cssi/SciSciGPT-SciSciNet huggingface dataset:
The ‘COVID’ crash of the 2020 U.S. Stock market

Abstract We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the SP the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.
Smart choices and the selection monad

Describing systems in terms of choices and their resulting costs and rewards promises to free algorithm designers and programmers from specifying how to make those choices. In implementations, the choices can be realized by optimization or machine-learning methods. We study this approach from a programming-language perspective. We define a small language that supports decision-making abstraction, rewards, and probabilities. We give a globally optimizing operational semantics, and, using the selection monad for decision-making, three denotational semantics with auxiliary monads for reward and probability; the three model various correlations between returned values and expected rewards. We show the two kinds of semantics coincide by proving adequacy theorems; we show that observational equivalence is characterized by semantic equality (at basic types) by proving full abstraction theorems; and we discuss program equations.
Online Optimal Control with Affine Constraints.

This paper considers online optimal control with affine constraints on the states and actions under linear dynamics with bounded random disturbances. The system dynamics and constraints are assumed to be known and time invariant but the convex stage cost functions change adversarially. To solve this problem, we propose Online Gradient Descent with Buffer Zones (OGD-BZ). Theoretically, we show that OGD-BZ with proper parameters can guarantee the system to satisfy all the constraints despite any admissible disturbances. Further, we investigate the policy regret of OGD-BZ, which compares OGD-BZ's performance with the performance of the optimal linear policy in hindsight. We show that OGD-BZ can achieve a policy regret upper bound that is square root of the horizon length multiplied by some logarithmic terms of the horizon length under proper algorithm parameters.
First principles reactive simulation for equation of state prediction.

The high cost of density functional theory (DFT) has hitherto limited the ab initio prediction of the equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction and thus the assessment of high explosives. In particular, we find that DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman–Jouguet state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. We also demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling.
Harnessing the power of antibodies to fight bone metastasis

Antibody-based therapies have proved to be of great value in cancer treatment. Despite the clinical success of these biopharmaceuticals, reaching targets in the bone microenvironment has proved to be difficult due to the relatively low vascularization of bone tissue and the presence of physical barriers. Here, we have used an innovative bone-targeting (BonTarg) technology to generate a first-in-class bone-targeting antibody. Our strategy involves the use of pClick antibody conjugation technology to chemically couple the bone-targeting moiety bisphosphonate to therapeutic antibodies. Bisphosphonate modification of these antibodies results in the delivery of higher conjugate concentrations to the bone metastatic niche, relative to other tissues. In xenograft mice models, this strategy provides enhanced inhibition of bone metastases and multiorgan secondary metastases that arise from bone lesions. Specific delivery of therapeutic antibodies to the bone, therefore, represents a promising strategy for the treatment of bone metastatic cancers and other bone diseases.
Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry.

Most high-dimensional datasets are thought to be inherently low-dimensional—that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace–Beltrami operator using the heat-trace expansion and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifold’s embedding to its curvature using the Second Fundamental Form and the Gauss–Codazzi equation. We found that the intrinsic approach fails to accurately estimate the curvature of even a two-dimensional constant-curvature manifold, whereas the extrinsic approach was able to handle more complex toy models, even when confounded by practical constraints like small sample sizes and measurement noise. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches and recapitulated its topological classification as a Klein bottle. Lastly, we applied the extrinsic approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells to quantify the Riemannian curvature of scRNAseq manifolds.
Boundary electromagnetic duality from homological edge modes

Recent years have seen a renewed interest in using ‘edge modes’ to extend the pre-symplectic structure of gauge theory on manifolds with boundaries. Here we further the investigation undertaken in [1] by using the formalism of homotopy pullback and Deligne- Beilinson cohomology to describe an electromagnetic (EM) duality on the boundary of M = B3 × ℝ. Upon breaking a generalized global symmetry, the duality is implemented by a BF-like topological boundary term. We then introduce Wilson line singularities on ∂M and show that these induce the existence of dual edge modes, which we identify as connections over a (−1)-gerbe. We derive the pre-symplectic structure that yields the central charge in [1] and show that the central charge is related to a non-trivial class of the (−1)-gerbe.
New mechanism and exact theory of superconductivity from strong repulsive interaction.

