cssi/SciSciGPT-SciSciNet huggingface dataset:
Weaker neural suppression in autism

Abnormal sensory processing has been observed in autism, including superior visual motion discrimination, but the neural basis for these sensory changes remains unknown. Leveraging well-characterized suppressive neural circuits in the visual system, we used behavioral and fMRI tasks to demonstrate a significant reduction in neural suppression in young adults with autism spectrum disorder (ASD) compared to neurotypical controls. MR spectroscopy measurements revealed no group differences in neurotransmitter signals. We show how a computational model that incorporates divisive normalization, as well as narrower top-down gain (that could result, for example, from a narrower window of attention), can explain our observations and divergent previous findings. Thus, weaker neural suppression is reflected in visual task performance and fMRI measures in ASD, and may be attributable to differences in top-down processing. Sensory hypersensitivity is common in autism spectrum disorders. Using functional MRI, psychophysics, and computational modeling, Schallmo et al. show that differences in visual motion perception in ASD are accompanied by weaker neural suppression in visual cortex.
Distributed Communication-Aware Motion Planning for Networked Mobile Robots Under Formal Specifications

Control and communication are often tightly coupled for networked mobile robots; motions of robots impact communication quality and communication quality of service (QoS), in turn, affecting coordination performance of robots. In this article, we propose a theoretical motion planning control framework for a team of networked mobile robots to accomplish high-level spatial and temporal motion specifications while optimizing communication QoS. Desired motion specifications are formulated as signal temporal logic (STL), whereas the communication QoS to be optimized is captured by spatial temporal reach and escape logic (STREL) formulas. Both the STL and STREL specifications are encoded as mixed integer linear constraints posed on the system and environment state variables of the mobile robot network, where satisfactory control strategies can be computed by exploiting a distributed model predictive control (MPC) approach. A two-layer hierarchical MPC procedure is proposed to efficiently solve the co-optimization problem, whose recursive feasibility is formally ensured. The effectiveness of the proposed framework is validated by simulation.
A Rule Based Method to Construct The Mohr’s Circle for Plane Stress

Mohr’s Circle is a graphical representation of the state of plane stress at a point. It is an important concept to learn for the understanding of plane stress transformation. However, it remains a challenge for both engineering as well as engineering technology students to understand the principles of Mohr’s Circle. In this paper, an emphasis is given to identify the source of pedagogical difficulty and a simple rule based method is presented as a new approach for effective teaching as well as learning. It is a viable method to understand the Mohr’s Circle and its application to plane stress transformation.Copyright © 2014 by ASME
Beyond Social Fragmentation: Coexistence of Cultural Diversity and Structural Connectivity Is Possible with Social Constituent Diversity

Social fragmentation caused by widening differences among constituents has recently become a highly relevant issue to our modern society. Theoretical models of social fragmentation using the adaptive network framework have been proposed and studied in earlier literature, which are known to either converge to a homogeneous, well-connected network or fragment into many disconnected subnetworks with distinct states. Here we introduced the diversities of behavioral attributes among social constituents and studied their effects on social network evolution. We investigated, using a networked agent-based simulation model, how the resulting network states and topologies would be affected when individual constituents’ cultural tolerance, cultural state change rate, and edge weight change rate were systematically diversified. The results showed that the diversity of cultural tolerance had the most direct effect to keep the cultural diversity within the society high and simultaneously reduce the average shortest path length of the social network, which was not previously reported in the earlier literature. Diversities of other behavioral attributes also had effects on final states of the social network, with some nonlinear interactions. Our results suggest that having a broad distribution of cultural tolerance levels within society can help promote the coexistence of cultural diversity and structural connectivity.
schuBERT: Optimizing Elements of BERT

Transformers have gradually become a key component for many state-of-the-art natural language representation models. A recent Transformer based model- BERTachieved state-of-the-art results on various natural language processing tasks, including GLUE, SQuAD v1.1, and SQuAD v2.0. This model however is computationally prohibitive and has a huge number of parameters. In this work we revisit the architecture choices of BERT in efforts to obtain a lighter model. We focus on reducing the number of parameters yet our methods can be applied towards other objectives such FLOPs or latency. We show that much efficient light BERT models can be obtained by reducing algorithmically chosen correct architecture design dimensions rather than reducing the number of Transformer encoder layers. In particular, our schuBERT gives 6.6% higher average accuracy on GLUE and SQuAD datasets as compared to BERT with three encoder layers while having the same number of parameters.
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information

