cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimizing deep learning model selection for angular feature extraction in satellite imagery

Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of convolutional neural network (CNN) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine learning problem that can be applied to nontraditional real-world feature extraction and automation tasks. Ursa Space Systems Inc. develops machine learning approaches to build custom solutions and extract answers from synthetic aperture radar satellite data fused with other remote sensing data sets. One application is identifying the orientation of nontexture linear features in imagery, such as an inlet pipe on top of a cylindrical oil storage tank. We propose a two-phase approach for determining this orientation: first an optimized CNN is used in a nontraditional way to probabilistically determine a coarse location and orientation of the inlet pipe, followed by a maximum likelihood voting scheme to automatically extract the orientation of the angular feature within 7.5 deg. We use a known hyperparameter optimization technique to determine the best deep learning CNN architecture for our specific problem and under user-defined optimization and accuracy constraints, by optimizing model hyperparameters (number of layers, size of the input image, and data set preprocessing) using a manual and grid search approach. The use of this systematic approach for hyperparameter optimization yields increased accuracy for our angular feature extraction and orientation finding algorithm from 86% to 94%. Additionally, this proposed algorithm shows how machine learning can be used to improve real-world remote sensing workflows.
Non-adaptive adaptive sampling on turnstile streams

Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation. Adaptive sampling repeatedly selects rows of an underlying matrix A∈ℝ n× d , where n≫ d, with probabilities proportional to their distances to the subspace of the previously selected rows. Intuitively, adaptive sampling seems to be limited to trivial multi-pass algorithms in the streaming model of computation due to its inherently sequential nature of assigning sampling probabilities to each row only after the previous iteration is completed. Surprisingly, we show this is not the case by giving the first one-pass algorithms for adaptive sampling on turnstile streams and using space poly(d,k,logn), where k is the number of adaptive sampling rounds to be performed. Our adaptive sampling procedure has a number of applications to various data summarization problems that either improve state-of-the-art or have only been previously studied in the more relaxed row-arrival model. We give the first relative-error algorithm for column subset selection on turnstile streams. We show our adaptive sampling algorithm also gives the first relative-error algorithm for subspace approximation on turnstile streams that returns k noisy rows of A. The quality of the output can be improved to a (1+є)-approximation at the tradeoff of a bicriteria algorithm that outputs a larger number of rows. We then give the first algorithm for projective clustering on turnstile streams that uses space sublinear in n. In fact, we use space poly(d,k,s,1/є,logn) to output a (1+є)-approximation, where s is the number of k-dimensional subspaces. Our adaptive sampling primitive also provides the first algorithm for volume maximization on turnstile streams. We complement our volume maximization algorithmic results with lower bounds that are tight up to lower order terms, even for multi-pass algorithms. By a similar construction, we also obtain lower bounds for volume maximization in the row-arrival model, which we match with competitive upper bounds.
Stochastically Dominant Distributional Reinforcement Learning

We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random returns induced by actions, producing a more comprehensive and robust evaluation of the environment's uncertainty. The necessary conditions for SSD require estimators to predict accurate second moments. To accommodate this, we map the distributional RL problem to a Wasserstein gradient flow, treating the distributional Bellman residual as a potential energy functional. We propose a particle-based algorithm for which we prove optimality and convergence. Our experiments characterize the algorithm performance and demonstrate how uncertainty and performance are better balanced using an \textsc{ssd} policy than with other risk measures.
Exploring the Design Space of Lunar GNSS in Frozen Orbit Conditions

We have witnessed a growing interest in lunar exploration missions in the last few years, with the announcement of NASA's Deep Space Gateway, the launch of several uncrewed missions and the involvement of private companies. However, there is currently no reliable means of accurate and instantaneous navigation in the vicinity of the Moon. This poses operational limitations on the autonomy of lunar robotic and small satellite missions. We present the preliminary results of a systems architecture study conducted on a new satellite navigation system orbiting the Moon that would effectively extend the Global Navigation Satellite Systems (GNSS) space service volume to cislunar space. We constrain the analysis to lunar frozen orbits under J2, C22 and third-body perturbations, to achieve stable lunar orbits. The problem formulation includes the following design decisions: (1) Orbit semi-major axis, (2) Number of satellites, (3) Number of orbital planes, (4) Satellite phasing in adjacent planes, (5) Orbit eccentricity and (6) Argument of periapsis. The Borg Multi-Objective Evolutionary Algorithm (MOEA) is used to optimize the satellite constellation design using multiple crossover and mutation operators that can adaptively be selected at runtime. The fitness function takes into account performance, cost, availability and station-keeping delta-V. Specifically, the performance metric assessment is based on the Geometric Dilution of Precision (GDOP), which is computed over a grid of 500 equidistant points on the lunar surface. The input satellite orbits used in the GDOP computation are obtained from high-fidelity orbit propagation using NASA's General Mission Analysis Toolbox (GMAT). The space segment cost model considers satellite constellation development and production costs. Single satellite costs are based on satellite dry mass estimates derived from the power budget analysis assuming a satellite lifetime of 10 years. The results of the study show that high-performing Lunar GNSS constellations relying on frozen orbits alone can be achieved with 20 satellites but they may be suboptimal for high-latitude regions.
Internal structure of shock waves in disparate mass mixtures

