cssi/SciSciGPT-SciSciNet huggingface dataset:
Distortion and the bridge distance of knots

We extend techniques due to Pardon to show that there is a lower bound on the distortion of a knot in $\mathbb{R}^3$ proportional to the minimum of the bridge distance and the bridge number of the knot. We also exhibit an infinite family of knots for which the minimum of the bridge distance and the bridge number is unbounded and Pardon's lower bound is constant.
BOLA: Near-Optimal Bitrate Adaptation for Online Videos

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes (rebuffers) and enhancing the quality of the video. A bitrate that is too high leads to frequent rebuffering, while a bitrate that is too low leads to poor video quality. Video providers segment videos into short segments and encode each segment at multiple bitrates. The video player adaptively chooses the bitrate of each segment to download, possibly choosing different bitrates for successive segments. We formulate bitrate adaptation as a utility-maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term $O(1/V)$ of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, BOLA does not require prediction of available network bandwidth. We empirically validate BOLA in a simulated network environment using a collection of network traces. We show that BOLA achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission. We also implemented an updated version of BOLA that is now part of the standard reference player dash.js and is used in production by several video providers such as Akamai, BBC, CBS, and Orange.
Dense superconducting phases of copper-bismuth at high pressure

Extensive $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ structural search resulted in the discovery of unexpected copper-bismuth compounds at high pressures. Bismuth, one of the heaviest stable elements, persistently refuses to mix with many other elements to form stable compounds. However, recent studies have shown that bismuth can be forced to bond with copper by squeezing them together using sufficiently high pressures, resulting in Cu${}_{11}$Bi${}_{7}$ and CuBi. In this work, the authors show that a new material, Cu${}_{2}$Bi, emerges at a pressure slightly below 60 GPa. At these extreme conditions, the Bi lone electron pairs are stereochemically inactive, allowing a denser packing of the constituent elements than in Cu${}_{11}$Bi${}_{7}$ and CuBi. According to their calculations, Cu${}_{2}$Bi is a conventional superconductor with a transition temperature above the values of any other Cu-Bi compound.
Mie scattering of phonons by point defects in IV-VI semiconductors PbTe and GeTe

Abstract Point defects in solids such as vacancy and dopants often cause large thermal resistance. Because the lattice site occupied by a point defect has a much smaller size than phonon wavelengths, the scattering of thermal acoustic phonons by point defects in solids has been widely assumed to be the Rayleigh scattering type. In contrast to this conventional perception, using an ab initio Green's function approach, we show that the scattering by point defects in PbTe and GeTe exhibits Mie scattering characterized by a weaker frequency dependence of the scattering rates and highly asymmetric scattering phase functions. These unusual behaviors occur because the strain field induced by a point defect can extend for a long distance much larger than the lattice spacing. Because of the asymmetric scattering phase functions, the widely used relaxation time approximation fails with an error of ~20% at 300 K in predicting lattice thermal conductivity when the vacancy fraction is 1%. Our results show that the phonon scattering by point defects in IV-VI semiconductors cannot be described by the simple kinetic theory combined with Rayleigh scattering.
PT-symmetric quantum electrodynamics and unitarity

More than 15 years ago, a new approach to quantum mechanics was suggested, in which Hermiticity of the Hamiltonian was to be replaced by invariance under a discrete symmetry, the product of parity and time-reversal symmetry, . It was shown that, if is unbroken, energies were, in fact, positive, and unitarity was satisfied. Since quantum mechanics is quantum field theory in one dimension—time—it was natural to extend this idea to higher-dimensional field theory, and in fact an apparently viable version of -invariant quantum electrodynamics (QED) was proposed. However, it has proved difficult to establish that the unitarity of the scattering matrix, for example, the Kallen spectral representation for the photon propagator, can be maintained in this theory. This has led to questions of whether, in fact, even quantum mechanical systems are consistent with probability conservation when Green’s functions are examined, since the latter have to possess physical requirements of analyticity. The status of QED will be reviewed in this paper, as well as the general issue of unitarity.
Future of jets, heavy flavor and EM probes at RHIC and RHIC II

Exciting results from the Relativistic Heavy Ion Collider (RHIC) have been presented at this Workshop. However, fundamental questions remain to be addressed in the future regarding whether the system is deconfined, chiral symmetry is restored, a color glass condensate exists in the initial state, and how the system evolves through eventual hadronization. Jets, heavy flavors and electromagnetic probes are sensitive to the initial high density stage of RHIC collisions, and should provide new insight. Significant additional capabilities will be added with a luminosity upgrade of RHIC (to RHIC II), upgrades of present detectors and a possible, new comprehensive detector at RHIC II.
Reactive learning strategies for iterated games.

