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From the near-Earth solar wind to the intracluster medium of galaxy clusters, collisionless, high-beta, magnetized plasmas pervade our universe. Energy and momentum transport from large-scale fields and flows to small-scale motions of plasma particles is ubiquitous in these systems, but a full picture of the underlying physical mechanisms remains elusive. The transfer is often mediated by a turbulent cascade of Alfvénic fluctuations as well as a variety of kinetic instabilities; these processes tend to be multi-scale and/or multi-dimensional, which makes them difficult to study using spacecraft missions and numerical simulations alone. Meanwhile, existing laboratory devices struggle to produce the collisionless, high ion beta ($$\beta _i \gtrsim 1$$), magnetized plasmas across the range of scales necessary to address these problems. As envisioned in recent community planning documents, it is therefore important to build a next generation laboratory facility to create a$$\beta _i \gtrsim 1$$, collisionless, magnetized plasma in the laboratory for the first time. A working group has been formed and is actively defining the necessary technical requirements to move the facility towards a construction-ready state. Recent progress includes the development of target parameters and diagnostic requirements as well as the identification of a need for source-target device geometry. As the working group is already leading to new synergies across the community, we anticipate a broad community of users funded by a variety of federal agencies (including National Aeronautics and Space Administration, Department of Energy and National Science Foundation) to make copious use of the future facility. 

ABSTRACT Reform‐oriented science classrooms encourage environments in which students engage in a collective enterprise of making sense of their science ideas together. Teachers who strive for these sorts of environments support students in collaboratively constructing and answering their own questions about phenomena and making sense of competing ideas together. However, to engage with one another productively, students must ask questions, share incomplete thoughts, and comment on each other's ideas, all of which can be seen as risky and unfamiliar behavior that may result in feelings of uncertainty or other negative classroom consequences. We conduct an explanatory case study using student and teacher interviews, teacher surveys, and classroom video collected over 2 years to investigate how one teacher used classroom norms to establish and maintain a culture in which students appeared committed to taking risks to improve their collective knowledge‐building. We found that norms were one practical tool the teacher used to encourage students to take risks and that also seemed helpful for negotiating individual and group uncertainty. Norms were also tools the teacher used to ensure that she and her students had similar expectations for classroom engagement. This study practically addresses some key challenges teachers face in enacting reform‐oriented science teaching and offers suggestions for how continued research regarding norms and uncertainty can continue to further science reform efforts. 

Light’sinteraction with objectsurfacesthrough anisotropic reflection– where reflected light varies with viewing angles–offers significant potential for enhancing visual capabilities and assisting informed decision-making. Such ubiquitous light transfer phenomenon supports directional information encoding in sensing and dynamic display applications. We present LumosX, a set of techniques for encoding and decoding information through light intensity changes using 3D-printed optical anisotropic properties. By optimizing directional reflection and brightness contrasts through off-the-shelf materials and precise control over processing parameters (e.g., extrusion volume, raster angles, layer height, nozzle positioning), we enable cost-effective fabrication of visually enhanced objects. Our method supports modular assembly for highly curved regular surfaces and direct printing on top of relatively flat curved surfaces, enabling flexible information encoding for diverse applications. We showcase LumosX’s effectiveness through various indoor and smart urban sensing scenarios, demonstrating significant improvements in both human interaction and autonomous machine perception. 

Diffusion models have begun to overshadow GANs and other generative models in industrial applications due to their superior image generation performance. The complex architecture of these models furnishes an extensive array of attack features. In light of this, we aim to design membership inference attacks (MIAs) catered to diffusion models. We first conduct an exhaustive analysis of existing MIAs on diffusion models, taking into account factors such as black-box/white-box models and the selection of attack features. We found that white-box attacks are highly applicable in real-world scenarios, and the most effective attacks presently are white-box. Departing from earlier research, which employs model loss as the attack feature for white-box MIAs, we employ model gradients in our attack, leveraging the fact that these gradients provide a more profound understanding of model responses to various samples. We subject these models to rigorous testing across a range of parameters, including training steps, timestep sampling frequency, diffusion steps, and data variance. Across all experimental settings, our method consistently demonstrated near-flawless attack performance, with attack success rate approaching 100% and attack AUCROC near 1.0. We also evaluated our attack against common defense mechanisms, and observed our attacks continue to exhibit commendable performance. 

Training a machine learning model with data following a meaningful order, i.e., from easy to hard, has been proven to be effective in accelerating the training process and achieving better model performance. The key enabling technique is curriculum learning (CL), which has seen great success and has been deployed in areas like image and text classification. Yet, how CL affects the privacy of machine learning is unclear. Given that CL changes the way a model memorizes the training data, its influence on data privacy needs to be thoroughly evaluated. To fill this knowledge gap, we perform the first study and leverage membership inference attack (MIA) and attribute inference attack (AIA) as two vectors to quantify the privacy leakage caused by CL. Our evaluation of 9 real-world datasets with attack methods (NN-based, metric-based, label-only MIA, and NN-based AIA) revealed new insights about CL. First, MIA becomes slightly more effective when CL is applied, but the impact is much more prominent to a subset of training samples ranked as difficult. Second, a model trained under CL is less vulnerable under AIA, compared to MIA. Third, the existing defense techniques like MemGuard and MixupMMD are not effective under CL. Finally, based on our insights into CL, we propose a new MIA, termed Diff-Cali, which exploits the difficulty scores for result calibration and is demonstrated to be effective against all CL methods and the normal training method. With this study, we hope to draw the community's attention to the unintended privacy risks of emerging machine-learning techniques and develop new attack benchmarks and defense solutions.