We introduce a general mechanism for superconductivity in Fermi systems with strong repulsive interaction. Because kinetic terms are small compared to the bare repulsion, the dynamics of charge carriers is constrained by the presence of other nearby carriers. By treating kinetic terms as a perturbation around the atomic limit, we show that pairing can be induced by correlated multiparticle tunneling processes that favor two itinerant carriers to be close together. Our analytically controlled theory provides a quantitative formula relating Tc to microscopic parameters, with maximum Tc reaching about 10% of the Fermi temperature. Our work demonstrates a powerful method for studying strong coupling superconductivity with unconventional pairing symmetry. It also offers a realistic new route to realizing finite angular momentum superfluidity of spin-polarized fermions in optical lattice.
Temporal analysis of social determinants associated with COVID-19 mortality

This study examines how social determinants associated with COVID-19 mortality change over time. Using US county-level data from July 5 and December 28, 2020, the effect of 19 high-risk factors on COVID-19 mortality rate was quantified at each time point with negative binomial mixed models. Then, these high-risk factors were used as controls in two association studies between 40 social determinants and COVID-19 mortality rates using data from the same time points. The results indicate that counties with certain ethnic minorities and age groups, immigrants, prevalence of diseases like pediatric asthma and diabetes and cardiovascular disease, socioeconomic inequalities, and higher social association are associated with increased COVID-19 mortality rates. Meanwhile, more mental health providers, access to exercise, higher income, chronic lung disease in adults, suicide, and excessive drinking are associated with decreased mortality. Our temporal analysis also reveals a possible decreasing impact of socioeconomic disadvantage and air quality, and an increasing effect of factors like age, which suggests that public health policies may have been effective in protecting disadvantaged populations over time or that analysis utilizing earlier data may have exaggerated certain effects. Overall, we continue to recognize that social inequality still places disadvantaged groups at risk, and we identify possible relationships between lung disease, mental health, and COVID-19 that need to be explored on a clinical level.
Quantum Features and Signatures of Quantum Thermal Machines

The aim of this book chapter is to indicate how quantum phenomena are affecting the operation of microscopic thermal machines, such as engines and refrigerators. As converting heat to work is one of the fundamental concerns in thermodynamics, the platform of quantum thermal machines sheds light on thermodynamics in the quantum regime. This chapter focuses on the basic features of quantum mechanics, such as energy quantization, the uncertainty principle, quantum coherence and correlations, and their manifestation in microscopic thermal devices. In addition to indicating the peculiar behaviors of thermal-machines due to their non-classical aspects, we present quantum-thermodynamic signatures of these machines. Any violation of the classical thermodynamic bounds observed in the outcome of measurements on theses machines is a sufficient condition to conclude that quantum effects are present in the operation of that thermal machine. Experimental setups demonstrating some of these results are also presented.
Geometry and entanglement in the scattering matrix

Abstract A formulation of nucleon–nucleon scattering is developed in which the S -matrix, rather than an effective-field theory (EFT) action, is the fundamental object. Spacetime plays no role in this description: the S -matrix is a trajectory that moves between RG fixed points in a compact theory space defined by unitarity. This theory space has a natural operator definition, and a geometric embedding of the unitarity constraints in four-dimensional Euclidean space yields a flat torus, which serves as the stage on which the S -matrix propagates. Trajectories with vanishing entanglement are special geodesics between RG fixed points on the flat torus, while entanglement is driven by an external potential. The system of equations describing S -matrix trajectories is in general complicated, however the very-low-energy S -matrix –that appears at leading-order in the EFT description– possesses a UV/IR conformal invariance which renders the system of equations integrable, and completely determines the potential. In this geometric viewpoint, inelasticity is in correspondence with the radius of a three-dimensional hyperbolic space whose two-dimensional boundary is the flat torus. This space has a singularity at vanishing radius, corresponding to maximal violation of unitarity. The trajectory on the flat torus boundary can be explicitly constructed from a bulk trajectory with a quantifiable error, providing a simple example of a holographic quantum error correcting code.
Measuring the density structure of an accretion hot spot

Magnetospheric accretion models predict that matter from protoplanetary disks accretes onto stars via funnel flows, which follow stellar magnetic field lines and shock on the stellar surfaces1–3, leaving hot spots with density gradients4–6. Previous work has provided observational evidence of varying density in hot spots7, but these observations were not sensitive to the radial density distribution. Attempts have been made to measure this distribution using X-ray observations8–10; however, X-ray emission traces only a fraction of the hot spot11,12 and also coronal emission13,14. Here we report periodic ultraviolet and optical light curves of the accreting star GM Aurigae, which have a time lag of about one day between their peaks. The periodicity arises because the source of the ultraviolet and optical emission moves into and out of view as it rotates along with the star. The time lag indicates a difference in the spatial distribution of ultraviolet and optical brightness over the stellar surface. Within the framework of a magnetospheric accretion model, this finding indicates the presence of a radial density gradient in a hot spot on the stellar surface, because regions of the hot spot with different densities have different temperatures and therefore emit radiation at different wavelengths. An observed one-day difference between the peaks of emission of ultraviolet and optical light from the hot spot on GM Aurigae indicates that the hot spot has a radial density gradient.
Directed sets and topological spaces definable in o-minimal structures