In an iterated game between two players, there is much interest in characterizing the set of feasible pay-offs for both players when one player uses a fixed strategy and the other player is free to...
The Properties of Heavy Elements in Giant Planet Envelopes

The core accretion model for giant planet formation suggests a two layer picture for the initial structure of Jovian planets, with heavy elements in a dense core and a thick H-He envelope. Late planetesimal accretion and core erosion could potentially enrich the H-He envelope in heavy elements, which is supported by the three-fold solar metallicity that was measured in Jupiter's atmosphere by the Galileo entry probe. In order to reproduce the observed gravitational moments of Jupiter and Saturn, models for their interiors include heavy elements, $Z$, in various proportions. However, their effect on the equation of state of the hydrogen-helium mixtures has not been investigated beyond the ideal mixing approximation. In this article, we report results from \textit{ab initio} simulations of fully interacting H-He-$Z$ mixtures in order to characterize their equation of state and to analyze possible consequences for the interior structure and evolution of giant planets. Considering C, N, O, Si, Fe, MgO and SiO$_2$, we show that the behavior of heavy elements in H-He mixtures may still be represented by an ideal mixture if the effective volumes and internal energies are chosen appropriately. In the case of oxygen, we also compute the effect on the entropy. We find the resulting changes in the temperature-pressure profile to be small. A homogeneous distribution of 2\% oxygen by mass changes the temperature in Jupiter's interior by only 80 K.
Form and index of Ginsparg-Wilson fermions

We clarify the questions rised by a recent example of a lattice Dirac operator found by Chiu. We show that this operator belongs to a class based on the Cayley transformation and that this class on the finite lattice generally does not admit a nonvanishing index, while in the continuum limit, due to operator properties in Hilbert space, this defect is no longer there. Analogous observations are made for the chiral anomaly. We also elaborate on various aspects of the underlying sum rule for the index.
Moments of a length function on the boundary of a hyperbolic manifold

In this paper we will study the statistics of the unit geodesic flow normal to the boundary of a hyperbolic manifold with nonempty totally geodesic boundary. Viewing the time it takes this flow to hit the boundary as a random variable, we derive a formula for its moments in terms of the orthospectrum. The first moment gives the average time for the normal flow acting on the boundary to again reach the boundary, which we connect to Bridgeman’s identity (in the surface case), and the zeroth moment recovers Basmajian’s identity. Furthermore, we are able to give explicit formulae for the first moment in the surface case as well as for manifolds of odd dimension. In dimension two, the summation terms are dilogarithms. In dimension three, we are able to find the moment generating function for this length function.
Inclusive and Exclusive Scatterings from Tensor Polarized Deuteron

The possibility of using a tensor polarized deuteron target in electroproduction reactions creates new opportunities for studying different phenomena related to the short-range hadronic and nuclear physics. The use of the tensor polarized deuteron allows us to isolate smaller than average inter-nucleon distances for the bound two-nucleon system. In this report we consider several high Q2 reactions which are particularly sensitive to the short-range two- nucleon configurations in the deuteron. One is the relativistic dynamics of electron-bound- nucleon scattering, which can be studied in both inclusive and exclusive reactions, and the other is the strong final state interaction in close proximity of two nucleons that can be used as a sensitive probe for color-transparency phenomena.
Non-diffracting broadband incoherent space–time fields

Space–time (ST) wave packets are coherent pulsed beams that propagate diffraction free and dispersion free by virtue of tight correlations introduced between their spatial and temporal spectral degrees of freedom. Less is known of the behavior of incoherent ST fields that maintain the spatio–temporal spectral structure of their coherent wave-packet counterparts while losing all purely spatial or temporal coherence. We show here that structuring the spatio–temporal spectrum of an incoherent field produces broadband incoherent ST fields that are diffraction free. The intensity profile of these fields consists of a narrow spatial feature atop a constant background. Spatio–temporal spectral engineering allows controlling the width of this spatial feature, tuning it from a bright to a dark diffraction-free feature, and varying its amplitude relative to the background. These results pave the way to new opportunities in the experimental investigation of optical coherence of fields jointly structured in space and time by exploiting the techniques usually associated with ultrafast optics.
An analysis of two common reference points for EEGS

Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical grounding used). This investigation explores the statistical differences present in two different referential montages: Linked Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately 45% of the data in the TUH EEG Corpus. In this study, we explore the impact this variability has on machine learning performance. We compare the statistical properties of features generated using these two montages, and explore the impact of performance on our standard Hidden Markov Model (HMM) based classification system. We show that a system trained on LE data significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We also demonstrate that performance of a system trained on both data sets is somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data suggests that mean, variance and channel normalization should be considered. However, cepstral mean subtraction failed to produce an improvement in performance, suggesting that the impact of these statistical differences is subtler.
Veiled symmetry of disordered Parity-Time lattices: protected PT-threshold and the fate of localization.

