cssi/SciSciGPT-SciSciNet huggingface dataset:
CEO Reputation: Who Benefits - The Firm and the CEO?

I examine the potential economic value of CEO reputation: performance improvement at the firm level and job retention benefits to the CEO. Two perspectives on CEO reputation offer different predictions regarding the benefits of CEO reputation. The ability perspective in the agency literature advocates the economic benefits of CEO reputation. The symbolic image perspective from recent CEO reputation studies, however, argues that CEO reputation does not necessarily improve firm performance or CEO job retention. I investigate which perspective is more consistent with empirical evidence. The results of firm performance tests show that CEOs with well-established reputations are able to sustain good firm performance but do not turn around poor performance in the near term. These results imply that stakeholders might have to consider replacing the CEO - no matter how highly regarded - with a turnaround specialist when a firm suffers financially. Another finding from job security tests shows that CEOs with high reputation are more likely to be dismissed than CEOs with low reputation when they perform poorly. These results suggest that the reputations of CEOs through promoting their own images in the media do not necessarily secure their job titles.
Reassessing the Inclusion of Race in Diagnosing Kidney Diseases: An Interim Report From the NKF-ASN Task Force

For almost 2 decades, equations that use serum creatinine, age, sex, and race to estimate glomerular filtration rate (GFR) have included "race" as Black or non-Black. Given considerable evidence of disparities in health and health care delivery in African American communities, some regard keeping a race term in GFR equations as a practice that differentially influences access to care and kidney transplantation. Others assert that race captures important non-GFR determinants of serum creatinine and its removal from the calculation may perpetuate other disparities. The National Kidney Foundation (NKF) and American Society of Nephrology (ASN) established a task force in 2020 to reassess the inclusion of race in the estimation of GFR in the United States and its implications for diagnosis and subsequent management of patients with, or at risk for, kidney diseases. This interim report details the process, initial assessment of evidence, and values defined regarding the use of race to estimate GFR. We organized activities in phases: (1) clarify the problem and examine evidence, (2) evaluate different approaches to address use of race in GFR estimation, and (3) make recommendations. In phase 1, we constructed statements about the evidence and defined values regarding equity and disparities; race and racism; GFR measurement, estimation, and equation performance; laboratory standardization; and patient perspectives. We also identified several approaches to estimate GFR and a set of attributes to evaluate these approaches. Building on evidence and values, the attributes of alternative approaches to estimate GFR will be evaluated in the next phases and recommendations will be made.
MIRA:Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks

We study the problem of deep recall model in industrial web search, which is, given a user query, retrieve hundreds of most relevant documents from billions of candidates. The common framework is to encoding queries and documents separately into distributed representations and match them in latent semantic space. However, all the exiting deep encoding models only leverage the information of the document itself, which is often not sufficient in practice when matching with query terms, especially for the hard tail queries. In this work we aim to leverage the additional information for documents from their co-click neighbours to help document retrieval. The challenges include how to effectively extract information and eliminate noise when involving co-click information while meet the demands of industrial scalability for real time online serving. To handle the noise in co-click relations, we firstly propose a web-scale Multi-Intention Co-click document Graph(MICG) which builds the co-click connections between documents on click intention level but not on document level. Then we present an encoding framework MIRA based on Bert and graph attention networks which leverages a two-factor attention mechanism to aggregate neighbours. To meet the online latency requirements, we only involve neighbour information in document side which can save the time-consuming query neighbor search in real time serving. We conduct extensive offline experiments on two public datasets and one private web-scale dataset from major commercial search engines(Bing1 and Sougou2) demonstrating the effectiveness and scalability of the proposed method compared with several baselines. And a further case study reveals that co-click relations mainly help improve web search quality from two aspects: key concept enhancing and query term complementary.
The Profitability of Revenue-Based Quotas Under Price Negotiation

We study how the presence of a monthly revenue-based quota impacts a retailer’s profits when prices are negotiated by a salesperson. Using transaction level data for refrigerators, we first provide...
Hodge decomposition of string topology

