cssi/SciSciGPT-SciSciNet huggingface dataset:
Bounded point evaluations for rationally multicyclic subnormal operators

Abstract Let S be a pure bounded rationally multicyclic subnormal operator on a separable complex Hilbert space H and let M z be the minimal normal extension on a separable complex Hilbert space K containing H . Let b p e ( S ) be the set of bounded point evaluations and let a b p e ( S ) be the set of analytic bounded point evaluations. We show a b p e ( S ) = b p e ( S ) ∩ I n t ( σ ( S ) ) . The result affirmatively answers a question asked by J. B. Conway concerning the equality of the interior of b p e ( S ) and a b p e ( S ) for a rationally multicyclic subnormal operator S. As a result, if λ 0 ∈ I n t ( σ ( S ) ) and d i m ( k e r ( S − λ 0 ) ⁎ ) = N , where N is the minimal number of cyclic vectors for S, then the range of S − λ 0 is closed, hence, λ 0 ∈ σ ( S ) ∖ σ e ( S ) .
Identifiability of restricted latent class models with binary responses

Statistical latent class models are widely used in social and psychological researches, yet it is often difficult to establish the identifiability of the model parameters. In this paper, we consider the identifiability issue of a family of restricted latent class models, where the restriction structures are needed to reflect pre-specified assumptions on the related assessment. We establish the identifiability results in the strict sense and specify which types of restriction structure would give the identifiability of the model parameters. The results not only guarantee the validity of many of the popularly used models, but also provide a guideline for the related experimental design, where in the current applications the design is usually experience based and identifiability is not guaranteed. Theoretically, we develop a new technique to establish the identifiability result, which may be extended to other restricted latent class models.
User Scheduling for Millimeter Wave Hybrid Beamforming Systems With Low-Resolution ADCs

We investigate uplink user scheduling for millimeter wave (mm-wave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mm-wave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have 1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and 2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mm-wave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. The simulation results validate the sum rate performance of the proposed algorithms and the derived ergodic rate expressions.
Pionic photons and neutrinos from cosmic ray accelerators

Abstract Identifying the accelerators that produce the Galactic and extragalactic cosmic rays has been a priority mission of several generations of high energy gamma ray and neutrino telescopes; success has been elusive so far. Detecting the gamma-ray and neutrino fluxes associated with cosmic rays reaches a new watershed with the completion of IceCube, the first neutrino detector with sensitivity to the anticipated fluxes, and the construction of CTA, a ground-based gamma ray detector that will map and study candidate sources with unprecedented precision. In this paper, we revisit the prospects for revealing the sources of the cosmic rays by a multiwavelength approach; after reviewing the methods, we discuss supernova remnants, gamma ray bursts, active galaxies and GZK neutrinos in some detail.
Examples of diffeomorphism group cocycles with no periodic approximation

We construct a finitely generated subgroup of $\text{Diff}^{\infty}(\mathbb{S}^3 \times \mathbb{S}^1)$ where every element is conjugate to an isometry but such that the group action itself is far from isometric (the group has "exponential growth of derivatives"). As a corollary, one obtains a locally constant $\text{Diff}^{\infty}(\mathbb{S}^3 \times \mathbb{S}^1)$ valued cocycle over a hyperbolic dynamical system which has elliptic behavior over its periodic orbits but which preserves a measure with non-zero top Fiber Lyapunov exponent. Additionally, we provide new examples of Banach cocycles not satisfying the periodic approximation property as first shown by Kalinin-Sadovskaya.
Eigenvalue Coincidences and $K$-orbits, I

Abstract We study the variety g ( l ) consisting of matrices x ∈ gl ( n , C ) such that x and its n − 1 by n − 1 cutoff x n − 1 share exactly l eigenvalues, counted with multiplicity. We determine the irreducible components of g ( l ) by using the orbits of GL ( n − 1 , C ) on the flag variety B of gl ( n , C ) . More precisely, let b ∈ B be a Borel subalgebra such that the orbit GL ( n − 1 , C ) ⋅ b in B has codimension l. Then we show that the set Y b : = { Ad ( g ) ( x ) : x ∈ b ∩ g ( l ) , g ∈ GL ( n − 1 , C ) } is an irreducible component of g ( l ) , and every irreducible component of g ( l ) is of the form Y b , where b lies in a GL ( n − 1 , C ) -orbit of codimension l. An important ingredient in our proof is the flatness of a variant of a morphism considered by Kostant and Wallach, and we prove this flatness assertion using an analogue of the Steinberg variety.
Reasoning About Pragmatics with Neural Listeners and Speakers

