cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal Active social Network De-anonymization Using Information Thresholds

In this paper, de-anonymizing internet users by actively querying their group memberships in social networks is considered. An anonymous victim visits the attacker's website, and the attacker uses the victim's browser history to query her social media activity for the purpose of de-anonymization using the minimum number of queries. A stochastic model of the problem is considered where the attacker has partial prior knowledge of the group membership graph and receives noisy responses to its real-time queries. The victim's identity is assumed to be chosen randomly based on a given distribution which models the users' risk of visiting the malicious website. A de-anonymization algorithm is proposed which operates based on information thresholds and its performance both in the finite and asymptotically large social network regimes is analyzed. Furthermore, a converse result is provided which proves the optimality of the proposed attack strategy.
Shared autonomy via hindsight optimization for teleoperation and teaming

In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user’s goal. As such, most prior works follow...
Visual dynamics: probabilistic future frame synthesis via cross convolutional networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-world video frames. We also show that our model can be applied to visual analogy-making, and present an analysis of the learned network representations.
Computing Weakly Reversible Deficiency Zero Network Translations Using Elementary Flux Modes

We present a computational method for performing structural translation, which has been studied recently in the context of analyzing the steady states and dynamical behavior of mass-action systems derived from biochemical reaction networks. Our procedure involves solving a binary linear programming problem where the decision variables correspond to interactions between the reactions of the original network. We call the resulting network a reaction-to-reaction graph and formalize how such a construction relates to the original reaction network and the structural translation. We demonstrate the efficacy and efficiency of the algorithm by running it on 508 networks from the European Bioinformatics Institutes' BioModels database. We also summarize how this work can be incorporated into recently proposed algorithms for establishing mono- and multistationarity in biochemical reaction systems.
Reuse of neural modules for general video game playing

A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.
A vanishing theorem for Dirac cohomology of standard modules

Abstract This paper studies the Dirac cohomology of standard modules in the setting of graded Hecke algebras with geometric parameters. We prove that the Dirac cohomology of a standard module vanishes if and only if the module is not twisted-elliptic tempered. The proof makes use of two deep results. One is some structural information from the generalized Springer correspondence obtained by S. Kato and Lusztig. Another one is a computation of the Dirac cohomology of tempered modules by Barbasch–Ciubotaru–Trapa and Ciubotaru. We apply our result to compute the Dirac cohomology of ladder representations for type A n . For each of such representations with non-zero Dirac cohomology, we associate to a canonical Weyl group representation. We use the Dirac cohomology to conclude that such representations appear with multiplicity one.
The motive of the Fano surface of lines

Abstract In this short note, we prove that the Chow motive of the Fano surface of lines on the smooth cubic threefold is finite-dimensional in the sense of Kimura. This gives an example of a variety not dominated by a product of curves whose Chow motive is of Abelian type.
Counting Numerical Semigroups

A numerical semigroup is an additive submonoid of the natural numbers with finite complement. The size of the complement is called the genus of the semigroup. How many numerical semigroups have gen...
Bounding sequence extremal functions with formations

An $(r, s)$-formation is a concatenation of $s$ permutations of $r$ letters. If $u$ is a sequence with $r$ distinct letters, then let $\mathit{Ex}(u, n)$ be the maximum length of any $r$-sparse sequence with $n$ distinct letters which has no subsequence isomorphic to $u$. For every sequence $u$ define $\mathit{fw}(u)$, the formation width of $u$, to be the minimum $s$ for which there exists $r$ such that there is a subsequence isomorphic to $u$ in every $(r, s)$-formation. We use $\mathit{fw}(u)$ to prove upper bounds on $\mathit{Ex}(u, n)$ for sequences $u$ such that $u$ contains an alternation with the same formation width as $u$. We generalize Nivasch's bounds on $\mathit{Ex}((ab)^{t}, n)$ by showing that $\mathit{fw}((12 \ldots l)^{t})=2t-1$ and $\mathit{Ex}((12\ldots l)^{t}, n) =n2^{\frac{1}{(t-2)!}\alpha(n)^{t-2}\pm O(\alpha(n)^{t-3})}$ for every $l \geq 2$ and $t\geq 3$, such that $\alpha(n)$ denotes the inverse Ackermann function. Upper bounds on $\mathit{Ex}((12 \ldots l)^{t} , n)$ have been used in other papers to bound the maximum number of edges in $k$-quasiplanar graphs on $n$ vertices with no pair of edges intersecting in more than $O(1)$ points. If $u$ is any sequence of the form $a v a v' a$ such that $a$ is a letter, $v$ is a nonempty sequence excluding $a$ with no repeated letters and $v'$ is obtained from $v$ by only moving the first letter of $v$ to another place in $v$, then we show that $\mathit{fw}(u)=4$ and $\mathit{Ex}(u, n) =\Theta(n\alpha(n))$. Furthermore we prove that $\mathit{fw}(abc(acb)^{t})=2t+1$ and $\mathit{Ex}(abc(acb)^{t}, n) = n2^{\frac{1}{(t-1)!}\alpha(n)^{t-1}\pm O(\alpha(n)^{t-2})}$ for every $t\geq 2$.
Scalar curvature rigidity and Ricci DeTurck flow on perturbations of Euclidean space

