Tootfinder

This https://arxiv.org/abs/2311.09736 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

CARE: Extracting Experimental Findings From Clinical Literature
Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world complexity and nuance required. Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities a…

:kitten: Say hello to the new Kitten web site! 🎉
https://kitten.small-web.org
Like Kitten itself, it’s a baby but will be evolving quickly as they approach API version 1 together.
Enjoy!
💕
PS. Of course it’s written in Kitten itself. It doesn’t do anything fancy but here…

EPA says it will step up enforcement to address ‘critical’ vulnerabilities within water sector https://therecord.media/epa-enforcement-vulnerabilities-critical-water-sector

EPA says it will step up enforcement to address ‘critical’ vulnerabilities within water sector
The Environmental Protection Agency on Monday urged water utilities to take action to improve their digital defenses, following a spate of recent cyberattacks.

Shaved off the goatee & sideburns.
Ravishing!

Seems dumb, what’s going on here? https://www.canarymedia.com/articles/virtual-power-plants/california-budget-cuts-could-decimate-key-virtual-power-plant-programs

California budget cuts could decimate key virtual power plant programs
Governor Gavin Newsom plans to slash funding for batteries and utility customers to help with grid emergencies — but money for fossil fuel generators keeps flowing.

New look. Spring has finally settled in!

This https://arxiv.org/abs/2406.03510 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Speech-based Clinical Depression Screening: An Empirical Study
This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios, including psychiatric interviews, chatbot conversations, and text readings. Participants includes depressed patients recruited from the outpatient clinics of Peking University Sixth Hospital and control group members from the community, all diagnosed by psychiatrists following standardized diagnostic protocols. We extracted acoustic and deep speech features from each part…

The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
Omar Besbes, Yash Kanoria, Akshit Kumar
https://arxiv.org/abs/2405.03948 https…

The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
Recommendation systems are widespread, and through customized recommendations, promise to match users with options they will like. To that end, data on engagement is collected and used. Most recommendation systems are ranking-based, where they rank and recommend items based on their predicted engagement. However, the engagement signals are often only a crude proxy for utility, as data on the latter is rarely collected or available. This paper explores the following question: By optimizing for m…

A Negotiator's Backup Plan: Optimal Concessions with a Reservation Value
Tamara C. P. Florijn, Pinar Yolum, Tim Baarslag
https://arxiv.org/abs/2404.19361

A Negotiator's Backup Plan: Optimal Concessions with a Reservation Value
Automated negotiation is a well-known mechanism for autonomous agents to reach agreements. To realize beneficial agreements quickly, it is key to employ a good bidding strategy. When a negotiating agent has a good back-up plan, i.e., a high reservation value, failing to reach an agreement is not necessarily disadvantageous. Thus, the agent can adopt a risk-seeking strategy, aiming for outcomes with a higher utilities. Accordingly, this paper develops an optimal bidding strategy called MIA-RVe…

Speech-based Clinical Depression Screening: An Empirical Study
Yangbin Chen, Chenyang Xu, Chunfeng Liang, Yanbao Tao, Chuan Shi
https://arxiv.org/abs/2406.03510

Speech-based Clinical Depression Screening: An Empirical Study
This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios, including psychiatric interviews, chatbot conversations, and text readings. Participants includes depressed patients recruited from the outpatient clinics of Peking University Sixth Hospital and control group members from the community, all diagnosed by psychiatrists following standardized diagnostic protocols. We extracted acoustic and deep speech features from each part…