cssi/SciSciGPT-SciSciNet huggingface dataset:
Ethics and entrepreneurship

Abstract As the study of entrepreneurship and the study of business ethics become increasingly established, the intersection of entrepreneurship and ethics is receiving increasing scholarly attention. In this paper, we review the research connecting ethics and entrepreneurship, classifying the literature into three broad themes; we also identify and integrate the key themes that emerge, and we offer suggestions for future research. We conclude by introducing the articles in this special issue.
Cofree objects in the centralizer and the center categories

We study cocompleteness, co-wellpoweredness, and generators in the centralizer category of an object or morphism in a monoidal category, and the center or the weak center of a monoidal category. We explicitly give some answers for when colimits, cocompleteness, co-wellpoweredness, and generators in these monoidal categories can be inherited from their base monidal categories. Most importantly, we investigate cofree objects of comonoids in these monoidal categories.
Heterogeneity-aware and communication-efficient distributed statistical inference

In multicenter research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only require sharing aggregated information, have been developed. The existing distributed algorithms usually assume the data are homogeneously distributed across sites. This assumption ignores the important fact that the data collected at different sites may come from various sub-populations and environments, which can lead to heterogeneity in the distribution of the data. Ignoring the heterogeneity may lead to erroneous statistical inference. In this paper, we propose distributed algorithms which account for the heterogeneous distributions by allowing site-specific nuisance parameters. The proposed methods extend the surrogate likelihood approach to the heterogeneous setting by applying a novel density ratio tilting method to the efficient score function. The proposed algorithms maintain the same communication cost as the existing communication-efficient algorithms. We establish a non-asymptotic risk bound for the proposed distributed estimator and its limiting distribution in the two-index asymptotic setting which allows both sample size per site and the number of sites to go to infinity. In addition, we show that the asymptotic variance of the estimator attains the Cramer-Rao lower bound when the number of sites is in rate smaller than the sample size at each site. Finally, we use simulation studies and a real data application to demonstrate the validity and feasibility of the proposed methods.
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations

Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory requirement is also significantly reduced. However, compared to start-of-the-art compact convolutional neural network (CNN) models, BNNs tend to produce a much lower accuracy on realistic datasets such as ImageNet. In addition, the input layer of BNNs has gradually become a major compute bottleneck, because it is conventionally excluded from binarization to avoid a large accuracy loss. This work proposes FracBNN, which exploits fractional activations to substantially improve the accuracy of BNNs. Specifically, our approach employs a dual-precision activation scheme to compute features with up to two bits, using an additional sparse binary convolution. We further binarize the input layer using a novel thermometer encoding. Overall, FracBNN preserves the key benefits of conventional BNNs, where all convolutional layers are computed in pure binary MAC operations (BMACs). We design an efficient FPGA-based accelerator for our novel BNN model that supports the fractional activations. To evaluate the performance of FracBNN under a resource-constrained scenario, we implement the entire optimized network architecture on an embedded FPGA (Xilinx Ultra96 v2). Our experiments on ImageNet show that FracBNN achieves an accuracy comparable to MobileNetV2, surpassing the best-known BNN design on FPGAs with an increase of 28.9% in top-1 accuracy and a 2.5x reduction in model size. FracBNN also outperforms a recently introduced BNN model with an increase of 2.4% in top-1 accuracy while using the same model size. On the embedded FPGA device, FracBNN demonstrates the ability of real-time image classification.
Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech

Speech processing is built upon encoding by the auditory nerve and brainstem, yet we know very little about how these processes unfold in specific subcortical structures. These structures are deep and respond quickly, making them difficult to study during ongoing speech. Recent techniques begin to address this problem, but yield temporally broad responses with consequently ambiguous neural origins. Here we describe a method that pairs re-synthesized 'peaky' speech with deconvolution analysis of EEG recordings. We show that in adults with normal hearing, the method quickly yields robust responses whose component waves reflect activity from distinct subcortical structures spanning auditory nerve to rostral brainstem. We further demonstrate the versatility of peaky speech by simultaneously measuring bilateral and ear-specific responses across different frequency bands, and discuss important practical considerations such as talker choice. The peaky speech method holds promise as a tool for investigating speech encoding and processing, and for clinical applications.
Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study

