cssi/SciSciGPT-SciSciNet huggingface dataset:
Moduli Spaces of Sheaves on K3 Surfaces and Galois Representations

We consider two K3 surfaces defined over an arbitrary field, together with a smooth proper moduli space of stable sheaves on each. When the moduli spaces have the same dimension, we prove that if the etale cohomology groups (with Ql coefficients) of the two surfaces are isomorphic as Galois representations, then the same is true of the two moduli spaces. In particular, if the field of definition is finite and the K3 surfaces have equal zeta functions, then so do the moduli spaces, even when the moduli spaces are not birational.
Biogeographic Study of Human Gut Associated crAssphage Suggests Impacts from Industrialization and Recent Expansion

CrAssphage (cross-assembly phage) is a bacteriophage that was first discovered in human gut metagenomic data. CrAssphage belongs to a diverse family of crAss-like bacteriophages thought to infect gut commensal bacteria belonging to Bacteroides species. However, not much is known about the biogeography of crAssphage and whether certain strains are associated with specific human populations. In this study, we screened publicly available human gut metagenomic data from 3,341 samples for the presence of crAssphage sensu stricto (NC_024711.1). We found that crAssphage prevalence is low in traditional, hunter-gatherer populations, such as the Hadza from Tanzania and Matses from Peru, as compared to industrialized, urban populations. Statistical comparisons showed no association of crAssphage prevalence with variables such as age, sex, body mass index, and health status of individuals. Phylogenetic analyses show that crAssphage strains reconstructed from the same individual over multiple time-points, cluster together. CrAssphage strains from individuals from the same study population do not always cluster together. Some evidence of clustering is seen at the level of broadly defined geographic regions, however, the relative positions of these clusters within the crAssphage phylogeny are not well-supported. We hypothesize that this lack of strong biogeographic structuring is suggestive of an expansion event within crAssphage. Using a Bayesian dating approach, we estimate that this expansion has occurred fairly recently. Overall, we determine that crAssphage presence is associated with an industrialized lifestyle and the absence of strong biogeographic structuring within global crAssphage strains is likely due to a recent population expansion within this bacteriophage.
Subset Selection for Multiple Linear Regression via Optimization

Subset selection in multiple linear regression aims to choose a subset of candidate explanatory variables that tradeoff fitting error (explanatory power) and model complexity (number of variables selected). We build mathematical programming models for regression subset selection based on mean square and absolute errors, and minimal-redundancy–maximal-relevance criteria. The proposed models are tested using a linear-program-based branch-and-bound algorithm with tailored valid inequalities and big M values and are compared against the algorithms in the literature. For high dimensional cases, an iterative heuristic algorithm is proposed based on the mathematical programming models and a core set concept, and a randomized version of the algorithm is derived to guarantee convergence to the global optimum. From the computational experiments, we find that our models quickly find a quality solution while the rest of the time is spent to prove optimality; the iterative algorithms find solutions in a relatively short time and are competitive compared to state-of-the-art algorithms; using ad-hoc big M values is not recommended.
Imaging the Subsurface with Ambient Noise Autocorrelations

Autocorrelations created by stacks of near‐offset traces from virtual source gathers are used to form an image of the deeper subsurface. We minimize the masking effects of the effective source time function by subtracting the survey‐wide average autocorrelation from each trace. The result is a zero‐offset reflection image of the subsurface generated by ambient noise correlation. The technique can be particularly useful for imaging the mid and lower crust, in which traditional seismic methods have penetration problems. We show examples from a one‐component 3D survey and a three‐component 2D profile. The 3D example shows the crust in the transition zone between the continent and the Inner Borderland in the Los Angeles, California, area, and for the first time, shows an image of the lower crust. The 2D profile provides both a P image and an S image of the basement interface in the San Bernardino basin in southern California.
Optimal Quantization Via Dynamics

Quantization for probability distributions refers broadly to estimating a given probability measure by a discrete probability measure supported by a finite number of points. We consider general geo...
All-or-Nothing Phenomena: From Single-Letter to High Dimensions

We consider the problem of estimating a $p$ -dimensional vector $\beta$ from $n$ observations $Y=X\beta+W$ , where $\beta_{j}\mathop{\sim}^{\mathrm{i.i.d}.}\pi$ for a real-valued distribution $\pi$ with zero mean and unit variance’ $X_{ij}\mathop{\sim}^{\mathrm{i.i.d}.}\mathcal{N}(0,1)$ , and $W_{i}\mathop{\sim}^{\mathrm{i.i.d}.}\mathcal{N}(0,\ \sigma^{2})$ . In the asymptotic regime where $n/p\rightarrow\delta$ and $p/\sigma^{2}\rightarrow$ snr for two fixed constants $\delta,\ \mathsf{snr}\in(0,\ \infty)$ as $p\rightarrow\infty$ , the limiting (normalized) minimum mean-squared error (MMSE) has been characterized by a single-letter (additive Gaussian scalar) channel. In this paper, we show that if the MMSE function of the single-letter channel converges to a step function, then the limiting MMSE of estimating $\beta$ converges to a step function which jumps from 1 to 0 at a critical threshold. Moreover, we establish that the limiting mean-squared error of the (MSE-optimal) approximate message passing algorithm also converges to a step function with a larger threshold, providing evidence for the presence of a computational-statistical gap between the two thresholds.
A Practical Two-Stage Training Strategy for Multi-Stream End-to-End Speech Recognition

