cssi/SciSciGPT-SciSciNet huggingface dataset:
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

Increasing the scale of reinforcement learning experiments has allowed researchers to achieve unprecedented results in both training sophisticated agents for video games, and in sim-to-real transfer for robotics. Typically such experiments rely on large distributed systems and require expensive hardware setups, limiting wider access to this exciting area of research. In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation. We present the "Sample Factory", a high-throughput training system optimized for a single-machine setting. Our architecture combines a highly efficient, asynchronous, GPU-based sampler with off-policy correction techniques, allowing us to achieve throughput higher than $10^5$ environment frames/second on non-trivial control problems in 3D without sacrificing sample efficiency. We extend Sample Factory to support self-play and population-based training and apply these techniques to train highly capable agents for a multiplayer first-person shooter game. The source code is available at this https URL
RasterNet: Modeling Free-Flow Speed Using LiDAR and Overhead Imagery

Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, planning, and policy decisions of highway systems. Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming. Traditional approaches for estimating free-flow speed use geometric properties of the underlying road segment, such as grade, curvature, lane width, lateral clearance and access point density, but for many roads such features are unavailable. We propose a fully automated approach, RasterNet, for estimating free-flow speed without the need for explicit geometric features. RasterNet is a neural network that fuses large-scale overhead imagery and aerial LiDAR point clouds using a geospatially consistent raster structure. To support training and evaluation, we introduce a novel dataset combining free-flow speeds of road segments, overhead imagery, and LiDAR point clouds across the state of Kentucky. Our method achieves state-of-the-art results on a benchmark dataset.
Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems.
Stopping and Bending Light in 2D Photonic Structures

The problem of light propagating through two dimensional photonic structures with a localized defect is addressed. Examples of potential engineering applications of such structures are rerouting of light pulses or optical memory. The governing mathematical model is the system of Coupled Mode Equations (CME) in two spatial dimensions with addition of potentials which account for the defect. As we briefly explain, unlike the one dimensional model of CME without potentials, the two dimensional (x-uniform grating) one does NOT support stable localized pulses as solutions. Because stable pulses are necessary for any physical application, we add grating also in the x direction making, in effect, a true 2D photonic structure, and as we show numerically, this allows for launching of stable pulses. Next, making sensitive assumptions on the shape of the defect, we first give here a derivation of exact linear defect modes, i.e. solutions to the linear system with potentials, and then outline our future study of whether these linear modes persist into the nonlinear regime. Our next future task is to study the interactions of pulses with the nonlinear defect modes.
Estimates on the Markov convexity of Carnot groups and quantitative nonembeddability

Abstract We show that every graded nilpotent Lie group G of step r, equipped with a left invariant metric homogeneous with respect to the dilations induced by the grading, (this includes all Carnot groups with Carnot-Caratheodory metric) is Markov p-convex for all p ∈ [ 2 r , ∞ ) . We also show that this is sharp whenever G is a Carnot group with r ≤ 3 , a free Carnot group, or a jet space group; such groups are not Markov p-convex for any p ∈ ( 0 , 2 r ) . This continues a line of research started by Li who proved this sharp result when G is the Heisenberg group. As corollaries, we obtain new estimates on the non-biLipschitz embeddability of some finitely generated nilpotent groups into nilpotent Lie groups of lower step. Sharp estimates of this type are known when the domain is the Heisenberg group and the target is a uniformly convex Banach space or L 1 , but not when the target is a nonabelian nilpotent group.
Prdm8 regulates pMN progenitor specification for motor neuron and oligodendrocyte fates by modulating Shh signaling response.

Spinal cord pMN progenitors sequentially produce motor neurons and oligodendrocyte precursor cells (OPCs). Some OPCs differentiate rapidly as myelinating oligodendrocytes whereas others remain into adulthood. How pMN progenitors switch from producing motor neurons to OPCs with distinct fates is poorly understood. pMN progenitors express prdm8, which encodes a transcriptional repressor, during motor neuron and OPC formation. To determine if prdm8 controls pMN cell fate specification, we used zebrafish as a model system to investigate prdm8 function. Our analysis revealed that prdm8 mutant embryos have a deficit of motor neurons resulting from a premature switch from motor neuron to OPC production. Additionally, prdm8 mutant larvae have excess oligodendrocytes and a concomitant deficit of OPCs. Notably, pMN cells of mutant embryos have elevated Shh signaling coincident with the motor neuron to OPC switch. Inhibition of Shh signaling restored the number of motor neurons to normal but did not rescue the proportion of oligodendrocytes. These data suggest that Prdm8 regulates the motor neuron-OPC switch by controlling the level of Shh activity in pMN progenitors and also regulates allocation of oligodendrocyte lineage cell fates.
Investment Horizons and Information

We examine the relation between institutions' investment horizons on firms' financing and investment decisions. Firms with larger short-term institutional ownership use less debt financing and invest more in corporate liquidity. In contrast, firms with larger long-term institutional ownership use more internal funds, less external equity financing, and preserve investments in long-term assets. These results are primarily driven by the variation in informational preferences of different institutions. We argue that short-term (long-term) institutions collect and use value-neutral (value-enhancing) information. [ABSTRACT FROM AUTHOR]
Co-Design of Delays and Sparse Controllers for Bandwidth-Constrained Cyber-Physical Systems.

