cssi/SciSciGPT-SciSciNet huggingface dataset:
Singularities and semistable degenerations for symplectic topology

Abstract We overview our work [7] , [8] , [9] , [10] , [11] , [6] defining and studying normal crossings varieties and subvarieties in symplectic topology. This work answers a question of Gromov on the feasibility of introducing singular (sub)varieties into symplectic topology in the case of normal crossings singularities. It also provides a necessary and sufficient condition for smoothing normal crossings symplectic varieties. In addition, we explain some connections with other areas of mathematics and discuss a few directions for further research.
Metric dimensions of minor excluded graphs and minor exclusion in groups

An infinite graph Γ is minor excluded if there is a finite graph that is not a minor of Γ. We prove that minor excluded graphs have finite Assouad–Nagata dimension and study minor exclusion for Cayley graphs of finitely generated groups. Our main results and observations are: (1) minor exclusion is not a group property: it depends on the choice of generating set; (2) a group with one end has a generating set for which the Cayley graph is not minor excluded; (3) there are groups that are not minor excluded for any set of generators, like ℤ3; (4) minor exclusion is preserved under free products; and (5) virtually free groups are minor excluded for any choice of finite generating set.
Path homotopy invariants and their application to optimal trajectory planning

We consider the problem of optimal path planning in different homotopy classes in a given environment. Though important in robotics applications, path-planning with reasoning about homotopy classes of trajectories has typically focused on subsets of the Euclidean plane in the robotics literature. The problem of finding optimal trajectories in different homotopy classes in more general configuration spaces (or even characterizing the homotopy classes of such trajectories) can be difficult. In this paper we propose automated solutions to this problem in several general classes of configuration spaces by constructing presentations of fundamental groups and giving algorithms for solving the word problem in such groups. We present explicit results that apply to knot and link complements in 3-space, discuss how to extend to cylindrically-deleted coordination spaces of arbitrary dimension, and also present results in the coordination space of robots navigating on an Euclidean plane.
Moving robots efficiently using the combinatorics of CAT(0) cubical complexes

Given a reconfigurable system $X$, such as a robot moving on a grid or a set of particles traversing a graph without colliding, the possible positions of $X$ naturally form a cubical complex ${\mathcal{S}}(X)$. When ${\mathcal{S}}(X)$ is a CAT(0) space, we can explicitly construct the shortest path between any two points for any of the four most natural metrics: distance, time, number of moves, and number of steps of simultaneous moves. Using Ardila, Owen, and Sullivant's result that CAT(0) cubical complexes are in correspondence with posets with inconsistent pairs (PIPs), we can prove that a state complex ${\mathcal{S}}(X)$ is CAT(0) by identifying the corresponding PIP. We illustrate this very general strategy with one known and one new example: Abrams and Ghrist's positive robotic arm on a square grid, and the robotic arm in a strip. We then use the PIP as a combinatorial “remote control" to move these robots efficiently from one position to another.
The Boolean Model in the Shannon Regime: Three Thresholds and Related Asymptotics

Consider a family of Boolean models, indexed by integers $n \ge 1$, where the $n$-th model features a Poisson point process in ${\mathbb{R}}^n$ of intensity $e^{n \rho_n}$ with $\rho_n \to \rho$ as $n \to \infty$, and balls of independent and identically distributed radii distributed like $\bar X_n \sqrt{n}$, with $\bar X_n$ satisfying a large deviations principle. It is shown that there exist three deterministic thresholds: $\tau_d$ the degree threshold; $\tau_p$ the percolation threshold; and $\tau_v$ the volume fraction threshold; such that asymptotically as $n$ tends to infinity, in a sense made precise in the paper: (i) for $\rho \tau_v$, the whole space covered. The analysis of this asymptotic regime is motivated by related problems in information theory, and may be of interest in other applications of stochastic geometry.
L-Store: A Real-time OLTP and OLAP System.

Arguably data is the new natural resource in the enterprise world with an unprecedented degree of proliferation. But to derive real-time actionable insights from the data, it is important to bridge the gap between managing the data that is being updated at a high velocity (i.e., OLTP) and analyzing a large volume of data (i.e., OLAP). However, there has been a divide where specialized solutions were often deployed to support either OLTP or OLAP workloads but not both; thus, limiting the analysis to stale and possibly irrelevant data. In this paper, we present Lineage-based Data Store (L-Store) that combines the real-time processing of transactional and analytical workloads within a single unified engine by introducing a novel lineage-based storage architecture. By exploiting the lineage, we develop a contention-free and lazy staging of columnar data from a write-optimized form (suitable for OLTP) into a read-optimized form (suitable for OLAP) in a transactionally consistent approach that also supports querying and retaining the current and historic data. Our working prototype of L-Store demonstrates its superiority compared to state-of-the-art approaches under a comprehensive experimental evaluation.
An improved global risk bound in concave regression