We present a model for pragmatically describing scenes, in which contrastive behavior results from a combination of inference-driven pragmatics and learned semantics. Like previous learned approaches to language generation, our model uses a simple feature-driven architecture (here a pair of neural "listener" and "speaker" models) to ground language in the world. Like inference-driven approaches to pragmatics, our model actively reasons about listener behavior when selecting utterances. For training, our approach requires only ordinary captions, annotated _without_ demonstration of the pragmatic behavior the model ultimately exhibits. In human evaluations on a referring expression game, our approach succeeds 81% of the time, compared to a 69% success rate using existing techniques.
Weighted Voting Via No-Regret Learning

Voting systems typically treat all voters equally. We argue that perhaps they should not: Voters who have supported good choices in the past should be given higher weight than voters who have supported bad ones. To develop a formal framework for desirable weighting schemes, we draw on no-regret learning. Specifically, given a voting rule, we wish to design a weighting scheme such that applying the voting rule, with voters weighted by the scheme, leads to choices that are almost as good as those endorsed by the best voter in hindsight. We derive possibility and impossibility results for the existence of such weighting schemes, depending on whether the voting rule and the weighting scheme are deterministic or randomized, as well as on the social choice axioms satisfied by the voting rule.
Ill-posedness of the Navier-Stokes equations in a critical space in 3D

Abstract We prove that the Cauchy problem for the three-dimensional Navier–Stokes equations is ill-posed in B ˙ ∞ − 1 , ∞ in the sense that a “norm inflation” happens in finite time. More precisely, we show that initial data in the Schwartz class S that are arbitrarily small in B ˙ ∞ − 1 , ∞ can produce solutions arbitrarily large in B ˙ ∞ − 1 , ∞ after an arbitrarily short time. Such a result implies that the solution map itself is discontinuous in B ˙ ∞ − 1 , ∞ at the origin.
TC: Throughput centric successive cancellation decoder hardware implementation for polar codes

This paper presents a hardware architecture of fast simplified successive cancellation (fast-SSC) algorithm for polar codes, which significantly reduces the decoding latency and dramatically increases the throughput. Algorithmically, fast-SSC algorithm suffers from the fact that its decoder scheduling and the consequent architecture depends on the code rate; this is a challenge for rate-compatible system. However, by exploiting the homogeneousness between the decoding processes of fast constituent polar codes and regular polar codes, the presented design is compatible with any rate. The scheduling plan and the intendedly designed processing core are also described. Results show that, compared with the state-of-art decoder, our design can achieve at least 60% latency reduction for the codes with length N = 1024. By using Nangate FreePDK 45nm process, proposed design can reach throughput up to 5.81 Gbps and 2.01 Gbps for (1024,870) and (1024, 512) polar code, respectively.
Hyperbolic relaxation of reaction-diffusion equations with dynamic boundary conditions

Under consideration is the hyperbolic relaxation of a semilinear reaction-diffusion equation on a bounded domain, subject to a dynamic boundary condition. We also consider the limit parabolic problem with the same dynamic boundary condition. Each problem is well-posed in a suitable phase space where the global weak solutions generate a Lipschitz continuous semiflow which admits a bounded absorbing set. We prove the existence of a family of global attractors of optimal regularity. After fitting both problems into a common framework, a proof of the upper-semicontinuity of the family of global attractors is given as the relaxation parameter goes to zero. Finally, we also establish the existence of exponential attractors.
Asking too much? The rhetorical role of questions in political discourse

Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse—such as the bifurcation in questioning behavior between government and opposition parties—and reveals new insights into the effects of a legislator’s tenure and political career ambitions.
Gorenstein Injective Filtrations Over Cohen-Macaulay Rings with Dualizing Modules

Over a noetherian ring, it is a classic result of Matlis that injective modules admit direct sum decompositions into injective hulls of quotients by prime ideals. We show that over a Cohen-Macaulay ring admitting a dualizing module, Gorenstein injective modules admit similar filtrations. We also investigate Tor-modules of Gorenstein injective modules over such rings. This extends work of Enochs and Huang over Gorenstein rings. Furthermore, we give examples showing the following: (1) the class of Gorenstein injective R-modules need not be closed under tensor products, even when R is local and artinian; (2) the class of Gorenstein injective R-modules need not be closed under torsion products, even when R is a local, complete hypersurface; and (3) the filtrations given in our main theorem do not yield direct sum decompositions, even when R is a local, complete hypersurface.
Mitigating Uncertainty in Document Classification.

