cssi/SciSciGPT-SciSciNet huggingface dataset:
Divisional managers and internal capital markets

Using hand-collected data on divisional managers at S&P 500 firms, we study their role in internal capital budgeting. Divisional managers with social connections to the CEO receive more capital. Connections to the CEO outweigh measures of managers’ formal influence, such as seniority and board membership, and affect both managerial appointments and capital allocations. The effect of connections on investment efficiency depends on the tradeoff between agency and information asymmetry. Under weak governance, connections reduce investment efficiency and firm value via favoritism. Under high information asymmetry, connections increase investment efficiency and firm value via information transfer. DIVISIONAL MANAGERS PLAY AN important role in theories of internal capital markets. The bright-side view posits that internal capital markets benefit from stronger control rights and superior information provided by divisional managers, which enable the CEO to make better allocation decisions. 1 The darkside view holds that internal capital markets suffer from agency motives of divisional managers and the CEO, who pursue private interests. 2 The importance of divisional managers in the theoretical literature is also supported by
Health Care Reform, Efficiency of Health Insurers, and Optimal Health Insurance Markets

This research examines the efficiency of the U.S. health insurers. It shows that more insurers are less efficient than in the previous sample year; however, the results suggest that the federal health care reform has no significant effect on the overall efficiency of all insurers as a whole, which is very low but does not change much over time. This research explores how to improve the efficiency of the health insurance market by proposing state, regional, and national efficiency-based goal-oriented market models and an efficiency duplicating system, and it discusses important implications to the health care compacts, the health insurance exchanges or marketplaces, and the national multistate programs. It also analyzes further moves for efficiency enhancement with regard to payment methods and the health care delivery system. One interesting finding is that the Medicaid program is very efficient because it provides support to the offering of Medicaid coverage and further expansion, which enhances the heal...
Defense Costs and Insurer Reserves in Medical Malpractice and Other Personal Injury Cases: Evidence from Texas, 1988–2004

We study defense costs for commercially insured personal injury tort claims in Texas over 1988�2004, and insurer reserves for those costs. We rely on detailed case-level data on defense legal fees and expenses, and Texas state bar data on lawyers� hourly rates. We study medical malpractice ("med mal") cases in detail, and other types of cases in less detail. Controlling for payouts, real defense costs in med mal cases rise by 4.6 percent per year, roughly doubling over this period. The rate of increase is similar for legal fees and for other expenses. Real hourly rates for personal injury defense counsel are flat. Defense costs in med mal cases correlate strongly with payouts, both in ordinary least squares (OLS) and in an instrumental variable analysis. They also correlate with the stage at which a case is resolved, and case duration. Mean duration declined over time. Med mal insurers predominantly use outside counsel. Case-level variation in initial expense reserves predicts a small fraction of actual defense costs. In other areas of tort litigation (auto, general commercial, multi-peril, and other professional liability), defense costs rose by 2.2 percent per year. Defense costs in these cases are predicted by the same factors as in med mal cases, plus the presence of multiple defendants. Insurer reserving practices raise some puzzles. Med mal insurers did not react to the sustained rise in defense costs by adjusting their expense reserves, either in real dollars or relative to reserves for payouts. Thus, expense reserves declined substantially relative to defense costs. In other litigation areas, expense reserves rose along with defense costs.
Judicial Power in Latin America: a Short Survey

This article, written by Teresa M. Miguel-Stearns, explores the vast differences in judicial authority not only between the common law and civil law traditions, but also among various countries steeped in the civil law tradition in Latin America. Judicial review, certiorari, precedent, and other functions and characteristics of the judiciaries of five distinctly different countries (Argentina, Bolivia, Brazil, Chile, and Mexico) are compared and contrasted with each other and with the common law tradition. This evaluation demonstrates that despite their, arguably, similar distant histories and legal foundations, each country has evolved into a unique legal system with significant differences in the treatment of the judiciary and its jurisprudence.
Do important inventions benefit from knowledge originating in other technological domains

Abstract A frequently made claim in the innovation literature is that important inventions involve the transfer of new knowledge from one technological domain to another. This study uses U.S. patents granted from 1976 to 2006 to identify the role of knowledge acquired from outside each patent's technological domain. Our results do not seem to support the claim above. Increasing citations to external prior art is a significantly less important predictor of forward citation frequency than citing prior art that is technologically closer. This result is robust across several model specifications and ways of defining whether each flow of knowledge is external. The result is even stronger in the most highly cited technology categories. We discuss possible explanations for this apparently negative impact of external knowledge—including both measurement issues and challenges associated with assimilating disparate knowledge.
Global Convergence of Sobolev Training for Overparameterized Neural Networks

Sobolev loss is used when training a network to approximate the values and derivatives of a target function at a prescribed set of input points. Recent works have demonstrated its successful applications in various tasks such as distillation or synthetic gradient prediction. In this work we prove that an overparameterized two-layer relu neural network trained on the Sobolev loss with gradient flow from random initialization can fit any given function values and any given directional derivatives, under a separation condition on the input data.
Effects of linker flexibility on phase behavior and structure of linked colloidal gels.