We prove a rigidity result for non-negative scalar curvature perturbations of the Euclidean metric on $$\mathbb {R}^n$$ , which may be regarded as a weak version of the rigidity statement of the positive mass theorem. We prove our result by analyzing long time solutions of Ricci DeTurck flow. As a byproduct in doing so, we extend known $$L^p$$ bounds and decay rates for Ricci DeTurck flow and prove regularity of the flow at the initial data.
A Decomposition of Parking Functions by Undesired Spaces

There is a well-known bijection between parking functions of a fixed length and maximal chains of the noncrossing partition lattice which we can use to associate to each set of parking functions a poset whose Hasse diagram is the union of the corresponding maximal chains. We introduce a decomposition of parking functions based on the largest number omitted and prove several theorems about the corresponding posets. In particular, they share properties with the noncrossing partition lattice such as local self-duality, a nice characterization of intervals, a readily computable M o bius function, and a symmetric chain decomposition. We also explore connections with order complexes, labeled Dyck paths, and rooted forests.
Delay optimal power aware opportunistic scheduling with mutual information accumulation

This paper considers optimization of power and delay in a time-varying wireless link using rateless codes. The link serves a sequence of variable-length packets. Each packet is coded and transmitted over multiple slots. Channel conditions can change from slot to slot and are unknown to the transmitter. The amount of mutual information accumulated on each slot depends on the random channel realization and the power used. The goal is to minimize average service delay subject to an average power constraint. We formulate this problem as a frame-based stochastic optimization problem and solve it via an online algorithm. We show that the subproblem within each frame is a simple integer program which can be effectively solved using a dynamic program. The optimality of this online algorithm is proved using the frame-based Lyapunov drift analysis.
Computational Social Choice and Computational Complexity: BFFs?

We discuss the connection between computational social choice (comsoc) and computational complexity. We stress the work so far on, and urge continued focus on, two less-recognized aspects of this connection. Firstly, this is very much a two-way street: Everyone knows complexity classification is used in comsoc, but we also highlight benefits to complexity that have arisen from its use in comsoc. Secondly, more subtle, less-known complexity tools often can be very productively used in comsoc.
Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles

We demonstrate how a novel synthesis of three methods — (1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural network algorithm — can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we employ this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data are comprised of interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual’s communication style. We then perform a proof-of-concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods.
NUMERICAL RELATIVITY SIMULATIONS OF MAGNETIZED BLACK HOLE–NEUTRON STAR MERGERS

We present new numerical techniques we developed for launching the first parameter study of {\it magnetized} black hole--neutron star (BHNS) mergers, varying the magnetic fields seeded in the initial neutron star. We found that magnetic fields have a negligible impact on the gravitational waveforms and bulk dynamics of the system during merger, regardless of magnetic field strength or BH spin. In a recent simulation, we seeded the remnant disk from an unmagnetized BHNS merger simulation with large-scale, purely poloidal magnetic fields, which are otherwise absent in the full simulation. The outcome appears to be a viable sGRB central engine.
Computer aided detection of anemia-like pallor