We study directed sets definable in o-minimal structures, showing that in expansions of ordered fields these admit cofinal definable curves, as well as a suitable analogue in expansions of ordered groups, and furthermore that no analogue holds in full generality. We use the theory of tame pairs to extend the results in the field case to definable families of sets with the finite intersection property. We then apply our results to the study of definable topologies. We prove that all definable topological spaces display properties akin to first countability, and give several characterizations of a notion of definable compactness due to Peterzil and Steinhorn generalized to this setting.
Data Characterization of Medicaid: Legacy and New Data Formats in the CMS Virtual Research Data Center

Medicaid is a significant health insurance plan providing healthcare coverage to up to a third of the population of the United Sates. We describe two different formats of Medicaid data within Center for Medicare and Medicaid Services Virtual Research Data Center. We analyze record length, age and enrollment justification among patients for both data formats. As of December 2016, the total size of Medicaid population available from CMS is 92,953,389; 45% of patients are aged 0 to 18, 26.6% are aged 19-35 and 23.2% are aged 36-64. In terms of Medicaid eligibility, 35.6% qualify due to (child) age and 26.8% qualify due to income. We also compare the volume of Medicaid to Medicare for year 2016. We conclude that Medicaid data includes patients with significant record lengths and relatively well documented enrollment justification, which are high value assets for data reuse researchers that are willing to balance known data limitations with careful analysis design and interpretation.
SwarmTCR: a computational approach to predict the specificity of T cell receptors.

With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. We compared the performance of SwarmTCR against another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable. Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub ( https://github.com/thecodingdoc/SwarmTCR ).
Finding Self Among Others: Navigating the Tensions Between Personal and Social Identity:

Personal and social identity are critical for entrepreneurs. However, tensions arise in entrepreneurial experiences due to the independence underlying personal identity and embeddedness underlying ...
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis

Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference however, adaptive data collection induces memory and c...
Automated hyperparameter selection for the PC algorithm

Abstract The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I α level. PC is however unsupervised, so we cannot tune α using traditional cross-validation. We therefore propose AutoPC, a fast procedure that optimizes α directly for a user chosen metric. We in particular force PC to double check its output by executing a second run on the recovered graph. We choose the final output as the one which maximizes stability between the two runs. AutoPC consistently outperforms the state of the art across multiple metrics.
Monosodium Urate Crystals Regulate a Unique JNK-Dependent Macrophage Metabolic and Inflammatory Response

The response of macrophages to monosodium urate crystals (MSUc) is incompletely understood partly due to the use of a toll-like receptor (TLR)-induced priming step in cell lines. Yet, MSUc induce inflammation in vivo without prior priming, raising the possibility of an initial cell-autonomous phase that is TLR-independent. Here, using genome wide transcriptomic analysis and biochemical assays we demonstrate that MSUc alone induce a metabolic-inflammatory transcriptional program in non-primed human and murine macrophages that is markedly distinct to that induced by LPS. Genes uniquely upregulated in response to MSUc belonged to lipid and amino acid metabolism, glycolysis, and SLC transporters. This upregulation led to a metabolic rewiring in sera from individuals and mice with acute gouty arthritis. Mechanistically, the initiating inflammatory-metabolic changes in acute gout flares are regulated through a persistent expression and increased binding of JUN to the promoter of target genes through JNK signaling -but not P38- in a process that is different than after LPS stimulation and unexpectedly independent of inflammasome activation. Finally, pharmacological JNK inhibition limited MSUc-induced inflammation in animal models of acute gouty inflammation. MSUc alone induce a unique strong macrophage activation and distinct phenotype that forces a reconsideration of initiating mechanisms in acute gout flares.
Analyzing relevance vector machines using a single penalty approach