The detailed flow structure of a normal shock wave for a gas mixture is investigated using the direct-simulation Monte Carlo method. A variable diameter hard-sphere (VDHS) model is employed to investigate the effect of different viscosity temperature exponents (VTE) for each species in a gas mixture. Special attention is paid to the irregular behavior in the density profiles which was previously observed in a helium-xenon experiment. It is shown that the VTE can have substantial effects in the prediction of the structure of shock waves. The variable hard-sphere model of Bird shows good agreement, but with some limitations, with the experimental data if a common VTE is chosen properly for each case. The VDHS model shows better agreement with the experimental data without adjusting the VTE. The irregular behavior of the light-gas component in shock waves of disparate mass mixtures is observed not only in the density profile, but also in the parallel temperature profile. The strength of the shock wave, the type of molecular interactions, and the mole fraction of heavy species have substantial effects on the existence and structure of the irregularities.
Finding Closed Quasigeodesics on Convex Polyhedra

A closed quasigeodesic is a closed loop on the surface of a polyhedron with at most $180^\circ$ of surface on both sides at all points; such loops can be locally unfolded straight. In 1949, Pogorelov proved that every convex polyhedron has at least three (non-self-intersecting) closed quasigeodesics, but the proof relies on a nonconstructive topological argument. We present the first finite algorithm to find a closed quasigeodesic on a given convex polyhedron, which is the first positive progress on a 1990 open problem by O'Rourke and Wyman. The algorithm's running time is pseudopolynomial, namely $O\left({n^2 \over \varepsilon^2} {L \over \ell} b\right)$ time, where $\varepsilon$ is the minimum curvature of a vertex, $L$ is the length of the longest edge, $\ell$ is the smallest distance within a face between a vertex and a nonincident edge (minimum feature size of any face), and $b$ is the maximum number of bits of an integer in a constant-size radical expression of a real number representing the polyhedron. We take special care with the model of computation, introducing the $O(1)$-expression RAM and showing that it can be implemented in the standard word RAM.
Habitat affinity and density-dependent movement as indicators of fish habitat restoration efficacy

Conceptual and methodological tools from behavioral ecology can inform studies of habitat quality and their potential for evaluating habitat restoration in conservation efforts is explored here. Such approaches provide mechanistic detail in understanding the relationship between organisms and their habitats and are thus more informative than correlations between density and habitat characteristics. Several Pacific salmon species have been the target of habitat restoration efforts for the past 2-3 decades, but most post-restoration effectiveness studies have been limited to correlative data described above. In mark-recapture assays from four different study years, the affinity of sub-yearling Chinook salmon (Oncorhynchus tschawytscha) and steelhead (O. mykiss) for stream pools restored with or created by engineered log structures was greater than that for pools without restoration, though with high interannual variability. From corresponding distribution and density data, it was clear that habitat affinity data are not always concordant with single observations of density. The same was true of the correlation between either affinity or density and physical characteristics of pools, although depth and current velocity had some explanatory power for both responses in Chinook. Movement into pools by Chinook during the assays indicated that restored pools can support more immigrants at a given density than can unrestored pools; however no such pattern emerged for steelhead. Variation among individuals in body condition has implications for population-wide fitness, and such low variation was correlated with stronger affinity for pools in Chinook regardless of restoration status. This suggests that pools may mediate habitat-related trade-offs and that restoring them might have a positive effect on fitness. Thus affinity, immigration, and condition data give much-needed mechanistic indication of habitat selection for restored habitat via an apparent capacity increase and those potential fitness benefits. This is stronger support for restoration effectiveness than density differences alone because density data 1) may simply indicate redistribution of fish from poor to good habitats and 2) are not adequate to show correlations between restoration and positive change in traits correlated with fitness.
Primer on ILC physics and SiD software tools