The uncertainty measurement of classifiers’ predictions is especially important in applications such as medical diagnoses that need to ensure limited human resources can focus on the most uncertain predictions returned by machine learning models. However, few existing uncertainty models attempt to improve overall prediction accuracy where human resources are involved in the text classification task. In this paper, we propose a novel neural-network-based model that applies a new dropout-entropy method for uncertainty measurement. We also design a metric learning method on feature representations, which can boost the performance of dropout-based uncertainty methods with smaller prediction variance in accurate prediction trials. Extensive experiments on real-world data sets demonstrate that our method can achieve a considerable improvement in overall prediction accuracy compared to existing approaches. In particular, our model improved the accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data.
Technological host-parasites co-evolution

Abctract. The fundamental problem in the field of technology studies is how technology evolves and sustains economic change in human society. This study confronts the problem here by proposing the theory of technological host-parasites coevolution, an approach that may be useful for bringing a new perspective to explain and generalize, whenever possible, the evolution of technology in human societies. Technological host-parasites coevolution is a mutual symbiotic relationship between a host technology and associated technologies directed to satisfy needs and/or to solve problems of human beings. To explore the potential of adopting a theory of technological host-parasites coevolution and to predict which technologies are likeliest to evolve rapidly, this study implements an empirical test based on historical data on the evolution of four example technologies (aircraft, tractor, locomotive and bicycle technology) to substantiate the theoretical framework. Empirical evidence is broadly consistent with the theoretical expectation that host technologies with many associated parasitic technologies advance rapidly, whereas host technologies with fewer parasitic technologies improve slowly. This study begins the process of clarifying and generalizing, as far as possible, the role of long-run coevolution between technologies in complex systems of technology. The proposed theoretical framework also lays a foundation for the development of more sophisticated concepts to explain technological and economic change in human society. The evolution of technology plays an important role in economic and social change of human society. However, little is known about how technologies evolve and sustain human progress, despite being a crucial process in socio-ecological systems for millennia. This study proposes, for the first time to our knowledge, a concept of technological host-parasites coevolution that may be useful for bringing a new perspective to explain the evolution of technology. Statistical results suggest that host technologies with many associated parasitic technologies have a higher rate of evolution than technologies with fewer associated parasitic systems and sub-systems. The mutual symbiotic relationship between a host and parasitic technologies seems to be an invariant property driving the evolution of technology in human society. Keywords. Evolution of technology, Technological parasitism, Technological host-parasites coevolution, Technological interaction, Technological evolution, Coevolution, Nature of technology, Technological change, Host technology, Technological innovation. JEL. B50, B52, O31, O32, O33, O39.
Competing phases in epitaxial vanadium dioxide at nanoscale

Phase competition in correlated oxides offers tantalizing opportunities as many intriguing physical phenomena occur near the phase transitions. Owing to a sharp metal-insulator transition (MIT) near room temperature, the correlated vanadium dioxide (VO2) exhibits a strong competition between insulating and metallic phases, which is important for practical applications. However, the phase boundary undergoes a strong modification when strain is involved, yielding complex phase transitions. Here, we report the emergence of nanoscale M2 phase domains in VO2 epitaxial films under anisotropic strain relaxation. The competing phases of the films are imaged by multilength-scale probes, detecting the structural and electrical properties in individual local domains. Competing evolution of the M1 and M2 phases indicates the critical role of lattice-strain on both the stability of the M2 Mott phase and the energetics of the MIT in VO2 films. This study demonstrates how strain engineering can be utilized to design phase states, which allow deliberate control of MIT behavior at the nanoscale in epitaxial VO2 films.
Microcanonical analysis of Boltzmann and Gibbs entropies in trapped cold atomic gases

Abstract We analyze a gas of noninteracting fermions confined to a one-dimensional harmonic oscillator potential, with the aim of distinguishing between two proposed definitions of the thermodynamic entropy in the microcanonical ensemble, namely the standard Boltzmann entropy and the Gibbs (or volume) entropy. The distinction between these two definitions is crucial for systems with an upper bound on allowed energy levels, where the Boltzmann definition can lead to the notion of negative absolute temperature. Although negative temperatures do not exist for the system of fermions studied here, we still find a significant difference between the Boltzmann and Gibbs entropies, and between the corresponding temperatures with the Gibbs temperature being closer (for small particle number) to the temperature based on a grand canonical picture.
High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images

