cssi/SciSciGPT-SciSciNet huggingface dataset:
A Simple and Efficient Tube-based Robust Output Feedback Model Predictive Control Scheme

The control of constrained systems using model predictive control (MPC) becomes more challenging when full state information is not available and when the nominal system model and measurements are corrupted by noise. Since these conditions are often seen in practical scenarios, techniques such as robust output feedback MPC have been developed to address them. However, existing approaches to robust output feedback MPC are challenged by increased complexity of the online optimization problem, increased computational requirements for controller synthesis, or both. In this work we present a simple and efficient methodology for synthesizing a tube-based robust output feedback MPC scheme for linear, discrete, time-invariant systems subject to bounded, additive disturbances. Specifically, we first formulate a scheme where the online MPC optimization problem has the same complexity as in the nominal full state feedback MPC by using a single tube with constant cross-section. This makes our proposed approach simpler to implement and less computationally demanding than previous methods for both online implementation and offline controller synthesis. Secondly, we propose a novel and simple procedure for the computation of robust positively invariant (RPI) sets that are approximations of the minimal RPI set, which can be used to define the tube in the proposed control scheme.
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions

In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties. In addition, we use the model to identify the most influential features for prediction of the growth of infection. We also analyze pairs of features and estimate the amount of observed second-order interaction between them. Experiments show that the proposed model obtains satisfactory predictive performance and fairly interpretable feature analysis results; hence, the proposed model could complement the standard epidemiological models for national-level surveillance of pandemics, such as COVID-19. The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies. To fast-track further development and experimentation, the code used to implement the proposed model has been made fully open source.
Novel Electrodes for Reliable EEG Recordings on Coarse and Curly Hair

EEG is a powerful and affordable brain sensing and imaging tool used extensively for the diagnosis of neurological disorders (e.g. epilepsy), brain computer interfacing, and basic neuroscience. Unfortunately, most EEG electrodes and systems are not designed to accommodate coarse and curly hair common in individuals of African descent. In neuroscience studies, this can lead to poor quality data that might be discarded in scientific studies after recording from a broader population set. In clinical diagnoses, it may lead to an uncomfortable and/or emotionally taxing experience, and, in the worst cases, misdiagnosis. Our prior work demonstrated that braiding hair in cornrows to expose the scalp at target locations leads to reduced electrode-skin impedance for existing electrodes. In this work, we design and implement novel electrodes that harness braided hair, and demonstrate that, across time, our electrodes, in conjunction with braiding, lower the impedance further, attaining 10x lower impedance than existing systems.
Characterization of Vibrio cholerae isolates from freshwater sources in northwest Ohio

Vibrio cholerae is a natural inhabitant of aquatic ecosystems worldwide, typically residing in coastal or brackish water. While more than 200 serogroups have been identified, only serogroups O1 and O139 have been associated with epidemic cholera. However, infections other than cholera can be caused by nonepidemic, non-O1/non-O139 V. cholerae strains, including gastroenteritis and extraintestinal infections. While V. cholerae can also survive in freshwater, that is typically only observed in regions of the world where cholera is endemic. We recently isolated V. cholerae from several locations in lakes and rivers in northwest Ohio. These isolates were all found to be non-O1/non-O139 V. cholerae strains, that would not cause cholera. However, these isolates contained a variety of virulence genes, including ctxA, rtxA, rtxC, hlyA, and ompU. Therefore, it is possible that some of these isolates have the potential to cause gastroenteritis or other infections in humans. We also investigated the relative motility of the isolates and their ability to form biofilms as this is important for V. cholerae survival in the environment. We identified one isolate that forms very robust biofilms, up to 4x that of our laboratory strains. Finally, we investigated the susceptibility of these isolates to a panel of antibiotics. We found that many of the isolates showed decreased susceptibility to some of the antibiotics tested, which could be of concern. While we do not know if these isolates are pathogenic to humans, increased surveillance to better understand the public health risk to the local community should be considered.
Detecting Generalized Replay Attacks via Time-Varying Dynamic Watermarking

Cyber-physical systems (CPS) often rely on external communication for supervisory control or sensing. Unfortunately, these communications render the system vulnerable to cyber attacks. Attacks that alter messages, such as replay attacks that record measurement signals, and then play them back to the system can cause devastating effects. Dynamic Watermarking methods, which inject a private excitation into control inputs to secure resulting measurement signals, have begun addressing the challenges of detecting these attacks, but have been restricted to linear time-invariant (LTI) systems. Though LTI models are sufficient for some applications, other CPS, such as autonomous vehicles, require more complex models. This article develops a linear time-varying (LTV) extension to previous dynamic watermarking methods by designing a matrix normalization factor to accommodate the temporal changes in the system. Implementable tests are provided with considerations for real-world systems. The proposed method is then shown to be able to detect generalized replay attacks both in theory and in simulation using an LTV vehicle model.
The Frisch–Waugh–Lovell theorem for standard errors