Relevance vector machine (RVM) is a popular sparse Bayesian learning model typically used for prediction. Recently it has been shown that improper priors assumed on multiple penalty parameters in RVM may lead to an improper posterior. Currently in the literature, the sufficient conditions for posterior propriety of RVM do not allow improper priors over the multiple penalty parameters. In this article, we propose a single penalty relevance vector machine (SPRVM) model in which multiple penalty parameters are replaced by a single penalty and we consider a semi Bayesian approach for fitting the SPRVM. The necessary and sufficient conditions for posterior propriety of SPRVM are more liberal than those of RVM and allow for several improper priors over the penalty parameter. Additionally, we also prove the geometric ergodicity of the Gibbs sampler used to analyze the SPRVM model and hence can estimate the asymptotic standard errors associated with the Monte Carlo estimate of the means of the posterior predictive distribution. Such a Monte Carlo standard error cannot be computed in the case of RVM, since the rate of convergence of the Gibbs sampler used to analyze RVM is not known. The predictive performance of RVM and SPRVM is compared by analyzing three real life datasets.
Robust Regression via Model Based Methods.

The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few. Despite computational advantages due to its differentiability, it is not robust to outliers. In contrast, \(\ell _p\) norms are known to be robust, but cannot be optimized via, e.g., stochastic gradient descent, as they are non-differentiable. We propose an algorithm inspired by so-called model-based optimization (MBO) [35, 36], which replaces a non-convex objective with a convex model function and alternates between optimizing the model function and updating the solution. We apply this to robust regression, proposing SADM, a stochastic variant of the Online Alternating Direction Method of Multipliers (OADM) [48] to solve the inner optimization in MBO. We show that SADM converges with the rate \(O(\log T/T)\). Finally, we demonstrate experimentally (a) the robustness of \(\ell _p\) norms to outliers and (b) the efficiency of our proposed model-based algorithms in comparison with gradient methods on autoencoders and multi-target regression.
Acoustic Communication and Sensing for Inflatable Modular Soft Robots

Modular soft robots combine the strengths of two traditionally separate areas of robotics. As modular robots, they can show robustness to individual failure and reconfigurability; as soft robots, they can deform and undergo large shape changes in order to adapt to their environment, and have inherent human safety. However, for sensing and communication these robots also combine the challenges of both: they require solutions that are scalable (low cost and complexity) and efficient (low power) to enable collectives of large numbers of robots, and these solutions must also be able to interface with the high extension ratio elastic bodies of soft robots. In this work, we seek to address these challenges using acoustic signals produced by piezoelectric surface transducers that are cheap, simple, and low power, and that not only integrate with but also leverage the elastic robot skins for signal transmission. Importantly, to further increase scalability, the transducers exhibit multi-functionality made possible by a relatively flat frequency response across the audible and ultrasonic ranges. With minimal hardware, they enable directional contact-based communication, audible-range communication at a distance, and exteroceptive sensing. We demonstrate a subset of the decentralized collective behaviors that these functions make possible with multi-robot hardware implementations. The use of acoustic waves in this domain is shown to provide distinct advantages over existing solutions.
Towards a Cost vs. Quality Sweet Spot for Monitoring Networks

Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can reduce the costs to monitor - in terms of collection, storage and analysis - by judiciously controlling how much and which measurements we collect. By positing that we can treat almost all measured signals as sampled time-series, we show that we can use signal processing techniques such as the Nyquist-Shannon theorem to avoid wasteful data collection. We show that large savings appear possible by analyzing tens of popular measurement systems from a production datacenter network. We also discuss some challenges that must be solved when applying these techniques in practice.
Driver State and Behavior Detection Through Smart Wearables

Integrating driver, in-cabin, and outside environ-ment's contextual cues into the vehicle's decision making is the centerpiece of semi-automated vehicle safety. Multiple systems have been developed for providing context to the vehicle, which often rely on video streams capturing drivers' physical and environmental states. While video streams are a rich source of information, their ability in providing context can be challenging in certain situations, such as low illuminance environments (e.g., night driving), and they are highly privacy-intrusive. In this study, we leverage passive sensing through smartwatches for classifying elements of driving context. Specifically, through using the data collected from 15 participants in a naturalistic driving study, and by using multiple machine learning algorithms such as random forest, we classify driver's activities (e.g., using phone and eating), outside events (e.g., passing intersection and changing lane), and outside road attributes (e.g., driving in a city versus a highway) with an average F1 score of 94.55, 98.27, and 97.86 % respectively, through 10-fold cross-validation. Our results show the applicability of multimodal data retrieved through smart wearable devices in providing context in real-world driving scenarios and pave the way for a better shared autonomy and privacy-aware driving data-collection, analysis, and feedback for future autonomous vehicles.
Neural Process for Black-box Model Optimization Under Bayesian Framework.

