Parton Energy Loss in Two-Stream Plasma System

The energy loss of a fast parton scattering elastically in a weakly coupled quark-gluon plasma is formulated as an initial value problem. The approach is designed to study an unstable plasma, but it also reproduces the well known result of energy loss in an equilibrium plasma. A two-stream system, which is unstable due to longitudinal chromoelectric modes, is discussed here some detail. In particular, a strong time and directional dependence of the energy loss is demonstrated.Comment: 6 pages; presented by K. Deja at the conference Strangeness in Quark Matter, Cracow, Poland, September 18-24, 201

Correlation between shape errors in flat grinding

Correlation between shape errors of the tool and ceramic work-pieces are presented in the paper. A new tool, on which different shape errors of convexity or concavity can be set, was used during experiments. Results from flat grinding, such as the shape errors, are presented and analyzed. Computational calculations concerning the local shape errors of the tool and the technological effects such as surface roughness and waviness parameters as well as the workpiece plane-parallelism are also presented

Learning Data Representations with Joint Diffusion Models

Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from a set of empirical observations that indicate the usefulness of internal representations built by contemporary deep diffusion-based generative models not only for generating but also predicting. We then propose to extend the vanilla diffusion model with a classifier that allows for stable joint end-to-end training with shared parameterization between those objectives. The resulting joint diffusion model outperforms recent state-of-the-art hybrid methods in terms of both classification and generation quality on all evaluated benchmarks. On top of our joint training approach, we present how we can directly benefit from shared generative and discriminative representations by introducing a method for visual counterfactual explanations.Comment: Code: https://github.com/KamilDeja/joint_diffusio

Mary Pauper: A Historical Exploration of Early Care and Education Compensation, Policy, and Solutions

The Early Educator Investment Collaborative is committed in our work to recognizing and understanding the historical context in which structural racism continues to present in the early childhood workforce and eliminating the systemic oppression that keeps many early childhood educators living in poverty. In 2021, Child Trends was selected to conduct a literature review and develop a policy and practice report to map the history of systemic racism in the U.S. and how it has influenced early childhood education (ECE) policy and practice, with a particular focus on educator pay and benefits, preparation, and workforce stability.This report articulates a landscape analysis and a set of recommendations for policy, practice, and future research to improve the professional status of early childhood educators. The intent of this work is to build a common understanding of the biggest equity issues impacting early childhood educators—historically and in the present day

Exploring Continual Learning of Diffusion Models

Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data. However, training a diffusion model from scratch is computationally expensive. This highlights the need to investigate the possibility of training these models iteratively, reusing computation while the data distribution changes. In this study, we take the first step in this direction and evaluate the continual learning (CL) properties of diffusion models. We begin by benchmarking the most common CL methods applied to Denoising Diffusion Probabilistic Models (DDPMs), where we note the strong performance of the experience replay with the reduced rehearsal coefficient. Furthermore, we provide insights into the dynamics of forgetting, which exhibit diverse behavior across diffusion timesteps. We also uncover certain pitfalls of using the bits-per-dimension metric for evaluating CL

Our Space: Being a Responsible Citizen of the Digital World

Our Space is a set of curricular materials designed to encourage high school students to reflect on the ethical dimensions of their participation in new media environments. Through role-playing activities and reflective exercises, students are asked to consider the ethical responsibilities of other people, and whether and how they behave ethically themselves online. These issues are raised in relation to five core themes that are highly relevant online: identity, privacy, authorship and ownership, credibility, and participation.Our Space was co-developed by The Good Play Project and Project New Media Literacies (established at MIT and now housed at University of Southern California's Annenberg School for Communications and Journalism). The Our Space collaboration grew out of a shared interest in fostering ethical thinking and conduct among young people when exercising new media skills