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Hjälporganisationer varnar: Massvält i Gaza https://www.dn.se/varlden/hjalporganisationer-varnar-massvalt-i-gaza/

Hjälporganisationer varnar: Massvält i Gaza
Över 100 hjälporganisationer, däribland Rädda Barnen och Läkare utan gränser, varnar för att massvält sprider sig i Gaza och de får…

WGRAMMAR: Leverage Prior Knowledge to Accelerate Structured Decoding
Ran Wang, Xiaoxuan Liu, Hao Ren, Gang Chen, Fanchao Qi, Maosong Sun
https://arxiv.org/abs/2507.16768

WGRAMMAR: Leverage Prior Knowledge to Accelerate Structured Decoding
Structured decoding enables large language models (LLMs) to generate outputs in formats required by downstream systems, such as HTML or JSON. However, existing methods suffer from efficiency bottlenecks due to grammar compilation, state tracking, and mask creation. We observe that many real-world tasks embed strong prior knowledge about output structure. Leveraging this, we propose a decomposition of constraints into static and dynamic components -- precompiling static structures offline and in…

Calamus 42 To the young man
A short poem but very hot, dripping with latent queer meaning. Or pederasty, if I'm being honest.
The poem is literally Whitman offering to teach a young man. "To absorb, to engraft, to develop". And "to help him become élève", a fancy French word for student, and the choice there certainly raises an eyebrow.
But then what qualifications does this student require? Here, in the negation:
If he be not silently selected by lovers, and do not silently select lovers
That's not the usual test scores and sports achievements! Instead Whitman seems to have a specific extracurricular activity in mind. (And note the "select" directly echoes the "picking me out" in the previous poem.)
This poem becomes particularly bold when understanding it as Whitman speaking directly to the reader. Perhaps a young man that Whitman can take under his wing.
I'm being a bit silly but this kind of mentorship has a long, sexy gay history.

eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
Paul R. B. Houssel, Siamak Layeghy, Priyanka Singh, Marius Portmann
https://arxiv.org/abs/2507.16241

eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems (NIDS) by leveraging Large Language Models (LLMs). In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Prompt Augmenter. This module extracts contextual information and Cyber Threat Intelligence (CTI)-related knowledge from these flows. This enriched, context-specific data is then integrated with an input…

Leveraging Cloud-Fog Automation for Autonomous Collision Detection and Classification in Intelligent Unmanned Surface Vehicles
Thien Tran, Quang Nguyen, Jonathan Kua, Minh Tran, Toan Luu, Thuong Hoang, Jiong Jin
https://arxiv.org/abs/2506.18024

Leveraging Cloud-Fog Automation for Autonomous Collision Detection and Classification in Intelligent Unmanned Surface Vehicles
Industrial Cyber-Physical Systems (ICPS) technologies are foundational in driving maritime autonomy, particularly for Unmanned Surface Vehicles (USVs). However, onboard computational constraints and communication latency significantly restrict real-time data processing, analysis, and predictive modeling, hence limiting the scalability and responsiveness of maritime ICPS. To overcome these challenges, we propose a distributed Cloud-Edge-IoT architecture tailored for maritime ICPS by leveraging d…

Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad, Anirudh Deodhar
https://arxiv.org/abs/2507.17273

Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
Analyzing large, complex output datasets from Discrete Event Simulations (DES) of warehouse operations to identify bottlenecks and inefficiencies is a critical yet challenging task, often demanding significant manual effort or specialized analytical tools. Our framework integrates Knowledge Graphs (KGs) and Large Language Model (LLM)-based agents to analyze complex Discrete Event Simulation (DES) output data from warehouse operations. It transforms raw DES data into a semantically rich KG, capt…

EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
Ruijie Yang, Yan Zhu, Peiyao Fu, Yizhe Zhang, Zhihua Wang, Quanlin Li, Pinghong Zhou, Xian Yang, Shuo Wang
https://arxiv.org/abs/2507.17323

EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have improved optical-assisted diagnostics, they often demand extensive labeled datasets and yield "black-box" outputs with limited interpretability. In this paper, we propose EndoFinder, an online polyp retrieval framework that leverages multi-view scene representations for explainable and scalable CRC diagnosis. First, we …

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Mohammad Asifur Rahim, Muhammad Nazmul Arefin, Md. Mizanur Rahman, Md Ali Hossain, Ahmed Moustafa
https://arxiv.org/abs/2507.15915

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Context: Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions, making accurate early diagnosis challenging. Artificial intelligence (AI), especially Deep Learning (DL), has a strong tool for medical image analysis; however, pre-trained models like CNNs and XAI techniques for mpox detection is underexplored. Objective: This study aims to evaluate the effectiveness of pre-trained CNN models (VGG16, VGG19, InceptionV3, MobileNetV2) for the early…

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
Rishemjit Kaur, Arshdeep Singh Bhankhar, Surangika Ranathunga, Jashanpreet Singh Salh, Sudhir Rajput, Vidhi, Kashish Mahendra, Bhavika Berwal, Ritesh Kumar
https://arxiv.org/abs/2507.16974…

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
Enabling farmers to access accurate agriculture-related information in their native languages in a timely manner is crucial for the success of the agriculture field. Although large language models (LLMs) can be used to implement Question Answering (QA) systems, simply using publicly available general-purpose LLMs in agriculture typically offer generic advisories, lacking precision in local and multilingual contexts due to insufficient domain-specific training and scarcity of high-quality, regio…

Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms
Cheng Ji, Huaiying Luo
https://arxiv.org/abs/2506.17900

Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms
With the increasing complexity and rapid expansion of the scale of AI systems in cloud platforms, the log data generated during system operation is massive, unstructured, and semantically ambiguous, which brings great challenges to fault location and system self-repair. In order to solve this problem, this paper proposes an intelligent log processing and automatic debugging framework based on Large Language Model (LLM), named Intelligent Debugger (LLM-ID). This method is extended on the basis o…