There are a large number of optimization problems in physical models where the relationships between model parameters and outputs are unknown or hard to track. These models are named as black-box models in general because they can only be viewed in terms of inputs and outputs, without knowledge of the internal workings. Optimizing the black-box model parameters has become increasingly expensive and time consuming as they have become more complex. Hence, developing effective and efficient black-box model optimization algorithms has become an important task. One powerful algorithm to solve such problem is Bayesian optimization, which can effectively estimates the model parameters that lead to the best performance, and Gaussian Process (GP) has been one of the most widely used surrogate model in Bayesian optimization. However, the time complexity of GP scales cubically with respect to the number of observed model outputs, and GP does not scale well with large parameter dimension either. Consequently, it has been challenging for GP to optimize black-box models that need to query many observations and/or have many parameters. To overcome the drawbacks of GP, in this study, we propose a general Bayesian optimization algorithm that employs a Neural Process (NP) as the surrogate model to perform black-box model optimization, namely, Neural Process for Bayesian Optimization (NPBO). In order to validate the benefits of NPBO, we compare NPBO with four benchmark approaches on a power system parameter optimization problem and a series of seven benchmark Bayesian optimization problems. The results show that the proposed NPBO performs better than the other four benchmark approaches on the power system parameter optimization problem and competitively on the seven benchmark problems.
Bright mid-infrared photoluminescence from high dislocation density epitaxial PbSe films on GaAs

We report on photoluminescence in the 3–7 µm mid-wave infrared (MWIR) range from sub-100 nm strained thin films of rocksalt PbSe(001) grown on GaAs(001) substrates by molecular beam epitaxy. These bare films, grown epitaxially at temperatures below 400 °C, luminesce brightly at room temperature and have minority carrier lifetimes as long as 172 ns. The relatively long lifetimes in PbSe thin films are achievable despite threading dislocation densities exceeding 109 cm−2 arising from island growth on the nearly 8% lattice- and crystal-structure-mismatched GaAs substrate. Using quasi-continuous-wave and time-resolved photoluminescence, we show that the Shockley–Read–Hall recombination is slow in our high dislocation density PbSe films at room temperature, a hallmark of defect tolerance. Power-dependent photoluminescence and high injection excess carrier lifetimes at room temperature suggest that degenerate Auger recombination limits the efficiency of our films, although the Auger recombination rates are significantly lower than equivalent III–V bulk materials and even a bit slower than expectations for bulk PbSe. Consequently, the combined effects of defect tolerance and low Auger recombination rates yield an estimated peak internal quantum efficiency of roughly 30% at room temperature, unparalleled in the MWIR for a severely lattice-mismatched thin film. We anticipate substantial opportunities for improving performance by optimizing crystal growth as well as understanding Auger processes in thin films. These results highlight the unique opportunity to harness the unusual chemical bonding in PbSe and related IV–VI semiconductors for heterogeneously integrated mid-infrared light sources constrained by tight thermal budgets in new device designs.
Cooperative SGD: A Unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms

Communication-efficient SGD algorithms, which allow nodes to perform local updates and periodically synchronize local models, are highly effective in improving the speed and scalability of distributed SGD. However, a rigorous convergence analysis and comparative study of different communication-reduction strategies remains a largely open problem. This paper presents a unified framework called Cooperative SGD that subsumes existing communication-efficient SGD algorithms such as periodic-averaging, elastic-averaging, and decentralized SGD. By analyzing Cooperative SGD, we provide novel convergence guarantees for existing algorithms. Moreover, this framework enables us to design new communication-efficient SGD algorithms that strike the best balance between reducing communication overhead and achieving fast error convergence with a low error floor.
Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems

Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. These pre-defined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. This leads to large codebook sizes with high beam training overhead which makes it hard for these systems to support highly mobile applications. To overcome these limitations, this paper develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on the receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, RF hardware, or user positions. To reduce the learning time, the proposed model designs a novel Wolpertinger-variant architecture that is capable of efficiently searching the large discrete action space. The proposed learning framework respects the RF hardware constraints such as the constant-modulus and quantized phase shifter constraints. Simulation results confirm the ability of the developed framework to learn near-optimal beam patterns for line-of-sight (LOS), non-LOS (NLOS), mixed LOS/NLOS scenarios and for arrays with hardware impairments without requiring any channel knowledge.
Judicial Deference, Procedural Protections, and Deal Outcomes in Freezeout Transactions: Evidence from the Effect of MFW

