cssi/SciSciGPT-SciSciNet huggingface dataset:
Comparisons between polypyridyl nitrosyl complexes of osmium(II) and ruthenium(II)

Comparaison entre les proprietes de [(trpy)(bpy)MNO] 3+ et [(trpy)(bpy)MNO 2 ] + : proprietes et reactivite
A probabilistic approach to identifying run scoring advantage in the order of playing cricket

In the game of cricket, the decision to bat first after winning the toss is often taken to make the best use of superior pitch conditions and set a big target for the opponent. However, the opponen...
MagBB: Wireless Charging for Batteryless Sensors Using Magnetic Blind Beamforming

Tiny batteryless sensors are desirable since they create negligible impacts on the operation of the system being monitored or the surrounding environment. Wireless energy transfer for batteryless sensors is challenging since they cannot cooperate with the charger due to the lack of energy. In this paper, a Magnetic Blind Beamforming (MagBB) algorithm is developed for wireless energy transfer for batteryless sensors in inhomogeneous media. Batteryless sensors with randomly orientated coils may experience significant orientation losses and they may not receive any energy from the charger. MagBB uses a set of optimized current vectors to generate rotating magnetic fields which can ensure that coils on batteryless sensors with arbitrary orientations can receive sufficient voltages for charging. It does not require any information regarding the batteryless sensor's coil orientation or location. The efficiency of MagBB is proven by extensive numerical simulations.
Dynamics of freely suspended drops translating through miscible environments

Our work focuses on an experimental investigation of droplets freely rising through a miscible, more viscous liquid. We report observations of water droplets rising through glycerol and corn syrup, which are common household ingredients. Immediately after the drops are formed, they take on prolate shapes and rise with constant velocity without expanding in size. However, after a critical time predicted by our scaling arguments, the drops continually grow into oblate spheroids, and as they mix with the ambient liquid, their volume increases and their velocity decreases, eventually following power laws. We present scaling relations that explain the main observed phenomena. However, the power laws governing the rate of the volumetric increase and the velocity decrease, namely, t 1 / 2 and t − 1 / 2, respectively, remain points of further investigation.
Renormalization group improvement of the effective potential: an EFT approach

We apply effective field theory (EFT) methods to compute the renormalization group improved effective potential for theories with a large mass hierarchy. Our method allows one to compute the effective potential in a systematic expansion in powers of the mass ratio, as well as to sum large logarithms of mass ratios using renormalization group evolution. The effective potential is the sum of one-particle irreducible diagrams (1PI) but information about which diagrams are 1PI is lost after matching to the EFT, since heavy lines get shrunk to a point. We therefore introduce a tadpole condition in place of the 1PI condition, and use the renormalization group improved value of the tadpole in computing the effective potential. We explain why the effective potential computed using an EFT is not the same as the effective potential of the EFT. We illustrate our method using the O(N) model, a theory of two scalars in the unbroken and broken phases, and the Higgs-Yukawa model. Our leading-log result, obtained by integrating the one-loop β-functions, correctly reproduces the log-squared term in explicit two-loop calculations. Our method does not have a Goldstone boson infrared divergence problem.
Cover’s Rebalancing Option with Discrete Hindsight Optimization

The author studies T. Cover’s rebalancing option (Ordentlich and Cover 1998) under discrete hindsight optimization in continuous time. The payoff in question is equal to the final wealth that would have accrued to an initial deposit of 1 unit of the numeraire into the best of some finite set of (perhaps levered) rebalancing rules determined in hindsight. A rebalancing rule (or fixed-fraction betting scheme) amounts to fixing an asset allocation (i.e., 200% equities and −100% bonds) and then continuously executing rebalancing trades so as to counteract allocation drift. Restricting the hindsight optimization to a small number of rebalancing rules (i.e., 2) has some advantages over the pioneering approach taken by Cover & Company in their theory of universal portfolios (1986, 1991, 1996, 1998), wherein one’s trading performance is benchmarked relative to the final wealth of the best unlevered rebalancing rule (of any kind) in hindsight. Our approach lets practitioners express an a priori view that one of the favored asset allocations (“bets”) in the set {b1, …, bn} will turn out to have performed spectacularly well in hindsight. In limiting our robustness to some discrete set of asset allocations (rather than all possible asset allocations) we reduce the price of the rebalancing option and guarantee that we will achieve a correspondingly higher percentage of the hindsight-optimized wealth at the end of the planning period. A practitioner who lives to delta-hedge this variant of Cover’s rebalancing option through several decades is guaranteed to see the day that his realized compound-annual capital growth rate is very close to that of the best bi in hindsight. Hence the point of the rock-bottom option price. TOPICS:Quantitative methods, statistical methods, portfolio construction, derivatives, options, performance measurement Key Findings ▪ The Cost of Achieving the Best [continuously-rebalanced] Portfolio in Hindsight must be reduced by hindsight-optimizing over just a few portfolios, rather than all possible portfolios. ▪ The new replicating strategy has lower regret relative to the (less aggressive) benchmark, which is better suited to a human lifespan. The customized option parameters can incorporate prior beliefs or institutional constraints into the corresponding universal portfolio. ▪ Discrete rebalancing options can be synthesized as a certain portfolio of Margrabe-Fischer exchange options. Thus, we unearth a hidden linkage between Cover’s universal portfolios and classical options theory in continuous time.
Integral quantum cluster structures