We address the problem of sparsity-promoting optimal control of cyber-physical systems with feedback delays. The delays are categorized into two classes - namely, intra-layer delay, and inter-layer delay between the cyber and the physical layers. Our objective is to minimize the H2-norm of the closed-loop system by designing an optimal combination of these two delays along with a sparse state-feedback controller, while respecting a given bandwidth constraint. We propose a two-loop optimization algorithm for this. The inner loop, based on alternating directions method of multipliers (ADMM), handles the conflicting directions of decreasing H2-norm and increasing sparsity of the controller. The outer loop comprises of semidefinite program (SDP)-based relaxations of non-convex inequalities necessary for stable co-design of the delays with the controller. We illustrate this algorithm using simulations that highlight various aspects of how delays and sparsity impact the stability and $\mc{H}_2$ performance of a LTI system.
Epigenome Prediction of Gene Expression using a Dynamical System Approach

Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigentic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN). Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN.
gBeam-ACO: a greedy and faster variant of Beam-ACO

Beam-ACO, a modification of the traditional Ant Colony Optimization (ACO) algorithms that incorporates a modified beam search, is one of the most effective ACO algorithms for solving the Traveling Salesman Problem (TSP). Although adding beam search to the ACO heuristic search process is effective, it also increases the amount of work (in terms of partial paths) done by the algorithm at each step. In this work, we introduce a greedy variant of Beam-ACO that uses a greedy path selection heuristic. The exploitation of the greedy path selection is offset by the exploration required in maintaining the beam of paths. This approach has the added benefit of avoiding costly calls to a random number generator and reduces the algorithms internal state, making it simpler to parallelize. Our experiments demonstrate that not only is our greedy Beam-ACO (gBeam-ACO) faster than traditional Beam-ACO, in some cases by an order of magnitude, but it does not sacrifice quality of the found solution, especially on large TSP instances. We also found that our greedy algorithm, which we refer to as gBeam-ACO, was less dependent on hyperparameter settings.
Mouse Model of SARS-CoV-2 Reveals Inflammatory Role of Type I Interferon Signaling

Severe acute respiratory syndrome-coronavirus 2 (SARS-Cov-2) has caused over 13,000,000 cases of coronavirus disease (COVID-19) with a significant fatality rate. Laboratory mice have been the stalwart of therapeutic and vaccine development; however, they do not support infection by SARS-CoV-2 due to the virus's inability to use the mouse orthologue of its human entry receptor angiotensin-converting enzyme 2 (hACE2). While hACE2 transgenic mice support infection and pathogenesis, these mice are currently limited in availability and are restricted to a single genetic background. Here we report the development of a mouse model of SARS-CoV-2 based on adeno-associated virus (AAV)-mediated expression of hACE2. These mice support viral replication and exhibit pathological findings found in COVID-19 patients. Moreover, we show that type I interferons do not control SARS-CoV-2 replication in vivo but are significant drivers of pathological responses. Thus, the AAV-hACE2 mouse model enables rapid deployment for in-depth analysis following robust SARS-CoV-2 infection with authentic patient-derived virus in mice of diverse genetic backgrounds.
Completely coarse maps are $\mathbb R$-linear

A map between operator spaces is called completely coarse if the sequence of its amplifications is equi-coarse. We prove that all completely coarse maps must be $\mathbb R$-linear. On the opposite direction of this result, we introduce a notion of embeddability between operator spaces and show that this notion is strictly weaker than complete $\mathbb R$-isomorphic embeddability (in particular, weaker than complete $\mathbb C$-isomorphic embeddability). Although weaker, this notion is strong enough for some applications. For instance, we show that if an infinite dimensional operator space $X$ embeds in this weaker sense into Pisier's operator space $\mathrm{OH}$, then $X$ must be completely isomorphic to $\mathrm{OH}$.
Timely Status Updating Through Intermittent Sensing and Transmission