Paleness or pallor is a manifestation of blood loss or low hemoglobin concentrations in the human blood that can be caused by pathologies such as anemia. This work presents the first automated screening system that utilizes pallor site images, segments, and extracts color and intensity-based features for multi-class classification of patients with high pallor due to anemia-like pathologies, normal patients and patients with other abnormalities. This work analyzes the pallor sites of conjunctiva and tongue for anemia screening purposes. First, for the eye pallor site images, the sclera and conjunctiva regions are automatically segmented for regions of interest. Similarly, for the tongue pallor site images, the inner and outer tongue regions are segmented. Then, color-plane based feature extraction is performed followed by machine learning algorithms for feature reduction and image level classification for anemia. In this work, a suite of classification algorithms image-level classifications for normal (class 0), pallor (class 1) and other abnormalities (class 2). The proposed method achieves 86% accuracy, 85% precision and 67% recall in eye pallor site images and 98.2% accuracy and precision with 100% recall in tongue pallor site images for classification of images with pallor. The proposed pallor screening system can be further fine-tuned to detect the severity of anemia-like pathologies using controlled set of local images that can then be used for future benchmarking purposes.
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap

Over the course of the past decade, a variety of randomized algorithms have been proposed for computing approximate least-squares (LS) solutions in large-scale settings. A longstanding practical issue is that, for any given input, the user rarely knows the actual error of an approximate solution (relative to the exact solution). Likewise, it is difficult for the user to know precisely how much computation is needed to achieve the desired error tolerance. Consequently, the user often appeals to worst-case error bounds that tend to offer only qualitative guidance. As a more practical alternative, we propose a bootstrap method to compute a posteriori error estimates for randomized LS algorithms. These estimates permit the user to numerically assess the error of a given solution, and to predict how much work is needed to improve a "preliminary" solution. In addition, we provide theoretical consistency results for the method, which are the first such results in this context (to the best of our knowledge). From a practical standpoint, the method also has considerable flexibility, insofar as it can be applied to several popular sketching algorithms, as well as a variety of error metrics. Moreover, the extra step of error estimation does not add much cost to an underlying sketching algorithm. Finally, we demonstrate the effectiveness of the method with empirical results.
Efficient Trajectory Optimization for Robot Motion Planning

Motion planning for multi-jointed robots is challenging. Due to the inherent complexity of the problem, most existing works decompose motion planning as easier subproblems. However, because of the inconsistent performance metrics, only sub-optimal solution can be found by decomposition based approaches. This paper presents an optimal control based approach to address the path planning and trajectory planning subproblems simultaneously. Unlike similar works which either ignore robot dynamics or require long computation time, an efficient numerical method for trajectory optimization is presented in this paper for motion planning involving complicated robot dynamics. The efficiency and effectiveness of the proposed approach is shown by numerical results. Experimental results are used to show the feasibility of the presented planning algorithm.
Being Robust (in High Dimensions) Can Be Practical

Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can tolerate only a tiny fraction of errors. Recent work in theoretical computer science has shown that, in appropriate distributional models, it is possible to robustly estimate the mean and covariance with polynomial time algorithms that can tolerate a constant fraction of corruptions, independent of the dimension. However, the sample and time complexity of these algorithms is prohibitively large for high-dimensional applications. In this work, we address both of these issues by establishing sample complexity bounds that are optimal, up to logarithmic factors, as well as giving various refinements that allow the algorithms to tolerate a much larger fraction of corruptions. Finally, we show on both synthetic and real data that our algorithms have state-of-the-art performance and suddenly make high-dimensional robust estimation a realistic possibility.
Robust moment closure method for the chemical master equation

The Chemical Master Equation (CME) is used to stochastically model biochemical reaction networks, under the Markovian assumption. The low-order statistical moments induced by the CME are often the key quantities that one is interested in. However, in most cases, the moments equation is not closed; in the sense that the first n moments depend on the higher order moments, for any positive integer n. In this paper, we develop a moment closure technique in which the higher order moments are approximated by an affine function of the lower order moments. We refer to such functions as the affine Moment Closure Functions (MCF) and prove that they are optimal in the worst-case context, in which no a priori information on the probability distribution is available. Furthermore, we cast the problem of finding the optimal affine MCF as a linear program, which is tractable. We utilize the affine MCFs to derive a finite dimensional linear system that approximates the low-order moments. We quantify the approximation error in terms of the induced norm of some linear system. Our results can be effectively used to approximate the low-order moments and characterize the noise properties of the biochemical network under study.
Game theory for cyber deception: a tutorial