A new risk bound is presented for the problem of convex/concave function estimation, using the least squares estimator. The best known risk bound, as had appeared in \citet{GSvex}, scaled like $\log(en) n^{-4/5}$ under the mean squared error loss, up to a constant factor. The authors in \cite{GSvex} had conjectured that the logarithmic term may be an artifact of their proof. We show that indeed the logarithmic term is unnecessary and prove a risk bound which scales like $n^{-4/5}$ up to constant factors. Our proof technique has one extra peeling step than in a usual chaining type argument. Our risk bound holds in expectation as well as with high probability and also extends to the case of model misspecification, where the true function may not be concave.
Existence of self-shrinkers to the degree-one curvature flow with a rotationally symmetric conical end

Abstract Given a smooth, symmetric, homogeneous of degree one function f ( λ 1 , ⋯ , λ n ) satisfying ∂ i f > 0 for all i = 1 , ⋯ , n , and a rotationally symmetric cone C in R n + 1 , we show that there is a f self-shrinker (i.e. a hypersurface Σ in R n + 1 which satisfies f ( κ 1 , ⋯ , κ n ) + 1 2 X ⋅ N = 0 , where X is the position vector, N is the unit normal vector, and κ 1 , ⋯ , κ n are principal curvatures of Σ) that is asymptotic to C at infinity.
Model order reduction for Linear Noise Approximation using time-scale separation

In this paper, we focus on model reduction of biomolecular systems with multiple time-scales, modeled using the Linear Noise Approximation. Considering systems where the Linear Noise Approximation can be written in singular perturbation form, with e as the singular perturbation parameter, we obtain a reduced order model that approximates the slow variable dynamics of the original system. In particular, we show that, on a finite time-interval, the first and second moments of the reduced system are within an O(e)-neighborhood of the first and second moments of the slow variable dynamics of the original system. The approach is illustrated on an example of a biomolecular system that exhibits time-scale separation.
Unsupervised discovery of El Nino using causal feature learning on microlevel climate data

We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka et al., 2015, 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific Ocean. The method identifies these unusual climate states on the basis of the relation between ZW and SST patterns without any input about past occurrences of El Nino or La Nina. The simpler alternatives of (i) clustering the SST fields while disregarding their relationship with ZW patterns, or (ii) clustering the joint ZW-SST patterns, do not discover El Nino. We discuss the degree to which our method supports a causal interpretation and use a low-dimensional toy example to explain its success over other clustering approaches. Finally, we propose a new robust and scalable alternative to our original algorithm (Chalupka et al., 2016), which circumvents the need for high-dimensional density learning.
Hypothesis Transfer Learning via Transformation Functions

We consider the Hypothesis Transfer Learning (HTL) problem where one incorporates a hypothesis trained on the source domain into the learning procedure of the target domain. Existing theoretical analysis either only studies specific algorithms or only presents upper bounds on the generalization error but not on the excess risk. In this paper, we propose a unified algorithm-dependent framework for HTL through a novel notion of transformation functions, which characterizes the relation between the source and the target domains. We conduct a general risk analysis of this framework and in particular, we show for the first time, if two domains are related, HTL enjoys faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge Regression than those of the classical non-transfer learning settings. We accompany this framework with an analysis of cross-validation for HTL to search for the best transfer technique and gracefully reduce to non-transfer learning when HTL is not helpful. Experiments on robotics and neural imaging data demonstrate the effectiveness of our framework.
A remark on the multipliers on spaces of Weak Products of functions

If $\mathcal{H}$ denotes a Hilbert space of analytic functions on a region $\Omega \subseteq \mathbb{C}^d$, then the weak product is defined by $$\mathcal{H}\odot\mathcal{H}=\left\{h=\sum_{n=1}^\infty f_n g_n : \sum_{n=1}^\infty \|f_n\|_{\mathcal{H}}\|g_n\|_{\mathcal{H}} <\infty\right\}.$$ We prove that if $\mathcal{H}$ is a first order holomorphic Besov Hilbert space on the unit ball of $\mathbb{C}^d$, then the multiplier algebras of $\mathcal{H}$ and of $\mathcal{H}\odot\mathcal{H}$ coincide.
Change of polarization for moduli of sheaves on surfaces as Bridgeland wall-crossing

We prove that the "Thaddeus flips" of $L$-twisted sheaves constructed by Matsuki and Wentworth can be obtained via Bridgeland wall-crossing. Similarly, we realize the change of polarization for moduli spaces of 1-dimensional Gieseker semistable sheaves on a surface by varying a family of stability conditions.
PAG2ADMG: A Novel Methodology to Enumerate Causal Graph Structures.

Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent causal relationships among variables in a model. Methods exist for learning DAGs and PAGs from data and for converting DAGs to PAGs. However, these methods only output a single causal graph consistent with the independencies/dependencies (the Markov equivalence class $M$) estimated from the data. However, many distinct graphs may be consistent with $M$, and a data modeler may wish to select among these using domain knowledge. In this paper, we present a method that makes this possible. We introduce PAG2ADMG, the first method for enumerating all causal graphs consistent with $M$, under certain assumptions. PAG2ADMG converts a given PAG into a set of acyclic directed mixed graphs (ADMGs). We prove the correctness of the approach and demonstrate its efficiency relative to brute-force enumeration.
Study of Oxygen Diffusion and Clustering in Silicon Using an Empirical Interatomic Potential

The diffusion path and diffusivity of oxygen in crystalline silicon are computed using an empirical interatomic potential which was recently developed [1] for modelling the interactions between oxygen and silicon atoms. The diffusion path is determined by constrained energy minimization, and the diffusivity is computed using jump rate theory. The calculated diffusivityD=0.025 exp(-2.43eV/kBT) cm2/sec is in excellent agreement with experimental data. The same interatomic potential also is used to study the formation of small clusters of oxygen atoms in silicon. The structures of these clusters are found by NPT molecular dynamics simulations, and their free energies are calculated by thermodynamic integration. These free energies are used to predict the temperature dependence of the equilibrium partitioning of oxygen atoms into clusters of different sizes. The calculations show that, for given total oxygen concentration, most oxygen atoms are in clusters at temperature below 1300K, and that the average cluster size increases with decreasing temperature. These results are in qualitative agreement with the effects of thermal annealing on oxygen precipitation in silicon crystals.
Inverse problem of potential theory

Abstract P. Novikov in 1938 has proved that if u 1 ( x ) = u 2 ( x ) for | x | > R , where R > 0 is a large number, u j ( x ) ≔ ∫ D j g 0 ( x , y ) d y , g 0 ( x , y ) ≔ 1 4 π | x − y | , and D j ⊂ R 3 , j = 1 , 2 , D j ⊂ B R , are bounded, connected, smooth domains, star-shaped with respect to a common point, then D 1 = D 2 . Here B R ≔ { x : | x | ≤ R } . Our basic results are: (a) the removal of the assumption about star-shapeness of D j , (b) a new approach to the problem, (c) the construction of counter-examples for a similar problem in which g 0 is replaced by g = e i k | x − y | 4 π | x − y | , where k > 0 is a fixed constant.
Bounded optimal exploration in MDP

Within the framework of probably approximately correct Markov decision processes (PAC-MDP), much theoretical work has focused on methods to attain near optimality after a relatively long period of learning and exploration. However, practical concerns require the attainment of satisfactory behavior within a short period of time. In this paper, we relax the PAC-MDP conditions to reconcile theoretically driven exploration methods and practical needs. We propose simple algorithms for discrete and continuous state spaces, and illustrate the benefits of our proposed relaxation via theoretical analyses and numerical examples. Our algorithms also maintain anytime error bounds and average loss bounds. Our approach accommodates both Bayesian and non-Bayesian methods.
Online Scoring with Delayed Information: A Convex Optimization Viewpoint

We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is partially observed, and the unobserved part comes after some delay. We assume that an agent, once left, cannot re-enter the system. Therefore, the job of the system is to provide an estimated score for the agent based on her instantaneous score and possibly some inference of the instantaneous score over the delayed score. In this paper, we estimate the delayed context via an online convex game between the agent and the system. We argue that the error in the score estimate accumulated over T iterations is small if the regret of the online convex game is small. Further, we leverage side information about the delayed context in the form of a correlation function with the known context. We consider the settings where the delay is fixed or arbitrarily chosen by an adversary. Furthermore, we extend the formulation to the setting where the contexts are drawn from some Banach space. Overall, we show that the average penalty for not knowing the delayed context while making a decision scales with $\mathcal { O } \left( \frac { 1 } { \sqrt { T } } \right)$, where this can be improved to $\mathcal { O } \left( \frac { \log T } { T } \right)$ under special setting.
Underapproximation of reach-avoid sets for discrete-time stochastic systems via Lagrangian methods