Colloidal nanocrystal gels can be assembled using a difunctional “linker” molecule to mediate bonding between nanocrystals. The conditions for gelation and the structure of the gel are controlled macroscopically by the linker concentration and microscopically by the linker’s molecular characteristics. Here, we demonstrate using a toy model for a colloid–linker mixture that linker flexibility plays a key role in determining both phase behavior and the structure of the mixture. We fix the linker length and systematically vary its bending stiffness to span the flexible, semiflexible, and rigid regimes. At fixed linker concentration, flexible-linker and rigid-linker mixtures phase separate at low colloid volume fractions, in agreement with predictions of first-order thermodynamic perturbation theory, but the semiflexible-linker mixtures do not. We correlate and attribute this qualitatively different behavior to undesirable “loop” linking motifs that are predicted to be more prevalent for linkers with end-to-end distances commensurate with the locations of chemical bonding sites on the colloids. Linker flexibility also influences the spacing between linked colloids, suggesting strategies to design gels with desired phase behavior, structure, and, by extension, structure-dependent properties.
Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses

Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ignores the uncertainty of the perception process both regarding the target’s and the surrounding objects’ poses. This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene. Then, an open-loop planning pipeline is proposed to return safe and effective solutions for moving a robotic arm to pick, which (a) minimizes the probability of collision with the obstructing objects; and (b) maximizes the probability of reaching the target item. The planning framework models the challenge as a stochastic variant of the Minimum Constraint Removal (MCR) problem. The effectiveness of the methodology is verified given both simulated and real data in different scenarios. The experiments demonstrate the importance of considering the uncertainty of the perception process in terms of safe execution. The results also show that the methodology is more effective than conservative MCR approaches, which avoid all possible object poses regardless of the reported uncertainty.
High Temporal and Spectral Resolution of Stimulated X-Ray Raman Signals with Stochastic Free-Electron-Laser Pulses

Temporal and spectral resolution of x-ray spectroscopy can be improved by exploiting correlations of existing stochastic x-ray fields, allowing for new investigations of electron and nuclear dynamics in molecules.
Cyclic Prefix Direct Sequence Spread Spectrum Capacity Analysis

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a novel waveform that is proposed as a solution to massive machine type communications (mMTC) for 5G and beyond. This paper analyzes the capacity of CP-DSSS in comparison with Orthogonal Frequency-Division Multiplexing (OFDM). We show that CP-DSSS achieves the same capacity as OFDM and can be optimized with similar precoding methods (e.g., water-filling). Because of its spread spectrum nature, CP-DSSS can operate as a secondary network using the same spectrum as the primary 4G or 5G network, but transmitting at much lower power. Accordingly, the combination of primary and secondary signals in the envisioned setup may be viewed as a power NOMA (non-orthogonal multiple access) technique where primary signals are detected and subtracted from the received signal first before detecting the secondary signals. In order to operate at a sufficiently low interference level to the primary network, details of CP-DSSS capacity for symbol rate reduction and multi-antenna operation are developed. The capacity limits established in this paper can be used as a baseline to evaluate the performance of future CP-DSSS receiver architectures for single- and multi-user scenarios.
Critiques, Ethics, Prestige and Status: A Survey of Editors in Economics.

This study examines survey data on the views of editors of economics journals on common critiques of the discipline, ethics and editorial practices, and the role of prestige and status in publishing. We utilize an ordered probit model to investigate whether editors or journal characteristics are systematically related to editors’ views, controlling for gender and editorial position. Regression results show that editors from top-ranked journals are less likely to agree with common disciplinary critiques, more likely to support market solutions and less likely to agree with concerns about editorial practices.
Lytic bacteriophages facilitate antibiotic sensitization of Enterococcus faecium .