The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. Our previous study offered a promising direction within end-to-end automatic speech recognition, where parallel encoders aim to capture diverse information followed by a stream-level fusion based on attention mechanisms to combine the different views. However, with an increasing number of streams resulting in an increasing number of encoders, the previous approach could require substantial memory and massive amounts of parallel data for joint training. In this work, we propose a practical two-stage training scheme. Stage-1 is to train a Universal Feature Extractor (UFE), where encoder outputs are produced from a single-stream model trained with all data. Stage-2 formulates a multi-stream scheme intending to solely train the attention fusion module using the UFE features and pretrained components from Stage-1. Experiments have been conducted on two datasets, DIRHA and AMI, as a multi-stream scenario. Compared with our previous method, this strategy achieves relative word error rate reductions of 8.2–32.4%, while consistently outperforming several conventional combination methods.
Variations in the California Emergency Medical Services Response to Opioid Use Disorder.

Author(s): Glober, Nancy K.; Hern, Gene; McBride, Owen; Mercer, Mary P. | Abstract: Introduction: Opioids contributed to over 300,000 deaths in the United States in the past 10 years. Most research on drug use occurs in clinics or hospitals; few studies have evaluated the impact of opioid use on emergency medical services (EMS) or the EMS response to opioid use disorder (OUD). This study describes the perceived burden of disease, data collection, and interventions in California local EMS agencies (LEMSA).Methods: We surveyed medical directors of all 33 California LEMSAs with 25 multiple-choice and free-answer questions. Results were collected in RedCap and downloaded into Excel (Microsoft Corporation, Redmond WA). This study was exempt from review by the Alameda Health System - Highland Hospital Institutional Review Board.Results: Of the 33 California LEMSAs, 100% responded, all indicating that OUD significantly affects their patients. Most (91%) had specific protocols directing care of those patients and repeat naloxone dosing. After naloxone administration, none permitted release to law enforcement custody, 6% permitted patient refusal of care, and 45% directed base hospital contact for refusal of care. Few protocols directed screening or treatment of OUD or withdrawal symptoms. Regular data collection occurred in 76% of LEMSAs, with only 48% linking EMS data with hospital or coroner outcomes. In only 30% did the medical director oversee regular quality improvement meetings. Of respondents, 64% were aware of public health agency-based outreach programs and 42% were aware of emergency department BRIDGE programs (Medication Assisted Treatment and immediate referral). Only 9% oversaw naloxone kit distribution (all under the medical director), and 6% had EMS-based outreach programs. In almost all (94%), law enforcement officers carried naloxone and administered it anywhere from a few times a year to greater than 200 in one LEMSA.Conclusion: This study represents an important description of EMS medical directors’ approaches to the impact of OUD as well as trends in protocols and interventions to treat and prevent overdoses. Through this study, we can better understand the variable response to patients with OUD across California.
Explicit Stencil Computation Schemes Generated by Poisson's Formula for the 2D Wave Equation

A novel approach to building explicit stencil computation schemes for the transient 2D scalar wave equation is proposed and implemented. It is based on using the integral representation formula (Poisson's formula) that provides the exact solution u(x,t) (where x ∈ R 2 , t is time) of the initial-value problem for the transient 2D scalar wave equation at t > 0 through the initial conditions for both u and v = ∂ u/∂t. For the purpose of constructing a two-step time-marching algorithm, an additional integral representation formula for the sum u(x,t * + τ) + u(x,t * − τ) is provided, with τ being a time step and t * ≥ τ. It is shown that integrals in the two representation formulas are easily calculated if the initial conditions and u(x,t *) as functions of spatial coordinates are approximated by stencil interpolation polynomials in the neighborhood of any point x i j in a 2D Cartesian grid {(ih, jh), (i, j) ∈ Z 2 } where h is a grid spacing. As a result, if a uniform time grid {t k = kτ, k ∈ N} is chosen, the proposed time-marching algorithm consists of two numerical procedures: (a1) the calculation of u(x i j ,t 1), at the very first time-step through the initial values of both u and v at stencil nodes; (a2) the calculation of u(x i j ,t n+1), n ≥ 1 at the second and next time-steps through a generated two-step scheme. Three computational stencils (with 5, 9 and 13 space points) are built using the proposed approach. Their stability is discussed. More attention is given to stencils with five and nine space points. The obtained numerical schemes are compared with corresponding finite-difference methods available in the literature. Simulation comparison results are presented for two benchmark problems that have exact solutions. It is demonstrated by simulation that using the new first time-step calculation procedure (a1) instead of the conventional one can lead to a significant improvement of accuracy including later time-steps.
Demographic perspectives on mortality of COVID-19 and other epidemics