We prove a general theorem for constructing integral quantum cluster algebras over Z[q±1/2], namely, that under mild conditions the integral forms of quantum nilpotent algebras always possess integral quantum cluster algebra structures. These algebras are then shown to be isomorphic to the corresponding upper quantum cluster algebras, again defined over Z[q±1/2]. Previously, this was only known for acyclic quantum cluster algebras. The theorem is applied to prove that, for every symmetrizable Kac–Moody algebra g and Weyl group element w, the dual canonical form Aq(n+(w))Z[q±1] of the corresponding quantum unipotent cell has the property that Aq(n+(w))Z[q±1]⊗Z[q±1]Z[q±1/2] is isomorphic to a quantum cluster algebra over Z[q±1/2] and to the corresponding upper quantum cluster algebra over Z[q±1/2].
A survey of optimal polynomial approximants, applications to digital filter design, and related open problems

In the last few years, the notion of optimal polynomial approximant has appeared in the mathematics literature in connection with the Hilbert spaces of analytic functions of one or more variables. In the 1970s, researchers in engineering and applied mathematics introduced least-squares inverses in the context of digital filters in signal processing. It turns out that in the Hardy space $$H^{2},$$ these objects are identical. This paper is a survey of known results about optimal polynomial approximants. In particular, we will examine their connections with orthogonal polynomials and reproducing kernels in weighted spaces and digital filter design. We will also describe what is known about the zeros of optimal polynomial approximants, their rates of decay, and convergence results. Throughout the paper, we state many open questions that may be of interest.
Neural Ring Homomorphisms and Maps Between Neural Codes

Neural codes are binary codes that are used for information processing and representation in the brain. In previous work, we have shown how an algebraic structure, called the neural ring, can be used to efficiently encode geometric and combinatorial properties of a neural code (Curto et al., Bull Math Biol 75(9), 2013). In this work, we consider maps between neural codes and the associated homomorphisms of their neural rings. In order to ensure that these maps are meaningful and preserve relevant structure, we find that we need additional constraints on the ring homomorphisms. This motivates us to define neural ring homomorphisms. Our main results characterize all code maps corresponding to neural ring homomorphisms as compositions of five elementary code maps. As an application, we find that neural ring homomorphisms behave nicely with respect to convexity. In particular, if \(\mathcal C\) and \(\mathcal D\) are convex codes, the existence of a surjective code map \(\mathcal C\rightarrow \mathcal D\) with a corresponding neural ring homomorphism implies that the minimal embedding dimensions satisfy \(d(\mathcal D) \leq d(\mathcal C)\).
Replica wormholes for an evaporating 2D black hole

Quantum extremal islands reproduce the unitary Page curve of an evaporating black hole. This has been derived by including replica wormholes in the gravitational path integral, but for the transient, evaporating black holes most relevant to Hawking’s paradox, these wormholes have not been analyzed in any detail. In this paper we study replica wormholes for black holes formed by gravitational collapse in Jackiw-Teitelboim gravity, and confirm that they lead to the island rule for the entropy. The main technical challenge is that replica wormholes rely on a Euclidean path integral, while the quantum extremal islands of an evaporating black hole exist only in Lorentzian signature. Furthermore, the Euclidean equations for the Schwarzian mode are non-local, so it is unclear how to connect to the local, Lorentzian dynamics of an evaporating black hole. We address these issues with Schwinger-Keldysh techniques and show how the non-local equations reduce to the local ‘boundary particle’ description in special cases.
Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study.