Observational data are often accompanied by natural structural indices, such as time stamps or geographic locations, which are meaningful to prediction tasks but are often discarded. We leverage semantically meaningful indexing data while ensuring robustness to potentially uninformative or misleading indices. We propose a post-estimation smoothing operator as a fast and effective method for incorporating structural index data into prediction. Because the smoothing step is separate from the original predictor, it applies to a broad class of machine learning tasks, with no need to retrain models. Our theoretical analysis details simple conditions under which post-estimation smoothing will improve accuracy over that of the original predictor. Our experiments on large scale spatial and temporal datasets highlight the speed and accuracy of post-estimation smoothing in practice. Together, these results illuminate a novel way to consider and incorporate the natural structure of index variables in machine learning.
Stabilization of the response of cyclically loaded lattice spring models with plasticity

This paper develops an analytic framework to design both stress-controlled and displacement-controlled T -periodic loadings which make the quasistatic evolution of a one-dimensional network of elastoplastic springs converging to a unique periodic regime. The solution of such an evolution problem is a function t ↦(e (t ), p (t )), where e i (t ) is the elastic elongation and p i (t ) is the relaxed length of spring i , defined on [t 0 , ∞ ) by the initial condition (e (t 0 ), p (t 0 )). After we rigorously convert the problem into a Moreau sweeping process with a moving polyhedron C (t ) in a vector space E of dimension d , it becomes natural to expect (based on a result by Krejci) that the elastic component t ↦e (t ) always converges to a T -periodic function as t →∞ . The achievement of this paper is in spotting a class of loadings where the Krejci’s limit doesn’t depend on the initial condition (e (t 0 ), p (t 0 )) and so all the trajectories approach the same T -periodic regime. The proposed class of sweeping processes is the one for which the normals of any d different facets of the moving polyhedron C (t ) are linearly independent. We further link this geometric condition to mechanical properties of the given network of springs. We discover that the normals of any d different facets of the moving polyhedron C (t ) are linearly independent, if the number of displacement-controlled loadings is two less the number of nodes of the given network of springs and when the magnitude of the stress-controlled loading is sufficiently large (but admissible). The result can be viewed as an analogue of the high-gain control method for elastoplastic systems. In continuum theory of plasticity, the respective result is known as Frederick-Armstrong theorem.
Survey of Telehealth Adoption by Neuro-ophthalmologists During the COVID-19 Pandemic: Benefits, Barriers, and Utility.

BACKGROUND: During the COVID-19 pandemic, telehealth modalities have come to prominence as a strategy for providing patient care when in-person care provision opportunities are limited. The degree of adoption by neuro-ophthalmologists has not been quantified. METHODS: Telehealth utilization pre-COVID-19 and peri-COVID-19 was surveyed among practicing neuro-ophthalmologists in and outside the United States using an online platform. Demographics, perceived benefits, barriers, and utility for different neuro-ophthalmic conditions were collected. Data collection occurred over a 2-week period in May 2020. RESULTS: Two hundred eight practicing neuro-ophthalmologists (81.3% United States, 50.2% females, age range 65, mode 35-44 years) participated in the survey. Utilization of all telehealth modalities increased from pre-COVID to peri-COVID (video visit 3.9%-68.3%, P < 0.0005, remote interpretation of testing 26.7%-32.2%, P = 0.09, online second opinion 7.9%-15.3%, P = 0.001, and interprofessional e-consult 4.4%-18.7%, P < 0.0005, McNemar). The majority selected access, continuity, and patient efficiency of care as benefits and data quality as a barrier. Telehealth was felt to be most helpful for conditions relying on history, external examination, and previously collected ancillary testing and not helpful for conditions requiring funduscopic examination. CONCLUSIONS: Telehealth modality usage by neuro-ophthalmologists increased during the COVID-19 pandemic. Identified benefits have relevance both during and beyond COVID-19. Further work is needed to address barriers in their current and future states to maintain these modalities as viable care delivery options.
Neuroophthalmologic Aspects of the Vasculitides

There have been significant advances in the understanding of the vasculitides in the past several years, leading to more precise classification and nosology. Ophthalmologic manifestations may be the presenting feature of and a clue to the diagnosis of vasculitis, or develop in the course of the illness owing to a common disease mechanism. Precise diagnosis and prompt treatment prevents short- and long-term ophthalmologic sequela.
Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware

In this work we explore recurrent representations of leaky integrate and fire neurons operating at a timescale equal to their absolute refractory period. Our coarse time scale approximation is obtained using a probability distribution function for spike arrivals that is homogeneously distributed over this time interval. This leads to a discrete representation that exhibits the same dynamics as the continuous model, enabling efficient large scale simulations and backpropagation through the recurrent implementation. We use this approach to explore the training of deep spiking neural networks including convolutional, all-to-all connectivity, and maxpool layers directly in PyTorch. We found that the recurrent model leads to high classification accuracy using just 4-long spike trains during training. We also observed a good transfer back to continuous implementations of leaky integrate and fire neurons. Finally, we applied this approach to some of the standard control problems as a first step to explore reinforcement learning using neuromorphic chips.
A Public Website for the Automated Assessment and Validation of SARS-CoV-2 Diagnostic PCR Assays.