Abstract The Frisch–Waugh–Lovell Theorem states the equivalence of the coefficients from the full and partial regressions. I further show the equivalence between various standard errors. Applying the new result to stratified experiments reveals the discrepancy between model-based and design-based standard errors.
Can Short and Partial Observations Reduce Model Error and Facilitate Machine Learning Prediction

Predicting complex nonlinear turbulent dynamical systems is an important and practical topic. However, due to the lack of a complete understanding of nature, the ubiquitous model error may greatly affect the prediction performance. Machine learning algorithms can overcome the model error, but they are often impeded by inadequate and partial observations in predicting nature. In this article, an efficient and dynamically consistent conditional sampling algorithm is developed, which incorporates the conditional path-wise temporal dependence into a two-step forward-backward data assimilation procedure to sample multiple distinct nonlinear time series conditioned on short and partial observations using an imperfect model. The resulting sampled trajectories succeed in reducing the model error and greatly enrich the training data set for machine learning forecasts. For a rich class of nonlinear and non-Gaussian systems, the conditional sampling is carried out by solving a simple stochastic differential equation, which is computationally efficient and accurate. The sampling algorithm is applied to create massive training data of multiscale compressible shallow water flows from highly nonlinear and indirect observations. The resulting machine learning prediction significantly outweighs the imperfect model forecast. The sampling algorithm also facilitates the machine learning forecast of a highly non-Gaussian climate phenomenon using extremely short observations.
Smart Home Energy Management System for Power System Resiliency

The need for resiliency of electricity supply is increasing due to increasing frequency of natural disasters-such as hurricanes-that disrupt supply from the power grid. Rooftop solar photovoltaic (PV) panels together with batteries can provide resiliency in many scenarios. Without intelligent and automated decision making that can trade off conflicting requirements, a large PV system and a large battery is needed to provide meaningful resiliency. By using forecast of solar generation and household demand, an intelligent decision maker can operate the equipment (battery and critical loads) to ensure that the critical loads are serviced to the maximum duration possible. With the aid of such an intelligent control system, a smaller (and thus lower cost) system can service the primary loads for the same duration that a much larger system will be needed to service otherwise. In this paper we propose such an intelligent control system. A model predictive control (MPC) architecture is used that uses available measurements and forecasts to make optimal decisions for batteries and critical loads in real time. The optimization problem is formulated as a MILP (mixed integer linear program) due to the on/off decisions for the loads. Performance is compared with a non-intelligent baseline controller, for a PV-battery system chosen carefully for a single family house in Florida. Simulations are conducted for a one week period during hurricane Irma in 2017. Simulations show that the cost of the PV +battery system to provide a certain resiliency performance, duration the primary load can be serviced successfully, can be halved by the proposed control system.
Label-free lipidome study of paraventricular thalamic nucleus (PVT) of rat brain with post-traumatic stress injury by Raman imaging

Post-traumatic stress disorder (PTSD) is a widespread psychiatric injury that develops serious life-threatening symptoms like substance abuse, severe depression, cognitive impairments, and persistent anxiety. However, the mechanisms of post-traumatic stress injury in brain are poorly understood due to the lack of practical methods to reveal biochemical alterations in various brain regions affected by this type of injury. Here, we introduce a novel method that provides quantitative results from Raman maps in the paraventricular nucleus of the thalamus (PVT) region. By means of this approach, we have shown a lipidome comparison in PVT regions of control and PTSD rat brains. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry was also employed for validation of the Raman results. Lipid alterations can reveal invaluable information regarding the PTSD mechanisms in affected regions of brain. We have showed that the concentration of cholesterol, cholesteryl palmitate, phosphatidylinositol, phosphatidylserine, phosphatidylethanolamine, sphingomyelin, ganglioside, glyceryl tripalmitate and sulfatide changes in the PVT region of PTSD compared to control rats. A higher concentration of cholesterol suggests a higher level of corticosterone in the brain. Moreover, concentration changes of phospholipids and sphingolipids suggest the alteration of phospholipase A2 (PLA2) which is associated with inflammatory processes in the brain. Our results have broadened the understanding of biomolecular mechanisms for PTSD in the PVT region of the brain. This is the first report regarding the application of Raman spectroscopy for PTSD studies. This method has a wide spectrum of applications and can be applied to various other brain related disorders or other regions of the brain.
Risk factors associated with hospital admission in COVID-19 patients initially admitted to an observation unit.

