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We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room. Our technique analyzes complex imperceptible changes in indirect illumination in a video of the wall to reveal a signal that is correlated with motion in the hidden part of a scene. We use this signal to classify between zero, one, or two moving people, or the activity of a person in the hidden scene. We train two convolutional neural networks using data collected from 20 different scenes, and achieve an accuracy of 94% for both tasks in unseen test environments and real-time online settings. Unlike other passive non-line-of-sight methods, the technique does not rely on known occluders or controllable light sources, and generalizes to unknown rooms with no recalibration. We analyze the generalization and robustness of our method with both real and synthetic data, and study the effect of the scene parameters on the signal quality. 

Abstract Superfluid 3 He, with unconventional spin-triplet p-wave pairing, provides a model system for topological superconductors, which have attracted significant interest through potential applications in topologically protected quantum computing. In topological insulators and quantum Hall systems, the surface/edge states, arising from bulk-surface correspondence and the momentum space topology of the band structure, are robust. Here we demonstrate that in topological superfluids and superconductors the surface Andreev bound states, which depend on the momentum space topology of the emergent order parameter, are fragile with respect to the details of surface scattering. We confine superfluid 3 He within a cavity of height D comparable to the Cooper pair diameter ξ 0 . We precisely determine the superfluid transition temperature T c and the suppression of the superfluid energy gap, for different scattering conditions tuned in situ, and compare to the predictions of quasiclassical theory. We discover that surface magnetic scattering leads to unexpectedly large suppression of T c , corresponding to an increased density of low energy bound states. 

In this article the recent developments of the open-source OpenMolcas chemistry software environment, since spring 2020, are described, with the main focus on novel functionalities that are accessible in the stable branch of the package and/or via interfaces with other packages. These community developments span a wide range of topics in computational chemistry, and are presented in thematic sections associated with electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report represents a useful summary of these developments, and it offers a solid overview of the chemical phenomena and processes that OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations. 

We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene. 

Despite efforts over the past few decades to promote diversity and foster inclusive campus climates, there is still underrepresentation of Blacks/ African Americans, Latinx/Hispanics, and Native Americans (including Native Hawaiians and Alaska Natives) within the STEM professoriate nationwide. For students who are members of these groups, the culturally isolating experience this deficit creates can weaken one's academic self-perception, and hinder performance in STEM disciplines. This paper explores the relationship between intentionality towards diversity and inclusion in faculty job postings and corresponding faculty demographics at a variety of US postsecondary institutions. The research questions we are investigating are: •In what ways are diversity and inclusion implicitly and explicitly addressed in the evaluated job postings? •Does intentionality towards diversity and inclusion in job postings vary based on the type of position advertised (i.e., tenured/tenure track versus non-tenure-track) or institution type (i.e., Basic Carnegie Classification)? Using HigherEdJobs.com, we conducted an advanced search of all open science and engineering faculty positions containing the keywords "data science", "data engineering", "data analysis", or "data analytics." Each result posted in September 2019 that advertised a full-time tenured/tenure-track or non-tenure track faculty appointment for at least one academic year at a US college or university was recorded. All qualifying job postings were qualitatively analyzed for active, intentional recruitment of URM candidates. Intentionality towards diversity and inclusion varied significantly across job postings. While some had no reference to diversity beyond a required one-sentence equal employment opportunity (EEO) statement, others explicitly addressed inclusion within the announcements, and still others required a standalone diversity statement as part of a complete application. The results will help to inform strategies for recruiting URM faculty in STEM disciplines, which may lead to improved opportunities to create cultures of inclusion and support for diverse students (undergraduate and graduate) and postdoctoral fellows. 

We present a physics-based model for ferroelectric/negative capacitance transistors (FEFETs/ NCFETs) without an inter-layer metal between ferroelectric and dielectric in the gate stack. The model self-consistently solves 2D Poisson's equation, non-equilibrium Green's function (NEGF) based charge and transport equations, and multi-domain Landau Khalatnikov (LK) equations with the domain interaction term. The proposed simulation framework captures the variation of ferroelectric (FE) polarization (P) along the gate length due to non-uniform electric field (E) along the channel. To calibrate the LK equations, we fabricate and characterize 10nm HZO films. Based on the calibrated model, we analyze the gate/drain voltage dependence of P distribution in the FE and its effect on the channel potential and current-voltage characteristics. Our results highlight the importance of larger domain interaction to boost the benefits of FEFETs with subthreshold swing (SS) as small as ~50mV/decade achieved at room temperature. As domain interaction increases, the characteristics of FEFETs without inter-layer metal (SS, negative drain induced barrier lowering (DIBL), negative output conductance) approach those of FEFETs with inter-layer metal. 

We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational-wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM onboard triggers and subthreshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma rays from binary black hole mergers.