Optical diffraction tomography (ODT) reconstructs a sample’s volumetric refractive index (RI) to create high-contrast, quantitative 3D visualizations of biological samples. However, standard implementations of ODT use interferometric systems, and so are sensitive to phase instabilities, complex mechanical design, and coherent noise. Furthermore, their reconstruction framework is typically limited to weakly scattering samples, and thus excludes a whole class of multiple-scattering samples. Here, we implement a new 3D RI microscopy technique that utilizes a computational multi-slice beam propagation method to invert the optical scattering process and reconstruct high-resolution (NA>1.0) 3D RI distributions of multiple-scattering samples. The method acquires intensity-only measurements from different illumination angles and then solves a nonlinear optimization problem to recover the sample’s 3D RI distribution. We experimentally demonstrate the reconstruction of samples with varying amounts of multiple-scattering: a 3T3 fibroblast cell, a cluster of C. elegans embryos, and a whole C. elegans worm, with lateral and axial resolutions of ≤240 nm and ≤900 nm, respectively. The results of this work lays groundwork for future studies into using optical wavelengths to probe 3D RI distributions of highly scattering biological organisms.
Linearized Gaussian Processes for Fast Data-driven Model Predictive Control

Data-driven Model Predictive Control (MPC), where the system model is learned from data with machine learning, has recently gained increasing interests in the control community. Gaussian Processes (GP), as a type of statistical models, are particularly attractive due to their modeling flexibility and their ability to provide probabilistic estimates of prediction uncertainty. GP-based MPC has been developed and applied, however the optimization problem is typically non-convex and highly demanding, and scales poorly with model size. This causes unsatisfactory solving performance, even with state-of-the-art solvers, and makes the approach less suitable for real-time control. We develop a method based on a new concept, called linearized Gaussian Process, and Sequential Convex Programming, that can significantly improve the solving performance of GP-based MPC. Our method is not only faster but also much more scalable and predictable than other commonly used methods, as it is much less influenced by the model size. The efficiency and advantages of the algorithm are demonstrated clearly in a numerical example.
Echo-cancellation in a rapidly time varying environment

This paper presents a new approach for system identification of rapidly time varying systems with applications to echo-cancellation in a cabin environment. These situations arise when the coherence time is significant relative to the system time-scale. Under these conditions it is not meaningful to track the system dynamics exactly as this would lead to significant variability in performance. The novelty of our approach relies on deliberately undermodeling the system in a lower complexity model class and reliably tracking the reduced order model. From a theoretical standpoint, under-modeling requires dealing with residual dynamics in addition to measurement noise. A novel technique based on first annihilating the residual error by exploiting the inherent 'orthogonal' decomposition between model class and unmodeled dynamics is obtained. We quantify the estimation error as a function of the rate of variation, complexity of the model class and the undermodeling error.
From combinatorial chemistry to nanocarriers for cancer therapy and imaging

The enabling one-bead-one-compound combinatorial library method allows us to rapidly develop optical probes and cancer targeting agents for cell surface receptors. The targeting agents can be incorporated into the novel nanocarriers for delivery of theranostic agents against cancer.
Dual-plane 3-photon microscopy with remote focusing

3-photon excitation enables in vivo fluorescence microscopy deep in densely labeled and highly scattering samples. To date, 3-photon excitation has been restricted to scanning a single focus, limiting the speed of volume acquisition. Here, for the first time to our knowledge, we implemented and characterized dual-plane 3-photon microscopy with temporal multiplexing and remote focusing, and performed simultaneous in vivo calcium imaging of two planes beyond 600 µm deep in the cortex of a pan-excitatory GCaMP6s transgenic mouse with a per-plane framerate of 7 Hz and an effective 2 MHz laser repetition rate. This method is a straightforward and generalizable modification to single-focus 3PE systems, doubling the rate of volume (column) imaging with off-the-shelf components and minimal technical constraints.
Arapaima Fish Scale: One of the Toughest Flexible Biological Materials

Summary For fish scales to provide protection from predators without severely compromising mobility, they must be lightweight, flexible, and tough. The arapaima fish scale is a superb example of this, enabling its survival in piranha-infested lakes of the Amazon. These elasmoid scales comprise two layers: a laminate composite of parallel collagen fibrils arranged in a Bouligand-like pattern and a highly mineralized surface layer that prevents initial penetration damage. Here, we measure its J-integral fracture toughness and find that the crack-growth toughness is ∼100–200 kJ⋅m−2, representing a very high fracture resistance for a natural material. This toughness results from multiple deformation mechanisms acting in concert in the twisted plywood structure of the scale, involving the collagenous lamellae at varying orientations retarding crack advance through stretching, reorientation, delamination and shear, and fracture. The toughness values obtained for the arapaima scales indicate that they are among the toughest of nature's flexible biological materials.
ELF: Embedded Localisation of Features in Pre-Trained CNN

