Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning.

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential for catastrophic forgetting across real-world heterogeneous devices. In this paper, we demonstrate that self-attention-based architectures (e.

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning.

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal research has seen limited resources to study (1) generalization across domains and modalities, (2) complexity during training and inference, and (3) robustness to noisy and missing modalities.

CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French.

Modeling multimodal language is a core research area in natural language processing. While languages such as English have relatively large multimodal language resources, other widely spoken languages across the globe have few or no large-scale datasets in this area. This disproportionately affects native speakers of languages other than English.

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors.

Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear.

Effect of reducing sample volume on the detection of drugs in urine by transmission mode direct analysis in real time mass spectrometry.

Rationale: Matrix interference attributed to urea and other nitrogenous substances in unprocessed urine is significant. In this study desorption ionization of sub-microliter volume samples is performed in an effort to improve the detection of drugs in unprocessed urine using transmission mode-direct analysis in real time mass spectrometry (TM-DART-MS).

Methods: Urine samples were spiked with analytical standards of two drugs of abuse, codeine and methadone.

High-throughput quantification of drugs of abuse in biofluids via 96-solid-phase microextraction-transmission mode and direct analysis in real time mass spectrometry.

Rationale: The workload of clinical laboratories has been steadily increasing over the last few years. High-throughput (HT) sample processing allows scientists to spend more time undertaking matters of critical thinking rather than laborious sample processing. Herein we introduce a HT 96-solid-phase microextraction (SPME) transmission mode (TM) system coupled to direct analysis in real time (DART) mass spectrometry (MS).

Multi-attention Recurrent Network for Human Communication Comprehension.

Human face-to-face communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our intentions. Humans easily process and understand face-to-face communication, however, comprehending this form of communication remains a significant challenge for Artificial Intelligence (AI).