SUMMARY: Polymerase chain reaction-based assays are the current gold standard for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we need to constantly assess whether existing PCR-based assays will continue to detect all known viral strains. To enable the continuous monitoring of SARS-CoV-2 assays, we have developed a web-based assay validation algorithm that checks existing PCR-based assays against the ever-expanding genome databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics. The assay screening results are displayed as a heatmap, showing the number of mismatches between each detection and each SARS-CoV-2 genome sequence. Using a mismatch threshold to define detection failure, assay performance is summarized with the true positive rate (recall) to simplify assay comparisons. AVAILABILITY AND IMPLEMENTATION: The assay evaluation website and supporting software are Open Source and freely available at https://covid19.edgebioinformatics.org/#/assayValidation, https://github.com/jgans/thermonucleotideBLAST, and https://github.com/LANL-Bioinformatics/assay_validation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
A framework to evaluate IMEX schemes for atmospheric models

Abstract. We present a new evaluation framework for implicit and explicit (IMEX) Runge–Kutta time-stepping schemes. The new framework uses a linearized nonhydrostatic system of normal modes. We utilize the framework to investigate the stability of IMEX methods and their dispersion and dissipation of gravity, Rossby, and acoustic waves. We test the new framework on a variety of IMEX schemes and use it to develop and analyze a set of second-order low-storage IMEX Runge–Kutta methods with a high Courant–Friedrichs–Lewy (CFL) number. We show that the new framework is more selective than the 2-D acoustic system previously used in the literature. Schemes that are stable for the 2-D acoustic system are not stable for the system of normal modes.
Finite-time analysis of vector autoregressive models under linear restrictions

This paper develops a unified finite-time theory for the ordinary least squares estimation of possibly unstable and even slightly explosive vector autoregressive models under linear restrictions, with the applicable region $\rho(A)\leq 1+c/n$, where $\rho(A)$ is the spectral radius of the transition matrix $A$ in the \VAR(1) representation, $n$ is the time horizon and $c>0$ is a universal constant. The linear restriction framework encompasses various existing models such as banded/network vector autoregressive models. We show that the restrictions reduce the error bounds via not only the reduced dimensionality but also a scale factor resembling the asymptotic covariance matrix of the estimator in the fixed-dimensional setup: as long as the model is correctly specified, this scale factor is decreasing in the number of restrictions. It is revealed that the phase transition from slow to fast error rate regimes is determined by the smallest singular value of $A$, a measure of the least excitable mode of the system. The minimax lower bounds are derived across different regimes. The developed non-asymptotic theory not only bridges the theoretical gap between stable and unstable regimes but precisely characterizes the effect of restrictions and its interplay with model parameters. Simulations support our theoretical results.
Further Insight Relative to Cavity Radiation III: Gedanken Experiments, Irreversibility, and Kirchhoff's Law

Recently, gedanken experiments have been proposed in order to examine the validity of Kirchhoff’s Law of Thermal Emission (P.-M. Robitaille, Further Insight Relative to Cavity Radiation: A Thought Experiment Refuting Kirchhoff’s Law, Prog. Phys., 2014, v. 10, no. 1, 38–40; P.-M. Robitaille, Further Insight Relative to Cavity Radiation II: Gedanken Experiments and Kirchhoff’s Law, Prog. Phys., 2014, v. 10, no. 2, 116–120). In the second of these works, real materials (i.e. graphite and silver) were utilized in order to construct two separate cavities at the same temperature which are then placed in thermal contact with one another. It was hypothesized that the graphite cavity initially contained blackbody radiation and that the silver cavity was devoid of radiation. In the case of the silver cavity, all of the energy of the system was assigned to the phonons in its walls. When the cavities were brought together and a small hole introduced between the cavities, it was hypothesized that thermal contact between the cavity walls would enable the transformation of phonon energy into photon energy, eventually resulting in filling the silver cavity with black radiation. Energy contained within the wall of the silver cavity was believed to be reversibly trapped. However, in allowing energy to flow reversibly out of the walls of the silver cavity in this context, it has been assumed that the silver conduction bands could be neglected and that only phonon energy need be considered. However, the reflectivity attributed to the silver cavity should be considered uniquely as a result of energy associated with the formation of its conduction bands. Such formation must be considered irreversible. It will be demonstrated that under these conditions Kirchhoff’s law, once again, does not hold. The lack of thermal radiation within the silver cavity does not lead to a violation of the second law of thermodynamics.
A Mirror Theorem for Toric Complete Intersections

We prove a generalized mirror conjecture for non-negative complete intersections in symplectic toric manifolds. Namely, we express solutions of the PDE system describing quantum cohomology of such a manifold in terms of suitable hypergeometric functions.
Prediction of drug metabolites using neural machine translation

Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzyme families and therefore lack generalization, and additionally may involve manual work from experts limiting scalability. We present a rule-free, end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer learning on a deep learning transformer model for sequence translation, originally trained on chemical reaction data, to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites. Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites through phase I and phase II drug metabolism as well as other enzymes. Compared to existing rule-based approaches, our method has equivalent performance on the major enzyme families while it additionally finds metabolites through less common enzymes. Our results indicate that the proposed approach can provide a comprehensive study of drug metabolism that does not restrict to the major enzyme families and does not require the extraction of transformation rules.
Operationalizing occupational fatigue in pharmacists: An exploratory factor analysis.