Abstract Background No set guidelines to guide disposition decisions from the emergency department (ED) in patients with COVID-19 exist. Our goal was to determine characteristics that identify patients at high risk for adverse outcomes who may need admission to the hospital instead of an observation unit. Methods We retrospectively enrolled 116 adult patients with COVID-19 admitted to an ED observation unit. We included patients with bilateral infiltrates on chest imaging, COVID-19 testing performed, and/or COVID-19 suspected as the primary diagnosis. The primary outcome was hospital admission. We assessed risk factors associated with this outcome using univariate and multivariable logistic regression. Results Of 116 patients, 33 or 28% (95% confidence interval [CI] 20–37%) required admission from the observation unit. On multivariable logistic regression analysis, we found that hypoxia defined as room-air oxygen saturation 48 years, bilateral infiltrates, hypoxia, and Hispanic race, bilateral infiltrates, hypoxia yield an OR for admission of 4.99 (CI 1.50–16.65) with an AUC of 0.59 (CI 0.51–0.67) and 6.78 (CI 2.11–21.85) with an AUC of 0.62 (CI 0.54–0.71), respectively. Conclusions Over 1/4 of suspected COVID-19 patients admitted to an ED observation unit ultimately required admission to the hospital. Risk factors associated with admission include hypoxia, bilateral infiltrates on chest radiography, or the combination of these two factors plus either age > 48 years or Hispanic race.
Evaluating brain structure traits as endophenotypes using polygenicity and discoverability.

Human brain structure traits have been hypothesized to be broad endophenotypes for neuropsychiatric disorders, implying that brain structure traits are comparatively "closer to the underlying biology." Genome-wide association studies from large sample sizes allow for the comparison of common variant genetic architectures between traits to test the evidence supporting this claim. Endophenotypes, compared to neuropsychiatric disorders, are hypothesized to have less polygenicity, with greater effect size of each susceptible SNP, requiring smaller sample sizes to discover them. Here, we compare polygenicity and discoverability of brain structure traits, neuropsychiatric disorders, and other traits (91 in total) to directly test this hypothesis. We found reduced polygenicity (FDR = 0.01) and increased discoverability (FDR = 3.68 × 10-9 ) of cortical brain structure traits, as compared to aggregated estimates of multiple neuropsychiatric disorders. We predict that ~8 M individuals will be required to explain the full heritability of cortical surface area by genome-wide significant SNPs, whereas sample sizes over 20 M will be required to explain the full heritability of depression. In conclusion, our findings are consistent with brain structure satisfying the higher power criterion of endophenotypes.
Inverting spectrogram measurements via aliased Wigner distribution deconvolution and angular synchronization

We propose a two-step approach for reconstructing a signal ${\bf x}\in\mathbb{C}^d$ from subsampled short-time Fourier transform magnitude (spectogram) measurements: First, we use an aliased Wigner distribution deconvolution approach to solve for a portion of the rank-one matrix ${\bf \widehat{\bf x}}{\bf \widehat{\bf x}}^{*}.$ Second, we use angular syncrhonization to solve for ${\bf \widehat{\bf x}}$ (and then for ${\bf x}$ by Fourier inversion). Using this method, we produce two new efficient phase retrieval algorithms that perform well numerically in comparison to standard approaches and also prove two theorems, one which guarantees the recovery of discrete, bandlimited signals ${\bf x}\in\mathbb{C}^{d}$ from fewer than $d$ STFT magnitude measurements and another which establishes a new class of deterministic coded diffraction pattern measurements which are guaranteed to allow efficient and noise robust recovery.
Pandemic Pulse: Unraveling and Modeling Social Signals During the COVID-19 Pandemic

COVID-19 has presented society with a unique set of challenges, including seeking a scientific understanding of the novel coronavirus, modeling its epidemiology, and inferring appropriate societal response. In this article, we posit that fighting a pandemic is as much a social endeavor as a medicinal and scientific one and focus on developing a platform for understand the social pulse of the United States during the COVID-19 crisis. We collected a multitude of data that includes longitudinal trends of news topics, social distancing behaviors, community mobility changes, web searches, and other descriptors of the COVID-19 pandemic’s effects on the United States. Our preliminary results show that the number of COVID-19-related news articles published immediately after the World Health Organization declared the pandemic on March 11 have steadily decreased—regardless of changes in the number of cases or public policies. Additionally, we found that politically moderate and scientifically grounded sources have, relative to baselines measured before the beginning of the pandemic, published a lower proportion of COVID-19 news articles than more politically extreme sources—a fact that has implications for the spread and consequences of misinformation during the pandemic. We suggest that further analysis of these multi-modal signals could produce meaningful social insights and present an interactive dashboard to aid further exploration.1
Jointly De-Biasing Face Recognition and Demographic Attribute Estimation.