Deceptive and anti-deceptive technologies have been developed for various specific applications. But there is a significant need for a general, holistic, and quantitative framework of deception. Game theory provides an ideal set of tools to develop such a framework of deception. In particular, game theory captures the strategic and self-interested nature of attackers and defenders in cybersecurity. Additionally, control theory can be used to quantify the physical impact of attack and defense strategies. In this tutorial, we present an overview of game-theoretic models and design mechanisms for deception and counter-deception. The tutorial aims to provide a taxonomy of deception and counter-deception and understand how they can be conceptualized, quantified, and designed or mitigated. This tutorial gives an overview of diverse methodologies from game theory that includes games of incomplete information, dynamic games, mechanism design theory to offer a modern theoretic underpinning of cyberdeception. The tutorial will also discuss open problems and research challenges that the HoTSoS community can address and contribute with an objective to build a multidisciplinary bridge between cybersecurity, economics, game and decision theory.
A WEAK TRAPEZOIDAL METHOD FOR A CLASS OF STOCHASTIC DIFFERENTIAL EQUATIONS

We present a numerical method for the approximation of solutions for the class of stochastic differential equations driven by Brownian motions which induce stochastic variation in fixed directions. This class of equations arises naturally in the study of population processes and chemical reaction kinetics. We show that the method constructs paths that are second order accurate in the weak sense. The method is simpler than many second order methods in that it neither requires the construction of iterated Ito integrals nor the evaluation of any derivatives. The method consists of two steps. In the first an explicit Euler step is used to take a fractional step. The resulting fractional point is then combined with the initial point to obtain a higher order, trapezoidal like, approximation. The higher order of accuracy stems from the fact that both the drift and the quadratic variation of the underlying SDE are approximated to second order.
Symplectic log Calabi-Yau surface - deformation class

We study the symplectic analogue of log Calabi-Yau surfaces and show that the symplectic deformation classes of these surfaces are completely determined by the homological information.
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation

Estimators of information theoretic measures, such as entropy and mutual information, are a basic workhorse for many downstream applications in modern data science. State-of-the-art approaches have been either geometric [nearest neighbor (NN)-based] or kernel-based (with a globally chosen bandwidth). In this paper, we combine both these approaches to design new estimators of entropy and mutual information that outperform the state-of-the-art methods. Our estimator uses local bandwidth choices of $k$ -NN distances with a finite $k$ , independent of the sample size. Such a local and data dependent choice ameliorates boundary bias and improves performance in practice, but the bandwidth is vanishing at a fast rate, leading to a non-vanishing bias. We show that the asymptotic bias of the proposed estimator is universal ; it is independent of the underlying distribution. Hence, it can be precomputed and subtracted from the estimate. As a byproduct, we obtain a unified way of obtaining both the kernel and NN estimators. The corresponding theoretical contribution relating the asymptotic geometry of nearest neighbors to order statistics is of independent mathematical interest.
Computing the Maslov Index from Singularities of a Matrix Riccati Equation

We study the Maslov index as a tool to analyze stability of steady state solutions to a reaction–diffusion equation in one spatial dimension. We show that the path of unstable subspaces associated to this equation is governed by a matrix Riccati equation whose solution S develops singularities when changes in the Maslov index occur. Our main result proves that at these singularities the change in Maslov index equals the number of eigenvalues of S that increase to $$+\infty $$ minus the number of eigenvalues that decrease to $$-\infty $$ .
Adaptive Normalized Risk-Averting Training for deep neural networks

This paper proposes a set of new error criteria and a learning approach, called Adaptive Normalized Risk-Averting Training (ANRAT) to attack the non-convex optimization problem in training deep neural networks without pretraining. Theoretically, we demonstrate its effectiveness based on the expansion of the convexity region. By analyzing the gradient on the convexity index λ, we explain the reason why our learning method using gradient descent works. In practice, we show how this training method is successfully applied for improved training of deep neural networks to solve visual recognition tasks on the MNIST and CIFAR-10 datasets. Using simple experimental settings without pretraining and other tricks, we obtain results comparable or superior to those reported in recent literature on the same tasks using standard ConvNets + MSE/cross entropy. Performance on deep/shallow multilayer perceptron and Denoised Auto-encoder is also explored. ANRAT can be combined with other quasi-Newton training methods, innovative network variants, regularization techniques and other common tricks in DNNs. Other than unsupervised pretraining, it provides a new perspective to address the non-convex optimization strategy in training DNNs.
Chance-constrained day-ahead hourly scheduling in distribution system operation

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.
Hölder's inequality for roots of symmetric operator spaces

We prove a version of Holder's inequality with a constant for p-th roots of symmetric operator spaces of operators affiliated to a semifinite von Neumann algebra factor, and with constant equal to 1 for strongly symmetric operator spaces.
Efficient Approximations for the Online Dispersion Problem