To put estimates of COVID-19 mortality into perspective, we estimate age-specific mortality for an epidemic claiming for illustrative purposes 1 million US lives, with results approximately scalable over a broad range of deaths. We calculate the impact on period life expectancy (down 2.94 y) and remaining life years (11.7 y per death). Avoiding 1.75 million deaths or 20.5 trillion person years of life lost would be valued at $10.2 to $17.5 trillion. The age patterns of COVID-19 mortality in other countries are quite similar and increase at rates close to each country's rate for all-cause mortality. The scenario of 1 million COVID-19 deaths is similar in scale to that of the decades-long HIV/AIDS and opioid-overdose epidemics but considerably smaller than that of the Spanish flu of 1918. Unlike HIV/AIDS and opioid epidemics, the COVID-19 deaths are concentrated in a period of months rather than spread out over decades.
The economic effects of private social security accounts

Since the 1980s, over 30 countries have implemented various kinds of personal social security accounts. Most counties have adopted them as a part of their social security systems, to also continue to fund their public pay-as-you-go system. This paper analyses the effects of adopting private accounts, as well as the percentage of income paid into private accounts, on GDP per capita growth. Global panel-data regressions over time are used, as well as sample splits for developing countries and separately for Latin America, where many countries adopted some form of private accounts following Chile's ground-breaking example of a complete switch from public to private social security system. The paper estimates statistically significant effects of privatising parts or all of social security on countries' GDP per capita growth. Private accounts also benefit stock market growth and enrolment rates in secondary and higher education, as well as in reducing government expenditures and national debt.
Near Total Versus Gross Total Resection of Large Vestibular Schwannomas: Facial Nerve Outcome.

BACKGROUND Large vestibular schwannomas (VSs) with brainstem compression are generally reserved for surgical resection. Surgical aggressiveness must be balanced with morbidity from cranial nerve injury. The purpose of the present investigation is to evaluate the clinical presentation, management modality, and patient outcomes following near total resection (NTR) vs gross total resection (GTR) of large VSs. OBJECTIVE To assess facial nerve outcome differences between GTR and NTR patient cohorts. METHODS Between January 2010 and March 2018, a retrospective chart review was completed to capture patients continuously who had VSs with Hannover grades T4a and T4b. NTR was decided upon intraoperatively. Primary data points were collected, including preoperative symptoms, tumor size, extent of resection, and postoperative neurological outcome. RESULTS A total of 37 patients underwent surgery for treatment of large and giant (grade 4a and 4b) VSs. Facial nerve integrity was preserved in 36 patients (97%) at the completion of surgery. A total of 27 patients underwent complete resection, and 10 had near total (>95%) resection. Among patients with GTR, 78% (21/27) had House-Brackmann (HB) grade I-II facial nerve function at follow-up, whereas 100% (10/10) of the group with NTR had HB grade I-II facial nerve function. Risk of meningitis, cerebrospinal fluid leak, and sinus thromboses were not statistically different between the 2 groups. There was no stroke, brainstem injury, or death. The mean follow-up was 36 mo. CONCLUSION NTR seems to offer a benefit in terms of facial nerve functional outcome compared to GTR in surgical management of large VSs without significant risk of recurrence.
Case–Control Study, Two‐Phase

Two-phase case–control studies, sometimes called two-stage case–control studies, employ a double sampling design. In the first phase, cases and controls are sampled from a general population, and some stratifying information, such as a crude measure of exposure (e.g. smoker or nonsmoker) is obtained. At the second phase, subsamples are selected within strata defined by disease status (case or control) and by other stratifying information, and more refined information on exposure and other covariates is obtained for the subsample. Judicious allocation of the subsampling fractions can improve the efficiency of estimation for main effects and especially for interactions. Two-phase designs can also be used to validate error-prone exposure assessments by including “gold standard” measurements in a second phase validation sample. We discuss the efficiency of the design and methods of estimation, including fully efficient semiparametric estimators and pseudo-score estimators. Keywords: case–control study; two-stage case–control study; two-phase case–control study; Horvitz–Thompson estimator; double sampling; validation study; semiparametric efficient estimate
Admissible Complexes for the Projective X-ray Transform over a Finite Field