We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups. We present a novel de-biasing adversarial network (DebFace) that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation. The proposed network consists of one identity classifier and three demographic classifiers (for gender, age, and race) that are trained to distinguish identity and demographic attributes, respectively. Adversarial learning is adopted to minimize correlation among feature factors so as to abate bias influence from other factors. We also design a new scheme to combine demographics with identity features to strengthen robustness of face representation in different demographic groups. The experimental results show that our approach is able to reduce bias in face recognition as well as demographics estimation while achieving state-of-the-art performance.
Populist Nationalism Threatens Health and Human Rights in the COVID-19 Response.

COVID-19 demands international cooperation, yet populist nationalism is resurgent, threatening public health, human rights, and global governance. In responding to the pandemic, populist nationalism and global solidarity represent distinct paths, with enduring consequences for health and human rights.
Deep HST/ACS Photometry of the M81 Halo

We present a deep color-magnitude diagram (CMD) for individual stars in the halo of the nearby spiral galaxy M81, at a projected distance of 19 kpc, based on data taken with the Advanced Camera for Surveys on the Hubble Space Telescope (HST). The CMD reveals a red giant branch (RGB) that is narrow and fairly blue, and a horizontal branch that has stars that lie mostly redward of the RR Lyrae instability strip. We derive a mean metallicity of [M/H] = –1.15 ± 0.11 and age of 9 ± 2 Gyr for the dominant population in our field, from the shape of the RGB, the magnitude of the red clump, and the location of the RGB bump. We compare our metallicity and age results with those found previously for stars in different locations within M81 and in the spheroids of other nearby galaxies.
STARS: a software application for the EBEX autonomous daytime star cameras

The E and B Experiment (EBEX) is a balloon-borne telescope designed to probe polarization signals in the CMB resulting from primordial gravitational waves, gravitational lensing, and Galactic dust emission. EBEX completed an 11 day flight over Antarctica in January 2013 and data analysis is underway. EBEX employs two star cameras to achieve its real-time and post-flight pointing requirements. We wrote a software application called STARS to operate, command, and collect data from each of the star cameras, and to interface them with the main flight computer. We paid special attention to make the software robust against potential in-flight failures. We report on the implementation, testing, and successful in flight performance of STARS.
Connecting Marginally Large Tableaux and Rigged Configurations via Crystals

We show that the bijection from rigged configurations to tensor products of Kirillov-Reshetikhin crystals extends to a crystal isomorphism between the \(B(\infty )\) models given by rigged configurations and marginally large tableaux.
Identifying Young Kepler Planet Host Stars from Keck–HIRES Spectra of Lithium*

The lithium doublet at 6708 A provides an age diagnostic for main sequence FGK dwarfs. We measured the abundance of lithium in 1305 stars with detected transiting planets from the Kepler mission using high-resolution spectroscopy. Our catalog of lithium measurements from this sample has a range of abundance from A(Li) = 3.11 ± 0.07 to an upper limit of −0.84 dex. For a magnitude-limited sample that comprises 960 of the 1305 stars, our Keck–HIRES spectra have a median signal-to-noise ratio of 45 per pixel at ~6700 A with spectral resolution λ/(Δλ) = R = 55,000. We identify 80 young stars that have A(Li) values greater than the Hyades at their respective effective temperatures; these stars are younger than ~650 Myr, the approximate age of the Hyades. We then compare the distribution of A(Li) with planet size, multiplicity, orbital period, and insolation flux. We find larger planets preferentially in younger systems, with an A–D two-sided test p-value = 0.002, a > 3σ confidence that the older and younger planet samples do not come from the same parent distribution. This is consistent with planet inflation/photoevaporation at early ages. The other planet parameters (Kepler planet multiplicity, orbital period, and insolation flux) are uncorrelated with age.
Spectral analyses of trans- and cis-DOCO transients via comb spectroscopy

We use time-resolved direct frequency comb spectroscopy in the mid-infrared to obtain high-resolution rovibrational spectra of products produced from the OD + CO reaction. In this work, we present ...
TRANSITING PLANETS WITH LSST. I. POTENTIAL FOR LSST EXOPLANET DETECTION

The Large Synoptic Survey Telescope (LSST) is designed to meet several scientific objectives over a 10 year synoptic sky survey. Beyond its primary goals, the large amount of LSST data can be exploited for additional scientific purposes. We show that LSST data are sufficient to detect the transits of exoplanets, including planets orbiting stars that are members of stellar populations that have so far been largely unexplored. Using simulated LSST light curves, we find that existing transit detection algorithms can identify the signatures of Hot Jupiters around solar-type stars, Hot Neptunes around K-dwarfs, and (in favorable cases) Super-Earths in habitable-zone orbits of M-dwarfs. We also find that LSST may identify Hot Jupiters orbiting stars in the Large Magellanic Cloud—a remarkable possibility that would advance exoplanet science into the extragalactic regime.
Mellin bootstrap for scalars in generic dimension