We examine Lagrangian techniques for computing under approximations of finite-time horizon, stochastic reach-avoid level sets for discrete-time, nonlinear systems. We use the concept of reachability of a target tube to define robust reach-avoid sets which are parameterized by the target set, safe set, and the set which the disturbance is drawn from. We unify two existing Lagrangian approaches to compute these sets, and establish that there exists an optimal control policy for the robust reach-avoid sets which is a Markov policy. Based on these results, we characterize the subset of the disturbance space whose corresponding robust reach-avoid set for a given target and safe set is a guaranteed under approximation of the stochastic reach-avoid level set of interest. The proposed approach dramatically improves the computational efficiency for obtaining an under approximation of stochastic reach-avoid level sets when compared to the traditional approaches based on gridding. Our method, while conservative, does not rely on a grid, implying scalability as permitted by constraints due to computational geometry. We demonstrate the method on two examples: a simple two-dimensional integrator, and a space vehicle rendezvous-docking problem.
The Vector Heat Method

This article describes a method for efficiently computing parallel transport of tangent vectors on curved surfaces, or more generally, any vector-valued data on a curved manifold. More precisely, it extends a vector field defined over any region to the rest of the domain via parallel transport along shortest geodesics. This basic operation enables fast, robust algorithms for extrapolating level set velocities, inverting the exponential map, computing geometric medians and Karcher/Frechet means of arbitrary distributions, constructing centroidal Voronoi diagrams, and finding consistently ordered landmarks. Rather than evaluate parallel transport by explicitly tracing geodesics, we show that it can be computed via a short-time heat flow involving the connection Laplacian. As a result, transport can be achieved by solving three prefactored linear systems, each akin to a standard Poisson problem. To implement the method, we need only a discrete connection Laplacian, which we describe for a variety of geometric data structures (point clouds, polygon meshes, etc.). We also study the numerical behavior of our method, showing empirically that it converges under refinement, and augment the construction of intrinsic Delaunay triangulations so that they can be used in the context of tangent vector field processing.
Homological shadows of attracting laminations

Given a free group $F_n$, a fully irreducible automorphism $f \in \aut$, and a generic element $x \in F_n$, the elements $f^k(x)$ converge in the appropriate sense to an object called an attracting lamination of $f$. When the action of $f$ on $\frac{F_n}{[F_n, F_n]}$ has finite order, we introduce a homological version of this convergence, in which the attracting object is a convex polytope with rational vertices, together with a measure supported at a point with algebraic coordinates.
Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms With Robust Performance Constraints

In this paper, we consider a stochastic deployment problem, where a robotic swarm is tasked with the objective of positioning at least one robot at each of a set of pre-assigned targets while meeting a temporal deadline. Travel times and failure rates are stochastic but related, inasmuch as failure rates increase with speed. To maximize chances of success while meeting the deadline, a control strategy has therefore to balance safety and performance. Our approach is to cast the problem within the theory of constrained Markov decision processes (CMDPs), whereby we seek to compute policies that maximize the probability of successful deployment while ensuring that the expected duration of the task is bounded by a given deadline. To account for uncertainties in the problem parameters, we consider a robust formulation and we propose efficient solution algorithms, which are of independent interest. Numerical experiments confirming our theoretical results are presented and discussed.
Matrix models of strength distributions

In this work, we use matrix models to study the problem of strength distributions. This is motivated by noticing near exponential falloffs of strengths in calculated magnetic dipole excitations. We...
Markov Decision Processes with Continuous Side Information

We consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-information or context that determines the dynamics of the MDP for that episode. Our setting is motivated by applications in healthcare where baseline measurements of a patient at the start of a treatment episode form the context that may provide information about how the patient might respond to treatment decisions. We propose algorithms for learning in such Contextual Markov Decision Processes (CMDPs) under an assumption that the unobserved MDP parameters vary smoothly with the observed context. We also give lower and upper PAC bounds under the smoothness assumption. Because our lower bound has an exponential dependence on the dimension, we consider a tractable linear setting where the context is used to create linear combinations of a finite set of MDPs. For the linear setting, we give a PAC learning algorithm based on KWIK learning techniques.
Spectrum of the free rod under tension and compression

ABSTRACTIn this paper, we study the spectrum of the one-dimensional vibrating free rod equation u(4)-τu″=μu under tension (τ>0) or compression (τ<0) . The eigenvalues μ as functions of the tension/...
Cubic Planar Graphs that cannot be Drawn on few Lines

For every integer l, we construct a cubic 3-vertex-connected planar bipartite graph G with O(l^3) vertices such that there is no planar straight-line drawing of G whose vertices all lie on l lines. This strengthens previous results on graphs that cannot be drawn on few lines, which constructed significantly larger maximal planar graphs. We also find apex-trees and cubic bipartite series-parallel graphs that cannot be drawn on a bounded number of lines.
Bee + : A 95-mg Four-Winged Insect-Scale Flying Robot Driven by Twinned Unimorph Actuators