Let X be a simply connected closed oriented manifold of rationally elliptic homotopy type. We prove that the string topology bracket on the -equivariant homology of the free loop space of X preserves the Hodge decomposition of , making it a bigraded Lie algebra. We deduce this result from a general theorem on derived Poisson structures on the universal enveloping algebras of homologically nilpotent finite-dimensional DG Lie algebras. Our theorem settles a conjecture of [7].
The limiting speeds of dislocations

The purpose of this article is to show that the limiting speeds of moving dislocations are determined by inertial effects, or by viscous drag, at their cores. Simple expressions for the limiting speeds are derived. The standard idea that the speeds are limited by the inertia of the elastic fields, accompanied by Lorentz contractions, is shown to be flawed because it neglects the angular momentum of a moving dislocation; or, equivalently, because it assumes that the motion is steady if the velocity is constant, but this is not possible because the motion creates plastic deformation. Finally, the standard theory considers the dislocation to be moving in an infinite medium but this is not acceptable, because then both the energy and the mass would then be infinite.
Jurisdictional Standards (and Rules)

This Article uses the jurisprudential dichotomy between two opposing types of legal requirements-"rules" and "standards"-to examine extraterritorial regulation by the United States. It argues that there is natural push toward standards in extraterritorial regulation because numerous institutional actors either see standards as the best option in extraterritorial regulation or accept standards as a second-best option when their first choice (a rule favorable to their interests or their worldview) is not feasible. The Article explores several reasons for this push toward standards, including: statutory text, statutory interpretation theories, the nonbinary nature of the domestic/foreign characterization, the tendency of extraterritorial regulation to favor plaintiffs, interest-group pressures, and interbranch struggles within the federal government. Since it appears standards are here to stay, this Article concludes by suggesting that they may have some underappreciated benefits, at least from the perspective of a regulating state. First, the uncertainty inherent in standards may be a necessary consequence of regulatory schemes permitting private civil litigants to enforce extraterritorial statutes. Second, this inherent uncertainty may permit a state's regulatory program to influence primary behavior abroad that would be difficult to reach through a rule-based model. * Assistant Professor of Law, Penn State University Dickinson School of Law. J.D., Yale Law School, 2003. Earlier versions of this paper were presented at the 2011 Midyear Research Forum of the American Society of International Law, the 2012 Arizona State University Legal Scholars Conference, the 2012 Big Ten Untenured Law Professor's Conference, and the 2012 International Conference on Law and Society. For insightful comments on earlier drafts, I am grateful to Daniel Abebe, Larry Backer, Bill Fox, Mary Anne Franks, David Kaye, Kit Kinports, Julia Lee, Catherine Rogers, Victor Romero, Steve Ross, and Sam Thompson. Kirill Lavinski and Brandon Merritt provided excellent research assistance.
Self-similar cauchy problems and generalized Mittag-Leffler functions

By observing that the fractional Caputo derivative can be expressed in terms of a multiplicative convolution operator, we introduce and study a class of such operators which also have the same self-similarity property as the Caputo derivative. We proceed by identifying a subclass which is in bijection with the set of Bernstein functions and we provide several representations of their eigenfunctions, expressed in terms of the corresponding Bernstein function, that generalize the Mittag-Leffler function. Each eigenfunction turns out to be the Laplace transform of the right-inverse of a non-decreasing self-similar Markov process associated via the so-called Lamperti mapping to this Bernstein function. Resorting to spectral theoretical arguments, we investigate the generalized Cauchy problems, defined with these self-similar multiplicative convolution operators. In particular, we provide both a stochastic representation, expressed in terms of these inverse processes and an explicit representation, given in terms of the generalized Mittag-Leffler functions, of the solution of these self-similar Cauchy problems.
A Hybrid Approach to Coded Compressed Sensing Where Coupling Takes Place Via the Outer Code

This article seeks to advance coded compressed sensing (CCS) as a practical scheme for unsourced random access. The CCS algorithm features a concatenated structure where an inner code is tasked with support recovery and an outer code conducts message disambiguation. Recently, the CCS scheme was improved through the use of approximate message passing (AMP) with a dynamic denoiser that shares soft information between the inner and outer decoders. This significantly improves performance at the cost of additional complexity. This work shows how the spatial coupling generated by the outer code is sufficiently strong to justify relaxing certain constraints on the inner code. It is shown that a block diagonal sensing matrix with the aforementioned dynamic denoiser forms an effective means to get good performance at reduced complexity. This novel architecture can be used to scale CCS to dimensions that were previously impractical. Findings are supported by numerical simulations.
Invariance and attraction properties of Galton–Watson trees