The dispersion problem has been widely studied in computational geometry and facility location and is closely related to the packing problem. The goal is to locate $n$ points (e.g., facilities or p...
Electromagnetic Wave Scattering by a Thin Layer in Which Many Small Particles Are Embedded

Scattering of electromagnetic (EM) waves by many small particles (bodies), embedded in a thin layer, is studied. Physical properties of the particles are described by their boundary impedances. The thin layer of depth of the order O(a), with many embedded small particles of characteristic size a, is described by a boundary condition on the surface of the layer. The limiting interface boundary condition is obtained for the efiective EM fleld in the limiting medium, in the limit a ! 0, where the number M(a) of the particles tends to inflnity at a suitable rate.
Avoiding a Tyranny of the Majority: Public Deliberation as Citizen Science, Sensitive Issues, and Vulnerable Populations.

Citizen science is touted as a means of making science more inclusive and democratic. However, when citizens are drawn from societies with significant socioeconomic and racial disparities, citizen ...
The Impact of Information Security Breach Incidents on CIO Turnover

ABSTRACT We investigate the relationship between security breaches and chief information officer (CIO) turnover. Because CIOs are directly responsible for IT performance, we argue that their turnov...
Adaptive Variance for Changing Sparse-Reward Environments

Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled way to adapt the policy for better exploration in changing sparse-reward environments. Unlike previous works which explicitly model environmental changes, we analyze the relationship between the value function and the optimal exploration for a Gaussian-parameterized policy and show that our theory leads to an effective strategy for adjusting the variance of the policy, enabling fast adapt to changes in a variety of sparse-reward environments.
Learning-Based Low-Rank Approximations

We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an $n \times d$ matrix $A$, and a parameter $k$, compute a rank-$k$ matrix $A'$ that minimizes the approximation loss $\|A-A'\|_F$. The algorithm uses a training set of input matrices in order to optimize its performance. Specifically, some of the most efficient approximate algorithms for computing low-rank approximations proceed by computing a projection $SA$, where $S$ is a sparse random $m \times n$ “sketching matrix”, and then performing the singular value decomposition of $SA$. We show how to replace the random matrix $S$ with a “learned” matrix of the same sparsity to reduce the error. Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes up to one order of magnitude. We also study mixed matrices where only some of the rows are trained and the remaining ones are random, and show that matrices still offer improved performance while retaining worst-case guarantees. Finally, to understand the theoretical aspects of our approach, we study the special case of $m=1$. In particular, we give an approximation algorithm for minimizing the empirical loss, with approximation factor depending on the stable rank of matrices in the training set. We also show generalization bounds for the sketch matrix learning problem.
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion.

Grapheme-to-phoneme (G2P) models are a key component in Automatic Speech Recognition (ASR) systems, such as the ASR system in Alexa, as they are used to generate pronunciations for out-of-vocabulary words that do not exist in the pronunciation lexicons (mappings like "e c h o" to "E k oU"). Most G2P systems are monolingual and based on traditional joint-sequence based n-gram models [1,2]. As an alternative, we present a single end-to-end trained neural G2P model that shares same encoder and decoder across multiple languages. This allows the model to utilize a combination of universal symbol inventories of Latin-like alphabets and cross-linguistically shared feature representations. Such model is especially useful in the scenarios of low resource languages and code switching/foreign words, where the pronunciations in one language need to be adapted to other locales or accents. We further experiment with word language distribution vector as an additional training target in order to improve system performance by helping the model decouple pronunciations across a variety of languages in the parameter space. We show 7.2% average improvement in phoneme error rate over low resource languages and no degradation over high resource ones compared to monolingual baselines.
Assessing Bayes Factor Surfaces Using Interactive Visualization and Computer Surrogate Modeling

AbstractBayesian model selection provides a natural alternative to classical hypothesis testing based on p-values. While many articles mention that Bayesian model selection can be sensitive to prio...
Uncertain times and early predictions of bank failure

The Great Financial Crisis shows that bank failure in the United States, while rare, is a concern during uncertain times. Interest here is in the ability to predict future failures at the start of a crisis, when the recent past has few events on which to base inferences. I show that policy makers using estimates based on the Savings and Loans crisis would identify in early 2009 that 2.0% of banks were in critical condition and 7.0% were unhealthy. This is comparable to the 1.7% of banks that failed within a year and the 3.9% of banks that would fail during the crisis.
Monetary Transmission through Shadow Banks