Enterococcus faecium, a commensal of the human intestine, has emerged as a hospital-adapted, multi-drug resistant (MDR) pathogen. Bacteriophages (phages), natural predators of bacteria, have regained attention as therapeutics to stem the rise of MDR bacteria. Despite their potential to curtail MDR E. faecium infections, the molecular events governing E. faecium-phage interactions remain largely unknown. Such interactions are important to delineate because phage selective pressure imposed on E. faecium will undoubtedly result in phage resistance phenotypes that could threaten the efficacy of phage therapy. In an effort to understand the emergence of phage resistance in E. faecium, three newly isolated lytic phages were used to demonstrate that E. faecium phage resistance is conferred through an array of cell wall-associated molecules, including secreted antigen A (SagA), enterococcal polysaccharide antigen (Epa), wall teichoic acids, capsule, and an arginine-aspartate-aspartate (RDD) protein of unknown function. We find that capsule and Epa are important for robust phage adsorption and that phage resistance mutations in sagA, epaR, and epaX enhance E. faecium susceptibility to ceftriaxone, an antibiotic normally ineffective due to its low affinity for enterococcal penicillin binding proteins. Consistent with these findings, we provide evidence that phages potently synergize with cell wall (ceftriaxone and ampicillin) and membrane-acting (daptomycin) antimicrobials to slow or completely inhibit the growth of E. faecium Our work demonstrates that the evolution of phage resistance comes with fitness defects resulting in drug sensitization and that lytic phages could serve as effective antimicrobials for the treatment of E. faecium infections.
Does common ownership really increase firm coordination

Abstract A growing number of studies suggest that common ownership caused cooperation among firms to increase and competition to decrease. We take a closer look at four approaches used to identify these effects. We find that the effects that some studies have attributed to common ownership are caused by other factors, such as differential responses of firms (or industries) to the 2008 financial crisis. We propose a modification to one of the previously used empirical approaches that is less sensitive to these issues. Using this to re-evaluate the link between common ownership and firm outcomes, we find little robust evidence that common ownership affects firm behavior.
Distinguished Cuspidal Representations over p-adic and Finite Fields

The author's work with Murnaghan on distinguished tame supercuspidal representations is re-examined using a simplified treatment of Jiu-Kang Yu's construction of tame supercuspidal representations of $p$-adic reductive groups. This leads to a unification of aspects of the theories of distinguished cuspidal representations over $p$-adic and finite fields.
A Divisor Formula and a Bound on the $\mathbb{Q}$-gonality of the Modular Curve $X_1(N)$

We give a formula for divisors of modular units on $$X_1(N)$$ and use it to prove that the $$\mathbb {Q}$$ -gonality of the modular curve $$X_1(N)$$ is bounded above by $$[{11N^2}/{840}]$$ , where $$[\cdot ]$$ denotes the nearest integer.
HERMES: Persistent spectral graph software

Persistent homology (PH) is one of the most popular tools in topological data analysis (TDA), while graph theory has had a significant impact on data science. Our earlier work introduced the persistent spectral graph (PSG) theory as a unified multiscale paradigm to encompass TDA and geometric analysis. In PSG theory, families of persistent Laplacian matrices (PLMs) corresponding to various topological dimensions are constructed via a filtration to sample a given dataset at multiple scales. The harmonic spectra from the null spaces of PLMs offer the same topological invariants, namely persistent Betti numbers, at various dimensions as those provided by PH, while the non-harmonic spectra of PLMs give rise to additional geometric analysis of the shape of the data. In this work, we develop an open-source software package, called highly efficient robust multidimensional evolutionary spectra (HERMES), to enable broad applications of PSGs in science, engineering, and technology. To ensure the reliability and robustness of HERMES, we have validated the software with simple geometric shapes and complex datasets from three-dimensional (3D) protein structures. We found that the smallest non-zero eigenvalues are very sensitive to data abnormality.
Uniqueness and regularity of unbounded weak solutions to a class of cross diffusion systems

We establish the uniqueness and regularity of weak (and very weak) solutions to a class of cross diffusion systems which is inspired by models in mathematical biology/ecology, in particular the Shigesada–Kawasaki–Teramoto model in population biology. No boundedness assumption on these solutions is supposed here as known techniques for scalar equations such as maximum/comparison principles are generally unavailable for systems. Furthermore, for planar domains we show that unbounded weak solutions satisfying mild integrability conditions are in fact smooth.
Accelerating volume rendering with texture hulls

Texture-mapping hardware has been successfully exploited for volume rendering. In this paper, we propose algorithms for texture-based volume rendering accelerated by texture hulls that avoid the transferring and compositing of empty voxels. We have developed methods that efficiently find all the bounding rectangles of the non-empty regions as well as the bounding contours that more accurately describe the borders of the non-empty regions. The bounding shapes are treated as the hulls of the non-empty sub-textures. Texels outside the hulls are skipped for storing and rendering. With our accelerations, the rendering speed is 2 to 12 times faster for a variety of data sets.
FOREIGN CURRENCY FUTURES