The Health and Retirement Study (HRS) is a longitudinal study of U.S. adults enrolled at age 50 and older. We were interested in investigating the effect of a sudden large decline in wealth on the cognitive ability of subjects measured using a dataset provided composite score. However, our analysis was complicated by the lack of randomization, time-dependent confounding, and a substantial fraction of the sample and population will die during follow-up leading to some of our outcomes being censored. The common method to handle this type of problem is marginal structural models (MSM). Although MSM produces valid estimates, this may not be the most appropriate method to reflect a useful real-world situation because MSM upweights subjects who are more likely to die to obtain a hypothetical population that over time, resembles that would have been obtained in the absence of death. A more refined and practical framework, principal stratification (PS), would be to restrict analysis to the strata of the population that would survive regardless of negative wealth shock experience. In this work, we propose a new algorithm for the estimation of the treatment effect under PS by imputing the counterfactual survival status and outcomes. Simulation studies suggest that our algorithm works well in various scenarios. We found no evidence that a negative wealth shock experience would affect the cognitive score of HRS subjects.
A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments.

Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on polygenic traits from population genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modelled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. The performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Callosobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect the causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open-source computer software (fsabc) implementing this method are available from github (https://github.com/zgompert/fsabc.git).
Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?

Identifying an efficient and fair allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. The recent heart allocation rule by the United Network for Organ Sharing (UNOS) has emphasized medical urgency to address the heart transplant crisis by further dividing the previous sickest patient group into three subgroups. However, there is a significant debate on optimality and fairness of this policy because although it can help reduce pre‐transplant mortality, it may reduce post‐transplant survival. We undertake a rigorous study to address this debate by measuring the impacts of a variety of perspectives on the waiting list and patients. We show that the optimal policy of our proposed fluid model is a dynamic priority rule, and provide insight on the impact of fairness constraints on such priorities. We quantify the price that the society pays for following a medical urgency approach, which favors the sickest patients, compared to a utilitarian approach, which seeks to maximize total quality‐adjusted life years (QALYs). Our results, produced by a validated simulation model, reveal that the said price is 7.7% of total QALYs and increases to 11% by considering a broader regional sharing aligned with four‐hour heart cold ischemic time. We study other relevant objectives/measures in transplantation and our results show that the utilitarian policy outperforms the medical urgency policy in other measures as well. Our analysis provides novel insights on optimal patient allocation and sheds light on the debate around this challenging problem.
Building and Calibrating the Binary Star Population Using Kepler Data

Modeling binary star populations is critical to linking the theories of star formation and stellar evolution with observations. In order to test these theories, we need accurate models of observable binary populations. The Kepler Eclipsing Binary Catalog (KEBC), with its estimated $>$90% completeness, provides an observational anchor on binary population models. In this work we present the results of a new forward-model of the binary star population in the Kepler field. The forward-model takes a single star population from a model of the galaxy and pairs the stars into binaries by applying the constraints on the population from the results of observational binary population surveys such as arXiv:1007.0414 and arXiv:1303.3028. A synthetic binary population is constructed from the initial distributions of orbital parameters. We identify the eclipsing binary sample from the generated binary star population and compare this with the observed sample of eclipsing binaries contained in the KEBC. Finally, we update the distributions of the synthetic population and repeat the process until the synthetic eclipsing binary sample agrees with the KEBC. The end result of this process is a model of the underlying binary star population that has been fit to observations. We find that for fixed flat mass ratio and eccentricity input distributions, the binary period distribution is logarithmically flat above $\sim$3.2d. With additional constraints on distributions from observations, we can further adjust the synthetic binary population by relaxing other input constraints, such as mass ratio and eccentricity.
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks.

We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environment that are not part of the low-dimensional state yet affect its temporal dynamics are inferred from the interaction history and visual observations, and are implicitly captured in the generated parameters. We test HyperDynamics on a set of object pushing and locomotion tasks. It outperforms existing dynamics models in the literature that adapt to environment variations by learning dynamics over high dimensional visual observations, capturing the interactions of the agent in recurrent state representations, or using gradient-based meta-optimization. We also show our method matches the performance of an ensemble of separately trained experts, while also being able to generalize well to unseen environment variations at test time. We attribute its good performance to the multiplicative interactions between the inferred system properties -- captured in the generated parameters -- and the low-dimensional state representation of the dynamical system.
The Psychology of Second Guesses: Implications for the Wisdom of the Inner Crowd

Prior research suggests that averaging two guesses from the same person can improve quantitative judgments, a phenomenon known as the “wisdom of the inner crowd.” In this article, we find that this...
Single-phase duodenary high-entropy fluorite/pyrochlore oxides with an order-disorder transition