We introduce Bee + , a 95-mg four-winged microrobot with improved controllability and open-loop-response characteristics with respect to those exhibited by state-of-the-art two-winged microrobots with the same size and similar weight (i.e., the 75-mg Harvard RoboBee). The key innovation that made possible the development of Bee+ is the introduction of an extremely light (28-mg) pair of twinned unimorph actuators, which enabled the design of a new microrobotic mechanism that flaps four wings independently. A first main advantage of the proposed design, compared to those of two-winged flyers, is that by increasing the number of actuators from two to four, the number of direct control inputs increases from three to four when simple sinusoidal excitations are employed. A second advantage of Bee + is that its four-wing configuration and flapping mode naturally damp the rotational disturbances that commonly affect the yaw degree of freedom of two-winged microrobots. In addition, the proposed design greatly reduces the complexity of the associated fabrication process compared to those of other microrobots, as the unimorph actuators are fairly easy to build. Finally, we hypothesize that given the relatively low wingloading affecting their flapping mechanisms, the life expectancy of Bee + s must be considerably higher than those of the two-winged counterparts. The functionality and basic capabilities of the robot are demonstrated through a set of simple control experiments.
Maximizing Algebraic Connectivity of Constrained Graphs in Adversarial Environments

This paper aims to maximize algebraic connectivity of networks via topology design under the presence of constraints and an adversary. We are concerned with three problems. First, we formulate the concave-maximization topology design problem of adding edges to an initial graph, which introduces a nonconvex binary decision variable, in addition to subjugation to general convex constraints on the feasible edge set. Unlike previous approaches, our method is justifiably not greedy and is capable of accommodating these additional constraints. We also study a scenario in which a coordinator must selectively protect edges of the network from a chance of failure due to a physical disturbance or adversarial attack. The coordinator needs to strategically respond to the adversary’s action without presupposed knowledge of the adversary’s feasible attack actions. We propose three heuristic algorithms for the coordinator to accomplish the objective and identify worst-case preventive solutions. Each algorithm is shown to be effective in simulation and their compared performance is discussed.
Special Operations Forces: A Global Immune System?

The use of special operations forces (SOF) in war fighting and peace keeping efforts has increased dramatically in recent decades. A scientific understanding of the reason for this increase would provide guidance as to the contexts in which SOF can be used to their best effect, and when conventional forces are better suited. Ashby’s law of requisite variety provides a scientific framework for understanding and analyzing a system’s ability to survive and prosper in the face of environmental challenges. We have developed a generalization of this law to extend the analysis to systems that must respond to disturbances at multiple scales. This analysis identifies a necessary tradeoff between scale and complexity in a multiscale control system. As with Ashby’s law, the framework applies to the characterization of successful biological and social systems in the context of complex environmental challenges. Here we apply this multiscale framework to provide a control theoretic understanding of the historical and increasing need for SOF, as well as conventional military forces. We propose that the essential role distinction is in the separation between high complexity fine scale challenges as opposed to large scale challenges. This leads to a correspondence between the role SOF can best serve and that of the immune system in complex organisms—namely, the ability to respond to fine-grained, high-complexity disruptors and preserve tissue health. Much like a multicellular organism, human civilization is composed of a set of distinct and heterogeneous social tissues, each with its own distinct characteristics and functional relationships with other tissues. Responding to disruption and restoring health in a system with highly diverse local social conditions requires an ability to distinguish healthy tissue from disruptors and to neutralize disruptive forces with minimal collateral damage, an essentially complex task. Damage to social tissue, either through the growth of malignant forces or large-scale intervention by conventional forces, leads to cascading crises that spread beyond the initial location of disruption. To prevent such crises, the healthy functioning of social systems must be maintained by responding to disruptive forces while they remain small. SOF have the potential to mitigate against harm without disrupting normal social tissue behavior. Three conditions for SOF to fulfill such a role are identified: (1) distinctive capabilities of special operators that enable unmediated interaction with local cultures and peoples, (2) persistent presence and embeddedness to foster cultural attunement and mutual trust, and (3) local autonomy and decision-making of SOF to achieve requisite variety for sensing and acting on fine-grained disturbances. We point out the inapplicability of traditional hierarchical control structures for high-complexity local tasks, which require a decentralized control architecture. This analysis suggests how SOF might be leveraged to support global stability and mitigate against cascading crises.
Learnable Image Encryption

The network-based machine learning algorithm is very powerful tools. However, it requires huge training dataset. Researchers often meet privacy issues when they collect image dataset especially for surveillance applications. A learnable image encryption scheme is introduced. The key idea of this scheme is to encrypt images, so that human cannot understand images but the network can be train with encrypted images. This scheme allows us to train the network without the privacy issues. In this paper, a simple learnable image encryption algorithm is proposed. Then, the proposed algorithm is validated with cifar dataset.
Solution to the Pompeiu problem and the related symmetry problem