IMPORTANCE: False negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied. OBJECTIVE: To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR. DESIGN: Retrospective cohort study. SETTING: Five hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020. PARTICIPANTS: Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization. EXPOSURE: We developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested. MAIN OUTCOMES AND MEASURES: This model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive. RESULTS: We included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70-0.83). Using a cutpoint for our risk prediction model at the 90th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients. CONCLUSION AND RELEVANCE: We show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.
A National Survey of Practice Patterns For Accepting Living Kidney Donors With Prior Covid-19.

Introduction: A critical question facing transplant programs is if, when and how to safely accept living kidney donors (LKD) who have recovered from COVID-19 infection. The purpose of the study is to understand current practices related to accepting these LKDs.â¯. Methods: We surveyed US transplant programs from September 3 through November 3, 2020. Center level and participant level responses were analyzed. Results: A total of 174 respondents from 115 unique centers responded, representing 59% of US LKD Programs and 72.4% of 2019 and 72.5% of 2020 LKD volume1(Organ Procurement and Transplantation Network-OPTN 2021). 48.6% of responding centers had received inquiries from such LKDs, while 44.3% were currently evaluating. 98 donors were in evaluation phase, while 27.8% centers had approved 42 such donors to proceed with donation. 50.8% of participants preferred to wait > 3 months while 91% would wait at least > 1 month from onset of infection to LD surgery. Most common reason to exclude LDs was evidence of COVID-19 related AKI (59.8%) even if resolved, followed by COVID-19 related Pneumonia (28.7%) and Hospitalization (21.3%). Most common concern in accepting such donors was kidney health post donation (59.2%), followed by risk of transmission to recipient (55.7%),donor perioperative pulmonary risk (41.4%) and donor pulmonary risk in the future (29.9%). Conclusions: Practice patterns for acceptance of COVID-19 recovered LKD showed considerable variability. Ongoing research and consensus building are needed to guide optimal practices to ensure safety of accepting such donors.â¯ Long term close follow up of such donors is warranted.
Three-Dimensional Piecewise-Continuous Class-Shape Transformation of Wings

Class-Shape Transformation (CST) is a popular method for creating analytical representations of the surface coordinates of various components of aerospace vehicles. A wide variety of two- and three-dimensional shapes can be represented analytically using only a modest number of parameters, and the surface representation is smooth and continuous to as fine a degree as desired. This paper expands upon the original two-dimensional representation of airfoils to develop a generalized three-dimensional CST parametrization scheme that is suitable for a wider range of aircraft wings than previous formulations, including wings with significant non-planar shapes such as blended winglets and box wings. The method uses individual functions for the spanwise variation of airfoil shape, chord, thickness, twist, and reference axis coordinates to build up the complete wing shape. An alternative formulation parameterizes the slopes of the reference axis coordinates in order to relate the spanwise variation to the tangents of the sweep and dihedral angles. Also discussed are methods for fitting existing wing surface coordinates, including the use of piecewise equations to handle discontinuities, and mathematical formulations of geometric continuity constraints. A subsonic transport wing model is used as an example problem to illustrate the application of the methodology and to quantify the effects of piecewise representation and curvature constraints.
A machining process planning activity model for systems integration

A key issue of integrating process planning systems with design systems and production planning systems is how to overcome barriers in data exchange and sharing amongst software systems. A machining process planning activity model was developed to address some of the barriers. This model represents functional components and data requirements in process planning systems. The purpose of the model is to create the context in which data requirements and data flow for numerically controlled machining process planning are defined. Furthermore, the model was developed as a unification of many previously developed process planning activity models.
Deformation and orientational order of chiral membranes with free edges