Abstract Improved thermomechanical properties have been reported for various high-entropy oxides containing typically five metal cations. This study further investigates a series of duodenary (11 metals + oxygen) high-entropy oxides by mixing different fractions of a five-cation fluorite-structured niobate and a seven-cation pyrochlore (both containing Yb) with matching lattice constants. Nine compositions of duodenary high-entropy oxides have been examined. All of them exhibit single high-entropy phases of either disordered fluorite or ordered pyrochlore structure. An order-disorder transition (ODT) is evident with changing composition, accompanied by a reduction in thermal conductivity ( k ). In comparison with the ODT criteria developed from ternary oxides, these duodenary oxides are more prone to disorder, but the ODT is still controlled by similar factors (but at different thresholds). Interestingly, there are abrupt increases in Young's modulus ( E ) at low mixing concentrations near both endmembers. The E / k ratios are increased, in comparison with both endmembers. This study suggests a new route to tailor high-entropy ceramics via controlling cation ordering vs. disordering.
On the Concentration of Allocations and Comparisons of Auctions in Large Economies

We analyze competitive pressures in a sequence of auctions with a growing number of bidders, in a model that includes private and common valuations as special cases. We show that the key determinant of bidders' surplus (and implicitly auction revenue) is how the goods are distributed. In any setting and sequence of auctions where the allocation of good(s) is concentrated among a shrinking proportion of the population, the winning bidders enjoy no surplus in the limit. If instead the good(s) are allocated in a dispersed manner so that a non-vanishing proportion of the bidders obtain objects, then in any of a wide class of auctions bidders enjoy a surplus that is bounded away from zero. Moreover, under dispersed allocations, the format of the auction matters. If bidders have constant marginal utilities for objects up to some limit, then uniform price auctions lead to higher revenue than discriminatory auctions. If agents have decreasing marginal utilities for objects, then uniform price auctions are asymptotically efficient, while discriminatory auctions are asymptotically inefficient. Finally, we show that in some cases where dispersed allocations are efficient, revenue may increase by bundling goods at the expense of efficiency.
Learning compositional models of robot skills for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible genera...
Towards Robust State Estimation by Boosting the Maximum Correntropy Criterion Kalman Filter With Adaptive Behaviors

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or general non-Gaussian noise, and is able to modify filter parameters online to improve performance. Two separate methods are developed – the Variational Bayesian AMCCKF (VB-AMCCKF) and Residual AMCCKF (R-AMCCKF) – that modify the process and measurement noise models in addition to the bandwidth of the kernel function used in MCCKF based on the quality of measurements received. The two approaches differ in computational complexity and overall performance which is experimentally analyzed. The method is demonstrated in real experiments on both aerial and ground robots and is part of the solution used by the COSTAR team participating at the DARPA Subterranean Challenge.
Calypso Venus Scout

This is a mission to explore the surface of Venus from low altitudes. The Calypso Venus Scout consists of a high-altitude balloon and a instrumented Descent Module (DM). The DM is deployed to an altitude of 10-25 km by means of a Tether where it obtains images, with meter and centimeter scale resolution, and rough IR spectra. It is reeled-in after several hours for a "cool down" cycle, then deployed again. The balloon remains at high-altitude with no need to be fortified to survive high-T and high-P of Venus' lower atmosphere.
Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies

Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic.They, however, are prone to over 1,800 mutations uncovered by a Mutation Tracker. It is urgent to understand how vaccines and antibodies in the development would be impacted by mutations. In this work, we first study the mechanism, frequency, and ratio of mutations on the spike (S) protein, which is the common target of most COVID-19 vaccines and antibody therapies. Additionally, we build a library of antibody structures and analyze their 2D and 3D characteristics. Moreover, we predict the mutation-induced binding free energy (BFE) changes for the complexes of S protein and antibodies or ACE2. By integrating genetics, biophysics, deep learning, and algebraic topology, we deduce that some of the mutations such as M153I, S254F, and S255F may weaken the binding of S protein and antibodies, and potentially disrupt the efficacy and reliability of antibody therapies and vaccines in the development. We provide a strategy to prioritize the selection of mutations for designing vaccines or antibody cocktails.
Optimal Causal Rate-Constrained Sampling of the Wiener Process