We use the recently developed framework of the Mellin bootstrap to study perturbatively free scalar CFTs in arbitrary dimensions. This approach uses the crossing-symmetric Mellin space formulation of correlation functions to generate algebraic bootstrap equations by demanding that only physical operators contribute to the OPE. We find that there are no perturbatively interacting CFTs with only fundamental scalars in d > 6 dimensions (to at least second order in the perturbation). Our results can be seen as a modest step towards understanding the space of interacting CFTs in d > 6 and are consistent with the intuition that no such CFTs exist.
Low Mach Number Modeling of Type IA Supernovae. IV. White Dwarf Convection

We present the first three-dimensional, full-star simulations of convection in a white dwarf preceding a Type Ia supernova, specifically the last few hours before ignition. For these long-time calculations, we use our low Mach number hydrodynamics code, MAESTRO, which we have further developed to treat spherical stars centered in a three-dimensional Cartesian geometry. The main change required is a procedure to map the one-dimensional radial base state to and from the Cartesian grid. Our models recover the dipole structure of the flow seen in previous calculations, but our long-time integration shows that the orientation of the dipole changes with time. Furthermore, we show the development of gravity waves in the outer, stable portion of the star. Finally, we evolve several calculations to the point of ignition and discuss the range of ignition radii.
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology

The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. This effort requires the engagement of scholars and practitioners from many disciplines. And yet, these disciplines often conceptualize the operative values very differently while referring to them using the same vocabulary. The resulting conflation of ideas confuses discussions about values in technology at disciplinary boundaries. In the service of improving this situation, this paper examines the value of shared vocabularies, analytics, and other tools that facilitate conversations about values in light of these disciplinary specific conceptualizations, the role such tools play in furthering research and practice, outlines different conceptions of "fairness" deployed in discussions about computer systems, and provides an analytic tool for interdisciplinary discussions and collaborations around the concept of fairness. We use a case study of risk assessments in criminal justice applications to both motivate our effort--describing how conflation of different concepts under the banner of "fairness" led to unproductive confusion--and illustrate the value of the fairness analytic by demonstrating how the rigorous analysis it enables can assist in identifying key areas of theoretical, political, and practical misunderstanding or disagreement, and where desired support alignment or collaboration in the absence of consensus.
Partition identities and quiver representations

We present a particular connection between classical partition combinatorics and the theory of quiver representations. Specifically, we give a bijective proof of an analogue of A. L. Cauchy’s Durfee square identity to multipartitions. We then use this result to give a new proof of M. Reineke’s identity in the case of quivers $${\mathcal {Q}}$$ of Dynkin type A. Our identity is stated in terms of the lacing diagrams of S. Abeasis–A. Del Fra, which parameterize orbits of the representation space of $${\mathcal {Q}}$$ for a fixed dimension vector.
Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models

We propose a safe exploration algorithm for deterministic Markov Decision Processes with unknown transition models. Our algorithm guarantees safety by leveraging Lipschitz-continuity to ensure that no unsafe states are visited during exploration. Unlike many other existing techniques, the provided safety guarantee is deterministic. Our algorithm is optimized to reduce the number of actions needed for exploring the safe space. We demonstrate the performance of our algorithm in comparison with baseline methods in simulation on navigation tasks.
Experimental searches for the chiral magnetic effect in heavy-ion collisions

Abstract The chiral magnetic effect (CME) in quantum chromodynamics (QCD) refers to a charge separation (an electric current) of chirality imbalanced quarks generated along an external strong magnetic field. The chirality imbalance results from interactions of quarks, under the approximate chiral symmetry restoration, with metastable local domains of gluon fields of non-zero topological charges out of QCD vacuum fluctuations. Those local domains violate the P and CP invariance, potentially offering a solution to the strong CP problem in explaining the magnitude of the matter–antimatter asymmetry in today’s universe. Relativistic heavy-ion collisions, with the likely creation of the high energy density quark–gluon plasma and restoration of the approximate chiral symmetry, and the possibly long-lived strong magnetic field, provide a unique opportunity to detect the CME. Early measurements of the CME-induced charge separation in heavy-ion collisions are dominated by physics backgrounds. Major efforts have been devoted to eliminate or reduce those backgrounds. We review those efforts, with a somewhat historical perspective, and focus on the recent innovative experimental undertakings in the search for the CME in heavy-ion collisions.
FOREST DIAGRAMS FOR ELEMENTS OF THOMPSON'S GROUP F