Motivated by experiments on colloidal membranes composed of chiral rod-like viruses, we use Monte Carlo methods to simulate these systems and determine the phase diagram for the liquid crystalline order of the rods and the membrane shape. We generalize the Lebwohl–Lasher model for a nematic with a chiral coupling to a curved surface with edge tension and a resistance to bending, and include an energy cost for tilting of the rods relative to the local membrane normal. The membrane is represented by a triangular mesh of hard beads joined by bonds, where each bead is decorated by a director. The beads can move, the bonds can reconnect and the directors can rotate at each Monte Carlo step. When the cost of tilt is small, the membrane tends to be flat, with the rods only twisting near the edge for low chiral coupling, and remaining parallel to the normal in the interior of the membrane. At high chiral coupling, the rods twist everywhere, forming a cholesteric state. When the cost of tilt is large, the emergence of the cholesteric state at high values of the chiral coupling is accompanied by the bending of the membrane into a saddle shape. Increasing the edge tension tends to flatten the membrane. These results illustrate the geometric frustration arising from the inability of a surface normal to have twist.
Peak-Bust rental spreads

Abstract Landlords appear to use stale information when setting rents. Among over 43,000 California rental houses in 2018–2019, those last purchased during 2005–2007 (the peak) rent for 2–3% more than those purchased during 2008–2010 (bust). Neither house nor landlord characteristics explain this “peak-bust rental spread.” To clarify the mechanism, we test cross-sectional predictions from a simple theory of rent-setting. We find empirical support for both reference dependence and distorted beliefs. In the first, monthly payments establish (recurring) reference points, against which gains or losses are measured. In the second, past sales prices distort landlords’ current estimates of house values/rents.
Sell‐side analyst recommendation revisions and hedge fund trading before and after regulation fair disclosure

We examine institutional trading in relation to changes in consensus recommendations over time. We find that pre-Reg FD’s positive contemporaneous relation between hedge fund trading and change in consensus becomes negative after Reg FD, but the positive relation between non-hedge fund trading and change in consensus continues even after Reg FD. Furthermore, during post-Reg FD, while the performance of hedge funds’ trades that contradict with analysts improve, non-hedge funds’ trades that agree with analysts significantly deteriorate. Our evidence suggests that hedge funds have better information processing skill and are more cognizant about the role of selective information in analyst recommendations.
V/STOL propulsion control technology

Results of a NASA sponsored study of V/STOL Propulsion Control Analysis are presented. The study involved propulsion control requirements, design concepts and procedures, and control designs for supersonic V/STOL. A variable cycle engine with a remote augmented lift system was used as a basis for establishing typical operating requirements and control concepts, and a nonlinear engine model was developed for control development as a precursor to a real-time simulation capability. A simplified aircraft model was also used to investigate transition requirements, and a long-range technology plan was developed to define subsequent program requirements for achieving a real-time piloted simulation capability.
Friction and Wear Behavior of Graphite Fiber Reinforced Polyimide Composites

The friction and wear rate characteristics of 50/50 (weight percent) graphite fiber-polyimide composites were studied by sliding metallic hemispherically tipped riders against disks made from the composites. Two different polyimides and two different graphite fibers were evaluated. Also studied were such variables as the effect of adding 10 percent weight additions of powdered (CF1.1)n CdI2, or CdO; the effect of moisture in an air atmosphere; the effect of temperature; and the effect of different sliding speeds. In general, wear to the metallic riders was negligible and composite wear increased at a constant rate as a function of number of sliding cycles. Presented at the 32nd Annual Meeting in Montreal, Quebec, Canada, May 9–12, 1977
On the Importance of Environments in Human-Robot Coordination

When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks. However, less emphasis has been placed on the effect of the environment on coordination behaviors. To thoroughly explore environments that result in diverse behaviors, we propose a framework for procedural generation of environments that are (1) stylistically similar to human-authored environments, (2) guaranteed to be solvable by the human-robot team, and (3) diverse with respect to coordination measures. We analyze the procedurally generated environments in the Overcooked benchmark domain via simulation and an online user study. Results show that the environments result in qualitatively different emerging behaviors and statistically significant differences in collaborative fluency metrics, even when the robot runs the same planning algorithm.
A Deep Emulator for Secondary Motion of 3D Characters

Fast and light-weight methods for animating 3D characters are desirable in various applications such as computer games. We present a learning-based approach to enhance skinning-based animations of 3D characters with vivid secondary motion effects. We design a neural network that encodes each local patch of a character simulation mesh where the edges implicitly encode the internal forces between the neighboring vertices. The network emulates the ordinary differential equations of the character dynamics, predicting new vertex positions from the current accelerations, velocities and positions. Being a local method, our network is independent of the mesh topology and generalizes to arbitrarily shaped 3D character meshes at test time. We further represent per-vertex constraints and material properties such as stiffness, enabling us to easily adjust the dynamics in different parts of the mesh. We evaluate our method on various character meshes and complex motion sequences. Our method can be over 30 times more efficient than ground-truth physically based simulation, and outperforms alternative solutions that provide fast approximations.
Illuminating economic growth