This paper documents a new transmission channel of monetary policy: the shadow money channel. Analyzing U.S. money supply data from 1987 to 2012, I find that shadow money, namely liquid deposits created by shadow banks, expands significantly when the Federal Reserve tightens monetary policy. Using a structural model of bank competition, I show that this new channel is a result of imperfect competition between commercial and shadow banks in the deposit market with heterogeneous depositors. Due to a lack of a bank charter, shadow banks offer lower transaction convenience and hence must compete on yields. During periods of monetary tightening, shadow banks pass through more rate hikes to depositors, thereby poaching yield-sensitive deposits from commercial banks. Fitting my model to institution-level data from commercial banks and money market funds, I show that shadow money creation offsets 35 cents of each dollar in commercial bank deposit reductions, significantly dampening the impact of monetary tightening. My results suggest that monetary tightening may unintentionally drive more deposits into the uninsured shadow banking sector, thereby amplifying the risk of bank runs.
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data

Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment. Recent years, deep neural networks have made groundbreaking success in medical image segmentation problem. However, due to the high variance in instrumental parameters, experimental protocols, and subject appearances, the generalization of deep learning models is often hindered by the inconsistency in medical images generated by different machines and hospitals. In this work, we present StyleSegor, an efficient and easy-to-use strategy to alleviate this inconsistency issue. Specifically, neural style transfer algorithm is applied to unlabeled data in order to minimize the differences in image properties including brightness, contrast, texture, etc. between the labeled and unlabeled data. We also apply probabilistic adjustment on the network output and integrate multiple predictions through ensemble learning. On a publicly available whole heart segmentation benchmarking dataset from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice accuracy surpassing current state-of-the-art method and notably, an improvement of the total score by 29.91%. StyleSegor is thus corroborated to be an accurate tool for 3D whole heart segmentation especially on highly inconsistent data, and is available at https://github.com/horsepurve/StyleSegor.
Recharacterizing Corruption to Encompass Illicit Financial Flows

Anticorruption treaties generally define corruption as the abuse of entrusted power for private gain. As such, global anticorruption efforts primarily target transactions involving the bribery of governmental officials. The definition excludes transactions in which multinational corporations deprive developing states of revenue by failing to pay taxes and other monies due. Yet such transactions are equally injurious to the development agenda of poor states. This essay argues that corruption should be redefined to encompass illicit financial flows, a term used by a growing network of tax and economic justice groups to refer to money that is “illegally earned, transferred or used.” Transactions such as trade misinvoicing, base-erosion, and abusive transfer pricing to illegally earn additional income undermine the ability of poor states to raise revenue for development. Expanding the definition of corruption would create a more realistic picture of the role of corporate actors and their involvement in corrupt and illicit dealings. It would also bring equivalency to the treatment of corporate actors and public officials. By focusing on illicit dealings involving corporate actors, this essay challenges the partial definition of corruption adopted in the heyday of the Washington Consensus, when skepticism about the role of the state, rather than of private actors, prevailed.
On the number of clonoids

A clonoid is a set of finitary functions from a set A to a set B that is closed under taking minors. Hence clonoids are generalizations of clones. By a classical result of Post, there are only countably many clones on a 2-element set. In contrast to that, we present continuum many clonoids for $$A = B = \{0,1\}$$. More generally, for any finite set A and any 2-element algebra $${\mathbf {B}}$$, we give the cardinality of the set of clonoids from A to $${\mathbf {B}}$$ that are closed under the operations of $${\mathbf {B}}$$. Further, for any finite set A and finite idempotent algebra $${\mathbf {B}}$$ without a cube term (with $$|A|,|B|\ge 2$$) there are continuum many clonoids from A to $${\mathbf {B}}$$ that are closed under the operations of $${\mathbf {B}}$$; if $${\mathbf {B}}$$ has a cube term there are countably many such clonoids.
Priors for genotyping polyploids.