We introduce forest diagrams to represent elements of Thompson's group F. These diagrams relate to a certain action of F on the real line in the same way that tree diagrams relate to the standard action of F on the unit interval. Using forest diagrams, we give a conceptually simple length formula for elements of F with respect to the {x0,x1} generating set, and we discuss the construction of minimum-length words for positive elements. Finally, we use forest diagrams and the length formula to examine the structure of the Cayley graph of F.
A note on distinct distance subsets

It is shown that given a set of N points in the plane, sphere or hyperbolic plane, there is a subset of size \({\gtrsim (N/\log N)^{1/3}}\) with all pairwise distances between points distinct.
Formulas and Asymptotics for the Asymmetric Simple Exclusion Process

This is an expanded version of a series of lectures delivered by the second author in June, 2009. It describes the results of three of the authors’ papers on the asymmetric simple exclusion process, from the derivation of exact formulas for configuration probabilities, through Fredholm determinant representation, to asymptotics with step initial condition establishing KPZ universality. Although complete proofs are in general not given, at least the main elements of them are.
Pre-big Bang Geometric Extensions of Inflationary Cosmologies

Robertson–Walker spacetimes within a large class are geometrically extended to larger cosmologies that include spacetime points with zero and negative cosmological times. In the extended cosmologies, the big bang is lightlike, and though singular, it inherits some geometric structure from the original spacetime. Spacelike geodesics are continuous across the cosmological time zero submanifold which is parameterized by the radius of Fermi space slices, i.e., by the proper distances along spacelike geodesics from a comoving observer to the big bang. The continuous extension of the metric, and the continuously differentiable extension of the leading Fermi metric coefficient $$g_{\tau \tau }$$ of the observer, restrict the geometry of spacetime points with pre-big bang cosmological time coordinates. In our extensions the big bang is two dimensional in a certain sense, consistent with some findings in quantum gravity.
On the ring of cooperations for 2‐primary connective topological modular forms

We analyze the ring tmf∗tmf of cooperations for the connective spectrum of topological modular forms (at the prime 2) through a variety of perspectives: (1) the E2-term of the Adams spectral sequence for tmf ∧ tmf admits a decomposition in terms of Ext groups for bo-Brown-Gitler modules, (2) the image of tmf∗tmf in TMF∗TMFQ admits a description in terms of 2-variable modular forms, and (3) modulo v2-torsion, tmf∗tmf injects into a certain product of copies of π∗TMF0(N), for various values of N . We explain how these different perspectives are related, and leverage these relationships to give complete information on tmf∗tmf in low degrees. We reprove a result of Davis-Mahowald-Rezk, that a piece of tmf ∧ tmf gives a connective cover of TMF0(3), and show that another piece gives a connective cover of TMF0(5). To help motivate our methods, we also review the existing work on bo∗bo, the ring of cooperations for (2-primary) connective K-theory, and in the process give some new perspectives on this classical subject matter.
The dualizing complex of $F$-injective and Du Bois singularities

Let $$(R,\mathfrak {m}, k)$$ be an excellent local ring of equal characteristic. Let j be a positive integer such that $$H_\mathfrak {m}^i(R)$$ has finite length for every $$0\le i <j$$ . We prove that if R is F-injective in characteristic $$p>0$$ or Du Bois in characteristic 0, then the truncated dualizing complex is quasi-isomorphic to a complex of k-vector spaces. As a consequence, F-injective or Du Bois singularities with isolated non-Cohen–Macaulay locus are Buchsbaum. Moreover, when R has F-rational or rational singularities on the punctured spectrum, we obtain stronger results generalizing Ishida (The dualizing complexes of normal isolated Du Bois singularities. Algebraic and topological theories, 387–390, 1984) and Ma (Math Ann 362:25–42, 2015).
Monsters, Black Holes and the Statistical Mechanics of Gravity

We review the construction of monsters in classical general relativity. Monsters have finite ADM mass and surface area, but potentially unbounded entropy. From the curved space perspective, they are objects with large proper volume that can be glued on to an asymptotically flat space. At no point is the curvature or energy density required to be large in Planck units, and quantum gravitational effects are, in the conventional effective field theory framework, small everywhere. Since they can have more entropy than a black hole of equal mass, monsters are problematic for certain interpretations of black hole entropy and the AdS/CFT duality. In the second part of the paper we review recent developments in the foundations of statistical mechanics which make use of properties of high-dimensional (Hilbert) spaces. These results primarily depend on kinematics — essentially, the geometry of Hilbert space — and are relatively insensitive to dynamics. We discuss how this approach might be adopted as a basis for the statistical mechanics of gravity. Interestingly, monsters and other highly entropic configurations play an important role.
Sharp Estimates for the Global Attractor of Scalar Reaction-Diffusion Equations with a Wentzell Boundary Condition