We consider a novel intermittent status updating model motivated by intermittent operations in energy harvesting nodes. In particular, we consider status updating through non-preemptive sensing and transmission operations, each costing a single energy recharge of the node. The sensing time for each update is independent with a general distribution. The transmission queue has a single server receiving packets generated after sensing operation, general service time distribution and a single data buffer to save the latest arriving update packet. Once energy is harvested, the node has to decide whether to activate sensing to generate a new update or transmission to send the existing update (if any) to the receiver. We prove that average peak age of information (AoI) at the receiver is minimized by a threshold-based stopping rule that accepts only young packets to the transmission server. We then use this result to address average AoI optimization over the considered stopping rules through novel hybrid waiting and thresholding schemes. Our numerical results show the improvements in average AoI maintained by hybrid schemes.
Galois connections for phylogenetic networks and their polytopes

We describe Galois connections which arise between two kinds of combinatorial structures, both of which generalize trees with labeled leaves, and then apply those connections to a family of polytopes. The graphs we study can be imbued with metric properties or associated with vectors. Famous examples are the Billera–Holmes–Vogtmann metric space of phylogenetic trees, and the Balanced Minimal Evolution polytopes of phylogenetic trees described by Eickmeyer, Huggins, Pachter and Yoshida. Recently, the space of trees has been expanded to split networks by Devadoss and Petti, while the definition of phylogenetic polytopes has been generalized to encompass 1-nested phylogenetic networks, by Durell and Forcey. The first Galois connection we describe is a reflection between the (unweighted) circular split networks and the 1-nested phylogenetic networks. Another Galois connection exists between certain metric versions of these structures. Reflection between the purely combinatorial posets becomes a coreflection in the geometric case. Our chief contributions here, beyond the discovery of the Galois connections, are: a translation between approaches using PC-trees and networks, a new way to look at weightings on networks, and a fuller characterization of faces of the phylogenetic polytopes.
Head stabilization during standing in people with persisting symptoms after mild traumatic brain injury

Abstract Increased postural sway is often observed in people with mild traumatic brain injury (mTBI), but our understanding of how individuals with mTBI control their head during stance is limited. The purpose of this study was to determine if people with mTBI exhibit increased sway at the head compared with healthy controls. People with persisting symptoms after mTBI (n = 59, 41 women) and control participants (n = 63, 38 women) stood quietly for one minute in four conditions: eyes open on a firm surface (EO-firm), eyes closed on a firm surface (EC-firm), eyes open on a foam pad (EO-foam), and eyes closed on foam (EC-foam). Inertial sensors at the head, sternum, and lumbar region collected tri-axial accelerations. Root-mean-square (RMS) accelerations in anteroposterior (AP) and mediolateral (ML) directions and sway ratios between the head and sternum, head and lumbar, and sternum and lumbar region were compared between groups. Temporal coupling of anti-phase motion between the upper and lower body angular accelerations was assessed with magnitude squared coherence and cross-spectral phase angles. People with mTBI demonstrated greater sway than controls across conditions and directions. During foam-surface conditions, the control group, but not the mTBI group, reduced ML sway at their head and trunk relative to their lumbar by increasing the expression of an anti-phase hip strategy within the frontal plane. These results are consistent with suggestions of inflexible or inappropriate postural control in people with mTBI.
Word-of-Mouth Communication, Noise-driven Volatility, and Public Disclosure

Abstract This paper examines how a firm adjusts its disclosure quality in response to technological innovations that improve investors' private information. We show that more precise private information can endogenously amplify supply shocks and, hence, increase noise-driven (or non-fundamental) price volatility. We study how the firm reacts to such changes and derive a necessary and sufficient condition under which the firm improves its disclosure quality when investors are informed with better private signals. We then apply our model to study investors' private word-of-mouth communication. Our analysis highlights a “dark side” of word-of-mouth communication and a call for better public disclosure even if private communication is assumed to be unbiased and truthful. We provide empirical predictions regarding how price volatility, market depth, and firms’ disclosure qualities would change as technological innovations, such as social media, facilitate information sharing among investors.
Novelty Detection Through Model-Based Characterization of Neural Networks

In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs that can significantly degrade the performance of machine learning algorithms. Majority of existing studies have focused on activation-based representations to detect abnormal inputs, which limits the characterization of abnormality from a data perspective. However, a model perspective can also be informative in terms of the novelties and abnormalities. To articulate the significance of the model perspective in novelty detection, we utilize back-propagated gradients. We conduct a comprehensive analysis to compare the representation capability of gradients with that of activation and show that the gradients outperform the activation in novel class and condition detection. We validate our approach using four image recognition datasets including MNIST, Fashion-MNIST, CIFAR10, and CURE-TSR. We achieve a significant improvement on all four datasets with an average AUROC of $0.953, 0.918, 0.582$, and 0.746, respectively.
The End of the Express Road for Hybrid Vehicles: Can Governments’ Green Product Incentives Backfire?