Abstract The theoretical nature of risk premiums in foreign currency futures markets is derived and studied empirically. Estimation problems encountered in using futures data are discussed. Since forward rates and futures prices have been found to be approximately equal, and because risk in daily risk premiums in the futures market. Unbiasedness of daily futures prices as predictors of the following day's futures price is rejected for all currencies. Reconciliation of daily and monthyl data requires positive serial correlation in daily risk premiums.
Some Remarks on Ricci Flow and the Quantum Potential

We indicate some formulas connecting Ricci flow and Perelman entropy to Fisher information, differential entropy, and the quantum potential. There is a known relation involving the Schroedinger equation in a Weyl space where the Weyl-Ricci curvature is proportional to the quantum potential. The quantum potential in turn is related to Fisher information which is given via the Perelman entropy functional arising from a differential entropy under Ricci flow. These relations are written out and seem to suggest connections between quantum mechanics and Ricci flow.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective crosses. Deep & Cross Network (DCN) was proposed to automatically and efficiently learn bounded-degree predictive feature interactions. Unfortunately, in models that serve web-scale traffic with billions of training examples, DCN showed limited expressiveness in its cross network at learning more predictive feature interactions. Despite significant research progress made, many deep learning models in production still rely on traditional feed-forward neural networks to learn feature crosses inefficiently. In light of the pros/cons of DCN and existing feature interaction learning approaches, we propose an improved framework DCN-V2 to make DCN more practical in large-scale industrial settings. In a comprehensive experimental study with extensive hyper-parameter search and model tuning, we observed that DCN-V2 approaches outperform all the state-of-the-art algorithms on popular benchmark datasets. The improved DCN-V2 is more expressive yet remains cost efficient at feature interaction learning, especially when coupled with a mixture of low-rank architecture. DCN-V2 is simple, can be easily adopted as building blocks, and has delivered significant offline accuracy and online business metrics gains across many web-scale learning to rank systems at Google. Our code and tutorial are open-sourced as part of TensorFlow Recommenders (TFRS)1.
Perturbations of a Rotating Black Hole

Decoupled, separable equations describing perturbations of a Kerr black hole are derived. These equations can be used to study black-hole processes involving scalar, electromagnetic, neutrino or gravitational fields. A number of astrophysical applications are made: Misner's idea that gravitational synchrotron radiation might explain Weber's observations is shown to be untenable; rotating black holes are shown to be stable against small perturbations; energy amplification by "superradiant scattering" of waves off a rotating black hole is computed; the "spin down" (loss of angular momentum) of a rotating black hole caused by a stationary non-axisymmetric perturbation is calculated.
Continuous Reachability Task Transition Using Control Barrier Functions

Abstract In this paper, a method to achieve smooth transitions between sequential reachability tasks for a continuous time mobile robotic system is presented. Control barrier functions provide formal guarantees of forward invariance of safe sets and finite-time reachability and are able to enforce task execution. Barrier functions used in quadratic programs result in implementation of controllers with real-time performance guarantees. Existing approaches for multi-objective task execution using control barrier functions leverage discretely switched, sequential quadratic programs to achieve successive tasks. However, discrete switching can lead to control input discontinuities which can affect a robot’s performance. Hence, we propose a method which ensures continuous transitions between sequential quadratic programs. In particular, a time varying component to the barrier function constraint is introduced which allows for a smooth transition between objectives. Robotic implementation results are also provided.
K-Classes Of Brill–Noether Loci And A Determinantal Formula

We compute the Euler characteristic of the structure sheaf of the Brill-Noether locus of linear series with special vanishing at up to two marked points. When the Brill-Noether number $\rho$ is zero, we recover the Castelnuovo formula for the number of special linear series on a general curve; when $\rho=1$, we recover the formulas of Eisenbud-Harris, Pirola, and Chan-Martin-Pflueger-Teixidor for the arithmetic genus of a Brill-Noether curve of special divisors. These computations are obtained as applications of a new determinantal formula for the K-theory class of certain degeneracy loci. Our degeneracy locus formula also specializes to new determinantal expressions for the double Grothendieck polynomials corresponding to 321-avoiding permutations, and gives double versions of the flagged skew Grothendieck polynomials recently introduced by Matsumura. Our result extends the formula of Billey-Jockusch-Stanley expressing Schubert polynomials for 321-avoiding permutations as generating functions for flagged skew tableaux.
Pre- and post-processing in quantum-computational hydrologic inverse analysis

It was recently shown that certain subsurface hydrological inverse problems—here framed as determining the composition of an aquifer from pressure readings—can be solved on a quantum annealer. However, the quantum annealer performance suffered when solving problems where the aquifer was composed of materials with vastly different permeability, which is often encountered in practice. In this paper, we study why this regime is difficult and use several pre- and post-processing tools to address these issues. This study has three benefits: it improves quantum annealing performance for real-world problems in hydrology, it studies the scaling behavior for these problems (which were previously studied at a fixed size), and it elucidates a challenging class of problems that are amenable to quantum annealers.
Node-level energy management for sensor networks in the presence of multiple applications