Open, non-equilibrium systems with balanced gain and loss, known as parity-time ( $${\mathscr{P}}{\mathscr{T}}$$ )-symmetric systems, exhibit properties that are absent in closed, isolated systems. A key property is the $${\mathscr{P}}{\mathscr{T}}$$ -symmetry breaking transition, which occurs when the gain-loss strength, a measure of the openness of the system, exceeds the intrinsic energy-scale of the system. We analyze the fate of this transition in disordered lattices with non-Hermitian gain and loss potentials ±iγ at reflection-symmetric sites. Contrary to the popular belief, we show that the $${\mathscr{P}}{\mathscr{T}}$$ -symmetric phase is protected in the presence of a periodic disorder which leads to a positive $${\mathscr{P}}{\mathscr{T}}$$ -symmetry breaking threshold. We uncover a veiled symmetry of such disordered systems that is instrumental for the said protection, and show that this symmetry leads to new localization behavior across the $${\mathscr{P}}{\mathscr{T}}$$ -symmetry breaking transition. We elucidate the interplay between such localization and the $${\mathscr{P}}{\mathscr{T}}$$ -symmetry breaking phenomena in disordered $${\mathscr{P}}{\mathscr{T}}$$ -symmetric lattices, with Hermitian disorder or gain-loss disorder, and support our conclusions with a beampropagation- method analysis. Our theoretical predictions provide avenues for experimental realizations of -symmetric systems with engineered disorder.
Characterization of simplices via the Bezout inequality for mixed volumes

We consider the following Bezout inequality for mixed volumes: $$V(K_1,\dots,K_r,\Delta[{n-r}])V_n(\Delta)^{r-1}\leq \prod_{i=1}^r V(K_i,\Delta[{n-1}])\ \text{ for }2\leq r\leq n.$$ It was shown previously that the inequality is true for any $n$-dimensional simplex $\Delta$ and any convex bodies $K_1, \dots, K_r$ in $\mathbb{R}^n$. It was conjectured that simplices are the only convex bodies for which the inequality holds for arbitrary bodies $K_1, \dots, K_r$ in $\mathbb{R}^n$. In this paper we prove that this is indeed the case if we assume that $\Delta$ is a convex polytope. Thus the Bezout inequality characterizes simplices in the class of convex $n$-polytopes. In addition, we show that if a body $\Delta$ satisfies the Bezout inequality for all bodies $K_1, \dots, K_r$ then the boundary of $\Delta$ cannot have strict points. In particular, it cannot have points with positive Gaussian curvature.
Machine learning formation enthalpies of intermetallics

Developing fast and accurate methods to discover intermetallic compounds is relevant for alloy design. While density-functional-theory (DFT)-based methods have accelerated design of binary and ternary alloys by providing rapid access to the energy and properties of the stable intermetallics, they are not amenable for rapidly screening the vast combinatorial space of multi-principal element alloys (MPEAs). Here, a machine-learning model is presented for predicting the formation enthalpy of binary intermetallics and is used to identify new ones. The model uses easily accessible elemental properties as descriptors and has a mean absolute error of 0.025 eV/atom in predicting the formation enthalpy of stable binary intermetallics reported in the Materials Project database. The model further predicts stable intermetallics to form in 112 binary alloy systems that do not have any stable intermetallics reported in the Materials Project database. DFT calculations confirm one such stable intermetallic identified by the model, NbV2, to be on the convex hull. Furthermore, an adaptive transfer learning method is used to generalize the model to predict ternary intermetallics with a similar accuracy as DFT, which suggests that it could be extended to identify compositionally complex intermetallics that may form in MPEAs.
Statistical mechanics of a two-dimensional black hole

The dynamics of a nearly-AdS2 spacetime with boundaries is reduced to that of two particles in the anti-de Sitter space. We determine the class of physically meaningful wavefunctions, and prescribe the statistical mechanics of a black hole. We demonstrate how wavefunctions for a two-sided black hole and a regularized notion of trace can be used to construct thermal partition functions, and more generally, arbitrary density matrices. We also obtain correlation functions of external operators.
A Beaming-Independent Estimate of the Energy Distribution of Long Gamma-Ray Bursts: Initial Results and Future Prospects