We empirically study the impact of the HOV-incentive launch and termination on green and non‐green product demand in the U.S. automobile industry.
Koszul and local cohomology, and a question of Dutta

For $$(A,\mathfrak {m})$$ a local ring, we study the natural map from the Koszul cohomology module $$H^{\dim A}(\mathfrak {m};\,A)$$ to the local cohomology module $$H^{\dim A}_\mathfrak {m}(A)$$ . We prove that the injectivity of this map characterizes the Cohen-Macaulay property of the ring A. We also answer a question of Dutta by constructing normal rings A for which this map is zero.
Search Costs and the Determinants of Job Search

Abstract This paper examines how individuals select into job search in terms of their individual qualifications and perceptions and measures how recruiting additional applicants with a modest job-search subsidy affects selection. I use experimental evidence to examine individuals’ decisions to attend and participate in a job fair. Thirteen percent of invited but unsubsidized respondents attend the job fair, and they are positively selected from the overall distribution of respondents. While the subsidy attracts those who are less qualified and less confident in their ability to find work abroad, the least qualified do not search intensively. Although the subsidy does not lead to any additional offers, it induces individuals with a high degree of uncertainty about their likelihood of job-finding to apply with recruitment agencies. These results demonstrate the importance of imperfect information about the returns to search and highlight how reducing search costs can increase search effort among those most uncertain about their prospects.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

Human collaborators can effectively communicate with their partners to finish a common task by inferring each other’s mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators’ mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user’s mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github.io/xCookingWeb/.
How big should this object be? Perceptual influences on viewing-size preferences

When viewing objects depicted in a frame, observers prefer to view large objects like cars in larger sizes and smaller objects like cups in smaller sizes. That is, the visual size of an object that "looks best" is linked to its typical physical size in the world. Why is this the case? One intuitive possibility is that these preferences are driven by semantic knowledge: For example, when we recognize a sofa, we access our knowledge about its real-world size, and this influences what size we prefer to view the sofa within a frame. However, might visual processing play a role in this phenomenon--that is, do visual features that are related to big and small objects look better at big and small visual sizes, respectively, even when observers do not have explicit access to semantic knowledge about the objects? To test this possibility, we used "texform" images, which are synthesized versions of recognizable objects, which critically retain local perceptual texture and coarse form information, but are no longer explicitly recognizable. To test for visual size preferences, we used a two-interval forced choice task, in which each texform was presented at the preferred visual size of its corresponding original image, and a visual size slightly bigger or smaller. Observers consistently selected the texform presented at the canonical visual size as the more aesthetically pleasing one. These results suggest that the preferred visual size of an object depends not only on explicit knowledge of its real-world size, but also can be evoked by mid-level visual features that systematically covary with an object9s real-world size.
Products of Independent Elliptic Random Matrices

For fixed $$m > 1$$ , we study the product of $$m$$ independent $$N \times N$$ elliptic random matrices as $$N$$ tends to infinity. Our main result shows that the empirical spectral distribution of the product converges, with probability $$1$$ , to the $$m$$ -th power of the circular law, regardless of the joint distribution of the mirror entries in each matrix. This leads to a new kind of universality phenomenon: the limit law for the product of independent random matrices is independent of the limit laws for the individual matrices themselves. Our result also generalizes earlier results of Gotze–Tikhomirov (On the asymptotic spectrum of products of independent random matrices, available at http://arxiv.org/abs/1012.2710 ) and O’Rourke–Soshnikov (J Probab 16(81):2219–2245, 2011) concerning the product of independent iid random matrices.
In silico design of metal-dielectric nanocomposites for solar energy applications

Recently, a homogenization procedure has been proposed, based on the tight lower bounds of the Bergman- Milton formulation, and successfully applied to dilute ternary nanocomposites to predict optical data without using any fitting parameters [Garcia et al. Phys. Rev. B, 75, 045439 (2007)]. The procedure has been extended and applied to predict the absorption coefficient of a quaternary nanocomposite consisting of Cu, Ag, and Au nanospheres embedded in a SiO 2 host matrix. Significant enhancement of the absorption coefficient is observed over the spectral range 350-800 nm. The magnitude of this enhancement can be controlled by varying the nanosphere diameter and the individual metal volume fraction with respect to the host matrix. We have determined the optimal composition resulting in enhanced broadband (350nm-800nm) absorption of the solar spectrum using a simulated annealing algorithm. Fabricating such composite materials with a desired optical absorption has potential applications in solar energy harvesting.
An Adaptive Feature Extraction Algorithm for Classification of Seismocardiographic Signals