We first outline the standard model of particle physics, particle production and decay, and the expected signal and background at a Higgs factory like the international linear collider (ILC). We then introduce high-energy colliders and collider detectors and briefly detail the ILC and the silicon detector (SiD), one of the two detectors proposed for the ILC. Next, we review the available software tools for ILC event generation, SiD detector simulation, and event reconstruction. Finally, we suggest open avenues in research for detector optimization and physics analysis. The pedagogical level is suitable for advanced undergraduate and beginning graduate students in physics and research scientists in related fields.
An Examination of the use of computer-based formative assessments

Abstract The purpose of this quantitative, correlational study was to investigate the computer-based formative assessment (CBFA) practices of core academic teachers within a one-to-one computing environment to better understand the relationships between CBFA usage rates of teachers and their perceptions of instructional technology. Survey data were collected from 261 academic teachers (63% response rate), which quantified teacher CBFA usage rates. The major findings of the study indicated that there were statistically significant correlations between CBFA usage rates and teacher comfort with technology, teacher belief in technology, and teacher autonomy. Significant differences in CBFA usage rates were found between different subjects, class levels, and grade levels. The findings provide insight into how teachers utilize CBFA in their classrooms to aid in developing targeted professional development to support teachers in using technology to formatively assess students. Future research into the effectiveness of increased CBFA usage could demonstrate how student achievement may be related to increased use of this instructional tool.
Estimation of Covid-19 prevalence from serology tests: A partial identification approach.

Abstract We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%–1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate ( ∼ 0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%–17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%–8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.
Lossless Source Coding in the Point-to-Point, Multiple Access, and Random Access Scenarios

This work studies point-to-point, multiple access, and random access lossless source coding in the finite-blocklength regime. In each scenario, a random coding technique is developed and used to analyze third-order coding performance. Asymptotic results include a third-order characterization of the Slepian-Wolf rate region with an improved converse that relies on a connection to composite hypothesis testing. For dependent sources, the result implies that the independent encoders used by Slepian-Wolf codes can achieve the same third-order-optimal performance as a single joint encoder. The concept of random access source coding is introduced to generalize multiple access (Slepian-Wolf) source coding to the case where encoders decide independently whether or not to participate and the set of participating encoders is unknown a priori to both the encoders and the decoder. The proposed random access source coding strategy employs rateless coding with scheduled feedback. A random coding argument proves the existence of a single deterministic code of this structure that simultaneously achieves the third-order-optimal Slepian-Wolf performance for each possible active encoder set.
Integrating semantic directions with concept mover’s distance to measure binary concept engagement

In an earlier article published in this journal (“Concept Mover’s Distance”, 2019), we proposed a method for measuring concept engagement in texts that uses word embeddings to find the minimum cost necessary for words in an observed document to “travel” to words in a “pseudo-document” consisting only of words denoting a concept of interest. One potential limitation we noted is that, because words associated with opposing concepts will be located close to one another in the embedding space, documents will likely have similar closeness to starkly opposing concepts (e.g., “life” and “death”). Using aggregate vector differences between antonym pairs to extract a direction in the semantic space pointing toward a pole of the binary opposition (following “The Geometry of Culture,” American Sociological Review, 2019), we illustrate how CMD can be used to measure a document’s engagement with binary concepts.
Carrots or Sticks? The Effectiveness of Subsidies and Tolls in Congestion Games

Are rewards or penalties more effective in influencing user behavior? This work compares the effectiveness of subsidies and tolls in incentivizing users in congestion games. The predominantly studied method of influencing user behavior in network routing problems is to institute taxes which alter users' observed costs in a manner that causes their self-interested choices to more closely align with a system-level objective. Another feasible method to accomplish the same goal is to subsidize the users' actions that are preferable from a system-level perspective. We show that, when users behave similarly and predictably, subsidies offer comparable performance guarantees to tolls while requiring smaller monetary transactions with users; however, in the presence of unknown player heterogeneity, subsidies fail to offer the same performance as tolls. We further investigate these relationships in affine congestion games, deriving explicit performance bounds under optimal tolls and subsidies with and without user heterogeneity; we show that the differences in performance can be significant.
Finite Horizon Density Steering for Multi-input State Feedback Linearizable Systems.