Energy related research in wireless ad hoc sensor networks (WASNs) is focusing on energy saving techniques in the application-, protocol-, service-, or hardware-level. Little has been done to manage the finite amount of energy for a given (possibly optimally-designed) set of applications, protocols and hardware. Given multiple candidate applications (i.e., distributed algorithms in a WASN) of different energy costs and different user rewards, how does one manage a finite energy amount? Where does one provide energy, so as to maximize the useful work done (i.e., maximize user rewards)? We formulate the problem at the node-level, by having system-level "hints" from the applications. In order to tackle the central problem we first identify the energy consumption patterns of applications in WASNs, we propose ways for real-time measurements of the energy consumption by individual applications, and we solve the problem of estimating the extra energy consumption that a new application brings to a set of executing applications. Having these tools at our disposal, and by properly abstracting the problem we present an optimal admission control policy and a post-admission policing mechanism at the node-level. The admission policy can achieve up to 48% increase in user rewards compared to the absence of energy management, for a variety of application mixes.
Effects of tDCS Dose and Electrode Montage on regional cerebral blood flow and motor behavior

Abstract We used three dose levels (Sham, 2 mA, and 4 mA) and two different electrode montages (unihemispheric and bihemispheric) to examine DOSE and MONTAGE effects on regional cerebral blood flow (rCBF) as a surrogate marker of neural activity, and on a finger sequence task, as a surrogate behavioral measure drawing on brain regions targeted by transcranial direct current stimulation (tDCS). We placed the anodal electrode over the right motor region (C4) while the cathodal or return electrode was placed either over a left supraorbital region (unihemispheric montage) or over the left motor region (C3 in the bihemispheric montage). Performance changes in the finger sequence task for both hands (left hand: p = 0.0026, and right hand: p = 0.0002) showed a linear tDCS dose response but no montage effect. rCBF in the right hemispheric perirolandic area increased with dose under the anodal electrode (p = 0.027). In contrast, in the perirolandic ROI in the left hemisphere, rCBF showed a trend to increase with dose (p = 0.053) and a significant effect of montage (p = 0.00004). The bihemispheric montage showed additional rCBF increases in frontomesial regions in the 4mA condition but not in the 2 mA condition. Furthermore, we found strong correlations between simulated current density in the left and right perirolandic region and improvements in the finger sequence task performance (FSP) for the contralateral hand. Our data support not only a strong direct tDCS dose effect for rCBF and FSP as surrogate measures of targeted brain regions but also indirect effects on rCBF in functionally connected regions (e.g., frontomesial regions), particularly in the higher dose condition and on FSP of the ipsilateral hand (to the anodal electrode). At a higher dose and irrespective of polarity, a wider network of sensorimotor regions is positively affected by tDCS.
Refactorization of Cauchy’s Method: A Second-Order Partitioned Method for Fluid–Thick Structure Interaction Problems

This work focuses on the derivation and the analysis of a novel, strongly-coupled partitioned method for fluid–structure interaction problems. The flow is assumed to be viscous and incompressible, and the structure is modeled using linear elastodynamics equations. We assume that the structure is thick, i.e., modeled using the same number of spatial dimensions as fluid. Our newly developed numerical method is based on Robin boundary conditions, as well as on the refactorization of the Cauchy’s one-legged ‘ $$\theta $$ -like’ method, written as a sequence of Backward Euler–Forward Euler steps used to discretize the problem in time. This family of methods, parametrized by $$\theta $$ , is B-stable for any $$\theta \in [\frac{1}{2} , 1]$$ and second-order accurate for $$\theta = \frac{1}{2} + {\mathcal {O}}(\tau )$$ , where $$\tau $$ is the time step. In the proposed algorithm, the fluid and structure sub-problems, discretized using the Backward Euler scheme, are first solved iteratively until convergence. Then, the variables are linearly extrapolated, equivalent to solving Forward Euler problems. We prove that the iterative procedure is convergent, and that the proposed method is stable provided $$\theta \in [\frac{1}{2},1]$$ . Numerical examples, based on the finite element discretization in space, explore convergence rates using different values of parameters in the problem, and compare our method to other strongly-coupled partitioned schemes from the literature. We also compare our method to both a monolithic and a non-iterative partitioned solver on a benchmark problem with parameters within the physiological range of blood flow, obtaining an excellent agreement with the monolithic scheme.
How Much is your Strangle Worth? On the Relative Value of the delta-Symmetric Strangle under the Black-Scholes Model