Abstract Assume that D ⊂ R 3 is a bounded domain with C 1 -smooth boundary. Our result is: Theorem 1.If D has P -property, then D is a ball. Four equivalent formulations of the Pompeiu problem are discussed. A domain D has P -property if there exists an f ≠ 0 , f ∈ L l o c 1 ( R 3 ) such that ∫ D f ( g x + y ) d x = 0 for all y ∈ R 3 and all g ∈ S O ( 2 ) , where S O ( 2 ) is the rotation group. The result obtained concerning the related symmetry problem is: Theorem 2.If ( ∇ 2 + k 2 ) u = 0 in D , u ∣ S = 1 , u N ∣ S = 0 , and k > 0 is a constant, then D is a ball.
Do More Professionalized Legislatures Discriminate Less? The Role of Staffers in Constituency Service:

Research suggests that organizational structure can influence the ability of actors to discriminate. In this research note, we examine whether the structure of state legislatures affects observed d...
Mechanisms and treatment of late-life depression

Depression predisposes to medical illnesses and advances biological aging indicated by shorter telomere length, accelerated brain aging and advanced epigenetic aging. Medical illnesses also increase the risk of late-life depression. The reciprocal relationships of depression with aging-related and disease-related processes have generated pathogenetic hypotheses and provided treatment targets. Targeting risk factors of vascular disease in mid-life is a logical approach in prevention of vascular depression. The depression-executive dysfunction and the vascular depression syndromes have clinical presentations and neuroimaging findings consistent with frontostriatal abnormalities. Dopamine D2/3 agonists are effective in depression of Parkinson’s disease and their efficacy needs to be assessed in these two syndromes. Computerized cognitive remediation targeting functions of the cognitive control network may improve both executive functions and depressive symptoms of late-life major depression. Significant progress has been made in neurostimulation treatments in depressed younger adults. TMS targeting deep structures responsible for mood regulation is well tolerated by older adults and its efficacy in syndromes of late-life depression needs to be studied. Efficacious psychotherapies for late-life depression exist, but are underutilized in part because of their complexity. Streamlined, stepped psychotherapies targeting behaviors assumed to result from dysfunction of brain networks implicated in late-life depression can be easy to learn and have potential for dissemination. However, their effectiveness needs further investigation. Depression increases the risk of dementing disorders. Antidepressants are rather ineffective in treating depression of demented patients, but long-term use of antidepressants may reduce the risk of dementia. However, confirmation studies are needed.
The diffusion geometry of fibre bundles: Horizontal diffusion maps

Abstract Kernel-based nonlinear dimensionality reduction methods, such as Local Linear Embedding (LLE) and Laplacian Eigenmaps, rely heavily upon pairwise distances or similarity scores, with which one can construct and study a weighted graph associated with the data set. When each individual data object carries additional structural details, however, the correspondence relations between these structures provide extra information that can be leveraged for studying the data set using the graph. Based on this observation, we generalize Diffusion Maps (DM) in manifold learning and introduce the framework of Horizontal Diffusion Maps (HDM). We model a data set with pairwise structural correspondences as a fibre bundle equipped with a connection. We demonstrate the advantage of incorporating such additional information and study the asymptotic behavior of HDM on general fibre bundles. In a broader context, HDM reveals the sub-Riemannian structure of high-dimensional data sets, and provides a nonparametric learning framework for data sets with structural correspondences.
The Role of Conspiracy Mentality in Denial of Science and Susceptibility to Viral Deception about Science

Members of the public can disagree with scientists in at least two ways: people can reject well-established scientific theories and they can believe fabricated, deceptive claims about science to be true. Scholars examining the reasons for these disagreements find that some individuals are more likely than others to diverge from scientists because of individual factors such as their science literacy, political ideology, and religiosity. This study builds on this literature by examining the role of conspiracy mentality in these two phenomena. Participants were recruited from a national online panel (N = 513) and in person from the first annual Flat Earth International Conference (N = 21). We found that conspiracy mentality and science literacy both play important roles in believing viral and deceptive claims about science, but evidence for the importance of conspiracy mentality in the rejection of science is much more mixed.
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a ‘Fog Robotics’ approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for low-latency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by $4\times$ to successfully declutter 86% of objects over 213 attempts.
Strategizing against No-regret Learners