In this paper, we study the feedback synthesis problem for steering the joint state density or ensemble subject to multi-input state feedback linearizable dynamics. This problem is of interest to many practical applications including that of dynamically shaping a robotic swarm. Our results here show that it is possible to exploit the structural nonlinearities to derive the feedback controllers steering the joint density from a prescribed shape to another while minimizing the expected control effort to do so. The developments herein build on our previous work, and extend the theory of the Schrodinger bridge problem subject to feedback linearizable dynamics.
Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions

An individualized risk prediction model that dynamically updates the probability of a clinical event from a specific cause is valuable for physicians to be able to optimize personalized treatment strategies in real-time by incorporating all available information collected over the follow-up. However, this is more complex and challenging when competing risks are present, because it requires simultaneously updating the overall survival and the cumulative incidence functions (CIFs) while adjusting for the time-dependent covariates and time-varying covariate effects. In this study, we developed a landmark proportional subdistribution hazards (PSH) model and a more comprehensive supermodel by extending the landmark method to the Fine-Gray model. The performance of our models was assessed via simulations and through analysis of data from a multicenter clinical trial for breast cancer patients. Our proposed models have appealing advantages over other dynamic prediction models for data with competing risks. First, our models are robust against violations of the PSH assumption and can directly predict the conditional CIFs bypassing the estimation of overall survival and greatly simplify the prediction procedure. Second, our landmark PSH supermodel enables researchers to make predictions at a set of landmark points in one step. Third, the proposed models can easily incorporate various types of time-dependent information using existing standard software without computational burden.
Modeling satellite-Earth quantum channel downlinks with adaptive-optics coupling to single-mode fibers

The efficient coupling of photons from a free-space quantum channel into a single-mode optical fiber (SMF) has important implications for quantum network concepts involving SMF interfaces to quantum detectors, atomic systems, integrated photonics, and direct coupling to a fiber network. Propagation through atmospheric turbulence, however, leads to wavefront errors that degrade mode matching with SMFs. In a free-space quantum channel, this leads to photon losses in proportion to the severity of the aberration. This is particularly problematic for satellite-Earth quantum channels, where atmospheric turbulence can lead to significant wavefront errors. This report considers propagation from low-Earth orbit to a terrestrial ground station and evaluates the efficiency with which photons couple either through a circular field stop or into an SMF situated in the focal plane of the optical receiver. The effects of atmospheric turbulence on the quantum channel are calculated numerically and quantified through the quantum bit error rate and secure key generation rates in a decoy-state BB84 protocol. Numerical simulations include the statistical nature of Kolmogorov turbulence, sky radiance, and an adaptive-optics system under closed-loop control.
Identifying materials with charge–spin physics using charge–spin susceptibility computed from first principles

The authors present a quantity termed charge-spin susceptibility, which measures the charge response to spin degrees of freedom in strongly correlated materials. This quantity is simple to evaluate using both standard density functional theory and many-body electronic structure techniques, enabling comparison between different levels of theory. A benchmark on 28 layered magnetic materials shows that large values of charge-spin susceptibility correlate with unconventional ground states such as disordered magnets and unconventional superconductivity.
Three and a half asymptotic properties

We define and discuss transfinite asymptotic notions of smoothability, type, and equal norm type. We prove distinctness of these notions for a proper class of ordinals and that each class is an ideal. We also extend some results of Godefroy, Kalton, and Lancien to operators and ordinals greater than zero regarding the equivalence of equal norm asymptotic type and uniform renormings with power type smoothness. Finally, we discuss an extension of a non-linear result for quasi-reflexive, asymptotically $p$-smoothable Banach spaces to quasi-reflexive Banach spaces with asymptotic equal norm type $p$.
Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

The COVID-19 pandemic has produced a flurry of online activity on social media sites. As such, analysis of social media data during the COVID-19 pandemic can produce unique insights into discussion topics and how those topics evolve over the course of the pandemic. In this study, we propose analyzing discussion topics on Twitter by clustering hashtags. In order to obtain high-quality clusters of the Twitter hashtags, we also propose a novel multi-view clustering technique that incorporates multiple different data types that can be used to describe how users interact with hashtags. The results of our multi-view clustering show that there are distinct temporal and topical trends present within COVID-19 twitter discussion. In particular, we find that some topical clusters of hashtags shift over the course of the pandemic, while others are persistent throughout, and that there are distinct temporal trends in hashtag usage. This study is the first to use multi-view clustering to analyze hashtags and the first analysis of the greater trends of discussion occurring online during the COVID-19 pandemic.
“They open the door, kick you out, and say, ‘Go’" : Reentry Challenges After Wrongful Imprisonment