This paper introduces a novel feature detector based only on information embedded inside a CNN trained on standard tasks (e.g. classification). While previous works already show that the features of a trained CNN are suitable descriptors, we show here how to extract the feature locations from the network to build a detector. This information is computed from the gradient of the feature map with respect to the input image. This provides a saliency map with local maxima on relevant keypoint locations. Contrary to recent CNN-based detectors, this method requires neither supervised training nor finetuning. We evaluate how repeatable and how ‘matchable’ the detected keypoints are with the repeatability and matching scores. Matchability is measured with a simple descriptor introduced for the sake of the evaluation. This novel detector reaches similar performances on the standard evaluation HPatches dataset, as well as comparable robustness against illumination and viewpoint changes on Webcam and photo-tourism images. These results show that a CNN trained on a standard task embeds feature location information that is as relevant as when the CNN is specifically trained for feature detection.
C^\infty smoothing for weak solutions of the inhomogeneous Landau equation

We consider the spatially inhomogeneous Landau equation with initial data that is bounded by a Gaussian in the velocity variable. In the case of moderately soft potentials, we show that weak solutions immediately become smooth, and remain smooth as long as the mass, energy, and entropy densities remain under control. For very soft potentials, we obtain the same conclusion with the additional assumption that a sufficiently high moment of the solution in the velocity variable remains bounded. Our proof relies on the iteration of local Schauder-type estimates.
Galactic Winds in Low-Mass Galaxies

Mass-loss via stellar-feedback driven outflows is predicted to play a critical role in the baryon cycle of low-mass galaxies. However, observational constraints on warm winds are limited as outflows are transient, intrinsically low-surface brightness events and, thus, difficult to detect. Here, we search for outflows in a sample of eleven nearby starburst dwarf galaxies which are strong candidates for outflows. Despite deep H? imaging on galaxies, only a fraction of the sample show evidence of winds. The spatial extent of all detected ionized gas is limited and would still be considered part of the ISM by simulations. These new observations indicate that the physical extent of warm phase outflows is modest and most of the mass will be recycled to the galaxy. The sample is part of the panchromatic STARBurst IRegular Dwarf Survey (STARBIRDS) designed to characterize the starburst phenomenon and its impact on the evolution of low-mass galaxies.
Digital Computation For Stiffness Matrix Analysis

Matrix method for static stress and normal mode analysis of highly indeterminate structures on large scale digital computers; application of technique to analysis of basic types of Civil Engineering structures; solution of simple building frame problem for stresses and deflections.
A Fine-Grained Variant of the Hierarchy of Lasserre

There has been much recent interest in hierarchies of progressively stronger convexifications of polynomial optimisation problems (POP). These often converge to the global optimum of the POP, asymptotically, but prove challenging to solve beyond the first level in the hierarchy for modest instances. We present a finer-grained variant of the Lasserre hierarchy, together with first-order methods for solving the convexifications, which allow for efficient warm-starting with solutions from lower levels in the hierarchy.
Thermal Emission Spectroscopy of Single, Isolated Carbon Nanoparticles: Effects of Particle Size, Material, Charge, Excitation Wavelength, and Thermal History

Results are presented for thermal emission from individually trapped carbon nanoparticles (NPs) in the temperature range from ∼1000 to ∼2100 K. We explore the effects on the magnitude and wavelengt...
Face Viewing Behavior Predicts Multisensory Gain During Speech Perception

Visual information from the face of an interlocutor complements auditory information from their voice, enhancing intelligibility. However, there are large individual differences in the ability to comprehend noisy audiovisual speech. Another axis of individual variability is the extent to which humans fixate the mouth or the eyes of a viewed face. We speculated that across a lifetime of face viewing, individuals who prefer to fixate the mouth of a viewed face might accumulate stronger associations between visual and auditory speech, resulting in improved comprehension of noisy audiovisual speech. To test this idea, we assessed interindividual variability in two tasks. Participants (n = 102) varied greatly in their ability to understand noisy audiovisual sentences (accuracy from 2-58%) and in the time they spent fixating the mouth of a talker enunciating clear audiovisual syllables (3-98% of total time). These two variables were positively correlated: a 10% increase in time spent fixating the mouth equated to a 5.6% increase in multisensory gain. This finding demonstrates an unexpected link, mediated by histories of visual exposure, between two fundamental human abilities: processing faces and understanding speech.
Bayesian median autoregression for robust time series forecasting