Trading option strangles is a highly popular strategy often used by market participants to mitigate volatility risks in their portfolios. In this paper we propose a measure of the relative value of a delta-Symmetric Strangle and compute it under the standard Black-Scholes option pricing model. This new measure accounts for the price of the strangle, relative to the Present Value of the spread between the two strikes, all expressed, after a natural re-parameterization, in terms of delta and a volatility parameter. We show that under the standard BS option pricing model, this measure of relative value is bounded by a simple function of delta only and is independent of the time to expiry, the price of the underlying security or the prevailing volatility used in the pricing model. We demonstrate how this bound can be used as a quick {\it benchmark} to assess, regardless the market volatility, the duration of the contract or the price of the underlying security, the market (relative) value of the $\delta-$strangle in comparison to its BS (relative) price. In fact, the explicit and simple expression for this measure and bound allows us to also study in detail the strangle's exit strategy and the corresponding {\it optimal} choice for a value of delta.
Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution.

We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages.
Higher Dimensional Graphics: Conceiving Worlds in Four Spatial Dimensions and Beyond

While the interpretation of high-dimensional datasets has become a necessity in most industries, and is supported by continuous advances in data science and machine learning, the spatial visualization of higher-dimensional geometry has mostly remained a niche research topic for mathematicians and physicists. Intermittent contributions to this field date back more than a century, and have had a non-negligible influence on contemporary art and philosophy. However, most contributions have focused on the understanding of specific mathematical shapes, with few concrete applications. In this work, we attempt to revive the community's interest in visualizing higher dimensional geometry by shifting the focus from the visualization of abstract shapes to the design of a broader hyper-universe concept, wherein 3D and 4D objects can coexist and interact with each other. Specifically, we discuss the content definition, authoring patterns, and technical implementations associated with the process of extending standard 3D applications as to support 4D mechanics. We operationalize our ideas through the introduction of a new hybrid 3D/4D videogame called Across Dimensions, which we developed in Unity3D through the integration of our own 4D plugin.
A Weighted Approach to the Maximum Cardinality Bipartite Matching Problem with Applications in Geometric Settings.

We present a weighted approach to compute a maximum cardinality matching in an arbitrary bipartite graph. Our main result is a new algorithm that takes as input a weighted bipartite graph $G(A\cup B,E)$ with edge weights of $0$ or $1$. Let $w \leq n$ be an upper bound on the weight of any matching in $G$. Consider the subgraph induced by all the edges of $G$ with a weight $0$. Suppose every connected component in this subgraph has $\mathcal{O}(r)$ vertices and $\mathcal{O}(mr/n)$ edges. We present an algorithm to compute a maximum cardinality matching in $G$ in $\tilde{\mathcal{O}}( m(\sqrt{w}+ \sqrt{r}+\frac{wr}{n}))$ time. When all the edge weights are $1$ (symmetrically when all weights are $0$), our algorithm will be identical to the well-known Hopcroft-Karp (HK) algorithm, which runs in $\mathcal{O}(m\sqrt{n})$ time. However, if we can carefully assign weights of $0$ and $1$ on its edges such that both $w$ and $r$ are sub-linear in $n$ and $wr=\mathcal{O}(n^{\gamma})$ for $\gamma < 3/2$, then we can compute maximum cardinality matching in $G$ in $o(m\sqrt{n})$ time. Using our algorithm, we obtain a new $\tilde{\mathcal{O}}(n^{4/3}/\varepsilon^4)$ time algorithm to compute an $\varepsilon$-approximate bottleneck matching of $A,B\subset\mathbb{R}^2$ and an $\frac{1}{\varepsilon^{\mathcal{O}(d)}}n^{1+\frac{d-1}{2d-1}}\mathrm{poly}\log n$ time algorithm for computing $\varepsilon$-approximate bottleneck matching in $d$-dimensions. All previous algorithms take $\Omega(n^{3/2})$ time. Given any graph $G(A \cup B,E)$ that has an easily computable balanced vertex separator for every subgraph $G'(V',E')$ of size $|V'|^{\delta}$, for $\delta\in [1/2,1)$, we can apply our algorithm to compute a maximum matching in $\tilde{\mathcal{O}}(mn^{\frac{\delta}{1+\delta}})$ time improving upon the $\mathcal{O}(m\sqrt{n})$ time taken by the HK-Algorithm.
Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance

Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.
Partially Phase-Locked Solutions to the Kuramoto Model