Abstract This paper seeks to illuminate national accounts GDP growth using satellite-recorded nighttime lights in a measurement error model framework. Using recently developed results in conjunction with reasonable assumptions about the exogeneity of the lights data generating process, we identify and estimate the relationship between nighttime light growth and GDP growth, as well as the nonparametric distribution of errors in both measures. We obtain three key results: (i) the elasticity of nighttime lights to GDP is about 1.3; (ii) national accounts GDP growth measures are less precise for low and middle income countries, and nighttime lights can play a big role in improving such measures; and (iii) our new measure of GDP growth, based on the optimal combination of nighttime lights and national accounts data under our identification assumptions, implies that China and India had considerably lower growth rates than official data suggested between 1993 and 2013. We expect our statistical framework and methodology to have a broad impact on measuring GDP using additional information.
Birds of a Feather: Capturing Avian Shape Models from Images

Animals are diverse in shape, but building a deformable shape model for a new species is not always possible due to the lack of 3D data. We present a method to capture new species using an articulated template and images of that species. In this work, we focus mainly on birds. Although birds represent almost twice the number of species as mammals, no accurate shape model is available. To capture a novel species, we first fit the articulated template to each training sample. By disentangling pose and shape, we learn a shape space that captures variation both among species and within each species from image evidence. We learn models of multiple species from the CUB dataset, and contribute new species-specific and multi-species shape models that are useful for downstream reconstruction tasks. Using a low-dimensional embedding, we show that our learned 3D shape space better reflects the phylogenetic relationships among birds than learned perceptual features.
Learning Multimodal Affinities for Textual Editing in Images

Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent. Unlike natural images that capture physical objects, document-images contain a significant amount of text with critical semantics and complicated layouts. In this work, we devise a generic unsupervised technique to learn multimodal affinities between textual entities in a document-image, considering their visual style, the content of their underlying text, and their geometric context within the image. We then use these learned affinities to automaticallycluster the textual entities in the image into different semantic groups. The core of our approach is a deep optimization scheme dedicated for an image provided by the user that detects and leverages reliable pairwise connections in the multimodal representation of the textual elements to properly learn the affinities. We show that our technique can operate on highly varying images spanning a wide range of documents and demonstrate its applicability for various editing operations manipulating the content, appearance, and geometry of the image.
Differentiable Programming of Reaction-Diffusion Patterns

Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature. Current RD system design practices boil down to trial-and-error parameter search. We propose a differentiable optimization method for learning the RD system parameters to perform example-based texture synthesis on a 2D plane. We do this by representing the RD system as a variant of Neural Cellular Automata and using task-specific differentiable loss functions. RD systems generated by our method exhibit robust, non-trivial 'life-like' behavior.
Algorithms and Decision-Making in the Public Sector

This article surveys the use of algorithmic systems to support decision-making in the public sector. Governments adopt, procure, and use algorithmic systems to support their functions within severa...
SARS-CoV-2 Nsp1 Suppresses Host But Not Viral Translation Through a Bipartite Mechanism

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a highly contagious virus that underlies the current COVID-19 pandemic. SARS-CoV-2 infection exhibits many features of viral immune evasion of host defense, and the SARS-CoV-2 nonstructural protein 1 (Nsp1) suppresses host defense by inhibiting host protein translation and could be a target for antiviral therapy against COVID-19. However, how SARS-CoV-2 circumvents this translational blockage for the production of its own proteins is an open question. Here we report a bipartite mechanism of SARS-CoV-2 Nsp1 in hijacking the host ribosome by a direct interaction with the small ribosomal subunit 40S through its C-terminal domain (CT) and in lifting this inhibition by a direct interaction with the 5' untranslated region (5’ UTR) of viral mRNA through its N-terminal domain (NT). We show that Nsp1-CT is sufficient for binding to 40S and inhibition of host protein translation, and that 5’ UTR of SARS-CoV-2 mRNA removes this inhibition by binding to Nsp1-NT, suggesting that the Nsp1-NT-UTR interaction is incompatible with the Nsp1-CT-40S interaction. Indeed, lengthening the linker between Nsp1-NT and Nsp1-CT of Nsp1 progressively reduced the ability of SARS-CoV-2 5’ UTR to escape the translational inhibition, supporting that the incompatibility is likely steric in nature. The short SL1 region of the 5’ UTR is required for viral mRNA translation in the presence of Nsp1. Thus, our data provide a comprehensive view on how Nsp1 switches infected cells from host mRNA translation to SARS-CoV-2 mRNA translation, and that Nsp1 and 5’ UTR may be targeted for anti-COVID-19 therapeutics. Funding: L.W. was supported by funding from an NIH T32 grant (5T32AI007512-34). T.-M.F. was supported by funding from an NIH T32 grant (5T32HL066987-18 to L.E.S.) and by start-up funds from the Ohio State University Comprehensive Cancer Center. Conflict of Interest: The authors declare no competing interests.
Leveraging the Fisher randomization test using confidence distributions: Inference, combination and fusion learning