In this paper, we derive optimal upper and lower bounds on the dimension of the attractor $\mathcal{A}_{\mathrm{W}}$ for scalar reaction–diffusion equations with a Wentzell (dynamic) boundary condition. We are also interested in obtaining explicit bounds on the constants involved in our asymptotic estimates, and to compare these bounds to previously known estimates for the dimension of the global attractor $\mathcal{A}_{K}$ , K∈{D,N,P}, of reaction–diffusion equations subject to Dirichlet, Neumann and periodic boundary conditions. The explicit estimates we obtain show that the dimension of the global attractor $\mathcal {A}_{\mathrm{W}}$ is of different order than the dimension of $\mathcal{A}_{K}$ , for each K∈{D,N,P}, in all space dimensions that are greater than or equal to three.
Beginning inflation in an inhomogeneous universe

Using numerical solutions of the full Einstein field equations coupled to a scalar inflaton field in 3+1 dimensions, we study the conditions under which a universe that is initially expanding, highly inhomogeneous and dominated by gradient energy can transition to an inflationary period. If the initial scalar field variations are contained within a sufficiently flat region of the inflaton potential, and the universe is spatially flat or open on average, inflation will occur following the dilution of the gradient and kinetic energy due to expansion. This is the case even when the scale of the inhomogeneities is comparable to the initial Hubble length, and overdense regions collapse and form black holes, because underdense regions continue expanding, allowing inflation to eventually begin. This establishes that inflation can arise from highly inhomogeneous initial conditions and solve the horizon and flatness problems, at least as long as the variations in the scalar field do not include values that exceed the inflationary plateau.
Algebraic properties of a disordered asymmetric Glauber model

We consider an asymmetric variant of disordered Glauber dynamics of Ising spins on a one-dimensional lattice, where each spin flips according to the relative state of the spin to its left. Moreover, each bond allows for two rates: flips which equalize nearest neighbour spins, and flips which 'unequalize' them. In addition, the leftmost spin flips depending on the spin at that site. We explicitly calculate all eigenvalues of the transition matrix for all system sizes and conjecture a formula for the normalization factor of the model. We then analyse two limits of this model, which are analogous to ferromagnetic and antiferromagnetic behaviour in the Ising model, for which we are able to prove an analogous formula for the normalization factor.
Quantum liquid from strange frustration in the trimer magnet Ba4Ir3O10

Quantum spin systems such as magnetic insulators usually show magnetic order, but such classical states can give way to quantum liquids with exotic entanglement through two known mechanisms of frustration: geometric frustration in lattices with triangle motifs, and spin-orbit-coupling frustration in the exactly solvable quantum liquid of Kitaev’s honeycomb lattice. Here we present the experimental observation of a new kind of frustrated quantum liquid arising in an unlikely place: the magnetic insulator Ba4Ir3O10 where Ir3O12 trimers form an unfrustrated square lattice. The crystal structure shows no apparent spin chains. Experimentally we find a quantum liquid state persisting down to 0.2 K that is stabilized by strong antiferromagnetic interaction with Curie–Weiss temperature ranging from −766 to −169 K due to magnetic anisotropy. The anisotropy-averaged frustration parameter is 2000, seldom seen in iridates. Heat capacity and thermal conductivity are both linear at low temperatures, a familiar feature in metals but here in an insulator pointing to an exotic quantum liquid state; a mere 2% Sr substitution for Ba produces long-range order at 130 K and destroys the linear-T features. Although the Ir4+(5d5) ions in Ba4Ir3O10 appear to form Ir3O12 trimers of face-sharing IrO6 octahedra, we propose that intra-trimer exchange is reduced and the lattice recombines into an array of coupled 1D chains with additional spins. An extreme limit of decoupled 1D chains can explain most but not all of the striking experimental observations, indicating that the inter-chain coupling plays an important role in the frustration mechanism leading to this quantum liquid.
Elementary Results for the Fundamental Representation of SU(3)

A general group element for the fundamental representation of SU(3) can always be expressed as a second order polynomial in a Hermitian generating matrix H, with coefficients consisting of elementary trigonometric functions dependent on the sole invariant det(H), in addition to the group parameter.
Design of Optimal Sparse Interconnection Graphs for Synchronization of Oscillator Networks