This paper proposes a novel adaptive feature extraction algorithm for seismocardiographic (SCG) signals. The proposed algorithm divides the SCG signal into a number of bins., where the length of each bin is determined based on the signal change within that bin. For example., when the signal variation is steeper., the bins are shorter and vice versa. The proposed algorithm was used to extract features of the SCG signals recorded from 7 healthy individuals (Age: 29.4±4.5 years) during different lung volume phases. The output of the feature extraction algorithm was fed into a support vector machines classifier to classify SCG events into two classes of high and low lung volume (HLV and LLV). The classification results were compared with currently available non-adaptive feature extraction methods for different number of bins. Results showed that the proposed algorithm led to a classification accuracy of ~90%. The proposed algorithm outperformed the non-adaptive algorithm., especially as the number of bins was reduced. For example, for 16 bins, F 1 score for the adaptive and non-adaptive methods were 0.91±0.05 and 0.63±0.08., respectively.
Characterizing the Gamma-Ray Variability of the Brightest Flat Spectrum Radio Quasars Observed with the Fermi LAT

Almost 10 yr of $\gamma$-ray observations with the Fermi Large Area Telescope (LAT) have revealed extreme $\gamma$-ray outbursts from flat spectrum radio quasars (FSRQs), temporarily making these objects the brightest $\gamma$-ray emitters in the sky. Yet, the location and mechanisms of the $\gamma$-ray emission remain elusive. We characterize long-term $\gamma$-ray variability and the brightest $\gamma$-ray flares of six FSRQs. Consecutively zooming in on the brightest flares, which we identify in an objective way through Bayesian blocks and a hill-climbing algorithm, we find variability on subhour time scales and as short as minutes for two sources in our sample (3C279, CTA102) and weak evidence for variability at time scales less than the Fermi satellite's orbit of 95 minutes for PKS1510-089 and 3C454.3. This suggests extremely compact emission regions in the jet. We do not find any signs for $\gamma$-ray absorption in the broad-line region (BLR), which indicates that $\gamma$-rays are produced at distances greater than hundreds of gravitational radii from the central black hole. This is further supported by a cross-correlation analysis between $\gamma$-ray and radio/millimeter light curves, which is consistent with $\gamma$-ray production at the same location as the millimeter core for 3C273, CTA102, and 3C454.3. The inferred locations of the $\gamma$-ray production zones are still consistent with the observed decay times of the brightest flares if the decay is caused by external Compton scattering with BLR photons. However, the minute-scale variability is challenging to explain in such scenarios.
Schur indices, BPS particles, and Argyres-Douglas theories

We conjecture a precise relationship between the Schur limit of the superconformal index of four-dimensional $$ \mathcal{N}=2 $$ field theories, which counts local operators, and the spectrum of BPS particles on the Coulomb branch. We verify this conjecture for the special case of free field theories, $$ \mathcal{N}=2 $$ QED, and SU(2) gauge theory coupled to fundamental matter. Assuming the validity of our proposal, we compute the Schur index of all Argyres-Douglas theories. Our answers match expectations from the connection of Schur operators with two-dimensional chiral algebras. Based on our results we propose that the chiral algebra of the generalized Argyres-Douglas theory (A k−1, A N−1) with k and N coprime, is the vacuum sector of the (k, k + N) W k minimal model, and that the Schur index is the associated vacuum character.
Analysis of Flows Inside Quiescent Prominences as Captured by Hinode /Solar Optical Telescope

Developing an understanding of how magnetic fields can become entangled in a prominence is important for predicting a possible eruption. This work investigates the kinetic energy and vorticity associated with plasma motion residing inside quiescent prominences (QPs). These plasma flow characteristics can be utilized to improve our understanding of how the prominence maintains a stable magnetic field configuration. Three different contrast-enhanced solar prominence observations from Hinode/Solar Optical Telescope were used to construct velocity maps—in the plane of the sky—via a Fourier local correlation tracking program. The resulting velocities were then used to perform the first-ever analysis of the two-dimensional kinetic energy and enstrophy spectra of a prominence. Enstrophy is introduced here as a means of quantifying the vorticity that has been observed in many QPs. The kinetic energy power spectral density (PSD) produced indices ranging from −1.00 to −1.60. There was a consistent anisotropy in the kinetic energy spectrum of all three prominences examined. Examination of the intensity PSD reveals that a different scaling relationship exists between the observed prominence structure and velocity maps. All of the prominences exhibited an inertial range of at least . Quasi-periodic oscillations were also detected in the centroid of the velocity distributions for one prominence. Additionally, a lower limit was placed on the kinetic energy density ( ~ 0.22–7.04 ) and enstrophy density (ω ~ 1.43–13.69 ) associated with each prominence.
Bosonic thermoelectric transport and breakdown of universality