The flexibility and wide applicability of the Fisher randomization test (FRT) makes it an attractive tool for assessment of causal effects of interventions from modern-day randomized experiments that are increasing in size and complexity. This paper provides a theoretical inferential framework for FRT by establishing its connection with confidence distributions Such a connection leads to development of (i) an unambiguous procedure for inversion of FRTs to generate confidence intervals with guaranteed coverage, (ii) generic and specific methods to combine FRTs from multiple independent experiments with theoretical guarantees and (iii) new insights on the effect of size of the Monte Carlo sample on the results of FRT. Our developments pertain to finite sample settings but have direct extensions to large samples. Simulations and a case example demonstrate the benefit of these new developments.
Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion

Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identi...
ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. A generalized Gaussian distribution (GGD) is used to model band-pass responses, while entropy variations between reference and distorted videos under the GGD model are used to capture video quality variations arising from frame rate changes. The entropic differences are calculated across multiple temporal and spatial subbands, and merged using a learned regressor. We show through extensive experiments that GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models. The features used in GREED are highly generalizable and obtain competitive performance even on standard, non-HFR VQA databases. The implementation of GREED has been made available online: https://github.com/pavancm/GREED .
New approaches to finite generation of cohomology rings

Abstract In support variety theory, representations of a finite dimensional (Hopf) algebra A can be studied geometrically by associating any representation of A to an algebraic variety using the cohomology ring of A. An essential assumption in this theory is the finite generation condition for the cohomology ring of A and that for the corresponding modules. In this paper, we introduce various approaches to study the finite generation condition. First, for any finite dimensional Hopf algebra A, we show that the finite generation condition on A-modules can be replaced by a condition on any affine commutative A-module algebra R under the assumption that R is integral over its invariant subring R A . Next, we use a spectral sequence argument to show that a finite generation condition holds for certain filtered, smash and crossed product algebras in positive characteristic if the related spectral sequences collapse. Finally, if A is defined over a number field over the rationals, we construct another finite dimensional Hopf algebra A ′ over a finite field, where A can be viewed as a deformation of A ′ , and prove that if the finite generation condition holds for A ′ , then the same condition holds for A.
Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science

Abstract One of the challenges of teaching applied data science courses is managing individual students’ local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There are additional challenges with using sensitive health information for clinical data science education. Here we describe the development and performance of a computing platform developed to support a series of MOOCs in clinical data science. This platform was designed to restrict and log all access to health datasets while also being scalable, accessible, secure, privacy preserving, and easy to access. Over the 19 months the platform has been live it has supported the computation of more than 2300 students from 101 countries.
DnaJC7 binds natively folded structural elements in tau to inhibit amyloid formation.

Molecular chaperones, including Hsp70/J-domain protein (JDP) families, play central roles in binding substrates to prevent their aggregation. How JDPs select different conformations of substrates remains poorly understood. Here, we report an interaction between the JDP DnaJC7 and tau that efficiently suppresses tau aggregation in vitro and in cells. DnaJC7 binds preferentially to natively folded wild-type tau, but disease-associated mutants in tau reduce chaperone binding affinity. We identify that DnaJC7 uses a single TPR domain to recognize a β-turn structural element in tau that contains the 275VQIINK280 amyloid motif. Wild-type tau, but not mutant, β-turn structural elements can block full-length tau binding to DnaJC7. These data suggest DnaJC7 preferentially binds and stabilizes natively folded conformations of tau to prevent tau conversion into amyloids. Our work identifies a novel mechanism of tau aggregation regulation that can be exploited as both a diagnostic and a therapeutic intervention. Protein binding by the Hsp70/J-domain protein (JDP) chaperones prevents aggregation of the client protein. Here, the authors show that DnaJC7 binds preferentially to natively folded wild-type tau, via a β-turn element in tau that contains the known amyloid motif, while aggregation-prone tau mutants are recognized with reduced affinity.
Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels-a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)-for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.
Generating Empirical Core Size Distributions of Hedonic Games Using a Monte Carlo Method