We consider the X-ray transform in a projective space over a finite field. It is well known (after Bolker) that this transform is injective. We formulate an analog of Gelfand’s admissibility problem for the Radon transform, which asks for a classification of all minimal sets of lines for which the restricted Radon transform is injective. The solution involves doubly ruled quadric surfaces.
Molecular state reconstruction by nonlinear wave packet interferometry

We show that time- and phase-resolved two-color nonlinear wave packet interferometry can be used to reconstruct the probability amplitude of an optically prepared molecular wave packet without prior knowledge of the underlying potential surface. We analyze state reconstruction in pure- and mixed-state model systems excited by shaped laser pulses and propose nonlinear wave packet interferometry as a tool for identifying optimized wave packets in coherent control experiments.
Chapter 2 – Series

Publisher Summary This chapter presents a study of the different kinds of series that are widely used as generating functions. It discusses formal power series. Operations on formal series involve corresponding operations on their coefficients. If the series actually converge and represent functions, operations on those functions correspond to certain operations on the power series coefficients of the expansions of those functions. The chapter explores a few of these relationships. It also discusses the calculus of formal exponential generating functions and reviews the basic analytic properties of power series and their coefficient sequences. Generating functionologists need reference lists of known power series and other series that occur frequently in applications of the theory. The chapter presents a list of some useful power series. For each series, it shows the series and its sum. It also discusses another kind of generating function that matches yet another kind of convolution of two sequences, a kind that also occurs naturally in many problems in combinatorics and number theory.
Gravitational collapse of a turbulent vortex with application to star formation

The gravitational collapse of a rotating cloud or vortex is analyzed by expanding the dependent variables in the equations of motion in two-dimensional Taylor series in the space variables. It is shown that the gravitational and rotation terms in the equations are of first order in the space variables, the pressure gradient terms are of second order, and the turbulent viscosity term is of third order. The presence of a turbulent viscosity ensures that the initial rotation is solid-body-like near the origin. The effect of pressure on the collapse process is found to depend on the shape of the initial density disturbance at the origin. Dimensionless collapse times, as well as the evolution of density and velocity, are calculated by solving numerically the system of nonlinear ordinary differential equations resulting from the series expansions. The axial flow is always inward and allows collapse to occur (axially) even when the rotation is large. An approximate solution of the governing partial differential equations is also given, in order to study the spatial distributions of the density and velocity. (AIP)
What’s Decidable About Program Verification Modulo Axioms?

We consider the decidability of the verification problem of programs modulo axioms — automatically verifying whether programs satisfy their assertions, when the function and relation symbols are interpreted as arbitrary functions and relations that satisfy a set of first-order axioms. Though verification of uninterpreted programs (with no axioms) is already undecidable, a recent work introduced a subclass of coherent uninterpreted programs, and showed that they admit decidable verification [26]. We undertake a systematic study of various natural axioms for relations and functions, and study the decidability of the coherent verification problem. Axioms include relations being reflexive, symmetric, transitive, or total order relations, functions restricted to being associative, idempotent or commutative, and combinations of such axioms as well. Our comprehensive results unearth a rich landscape that shows that though several axiom classes admit decidability for coherent programs, coherence is not a panacea as several others continue to be undecidable.
4 – EXPONENTIAL AND LOGARITHMIC FUNCTIONS

Publisher Summary This chapter focuses on the exponential and logarithmic functions. Exponential functions are useful in chemistry, biology, economics, mathematics, and engineering. The chapter describes the applications of exponential functions in calculating such quantities as compound interest and the growth rate of bacteria in a culture medium. Exponential functions occur in a wide variety of applied problems. The chapter reviews some problems dealing with population growth: predicting the growth of bacteria in a culture medium; radioactive decay, such as determining the half-life of strontium 90; and the interest earned when an interest rate is compounded. Logarithms can be viewed as another way of writing exponents. Logarithms have been used to simplify calculations; the slide rule, a device long used by engineers, is based on logarithmic scales. In today's world of inexpensive hand calculators, the need for manipulating logarithms is reduced.
Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning

We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will facilitate the automated processing of large dispersion curve data sets.
Converting inventions into innovations in large firms: How inventors at Xerox navigated the innovation process to commercialize their ideas

Research Summary How can inventors in large firms navigate their organizations' innovation processes to commercialize breakthrough inventions? Using historical case studies of three breakthrough inventions at Xerox—office workstations, personal computers, and laser printers, we illustrate how inventors navigated multiple evaluation criteria across different organizational units to attract resources toward inventions. These criteria stemmed from Xerox's first successful breakthrough invention, the 914 copier and the specific objectives of the organizational units. We highlight two approaches deployed by Xerox inventors—searching across the organization for more favorable evaluation criteria and shaping the evaluation criteria to help attract resources. While searching leveraged the heterogeneity of evaluation criteria across the different organizational units, shaping required the presence of evaluative uncertainty with respect to the appropriate criteria for evaluating breakthrough inventions. Managerial Summary The challenges of commercializing breakthrough inventions in large firms have been studied extensively through a lens of managerial decision‐making and resource allocation. This perspective has characterized the innovation process in large firms as one in which inventors confine themselves to idea generation, leaving idea commercialization to other actors, subject to organizational inertia. We develop a complementary perspective of the innovation process in which inventors may navigate organizational inertia by going beyond idea generation to attracting resources toward commercializing their breakthrough inventions. By offering a novel account of how inventors at Xerox navigated multiple evaluation criteria to commercialize their inventions, the study sheds light on an important yet overlooked aspect of the innovation process in large firms that can facilitate the commercialization of breakthrough inventions.
Partial Weight Adaptation for Robust DNN Inference