We discuss the general principles of transport in normal phase atomic gases, comparing Bose and Fermi systems. Our study shows that two-dimensional bosonic transport is non-universal with respect to different dissipation mechanisms. Near the superfluid transition temperature Tc, a striking similarity between the fermionic and bosonic transport emerges because super-conducting (fluid) fluctuation transport for Fermi gases is dominated by the bosonic, Cooper pair component. As in fluctuation theory, one finds that the Seebeck coefficient changes sign at Tc and the Lorenz number approaches zero at Tc. Our findings appear quantitatively consistent with recent Bose gas experiments.
On knot Floer homology in double branched covers

Let L be a link in A×I where A is an annulus. We consider A×I to be embedded in R2×R respecting the obvious fibration and embedding A into a round annulus in R2. We always project L into R2 (or A) along the R-fibration. The complement of L in A × I is thereby identified with the complement of B∪L in S3 where B an unknot as depicted below, called the axis of L. We assume throughout that L intersects the spanning disc of B in an odd number of points. For example,
Axiomatic $S^1$ Morse–Bott theory

In various situations in Floer theory, one extracts homological invariants from “Morse–Bott” data in which the “critical set” is a union of manifolds, and the moduli spaces of “flow lines” have evaluation maps taking values in the critical set. This requires a mix of analytic arguments (establishing properties of the moduli spaces and evaluation maps) and formal arguments (defining or computing invariants from the analytic data). The goal of this paper is to isolate the formal arguments, in the case when the critical set is a union of circles. Namely, we state axioms for moduli spaces and evaluation maps (encoding a minimal amount of analytical information that one needs to verify in any given Floer-theoretic situation), and using these axioms we define homological invariants. More precisely, we define an (almost) category of “Morse–Bott systems”. We construct a “cascade homology” functor on this category, based on ideas of Bourgeois and Frauenfelder, which is “homotopy invariant”. This machinery is used in our work on cylindrical contact homology.
The All-or-Nothing Phenomenon in Sparse Tensor PCA

We study the statistical problem of estimating a rank-one sparse tensor corrupted by additive Gaussian noise, a model also known as sparse tensor PCA. We show that for Bernoulli and Bernoulli-Rademacher distributed signals and \emph{for all} sparsity levels which are sublinear in the dimension of the signal, the sparse tensor PCA model exhibits a phase transition called the \emph{all-or-nothing phenomenon}. This is the property that for some signal-to-noise ratio (SNR) $\mathrm{SNR_c}$ and any fixed $\epsilon>0$, if the SNR of the model is below $\left(1-\epsilon\right)\mathrm{SNR_c}$, then it is impossible to achieve any arbitrarily small constant correlation with the hidden signal, while if the SNR is above $\left(1+\epsilon \right)\mathrm{SNR_c}$, then it is possible to achieve almost perfect correlation with the hidden signal. The all-or-nothing phenomenon was initially established in the context of sparse linear regression, and over the last year also in the context of sparse 2-tensor (matrix) PCA, Bernoulli group testing, and generalized linear models. Our results follow from a more general result showing that for any Gaussian additive model with a discrete uniform prior, the all-or-nothing phenomenon follows as a direct outcome of an appropriately defined "near-orthogonality" property of the support of the prior distribution.
2FGL J1311.7-3429 Joins the Black Widow Club

We have found an optical/X-ray counterpart candidate for the bright, but presently unidentified, Fermi source 2FGL J1311.7-3429. This counterpart undergoes large-amplitude quasi-sinusoidal optical modulation with a 1.56 hr (5626 s) period. The modulated flux is blue at peak, with T{sub eff} Almost-Equal-To 14, 000 K, and redder at minimum. Superimposed on this variation are dramatic optical flares. Archival X-ray data suggest modest binary modulation, but no eclipse. With the {gamma}-ray properties, this appears to be another black-widow-type millisecond pulsar. If confirmation pulses can be found in the GeV data, this binary will have the shortest orbital period of any known spin-powered pulsar. The flares may be magnetic events on the rapidly rotating companion or shocks in the companion-stripping wind. While this may be a radio-quiet millisecond pulsar, we show that such objects are a small subset of the {gamma}-ray pulsar population.
Graphite based Schottky diodes formed on Si, GaAs, and 4H-SiC substrates