We give a description of invariants and attractors of the critical and subcritical Galton–Watson tree measures under the operation of Horton pruning (cutting tree leaves with subsequent series reduction). Under a regularity condition, the class of invariant measures consists of the critical binary Galton–Watson tree and a one-parameter family of critical Galton–Watson trees with offspring distribution {qk} that has a power tail qk∼Ck−(1+1/q0), where q0∈(1/2,1). Each invariant measure has a non-empty domain of attraction under consecutive Horton pruning, specified by the tail behavior of the initial Galton–Watson offspring distribution. The invariant measures satisfy the Toeplitz property for the Tokunaga coefficients and obey the Horton law with exponent R=(1−q0)−1/q0.
Acute stress reduces reward-related neural activity: Evidence from the reward positivity

Stress and blunted reward processing are risk factors for Major Depressive Disorder (MDD). The experience of acute stress reduces fMRI correlates of reward-related neural activity; however, few studies have examined how acute stress impacts measures of reward derived from event-related potentials (ERPs). The current study examined the impact of an acute stressor on the Reward Positivity (RewP), an ERP that indexes reward sensitivity, in twenty-six college students. Participants completed a monetary reward task while they placed their left hand in cold water set at 13 °C (i.e. acute stress condition) and again while their hand was placed in room temperature water (i.e. control condition). These conditions were separated by one week and performed in a counter-balanced order across participants. The results revealed that the RewP amplitude was blunted in the acute stress condition compared to the control condition. Moreover, there was a trend toward this effect interacting with self-reported depressive symptoms: the RewP was reduced only among individuals who reported low depressive symptoms. The current study suggests that an acute stressor reduces the RewP, and that this effect might be moderated by current depressive symptoms. Future studies might examine the temporal association between reward processing and stress, and how they interact to predict depressive symptoms.LAY SUMMARYThe current study examined the impact of acute stress on the brain's reward system. The results indicated that acute stress reduced activity within the brain's reward system, particularly among individuals with low depressive symptoms.
Dysregulation of Pulmonary Responses in Severe COVID-19

Patients with coronavirus disease 2019 (COVID-19) predominantly have a respiratory tract infection with various symptoms and high mortality is associated with respiratory failure second to severe disease. The risk factors leading to severe disease remain unclear. Here, we reanalyzed a published single-cell RNA-Seq (scRNA-Seq) dataset and found that bronchoalveolar lavage fluid (BALF) of patients with severe disease compared to those with mild disease contained decreased TH17-type cells, decreased IFNA1-expressing cells with lower expression of toll-like receptor 7 (TLR7) and TLR8, increased IgA-expressing B cells, and increased hyperactive epithelial cells (and/or macrophages) expressing matrix metalloproteinases (MMPs), hyaluronan synthase 2 (HAS2), and plasminogen activator inhibitor-1 (PAI-1), which may together contribute to the pulmonary pathology in severe COVID-19. We propose IFN-I (and TLR7/TLR8) and PAI-1 as potential biomarkers to predict the susceptibility to severe COVID-19.
Dark Trading and Post-Earnings-Announcement Drift

Both theory and evidence are mixed regarding the impact on prices of trading on “dark” venues partially exempt from National Market System requirements. Theory predicts that price discovery improve...
Deep Motion Blur Removal Using Noisy/Blurry Image Pairs

Removing spatially variant motion blur from a blurry image is a challenging problem as image blur can be complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel-free single image deblurring can be achieved, but questions about deblurring performance persist. To improve performance, we proposed a deep convolutional neural network to restore a sharp image from a noisy/blurry image pair captured in quick succession. Two neural network structures, Deblur Long Short-Term Memory (LSTM) and DeblurMerger, are presented to fuse the pair of images in either sequential or parallel manner. To boost the training, gradient loss, adversarial loss, and spectral normalization are leveraged. The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO. We evaluated the trained networks on a variety of synthetic datasets and real image pairs. The results demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively. DeblurLSTM achieves the best debluring performance, while DeblurMerger achieves nearly the same result but with significantly less computation time.
Artifact Detection and Correction in EEG data: A Review

Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have been proposed to detect and correct these artifacts. These techniques range from simply detecting and rejecting artifact ridden segments, to extracting the noise component from the EEG signal. In this paper we review a variety of recent and classical techniques for EEG data artifact detection and correction with a focus on the last half-decade. We compare the strengths and weaknesses of the approaches and conclude with proposed future directions for the field.
Nearly Linear-Time, Parallelizable Algorithms for Non-Monotone Submodular Maximization

We study parallelizable algorithms for maximization of a submodular function, not necessarily monotone, with respect to a cardinality constraint $k$. We improve the best approximation factor achieved by an algorithm that has optimal adaptivity and query complexity, up to logarithmic factors in the size $n$ of the ground set, from $0.039 - \epsilon$ to $0.193 - \epsilon$. We provide two algorithms; the first has approximation ratio $1/6 - \epsilon$, adaptivity $O( \log n )$, and query complexity $O( n \log k )$, while the second has approximation ratio $0.193 - \epsilon$, adaptivity $O( \log^2 n )$, and query complexity $O(n \log k)$. Heuristic versions of our algorithms are empirically validated to use a low number of adaptive rounds and total queries while obtaining solutions with high objective value in comparison with state-of-the-art approximation algorithms, including continuous algorithms that use the multilinear extension.
The Impact of Mandatory Auditor Tenure Disclosures on Ratification Voting, Auditor Dismissal, and Audit Pricing†

Recent amendments to Auditing Standard (AS) 3101 require disclosure of the initial year of the auditor-client relationship in the audit report. As the standard was being discussed, auditors, clients, and some PCAOB members expressed reservations about the necessity of tenure disclosures and were particularly concerned about disclosing tenure in the audit report. Our purpose is to investigate whether the tenure disclosures mandated by AS 3101 are associated with changes in stakeholder behavior. We predict and find that after the implementation date, ratification votes against the auditor and the probability of subsequent auditor dismissal increase for long-tenured versus short-tenured auditors. Results from a path analysis further suggest that the relative increase in dismissal for long-tenured auditors appears to be influenced directly through tenure disclosures and indirectly through ratification voting. We also predict and find that negotiating power decreases for long-tenured auditors as evidenced by lower audit fees. Our results are comparable for companies that voluntarily disclosed and companies that did not disclose auditor tenure in their proxy statements prior to the AS 3101 amendment. Overall, our study suggests that mandatory disclosure of auditor tenure in the audit report significantly affects stakeholder behavior. The PCAOB should consider our findings carefully as they evaluate whether AS 3101 is achieving its intended purpose.
Bank Balance Sheets and External Shocks in Asia: The Role of Fxi, Mpms and Cfms

In emerging Asia, banks constitute the dominant source of financing consumption and investment, and bank balance sheets comprise large gross FX assets and liabilities. This paper extends the DSGE model of Gertler and Karadi (2011) to incorporate these key features and estimates a panel vector autoregression on ten Asian economies to understand the role of the banking sector in transmitting spillovers from the global financial cycle to small open economies. It also evaluates the effectiveness of foreign exchange intervention (FXI) and other macroeconomic policies in responding to external financing shocks. External financial shocks affect net external liabilities of banks and the exchange rate, leading to changes in credit supply by banks and investment. For example, a capital outflow shock leads to a deprecation that reduces the net worth and intermediation capacity of banks exposed to foreign currency liabilities. In such cases, the exchange rate acts as shock amplifier and sterilized FXI, often deployed by Asian economies, can help cushion the economy. By contrast, with real shocks, the exchange rate serves as a shock absorber, and any FXI that weakens that function can be costly. We also explore the effectiveness of the monetary policy interest rate, macroprudential policies (MPMs) and capital flow management measures (CFMs).
Quantitative NDE Applied to Composites and Metals