We present single-epoch radio afterglow observations of 24 long-duration gamma-ray burst (GRB) on a timescale of {approx}> 100 days after the burst. These observations trace the afterglow evolution when the blast wave has decelerated to mildly or non-relativistic velocities and has roughly isotropized. We infer beaming-independent kinetic energies using the Sedov-Taylor self-similar solution, and find a median value for the sample of detected bursts of about 7 x 10{sup 51} erg, with a 90% confidence range of 1.1 x 10{sup 50}-3.3 x 10{sup 53} erg. Both the median and 90% confidence range are somewhat larger than the results of multi-wavelength, multi-epoch afterglow modeling (including large beaming corrections), and the distribution of beaming-corrected {gamma}-ray energies. This is due to bursts in our sample with only a single-frequency observation for which we can only determine an upper bound on the peak of the synchrotron spectrum. This limitation leads to a wider range of allowed energies than for bursts with a well-measured spectral peak. Our study indicates that single-epoch centimeter-band observations covering the spectral peak on a timescale of {delta}t {approx} 1 yr can provide a robust estimate of the total kinetic energy distribution with a small investment of telescope time. The substantialmore » increase in bandwidth of the Expanded Very Large Array (up to 8 GHz simultaneously with full coverage at 1-40 GHz) will provide the opportunity to estimate the kinetic energy distribution of GRBs with only a few hours of data per burst.« less
SPACES OF POLYNOMIALS RELATED TO MULTIPLIER MAPS

Let f(x) be a complex polynomial of degree n. We associate f with a ℂ-vector space W(f) that consists of complex polynomials p(x) of degree at most n — 2 such that f(x) divides f”(x)p(x) — f’(x)p’(x). The space W(f) first appeared in Yu. G. Zarhin’s work, where a problem concerning dynamics in one complex variable posed by Yu. S. Ilyashenko was solved. In this paper, we show that W(f) is nonvanishing if and only if q(x)2 divides f(x) for some quadratic polynomial q(x). In that case, W(f) has dimension (n — 1) — (n1 + n2 + 2N3) under certain conditions, where ni is the number of distinct roots of f with multiplicity i and N3 is the number of distinct roots of f with multiplicity at least 3.
Minimax Lower Bounds on Dictionary Learning for Tensor Data

This paper provides fundamental limits on the sample complexity of estimating dictionaries for tensor data. The specific focus of this work is on $K$ th-order tensor data and the case where the underlying dictionary can be expressed in terms of $K$ smaller dictionaries. It is assumed the data are generated by linear combinations of these structured dictionary atoms and observed through white Gaussian noise. This work first provides a general lower bound on the minimax risk of dictionary learning for such tensor data and then adapts the proof techniques for specialized results in the case of sparse and sparse-Gaussian linear combinations. The results suggest the sample complexity of dictionary learning for tensor data can be significantly lower than that for unstructured data: for unstructured data it scales linearly with the product of the dictionary dimensions, whereas for tensor-structured data the bound scales linearly with the sum of the product of the dimensions of the (smaller) component dictionaries. A partial converse is provided for the case of 2nd-order tensor data to show that the bounds in this paper can be tight. This involves developing an algorithm for learning highly-structured dictionaries from noisy tensor data. Finally, numerical experiments highlight the advantages associated with explicitly accounting for tensor data structure during dictionary learning.
Multidimensional nonlinear geometric optics for transport operators with applications to stable shock formation

In n≥1 spatial dimensions, we study the Cauchy problem for a genuinely nonlinear quasilinear transport equation coupled to a quasilinear symmetric hyperbolic subsystem of a rather general type. For an open set (relative to a suitable Sobolev topology) of regular initial data that are close to the data of a simple plane wave, we give a sharp, constructive proof of shock formation in which the transport variable remains bounded but its first-order Cartesian coordinate partial derivatives blow up in finite time. Moreover, we prove that, at least at the low derivative levels, the singularity does not propagate into the symmetric hyperbolic variables: they and their first-order Cartesian coordinate partial derivatives remain bounded, even though they interact with the transport variable all the way up to its singularity. The formation of the singularity is tied to the finite-time degeneration, relative to the Cartesian coordinates, of a system of geometric coordinates adapted to the characteristics of the transport operator. Two crucial features of the proof are that relative to the geometric coordinates, all solution variables remain smooth, and that the finite-time degeneration coincides with the intersection of the transport characteristics. Compared to prior shock formation results in more than one spatial dimension, in which the blowup occurred in solutions to quasilinear wave equations, the main new features of the present work are: (i) we develop a theory of nonlinear geometric optics for transport operators, which is compatible with the coupling and which allows us to implement a quasilinear geometric vector field method, even though the regularity properties of the corresponding eikonal function are less favorable compared to the wave equation case and (ii) we allow for a full quasilinear coupling; i.e., the principal coefficients in all equations are allowed to depend on all solution variables.
Ghost D-brane, supersymmetry and matrix model