Mainstream video analytics uses a pre-trained DNN model with an assumption that inference input and training data follow the same probability distribution. However, this assumption does not always hold in the wild: autonomous vehicles may capture video with varying brightness; unstable wireless bandwidth calls for adaptive bitrate streaming of video; and, inference servers may serve inputs from heterogeneous IoT devices/cameras. In such situations, the level of input distortion changes rapidly, thus reshaping the probability distribution of the input. We present GearNN, an adaptive inference architecture that accommodates DNN inputs with varying distortions. GearNN employs an optimization algorithm to identify a tiny set of "distortion-sensitive" DNN parameters, given a memory budget. Based on the distortion level of the input, GearNN then adapts only the distortion-sensitive parameters, while reusing the rest of constant parameters across all input qualities. In our evaluation of DNN inference with dynamic input distortions, GearNN improves the accuracy (mIoU) by an average of 18.12% over a DNN trained with the undistorted dataset and 4.84% over stability training from Google, with only 1.8% extra memory overhead.
Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to the challenges of collecting effective training data and learning efficiently, most grasping algorithms today are limited to top-down movements and open-loop execution. In this work, we propose a new low-cost hardware interface for collecting grasping demonstrations by people in diverse environments. This data makes it possible to train a robust end-to-end 6DoF closed-loop grasping model with reinforcement learning that transfers to real robots. A key aspect of our grasping model is that it uses “action-view” based rendering to simulate future states with respect to different possible actions. By evaluating these states using a learned value function (e.g., Q-function), our method is able to better select corresponding actions that maximize total rewards (i.e., grasping success). Our final grasping system is able to achieve reliable 6DoF closed-loop grasping of novel objects across various scene configurations, as well as in dynamic scenes with moving objects.
Reparations, Responsibility, and Formalism : A Reply to Carnes

In a recent paper, Thomas Carnes develops a novel argument for reparations for historical injustices. This Reply shows that Carnes succeeds only at the cost of invoking an implausible formalism. The Reply also presents in brief a simpler argument for reparations.
Do preferred thermostat settings differ by sex

Abstract This paper examines the extent to which males and females report different thermostat settings using data from single-occupant homes in the United States. Reported thermostat settings for heating and cooling at night, at home, and when away from home are rigorously tested for behavioral differences between the sexes. In general, single-occupant males and females report the same thermostat settings on average. The lack of differences between sexes is robust to controlling for demographic characteristics and environmental variables that differ systematically between the samples using both regression and decomposition approaches. These findings have important implications for thermostat settings and thermal comfort in shared spaces.
Topology optimization of permanent magnets for stellarators

We introduce a topology optimization method to design permanent magnets for advanced stellarators. Recent researches show that permanent magnets have great potentials to simplify stellarator coils. We adopt state-of-the-art numerical techniques to determine the presence of magnets in the entire designing space. The FAMUS code is developed and it can design engineering-feasible permanent magnets for general stellarators satisfying the constraints of the maximum material magnetization and explicitly forbidden regions. FAMUS has been successfully verified against the previously proposed linear method. Three different permanent magnet designs together with planar TF coils for a half-Tesla NCSX configuration have been obtained for demonstrations. The designs have good accuracy in generating the desired equilibrium and offer considerably large plasma access on the outboard side. The results show that FAMUS is a flexible, advanced numerical tool for future permanent magnet stellarator designs.
Metal-chelating benzothiazole multifunctional compounds for the modulation and 64Cu PET imaging of Aβ aggregation