This paper reviews recent advances at LaRC in quantitative measurement science applied to characterizing materials in a nondestructive environment. Recent demands on NDE have resulted in new thrusts to achieve measurements that represent material properties rather than indications or anomalies in a background measurement. Good physical models must be developed of the geometry, material properties, and the interaction of the probing energy with the material to interpret the results quantitatively. In this paper are presented NDE models that were used to develop measurement technologies for characterizing the curing of a polymer system for composite materials. The procedure uses the changes in ultrasonic properties of the material to determine the glass transition temperature, the degree of cure, and the cure rate. A practical application of this technology is a closed feedback system for controlling autoclave processing of composite materials. An additional example is in the area of thermal NDE. Thermal diffusion models combined with controlled thermal input/measurement have been used to determine the thermal diffusivity of materials. These measurements are remote, require no contact with the material under test and thus have interesting promise for NDE applications.
Assessing Dual-Sensor Enhanced Flight Vision Systems to Enable Equivalent Visual Operations

Synthetic vision and enhanced flight vision systems may enable a Next Generation Air Transportation System equivalent visual operations concept: that is, the ability to achieve safety and operation...
Accelerated, scalable and reproducible AI-driven gravitational wave detection

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month’s worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month’s worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.
On a nonlocal Cahn–Hilliard model permitting sharp interfaces

A nonlocal Cahn–Hilliard model with a non-smooth potential of double-well obstacle type that promotes sharp interfaces in the solution is presented. To capture long-range interactions between parti...
Low-temperature oxidation reactions and cool flames at earth and reduced gravity

Non-isothermal studies of cool flames and low temperature oxidation reactions in unstirred closed vessels are complicated by the perturbing effects of natural convection at earth gravity. Buoyant convection due to self-heating during the course of slow reaction produces spatio-temporal variations in the thermal and thus specie concentration fields due to the Arrhenius temperature dependence of the reaction rates. Such complexities have never been quantitatively modeled and were the primary impetus for the development of CSTR's (continuously stirred tank reactors) 30 years ago. While CSTR's have been widely adopted since they offer the advantage of spatial uniformity in temperature and concentration, all gradients are necessarily destroyed along with any structure that may otherwise develop. Microgravity offers a unique environment where buoyant convection can be effectively minimized and the need for stirring eliminated. Moreover, eliminating buoyancy and the need for stirring eliminates complications associated with the induced hydrodynamic field whose influence on heat transport and hot spot formation, hence explosion limits, is not fully realized. The objective of this research is to quantitatively determine and understand the fundamental mechanisms that control the onset and evolution of low temperature reactions and cool flames in both static and flow reactors. Microgravity experiments will be conducted to obtain benchmark data on the structure (spatio-temporal temperature, concentration, flow fields), the dynamics of the chemical fronts, and the ignition diagrams (pressure vs. temperature). Ground-based experiments will be conducted to ascertain the role of buoyancy. Numerical simulations including detailed kinetics will be conducted and compared to experiment.
A Complexity Chasm for Solving Univariate Sparse Polynomial Equations Over p-adic Fields

We reveal a complexity chasm, separating the trinomial and tetranomial cases, for solving univariate sparse polynomial equations over certain local fields. First, for any fixed field K∈ Q2,,Q3,Q5, …, we prove that any polynomial f Z [x] with exactly 3 monomial terms, degree d, and all coefficients having absolute value at most H, can be solved over K within deterministic time logO(1) (dH) in the classical Turing model. (The best previous algorithms were of complexity exponential in log d, even for just counting roots in Qp.) In particular, our algorithm generates approximations in Q with bit-length log O(1) (dH) to all the roots of f in K, and these approximations converge quadratically under Newton iteration. On the other hand, we give a unified family of tetranomials requiring Ω(d log H) digits to distinguish the base-p expansions of their roots in K.
Fast Strategies for the Implementation of SIKE Round 3 on ARM Cortex-M4