Data analytics allows an analyst to gain insight into underlying populations through the use of various computational approaches, including Monte Carlo methods. This paper discusses an approach to apply Monte Carlo methods to hedonic games. Hedonic games have gain popularity over the last two decades leading to several research articles that are concerned with the necessary, sufficient, or both conditions of the existence of a core partition. Researchers have used analytical methods for this work. We propose that using a numerical approach will give insights that might not be available through current analytical methods. In this paper, we describe an approach to representing hedonic games, with strict preferences, in a matrix form that can easily be generated; that is, a hedonic game with randomly generated preferences for each player. Using this generative approach, we were able to create and solve, i.e., find any core partitions, of millions of hedonic games. Our Monte Carlo experiment generated games with up to thirteen players. The results discuss the distribution form of the core size of the games of a given number of players. We also discuss computational considerations. Our numerical study of hedonic games gives insight into the underlying properties of hedonic games.
The perfect groups of order up to two million

We enumerate the 15768 perfect groups of order up to $2\cdot 10^6$, up to isomorphism, thus also completing the missing cases in the prior classification. The work supplements the by now well-understood computer classifications of solvable groups, illustrating scope and feasibility of the enumeration process for nonsolvable groups.
Pressure reconstruction of a planar turbulent flow field within a multiply connected domain with arbitrary boundary shapes

This paper reports for the first time the implementation procedures and validation results for pressure reconstruction of a planar turbulent flow field within a multiply connected domain that has arbitrary inner and outer boundary shapes. The pressure reconstruction algorithm used in this study is the rotating parallel-ray omni-directional integration algorithm that offers high-level of accuracy in the reconstructed pressure. While preserving the nature and advantage of the parallel ray omni-directional pressure reconstruction at places with flow data, the new implementation of the algorithm is capable of processing an arbitrary number of inner void areas with arbitrary boundary shapes. Validation of the multiply connected domain pressure reconstruction code is conducted using the Johns Hopkins DNS (Direct Numerical Simulation) isotropic turbulence databases [J. Graham et al., J. Turbul. 17(2), 181 (2016)], with 1000 statistically independent pressure gradient field realizations embedded with random noise used to gauge the code performance. For further validation, the code is also applied for pressure reconstruction from the DNS data [E. Johnsen and T. Colonius, J. Fluid Mech., 629, 231 (2009)] about a shock-induced non-spherical bubble collapse in water. It is demonstrated that the parallel-ray omni-directional integration algorithm outperforms the Poisson equation approach in terms of the accuracy for the pressure reconstruction from error embedded pressure gradients in both simply connected and multiply connected domains.
Spectral Methods for Data Science: A Statistical Perspective

In contemporary science and engineering applications, the volume of available data is growing at an enormous rate. Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. A diverse array of applications have been found in machine learning, imaging science, financial and econometric modeling, and signal processing. This monograph presents a systematic, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large-scale applications. The authors provide a unified and comprehensive treatment that establishes the theoretical underpinnings for spectral methods, particularly through a statistical lens. Building on years of research experience in the field, the authors present a powerful framework, called leave-one-out analysis, that proves effective and versatile for delivering fine-grained performance guarantees for a variety of problems. This book is essential reading for all students, researchers and practitioners working in Data Science.
Who Watches the Watchmen: Evidence of the Effect of Body-Worn Cameras on New York City Policing

We present a multi-year study of the rollout of Body-Worn Cameras (BWCs) to the New York City Police Department (NYPD). Our study adds to the prior body of work by clarifying some of the discord within it, particularly with respect to large urban police departments. We estimate the effect of BWC deployment on precinct volumes of citizen stops, arrests, complaints against officers and use-of-force incidents. Results indicate that BWCs drive significant increases in stops and decreases in arrests and citizen complaints. We observe no effect on use of force. We also document heterogeneity in affected stops and complaints. Our findings speak to three potential benefits of BWCs in urban law enforcement: an increase in legitimate stops made by police; a decrease in complaints alleging officers’ abuse of authority; and a reduction in arrests (which appears beneficial, regardless of whether this results from improved behavior among police or citizens).
Tracking Evaporative Cooling of a Mesoscopic Atomic Quantum Gas in Real Time