In this note we study the world volume theory of pairs of D-brane and ghost D-brane, which is shown to have 16 linear supersymmetries and 16 nonlinear supersymmetries. In particular we study a matrix model based on the pairs of D(-1)-brane and ghost D(-1)-brane. Since such pairs are supposed to be equivalent to the closed string vacuum, we expect all 32 supersymmetries should be unbroken. We show that the world volume theory of the pairs of D-brane and ghost D-brane has unbroken 32 supersymmetries even though a half of them are nonlinearly realized.
Eigen-CAM: Class Activation Map using Principal Components

Deep neural networks are ubiquitous due to the ease of developing models and their influence on other domains. At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data. Making sense of such complex models (i.e., millions of parameters and hundreds of layers) remains challenging for developers as well as the end-users. This is partially due to the lack of tools or interfaces capable of providing interpretability and transparency. A growing body of literature, for example, class activation map (CAM), focuses on making sense of what a model learns from the data or why it behaves poorly in a given task. This paper builds on previous ideas to cope with the increasing demand for interpretable, robust, and transparent models. Our approach provides a simpler and intuitive (or familiar) way of generating CAM. The proposed Eigen-CAM computes and visualizes the principle components of the learned features/representations from the convolutional layers. Empirical studies were performed to compare the Eigen-CAM with the state- of-the-art methods (such as Grad-CAM, Grad-CAM++, CNN- fixations) by evaluating on benchmark datasets such as weakly- supervised localization and localizing objects in the presence of adversarial noise. Eigen-CAM was found to be robust against classification errors made by fully connected layers in CNNs, does not rely on the backpropagation of gradients, class relevance score, maximum activation locations, or any other form of weighting features. In addition, it works with all CNN models without the need to modify layers or retrain models. Empirical results show up to 12% improvement over the best method among the methods compared on weakly supervised object localization.
Space-Time-Modulated Metasurfaces with Spatial Discretization: Free-Space N -Path Systems

This work theoretically and experimentally studies metasurfaces with spatially discrete, traveling-wave modulation (SDTWM). A representative metasurface is considered consisting of columns of time-modulated subwavelength unit cells, referred to as stixels. SDTWM is achieved by enforcing a time delay between temporal waveforms applied to adjacent columns. In contrast to the continuous traveling-wave modulation commonly assumed in studies of space-time metasurfaces, here the modulation is spatially discretized. In order to account for the discretized spatial modulation, a modified Floquet analysis is introduced based on a new boundary condition that has been derived for SDTWM structures. The modified Floquet analysis separates the scattered field into its macroscopic and microscopic variations. The reported theoretical and experimental results reveal that the electromagnetic behavior of a SDTWM metasurface can be categorized into three regimes. For electrically large spatial-modulation periods, the microscopic field variation across each stixel can be neglected. In this regime, the space-time metasurface allows simultaneous frequency translation and angular deflection. When the spatial-modulation period on the metasurface is electrically small, the microscopic variation results in unique metasurface capabilities such as subharmonic mixing. When the spatial-modulation period of the metasurface is wavelength scale, the metasurface allows both subharmonic mixing and angular deflection to be achieved simultaneously. To verify our analysis, a dual-polarized, spatiotemporally modulated metasurface, is developed and measured at $X$-band frequencies.
Sizing of Movable Energy Resources for Service Restoration and Reliability Enhancement

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, power plants, and distribution system) destruction. Distribution system failures and outages are major contributors to power supply interruptions. Network reconfiguration and movable energy resources (MERs) can play a vital role in supplying loads during and after contingencies. This paper proposes a two-stage strategy to determine the minimum sizes of MERs with network reconfiguration for distribution service restoration and supplying local and isolated loads. Sequential Monte Carlo simulations are used to model the outages of distribution system components. After a contingency, the first stage determines the network reconfiguration based on the spanning tree search algorithm. In the second stage, if some system loads cannot be fed by network reconfiguration, MERs are deployed and the optimal routes to reach isolated areas are determined based on the Dijkstra’s shortest path algorithm (DSPA). The traveling time obtained from the DSPA is incorporated with the proposed sequential Monte Carlo simulation-based approach to determine the sizes of MERs. The proposed method is applied on several distribution systems including the IEEE-13 and IEEE-123 node test feeders. The results show that network reconfiguration can reduce the required sizes of MERs to supply the isolated areas.
On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics

Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as land and sea surface temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle. However, an often overlooked feature of the climate seasonal cycle is the semi-annual harmonic, which can account for a significant portion of the variance of the seasonal cycle and varies in amplitude and phase across space. Together, the spatial variation in the annual and semi-annual harmonics can play an important role in driving processes that are tied to seasonality (e.g., ecological and agricultural processes). We propose a multivariate spatio-temporal model to quantify the spatial and temporal change in minimum and maximum temperature seasonal cycles as a function of the annual and semi-annual harmonics. Our approach captures spatial dependence, temporal dynamics, and multivariate dependence of these harmonics through spatially and temporally-varying coefficients. We apply the model to minimum and maximum temperature over North American for the years 1979 to 2018. Formal model inference within the Bayesian paradigm enables the identification of regions experiencing significant changes in minimum and maximum temperature seasonal cycles due to the relative effects of changes in the two harmonics.
A Reactive Autonomous Camera System for the RAVEN II Surgical Robot

The endoscopic camera of a surgical robot provides surgeons with a magnified 3D view of the surgical field, but repositioning it increases mental workload and operation time. Poor camera placement contributes to safety-critical events when surgical tools move out of the view of the camera. This paper presents a proof of concept of an autonomous camera system for the Raven II surgical robot that aims to reduce surgeon workload and improve safety by providing an optimal view of the workspace showing all objects of interest. This system uses transfer learning to localize and classify objects of interest within the view of a stereoscopic camera. The positions and centroid of the objects are estimated and a set of control rules determines the movement of the camera towards a more desired view. Our perception module had an accuracy of 61.21% overall for identifying objects of interest and was able to localize both graspers and multiple blocks in the environment. Comparison of the commands proposed by our system with the desired commands from a survey of 13 participants indicates that the autonomous camera system proposes appropriate movements for the tilt and pan of the camera.
Heterogeneous turnover-performance relations

Abstract We document the heterogeneous effects of turnover on mutual fund performance, which help explain the weak cross-sectional turnover-performance relations reported in existing studies. For funds skilled in exploiting short-term investment opportunities, there is a positive empirical relation between turnover and performance. For unskilled funds, the relation turns negative. As a result, performance persistence is stronger among funds with higher turnover. Further, we find that the heterogeneous effects of turnover on performance are not driven by liquidity premium or trade execution skills, but rather due to substantial dispersion in short-term stock selection information.
A numerical fitting routine for frequency-domain thermoreflectance measurements of nanoscale material systems having arbitrary geometries

In this work, we develop a numerical fitting routine to extract multiple thermal parameters using frequency-domain thermoreflectance (FDTR) for materials having non-standard, non-semi-infinite geometries. The numerical fitting routine is predicated on either a 2D or 3D finite element analysis that permits the inclusion of non-semi-infinite boundary conditions, which cannot be considered in the analytical solution to the heat diffusion equation in the frequency domain. We validate the fitting routine by comparing it with the analytical solution to the heat diffusion equation used within the wider literature for FDTR and known values of thermal conductivity for semi-infinite substrates ( SiO 2, Al 2 O 3, and Si). We then demonstrate its capacity to extract the thermal properties of Si when etched into micropillars that have radii on the order of the pump beam. Experimental measurements of Si micropillars with circular and square cross sections are provided and fit using the numerical fitting routine established as part of this work. Likewise, we show that the analytical solution is unsuitable for the extraction of thermal properties when the geometry deviates significantly from the standard semi-infinite case. This work is critical for measuring the thermal properties of materials having arbitrary geometries, including ultra-drawn glass fibers and laser gain media.
Numerically Stable Polynomially Coded Computing

We study the numerical stability of polynomial based encoding methods, which has emerged to be a powerful class of techniques for providing straggler and fault tolerance in the area of coded computing. Our contributions are as follows: 1) We construct new codes for matrix multiplication that achieve the same fault/straggler tolerance as the previously constructed MatDot Codes and Polynomial Codes . 2) We show that the condition number of every $m \times m$ sub-matrix of an $m \times n, n \geq m$ Chebyshev-Vandermonde matrix, evaluated on the $n$ -point Chebyshev grid, grows as $O(n^{2(n-m)})$ for $n > m$ . 3) By specializing our orthogonal polynomial based constructions to Chebyshev polynomials, and using our condition number bound for Chebyshev-Vandermonde matrices, we construct new numerically stable techniques for coded matrix multiplication. We empirically demonstrate that our constructions have significantly lower numerical errors compared to previous approaches which involve inversion of Vandermonde matrices. We generalize our constructions to explore the trade-off between computation/communication and fault-tolerance. 4) We propose a numerically stable specialization of Lagrange coded computing. Our approach involves the choice of evaluation points and a suitable decoding procedure. Our approach is demonstrated empirically to have lower numerical errors as compared to standard methods.
Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following parameter quantification with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we ranked model parameters through sensitivity analysis for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination were simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy after infection.
Competition in Loan Contracts