The Supersingular Isogeny Key Encapsulation mechanism (SIKE) is the only post-quantum key encapsulation protocol based on elliptic curves and isogeny maps between them. Despite the quantum security of the protocol, SIKE requires a greater number of clock cycles and hence does not provide competitive timing and energy consumption results. However, it is more attractive offering the smallest public key as well as ciphertext sizes, which considering the impact of the communication costs and storage of the keys could become a good fit for resource-constrained devices. In this work, we present the fastest practical implementation of SIKE, targeting the platform Cortex-M4 based on the ARMv7-M architecture. We performed our measurements on the STM32F407VG microcontroller for benchmarking the clock cycles and on Nucleo-F411RE attached to X-NUCLEO-LPM01A (Power Shield) for measuring the energy consumption of the protocol. The low-level finite field arithmetic operations play main role in determining the efficiency of SIKE. Therefore, we mainly focus on their optimization and apply them to all NIST-required security levels. Our SIKEp434 implementation for NIST security level 1 is about 22.97% faster than the counterparts appeared in Seo et al. (2020), where for the SIKEp503, SIKEp610 and SIKEp751 the speedup reaches 21.10%, 19.21% and 19.08%. Finally, we benchmark energy consumption and report optimization of up to 11.9% depending on the NIST security level implementation.
A Resolvent Criterion Approach to Strong Decay of a Multilayered Lam\'e-Heat System

We consider a multilayer hyperbolic-parabolic PDE system which constitutes a coupling of 3D thermal – 2D elastic – 3D elastic dynamics, in which the boundary interface coupling between 3D fluid and...
Do attitudes toward risk taking affect entrepreneurship? Evidence from second-generation Americans

This paper empirically investigates the impact of willingness to take risks on entrepreneurship. We use a quarter century of data on second-generation Americans from Current Population Surveys in conjunction with a measure of willingness to take risks based on the Global Preference Survey. The level of risk taking in the country of origin is found to have a positive and significant impact on the likelihood of being an entrepreneur. A one-standard deviation increase in risk taking increases the probability of being an entrepreneur by 18%. We find that risk taking is also robust to other preference and cultural factors such as trust, patience, and individualism, as well as several deep-root determinants of development.
The New Kinship

Over the past century, the Supreme Court has articulated numerous doctrines that protect family privacy. These doctrines are not, however, well-suited to the brave new world of families formed through donor eggs, sperm, and embryos. As the number of donor-conceived children born to same-sex and heterosexual couples and to single parents increases, and as these families develop connections to one another, the law has not yet adjusted. This Article provides an extensive mapping of these “donor-conceived family communities,” and it reaches two major conclusions that support the development of these new families. First, relational interests, the traditional focus in family law, should govern the regulation of the donor world. Second, legal recognition should be given to the emotional and psychological ties between donor families in order to provide guidance to the development of donor-conceived family communities. These two principles point the way to integrating changing social realities into a new legal framework for donor families, allowing children from the same donor to connect to one another. While further regulation of relationships has its dangers, this paradigm shift in the donor world could prompt broader beneficial changes, creating options beyond framing all families within the dyadic nuclear-family model.
Hyperspectral Neutron CT with Material Decomposition

Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. The extremely high noise due to low count measurements is overcome using a sparse coding approach. It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.
Improved Approximation Algorithms for Tverberg Partitions

Tverberg’s theorem states that a set of n points in ℝ^d can be partitioned into ⌈n/(d+1)⌉ sets whose convex hulls all intersect. A point in the intersection (aka Tverberg point) is a centerpoint, or high-dimensional median, of the input point set. While randomized algorithms exist to find centerpoints with some failure probability, a partition for a Tverberg point provides a certificate of its correctness. Unfortunately, known algorithms for computing exact Tverberg points take n^{O(d²)} time. We provide several new approximation algorithms for this problem, which improve running time or approximation quality over previous work. In particular, we provide the first strongly polynomial (in both n and d) approximation algorithm for finding a Tverberg point.
Astrophysical explosions revisited: collisionless coupling of debris to magnetized plasma

The coupling between a rapidly expanding cloud of ionized debris and an ambient magnetized plasma is revisited with a hybrid (kinetic ion/fluid electron) simulation code that allows a study over a wide range of plasma parameters. Over a specified range of hypothetical conditions, simple scaling laws in terms of the total debris mass and explosion speed are derived and verified for the maximal size of the debris cloud and the fraction of debris that free-streams from the burst along the magnetic field. The amount of debris that escapes from the burst with minimal coupling to the background magnetic field increases with the debris gyroradius. Test cases with two different debris species--including a heavy minority species with a relatively large gyroradius--highlight how the collisionless coupling of the debris depends on the single-particle trajectories as well as the overall conservation of energy and momentum.
GOE statistics for Lévy matrices

