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Tuesday 2025-05-13 21:00 CEST semi-final 1

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"...and again we are using sixty jit codes as identifiers here, which relate to the sixty jit codes in that table over there..."
Me: "What are these sixty jit codes exactly? Is that some special format?"
"It's a code with the length of sixty jits."
Me: "And what is a jit? ... ... ... Oh.. SIX DIGITS!" 🤦‍♂️🤦‍♂️🤦‍♂️

A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma
Garima Jain, Sanghamitra Pati, Mona Duggal, Amit Sethi, Abhijeet Patil, Gururaj Malekar, Nilesh Kowe, Jitender Kumar, Jatin Kashyap, Divyajeet Rout, Deepali, Hitesh, Nishi Halduniya, Sharat Kumar, Heena Tabassum, Rupinder Singh Dhaliwal, Sucheta Devi Khuraijam, Sushma Khuraijam, Sharmila Laishram, Simmi Kharb, Sunita Singh, K. Swaminadtan, Ranjana Solanki, Deepika Hemranjani, Shashank Nath Singh, Uma Handa, Manvee…

A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma
Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection dramatically improves outcomes, with stage I cancers achieving up to 90 percent survival. However, traditional diagnosis based on histopathology has limited accessibility in low-resource settings because it is invasive, resource-intensive, and reliant on expert pathologists. On t…

The tool that transforms a very precise description of a problem and how it shall be solved into executable machine code is called a compiler¹ and not AI.
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¹or interpreter or anything between, these things get fuzzy, especially with JIT and virtual runtime environments

Influence of preparation and architecture on the elastic modulus of polymer networks
Jiting Tian, Jean-Louis Barrat, Walter Kob
https://arxiv.org/abs/2506.09670

Influence of preparation and architecture on the elastic modulus of polymer networks
The elastic modulus $G$ of a polymer network depends notably on parameters such as the initial concentration of the monomers before the synthesis ($ρ_0$), the density of the cross-linker, or the topology of the network. Understanding how these factors influence the stiffness of the sample is hampered by the fact that in experiments it is difficult to tune them individually. Here we use coarse-grained molecular dynamics simulations to study how these quantities, as well as excluded volume inter…

Collective Communication Profiling of Modern-day Machine Learning Workloads
Jit Gupta, Andrew Li, Tarun Banka, Ariel Cohen, T. Sridhar, Raj Yavatkar
https://arxiv.org/abs/2507.07117

Collective Communication Profiling of Modern-day Machine Learning Workloads
Machine Learning jobs, carried out on large number of distributed high performance systems, involve periodic communication using operations like AllReduce, AllGather, and Broadcast. These operations may create high bandwidth and bursty traffic patterns, leading to network congestion and packet loss, thus impacting the performance of these jobs. Hence it is imperative to analyze these patterns, which can be helpful in provisioning network resources depending on the type of machine learning workl…

Clock Synchronization for Drone-Based Entanglement Quantum Key Distribution
Jinquan Huang, Bangying Tang, Hui Han, JianJi Yi, Bo Xu, Chunqing Wu, Xiangwei Zhu, Wanrong Yu, Huicun Yu, Jiahao Li, Shihai Sun, Bo Liu
https://arxiv.org/abs/2506.07831

Clock Synchronization for Drone-Based Entanglement Quantum Key Distribution
Drone-based entanglement distribution provides full spatiotemporal coverage for quantum networks, enabling quantum key distribution (QKD) in dynamic environments. The security of QKD fundamentally depends on high-fidelity quantum state measurements, for which high-precision clock synchronization is indispensable, as timing jitter is inversely correlated with quantum state fidelity. However, drone-based clock synchronization is constrained by SWaP (Size, Weight, and Power) limitations and dynami…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation
Yiren Liu, Pranav Sharma, Mehul Jitendra Oswal, Haijun Xia, Yun Huang

I replaced my ancient phone with a Fairphone 5 and decided to do the ultimate comparison test: how accurate is the GPS when I'm riding my bike? Answer: on a map the tracks are v close but the F5 is consistently better, fewer wobbles. Errors in location mean errors in distance mean errors in speed: this image shows the new speed readings are much more steady. (NB: all speeds above 25 km/h are downhill.)

Kicking off this year's #jfs2025 with Dmitry Chuyko showing us how to profile a native image compiled from #java.
Java has options for running JITed or as a native compiled program for reasons!