We present a model of an unsecured loan market. Many lenders simultaneously offer loan contracts (a debt level and an interest rate) to a borrower. The borrower may accept more than one contract. Her payoff if she defaults increases in the total amount borrowed. If this payoff is high enough, deterministic zero-profit equilibria cannot be sustained. Lenders earn a positive profit, and may even charge the monopoly price. The positive-profit equilibria are robust to increases in the number of lenders. Despite the absence of asymmetric information, the competitive outcome does not obtain in the limit.
Understanding Systematic Risk: A High‐Frequency Approach

Based on a novel high‐frequency data set for a large number of firms, I estimate the time‐varying latent continuous and jump factors that explain individual stock returns. The factors are estimated using principal component analysis applied to a local volatility and jump covariance matrix. I find four stable continuous systematic factors, which can be well approximated by a market, oil, finance, and electricity portfolio, while there is only one stable jump market factor. The exposure of stocks to these risk factors and their explained variation is time‐varying. The four continuous factors carry an intraday risk premium that reverses overnight.
Capital requirements, risk choice, and liquidity provision in a business-cycle model

Abstract This paper develops a dynamic general equilibrium model to quantify the effects of bank capital requirements. Households’ preferences for liquid assets imply a liquidity premium on deposits. The banking sector supplies deposits and has excessive risk-taking incentives. I show that the scarcity of deposits created by an increased capital requirement can reduce the cost of capital for banks and increase bank lending. A higher capital requirement also increases banks’ monitoring incentives, which improves the efficiency of banks’ activities. Under reasonable parameterizations, the marginal benefit of a higher capital requirement related to this channel significantly exceeds the marginal cost, indicating that US capital requirements have been suboptimally low.
Presidential Signing Statements and Their Implications for Public Administration

For most of U.S. history, presidents have issued signing statements to comment on bills being signed into law. These statements often are hortatory and comment on the merits of the new law. In recent decades, presidents also have used signing statements to indicate portions of laws that they consider unconstitutional. Pointing out such parts of new statutes is not a problem, but indicating that the president may not execute part of the law is problematic. President George W. Bush used signing statements in an aggressive way to imply that he might not faithfully execute more than 1,000 provisions of statutes that he signed into law. This essay argues that this practice undermines the rule of law and threatens the separation of powers system.
Compensating Financial Experts

We propose a labor market model in which financial firms compete for a scarce supply of workers who can be employed as either bankers or traders. While hiring bankers helps create a surplus that can be split between a firm and its trading counterparties, hiring traders helps the firm appropriate a greater share of that surplus away from its counterparties. Firms bid defensively for workers bound to become traders, who then earn more than bankers. As counterparties employ more traders, the benefit of employing bankers decreases. The model sheds light on the historical evolution of compensation in finance. [ABSTRACT FROM AUTHOR]
English Proficiency and Latino Participation in U.S. Elections

This article focuses on how the ability to speak and read English affects Latino citizens' participation in U.S. elections, and evaluates the extent to which this language barrier might be overcome by living in a community with a relatively large Latino population and having access to registration materials and ballots in Spanish. Using data primarily from the Pew Hispanic Center, we find that the inability to speak and read English hinders registration and turnout among Latino citizens. While the language barrier to turnout is mitigated by several factors, the barrier to registration is more intractable. These results have implications for researchers and practitioners interested in the political participation of this increasingly prominent group. In particular, understanding the role of English proficiency is a critical first step in thinking about policy prescriptions that will bring into the political process a growing number of Latino citizens, particularly those with limited English skills. Este estudio se enfoca en como las habilidades de hablar y leer el ingles afectan la participacion de ciudadanos latinos en las elecciones estadounidenses, y evalua la medida en que esta barrera del idioma podria ser superada viviendo en una comunidad con una poblacion latina relativamente grande y teniendo acceso a materiales de registro electoral y boletas en espanol. Usando datos principalmente del Pew Hispanic Center, encontramos que la baja habilidad para hablar y leer en ingles obstaculiza el registro y participacion entre ciudadanos de origen latino. Mientras que la barrera del lenguaje para ejercer el derecho al voto es mitigada al vivir en una comunidad con una poblacion latina relativamente grande y con acceso a boletas bilingues, la barrera para inscribirse en el registro de electores es mas restrictiva. Estos resultados tienen implicaciones para investigadores, practicantes, y legisladores interesados en la participacion politica de este grupo cada vez mas prominente de electores. Entender el papel del dominio de el ingles es, particularmente, un primer paso critico para pensar recetas de politicas que incorporen en el proceso politico un creciente numero de ciudadanos latinos, especialmente aquellos con un limitado dominio del idioma ingles.
Anticipating the Unthinkable: The Adequacy of Risk Management in Finance and Environmental Studies