Abstract Background The Quadruple Aim recognizes that caring for the healthcare employee is necessary to optimize patient outcomes and health system performance. Although previous research has assessed pharmacists' workload, this study is the first to describe pharmacist occupational fatigue—a characteristic of excessive workload that inhibits workers’ abilities to function at normal capacity. Objective (s): The purpose of this study was to describe occupational fatigue in pharmacists using exploratory factor analysis (EFA)—assessing whether dimensional structures used to describe occupational fatigue in other health professions fit pharmacist perceptions. Methods A model was created to conceptualize two “fatigue” domains found in the literature—physical fatigue (ex. Physical discomfort) and mental fatigue (ex. trouble thinking clearly). These domains were operationalized and used to create a survey that was distributed to licensed pharmacists at a conference. An EFA was conducted to identify the key domains underlying pharmacist perceptions of fatigue. Results A total of 283 surveys were distributed, and 115 were returned and useable. Respondents were primarily white, female, and worked 9.5 h-per-day on average. The EFA suggested a statistically significant two-factor model (Χ2 9.73, p = 0.28), which included physical fatigue (α = 0.87) and mental fatigue (α = 0.82) dimensions. Conclusions The EFA yielded a structure similar to what was anticipated from the literature. While working, pharmacists may not be aware of fatigue related short-cuts or lapses that pose risks to patient safety. This study is just the first step in promoting systematic interventions to prevent or cope with fatigue and prevent the patient, pharmacist, and institutional outcomes.
An Independent Analysis of the Six Recently Claimed Exomoon Candidates

It has been recently claimed that KOIs-268.01, 303.01, 1888.01, 1925.01, 2728.01 & 3320.01 are exomoon candidates, based on an analysis of their transit timing. Here, we perform an independent investigation, which is framed in terms of three questions: 1) Are there significant excess TTVs? 2) Is there a significant periodic TTV? 3) Is there evidence for a non-zero moon mass? We applied rigorous statistical methods to these questions alongside a re-analysis of the Kepler photometry and find that none of the KOIs satisfy these three tests. Specifically, KOIs-268.01 & 3220.01 pass none of the tests and KOIs-303.01, 1888.01 & 1925.01 pass a single test each. Only KOI-2728.01 satisfies two, but fails the cross-validation test for predictions. Further, detailed photodynamical modeling reveals that KOI-2728.01 favours a negative radius moon (as does KOI-268.01). We also note that we find a significant photoeccentric for KOI-1925.01 indicating an eccentric orbit of e>(0.62+/-0.06). For comparison, we applied the same tests to Kepler-1625b, which reveals that 1) and 3) are passed, but 2) cannot be checked with the cross-validation method used here, due to the limited number of available epochs. In conclusion, we find no compelling evidence for exomoons amongst the six KOIs. Despite this, we're able to derive exomoon mass upper limits versus semi-major axis, with KOI-3220.01 leading to particularly impressive constraints of Ms/Mp < 0.4% [2 sigma] at a similar relative semi-major to that of the Earth-Moon.
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors

The mixed membership stochastic blockmodel (MMSB) is a popular framework for community detection and network generation. It learns a low-rank mixed membership representation for each node across communities by exploiting the underlying graph structure. MMSB assumes that the membership distributions of the nodes are independently drawn from a Dirichlet distribution, which limits its capability to model highly correlated graph structures that exist in real-world networks. In this paper, we present a flexible richly structured MMSB model, \textit{Struct-MMSB}, that uses a recently developed statistical relational learning model, hinge-loss Markov random fields (HL-MRFs), as a structured prior to model complex dependencies among node attributes, multi-relational links, and their relationship with mixed-membership distributions. Our model is specified using a probabilistic programming templating language that uses weighted first-order logic rules, which enhances the model's interpretability. Further, our model is capable of learning latent characteristics in real-world networks via meaningful latent variables encoded as a complex combination of observed features and membership distributions. We present an expectation-maximization based inference algorithm that learns latent variables and parameters iteratively, a scalable stochastic variation of the inference algorithm, and a method to learn the weights of HL-MRF structured priors. We evaluate our model on six datasets across three different types of networks and corresponding modeling scenarios and demonstrate that our models are able to achieve an improvement of 15\% on average in test log-likelihood and faster convergence when compared to state-of-the-art network models.
3D computational cannula fluorescence microscopy enabled by artificial neural networks

Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of ∼6µm, field of view ∼200µm and axial sectioning of ∼50µm for depths down to ∼700µm, all achieved with computation time of ∼3ms/frame on a desktop computer.
THE RENORMALIZATION GROUP ACCORDING TO BALABAN, I. SMALL FIELDS

This is an expository account of Balaban's approach to the renormalization group. The method is illustrated with a treatment of the ultraviolet problem for the scalar ϕ4 model on toroidal lattice in dimension d = 3. This yields another proof of the stability bound. In this first paper we analyze the small field contribution to the partition function.
Diagrammatic categorification of the Chebyshev polynomials of the second kind

Abstract We develop a diagrammatic categorification of the polynomial ring Z[x], based on a geometrically defined graded algebra. This construction generalizes to categorification of some special functions, such as Chebyshev polynomials. Diagrammatic algebras featured in these categorifications lead to the first topological interpretations of the Bernstein-Gelfand-Gelfand reciprocity property.
Graph Signal Processing for Infrastructure Resilience: Suitability and Future Directions

Graph signal processing (GSP) is an emerging field developed for analyzing signals defined on irregular spatial structures modeled as graphs. Given the considerable literature regarding the resilience of infrastructure networks using graph theory, it is not surprising that a number of applications of GSP can be found in the resilience domain. GSP techniques assume that the choice of graphical Fourier transform (GFT) imparts a particular spectral structure on the signal of interest. We assess a number of power distribution systems with respect to metrics of signal structure and identify several correlates to system properties and further demonstrate how these metrics relate to performance of some GSP techniques. We also discuss the feasibility of a data-driven approach that improves these metrics and apply it to a water distribution scenario. Overall, we find that many of the candidate systems analyzed are properly structured in the chosen GFT basis and amenable to GSP techniques, but identify considerable variability and nuance that merits future investigation.
Using machine learning to identify the most at-risk students in physics classes

Demographic variables such as gender, underrepresented minority status, first-generation college student status, and low socioeconomic status are not important predictor variables in models to identify students likely to receive a D, F, or withdraw in their introductory physics course.
Info3D: Representation Learning on 3D Objects Using Mutual Information Maximization and Contrastive Learning.

A major endeavor of computer vision is to represent, understand and extract structure from 3D data. Towards this goal, unsupervised learning is a powerful and necessary tool. Most current unsupervised methods for 3D shape analysis use datasets that are aligned, require objects to be reconstructed and suffer from deteriorated performance on downstream tasks. To solve these issues, we propose to extend the InfoMax and contrastive learning principles on 3D shapes. We show that we can maximize the mutual information between 3D objects and their “chunks” to improve the representations in aligned datasets. Furthermore, we can achieve rotation invariance in SO(3) group by maximizing the mutual information between the 3D objects and their geometric transformed versions. Finally, we conduct several experiments such as clustering, transfer learning, shape retrieval, and achieve state of art results.
Low temperature thermal resistance for a new design of silver sinter heat exchanger

We have developed a novel procedure for constructing high surface area silver sinter heat exchangers. Our recipe incorporates nylon fibers having a diameter of ~50 μm and thin wires of bulk silver in the heat exchanger. In order to increase the thermal conductance of liquid helium within the heat exchanger, prior to sintering, the nylon fibers are dissolved with an organic acid leaving a network of channels. In addition, the silver wires reinforce the structural integrity, and reduce the resistance, of the silver sinter. We have constructed a 3He melting curve thermometer (MCT) with this type of heat exchanger and measured the thermal time response of the liquid 3He inside the MCT in the temperature range T ≈ 2 — 150 mK. We find a thermal relaxation time of ~490 s at T ≈1 mK. We have used scanning electron microscopy (SEM) to characterize the heat exchanger and BET absorption for determination of the specific surface area.
Turbulent heat exchange between water and ice at an evolving ice–water interface

We conduct laboratory experiments on the time evolution of an ice layer cooled from below and subjected to a turbulent shear flow of warm water from above. Our study is motivated by observations of warm water intrusion into the ocean cavity under Antarctic ice shelves, accelerating the melting of their basal surfaces. The strength of the applied turbulent shear flow in our experiments is represented in terms of its Reynolds number , which is varied over the range . Depending on the water temperature, partial transient melting of the ice occurs at the lower end of this range of and complete transient melting of the ice occurs at the higher end. Following these episodes of transient melting, the ice reforms at a rate that is independent of . We fit our experimental measurements of ice thickness and temperature to a one-dimensional model for the evolution of the ice thickness in which the turbulent heat transfer is parameterized in terms of the friction velocity of the shear flow. Applying our model to field measurements at a site under the Antarctic Pine Island Glacier ice shelf yields a predicted melt rate that exceeds present-day observations.
Tensor Powers of the Defining Representation of $$S_n$$