Continuously tracking the number of atoms in an evaporating ultracold quantum gas uncovers stochastic fluctuations, providing a platform for exploring the interplay between randomness and nonlinear dynamics.
Equiangular lines with a fixed angle

Solving a longstanding problem on equiangular lines, we determine, for each given fixed angle and in all sufficiently large dimensions, the maximum number of lines pairwise separated by the given angle. Fix $0 < \alpha < 1$. Let $N_\alpha(d)$ denote the maximum number of lines through the origin in $\mathbb{R}^d$ with pairwise common angle $\arccos \alpha$. Let $k$ denote the minimum number (if it exists) of vertices in a graph whose adjacency matrix has spectral radius exactly $(1-\alpha)/(2\alpha)$. If $k < \infty$, then $N_\alpha(d) = \lfloor k(d-1)/(k-1) \rfloor$ for all sufficiently large $d$, and otherwise $N_\alpha(d) = d + o(d)$. In particular, $N_{1/(2k-1)}(d) = \lfloor k(d-1)/(k-1) \rfloor$ for every integer $k\ge 2$ and all sufficiently large $d$. A key ingredient is a new result in spectral graph theory: the adjacency matrix of a connected bounded degree graph has sublinear second eigenvalue multiplicity.
Error Mitigation for Universal Gates on Encoded Qubits.

The Eastin-Knill theorem states that no quantum error-correcting code can have a universal set of transversal gates. For Calderbank-Shor-Steane codes that can implement Clifford gates transversally, it suffices to provide one additional non-Clifford gate, such as the $T$ gate, to achieve universality. Common methods to implement fault-tolerant $T$ gates, e.g., magic state distillation, generate a significant hardware overhead that will likely prevent their practical usage in the near-term future. Recently, methods have been developed to mitigate the effect of noise in shallow quantum circuits that are not protected by error correction. Error mitigation methods require no additional hardware resources but suffer from a bad asymptotic scaling and apply only to a restricted class of quantum algorithms. In this Letter, we combine both approaches and show how to implement encoded $\text{Clifford}+T$ circuits where Clifford gates are protected from noise by error correction while errors introduced by noisy encoded $T$ gates are mitigated using the quasiprobability method. As a result, $\text{Clifford}+T$ circuits with a number of $T$ gates inversely proportional to the physical noise rate can be implemented on small error-corrected devices without magic state distillation. We argue that such circuits can be out of reach for state-of-the-art classical simulation algorithms.
Asymptotic nonlocality in gauge theories

Asymptotically nonlocal field theories represent a sequence of higher-derivative theories whose limit point is a ghost-free, infinite-derivative theory. Here we extend this framework, developed previously in a theory of real scalar fields, to gauge theories. We focus primarily on asymptotically nonlocal scalar electrodynamics, first identifying equivalent gauge-invariant formulations of the Lagrangian, one with higher-derivative terms and the other with auxiliary fields instead. We then study mass renormalization of the complex scalar field in each formulation, showing that an emergent nonlocal scale (i.e., one that does not appear as a fundamental parameter in the Lagrangian of the finite-derivative theories) regulates loop integrals as the limiting theory is approached, so that quadratic divergences can be hierarchically smaller than the lightest Lee-Wick partner. We conclude by making preliminary remarks on the generalization of our approach to non-Abelian theories, including an asymptotically nonlocal standard model.
Price leadership and asynchronous movements of multi-market listed stocks

Abstract We examine the asynchronous price movements of the same assets traded on multiple markets, each of which has its unique characteristics. Differently from the existing literature, we use a dynamic structural Vector Autoregressive (VAR) setting to explore the effects of market-wide and idiosyncratic shocks on both home and host listings. We find strong evidence that foreign prices lead home prices, but not the reverse effect. Contrary to theory predictions, investors in the firms’ home market respond to idiosyncratic fluctuations in the stock returns in the host markets. Our results suggest that investors pay attention to fluctuations in the stocks listed on the more institutionally developed markets.