Abstract We develop a Bayesian median autoregressive (BayesMAR) model for time series forecasting. The proposed method utilizes time-varying quantile regression at the median, favorably inheriting the robustness of median regression in contrast to the widely used mean-based methods. Motivated by a working Laplace likelihood approach in Bayesian quantile regression, BayesMAR adopts a parametric model bearing the same structure as autoregressive models by altering the Gaussian error to Laplace, leading to a simple, robust, and interpretable modeling strategy for time series forecasting. We estimate model parameters by Markov chain Monte Carlo. Bayesian model averaging is used to account for model uncertainty, including the uncertainty in the autoregressive order, in addition to a Bayesian model selection approach. The proposed methods are illustrated using simulations and real data applications. An application to U.S. macroeconomic data forecasting shows that BayesMAR leads to favorable and often superior predictive performance compared to the selected mean-based alternatives under various loss functions that encompass both point and probabilistic forecasts. The proposed methods are generic and can be used to complement a rich class of methods that build on autoregressive models.
Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation

Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples. Inspired by binary matrix permutation theories and geometric segmentation, we developed a fast and efficient BMF approach, called MEBF (Median Expansion for Boolean Factorization). Overall, MEBF adopted a heuristic approach to locate binary patterns presented as submatrices that are dense in 1's. At each iteration, MEBF permutates the rows and columns such that the permutated matrix is approximately Upper Triangular-Like (UTL) with so-called Simultaneous Consecutive-ones Property (SC1P). The largest submatrix dense in 1 would lie on the upper triangular area of the permutated matrix, and its location was determined based on a geometric segmentation of a triangular. We compared MEBF with other state of the art approaches on data scenarios with different density and noise levels. MEBF demonstrated superior performances in lower reconstruction error, and higher computational efficiency, as well as more accurate density patterns than popular methods such as ASSO, PANDA and Message Passing. We demonstrated the application of MEBF on both binary and non-binary data sets, and revealed its further potential in knowledge retrieving and data denoising.
Predicting the equity premium with the implied volatility spread

Abstract We show that the call-put implied volatility spread (IVS) outperforms many well-known predictors of the U.S. equity premium at return horizons up to six months over the period from 1996:1 to 2017:12. The predictive ability of the IVS is unrelated to the dividend yield and is useful in explaining the cross-section of returns. Decomposing the IVS, we find the longer run predictive ability of the IVS operates primarily through a cash flow channel. We also find the IVS is significantly related to indicators of aggregate market direction and expected market conditions. Our results are consistent with the IVS reflecting market sentiment as well as information about informed trading.
Two Threats to Precision Medicine Equity

In January 2015, President Barack Obama unveiled the “Precision Medicine Initiative,” a nationwide research effort to help bring an effective, preventive, and therapeutic approach to medicine. The purpose of the initiative is to bring a precise understanding of the genetic and environmental determi­nants of disease into clinical settings across the United States.1 The announcement was coupled with $216 million provided in the President’s proposed budget for a million-person national research cohort including public and private partnerships with academic medical centers, research­ers, foundations, privacy experts, medical ethicists, and medical product innovators. The Initiative promises to expand the use of precision medicine in cancer research and modernize regulatory approval processes for genome sequencing technologies. In response, Congress passed the 21st Century Cures Act in December 2016, authorizing a total of $1.5 billion over 10 years for the program.2 Although the Precision Medicine Initiative heralds great promise for the future of disease treatment and eradication, its implementation and development must be carefully guided to ensure that the millions of federal dollars expended will be spent equitably. This commentary discusses two key threats to the Precision Medicine Initia­tive’s ability to proceed in a manner consis­tent with the United States Constitutional requirement that the federal government shall not “deny to any person . . . the equal protection of the laws.”3 In short, this com­mentary sounds two cautionary notes, in order to advance precision medicine equity. First, achieving precision medicine equity will require scientists and clinicians to fulfill their intellectual, moral, and indeed legal duty to work against abusive uses of preci­sion medicine science to advance distorted views of racial group variation. Precision medicine scientists must decisively denounce and distinguish this Initiative from the pseudo-science of eugenics – the im­moral and deadly pseudo-science that gave racist and nationalist ideologies what Troy Duster called a “halo of legitimacy” during the first half of the 20th century.4 Second, to combat the social threat to precision medicine, scientists must incorporate a comprehensive, ecological understanding of the fundamental social and environ­mental determinants of health outcomes in all research. Only then will the Precision Medicine Initiative live up to its potential to improve and indeed transform health care delivery for all patients, regardless of race, color, or national origin. Ethn Dis: 2019;29(Suppl 3):629-640; doi:10.18865/ed.29.S3.629
A linear algorithm to find a rectangular dual of a planar triangulated graph

We develop anO(n) algorithm to construct a rectangular dual of ann-vertex planar triangulated graph.
Spatial Arrangement Drastically Changes the Neural Representation of Multiple Visual Stimuli That Compete in More Than One Feature Domain.