How should a player who repeatedly plays a game against a no-regret learner strategize to maximize his utility? We study this question and show that under some mild assumptions, the player can always guarantee himself a utility of at least what he would get in a Stackelberg equilibrium. When the no-regret learner has only two actions, we show that the player cannot get any higher utility than the Stackelberg equilibrium utility. But when the no-regret learner has more than two actions and plays a mean-based no-regret strategy, we show that the player can get strictly higher than the Stackelberg equilibrium utility. We construct the optimal game-play for the player against a mean-based no-regret learner who has three actions. When the no-regret learner's strategy also guarantees him a no-swap regret, we show that the player cannot get anything higher than a Stackelberg equilibrium utility.
Access control mechanisms for a network operating system

The growing recognition of the need for computer system security has resulted in the design, development and installation of "patches," packages and even new operating systems intended to provide higher degrees of data and systems protection. With the increased utilization of computer networks and current developments in the area of network operating systems (NOSs) 40 , 19 , 30 the requirements for security in networking environments are also coming under investigation. 26 , 42 While research and development are still ongoing in the NOS area, it is vital to ensure that requirements for the security and integrity of data are well specified and that mechanisms for achieving the needed levels of systems protection are included in the design of the NOS. This will ensure that subsequent production versions of NOSs incorporate such mechanisms—thus, charting a course away from the otherwise inevitable "retrofit security" situation.
Deep Contextualized Biomedical Abbreviation Expansion.

Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.
A Correlation between Wnt/Beta-catenin Signaling and the Rate of Dentin Secretion.

Abstract Introduction Odontoblasts produce dentin throughout life and in response to trauma. The purpose of this study was to identify the roles of endogenous Wnt signaling in regulating the rate of dentin accumulation. Methods Histology, immunohistochemistry, vital dye labeling, and histomorphometric assays were used to quantify the rate of dentin accumulation as a function of age. Two strains of Wnt reporter mice were used to identify and follow the distribution and number of Wnt-responsive odontoblasts as a function of age. To show a causal relationship between dentin secretion and Wnt signaling, dentin accumulation was monitored in a strain of mice in which Wnt signaling was aberrantly elevated. Results Dentin deposition occurs throughout life, but the rate of accumulation slows with age. This decline in dentin secretion correlates with a decrease in endogenous Wnt signaling. In a genetically modified strain of mice, instead of tubular dentin, aberrantly elevated Wnt signaling resulted in accumulation of reparative dentin or osteodentin secreted from predontoblasts. Conclusions Wnt signaling regulates dentin secretion by odontoblasts, and the formation of reparative or osteodentin is the direct consequence of elevated Wnt signaling. These preclinical data have therapeutic implications for the development of a biologically based pulp capping medicant.
Automated Threshold Selection for Cryo-EM Density Maps

Recent advances in cryo-EM have made it possible to create protein density maps with a near-atomic resolution. This has contributed to its wide popularity, resulting in a rapidly growing number of available cryo-EM density maps. In order to computationally process them, an electron density threshold level is required which defines a lower bound for density values. In the context of this paper the threshold level is required in a pre-processing step of the backbone structure prediction project which predicts the location of Cα atoms of the backbone of a protein based on its cryo-EM density map using deep learning techniques. A custom threshold level has to be selected for each prediction in order to reduce noise that could irritate the deep learning model. Automatizing this threshold selection process makes it easier to run predictions as well as it removes the dependency of the prediction accuracy to the ability of someone to choose the right threshold value. This paper presents a method to automatize the threshold selection for the previously mentioned project as well as for other problems which require a density threshold level. The method uses the surface area to volume ratio and the ratio of voxels that lie above the threshold level to non-zero voxels as metrics to derive characteristics about suitable threshold levels based on a training dataset. The threshold level selection was tested by integrating it in the backbone prediction project and evaluating the accuracy of predictions using automatically as well as manually selected thresholds. We found that there was no loss in accuracy using the automatically selected threshold levels indicating that they are equally good as manually selected ones. The source code related to this paper can be found at https://github.com/DrDongSi/Auto-Thresholding.
Universal skein theory for group actions

Abstract Given a group action on a finite set, we define the group-action model which consists of tensor network diagrams which are invariant under the group symmetry. In particular, group-action models can be realized as the even part of group-subgroup subfactor planar algebras. Moreover, all group-subgroup subfactor planar algebras arise in this way from transitive actions. In this paper, we provide a universal skein theory for those planar algebras. With the help of this skein theory, we give a positive answer to a question asked by Vaughan Jones in the late nineties.
Monitoring Global Supply Chains