An encoder causally observes the Wiener process and decides when and what to transmit about it. A decoder makes real-time estimation of the process using causally received codewords. We determine the causal encoding and decoding policies that minimize the mean-square estimation error, under the long-term communication rate constraint of R bits/s. We show that an optimal encoding policy can be implemented as a causal sampling policy followed by a causal compressing policy. We prove that the optimal encoding policy samples the Wiener process once the innovation passes some threshold, and compresses the sign of the innovation (SOI) using a 1-bit codeword. The SOI coding scheme achieves the operational distortion-rate function. This is better than the distortion-rate tradeoff achieved by the best non-causal code, because the SOI coding scheme uses the free timing information supplied by the zero-delay channel between the encoder and the decoder. The distortion-rate tradeoffs achieved with deterministic sampling policies are much worse. The optimal strategy is to sample the process as fast as possible and to transmit 1-bit codewords to the decoder without delay.
Single-Nucleus RNA-Seq Reveals Dysregulation of Striatal Cell Identity Due to Huntington's Disease Mutations

Huntington9s disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the huntingtin (HTT) gene. Cell death in HD occurs primarily in striatal medium spiny neurons (MSNs), but the involvement of specific MSN subtypes and of other striatal cell types remains poorly understood. To gain insight into cell type-specific disease processes, we studied the nuclear transcriptomes of 4524 cells from the striatum of a genetically precise knock-in mouse model of the HD mutation, HttQ175/+, and from wild-type controls. We used 14- to 15-month-old male mice, a time point at which multiple behavioral, neuroanatomical, and neurophysiological changes are present but at which there is no known cell death. Thousands of differentially expressed genes (DEGs) were distributed across most striatal cell types, including transcriptional changes in glial populations that are not apparent from RNA-seq of bulk tissue. Reconstruction of cell type-specific transcriptional networks revealed a striking pattern of bidirectional dysregulation for many cell type-specific genes. Typically, these genes were repressed in their primary cell type, yet de-repressed in other striatal cell types. Integration with existing epigenomic and transcriptomic data suggest that partial loss-of-function of the polycomb repressive complex 2 (PRC2) may underlie many of these transcriptional changes, leading to deficits in the maintenance of cell identity across virtually all cell types in the adult striatum. SIGNIFICANCE STATEMENT Huntington9s disease (HD) is a dominantly inherited neurodegenerative disorder characterized by specific loss of medium spiny neurons (MSNs) in the striatum, accompanied by more subtle changes in many other cell types. It is thought that changes in transcriptional regulation are an important underlying mechanism, but cell type-specific gene expression changes are not well understood, particularly at time points relevant to the onset of disease-related symptoms. Single-nucleus (sn)RNA-seq in a genetically precise mouse model enabled us to identify novel patterns of transcriptional dysregulation because of HD mutations, including bidirectional dysregulation of many cell type identity genes that may be driven by partial loss-of-function of the polycomb repressive complex (PRC). Identifying these regulators of transcriptional dysregulation in HD can be leveraged to design novel disease-modifying therapeutics.
DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0.
Stability properties of a crack inverse problem in half space

We show in this paper a Lipschitz stability result for a crack inverse problem in half space. The direct problem is a Laplace equation with zero Neumann condition on the top boundary. The forcing term is a discontinuity across the crack. This formulation can be related to geological faults in elastic media or to irrotational incompressible flows in a half space minus an inner wall. The direct problem is well posed in an appropriate functional space. We study the related inverse problem where the jump across the crack is unknown, and more importantly, the geometry and the location of the crack are unknown. The data for the inverse problem is of Dirichlet type over a portion of the top boundary. We prove that this inverse problem is uniquely solvable under some assumptions on the geometry of the crack. The highlight of this paper is showing a stability result for this inverse problem. Assuming that the crack is planar, we show that reconstructing the plane containing the crack is Lipschitz stable despite the fact that the forcing term for the underlying PDE is unknown. This uniform stability result holds under the assumption that the forcing term is bounded above and the Dirichlet data is bounded below away from zero in appropriate norms.
Standard and non-standard measurements of acidity and the bacterial ecology of northern temperate mineral soils