Natural scenes often contain multiple objects and surfaces. However, how neurons in the visual cortex represent multiple visual stimuli is not well understood. Previous studies have shown that, when multiple stimuli compete in one feature domain, the evoked neuronal response is biased toward the stimulus that has a stronger signal strength. We recorded from two male macaques to investigate how neurons in the middle temporal cortex (MT) represent multiple stimuli that compete in more than one feature domain. Visual stimuli were two random-dot patches moving in different directions. One stimulus had low luminance contrast and moved with high coherence, whereas the other had high contrast and moved with low coherence. We found that how MT neurons represent multiple stimuli depended on the spatial arrangement. When two stimuli were overlapping, MT responses were dominated by the stimulus component that had high contrast. When two stimuli were spatially separated within the receptive fields, the contrast dominance was abolished. We found the same results when using contrast to compete with motion speed. Our neural data and computer simulations using a V1-MT model suggest that the contrast dominance found with overlapping stimuli is due to normalization occurring at an input stage fed to MT, and MT neurons cannot overturn this bias based on their own feature selectivity. The interaction between spatially separated stimuli can largely be explained by normalization within MT. Our results revealed new rules on stimulus competition and highlighted the impact of hierarchical processing on representing multiple stimuli in the visual cortex. SIGNIFICANCE STATEMENT Previous studies have shown that the neural representation of multiple visual stimuli can be accounted for by a divisive normalization model. By using multiple stimuli that compete in more than one feature domain, we found that luminance contrast has a dominant effect in determining competition between multiple stimuli when they are overlapping but not spatially separated. Our results revealed that neuronal responses to multiple stimuli in a given cortical area cannot be simply predicted by the population neural responses elicited in that area by the individual stimulus components. To understand the neural representation of multiple stimuli, rather than considering response normalization only within the area of interest, one must consider the computations including normalization occurring along the hierarchical visual pathway.
Physical Models for the Clustering of Obscured and Unobscured Quasars

Clustering measurements of obscured and unobscured quasars show that obscured quasars reside in more massive dark matter halos than their unobscured counterparts. These results are inconsistent with simple unified (torus) scenarios, but might be explained by models in which the distribution of obscuring material depends on Eddington ratio or galaxy stellar mass. We test these possibilities by constructing simple physical models to compare to observed AGN populations. We find that previously observed relationships between obscuration and Eddington ratio or stellar mass are not sufficient reproduce the observed quasar clustering results ($\langle \log M_{\text{halo}}/M_{\odot} \rangle = 12.94 ^{+ 0.10}_{- 0.11}$ and $\langle \log M_{\text{halo}}/M_{\odot} \rangle = 12.49 ^{+ 0.08}_{- 0.08}$ for obscured and unobscured populations, respectively) while maintaining the observed fraction of obscured quasars (30-65$\%$). This work suggests that evolutionary models, in which obscuration evolves on the typical timescale for black hole growth, are necessary to understand the observed clustering of mid-IR selected quasars.
Relationship Between Motor Capacity of the Contralesional and Ipsilesional Hand Depends on the Side of Stroke in Chronic Stroke Survivors With Mild-to-Moderate Impairment.

There is growing evidence that after a stroke, sensorimotor deficits in the ipsilesional hand are related to the degree of impairment in the contralesional upper extremity. Here, we asked if the relationship between the motor capacities of the two hands differs based on the side of stroke. Forty-two pre-morbidly right-handed chronic stroke survivors (left hemisphere damage, LHD = 21) with mild-to-moderate paresis performed distal items of the Wolf Motor Function Test (dWMFT). We found that compared to right hemisphere damage (RHD), the relationship between contralesional arm impairment (Upper Extremity Fugl- Meyer, UEFM) and ipsilesional hand motor capacity was stronger for LHD (R^2_LHD = 0.42; R^2_RHD < 0.01; z = 2.12; p= 0.03) and the slope was steeper for LHD (t = -2.03; p= 0.04). Similarly, the relationship between contralesional dWMFT and ipsilesional hand motor capacity was stronger for LHD (R^2_LHD = 0.65; R^2_RHD = 0.09; z = 2.45; p= 0.01) and the slope was steeper for LHD (t = 2.03; p= 0.04) compared to RHD. Multiple regression analysis confirmed the presence of an interaction between contralesional UEFM and side of stroke (β3 = 0.66 ± 0.30; p= 0.024) and between contralesional dWMFT and side of stroke (β3 = -0.51 ± 0.34; p= 0.05). Our findings suggest that the relationship between contra- and ipsi-lesional motor capacity depends on the side of stroke in chronic stroke survivors with mild-to-moderate impairment. When contralesional impairment is more severe, the ipsilesional hand is correspondingly slower in those with LHD compared to those with RHD.
Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories

Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells. Proper characterization of the cell's shape, evolution, and their movement over time is crucial to understanding and modeling the mechanobiology of cell migration in many diseases. One in particular, toxoplasmosis is the disease caused by the parasite Toxoplasma gondii. Roughly, one-third of the world's population tests positive for T. gondii. Its virulence is linked to its lytic cycle, predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its motility patterns are critical to the eventual development of therapeutic strategies. Here, we present a computational framework for fast and scalable detection, tracking, and identification of T. gondii motion phenotypes in 3D videos, in a completely unsupervised fashion. Our pipeline consists of several different modules including preprocessing, sparsification, cell detection, cell tracking, trajectories extraction, parametrization of the trajectories; and finally, a clustering step. Additionally, we identified the computational bottlenecks, and developed a lightweight and highly scalable pipeline through a combination of task distribution and parallelism. Our results prove both the accuracy and performance of our method.
Algorithmic Fairness from a Non-ideal Perspective

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In the hopes of mitigating these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might hope to observe in a fair world, offering a variety of algorithms that attempt to satisfy subsets of these parities or to trade off the degree to which they are satisfied against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and ideal worlds. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of their actions, naive applications of ideal thinking can lead to misguided policies. In this paper, we demonstrate a connection between the recent literature on fair machine learning and the ideal approach in political philosophy, and show that some recently uncovered shortcomings in proposed algorithms reflect broader troubles faced by the ideal approach. We work this analysis through for different formulations of fairness and conclude with a critical discussion of real-world impacts and directions for new research.
The Clinical Kinase Index: Prioritizing Understudied Kinases as Targets for the Treatment of Cancer

Abstract The approval of the first kinase inhibitor, Gleevec, in 2001, ushered in a paradigm shift for oncological treatment—the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the established role deregulated kinase activity plays in cancer, only 8% of the entire kinome has been effectively “drugged”. Moreover, a quarter of the 634 human kinases are vastly understudied. We have developed a comprehensive scoring system which utilizes differential gene expression, clinical and pathological parameters, overall survival and mutational hotspot analysis to rank and prioritize clinically-relevant kinase targets across 17 solid tumor cancers from The Cancer Genome Atlas (TCGA). Collectively, we report that dark kinases have potential clinical value as biomarkers or as new drug targets that warrant further study.
The Elephant in the room: What can we learn from California regarding the use of sport hunting of pumas (Puma concolor) as a management tool?

Puma (Puma concolor) management in the western United States is highly contentious, particularly with regard to the use of sport hunting as a management tool. Since the 1970s, puma in ten western states have been managed by state fish and game agencies through the use of a sport hunt. The rationale presented by wildlife managers is that sport hunting, in addition to providing recreational hunting opportunities, also reduces threats to human safety and livestock safety, and increases populations of the puma’s ungulate prey, namely deer (Odocoileus sp.) and elk (Cervus elepus). We evaluated these claims using the state of California as a control, which has prohibited sport hunting since 1972, and employing data obtained from state and federal agencies with authority and control over puma management. Specifically, we tested four hypotheses: 1) sport hunting will suppress puma populations, 2) sport hunting will reduce the number of problematic puma-human encounters; 3) sport hunting will reduce puma predation on domestic livestock, and 4) sport hunting will reduce the impact of puma predation on wild ungulate numbers, resulting in increased hunting opportunities for the sport hunt of ungulates. Our results indicated, respectively, that relative to the 10 states where puma are hunted, California had 1) similar puma densities, 2) the 3rd lowest per capita problematic puma-human encounters, 3) similar per capita loss of cattle (P = 0.13) and a significantly lower (t = 5.7, P < 0.001) per capita loss of sheep, and 4) similar average deer densities while changes in annual deer populations correlated with changes in other states (F = 95.4, P < 0.001, R2 = 0.68). In sum, our analysis of the records obtained from state and federal wildlife agencies found no evidence that sport hunting of pumas has produced the management outcomes sought by wildlife managers aside from providing a sport hunting opportunity. Consequently, and particularly because other research suggests that sport hunting actually exacerbate conflicts between pumas and humans, we recommend that state agencies re-assess the use of sport hunting as a management tool for pumas.