Judicial Deference, Procedural Protections, and Deal Outcomes in Freezeout Transactions: Evidence from the Effect of MFW
Learning to delegate for large-scale vehicle routing

Vehicle routing problems (VRPs) are a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions on small problem instances of up to 100 customers, their performance does not scale to large problems. This article presents a novel learning-augmented local search algorithm to solve large-scale VRP. The method iteratively improves the solution by identifying appropriate subproblems and $\textit{delegating}$ their improvement to a black box subsolver. At each step, we leverage spatial locality to consider only a linear number of subproblems, rather than exponential. We frame subproblem selection as a regression problem and train a Transformer on a generated training set of problem instances. We show that our method achieves state-of-the-art performance, with a speed-up of up to 15 times over strong baselines, on VRPs with sizes ranging from 500 to 3000.
Diagnosis of Impact Damage in Composite Structures With Built-In Piezoelectrics Network

An active diagnostic technique for identifying impact damage in composites is presented. A network of piezoelectric actuators and sensors were built into composites to generate and receive ultrasonic stress waves in the structures. Scattered waves were produced from the damage and were detected by sensors. A signal-processing scheme composed of smoothing filtering a joint time-frequency analysis was applied to minimize noise interference and convert sensor measurements into time-frequency spectrograms for interpretation. An identification method based on time-of- flight information obtained from the spectrograms was developed. Experiments were performed on composites with different configurations. It was demonstrated that the method was able to detect the presence of the damage and characterize the damage to a satisfactory precision. Identification results were verified with X-ray images of the structures.
Development, damage and repair of the mammalian kidney (208.1)

The paired kidneys are vital organs controlling water, salt and pH balance for body fluids, removal of toxic by-products of metabolism, the pressure and composition of blood, and bone formation. Th...
Calculated late time spectra of Supernovae

We consider here the nebular phase spectra of supernovae whose late time luminosity is provided by the radioactive decay of /sup 56/Ni and /sup 56/Co synthesized in the explosion. A broad variety of supernovae are known or suspected to fall in this category. This includes all SNIa and SNIb, and at least some SNII, in particular SN1987a. At sufficiently late times the expanding supernova becomes basically nebular in character due to its decreasing optical depth. The spectra produced during this stage contain information on the density and abundance structure of the entire supernova, as opposed to spectra near maximum light which are affected only by the outermost layers. A numerical model for nebular spectrum formation is therefore potentially very valuable for answering currently outstanding questions about the post-explosion supernova structure. As an example, we can hope to determine the degree of mixing which occurs between the layers of the ''onion-skin'' abundance structure predicted by current one dimensional explosion calculations. In the sections which follow, such a numerical model is briefly described and then applied to SN1972e, a typical SNIa, SN1985f, an SNIb, and finally to SN1987a. In the case of SN1987a predicted spectra are presented for the wavelength range frommore » 1 to 100 microns at a time 300 days after explosion. 18 refs., 6 figs.« less
Yeast AMP deaminase. Catalytic activity in Schizosaccharomyces pombe and chromosomal location in Saccharomyces cerevisiae.

The AMP deaminase gene was mapped to chromosome XIII of Saccharomyces cerevisiae strain JM1901. The AMP deaminase gene is located near SUP5, GAL80, SUF7, and SUF22. The presence of AMP deaminase in the fission yeast Schizosaccharomyces pombe was examined by comparing DNA hybridization, protein immunoreactivity, and catalytic activity from S. cerevisiae, known to contain the protein, to S. pombe. DNA hybridization experiments using the cloned S. cerevisiae AMP deaminase gene failed to hybridize to the genomic DNA from S. pombe strain 972h-s. Protein extracts from S. pombe and S. cerevisiae were analyzed in parallel and exhibited comparable AMP deaminase activities. Analysis of reaction intermediates in cell extracts of S. pombe established that IMP is formed directly from AMP without intervening steps. The AMP deaminase of S. pombe was purified 1,100-fold to a specific catalytic activity of 67 mumol/min/mg of protein. Purified protein interacted weakly with polyclonal antibodies prepared against S. cerevisiae AMP deaminase. AMP deaminases from both S. cerevisiae and S. pombe were activated by ATP with micromolar activation constants, are inhibited by coformycin, and are specific for AMP when compared to other purine nucleosides and nucleotides. The results establish that S. pombe contains an AMP deaminase with catalytic properties similar to that from S. cerevisiae, even though the DNA sequences of the genes and the immunoreactivity of the protein from S. pombe differs considerably from the AMP deaminase of S. cerevisiae. Genetic analysis of the pathways of purine metabolism in S. pombe (Pourquie, J., and Heslot, H. (1971) Genet. Res. 18, 33-44) had indicated the absence of AMP deaminase. The presence of a regulated AMP deaminase in S. pombe supports the hypothesis that eukaryotes regulate adenine nucleotide pools by the activity of AMP deaminase.
The Development of Herman Dooyeweerd's Concept of Rights