In this paper we establish eigenvector delocalization and bulk universality for Levy matrices, which are real, symmetric, $N \times N$ random matrices $\textbf{H}$ whose upper triangular entries are independent, identically distributed $\alpha$-stable laws. First, if $\alpha \in (1, 2)$ and $E \in \mathbb{R}$ is any energy bounded away from $0$, we show that every eigenvector of $\textbf{H}$ corresponding to an eigenvalue near $E$ is completely delocalized and that the local spectral statistics of $\textbf{H}$ around $E$ converge to those of the Gaussian Orthogonal Ensemble (GOE) as $N$ tends to $\infty$. Second, we show for almost all $\alpha \in (0, 2)$, there exists a constant $c(\alpha) > 0$ such that the same statements hold if $|E| < c (\alpha)$.
LSD-StructureNet: Modeling Levels of Structural Detail in 3D Part Hierarchies

Generative models for 3D shapes represented by hierarchies of parts can generate realistic and diverse sets of outputs. However, existing models suffer from the key practical limitation of modelling shapes holistically and thus cannot perform conditional sampling, i.e. they are not able to generate variants on individual parts of generated shapes without modifying the rest of the shape. This is limiting for applications such as 3D CAD design that involve adjusting created shapes at multiple levels of detail. To address this, we introduce LSD-StructureNet, an augmentation to the StructureNet architecture that enables re-generation of parts situated at arbitrary positions in the hierarchies of its outputs. We achieve this by learning individual, probabilistic conditional decoders for each hierarchy depth. We evaluate LSD-StructureNet on the PartNet dataset, the largest dataset of 3D shapes represented by hierarchies of parts. Our results show that contrarily to existing methods, LSD-StructureNet can perform conditional sampling without impacting inference speed or the realism and diversity of its outputs.
Software compensation of undesirable racking motion of H-frame 3D printers using filtered B-splines

Abstract The H-frame (also known as H-Bot) architecture is a simple and elegant two-axis parallel positioning system used to construct the XY stage of 3D printers. It holds potential for high speed and high dynamic performance due to the use of frame-mounted motors that reduce the moving mass of the printer while allowing for the use of (heavy) higher torque motors. However, the H-frame’s dynamic accuracy is limited during high-acceleration and high-speed motion due to racking – i.e., parasitic torsional motions of the printer’s gantry due to a force couple. Mechanical solutions to the racking problem are either costly or detract from the simplicity of the H-frame. In this paper, we introduce a feedforward software compensation algorithm, based on the filtered B-splines (FBS) method, that rectifies errors due to racking. The FBS approach expresses the motion command to the machine as a linear combination of B-splines. The B-splines are filtered through an identified model of the machine dynamics and the control points of the B-spline based motion command are optimized such that the tracking error is minimized. To compensate racking using the FBS algorithm, an accurate frequency response function of the racking motion is obtained and coupled to the H-frame’s and y -axis dynamics with a kinematic model. The result is a coupled linear parameter varying model of the H-frame that is utilized in the FBS framework to compensate racking. An approximation of the proposed racking compensation algorithm, that decouples the x - and y -axis compensation, is developed to significantly improve its computational efficiency with almost no loss of compensation accuracy. Experiments on an H-frame 3D printer demonstrate a 43% improvement in the shape accuracy of a printed part using the proposed algorithm compared to the standard FBS approach without racking compensation. The proposed racking compensation algorithm can be used in-conjunction with mechanical solutions, or as a stand-alone solution, to improve the performance of H-frame architectures.
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification. To this end, we propose a dual-branch transformer to combine image patches (i.e., tokens in a transformer) of different sizes to produce stronger image features. Our approach processes small-patch and large-patch tokens with two separate branches of different computational complexity and these tokens are then fused purely by attention multiple times to complement each other. Furthermore, to reduce computation, we develop a simple yet effective token fusion module based on cross attention, which uses a single token for each branch as a query to exchange information with other branches. Our proposed cross-attention only requires linear time for both computational and memory complexity instead of quadratic time otherwise. Extensive experiments demonstrate that our approach performs better than or on par with several concurrent works on vision transformer, in addition to efficient CNN models. For example, on the ImageNet1K dataset, with some architectural changes, our approach outperforms the recent DeiT by a large margin of 2\% with a small to moderate increase in FLOPs and model parameters. Our source codes and models are available at \url{this https URL}.
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-On and Outfit Editing

We proposes a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Joint training on pose transfer and inpainting helps with detail preservation and coherence of generated garments. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.
Latent map Gaussian processes for mixed variable metamodeling

Abstract Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs , however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation . Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.
Gate Set Tomography.

Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is calibration-free. GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a gate set simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST's intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the pyGSTi implementation.
Stellar migration and chemical enrichment in the milky way disc: a hybrid model

We develop a hybrid model of galactic chemical evolution that combines a multi-ring computation of chemical enrichment with a prescription for stellar migration and the vertical distribution of stellar populations informed by a cosmological hydrodynamic disc galaxy simulation. Our fiducial model adopts empirically motivated forms of the star formation law and star formation history, with a gradient in outflow mass loading tuned to reproduce the observed metallicity gradient. With this approach, the model reproduces many of the striking qualitative features of the Milky Way disc's abundance structure: (i) the dependence of the [O/Fe]-[Fe/H] distribution on radius $R_\text{gal}$ and midplane distance $|z|$; (ii) the changing shapes of the [O/H] and [Fe/H] distributions with $R_\text{gal}$ and $|z|$; (iii) a broad distribution of [O/Fe] at sub-solar metallicity and changes in the [O/Fe] distribution with $R_\text{gal}$, $|z|$, and [Fe/H]; (iv) a tight correlation between [O/Fe] and stellar age for [O/Fe] $>$ 0.1; (v) a population of young and intermediate-age $\alpha$-enhanced stars caused by migration-induced variability in the Type Ia supernova rate; (vi) non-monotonic age-[O/H] and age-[Fe/H] relations, with large scatter and a median age of $\sim$4 Gyr near solar metallicity. Observationally motivated models with an enhanced star formation rate $\sim$2 Gyr ago improve agreement with the observed age-[Fe/H] and age-[O/H] relations, but worsen agreement with the observed age-[O/Fe] relation. None of our models predict an [O/Fe] distribution with the distinct bimodality seen in the observations, suggesting that more dramatic evolutionary pathways are required. All code and tables used for our models are publicly available through the Versatile Integrator for Chemical Evolution (VICE; this https URL).
Extracting Adverse Drug Events from Clinical Notes

Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, dosage), the reason why the drug was initially prescribed, and any adverse reaction to the drug. This paper explores the relationship between a drug and its associated attributes using relation extraction techniques. We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our experimental results demonstrate that the contextualized language model-based approach outperformed other models overall and obtain the state-of-the-art performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an $F_1$ score of 0.94; however, for certain relation types, the rule-based approach obtained a higher Precision and Recall than either learning approach.
The unusually high dark matter concentration of the galaxy group NGC 1600

We investigate the properties of the dark matter (DM) halo surrounding the nearby galaxy group NGC 1600. Through the use of deep (252 ks) Chandra observations and 64.3 ks of XMM-Newton observations, we construct surface brightness profiles in multiple energy bands in order to perform hydrostatic equilibrium analysis of the hot plasma within NGC 1600. Regardless of the DM model profile assumed, we measure a halo concentration (c$_{200}$) that is an extreme, positive outlier of the $\Lambda$CDM c$_{200}$-M$_{200}$ relation. For a typical NFW DM profile, we measure c$_{200}\!=\!26.7\pm1.4$ and M$_{200}\!=\!(2.0\pm0.2)\times10^{13}$ M$_\odot$; assuming a similar halo mass, the average concentration expected is c$_{200}=6-7$ for the theoretical $\Lambda$CDM c-M relation. Such a high concentration is similar to that of well-known fossil groups MRK 1216 and NGC 6482. While NGC 1600 exhibits some properties of a fossil group, it fails to meet the X-ray luminosity threshold of L$_X>5\times10^{41}$ erg s$^{-1}$. Whether or not it is considered a fossil group, the high concentration value makes it part of a select group of galaxy groups.