The purpose of this Article is to consider the role of risk management in the current financial crisis. In general, risk management is the practice of assessing and identifying the different kinds of risks facing a person, an institution, or society because of its activities and environment, determining the likelihood of losses and other consequences from those risks, and taking appropriate actions, which include monitoring the risks and reducing the losses and other consequences from them. In financial institutions, financial risk managers, who are part of a riskmanagement department or group, are generally charged with assessing and measuring the risks facing the financial institution as a result of its activities and the business environment, monitoring the risks for any change, determining whether the institution has the resources to deal with the risks, alerting senior managers and boards of directors about the risk information, and suggesting courses of action for the institution to take to deal with the risks. My argument is that risk management in systemically important financial institutions failed, which contributed to the recent collapse of the financial system. For a number of reasons that I shall explore below, risk management did not fulfill its purpose, which was to prevent financial institutions from suffering the kinds of losses that they experienced in the crisis. This Article identifies these risk-management failings and offers remedies to them. At times, environmental risk management, which deals with extreme environmental risks such as global warming, inspires my
Elitism in mathematics and inequality

The Fields Medal, often referred as the Nobel Prize of mathematics, is awarded to no more than four mathematicians under the age of 40, every 4 years. In recent years, its conferral has come under scrutiny of math historians, for rewarding the existing elite rather than its original goal of elevating under-represented mathematicians. Prior studies of elitism focus on citational practices while a characterization of the structural forces that prevent access remain unclear. Here we show the flow of elite mathematicians between countries and lingo-ethnic identity, using network analysis and natural language processing on 240,000 mathematicians and their advisor–advisee relationships. We present quantitative evidence of how the Fields Medal helped integrate Japan after WWII, through analysis of the elite circle formed around Fields Medalists. We show increases in pluralism among major countries, though Arabic, African, and East Asian identities remain under-represented at the elite level. Our results demonstrate concerted efforts by academic committees, such as prize giving, can either reinforce the existing elite or reshape its definition. We anticipate our methodology of academic genealogical analysis can serve as a useful diagnostic for equity and systemic bias within academic fields.
A Dynamic Analysis of Fairness in Global Warming Policy: Kyoto, Buenos Aires, and Beyond

(1998). A Dynamic Analysis of Fairness in Global Warming Policy: Kyoto, Buenos Aires, and Beyond. Journal of Applied Economics: Vol. 1, No. 2, pp. 329-362.
A Simple Stock–Bond Categorization of Alternative Investments

In this article, the author creates a tool to help investors categorize an investment in a diversifying, alternative asset class. Using standard portfolio mathematics, he obtains a precise analytical solution to the proportion of the new investment that should be considered stock-like and the proportion that should be considered bond-like. These proportions rely on whether the alternative asset is funded from stocks or bonds. The resulting formula can readily incorporate the parameter ambiguity that often surrounds alternative assets. While the approach is reductionist, categorical thinking about alternative assets can help investment decision making, particularly by non-specialists.
Do Analysts Say Anything About Earnings Without Revising Their Earnings Forecasts

ABSTRACT Analysts are selective about which forecasts they update and, thus, convey information about current quarter earnings even when not revising the current quarter earnings (CQE) forecast. We...
Detecting jumps from Lévy jump diffusion processes.

Abstract Recent asset-pricing models incorporate jump risk through Levy processes in addition to diffusive risk. This paper studies how to detect stochastic arrivals of small and big Levy jumps with new nonparametric tests. The tests allow for robust analysis of their separate characteristics and facilitate better estimation of return dynamics. Empirical evidence of both small and big jumps based on these tests suggests that models for individual equities and overall market indices require incorporating Levy-type jumps. The evidence of small jumps also helps explain why jumps in the market index are uncorrelated with jumps in its component equities.
Show or Tell? Improving Inventory Support for Agent-Based Businesses at the Base of the Pyramid

Problem definition: Firms providing products and services to low-income base of the pyramid (BOP) customers are increasingly utilizing independent contractor agents rather than employees in their d...