Abstract Databases of soil pH values today not only guide the decisions of land managers but also the biogeochemical models and experimental designs of microbiologists and soil ecologists. Soil acidity underpins fundamental properties and functions in the soil, such as the solubilities of exchangeable ions and nutrients, or bacterial use of gradients of internal and external acidity to generate ATP and turn flagellar motors. Therefore, it is perhaps unsurprising that soil pH has emerged as the strongest predictor of soil bacterial community composition. However, the measurement of these particular values today does not address whether soil pH accurately represents the in situ acidity of soil microhabitats where microorganisms survive and reproduce. This study analyzes and compares soils of a large-scale natural soil pH gradient and a long-term experimental soil pH gradient for the purposes of testing new methods of measuring and interpreting soil acidity when applied to soil ecology. We extracted and prepared soil solutions using laboratory simulation of levels of carbon dioxide and soil moisture more typical of soil conditions while also miniaturizing traditional methods using a centrifuge for extractions. The simulation of in situ soil conditions resulted in significantly different estimates of soil pH. Furthermore, for soils from the long-term experimental soil pH gradient trial, the simulated soil pH values substantially improved predictions of bacterial community composition. We offer suggestions and cautions for researchers considering how to better represent soil acidity.
From crystal color symmetry to quantum spacetime.

This perspective article elucidates both the importance and the implications of relativistic spacetime crystals as well as the renormalized blended coordinates transformation. It alludes to possible applications in materials science, condensed matter physics and quantum gravity.
Optimal Fitting of the Freidberg Solution to In Situ Spacecraft Measurements of Magnetic Clouds

We report, in detail, an optimization approach for fitting a three-dimensional (3D) magnetic cloud (MC) model to in situ spacecraft measurements. The model, dubbed the Freidberg solution, encompasses 3D spatial variations in a generally cylindrical geometry, as derived from a linear force-free formulation. The approach involves a least-squares minimization implementation with uncertainty estimates from magnetic field measurements. We present one case study of the MC event on 22 May 2007 to illustrate the method and demonstrate the satisfying result of the minimum reduced $\chi^{2}\lesssim1$ , obtained from the Solar and TErrestrial RElations Observatory (STEREO) Behind spacecraft measurements. In addition, since the Advanced Composition Explorer (ACE) spacecraft at Earth crossed the STEREO Behind solution domain with an appropriate separation distance, the result from the optimally fitted Freidberg solution along the ACE spacecraft path is compared with the actual measurements of magnetic field components. A correlation coefficient of 0.89 is obtained between the two sets of data.
Poset Ramsey Numbers for Boolean Lattices

For each positive integer n, let Qn denote the Boolean lattice of dimension n. For posets $P, P^{\prime }$ , define the poset Ramsey number $R(P,P^{\prime })$ to be the least N such that for any red/blue coloring of the elements of QN, there exists either a subposet isomorphic to P with all elements red, or a subposet isomorphic to $P^{\prime }$ with all elements blue. Axenovich and Walzer introduced this concept in Order (2017), where they proved R(Q2,Qn) ≤ 2n + 2 and R(Qn,Qm) ≤ mn + n + m. They later proved 2n ≤ R(Qn,Qn) ≤ n2 + 2n. Walzer later proved R(Qn,Qn) ≤ n2 + 1. We provide some improved bounds for R(Qn,Qm) for various $n,m \in \mathbb {N}$ . In particular, we prove that R(Qn,Qn) ≤ n2 − n + 2, $R(Q_{2}, Q_{n}) \le \frac {5}{3}n + 2$ , and $R(Q_{3}, Q_{n}) \le \lceil \frac {37}{16}n + \frac {55}{16}\rceil $ . We also prove that R(Q2,Q3) = 5, and $R(Q_{m}, Q_{n}) \le \left \lceil \left (m - 1 + \frac {2}{m+1} \right )n + \frac {1}{3} m + 2\right \rceil $ for all n > m ≥ 4.
Transcriptional profiling of sequentially generated septal neuron fates.

The septum is a ventral forebrain structure known to regulate innate behaviors. During embryonic development, septal neurons are produced in multiple proliferative areas from neural progenitors following transcriptional programs that are still largely unknown. Here, we use a combination of single cell RNA sequencing, histology and genetic models to address how septal neuron diversity is established during neurogenesis. We find that the transcriptional profiles of septal progenitors change along neurogenesis, coinciding with the generation of distinct neuron types. We characterize the septal eminence, an anatomically distinct and transient proliferative zone composed of progenitors with distinctive molecular profiles, proliferative capacity and fate potential compared to the rostral septal progenitor zone. We show that Nkx2.1-expressing septal eminence progenitors give rise to neurons belonging to at least three morphological classes, born in temporal cohorts that are distributed across different septal nuclei in a sequential fountain-like pattern. Our study provides insight into the molecular programs that control the sequential production of different neuronal types in the septum, a structure with important roles in regulating mood and motivation.
The financial impact of street-level greenery on New York commercial buildings