We give a decomposition formula for tensor powers of the defining representation of \(S_n\) and apply it to bound the mixing time of a Markov chain on \(S_n\).
Equilibration of a dissipative quantum oscillator

An explicit demonstration is given of a harmonic oscillator in equilibrium approaching the equilibrium of a corresponding interacting system by coupling it to a thermal bath consisting of a continuum of harmonic oscillators.
Particle-in-cell simulations of particle energization via shock drift acceleration from low Mach number quasi-perpendicular shocks in solar flares

Low Mach number, high beta fast mode shocks can occur in the magnetic reconnection outflows of solar flares. These shocks, which occur above flare loop tops, may provide the electron energization responsible for some of the observed hard X-rays and contemporaneous radio emission. Here we present new two-dimensional particle-in-cell simulations of low Mach number/high beta quasi-perpendicular shocks. The simulations show that electrons above a certain energy threshold experience shock-drift-acceleration. The transition energy between the thermal and non-thermal spectrum and the spectral index from the simulations are consistent with some of the X-ray spectra from RHESSI in the energy regime of E 40 ~ 100 keV. Plasma instabilities associated with the shock structure such as the modified-two-stream and the electron whistler instabilities are identified using numerical solutions of the kinetic dispersion relations. We also show that the results from PIC simulations with reduced ion/electron mass ratio can be scaled to those with the realistic mass ratio.
Local Density for Two-Dimensional One-Component Plasma

We study the classical two-dimensional one-component plasma of N positively charged point particles, interacting via the Coulomb potential and confined by an external potential. For the specific inverse temperature $${\beta=1}$$ (in our normalization), the charges are the eigenvalues of random normal matrices, and the model is exactly solvable as a determinantal point process. For any positive temperature, using a multiscale scheme of iterated mean-field bounds, we prove that the equilibrium measure provides the local particle density down to the optimal scale of $${N^{o(1)}}$$ particles. Using this result and the loop equation, we further prove that the particle configurations are rigid, in the sense that the fluctuations of smooth linear statistics on any scale are $${N^{o(1)}}$$ .
Dark energy and stabilization of extra dimensions

We discuss the role Casimir energies may play in addressing issues of moduli stabilization and dark energy. In particular, we examine a (non-supersymmetric) brane world scenario with toroidal extra dimensions in which Casimir energies of bulk fields generate a stabilizing potential for the toroidal volume while driving accelerated expansion in the non-compact directions. We speculate that such a scenario might establish a link between asymmetric topology and asymmetric geometry; that is, asymmetric topology could be linked to the hierarchy between large and small dimensions.
THE PROPELLER AND THE FROG

'Propellers' in planetary rings are disturbances in ring material excited by moonlets that open only partial gaps. We describe a new type of co-orbital resonance that can explain the observed non-Keplerian motions of propellers. The resonance is between the moonlet underlying the propeller and co-orbiting ring particles downstream of the moonlet where the gap closes. The moonlet librates within the gap about an equilibrium point established by co-orbiting material and stabilized by the Coriolis force. In the limit of small libration amplitude, the libration period scales linearly with the gap azimuthal width and inversely as the square root of the co-orbital mass. The new resonance recalls but is distinct from conventional horseshoe and tadpole orbits; we call it the 'frog' resonance, after the relevant term in equine hoof anatomy. For a ring surface density and gap geometry appropriate for the propeller Bleriot in Saturn's A ring, our theory predicts a libration period of {approx}4 years, similar to the {approx}3.7 year period over which Bleriot's orbital longitude is observed to vary. These librations should be subtracted from the longitude data before any inferences about moonlet migration are made.
New cubic fourfolds with odd-degree unirational parametrizations

We prove that the moduli space of cubic fourfolds $\mathcal{C}$ contains a divisor $\mathcal{C}_{42}$ whose general member has a unirational parametrization of degree 13. This result follows from a thorough study of the Hilbert scheme of rational scrolls and an explicit construction of examples. We also show that $\mathcal{C}_{42}$ is uniruled.
Pressure in the Landau-Ginzburg functional: Pascal's law, nucleation in fluid mixtures, a meanfield theory of amphiphilic action, and interface wetting in glassy liquids