We study the optimal design of a conductance network as a means for synchronizing a given set of oscillators. Synchronization is achieved when all oscillator voltages reach consensus, and performance is quantified by the mean-square deviation from the consensus value. We formulate optimization problems that address the tradeoff between synchronization performance and the number and strength of oscillator couplings. We promote the sparsity of the coupling network by penalizing the number of interconnection links. For identical oscillators, we establish convexity of the optimization problem and demonstrate that the design problem can be formulated as a semidefinite program. Finally, for special classes of oscillator networks we derive explicit analytical expressions for the optimal conductance values.
Nonperturbative True Muonium on the Light Front with TMSWIFT

The true muonium \({(\mu\bar{\mu})}\) bound state presents an interesting test of light-cone quantization techniques. In addition to exhibiting the standard problems of handling non-perturbative calculations, true muonium requires correct treatment of \({e\bar{e}}\) Fock-state contributions. Having previously produced a crude model of true muonium using the method of iterated resolvents, our current work has focused on the inclusion of the box diagrams to improve the cutoff-dependent issues of the model. Further, a parallel computer code, TMSWIFT, allowing for smaller numerical uncertainties, has been developed. This work focuses on the current state of these efforts to develop a model of true muonium that is testable at near-term experiments.
Bounds for convection between rough boundaries

We consider Rayleigh–Benard convection in a layer of fluid between rough no-slip boundaries where the top and bottom boundary heights are functions of the horizontal coordinates with square-integrable gradients. We use the background method to derive an upper bound on the mean heat flux across the layer for all admissible boundary geometries. This flux, normalized by the temperature difference between the boundaries, can grow with the Rayleigh number ( ) no faster than as . Our analysis yields a family of similar bounds, depending on how various estimates are tuned, but every version depends explicitly on the boundary geometry. In one version the coefficient of the leading term is , where is the mean squared magnitude of the boundary height gradients. Application to a particular geometry is illustrated for sinusoidal boundaries.
"I’d rather just go to bed”: Understanding Indirect Answers

We revisit a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. We create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task. The data was collected via elaborate crowdsourcing, and contains utterances with yes/no meaning, as well as uncertain, middle-ground, and conditional responses. We also present BERT-based neural models to predict such categories for a question-answer pair. We find that while transfer learning from entailment works reasonably, performance is not yet sufficient for robust dialog. Our models reach 82-88% accuracy for a 4-class distinction, and 74-85% for 6 classes.
Low-Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons

Binary stochastic neurons (BSNs) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has been recognized that hardware BSNs can be implemented using low-barrier magnets (LBMs) by minimally modifying present-day magnetoresistive random-access memory (MRAM) devices. A crucial parameter that determines the response of these LBM-based BSN designs is the correlation time of magnetization $\tau _c$ . In this letter, we show that, for magnets with low-energy barriers ( $\Delta \approx k_BT$ and below), circular disk magnets with in-plane magnetic anisotropy (IMA) lead to $\tau _c$ values that are two orders of magnitude smaller than $\tau _c$ of magnets with perpendicular magnetic anisotropy (PMA). Analytical descriptions demonstrate that this striking difference in $\tau _c$ is due to a precessionlike fluctuation mechanism that is enabled by the large demagnetization field in IMA magnets. We provide a detailed energy-delay performance evaluation of previously proposed BSN designs based on spin-orbit torque MRAM and spin-transfer torque MRAM employing low-barrier circular IMA magnets by SPICE simulations. The designs exhibit subnanosecond response times leading to energy requirements of approximately a few femtojoules to evaluate the BSN function, orders of magnitude lower than digital CMOS implementations with a much larger surface area. While modern MRAM technology is based on PMA magnets, results in this letter suggest that low-barrier circular IMA magnets may be more suitable for this application.
Wetting and energetics in nanoparticle etching of graphene

Molten metallic nanoparticles have recently been used to construct graphene nanostructures with crystallographic edges. The mechanism by which this happens, however, remains unclear. Here, we present a simple model that explains how a droplet can etch graphene. Two factors possibly contribute to this process: a difference between the equilibrium wettability of graphene and the substrate that supports it, or the large surface energy associated with the graphene edge. We calculate the etching velocities due to either of these factors and make testable predictions for evaluating the significance of each in graphene etching. This model is general and can be applied to other materials systems as well. As an example, we show how our model can be used to extend a current theory of droplet motion on binary semiconductor surfaces.
Higher dimensional supersymmetry in 4D superspace

We present an explicit formulation of supersymmetric Yang-Mills theories from = 5 to 10 dimensions in the familiar = 1, = 4 superspace. This provides the rules for globally supersymmetric model building with extra dimensions and in particular allows us to simply write down = 1 SUSY preserving interactions between bulk fields and fields localized on branes. We present a few applications of the formalism by way of illustration, including supersymmetric ``shining'' of bulk fields, orbifolds and localization of chiral fermions, anomaly inflow and super-Chern-Simons theories.