We demonstrate the formation of semimetal graphite/semiconductor Schottky barriers where the semiconductor is either silicon (Si), gallium arsenide (GaAs), or 4H-silicon carbide (4H-SiC). Near room temperature, the forward-bias diode characteristics are well described by thermionic emission, and the extracted barrier heights, which are confirmed by capacitance voltage measurements, roughly follow the Schottky–Mott relation. Since the outermost layer of the graphite electrode is a single graphene sheet, we expect that graphene/semiconductor barriers will manifest similar behavior.
Charge densities for conducting ellipsoids

The volume charge density for a conducting ellipsoid is expressed in simple geometrical terms, and then used to obtain the known surface charge density as well as the uniform charge per length along any principal axis. Corresponding results are presented for conducting hyperellipsoids in any number of spatial dimensions. The presentation is at a level suitable for use in graduate courses on electrostatics, as a supplement to more traditional material.
Quantum anomalous Hall effect in time-reversal-symmetry breaking topological insulators

The quantum anomalous Hall effect (QAHE), the last member of Hall family, was predicted to exhibit quantized Hall conductivity σ(yx) = e2/h without any external magnetic field. The QAHE shares a similar physical phenomenon with the integer quantum Hall effect (QHE), whereas its physical origin relies on the intrinsic topological inverted band structure and ferromagnetism. Since the QAHE does not require external energy input in the form of magnetic field, it is believed that this effect has unique potential for applications in future electronic devices with low-power consumption. More recently, the QAHE has been experimentally observed in thin films of the time-reversal symmetry breaking ferromagnetic (FM) topological insulators (TI), Cr- and V- doped (Bi,Sb)2Te3. In this topical review, we review the history of TI based QAHE, the route to the experimental observation of the QAHE in the above two systems, the current status of the research of the QAHE, and finally the prospects for future studies.
Curved non-relativistic spacetimes, Newtonian gravitation and massive matter

There is significant recent work on coupling matter to Newton-Cartan spacetimes with the aim of investigating certain condensed matter phenomena. To this end, one needs to have a completely general spacetime consistent with local non-relativistic symmetries which supports massive matter fields. In particular, one cannot impose a priori restrictions on the geometric data if one wants to analyze matter response to a perturbed geometry. In this paper, we construct such a Bargmann spacetime in complete generality without any prior restrictions on the fields specifying the geometry. The resulting spacetime structure includes the familiar Newton-Cartan structure with an additional gauge field which couples to mass. We illustrate the matter coupling with a few examples. The general spacetime we construct also includes as a special case the covariant description of Newtonian gravity, which has been thoroughly investigated in previous works. We also show how our Bargmann spacetimes arise from a suitable non-relativistic limit of Lorentzian spacetimes. In a companion paper [M. Geracie et al., e-print arXiv:1503.02680], we use this Bargmann spacetime structure to investigate the details of matter couplings, including the Noether-Ward identities, and transport phenomena and thermodynamics of non-relativistic fluids.
On the prevalence of elliptic and genus one fibrations among toric hypersurface Calabi-Yau threefolds

We systematically analyze the fibration structure of toric hypersurface Calabi-Yau threefolds with large and small Hodge numbers. We show that there are only four such Calabi-Yau threefolds with h1,1 ≥ 140 or h2,1 ≥ 140 that do not have manifest elliptic or genus one fibers arising from a fibration of the associated 4D polytope. There is a genus one fibration whenever either Hodge number is 150 or greater, and an elliptic fibration when either Hodge number is 228 or greater. We find that for small h1,1 the fraction of polytopes in the KS database that do not have a genus one or elliptic fibration drops exponentially as h1,1 increases. We also consider the different toric fiber types that arise in the polytopes of elliptic Calabi-Yau threefolds.
Big Bounce or Double Bang? A Reply to Craig and Sinclair on the Interpretation of Bounce Cosmologies

On the orthodox interpretation of bounce cosmologies, a preceding universe was compressed to a small size before "bouncing" to form the present expanding universe. William Lane Craig and James Sinclair have argued that the orthodox interpretation is incorrect if the entropy reaches a minimum at the bounce. In their view, the interface between universes represents the birth of two expanding universes, i.e., a ``double bang'' instead of a ``big bounce''. Here, I reply to Craig and Sinclair in defense of the orthodox interpretation. Contrary to their interpretation, features of one universe explain features of the other universe and so must precede the other universe in time. Moreover, contrary to a crucial part of Craig and Sinclair's interpretation, there are bounce cosmologies in which the thermodynamic arrow of time is continuous through the bounce even though the entropy is ``reset'' at the bounce.
Relative entropy of steering: on its definition and properties