MOTIVATION Empirical Bayes techniques to genotype polyploid organisms usually either (i) assume technical artifacts are known a priori or (ii) estimate technical artifacts simultaneously with the prior genotype distribution. Case (i) is unappealing as it places the onus on the researcher to estimate these artifacts, or to ensure that there are no systematic biases in the data. However, as we demonstrate with a few empirical examples, case (ii) makes choosing the class of prior genotype distributions extremely important. Choosing a class is either too flexible or too restrictive results in poor genotyping performance. RESULTS We propose two classes of prior genotype distributions that are of intermediate levels of flexibility: the class of proportional normal distributions and the class of unimodal distributions. We provide a complete characterization of and optimization details for the class of unimodal distributions. We demonstrate, using both simulated and real data that using these classes results in superior genotyping performance. AVAILABILITY AND IMPLEMENTATION Genotyping methods that use these priors are implemented in the updog R package available on the Comprehensive R Archive Network: https://cran.r-project.org/package=updog. All code needed to reproduce the results of this article is available on GitHub: https://github.com/dcgerard/reproduce_prior_sims. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Electromagnetic Guided Percutaneous Renal Access Outcomes Among Surgeons and Trainees of Different Experience Levels: A Pilot Study

Abstract Objective To determine feasibility of an electromagnetic (EM) guidance system (Auris Health, Redwood City, CA) in obtaining percutaneous renal access among urologists and trainees of different experience levels. EM-guidance is appealing for access as it allows real time, 3-dimensional targeting without radiation. Few studies have explored this for percutaneous nephrolithotomy (PCNL) and none have assessed its potential to decrease the learning curve in obtaining access using traditional techniques. Methods Institutional Animal Care and Use Committee approval was obtained to compare EM-guided percutaneous access to fluoroscopic guided access in a porcine model. Voluntary participants included urology trainees and faculty. They were categorized as beginner (no prior primary percutaneous nephrolithotomyexperience), intermediate (10-100 prior) and advanced (>100). Each participant attempted an EM and fluoroscopic guided puncture. Primary outcome was successful puncture. Secondary outcomes included access time, fluoroscopy time, and number of attempts. Participants were limited to 3 attempts and 10 minutes total to obtain access using each technique. Results Fourteen participants (6 beginners, 4 intermediates, and 4 experts) attempted 28 punctures. Overall success using EM-guidance was 93% compared to 71% using fluoroscopy (P = .33). EM punctures had shorter access times (85 vs 255 seconds, P Conclusion EM-guidance is a promising new technique to decrease the learning curve of percutaneous access with high success rates and minimal radiation.
Quasi-Hopf Deformations of Quantum Groups

The search for elliptic quantum groups leads to a modified quantum Yang–Baxter relation and to a special class of quasi-triangular quasi-Hopf algebras. This Letter calculates deformations of standard quantum groups (with or without spectral parameter) in the category of quasi-Hopf algebras. An earlier investigation of the deformations of quantum groups, in the category of Hopf algebras, showed that quantum groups are generically rigid: Hopf algebra deformations exist only under some restrictions on the parameters. In particular, affine Kac–Moody algebras are more rigid than their loop algebra quotients and only the latter (in the case of sl(n)) can be deformed to elliptic Hopf algebras. The generalization to quasi-Hopf deformations lifts this restriction. The full elliptic quantum groups (with central extension) associated with sl(n) are thus quasi-Hopf algebras. The universal R-matrices satisfy a modified Yang–Baxter relation and are calculated more or less explicitly. The modified classical Yang–Baxter relation is obtained and the elliptic solutions are worked out explicitly.
Coordination and contagion: Individual connections and peer mechanisms in a randomized field experiment

Abstract This paper investigates peer effects at the level of individual connections, leveraging the approach to shed light on peer mechanisms. In a field experiment using college freshmen, we elicited best friends and offered monetary incentives for gym visits to a treated subset. We find large spillovers from treated subjects to treated best friends but none from treated subjects to control best friends. We also find evidence of a mechanism: Subjects coordinate by visiting the gym with best friends, indicating that the intervention harnesses complementarities in utility or commitment mechanisms. Results highlight subtle peer effects and mechanisms that often go undetected.
Potential Data Link Candidates for Civilian Unmanned Aircraft Systems: A Survey

This survey studies the potential data link candidates for unmanned aircraft vehicles (UAVs). There has been tremendous growth in different applications of UAVs such as lifesaving and rescue missions, commercial use, recreations, etc. Unlike the traditional wireless communications, the data links for these systems do not have any general standardized framework yet to ensure safe co-existence of UAVs with other flying vehicles. This motivated us to provide a comprehensive survey of potential data link technologies available for UAVs. Our goal is to study the current trends and available candidates and carry out a comprehensive comparison among them. The contribution of this survey is to highlight the strength and weakness of the current data link options and their suitability to satisfy the UAVs communication requirements. Satellite links, cellular technologies, Wi-Fi and several similar wireless technologies are studied thoroughly in this paper. We also focus on several available promising standards that can be modified for these data links. Then, we discuss standard-related organizations that are working actively in the area of civilian unmanned systems. Finally, we bring up some future challenges in this area with several potential solutions to motivate further research work.
An Improved Lower Bound for the Traveling Salesman Constant