Drawing on 24 in-depth semi-structured interviews with exonerees, this study explores the post-release experiences and struggles upon reentry. Findings highlight the urgent need to provide support to individuals who have been victimized by the very system that is supposed to protect their fundamental rights. It is essential that more customized holistic approaches be implemented to address the wide range of often-interrelated practical, social and psychological issues. Furthermore, services should be provided immediately upon release and be offered indefinitely as hardships often linger well after release. Ultimately, the goal of this paper is to encourage action toward comprehensive support after wrongful imprisonment.
A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Abstract The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional networ...
Transport phenomena in fluid films with curvature elasticity

Cellular membranes are elastic lipid bilayers that contain a variety of proteins, including ion channels, receptors and scaffolding proteins. These proteins are known to diffuse in the plane of the membrane and to influence the bending of the membrane. Experiments have shown that lipid flow in the plane of the membrane is closely coupled with the diffusion of proteins. Thus, there is a need for a comprehensive framework that accounts for the interplay between these processes. Here, we present a theory for the coupled in-plane viscous flow of lipids, diffusion of transmembrane proteins and elastic deformation of lipid bilayers. The proteins in the membrane are modelled such that they influence membrane bending by inducing a spontaneous curvature. We formulate the free energy of the membrane with a Helfrich-like curvature elastic energy density function modified to account for the chemical potential energy of proteins. We derive the conservation laws and equations of motion for this system. Finally, we present results from dimensional analysis and numerical simulations and demonstrate the effect of coupled transport processes in governing the dynamics of membrane bending and protein diffusion.
A survey on physical unclonable function (PUF)-based security solutions for Internet of Things

Abstract The vast areas of applications for IoTs in future smart cities, smart transportation systems, and so on represent a thriving surface for several security attacks with economic, environmental and societal impacts. This survey paper presents a review of the security challenges of emerging IoT networks and discusses some of the attacks and their countermeasures based on different domains in IoT networks. Most conventional solutions for IoT networks are adopted from communication networks while noting the particular characteristics of IoT networks such as the nodes quantity, heterogeneity, and the limited resources of the nodes, these conventional security methods are not adequate. One challenge towards utilizing common secret key-based cryptographic methods in large-scale IoTs is the problem of secret key generation, distribution, and storage and protecting these secret keys from physical attacks. Physically unclonable functions (PUFs) can be utilized as a possible hardware remedy for identification and authentication in IoTs. Since PUFs extract the unique hardware characteristics, they potentially offer an affordable and practical solution for secret key generation. However, several barriers limit the PUFs’ applications for key generation purposes. We discuss the advantages of PUF-based key generation methods, and we present a survey of state-of-the-art techniques in this domain. We also present a proof-of-concept PUF-based solution for secret key generation using resistive random-access memories (ReRAM) embedded in IoTs.
Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots

Although cognitive engagement (CE) is crucial for motor learning, it remains underutilized in rehabilitation robots, partly because its assessment currently relies on subjective and gross measurements taken intermittently. Here, we propose an end-to-end computational framework that assesses CE in near real-time, using electroencephalography (EEG) signals as objective measurements. The framework consists of i) a deep convolutional neural network that extracts task-discriminative spatiotemporal EEG features to predict the level of CE for two classes- cognitively engaged vs. disengaged; and ii) a novel sliding window method that predicts continuous levels of CE in short time intervals. We evaluated our framework on 8 healthy subjects using an in-house Go/No-Go experiment that adapted its gameplay parameters to induce cognitive fatigue. The proposed CNN had an average leave-one-subject-out accuracy of 88.19%. The CE prediction correlated well with a commonly used behavioral metric based on self-reports taken every 5 minutes $(\rho =0.93)$. Our results objectify CE measurement in near real-time and pave the way for using CE as a rehabilitation parameter for tailoring robotic therapy to each patient’s needs and skills.
Perifosine induces differentiation and cell death in prostate cancer cells

AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA 1481 We analyzed the mechanism of action for perifosine (D-21266) a new synthetic alkylphospholipid Akt inhibitor using LNCaP and PC-3 prostate cancer cells. Perifosine treatment of PC-3 cells resulted in cytostatic and cytotoxic effects. Cytostatic effects were characterized by cell growth arrest, cell cycle block and morphological changes, such as a cell enlargement and granulation, hallmarks of differentiating PC-3 cells. Specific differentiation markers including prostasomal, secretory and plasma membrane proteins, and keratins were induced by perifosine. Among them, we detected strong induction and secretion of CEACAM5 protein. In contrast, perifosine strongly reduced caveolin-1 RNA levels. Cytotoxic effects included para-apoptosis, apoptosis, and necrosis. To pursue the mechanisms responsible for these activities we focused on signaling pathways that lie downstream of Akt. Perifosine-triggered GSK-3β activation in PC-3 and LNCaP cells resulted in the expression of GSK-3β-related differentiation markers. This expression was reduced in the presence of specific siRNA for GSK-3β or for its target CREB protein. The use of the GSK-3β inhibitor lithium chloride indicated that GSK-3β partially protects prostate cancer cells from the cytotoxic effects of perifosine. Together, these findings indicate that perifosine induces GSK-3β-related differentiation and caspase-independent cell death in prostate cancer PC-3 cells. In addition our results identify specific biomarkers for perifosine therapy.
Existence conditions for phononic frequency combs

The mechanical analog of optical frequency combs, phononic frequency combs, has recently been demonstrated in mechanical resonators and has been attributed to coupling between multiple phonon modes. This paper investigates the influence of the mode structure on comb generation using a model of two nonlinearly coupled phonon modes. The model predicts that there is only one region within the amplitude-frequency space where combs exist, and this region is a subset of the Arnold tongue that describes a 2:1 autoparametric resonance between the two modes. In addition, the location and shape of the comb region are analytically defined by the resonance frequencies, quality factors, mode coupling strength, and detuning of the driving force frequency from the mechanical resonances, providing clear conditions for comb generation. These results enable comb structure engineering for applications in areas as broad as sensing, communications, quantum information science, materials science, and molecular science.
A Lower Bound for One-Round Oblivious RAM

We initiate a fine-grained study of the round complexity of Oblivious RAM (ORAM). We prove that any one-round balls-in-bins ORAM that does not duplicate balls must have either \(\varOmega (\sqrt{N})\) bandwidth or \(\varOmega (\sqrt{N})\) client memory, where N is the number of memory slots being simulated. This shows that such schemes are strictly weaker than general (multi-round) ORAMs or those with server computation, and in particular implies that a one-round version of the original square-root ORAM of Goldreich and Ostrovksy (J. ACM 1996) is optimal. We prove this bound via new techniques that differ from those of Goldreich and Ostrovksy, and of Larsen and Nielsen (CRYPTO 2018), which achieved an \(\varOmega (\log N)\) bound for balls-in-bins and general multi-round ORAMs respectively. Finally we give a weaker extension of our bound that allows for limited duplication of balls, and also show that our bound extends to multiple-round ORAMs of a restricted form that include the best known constructions.
Spacecraft environmental anomalies expert system

Abstract : An expert system has been developed by The Aerospace Corporation, Space and Environment Technology Center for use in the diagnosis of satellite anomalies caused by the space environment. The expert system is designed to determine the probable cause of an anomaly from the following candidates: surface charging, bulk charging, single-event effects, total radiation dose, and space-plasma effects. Such anomalies depend on the orbit of the satellite, the local plasma and radiation environment (which is highly variable), the satellite-exposure time, and the hardness of the circuits and components in the satellite. The expert system is a rule-based system that uses the Texas Instrument's Personal Consultant Plus expert-system shell. The expert system's knowledgebase includes about 200 rules, as well as a number of databases that contain information on spacecraft and their orbits, previous spacecraft anomalies, and the environment. Space environment, Satellite anomalies, Expert system, Spacecraft charging, Single-event upsets, Radiation dose
Length distribution of sequencing by synthesis: fixed flow cycle model

Sequencing by synthesis is the underlying technology for many next- generation DNA sequencing platforms. We developed a new model, the fixed flow cycle model, to derive the distributions of sequence length for a given number of flow cycles under the general conditions where the nucleotide incorporation is probabilistic and may be incomplete, as in some single-molecule sequencing technologies. Unlike the previous model, the new model yields the probability distribution for the sequence length. Explicit closed form formulas are derived for the mean and variance of the distribution.
Unified Theory of Activated Relaxation in Liquids over 14 Decades in Time

We formulate a predictive theory at the level of forces of activated relaxation in hard-sphere fluids and thermal liquids that covers in a unified manner the apparent Arrhenius, crossover, and deeply supercooled regimes. The alpha relaxation event involves coupled cage-scale hopping and a long-range collective elastic distortion of the surrounding liquid, which results in two inter-related, but distinct, barriers. The strongly temperature and density dependent collective barrier is associated with a growing length scale, the shear modulus, and density fluctuations. Thermal liquids are mapped to an effective hard-sphere fluid based on matching long wavelength density fluctuation amplitudes, resulting in a zeroth-order quasi-universal description. The theory is devoid of fit parameters, has no divergences at finite temperature nor below jamming, and captures the key features of the alpha time of molecular liquids from picoseconds to hundreds of seconds.
Exact and numerical results on entanglement entropy in (5 + 1)-dimensional CFT

