Related papers: New Directions in International Masterclasses
The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle…
In the last years it is becoming more and more evident the importance in providing to the students, and people in general, a more realistic view on the scientific research world. In this framework many initiatives are beeing carried out to…
The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube -- Neutrinos in Deep ice" was a public machine learning…
Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent differences in the learning difficulties of classes.…
This research aims to investigate the gender-based learning experiences of engineering students enrolled in the Probability and Statistics course, focusing on the four different assessment methods employed namely direct conceptual learning…
In-context learning (ICL) is a new paradigm for natural language processing (NLP), where a large language model (LLM) observes a small number of demonstrations and a test instance as its input, and directly makes predictions without…
The past year has witnessed discovery of the first identified counterparts to a gravitational wave transient (GW 170817A) and a very high-energy neutrino (IceCube-170922A). These source identifications, and ensuing detailed studies, have…
We review the physics potential of top mass measurements and the GigaZ/MegaW options of the International Linear Collider (ILC) for probing New Physics models and especially the Minimal Supersymmetric Standard Model (MSSM). We demonstrate…
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectively learn from imperfect demonstrations, we propose a novel…
Funded by a $3M Department of Defense (DoD) National Defense Education Program (NDEP) award, we are developing and deploying on a national scale a follow-up curriculum to "Our Place In Space!", or OPIS!, in which approx. 3,500 survey-level…
We report on a project to introduce interactive learning strategies (ILS) to physics classes at the Universit\'e Pierre et Marie Curie (UPMC), one of the leading science universities in France. In Spring 2012, instructors in two large…
In the last decade, the Winograd Schema Challenge (WSC) has become a central aspect of the research community as a novel litmus test. Consequently, the WSC has spurred research interest because it can be seen as the means to understand…
The primary motivation for building neutrino telescopes is to open the road for neutrino astronomy, and to offer another observational window for the study of cosmic ray origins. Other physics topics, such as the search for WIMPs, can also…
Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for…
This paper describes our large reformed introductory physics course at UC Davis, which bioscience students have been taking since 1996. The central feature of this course is a focus on sense-making by the students during the five hours per…
Neutrino telescopes allow powerful probes of high-energy astrophysics and particle physics. Their power is increased when they can isolate different event classes, e.g., by flavor, though that is not the only possibility. Here we focus on a…
Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to…
Imitation learning (IL) enables agents to acquire skills by observing and replicating the behavior of one or multiple experts. In recent years, advances in deep learning have significantly expanded the capabilities and scalability of…
A large, world-wide community of physicists is working to realise an exceptional physics program of energy-frontier, electron-positron collisions with the International Linear Collider (ILC). This program will begin with a central focus on…
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models. This paper first empirically finds that 1) the vanilla MWA can benefit the class-imbalanced learning,…