While Alzheimer's Disease (AD) is the most common neurodegenerative disease, there is still a dearth of efficient therapeutic and diagnostic agents for this disorder. Reported herein are a series of new multifunctional compounds (MFCs) with appreciable affinity for amyloid aggregates that can be potentially used for both the modulation of Aβ aggregation and its toxicity, as well as positron emission tomography (PET) imaging of Aβ aggregates. Firstly, among the six compounds tested HYR-16 is shown to be capable to reroute the toxic Cu-mediated Aβ oligomerization into the formation of less toxic amyloid fibrils. In addition, HYR-16 can also alleviate the formation of reactive oxygen species (ROS) caused by Cu2+ ions through Fenton-like reactions. Secondly, these MFCs can be easily converted to PET imaging agents by pre-chelation with the 64Cu radioisotope, and the Cu complexes of HYR-4 and HYR-17 exhibit good fluorescent staining and radiolabeling of amyloid plaques both in vitro and ex vivo. Importantly, the 64Cu-labeled HYR-17 is shown to have a significant brain uptake of up to 0.99 ± 0.04 %ID per g. Overall, by evaluating the various properties of these MFCs valuable structure–activity relationships were obtained that should aid the design of improved therapeutic and diagnostic agents for AD.
Numerical analysis of gas bearings in oil-free linear compressors:

Recently, linear compressor technology has gained increased attention in vapor compression systems due to its compactness and lower frictional losses compared with the conventional reciprocating co...
Bio-inspired Learning of Sensorimotor Control for Locomotion

This paper presents a bio-inspired central pattern generator (CPG)-type architecture for learning optimal maneuvering control of periodic locomotory gaits. The architecture is presented here with the aid of a snake robot model problem involving planar locomotion of coupled rigid body systems. The maneuver involves clockwise or counterclockwise turning from a nominally straight path. The CPG circuit is realized as a coupled oscillator feedback particle filter. The collective dynamics of the filter are used to approximate a posterior distribution that is used to construct the optimal control input for maneuvering the robot. A Q-learning algorithm is applied to learn the approximate optimal control law. The issues surrounding the parametrization of the Q-function are discussed. The theoretical results are illustrated with numerics for a 5-link snake robot system.
Acceleration techniques for semiclassical Maxwell–Bloch systems: An application to discrete quantum dot ensembles

Abstract The solution to Maxwell–Bloch systems using an integral-equation-based framework has proven effective at capturing collective features of laser-driven and radiation-coupled quantum dots, such as light localization and modifications of Rabi oscillations. Importantly, it enables observation of the dynamics of each quantum dot in large ensembles in a rigorous, error-controlled, and self-consistent way without resorting to spatial averaging. Indeed, this approach has demonstrated convergence in ensembles containing up to 1 0 4 interacting quantum dots (Glosser et al., 2017). Scaling beyond 1 0 4 quantum dots tests the limit of computational horsepower, however, due to the O ( N t N s 2 ) scaling (where N t and N s denote the number of temporal and spatial degrees of freedom). In this work, we present an algorithm that reduces the cost of analysis to O ( N t N s log 2 N s ) . While the foundations of this approach rely on well-known particle–particle/particle–mesh and adaptive integral methods, we add refinements specific to transient systems and systems with multiple spatial and temporal derivatives. Accordingly, we offer numerical results that validate the accuracy, effectiveness and utility of this approach in analyzing the dynamics of large ensembles of quantum dots.
Black dwarf supernova in the far future

In the far future long after star formation has ceased the universe will be populated by sparse degenerate remnants, mostly white dwarfs, though their ultimate fate is an open question. These white dwarfs will cool and freeze solid into black dwarfs while pycnonuclear fusion will slowly process their composition to iron-56. However, due to the declining electron fraction the Chandrasekhar limit of these stars will be decreasing and will eventually be below that of the most massive black dwarfs. As such, isolated dwarf stars with masses greater than $\sim 1.2 M_\odot$ will collapse in the far future due to the slow accumulation of iron-56 in their cores. If proton decay does not occur then this is the ultimate fate of about $10^{21}$ stars, approximately one percent of all stars in the observable universe. We present calculations of the internal structure of black dwarfs with iron cores as a model for progenitors. From pycnonuclear fusion rates we estimate their lifetime and thus delay time to be $10^{1100}$ years. We speculate that high mass black dwarf supernovae resemble accretion induced collapse of O/Ne/Mg white dwarfs while later low mass transients will be similar to stripped-envelope core-collapse supernova, and may be the last interesting astrophysical transients to occur prior to heat death.
Visible-Light-Enabled Paternò-Büchi Reaction via Triplet Energy Transfer for the Synthesis of Oxetanes.

One of the most efficient ways to synthesize oxetanes is the light-enabled [2 + 2] cycloaddition reaction of carbonyls and alkenes, referred to as the Paterno-Buchi reaction. The reaction conditions for this transformation typically require the use of high energy UV light to excite the carbonyl, limiting the applications, safety, and scalability. We herein report the development of a visible-light-mediated Paterno-Buchi reaction protocol that relies on triplet energy transfer from an iridium-based photocatalyst to the carbonyl substrates. This mode of activation is demonstrated for a variety of aryl glyoxylates and negates the need for both visible-light-absorbing carbonyl starting materials and UV light to enable access to a variety of functionalized oxetanes in up to 99% yield.
Atomic Force Microscopy Examination of the Evolution of the Surface Morphology of Bi4Ti3O12 grown by Molecular Beam Epitaxy