We calculate the shape dependence of entanglement entropy in (5 + 1)-dimensional conformal field theory in terms of the extrinsic curvature of the entangling surface, the opening angles of possible conical singularities, and the conformal anomaly coefficients, which are required to obey a single constraint. An important special case of this result is given by the interacting (2, 0) theory describing a large number of coincident M5-branes. To derive the more general result we rely crucially on the holographic prescription for calculating entanglement entropy using Lovelock gravity. We test the conjecture by relating the entanglement entropy of the free massless (1, 0) hypermultiplet in (5 + 1)-dimensions to the entanglement entropy of the free massive chiral multiplet in (2 + 1)-dimensions, which we calculate numerically using lattice techniques. We also present a numerical calculation of the (2 + 1)-dimensional renormalized entanglement entropy for the free massive Dirac fermion, which is shown to be consistent with the F-theorem.
Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST

Nine transiting Earth-sized planets have recently been discovered around nearby late M dwarfs, including the TRAPPIST-1 planets and two planets discovered by the MEarth survey, GJ 1132b and LHS 1140b. These planets are the smallest known planets that may have atmospheres amenable to detection with JWST. We present model thermal emission and transmission spectra for each planet, varying composition and surface pressure of the atmosphere. We base elemental compositions on those of Earth, Titan, and Venus and calculate the molecular compositions assuming chemical equilibrium, which can strongly depend on temperature. Both thermal emission and transmission spectra are sensitive to the atmospheric composition; thermal emission spectra are sensitive to surface pressure and temperature. We predict the observability of each planet's atmosphere with JWST. GJ 1132b and TRAPPIST-1b are excellent targets for emission spectroscopy with JWST/MIRI, requiring fewer than 10 eclipse observations. Emission photometry for TRAPPIST-1c requires 5-15 eclipses; LHS 1140b and TRAPPIST-1d, TRAPPIST-1e, and TRAPPIST-1f, which could possibly have surface liquid water, may be accessible with photometry. Seven of the nine planets are strong candidates for transmission spectroscopy measurements with JWST, though the number of transits required depends strongly on the planets' actual masses. Using the measured masses, fewer than 20 transits are required for a 5 sigma detection of spectral features for GJ 1132b and six of the TRAPPIST-1 planets. Dedicated campaigns to measure the atmospheres of these nine planets will allow us, for the first time, to probe formation and evolution processes of terrestrial planetary atmospheres beyond our solar system.
Probing embryonic tissue mechanics with laser hole drilling.

We use laser hole drilling to assess the mechanics of an embryonic epithelium during development—in vivo and with subcellular resolution. We ablate a subcellular cylindrical hole clean through the epithelium and track the subsequent recoil of adjacent cells (on ms time scales). We investigate dorsal closure in the fruit fly with emphasis on apical constriction of amnioserosa cells. The mechanical behavior of this epithelium falls between that of a continuous sheet and a 2D cellular foam (a network of tensile interfaces). Tensile stress is carried both by cell–cell interfaces and by the cells' apical actin networks. Our results show that stress is slightly concentrated along interfaces (1.6-fold), but only in early closure. Furthermore, closure is marked by a decrease in the recoil power-law exponent, implying a transition to a more solid-like tissue. We use the site and stage dependence of the recoil kinetics to constrain how the cellular mechanics change during closure. We apply these results to test extant computational models.
Comparison of optical, radio, and acoustical detectors for ultrahigh-energy neutrinos

For electromagnetic cascades induced by electron-neutrinos in South Pole ice, the effective volume per detector element (phototube, radio antenna, or acoustic transducer) as a function of cascade energy is estimated, taking absorption and scattering into account. A comparison of the three techniques shows that the optical technique is most effective for energies below0.5 PeV, that the radio technique shows promise of being the most effective for higher energies, and that the acoustic method is not competitive. Due to the great transparency of ice, the event rate of AGN νe-induced cascades may be as much as an order of magnitude greater in ice than in water. The ratio of Glashow resonance events to non-resonant events ranges from1/3 for the hard spectrum of Stecker and Salamon to zero for the softest spectrum of Szabo and Protheroe. The radio technique will be particularly useful in the search for Glashow events and in studies of sources with very hard energy spectra.
Sparse Bayesian Inference and the Temperature Structure of the Solar Corona