Abstract Urban street-level greenery is empirically documented to improve mental and physical health, increase productivity, increase urban environmental equality and reduce carbon footprints. In addition, these benefits raise residents’ welfare, which has been correlated with increases in residential house prices. We measure street-level greenness in New York City through a novel Green View Index (GVI) using Google Street View images, and assess the impacts of greenness on commercial real estate prices. Using a sample of office transactions, we spatially correlate Google Street View Images for New York City over the 2010 to 2017 period. We find an 8.9% to 10.5% statistically, economically and positive transaction premium and a 5.6% to 7.8% rent premium for offices with low to high street-level greenness relative to those building transactions spatially correlated with very low greenness. Estimations are robust t with proximity to parks, subway stations, sidewalk widths, household income levels and investments by Building Improvement Districts, as well as other vital and standard office valuation features. By documenting the role of greenery in commercial building valuations, our results give a more complete understanding of the value of greenness in urban environments, as well as the economic role that urban landscape architecture, planning and development has upon cities.
Wheel configurations for combined energy storage and attitude control systems

Integrated power and attitude control system (IPACS) studies performed over a decade ago established the feasibility of simultaneously storing electrical energy in wheels and utilizing the resulting momentum for spacecraft attitude control. It was shown that such a system possessed many advantages over other contemporary energy storage and attitude control systems in many applications. More recent technology advances in composite rotors, magnetic bearings, and power control electronics have triggered new optimism regarding the feasibility and merits of such a system. This paper presents the results of a recent study whose focus was to define an advanced IPACS and to evaluate its merits for the Space Station application. Emphasis is given to the selection of the wheel configuration to perform the combined functions. A component design concept is developed to establish the system performance capability. A system-level trade study, including life-cycle costing, is performed to define the merits of the system relative to two other candidate systems. It is concluded that an advanced IPACS concept is not only feasible but offers substantial savings in mass and life-cycle cost.
Genetically) Improving Novelty in Procedural Story Generation

Procedural story generation (PCG) tailors a unique narrative experience for a player and can be accomplished via multiple techniques, from matching storylets to grammar-based generation. There exists a rich opportunity for evolutionary algorithms to be applied to this domain for intelligently constructing game narratives. This paper describes a conceptual procedure for applying genetic improvement to a grammar-driven procedural narrative within the context of a browser-based game.
Thermal effects on the Casimir energy of a Lorentz-violating scalar in magnetic field

In this work, I investigate the finite temperature Casimir effect due to a massive and charged scalar field that breaks Lorentz invariance in a CPT-even, aether-like way. I study the cases of Diric...
Multimodal Breast Lesion Classification Using Cross-Attention Deep Networks

Accurate breast lesion risk estimation can significantly reduce unnecessary biopsies and help doctors decide optimal treatment plans. Most existing computer-aided systems rely solely on mammogram features to classify breast lesions. While this approach is convenient, it does not fully exploit useful information in clinical reports to achieve the optimal performance. Would clinical features significantly improve breast lesion classification compared to using mammograms alone? How to handle missing clinical information caused by variation in medical practice? What is the best way to combine mammograms and clinical features? There is a compelling need for a systematic study to address these fundamental questions. This paper investigates several multimodal deep networks based on feature concatenation, cross-attention, and co-attention to combine mammograms and categorical clinical variables. We show that the proposed architectures significantly increase the lesion classification performance (average area under ROC curves from 0.89 to 0.94). We also evaluate the model when clinical variables are missing.
A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment

A primary goal of science and engineering (S&E) education is to produce good problem solvers, but how to best teach and measure the quality of problem solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here, we present a detailed characterization of the S&E problem-solving process as a set of specific interlinked decisions. This framework of decisions is empirically grounded and describes the entire process. To develop this, we interviewed 52 successful scientists and engineers ("experts") spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just 29 specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S&E problem solving. This decision framework also provides a more specific, complete, and empirically based description of the "practices" of science.
First-principles feasibility assessment of a topological insulator at the InAs/GaSb interface