For all his renown as a general philosopher and Christian apologist, Herman Dooyeweerd (1894-1977) was first and foremost a jurist. He studied law for five years at the Free University of Amsterdam and in 1917 completed his dissertation on the role of the Cabinet in Dutch public law. For three years thereafter he worked in the Dutch Department of Labour as a clerk and legislative draftsman. From 1926 until 1965 he served on the Faculty of Law of the Free University of Amsterdam, where he taught legal philosophy, legal history, and legal science. He presided over the distinguished Society of Legal (Philosophy and numerous other legal organizations. He engaged in ample and able debate with such leading jurists of his day as Giorgio del Vecchio, Georg Jellinek and Hans Kelsen, and filled his library with a vast array of legal tomes. Well over half of his 200-odd professional publications are on legal subjects. This article focuses on one small part of Dooyeweerd's jurisprudential legacy, his concept of rights. Dooyeweerd addressed the subject of rights several times in his career, each time seeking to develop a comprehensive Calvinist concept of rights. His initial efforts led him to a rather traditional Calvinist concept of political liberties, rooted in simple theological principles. His later efforts yielded an intricate modal concept of legal competences and subjective rights, rooted in a complex philosophical system. The analysis that follows will pay particular attention to the analytical stages through which Dooyeweerd passed to develop his concept.
The Concept of Election and Second Isaiah: Recent Literature

In this article I contend that the conceptual categories utilized by many recent scholars engaged in analyzing the idea of election in the Hebrew Bible have led to a variety of interrelated misunderstandings, both of the idea of election in general and of specific texts invoked in such discussions. This article traces out the distortions in the scholarship on this central theological concept and shows how similarly problematic trends also occur in discussions of Second Isaiah, a text frequently cited in studies of election. I conclude by offering a brief sketch of both a new possible reading of Second Isaiah and the theological implications of such a reading for the contemporary situation.
Evidence that Autophosphorylation of the Major Sporulation Kinase in Bacillus subtilis Is Able To Occur in trans

ABSTRACT Entry into sporulation in Bacillus subtilis is governed by a multicomponent phosphorelay, a complex version of a two-component system which includes at least three histidine kinases (KinA to KinC), two phosphotransferases (Spo0F and Spo0B), and a response regulator (Spo0A). Among the three histidine kinases, KinA is known as the major sporulation kinase; it is autophosphorylated with ATP upon starvation and then transfers a phosphoryl group to the downstream components in a His-Asp-His-Asp signaling pathway. Our recent study demonstrated that KinA forms a homotetramer, not a dimer, mediated by the N-terminal domain, as a functional unit. Furthermore, when the N-terminal domain was overexpressed in the starving wild-type strain, sporulation was impaired. We hypothesized that this impairment of sporulation could be explained by the formation of a nonfunctional heterotetramer of KinA, resulting in the reduced level of phosphorylated Spo0A (Spo0A∼P), and thus, autophosphorylation of KinA could occur in trans. To test this hypothesis, we generated a series of B. subtilis strains expressing homo- or heterogeneous KinA protein complexes consisting of various combinations of the phosphoryl-accepting histidine point mutant protein and the catalytic ATP-binding domain point mutant protein. We found that the ATP-binding-deficient protein was phosphorylated when the phosphorylation-deficient protein was present in a 1:1 stoichiometry in the tetramer complex, while each of the mutant homocomplexes was not phosphorylated. These results suggest that ATP initially binds to one protomer within the tetramer complex and then the γ-phosphoryl group is transmitted to another in a trans fashion. We further found that the sporulation defect of each of the mutant proteins is complemented when the proteins are coexpressed in vivo . Taken together, these in vitro and in vivo results reinforce the evidence that KinA autophosphorylation is able to occur in a trans fashion. IMPORTANCE Autophosphorylation of histidine kinases is known to occur by either the cis (one subunit of kinase phosphorylating itself within the multimer) or the trans (one subunit of the multimer phosphorylates the other subunit) mechanism. The present study provided direct in vivo and in vitro evidence that autophosphorylation of the major sporulation histidine kinase (KinA) is able to occur in trans within the homotetramer complex. While the physiological and mechanistic significance of the trans autophosphorylation reaction remains obscure, understanding the detailed reaction mechanism of the sporulation kinase is the first step toward gaining insight into the molecular mechanisms of the initiation of sporulation, which is believed to be triggered by unknown factors produced under conditions of nutrient depletion.
Joyous or Reckless