Abstract Let X 1 , X 2 , … , X n be independent uniform random variables on [ 0 , 1 ] 2 . Let L ( X 1 , … , X n ) be the length of the shortest Traveling Salesman tour through these points. Beardwood et al (1959) showed that there exists a constant β such that lim n → ∞ L ( X 1 , … , X n ) n = β almost surely. It was shown that β ≥ 0 . 625 . Building upon an approach proposed by Steinerberger (2015), we improve the lower bound to β ≥ 0 . 6277 .
Singular integral equations, asymptotic behavior and analyticity in the coupling constant

The inverse Mellin transform is exploited in connection with linear singular integral equations and nonanalyticity in the coupling constant at the origin. A difference integral equation in the transforming variable (1) is obtained, by which the asymptotic behavior of the solution near the singular point can be discussed.
National update on measles cases and outbreaks — United States, January 1 – October 1, 2019

During January 1-October 1, 2019, a total of 1,249 measles cases and 22 measles outbreaks were reported in the United States. This represents the most U.S. cases reported in a single year since 1992 (1), and the second highest number of reported outbreaks annually since measles was declared eliminated* in the United States in 2000 (2). Measles is an acute febrile rash illness with an attack rate of approximately 90% in susceptible household contacts (3). Domestic outbreaks can occur when travelers contract measles outside the United States and subsequently transmit infection to unvaccinated persons they expose in the United States. Among the 1,249 measles cases reported in 2019, 1,163 (93%) were associated with the 22 outbreaks, 1,107 (89%) were in patients who were unvaccinated or had an unknown vaccination status, and 119 (10%) measles patients were hospitalized. Closely related outbreaks in New York City (NYC) and New York State (NYS; excluding NYC), with ongoing transmission for nearly 1 year in large and close-knit Orthodox Jewish communities, accounted for 934 (75%) cases during 2019 and threatened the elimination status of measles in the United States. Robust responses in NYC and NYS were effective in controlling transmission before the 1-year mark; however, continued vigilance for additional cases within these communities is essential to determine whether elimination has been sustained. Collaboration between public health authorities and undervaccinated communities is important for preventing outbreaks and limiting transmission. The combination of maintenance of high national vaccination coverage with measles, mumps, and rubella vaccine (MMR) and rapid implementation of measles control measures remains the cornerstone for preventing widespread measles transmission (4).
Entorhinal velocity signals reflect environmental geometry.

The entorhinal cortex contains neurons that represent self-location, including grid cells that fire in periodic locations and velocity signals that encode running speed and head direction. Although the size and shape of the environment influence grid patterns, whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here we report that speed cells rescale after changes to the size and shape of the environment. Moreover, head direction cells reorganize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows dissociation of the coordination between cell types, with grid and speed cells, but not head direction cells, responding in concert to environmental change. These results point to malleability in the coding features of multiple entorhinal cell types and have implications for which cell types contribute to the velocity signal used by computational models of grid cells. Munn et al. provide evidence that medial entorhinal speed signals scale to reflect the geometry of the environment, whereas entorhinal head direction signals reflect learned information about the geometric symmetry of the environment.
Almost Sure Local Well-Posedness for a Derivative Nonlinear Wave Equation

We study the derivative nonlinear wave equation \( - \partial_{tt} u + \Delta u = |\nabla u|^2 \) on \( \mathbb{R}^{1+3} \). The deterministic theory is determined by the Lorentz-critical regularity \( s_L = 2 \), and both local well-posedness above \( s_L \) as well as ill-posedness below \( s_L \) are known. In this paper, we show the local existence of solutions for randomized initial data at the super-critical regularities \( s\geq 1.984\). In comparison to the previous literature in random dispersive equations, the main difficulty is the absence of a (probabilistic) nonlinear smoothing effect. To overcome this, we introduce an adaptive and iterative decomposition of approximate solutions into rough and smooth components. In addition, our argument relies on refined Strichartz estimates, a paraproduct decomposition, and the truncation method of de Bouard and Debussche.
Modulation of Surface Bonding Topology: Oxygen Bridges on OH-Terminated InP (001)

An understanding and control of complex physiochemical processes at the photoelectrode/electrolyte interface in photoelectrochemical cells (PECs) are essential for developing advanced solar-driven ...