First-principles simulations are conducted to shed light on the question of whether a two-dimensional topological insulator (2DTI) phase may be obtained at the interface between InAs and GaSb. To this end, the InAs/GaSb interface is compared and contrasted with the HgTe/CdTe interface. Density functional theory (DFT) simulations of these interfaces are performed using a machine-learned Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. For the HgTe/CdTe interface, our simulations show that band crossing is achieved and an inverted gap is obtained at a critical thickness of 5.1 nm of HgTe, in agreement with experiment and previous DFT calculations. In contrast, for InAs/GaSb, the gap narrows with increasing thickness of InAs; however, the gap does not close for interfaces with up to 50 layers (about 15 nm) of each material. When an external electric field is applied across the InAs/GaSb interface, the GaSb-derived valence band maximum is shifted up in energy with respect to the InAs-derived conduction band minimum until eventually the bands cross and an inverted gap opens. Our results show that it may be possible to reach the topological regime at the InAs/GaSb interface under the right conditions. However, it may be challenging to realize these conditions experimentally, which explains the difficulty of experimentally demonstrating an inverted gap in InAs/GaSb.
Information-Theoretic Bounds for Integral Estimation

In this paper, we consider a zero-order stochastic oracle model of estimating definite integrals. In this model, integral estimation methods may query an oracle function for a fixed number of noisy values of the integrand function and use these values to produce an estimate of the integral. We first show that the information-theoretic error lower bound for estimating the integral of a $d$ -dimensional function over a region with $l_{\infty}$ radius $r$ using at most $T$ queries to the oracle function is $\Omega\left(2^{d}r^{d+1}\sqrt{d/T}\right)$ . Additionally, we find that the Gaussian Quadrature method under the same model achieves a rate of $O\left(2^{d}r^{d}/\sqrt{T}\right)$ for functions with zero fourth and higherorder derivatives with respect to individual dimensions, and for Gaussian oracles, this rate is tight. For functions with nonzero fourth derivatives, the Gaussian Quadrature method achieves an upper bound which is not tight with the information-theoretic lower bound. Therefore, it is not minimax optimal, so there is space for the development of better integral estimation methods for such functions.
Refining the general equilibrium relation that subsists between stock returns, and each of investors’ risk preferences and information sets

Abstract Typically, models of stock prices or returns assume homogeneity of risk preference parameters. This study shows modeling of IPO prices necessarily is with reference to the distribution of risk preference parameters that already are represented in secondary equity markets. Modeling of stock returns is shown predicated only on changes to investors’ information sets, but is required to be robust to each of heterogeneity of risk preference parameters and existence, as an outcome, of representative agents. Non-bindingness of capital constraints facilitates the rational expectation that it is pricing of risk sharing benefits, not raw information, that determines stock prices.
Tonal harmony and the topology of dynamical score networks

I introduce the concept of dynamical score networks for the representation and analysis of tonal compositions: a score is interpreted as a dynamical network where every chord is a node and each pro...
Entropy stable modal discontinuous Galerkin schemes and wall boundary conditions for the compressible Navier-Stokes equations

Abstract Entropy stable schemes ensure that physically meaningful numerical solutions also satisfy a semi-discrete entropy inequality under appropriate boundary conditions. In this work, we describe a discretization of viscous terms in the compressible Navier-Stokes equations which enables a simple and explicit imposition of entropy stable no-slip and reflective (symmetry) wall boundary conditions for discontinuous Galerkin (DG) discretizations. Specifically, we derive methods for imposing adiabatic no-slip and reflective (symmetry) boundary conditions for modal entropy stable DG formulations which preserve a semi-discrete entropy inequality. Numerical results confirm the robustness and accuracy of the proposed approaches.
Phase transitions and critical behavior in hadronic transport with a relativistic density functional equation of state

We develop a flexible, relativistically covariant parameterization of dense nuclear matter equation of state suited for inclusion in computationally demanding hadronic transport simulations. Within an implementation in the hadronic transport code SMASH, we show that effects due to bulk thermodynamic behavior are reproduced in dynamic hadronic systems, demonstrating that hadronic transport can be used to study critical behavior in dense nuclear matter, both at and away from equilibrium. We also show that two-particle correlations calculated from hadronic transport simulation data follow theoretical expectations based on the second order cumulant ratio, and constitute a clear signature of crossing the phase diagram above the critical point.
Influence of Timing Between Androgen Deprivation Therapy and External Beam Radiation Therapy in Patients With Localized, High-Risk Prostate Cancer.