We set up the problem of finding the transition state for phase nucleation in multi-component fluid mixtures, within the Landau-Ginzburg density functional. We establish an expression for the coordinate-dependent local pressure that applies to mixtures, arbitrary geometries, and certain non-equilibrium configurations. The expression allows one to explicitly evaluate the pressure in spherical geometry, a la van der Waals. Pascal’s law is recovered within the Landau-Ginzburg density functional theory, formally analogously to how conservation of energy is recovered in the Lagrangian formulation of mechanics. We establish proper boundary conditions for certain singular functional forms of the bulk free energy density that allow one to obtain droplet solutions with thick walls in essentially closed form. The hydrodynamic modes responsible for mixing near the interface are explicitly identified in the treatment; the composition at the interface is found to depend only weakly on the droplet size. Next we develop a Landau-Ginzburg treatment of the effects of amphiphiles on the surface tension; the amphiphilic action is seen as a violation of Pascal’s law. We explicitly obtain the binding potential for the detergent at the interface and the dependence of the down-renormalization of the surface tension on the activity of the detergent. Finally, we argue that the renormalization of the activation barrier for escape from long-lived structures in glassy liquids can be viewed as an action of uniformly seeded, randomly oriented amphiphilic molecules on the interface separating two dissimilar aperiodic structures. This renormalization is also considered as a “wetting” of the interface. The resulting conclusions are consistent with the random first order transition theory.
A KINETIC ALFVÉN WAVE CASCADE SUBJECT TO COLLISIONLESS DAMPING CANNOT REACH ELECTRON SCALES IN THE SOLAR WIND AT 1 AU

Turbulence in the solar wind is believed to generate an energy cascade that is supported primarily by Alfven waves or Alfvenic fluctuations at MHD scales and by kinetic Alfven waves (KAWs) at kinetic scales k ⊥ρ i 1. Linear Landau damping of KAWs increases with increasing wavenumber and at some point the damping becomes so strong that the energy cascade is completely dissipated. A model of the energy cascade process that includes the effects of linear collisionless damping of KAWs and the associated compounding of this damping throughout the cascade process is used to determine the wavenumber where the energy cascade terminates. It is found that this wavenumber occurs approximately when |γ/ω| 0.25, where ω(k) and γ(k) are, respectively, the real frequency and damping rate of KAWs and the ratio γ/ω is evaluated in the limit as k ⊥ k ∥. For plasma parameters typical of high-speed solar wind streams at 1 AU, the model suggests that the KAW cascade in the solar wind is almost completely dissipated before reaching the wavenumber k ⊥ρ i 25. Consequently, an energy cascade consisting solely of KAWs cannot reach scales on the order of the electron gyro-radius, k ⊥ρ e ~ 1. This conclusion has important ramifications for the interpretation of solar wind magnetic field measurements. It implies that power-law spectra in the regime of electron scales must be supported by wave modes other than the KAW.
Exotic Sterile Neutrinos and Pseudo-Goldstone Phenomenology

We study the phenomenology of a light (GeV scale) sterile neutrino sector and the pseudo-Goldstone boson (not the majoron) associated with a global symmetry in this sector that is broken at a high scale. Such scenarios can be motivated from considerations of singlet fermions from a hidden sector coupling to active neutrinos via heavy right-handed seesaw neutrinos, effectively giving rise to a secondary, low-energy seesaw framework. This framework involves rich phenomenology with observable implications for cosmology, dark matter, and direct searches, involving novel sterile neutrino dark matter production mechanisms from the pseudo-Goldstone-mediated scattering or decay, modifications of BBN bounds on sterile neutrinos, suppression of canonical sterile neutrino decay channels at direct search experiments, late injection of an additional population of neutrinos in the Universe after neutrino decoupling, and measurable dark radiation.
Cusp Annihilation on Ordinary Cosmic Strings

The order of magnitude of energy emission from cusps to light bosons on ordinary cosmic string is calculated perturbatively. The analysis is applicable to both closed string loops and long cosmic strings. The perturbative result obtained here is much less than what is found by nonperturbative approximations.
Measurement of the Planck constant at the National Institute of Standards and Technology from 2015 to 2017

Researchers at the National Institute of Standards and Technology (NIST) have measured the value of the Planck constant to be J s (relative standard uncertainty ). The result is based on over 10 000 weighings of masses with nominal values ranging from 0.5 kg to 2 kg with the Kibble balance NIST-4. The uncertainty has been reduced by more than twofold relative to a previous determination because of three factors: (1) a much larger data set than previously available, allowing a more realistic, and smaller, Type A evaluation; (2) a more comprehensive measurement of the back action of the weighing current on the magnet by weighing masses up to 2 kg, decreasing the uncertainty associated with magnet non-linearity; (3) a rigorous investigation of the dependence of the geometric factor on the coil velocity reducing the uncertainty assigned to time-dependent leakage of current in the coil.
Neutralino versus axion/axino cold dark matter in the 19 parameter SUGRA model

