Publications

Regional Adaptive Metropolis Light Transport
Hisanari Otsu
Killian Herveau
Johannes Hanika
D. Nowrouzezahrai
Carsten Dachsbacher
The design of the proposal distributions, and most notably the kernel parameters, are crucial for the performance of Markov chain Monte Carl… (voir plus)o (MCMC) rendering. A poor selection of parameters can increase the correlation of the Markov chain and result in bad rendering performance. We approach this problem by a novel path perturbation strategy for online-learning of state-dependent kernel parameters. We base our approach on the theoretical framework of regional adaptive MCMC which enables the adaptation of parameters depending on the region of the state space which contains the current sample, and on information collected from previous samples. For this, we define a partitioning of the path space on a low-dimensional canonical space to capture the characteristics of paths, with a focus on path segments closer to the sensor. Fast convergence is achieved by adaptive refinement of the partitions. Exemplarily, we present two novel regional adaptive path perturbation techniques akin to lens and multi-chain perturbations. Our approach can easily be used on top of existing path space MLT methods to improve rendering efficiency, while being agnostic to the initial choice of kernel parameters.
2024-02-12
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
An enhanced wideband tracking method for characteristic modes
Chao Huang
Chenjiang Guo
Xia Ma
Yi Yuan
Jun Ding
An enhanced wideband tracking method for characteristic modes (CMs) is investigated in this paper. The method consists of three stages, and … (voir plus)its core tracking stage (CTS) is based on a classical eigenvector correlation-based algorithm. To decrease the tracking time and eliminate the crossing avoidance (CRA), we append a commonly used eigenvalue filter (EF) as the preprocessing stage and a novel postprocessing stage to the CTS. The proposed postprocessing stage can identify all CRA mode pairs by analyzing their trajectory and correlation characteristics. Subsequently, it can predict corresponding CRA frequencies and correct problematic qualities rapidly. Considering potential variations in eigenvector numbers at consecutive frequency samples caused by the EF, a new execution condition for the adaptive frequency adjustment in the CTS is introduced. Finally, CMs of a conductor plate and a fractal structure are investigated to demonstrate the performance of the proposed method, and the obtained results are discussed.
2024-02-11
International Journal of Microwave and Wireless Technologies (publié)
doi.org
Leveraging Function Space Aggregation for Federated Learning at Scale
Nikita Dhawan
Nicole Elyse Mitchell
Zachary Charles
Zachary Garrett
Gintare Karolina Dziugaite
The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model,… (voir plus) without sharing client data. Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization. In this work, we adopt a function space perspective and propose a new algorithm, FedFish, that aggregates local approximations to the functions learned by clients, using an estimate based on their Fisher information. We evaluate FedFish on realistic, large-scale cross-device benchmarks. While the performance of FedAvg can suffer as client models drift further apart, we demonstrate that FedFish is more robust to longer local training. Our evaluation across several settings in image and language benchmarks shows that FedFish outperforms FedAvg as local training epochs increase. Further, FedFish results in global networks that are more amenable to efficient personalization via local fine-tuning on the same or shifted data distributions. For instance, federated pretraining on the C4 dataset, followed by few-shot personalization on Stack Overflow, results in a 7% improvement in next-token prediction by FedFish over FedAvg.
2024-02-11
TMLR (accepté)
doi.org
openreview.net
Metrics reloaded: recommendations for image analysis validation.
Lena Maier-Hein
Annika Reinke
Evangelia Christodoulou
Ben Glocker
PATRICK GODAU
Fabian Isensee
Jens Kleesiek
Michal Kozubek
Mauricio Reyes
MICHAEL A. RIEGLER
Manuel Wiesenfarth
Michael Baumgartner
Matthias Eisenmann
DOREEN HECKMANN-NÖTZEL
A. EMRE KAVUR
TIM RÄDSCH
Minu Dietlinde Tizabi
Laura Acion
Michela Antonelli
Tal Arbel … (voir 47 de plus)
Spyridon Bakas
Peter Bankhead
Allison Benis
M. Jorge Cardoso
Veronika Cheplygina
BETH A. CIMINI
Gary S. Collins
Keyvan Farahani
Bram van Ginneken
Daniel A. Hashimoto
Michael M. Hoffman
Merel Huisman
Pierre Jannin
CHARLES E. KAHN
Alexandros Karargyris
Alan Karthikesalingam
H. Kenngott
Annette Kopp-Schneider
Anna Kreshuk
Tahsin Kurc
Bennett Landman
GEERT LITJENS
Amin Madani
Klaus Maier-Hein
Anne L. Martel
Peter Mattson
Erik Meijering
Bjoern Menze
David Moher
Karel G.M. Moons
Henning Müller
Felix Nickel
Brennan Nichyporuk
Jens Petersen
Nasir Rajpoot
Nicola Rieke
Julio Saez-Rodriguez
Clarisa S'anchez Guti'errez
Shravya Shetty
M. Smeden
Carole H. Sudre
Ronald M. Summers
Abdel Aziz Taha
Sotirios A. Tsaftaris
Ben Van Calster
Gael Varoquaux
PAUL F. JÄGER
2024-02-11
Nature Methods (publié)
doi.org
arxiv.org
The impact of gender on pediatric surgical access and outcomes in Africa
Sacha Williams
Olivia Serhan
Jenny Wang
Christian Guindi,
Elena Guadagno
Maeve Trudeau
Emmanuel Ameh
Kokila Lakhoo
Dan Poenaru
Girls, whose care is often affected by barriers steeped in gender inequity, may be at higher risk of poor surgical outcomes. This study expl… (voir plus)ored the impact of gender on pediatric surgical care in Africa. Differences in access to care and clinical outcomes for boys and girls were examined for pediatric surgical conditions that do not differ by physiological sex. A systematic review of African pediatric surgical studies ensued, followed by a random effects meta-analysis, and risk of bias assessment. Of the 12281 records retrieved, 54 were selected for review. Most studies were retrospective (57.4%), single-site (94.4%), from Egypt, Nigeria, Ghana, or Ethiopia (55.6%), focussed on gastrointestinal conditions (63.0%), published in 2010 or sooner (85.1%), had study durations of 5 years or less (68.5%), and cohorts of less than 200 children (57.4%). Sixty percent reported the outcome of mortality. Meta-analysis odds ratios revealed surgery was performed 3.6 times more often on boys (95% CI: 2.6, 4.9); and mortality was 1.6 times greater for girls (95% CI: 1.3, 2.0). African girls appear to face gender inequities in pediatric surgical care. Findings will be further explored in a mixed-methods study. I Gender disparities in global surgical care have been documented in the African adult population. However gender specific differentials in surgical access and outcomes have yet to be documented for African pediatric populations. This study provides first-time evidence of gender inequity in pediatric surgical care in Africa.
2024-02-10
Pediatric surgery international (Print) (publié)
doi.org
The Leukemoid Reaction in Severe Alcoholic Hepatitis: A Case Report
Siva Reddy
Sachin Agrawal
Sunil Kumar
Sourya Acharya
2024-02-10
Cureus (publié)
doi.org