Abstract Purpose Treatment with long-term androgen deprivation therapy (ADT) and radiation therapy (RT) is the nonsurgical standard-of-care for patients with high- or very high-risk prostate cancer (HR-PC), but the optimal timing between ADT and RT initiation is unknown. We evaluate the influence of timing between ADT and RT on outcomes in patients with HR-PC using a large national cancer database. Methods and Materials Data for patients with clinical T1-T4 N0, M0, National Cancer Comprehensive Network HR-PC who were treated with definitive external RT (≥60 Gy) and ADT starting either before or within 14 days after RT start were extracted from the National Cancer Database (2004-2015). Patients were grouped on the basis of ADT initiation: (1) >11 weeks before RT, (2) 8 to 11weeks before RT, and (3) Results With a median follow-up of 68.9 months, 37,606 patients with HR-PC were eligible for analysis: 13,346 (35.5%) with >11 weeks of neoadjuvant ADT, 11,456 (30.5%) with 8 to 11 weeks of neoadjuvant ADT; and 12,804 (34%) patients with 11 weeks, 8 to 11 weeks, and 11 weeks group. Conclusions Neoadjuvant ADT initiation 8 to 11 weeks before RT is associated with significantly improved OS compared with shorter neoadjuvant ADT duration. Although prospective validation is warranted, this analysis is the largest retrospective study suggesting an influence of timing between ADT and RT initiation in HR-PC.
Scattering diagrams, theta functions, and refined tropical curve counts

Working over various graded Lie algebras and in arbitrary dimension, we express scattering diagrams and theta functions in terms of counts of tropical curves/disks, weighted by multiplicities given in terms of iterated Lie brackets. Over the tropical vertex group, our tropical curve counts are known to give certain descendant log Gromov-Witten invariants. Working over the quantum torus algebra yields theta functions for quantum cluster varieties, and our tropical description sets up for geometric interpretations of these. As an immediate application, we prove the quantum Frobenius conjecture of Fock and Goncharov. We also prove a refined version of the Carl-Pumperla-Siebert result on consistency of theta functions, and we prove the non-degeneracy of the trace-pairing for the Gross-Hacking-Keel Frobenius structure conjecture.
Bombesin-like peptide recruits disinhibitory cortical circuits and enhances fear memories.

Disinhibitory neurons throughout the mammalian cortex are powerful enhancers of circuit excitability and plasticity. The differential expression of neuropeptide receptors in disinhibitory, inhibitory, and excitatory neurons suggests that each circuit motif may be controlled by distinct neuropeptidergic systems. Here, we reveal that a bombesin-like neuropeptide, gastrin-releasing peptide (GRP), recruits disinhibitory cortical microcircuits through selective targeting and activation of vasoactive intestinal peptide (VIP)-expressing cells. Using a genetically encoded GRP sensor, optogenetic anterograde stimulation, and trans-synaptic tracing, we reveal that GRP regulates VIP cells most likely via extrasynaptic diffusion from several local and long-range sources. In vivo photometry and CRISPR-Cas9-mediated knockout of the GRP receptor (GRPR) in auditory cortex indicate that VIP cells are strongly recruited by novel sounds and aversive shocks, and GRP-GRPR signaling enhances auditory fear memories. Our data establish peptidergic recruitment of selective disinhibitory cortical microcircuits as a mechanism to regulate fear memories.
Optimal spin- and planar-quantum squeezing in superpositions of spin coherent states

We investigate the presence of spin- and planar-squeezing in generalized superpositions of atomic (or spin) coherent states. Spin-squeezing has been shown to be a useful tool in determining the presence of entanglement in multipartite systems, such as collections of two-level atoms, as well as being an indication of reduced projection noise and sub-shot-noise-limited phase uncertainty in Ramsey spectroscopy, suitable for measuring phases ϕ∼0. On the other hand, planar-squeezed states display reduced projection noise in two directions simultaneously and have been shown to lead to enhanced metrological precision in measuring phases without the need for explicit prior knowledge of the phase value. In this paper, we show that the generalized superposition state can be parametrized to display both spin-squeezing along all orthogonal axes and planar-squeezing along all orthogonal planes for all values of J>1/2. We close with an application of the maximally spin- and planar-squeezed states to quantum metrology.
Work, Entropy Production, and Thermodynamics of Information under Protocol Constraints

A thermodynamic framework provides new bounds on how much work can be extracted from a system under nonidealized, real-world conditions.