We calculate the relic abundance of thermally produced neutralino cold dark matter in the general 19 parameter supergravity (SUGRA-19) model. A scan over GUT scale parameters reveals that models with a bino-like neutralino typically give rise to a dark matter density \( {\Omega_{\tilde{\chi }_1^0}}{h^2} \sim 1 - 1000 \), i.e. between 1 and 4 orders of magnitude higher than the measured value. Models with higgsino or wino cold dark matter can yield the correct relic density, but mainly for neutralino masses around 700-1300 GeV. Models with mixed bino-wino or bino-higgsino CDM, or models with dominant co-annihilation or A-resonance annihilation can yield the correct abundance, but such cases are extremely hard to generate using a general scan over GUT scale parameters; this is indicative of high fine-tuning of the relic abundance in these cases. Requiring that \( {m_{\tilde{\chi }_1^0}}\mathop { < }\limits_\sim 500\,{\text{GeV}} \) (as a rough naturalness requirement) gives rise to a minimal probably dip in paramet er space at the measured CDM abundance. For comparison, we also scan over mSUGRA space with four free parameters. Finally, we investigate the Peccei-Quinn augmented MSSM with mixed axion/axino cold dark matter. In this case, the relic abundance agrees more naturally with the measured value. In light of our cumulative results, we conclude that future axion searches should probe much more broadly in axion mass, and deeper into the axion coupling.
Disorder and mimesis at hadron colliders

We discuss how systems with a large number of degrees of freedom and disorder in their mass matrix can play a role in particle physics. We derive results on their mass spectra using, where applicable, QFT techniques. We study concrete realizations of these scenarios in the context of the LHC and HL-LHC, showing that collider events with a large number of soft b-quark jets can be common. Such final states can hide these models from current searches at the LHC. This motivates the ongoing effort aimed at lowering trigger thresholds and expanding data scouting.
Productive Interactions: heavy particles and non-Gaussianity

We analyze the shape and amplitude of oscillatory features in the primordial power spectrum and non-Gaussianity induced by periodic production of heavy degrees of freedom coupled to the inflaton phi. We find that non-adiabatic production of particles can contribute effects which are detectable or constrainable using cosmological data even if their time-dependent masses are always heavier than the scale phi(1/2), much larger than the Hubble scale. This provides a new role for UV completion, consistent with the criteria from effective field theory for when heavy fields cannot be integrated out. This analysis is motivated in part by the structure of axion monodromy, and leads to an additional oscillatory signature in a subset of its parameter space. At the level of a quantum field theory model that we analyze in detail, the effect arises consistently with radiative stability for an interesting window of couplings up to of order lesssim 1. The amplitude of the bispectrum and higher-point functions can be larger than that for Resonant Non-Gaussianity, and its signal/noise may be comparable to that of the corresponding oscillations in the power spectrum (and even somewhat larger within a controlled regime of parameters). Its shape is distinct from previously analyzed templates, but was partly motivated by the oscillatory equilateral searches performed recently by the Planck collaboration. We also make some general comments about the challenges involved in making a systematic study of primordial non-Gaussianity.
NEOWISE-R Observation of the Coolest Known Brown Dwarf

The Wide-field Infrared Survey Explorer (WISE) spacecraft has been reactivated as NEOWISE-R to characterize and search for near-Earth objects. The brown dwarf WISE J085510.83–071442.5 has now been re-observed by NEOWISE-R, and we confirm the results of Luhman, who found a very low effective temperature (≈250 K), a very high proper motion (8."1 ± 0."1 yr^(–1)), and a large parallax (454 ± 45 mas). The large proper motion has separated the brown dwarf from the background sources that influenced the 2010 WISE data, allowing a measurement of a very red WISE color of W1 – W2 >3.9 mag. A re-analysis of the 2010 WISE astrometry using only the W2 band, combined with the new NEOWISE-R 2014 position, gives an improved parallax of 448 ± 33 mas and a proper motion of 8."08 ± 0."05 yr^(–1). These are all consistent with values from Luhman.
Highly pure single photon emission from spectrally uniform surface-curvature directed mesa top single quantum dot ordered array

Realizing ordered and spectrally uniform single photon source arrays integrable on-chip with light manipulating elements in a scalable architecture lies at the core of building monolithic quantum optical circuits (QOCs). We demonstrate here a spatially ordered 5 × 8 array of surface-curvature driven mesa-top GaAs(001)/InGaAs/GaAs single quantum dots (MTSQDs) that exhibit highly pure (∼99%) single photon emission as deduced from the measured g(2)(0) < 0.02 at 9.4 K. Polarization-independent and polarization-resolved high resolution photoluminescence measurements show that these ordered and spectrally uniform QDs have neutral exciton emission with an intrinsic linewidth ∼ 10 μeV and a fine structure splitting <10 μeV, an important figure of merit for the use of QDs in QOCs. The findings point to the high potential of using such MTSQD based single photon source arrays as a promising platform for on-chip scalable integration with light manipulating units (connected resonant cavity, waveguide, beam splitter, etc.) to enable constructing QOCs.