Tootfinder

Estimation of Treatment Effects in Extreme and Unobserved Data
Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis
https://arxiv.org/abs/2506.14051 https://

Estimation of Treatment Effects in Extreme and Unobserved Data
Causal effect estimation seeks to determine the impact of an intervention from observational data. However, the existing causal inference literature primarily addresses treatment effects on frequently occurring events. But what if we are interested in estimating the effects of a policy intervention whose benefits, while potentially important, can only be observed and measured in rare yet impactful events, such as extreme climate events? The standard causal inference methodology is not designed …

Numerical evaluation of deliberative discussions of the UK food system: stimuli, demographics, and opinion reversion
John Buckell, Thomas Hancock
https://arxiv.org/abs/2506.14102 …

Numerical evaluation of deliberative discussions of the UK food system: stimuli, demographics, and opinion reversion
There is increasing acknowledgement - including from the UK government - of the benefit of employing deliberative processes (deliberative fora, citizens' juries, etc.). Evidence suggests that the qualitative reporting of deliberative fora are often unclear or imprecise. If this is the case, their value to policymakers could be diminished. In this study we develop numerical methods of deliberative processes to document people's preferences, as a complement to qualitative analysis. Data are taken…

Distributed Computing From First Principles
Kenneth Odoh
https://arxiv.org/abs/2506.12959 https://arxiv.org/pdf/2506.12959

Distributed Computing From First Principles
This book on Distributed Computing aims to benefit a diverse audience, ranging from aspiring engineers, and seasoned researchers, to a wide range of professionals. Driven by my passion for making the core concepts of distributed computing accessible, this work is a significant undertaking designed to empower individuals from all backgrounds to gain valuable insight. Have you ever wondered how a typical distributed system works under the hood? Are you looking for a pedagogical guide with complet…

Join Adrien Grand and Luca Cavanna at this year's Berlin Buzzwords as they share the fascinating journey to the release of version 10.0 of the popular Java search engine Apache Lucene, discussing the ups and downs, the team effort it took to get there, and much more.
Learn more: https://

We Are AI: Taking Control of Technology
Julia Stoyanovich, Armanda Lewis, Eric Corbett, Lucius E. J. Bynum, Lucas Rosenblatt, Falaah Arif Khan
https://arxiv.org/abs/2506.08117

We Are AI: Taking Control of Technology
Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable--benefiting diverse stakeholders while controlling the risks. Achieving this goal requires active engagement and participation from the broader public. This paper introduces "We are AI: Taking Control of Technology," a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting. We outline the goals behind t…

Join Adrien Grand and Luca Cavanna at this year's Berlin Buzzwords as they share the fascinating journey to the release of version 10.0 of the popular Java search engine Apache Lucene, discussing the ups and downs, the team effort it took to get there, and much more.
Learn more: https://

Game Theory in Social Media: A Stackelberg Model of Collaboration, Conflict, and Algorithmic Incentives
Arjan Khadka
https://arxiv.org/abs/2506.05373 https…

Game Theory in Social Media: A Stackelberg Model of Collaboration, Conflict, and Algorithmic Incentives
Social media platforms are ecosystems in which many decisions are constantly made for the benefit of the creators in order to maximize engagement, which leads to a maximization of income. The decisions, ranging from collaboration to public conflict or ``beefing,'' are heavily influenced by social media algorithms, viewer preferences, and sponsor risk. This paper models this interaction as a Stackelberg game in which the algorithm is the leader, setting exposure and reward rules, and the content…

Engineering Cryogenic FETs: Addressing SCEs and Impact of Interface Traps Down to 2 K Temperature
Nilesh Pandey, Dipanjan Basu, Yogesh Singh Chauhan, Leonard F. Register, Sanjay K. Banerjee
https://arxiv.org/abs/2506.09356

Engineering Cryogenic FETs: Addressing SCEs and Impact of Interface Traps Down to 2 K Temperature
This paper presents a comprehensive design and benchmarking of cryogenic bulk-FET using a calibrated TCAD model integrating 2-D electrostatics and interface trap effects across temperatures from T = 2 K to 300 K. For a 28-nm node FET, transport appears predominately ballistic at T = 2 K, shifting toward quasi-ballistic transport with increasing temperature. Short-channel effects (SCEs) dominate cryogenic benefits at Lg = 25 nm for T < 25 K, as evidenced by a modest improvement in the minimum su…

This https://arxiv.org/abs/2412.04629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Argumentative Experience: Reducing Confirmation Bias on Controversial Issues through LLM-Generated Multi-Persona Debates
Large language models (LLMs) are enabling designers to give life to exciting new user experiences for information access. In this work, we present a system that generates LLM personas to debate a topic of interest from different perspectives. How might information seekers use and benefit from such a system? Can centering information access around diverse viewpoints help to mitigate thorny challenges like confirmation bias in which information seekers over-trust search results matching existing …

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider
Jiahao Wang, Jinbo Han, Xingda Wei, Sijie Shen, Dingyan Zhang, Chenguang Fang, Rong Chen, Wenyuan Yu, Haibo Chen
https://arxiv.org/abs/2506.02634

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider
Serving large language models (LLMs) is important for cloud providers, and caching intermediate results (KV\$) after processing each request substantially improves serving throughput and latency. However, there is limited understanding of how LLM serving benefits from KV\$ caching, where system design decisions like cache eviction policies are highly workload-dependent. In this paper, we present the first systematic characterization of the KV\$ workload patterns from one of the leading LLM serv…