two interannual anomalies, we are able to estimate the density of snow/ice changing interannually over regions in the AIS. Especially over the Amundsen Sea sector with significant interannual signals, the temporal variability of the density of snow/ice associated with interannual anomalies is shown for the first time, which agrees with the events of excess snow accumulation and the accelerated ice discharge occurring there. Furthermore, we demonstrate the feasibility of obtaining higher resolution interannual mass anomalies over the west AIS, based on the density-corrected ENVISAT data. Negative correlations, which were also found in a previous study, are likely related to errors in the relatively weak interannual signals.