The surface morphology of (0 0 1) Bi4Ti3O12 grown on (0 0 1) SrTiO3 by reactive molecular beam epitaxy (MBE) has been examined using atomic force microscopy (AFM). Initial nucleation of a 1/4 unit cell thick layer is followed by growth of 1/2 unit cell thick layers. Between 9 and 16 layers, a transition to 3-dimensional growth occurs, leading to well-defined mounds. This implies a Stranski-Krastonov growth mode. During growth, the morphology follows a behavior consistent with the dynamic scaling hypothesis and we extract values for the scaling exponents α and β from the AFM data. A thickness variation in α is observed and reflects the strain relief associated with the Stranski-Krastonov growth.
Optimal Decisions of a Rational Agent in the Presence of Biased Information Providers

We consider information networks whereby multiple biased-information-providers (BIPs), e.g., media outlets/social network users/sensors, share reports of events with rational-information-consumers (RICs). Making the reasonable abstraction that an event can be reported as an answer to a logical statement, we model the input-output behavior of each BIP as a binary channel. For various reasons, some BIPs might share incorrect reports of the event. Moreover, each BIP is: ‘biased’ if it favors one of the two outcomes while reporting, or ‘unbiased’ if it favors neither outcome. Such biases occur in information/social networks due to differences in users’ characteristics/worldviews.We study the impact of the BIPs’ biases on an RIC’s choices while deducing the true information. Our work reveals that a “graph-blind” RIC looking for n BIPs among its neighbors, acts peculiarly in order to minimize its probability of making an error white deducing the true information. First, we establish the counter-intuitive fact that the RIC’s expected error is minimized by choosing BIPs that are fully-biased against the a-priori likely event. Then, we study the gains that fully-biased BIPs provide over unbiased BIPs when the error rates of their binary channels are equalized, for fair comparison, at some $r\gt0$. Specifically, the unbiased-to-fully-biased ratio of the $\mathbf{R I C}$ ’s expected error probabilities grows exponentially with the exponent $\displaystyle \frac{n}{2}\ln\left(4\rho_{0}^{2}\left(\frac{1}{r}-1\right)\right)$, where $\rho_{0}$ is the event’s prior probability of being 0. This shows not only that fully-biased BIPs are preferable to unbiased or heterogeneously-biased BIPs, but also that the gains can be substantial for small r.
Defect of Euclidean distance degree

Abstract Two well studied invariants of a complex projective variety are the unit Euclidean distance degree and the generic Euclidean distance degree. These numbers give a measure of the algebraic complexity for “nearest” point problems of the algebraic variety. It is well known that the latter is an upper bound for the former. While this bound may be tight, many varieties appearing in optimization, engineering, statistics, and data science, have a significant gap between these two numbers. We call this difference the defect of the ED degree of an algebraic variety. In this paper we compute this defect by classical techniques in Singularity Theory, thereby deriving a new method for computing ED degrees of smooth complex projective varieties.
EQUIVARIANT DERIVED CATEGORIES FOR TOROIDAL GROUP IMBEDDINGS

Let X denote a projective variety over an algebraically closed field on which a linear algebraic group acts with finitely many orbits. Then, a conjecture of Soergel and Lunts in the setting of Koszul duality and Langlands' philosophy, postulates that the equivariant derived category of bounded complexes with constructible equivariant cohomology sheaves on X is equivalent to a full subcategory of the derived category of modules over a graded ring defined as a suitable graded Ext. Only special cases of this conjecture have been proven so far. The purpose of this paper is to provide a proof of this conjecture for all projective toroidal imbeddings of complex reductive groups. In fact, we show that the methods used by Lunts for a proof in the case of toric varieties can be extended with suitable modifications to handle the toroidal imbedding case. Since every equivariant imbedding of a complex reductive group is dominated by a toroidal imbedding, the class of varieties for which our proof applies is quite large. We also show that, in general, there exist a countable number of obstructions for this conjecture to be true and that half of these vanish when the odd dimensional equivariant intersection cohomology sheaves on the orbit closures vanish. This last vanishing condition had been proven to be true in many cases of spherical varieties by Michel Brion and the author in prior work.
Electrically-Tunable Stochasticity for Spin-based Neuromorphic Circuits: Self-Adjusting to Variation

Energy-efficient methods are addressed for lever-aging low energy barrier nanomagnetic devices within neuro-morphic architectures. Using a Magnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) as the basis of neuronal structures in Deep Belief Networks (DBNs), the impact of increasing the Magnetic Tunnel Junction's (MTJ's) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. A self-compensating circuit is developed herein providing a compact, and low complexity approach to mitigating process variation impacts towards practical implementation and fabrication. As evaluated for the MNIST dataset for energy barriers (E B ) at near-zero kT to 2.0 kT, it is shown that the proposed variation-tolerant circuit can effectively increase the reduced probabilistic fluctuation speed of the nanomagnet in p-bits with E B > OkT achieving approximately 5x improvement in the total energy consumption of a 784 × 200 × 10 DBN.
Deep Reinforcement Learning for Option Replication and Hedging

