18 GHz Ultraviolet Astrocomb via Chip-Integrated Harmonic Generation

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Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimoda ...

While large language models (LLMs) are increasingly playing a pivotal role in education by providing instantaneous, adaptive responses, their potential to promote critical thinking remains understudied. In this paper, we fill such a gap and present an inno ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Instruction-tuning is a widely adopted finetuning method that enables large language models (LLMs) to generate output that more closely resembles human responses. However, no studies have shown that instruction-tuning actually teaches LLMs to process langu ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score. In this work, we propose PERFECT, a ...

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

This dataset includes a total of 635 annotated image patches from historical city maps. It is designed for the semantic segmentation of the maps into 5 semantic classes (building blocks, non-built, water, road network, background frame). 330 patches are ta ...

Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent developments in machine learning and in particular deep learning h ...

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learnin ...

Seeing and recognizing an object whose size is much smaller than the illumination wavelength is a challenging task for an observer placed in the far field, due to the diffraction limit. Recent advances in near- and far-field microscopy have offered several ...

Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a suf ...

Should we judge the quality of the class by the grades the students and teacher get at the end of the semester or how the group collaborated during the semester towards acquiring new knowledge? Up until recently, the later approach was all too inaccessible ...

18 GHz Ultraviolet Astrocomb via Chip-Integrated Harmonic Generation

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Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

Enhancing Critical Thinking in Education by Means of a Socratic Chatbot

Reliable data-driven decision-making through optimal transport

Instruction-tuned LLMs with World Knowledge are More Aligned to the Human Brain

Aggregating Spatial and Photometric Context for Photometric Stereo

PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Discrete-Choice Mining of Social Processes

Historical City Maps Semantic Segmentation Dataset

Business Entity Matching with Siamese Graph Convolutional Networks

Medical Image Classification Using Generalized Zero Shot Learning

Far-Field Subwavelength Acoustic Imaging by Deep Learning

Editorial: Computational Pathology

Unsupervised Geometry-Aware Representation Learning for 3D Human Pose Estimation

Holistic Analysis of the Classroom