Measuring the temperature structure of the solar atmosphere is critical to understanding how it is heated to high temperatures. Unfortunately, the temperature of the upper atmosphere cannot be observed directly, but must be inferred from spectrally resolved observations of individual emission lines that span a wide range of temperatures. Such observations are "inverted" to determine the distribution of plasma temperatures along the line of sight. This inversion is ill-posed and, in the absence of regularization, tends to produce wildly oscillatory solutions. We introduce the application of sparse Bayesian inference to the problem of inferring the temperature structure of the solar corona. Within a Bayesian framework a preference for solutions that utilize a minimum number of basis functions can be encoded into the prior and many ad hoc assumptions can be avoided. We demonstrate the efficacy of the Bayesian approach by considering a test library of 40 assumed temperature distributions.
Performance of Topological Insulator Interconnects

The poor performance of copper interconnects at the nanometer scale calls for new material solutions for continued scaling of integrated circuits. We propose the use of 3-D time-reversal-invariant topological insulators (TIs), which host backscattering-protected surface states, for this purpose. Using semiclassical methods, we demonstrate that nanoscale TI interconnects have a resistance 1–3 orders of magnitude lower than copper interconnects and graphene nanoribbons at the nanometer scale. We use the nonequilibrium Green function formalism to measure the change in conductance of nanoscale TI and metal interconnects caused by the presence of impurity disorder. We show that metal interconnects suffer a resistance increase, relative to the clean limit, in excess of 500% due to disorder, while the TI’s surface states increase less than 35% in the same regime.
Dark matter freeze out during an early cosmological period of QCD confinement

Standard lore states that there is tension between the need to accommodate the relic density of a weakly interacting massive particle and direct searches for dark matter. However, the estimation of the relic density rests on an extrapolation of the cosmology of the early Universe to the time of freeze out, untethered by observations. We explore a nonstandard cosmology in which the strong coupling constant evolves in the early Universe, triggering an early period of QCD confinement at the time of freeze out. We find that depending on the nature of the interactions between the dark matter and the Standard Model, freeze out during an early period of confinement can lead to drastically different expectations for the relic density, allowing for regions of parameter space which realize the correct abundance but would otherwise be excluded by direct searches.
High Energy Vision: Processing X-rays

Astronomy is by nature a visual science. The high quality imagery produced by the world’s observatories can be a key to effectively engaging with the public and helping to inspire the next generation of scientists. Creating compelling astronomical imagery can, however, be particularly challenging in the non-optical wavelength regimes. In the case of X-ray astronomy, where the amount of light available to create an image is severely limited, it is necessary to employ sophisticated image processing algorithms to translate light beyond human vision into imagery that is aesthetically pleasing while still being scientifically accurate. This paper provides a brief overview of the history of X-ray astronomy leading to the deployment of NASA’s Chandra X-ray Observatory, followed by an examination of the specific challenges posed by processing X-ray imagery. The authors then explore image processing techniques used to mitigate such processing challenges in order to create effective public imagery for X-ray astronomy. A follow-up paper to this one will take a more in-depth look at the specific techniques and algorithms used to produce press-quality imagery.
The deflagration stage of Chandrasekhar mass models for type Ia supernovae. I. Early evolution

We present high-resolution, full-star simulations of the post-ignition phase of Type Ia supernovae using the compressible hydrodynamics code Castro. Initial conditions, including the turbulent velocity field and ignition site, are imported directly from a simulation of the last few hours of presupernova convection using a low Mach number code, Maestro. Adaptive mesh refinement allows the initial burning front to be modeled with an effective resolution of 36,8643 zones (136 m zone–1). The initial rise and expansion of the deflagration front are tracked until burning reaches the star's edge and the role of the background turbulence on the flame is investigated. The effect of artificially moving the ignition location closer to the star's center is explored. The degree to which turbulence affects the burning front decreases with increasing ignition radius since the buoyancy force is stronger at larger radii. Even central ignition—in the presence of a background convective flow field—is rapidly carried off-center as the flame is carried by the flow field. We compare our results to analytic models for burning thermals, and find that they reproduce the general trends of the bubble's size and mass, but underpredict the amount of buoyant acceleration due to simplifying assumptions of the bubble's properties. Overall, we find that the amount of mass that burns prior to flame break out is small, consistent with a "gravitationally confined detonation" occurring at a later epoch, but additional burning will occur following breakout that may modify this conclusion.