James F. Weiner. The Heart of the Pearlshell: The Mythological Dimension of Foi Sociality. Berkeley: University of California Press. 1988. 322 pp. $40.00 (cloth).
A Post-Fractionated Strip-Strip-Block Design for Multi-Stage Processes

It is shown that costs and experimental efforts can be reduced by using a post-fractionated strip-strip block design to investigate the effects of factors and their interactions in three-stage industrial processes. Also considered is how to generate the..
The isolation, characterization and sequence of two divergent β-tubulin genes from soybean (Glycine max L.)

Two divergent β-tubulin genes (designated Sβ-1 and Sβ-2) were isolated by screening a soybean genomic library with a Chlamydomonas reinhardtii β-tubulin cDNA probe. Restriction fragment analysis of the clones recovered, and of soybean genomic DNA, indicated that these represent two unique classes of structurally different β-tubulin genes in the soybean genome. However, it is possible that unidentified members of these classes or additional highly divergent classes of β-tubulin genes (thus far undetected) exist in the soybean genome. The Sβ-1 and Sβ-2 genomic clones were sequenced, revealing that both are potentially functional genes which would encode β-tubulins of 445 and 449 amino acids, respectively. A comparison of their derived amino acid sequences with β-tubulins from several organisms showed that they are most homologous to Chlamydomonas β-tubulin (85–87%), with lesser degrees of homology to β-tubulins of vertebrate species (79–83%), Trypanosoma brucei (80–81%) and Saccharomyces cerevisiae (66–68%). The amino acid sequences of Sβ-1 and Sβ-2 are as divergent from each other as they are from the Chlamydomonas β-tubulin. The amino acids at the diverged positions in Sβ-2 are nearly all conservative substitutions while in Sβ-1, 18 of the 69 substitutions were non-conservative. Both soybean β-tubulin genes contain two introns in exactly the same positions. The first soybean intron is located in the same position as the third intron of the Chlamydomonas β-tubulin genes. Codon usage in the two soybean β-tubulins is remarkably similar (D2=0.87), but differs from codon usage in other soybean genes.
JAK2V617F: more than a clonal marker?

Little was known about the molecular pathogenesis of BCR-ABL1-negative classic myeloproliferative neoplasms (MPN) until early 2005 when several groups reported a Janus kinase 2 (JAK2) gain-of-funct...
Nonalloyed contacts to p-type GaAs

ABSTRACT The formation of low resistance ohmic contacts to semiconductor devices in which no melting("alloying") of the contacting metals occurs is highly desirable. To form these contacts a very thindepletion layer must be created at the metal-semiconductor, MS, interface with tunneling occurring atthe Fermi level. We describe two contacting schemes to p-type GaAs that avoid the alloying step andpermit the use of robust refractory metals for the contacts. The p-contact will be important in proposedphoto conductive switches using a P-i-N structure instead of the more common N-i-N structure. Thework reported describes our studies of (a) a simple diffusion apparatus to create degenerate holeconcentrations near the semiconductor interface and (b) a very stable highly doped epitaxial layer grownusing Liquid Phase Epitaxy (LPE). 2. INTRODUCTION. The standard optical switch structure is shown schematically in Fig. 1 in which the material ofthe switch is assumed to be semi-insulating GaAs in which the majority carriers upon switching are
Ethical issues in the treatment of applicants to APA-accredited Ph.D. programs.

This paper examines some of the ethical issues involved in the treatment of applicants to highly competitive APA-accredited clinical and counseling psychology Ph.D. programs. These issues are analyzed in terms of the 1992 APA Ethics Code as well as the basic ethical principles of nonmaleficence, beneficence, justice, fidelity, and autonomy. Issues considered include the applicability of the 1992 APA Ethics Code to the selection process, the obligation to provide applicants accurate and complete information, dual relationships in the selection process, the treatment of rejected or wait-listed applicants, discrimination in the selection process, psychological assessment and research in the selection process, and legal resolutions which pertain to the selection process. In analyzing these issues, the paper calls attention to the need for psychologists functioning as admissions evaluators to be aware of and sensitive to the ethical issues relevant to the selection process.