The Kuramoto model is a canonical model for understanding phase-locking phenomenon. It is well-understood that, in the usual mean-field scaling, full phase-locking is unlikely and that it is partially phase-locked states that are important in applications. Despite this, while there has been much attention given to existence and stability of fully phase-locked states in the finite N Kuramoto model, the partially phase-locked states have received much less attention. In this paper we present two related results. Firstly, we derive an analytical criteria that, for sufficiently strong coupling, guarantees the existence of a partially phase-locked state by proving the existence of an attracting ball around a fixed point of a subset of the oscillators. We also derive a larger invariant ball such that any point in it will asymptotically converge to the attracting ball. Secondly, we consider the large N behavior of the finite N Kuramoto system with randomly distributed frequencies. In the case where the frequencies are independent and identically distributed we use a result of De Smet and Aeyels on partial entrainment to derive a condition giving (with high probability) the existence of a partially entrained cluster. We also derive upper and lower bounds on the size of the largest entrained cluster, together with a lower bound on the order parameter. Interestingly in a series of numerical experiments we find that the observed size of the largest entrained cluster is predicted extremely well by the upper bound.
RCC: Resilient Concurrent Consensus for High-Throughput Secure Transaction Processing

Recently, we saw the emergence of consensus-based database systems that promise resilience against failures, strong data provenance, and federated data management. Typically, these fully-replicated systems are operated on top of a primary-backup consensus protocol, which limits the throughput of these systems to the capabilities of a single replica (the primary).To push throughput beyond this single-replica limit, we propose concurrent consensus. In concurrent consensus, replicas independently propose transactions, thereby reducing the influence of any single replica on performance. To put this idea in practice, we propose our RCC paradigm that can turn any primary-backup consensus protocol into a concurrent consensus protocol by running many consensus instances concurrently. RCC is designed with performance in mind and requires minimal coordination between instances. Furthermore, RCC also promises increased resilience against failures. We put the design of RCC to the test by implementing it in ResilientDB, our high-performance resilient blockchain fabric, and comparing it with state-of-the-art primary-backup consensus protocols. Our experiments show that RCC achieves up to 2.75× higher throughput than other consensus protocols and can be scaled to 91 replicas.
PSB2: the second program synthesis benchmark suite

For the past six years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite to benchmark many aspects of automatic program synthesis systems. These problems have been used to make notable progress toward the goal of general program synthesis: automatically creating the types of software that human programmers code. Many of the systems that have attempted the problems in the original benchmark suite have used it to demonstrate performance improvements granted through new techniques. Over time, the suite has gradually become outdated, hindering the accurate measurement of further improvements. The field needs a new set of more difficult benchmark problems to move beyond what was previously possible. In this paper, we describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite. These problems are curated from a variety of sources, including programming katas and college courses. We selected these problems to be more difficult than those in the original suite, and give results using PushGP showing this increase in difficulty. These new problems give plenty of room for improvement, pointing the way for the next six or more years of general program synthesis research.
Transonic pressure distributions on a rectangular supercritical wing oscillating in pitch

Steady and unsteady aerodynamic data were measured on a rectangular wing with a 12 percent thick supercritical airfoil mounted in the NASA Langley Transonic Dynamics Tunnel. The wing was oscillated in pitch to generate the unsteady aerodynamic data. The purpose of the wind-tunnel test was to measure data for use in the development and assessment of transonic analytical codes. The effects on the wing pressure distributions of Mach number, mean angle of attack, and oscillation frequency and amplitude were measured. Results from the newly-developed XTRAN3S program (a non-linear transonic small disturbance code) and from the RHOIV program (a linear lifting surface kernel function code) were compared to measured data for a Mach number of 0.7 and for oscillation frequencies ranging from 0 to 20 Hz. The XTRAN3S steady and unsteady results agreed fairly well with the measured data. The RHOIV unsteady-result agreement was fair but, of course, did not predict shock effects.
Attention-based Contextual Language Model Adaptation for Speech Recognition

Language modeling (LM) for automatic speech recognition (ASR) does not usually incorporate utterance level contextual information. For some domains like voice assistants, however, additional context, such as the time at which an utterance was spoken, provides a rich input signal. We introduce an attention mechanism for training neural speech recognition language models on both text and non-linguistic contextual data. When applied to a large de-identified dataset of utterances collected by a popular voice assistant platform, our method reduces perplexity by 7.0% relative over a standard LM that does not incorporate contextual information. When evaluated on utterances extracted from the long tail of the dataset, our method improves perplexity by 9.0% relative over a standard LM and by over 2.8% relative when compared to a state-of-the-art model for contextual LM.
Modeling of Personalized Anatomy Using Plastic Strains

