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新论文 · v1arXiv:2611.14744

Beyond Ground States: Learning Many-Body Hamiltonians for p-Bit Invertible Arithmetic

Tian Feng, Xutang Fan, Yiyun Chen

原文摘要

Probabilistic-bit (p-bit) invertible logic encodes a Boolean relation as an energy whose ground states are its legal assignments, computing in any direction by sampling. Traditional constructions target ground-state membership and a wide minimum energy gap, yet finite-time samplers traverse excited states. We cast Hamiltonian design as inverse statistical mechanics: fit an energy with interactions up to order four whose Gibbs law approaches uniformity on each clamp case's legal states, under a gap floor keeping every legal state below every illegal one. Constrained maximum likelihood with pruning and refitting fits terms from a fixed pool, and every delivered energy is audited exactly on all cases. Each energy is scored by time to a verified solution under restarts with its best of nine tuned samplers. In underdetermined modes, learned multipliers factor in 0.55, 0.44, 0.52 and 0.46 [0.38, 0.56] of the hand-written encodings' updates from 4x4 to 7x7, faster within every family below 7x7 at no resolved work penalty; learned adders solve the reverse mode in 0.71 of the updates at 8 bits and about a quarter at 24 and 32 bits, at 0.55 of the work at 32 bits. At 5x5 under equal pool, budget and floor, a gap-maximizing fit reaches 0.79 [0.73, 0.88] in updates and 1.26 in work against the hand-written encoding, while likelihood fitting takes 0.55 [0.46, 0.58] and 0.58 [0.48, 0.61] of the gap fit's updates and work. Rounding coefficients to the narrowest exact integer words tested, we run both energies at each pair's common word on one FPGA color-sweep SSA core, bit-exact with software: learned multipliers take 0.41 of the hand-written encoding's cycles at 4x4 and 0.057 [0.045, 0.068] at 7x7, at up to 2.4 times its lookup tables. The method requires only a circuit's legal assignments; in determined modes, hand-written encodings take less work, and from 12 bits on the adder fewer updates.

cond-mat.dis-nn · cs.ET · physics.app-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.19019

Smiling Regulates Emotion During Traumatic Recollection

Marcus Ma, Emily Zhou, Leonard Ludwig, Julia Hörath, Christina Winkler, Kleanthis Avramidis, Tiantian Feng, Gabor Mihaly Toth 等 10 位作者

原文摘要

We study when, where, and why 978 Holocaust survivors smile in video testimonies. We create an automatic smile detection model from facial features with an F1 of 85% and annotate detected smiles under two established taxonomies of smiling. We produce narrative features on 1,083,417 transcript sentences as well as emotional valence from three different modalities: audio, eye gaze, and text transcript. Smiling rates are associated with specific semantic topics, narrative structures, and temporal syntaxes across the corpus. Smiles often occur during periods of intense negative affect and we find negative-affect smiles are associated with more positive subsequent valence trajectories across all three modalities. Smiling reduces eye dynamics and blink rates, with both of these effects modulated by narrative valence. Taken together, we conclude that smiling plays a critical role in regulating emotion and social interaction during traumatic recollection.

cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37270

Understanding Everyday Communication Patterns, Stress, and Productivity in Hybrid Work through Egocentric Wearable Audio Sensing

Tiantian Feng, Aditya Kommineni, Emily Zhou, Burcin Becerik-Gerber, Gale Lucas, Shawn C. Roll, Shrikanth Narayanan

原文摘要

The hybrid work trend in the post-COVID period has transformed how people communicate while at work. However, there is a lack of quantitative understanding of how home and office environments shape communicative behavior and affective well-being. To study this, as a part of a larger study of the role of work environment on stress, we designed an egocentric wearable audio sensing system that captured interpersonal communication across both working locations. The system extracts real-time acoustic features on a wearable audio sensor and performs offline foreground speaker diarization to derive speaking load and activeness features. To improve diarization performance under low-dimensional feature constraints, we apply a teacher-student knowledge distillation framework that transfers knowledge from large-scale speech foundation models into a compact ResNet-based model, achieving consistent improvements in diarization performance over the baseline across three public egocentric speech datasets. In a four-month longitudinal deployment with 16 participants, our system revealed location-dependent differences in work communication patterns. Moreover, the mixed linear model analyses showed that office-based speaking load was significantly associated with higher self-reported stress and lower self-reported productivity, while speaking activeness during home-based work amplified both productivity and affective load in this cohort.

cs.SD

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.58ACL Anthology · ACL 2026

RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification

Shakhrul Iman Siam, Tiantian Feng University of Southern California, Jiankun Zhang, Shrikanth Narayanan University of Southern California, Mi Zhang The Ohio State University

原文摘要

Respiratory diseases remain a leading cause of global mortality, where timely and accurate diagnosis is critical to improving patient outcomes and reducing healthcare burdens. While prior work has explored audio-based models for respiratory disease detection, such unimodal approaches often suffer from limited generalizability and diagnostic precision. In this paper, we propose RespiraMFM, a Multimodal Foundation Model that integrates respiratory sounds with patient medical history and symptoms to enhance diagnostic accuracy and disease detection capabilities. We introduce an effective contrastive alignment strategy for audio-text multimodal integration, allowing the model to learn better cross-modal representations between respiratory sounds and corresponding textual clinical information. We evaluate RespiraMFM across five major respiratory diseases using seven real-world datasets in both supervised fine-tuning and zero-shot settings, achieving a 9.15% improvement in AUROC on supervised tasks and a 20.98% gain on zero-shot tasks over existing baselines. These findings underscore the potential of our framework to advance early diagnosis and improve clinical decision-making in respiratory disease management.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表