Research summary: Firms seeking to avoid reputational spillovers that can arise from dangerous, illegal, and unethical behavior at supply chain factories are increasingly relying on private social auditors to provide strategic information about suppliers' conduct. But little is known about what influences auditors' ability to identify and report problems. Our analysis of nearly 17,000 supplier audits reveals that auditors report fewer violations when individual auditors have audited the factory before, when audit teams are less experienced or less trained, when audit teams are all male, and when audits are paid for by the audited supplier. This first comprehensive and systematic analysis of supply chain monitoring identifies previously overlooked transaction costs and suggests strategies to develop governance structures to mitigate reputational risks by reducing information asymmetries in supply chains. Managerial summary: Firms reliant on supply chains to manufacture their goods risk reputational harm if the working conditions in those factories are revealed to be dangerous, illegal, or otherwise problematic. While firms are increasingly relying on private-sector “social auditors” to assess factory conditions, little has been known about the accuracy of those assessments. We analyzed nearly 17,000 code-of-conduct audits conducted at nearly 6,000 suppliers around the world. We found that audits yield fewer violations when the audit team has been at that particular supplier before, when audit teams are less experienced or less trained, when audit teams are all male, and when the audits were paid for by the supplier instead of by the buyer. We describe implications for firms relying on social auditors and for auditing firms. Copyright © 2015 John Wiley & Sons, Ltd.
On Positive Solutions of a Delay Equation Arising When Trading in Financial Markets

We consider a discrete-time linear state equation with delay, which arises as a model for a trader's account value when buying and selling a risky asset in a financial market. The state equation includes a nonnegative feedback gain $\alpha$ and a sequence $v(k)$ , which models asset returns that are within known bounds but otherwise arbitrary. We introduce two thresholds, $\alpha _-$ and $\alpha _+$ , depending on these bounds, and prove that for $\alpha , state positivity is guaranteed for all time and all asset-return sequences, i.e., bankruptcy is ruled out and positive solutions of the state equation are continuable indefinitely. On the other hand, for $\alpha > \alpha _+$ , we show that there is always a sequence of asset returns for which the state fails to be positive for all time, i.e., along this sequence, bankruptcy occurs and the solution of the state equation ceases to be meaningful after some finite time. Finally, this article also includes a conjecture, which says that for the “gap” interval $\alpha _- \leq \alpha \leq \alpha _+,$ state positivity is also guaranteed for all time. Support for the conjecture, both theoretical and computational, is provided.
Missing them yet? Investment banker directors in the 21st century

Abstract Subsequent to the stricter corporate governance listing standards adopted by the NYSE and NASDAQ in the early part of this century and the independence requirements of the Sarbanes Oxley Act of 2002 (SOX), the number of investment bankers (IB) serving on corporate boards has declined significantly. We document that the firms that lose the relationship with the investment bank after SOX become relatively more financially constrained soon after. The evidence is similar, albeit weaker, after departures of investment bankers at the advent of the financial crisis. We examine the mechanisms through which the constraints might be lowered, and observe that firms with IB directors face lower underwriting spreads when they issue equity and debt. Inconsistent with the hold-up problem associated with IB directors, the market reaction to seasoned equity offerings in firms with IB directors is less negative than comparable firms. The results point to costs associated with the increased attempts to improve board independence.
Right-cyclic Hadamard coding schemes and fast Fourier transforms for use in computing spectrum estimates in Hadamard-transform spectrometry

Two computationally efficient spectrum-recovery schemes were recently developed for use by Hadamard-transform spectrometers that have static and dynamic nonidealities in their encoding masks. These methods make use of a left-cyclic Hadamard encodement scheme and the ability to express the left-cyclic W/sub D/ matrix in factored form as W/sub D/=ST/sub D/. The matrix W/sub D/ describes the dynamic characteristics of and the encodement scheme for the mask. This paper focuses on the use of a right-cyclic Hadamard pattern to encode the mask and computationally efficient methods that can be used to obtain the spectrum-estimate. The major advantage of right-cyclic over left-cyclic encodement schemes is due to the resulting right-cyclic nature of both W/sub D/ and W/sub D//sup -1/. Fast algorithms, such as a fast Fourier transform (FFT) or a Trench algorithm, that take advantage of the right-cyclic nature of W/sub D/ can be used to obtain W/sub D//sup -1/ directly. In general, the number of mask elements is not an integer power of two, and non-radix-2 FFT's must be used to compute W/sub D//sup -1/. Since W/sub D//sup -1/ is right-cyclic, the vector-matrix product of W/sub D//sup -1/ and the measurement vector can be expressed as a circular correlation and implemented indirectly via FFT's. With appropriate zero-padding of the vectors, radix-2 FFT's can be used for this computation. Various algorithms were used at each step in the overall computation of the spectrum-estimate, and the total computation times are presented and compared. The size of the mask is important in determining which algorithms are the most efficient in recovering the spectrum-estimate.