We present a method for modeling solid objects undergoing large spatially varying and/or anisotropic strains, and use it to reconstruct human anatomy from medical images. Our novel shape deformation method uses plastic strains and the finite element method to successfully model shapes undergoing large and/or anisotropic strains, specified by sparse point constraints on the boundary of the object. We extensively compare our method to standard second-order shape deformation methods, variational methods, and surface-based methods, and demonstrate that our method avoids the spikiness, wiggliness, and other artifacts of previous methods. We demonstrate how to perform such shape deformation both for attached and un-attached (“free flying”) objects, using a novel method to solve linear systems with singular matrices with a known nullspace. Although our method is applicable to general large-strain shape deformation modeling, we use it to create personalized 3D triangle and volumetric meshes of human organs, based on magnetic resonance imaging or computed tomography scans. Given a medically accurate anatomy template of a generic individual, we optimize the geometry of the organ to match the magnetic resonance imaging or computed tomography scan of a specific individual. Our examples include human hand muscles, a liver, a hip bone, and a gluteus medius muscle (“hip abductor”).
Playing with Food: Learning Food Item Representations Through Interactive Exploration

A key challenge in robotic food manipulation is modeling the material properties of diverse and deformable food items. We propose using a multimodal sensory approach to interact and play with food that facilitates the ability to distinguish these properties across food items. First, we use a robotic arm and an array of sensors, which are synchronized using ROS, to collect a diverse dataset consisting of 21 unique food items with varying slices and properties. Afterwards, we learn visual embedding networks that utilize a combination of proprioceptive, audio, and visual data to encode similarities among food items using a triplet loss formulation. Our evaluations show that embeddings learned through interactions can successfully increase performance in a wide range of material and shape classification tasks. We envision that these learned embeddings can be utilized as a basis for planning and selecting optimal parameters for more material-aware robotic food manipulation skills. Furthermore, we hope to stimulate further innovations in the field of food robotics by sharing this food playing dataset with the research community.
Taxon-specific phytoplankton growth, nutrient utilization, and light limitation in the oligotrophic Gulf of Mexico

The highly stratified, oligotrophic regions of the oceans are predominantly nitrogen limited in the surface ocean and light limited at the deep chlorophyll maximum (DCM). Hence, determining light and nitrogen co-limitation patterns for diverse phytoplankton taxa is crucial to understanding marine primary production throughout the euphotic zone. During two cruises in the deep-water Gulf of Mexico, we measured primary productivity (H13CO3-), nitrate uptake (15NO3-), and ammonium uptake (15NH4+) throughout the water column. Primary productivity declined with depth from the mixed-layer to the DCM, averaging 27.1 mmol C m-2 d-1. The fraction of growth supported by NO3- was consistently low, with upper euphotic zone values ranging from 0.01 to 0.14 and lower euphotic zone values ranging from 0.03 to 0.44. Nitrate uptake showed strong diel patterns (maximum during the day), while ammonium uptake exhibited no diel variability. To parameterize taxon-specific phytoplankton nutrient and light utilization, we used a data assimilation approach (Bayesian Markov Chain Monte Carlo) including primary productivity, nutrient uptake, and taxon-specific growth rate measurements. Parameters derived from this analysis define distinct niches for five phytoplankton taxa (Prochlorococcus, Synechococcus, diatoms, dinoflagellates, and prymnesiophytes) and may be useful for constraining biogeochemical models of oligotrophic open-ocean systems.
Tracking the relation between gist and item memory over the course of long-term memory consolidation

Our experiences in the world support memories not only of specific episodes but also of the generalities (the 'gist') across related experiences. It remains unclear how these two types of memories evolve and influence one another over time. In two experiments, 173 human participants encoded spatial locations from a distribution and reported both item memory (specific locations) and gist memory (center for the locations) across 1-2 months. Experiment 1 demonstrated that after 1 month, gist memory was preserved relative to item memory, despite a persistent positive correlation between them. Critically, item memories were biased toward the gist over time. Experiment 2 showed that a spatial outlier item changed this relationship and that the extraction of gist is sensitive to the regularities of items. Our results suggest that the gist starts to guide item memories over longer durations as their relative strengths change.
Dirichlet depths for point process

Statistical depths have been well studied for multivariate and functional data over the past few decades, but remain under-explored for point processes. A first attempt on the notion of point process depth was conducted recently where the depth was defined as a weighted product of two terms: (1) the probability of the number of events in each process and (2) the depth of the event times conditioned on the number of events by using a Mahalanobis depth. We point out that multivariate depths such as the Mahalanobis depth cannot be directly used because they often neglect the important ordering property in the point process events. To deal with this problem, we propose a model-based approach for point process systematically. In particular, we develop a Dirichlet-distribution-based framework on the conditional depth term, where the new methods are referred to as Dirichlet depths. We examine mathematical properties of the new depths and conduct asymptotic analysis. In addition, we illustrate the new methods using various simulated and real experiment data. It is found that the proposed framework provides a reasonable center-outward rank and the new methods have accurate decoding in one neural spike train dataset.