The authors propose models for the solution of the fundamental problem of option replication subject to discrete trading, round lotting, and nonlinear transaction costs using state-of-the-art methods in deep reinforcement learning (DRL), including deep Q-learning, deep Q-learning with Pop-Art, and proximal policy optimization (PPO). Each DRL model is trained to hedge a whole range of strikes, and no retraining is needed when the user changes to another strike within the range. The models are general, allowing the user to plug in any option pricing and simulation library and then train them with no further modifications to hedge arbitrary option portfolios. Through a series of simulations, the authors show that the DRL models learn similar or better strategies as compared to delta hedging. Out of all models, PPO performs the best in terms of profit and loss, training time, and amount of data needed for training. TOPICS:Big data/machine learning, options, risk management, simulations Key Findings • The authors propose models for the replication of options over a whole range of strikes subject to discrete trading, round lotting, and nonlinear transaction costs based on state-of-the-art methods in deep reinforcement learning including deep Q-learning and proximal policy optimization. • The models allow the user to plug in any option pricing and simulation library and then train them with no further modifications to hedge arbitrary option portfolios. • A series of simulations demonstrates that the deep reinforcement learning models learn similar or better strategies as compared to delta hedging. • Proximal policy optimization outperforms the other models in terms of profit and loss, training time, and amount of data needed for training.
Validation of a PNA Clamping Method for Reducing Host DNA Amplification and Increasing Eukaryotic Diversity in Rhizosphere Microbiome Studies

Protists and microscopic animals are important but poorly understood determinants of plant health. Plant-associated eukaryotes could be surveyed by high-throughput sequencing of 18S ribosomal RNA (...
Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams

Multi-robot systems of increasing size and complexity are used to solve large-scale problems, such as area exploration and search and rescue. A key decision in human-robot teaming is dividing a multi-robot system into teams to address separate issues or to accomplish a task over a large area. In order to address the problem of selecting teams in a multi-robot system, we propose a new multimodal graph embedding method to construct a unified representation that fuses multiple information modalities to describe and divide a multi-robot system. The relationship modalities are encoded as directed graphs that can encode asymmetrical relationships, which are embedded into a unified representation for each robot. Then, the constructed multimodal representation is used to determine teams based upon unsupervised learning. We per-form experiments to evaluate our approach on expert-defined team formations, large-scale simulated multi-robot systems, and a system of physical robots. Experimental results show that our method successfully decides correct teams based on the multifaceted internal structures describing multi-robot systems, and outperforms baseline methods based upon only one mode of information, as well as other graph embedding-based division methods.
Two Routes to Scalable Credit Assignment without Weight Symmetry

The neural plausibility of backpropagation has long been disputed, primarily for its use of non-local weight transport $-$ the biologically dubious requirement that one neuron instantaneously measure the synaptic weights of another. Until recently, attempts to create local learning rules that avoid weight transport have typically failed in the large-scale learning scenarios where backpropagation shines, e.g. ImageNet categorization with deep convolutional networks. Here, we investigate a recently proposed local learning rule that yields competitive performance with backpropagation and find that it is highly sensitive to metaparameter choices, requiring laborious tuning that does not transfer across network architecture. Our analysis indicates the underlying mathematical reason for this instability, allowing us to identify a more robust local learning rule that better transfers without metaparameter tuning. Nonetheless, we find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several non-local learning rules that relax the need for instantaneous weight transport into a more biologically-plausible "weight estimation" process, showing that these rules match state-of-the-art performance on deep networks and operate effectively in the presence of noisy updates. Taken together, our results suggest two routes towards the discovery of neural implementations for credit assignment without weight symmetry: further improvement of local rules so that they perform consistently across architectures and the identification of biological implementations for non-local learning mechanisms.
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of any Number of Speakers.

We propose an end-to-end speaker-attributed automatic speech recognition model that unifies speaker counting, speech recognition, and speaker identification on monaural overlapped speech. Our model is built on serialized output training (SOT) with attention-based encoder-decoder, a recently proposed method for recognizing overlapped speech comprising an arbitrary number of speakers. We extend SOT by introducing a speaker inventory as an auxiliary input to produce speaker labels as well as multi-speaker transcriptions. All model parameters are optimized by speaker-attributed maximum mutual information criterion, which represents a joint probability for overlapped speech recognition and speaker identification. Experiments on LibriSpeech corpus show that our proposed method achieves significantly better speaker-attributed word error rate than the baseline that separately performs overlapped speech recognition and speaker identification.