In [Gallego and Aolita, Physical Review X 5, 041008 (2015)], the authors proposed a definition for the relative entropy of steering and showed that the resulting quantity is a convex steering monotone. Here we advocate for a different definition for relative entropy of steering, based on well grounded concerns coming from quantum Shannon theory. We prove that this modified relative entropy of steering is a convex steering monotone. Furthermore, we establish that it is uniformly continuous and faithful, in both cases giving quantitative bounds that should be useful in applications. We also consider a restricted relative entropy of steering which is relevant for the case in which the free operations in the resource theory of steering have a more restricted form (the restricted operations could be more relevant in practical scenarios). The restricted relative entropy of steering is convex, monotone with respect to these restricted operations, uniformly continuous, and faithful.
A natural barrier in random greedy hypergraph matching

Let r ⩾ 2 be a fixed constant and let $ {\cal H} $ be an r -uniform, D -regular hypergraph on N vertices. Assume further that D → ∞ as N → ∞ and that degrees of pairs of vertices in $ {\cal H} $ are at most L where L = D/ ( log N ) ω (1) . We consider the random greedy algorithm for forming a matching in $ {\cal H} $ . We choose a matching at random by iteratively choosing edges uniformly at random to be in the matching and deleting all edges that share at least one vertex with a chosen edge before moving on to the next choice. This process terminates when there are no edges remaining in the graph. We show that with high probability the proportion of vertices of $ {\cal H} $ that are not saturated by the final matching is at most ( L / D ) (1/(2( r −1)))+ o (1) . This point is a natural barrier in the analysis of the random greedy hypergraph matching process.
Use of Eigenvector Centrality to Detect Graph Isomorphism

Graph Isomorphism is one of the classical problems of graph theory for which no deterministic polynomial-time algorithm is currently known, but has been neither proven to be NP-complete. Several heuristic algorithms have been proposed to determine whether or not two graphs are isomorphic (i.e., structurally the same). In this research, we propose to use the sequence (either the non-decreasing or nonincreasing order) of eigenvector centrality (EVC) values of the vertices of two graphs as a precursor step to decide whether or not to further conduct tests for graph isomorphism. The eigenvector centrality of a vertex in a graph is a measure of the degree of the vertex as well as the degrees of its neighbors. We hypothesize that if the non-increasing (or non-decreasing) order of listings of the EVC values of the vertices of two test graphs are not the same, then the two graphs are not isomorphic. If two test graphs have an identical non-increasing order of the EVC sequence, then they are declared to be potentially isomorphic and confirmed through additional heuristics. We test our hypothesis on random graphs (generated according to the Erdos-Renyi model) and we observe the hypothesis to be indeed true: graph pairs that have the same sequence of non-increasing order of EVC values have been confirmed to be isomorphic using the well-known Nauty software.
FRACTIONAL-SPIN INTEGRALS OF MOTION FOR THE BOUNDARY SINE–GORDON MODEL AT THE FREE FERMION POINT

We construct integrals of motion (IM) for the sine–Gordon model with boundary at the free fermion (β2=4π) which correctly determine the boundary S matrix. The algebra of these IM ("boundary quantum group" at q=1) is a one-parameter family of infinite-dimensional subalgebras of twisted . We also propose the structure of the fractional-spin IM away from the free fermion point (β2≠4π).
Sub-Chandrasekhar-mass white dwarf detonations revisited

The detonation of a sub-Chandrasekhar-mass white dwarf (WD) has emerged as one of the most promising Type Ia supernova (SN Ia) progenitor scenarios. Recent studies have suggested that the rapid transfer of a very small amount of helium from one WD to another is sufficient to ignite a helium shell detonation that subsequently triggers a carbon core detonation, yielding a "dynamically-driven double degenerate double detonation" SN Ia. Because the helium shell that surrounds the core explosion is so minimal, this scenario approaches the limiting case of a bare C/O WD detonation. Motivated by discrepancies in previous literature and by a recent need for detailed nucleosynthetic data, we revisit simulations of naked C/O WD detonations in this paper. We disagree to some extent with the nucleosynthetic results of previous work on sub-Chandrasekhar-mass bare C/O WD detonations; e.g., we find that a median-brightness SN Ia is produced by the detonation of a 1.0 Msol WD instead of a more massive and rarer 1.1 Msol WD. The neutron-rich nucleosynthesis in our simulations agrees broadly with some observational constraints, although tensions remain with others. There are also discrepancies related to the velocities of the outer ejecta and light curve shapes, but overall our synthetic light curves and spectra are roughly consistent with observations. We are hopeful that future multi-dimensional simulations will resolve these issues and further bolster the dynamically-driven double degenerate double detonation scenario's potential to explain most SNe Ia.