cssi/SciSciGPT-SciSciNet huggingface dataset:
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs.

Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning. While MF based models have been extensively exploited in the graph modeling and link prediction literature, the AE family has not gained much attention. In this paper we investigate both MF and AE’s application to the link prediction problem in sparse graphs. We show the connection between AE and MF from the perspective of multiview learning, and further propose MF+AE: a model training MF and AE jointly with shared parameters. We apply dropout to training both the MF and AE parts, and show that it can significantly prevent overfitting by acting as an adaptive regularization. We conduct experiments on six real world sparse graph datasets, and show that MF+AE consistently outperforms the competing methods, especially on datasets that demonstrate strong non-cohesive structures.
Joint estimation of sparse multivariate regression and conditional graphical models

Multivariate regression model is a natural generalization of the classical univariate regression model for fitting multiple responses. In this paper, we propose a high-dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency structure among the multiple responses. The proposed method decomposes the multivariate regression problem into a series of penalized conditional log-likelihood of each response conditional on the covariates and other responses. It allows simultaneous estimation of the sparse regression coefficient matrix and the sparse inverse covariance matrix. The asymptotic selection consistency and normality are established for the diverging dimension of the covariates and number of responses. The effectiveness of the proposed method is demonstrated in a variety of simulated examples as well as an application to the Glioblastoma multiforme cancer data.
Weakly interacting particle systems on inhomogeneous random graphs

Abstract We consider weakly interacting diffusions on time varying random graphs. The system consists of a large number of nodes in which the state of each node is governed by a diffusion process that is influenced by the neighboring nodes. The collection of neighbors of a given node changes dynamically over time and is determined through a time evolving random graph process. A law of large numbers and a propagation of chaos result is established for a multi-type population setting where at each instant the interaction between nodes is given by an inhomogeneous random graph which may change over time. This result covers the setting in which the edge probabilities between any two nodes are allowed to decay to 0 as the size of the system grows. A central limit theorem is established for the single-type population case under stronger conditions on the edge probability function.
Parabolic Harnack inequality on fractal-type metric measure Dirichlet spaces

This paper proves the strong parabolic Harnack inequality for local weak solutions to the heat equation associated with time-dependent (nonsymmetric) bilinear forms. The underlying metric measure Dirichlet space is assumed to satisfy the volume doubling condition, the strong Poincare inequality, and a cutoff Sobolev inequality. The metric is not required to be geodesic. Further results include a weighted Poincare inequality, as well as upper and lower bounds for non-symmetric heat kernels.
Revenue Management on an On-Demand Service Platform

Abstract I consider a worker’s optimal per-unit-time pricing problem on an on-demand service platform. I establish that if the customer arrival process is Poisson, then any price discrimination must result only from differences in the customers’ willingness-to-pay distributions, and not from differences in their arrival or service-time characteristics. For multiple customer classes that differ in their willingness-to-pay distributions, I present a simple procedure to compute the optimal prices. Finally, I analyze price competition across workers in the presence of reputation effects.
3D whole brain segmentation using spatially localized atlas network tiles

Abstract Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation (>100 labels), however, whose performance are still commonly inferior compared with state-of-the-art multi-atlas segmentation methods (MAS) due to the following challenges: (1) a single network is typically used to learn both spatial and contextual information for the patches, (2) limited manually traced whole brain volumes are available (typically less than 50) for training a network. In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation. To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by multi-atlas segmentation for training. Since the method integrated multiple traditional medical image processing methods with deep learning, we developed a containerized pipeline to deploy the end-to-end solution. From the results, the proposed method achieved superior performance compared with multi-atlas segmentation methods, while reducing the computational time from >30 h to 15 min. The method has been made available in open source ( https://github.com/MASILab/SLANTbrainSeg ).
Depth-Adaptive Computational Policies for Efficient Visual Tracking

Current convolutional neural networks algorithms for video object tracking spend the same amount of computation for each object and video frame [3]. However, it is harder to track an object in some frames than others, due to the varying amount of clutter, scene complexity, amount of motion, and object’s distinctiveness against its background. We propose a depth-adaptive convolutional siamese network that performs video tracking adaptively at multiple neural network depths. Parametric gating functions are trained to control the depth of the convolutional feature extractor by minimizing a joint loss of computational cost and tracking error. Our network achieves accuracy comparable to the state-of-the-art on the VOT2016 benchmark. Furthermore, our adaptive depth computation achieves higher accuracy for a given computational cost than traditional fixed-structure neural networks. The presented framework extends to other tasks that use convolutional neural networks and enables trading speed for accuracy at runtime.
Accounting for phenology in the analysis of animal movement

The analysis of animal tracking data provides important scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Such features may correspond to a single point (eg, dens or kill sites), or to higher dimensional subspaces (eg, rivers or lakes). Features may be relatively static in time (eg, coastlines or home-range centers), or may be dynamic (eg, sea ice extent or areas of high-quality forage for herbivores). We introduce a novel model for animal movement that incorporates active selection for dynamic features in a landscape. Our approach is motivated by the study of polar bear (Ursus maritimus) movement. During the sea ice melt season, polar bears spend much of their time on sea ice above shallow, biologically productive water where they hunt seals. The changing distribution and characteristics of sea ice throughout the year mean that the location of valuable habitat is constantly shifting. We develop a model for the movement of polar bears that accounts for the effect of this important landscape feature. We introduce a two-stage procedure for approximate Bayesian inference that allows us to analyze over 300 000 observed locations of 186 polar bears from 2012 to 2016. We use our model to estimate a spatial boundary of interest to wildlife managers that separates two subpopulations of polar bears from the Beaufort and Chukchi seas.
Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD

The goal of this paper is to study a distributed version of the gradient temporal-difference (GTD) learning algorithm for multi-agent Markov decision processes (MDPs). The temporal-difference (TD) learning is a reinforcement learning (RL) algorithm that learns an infinite horizon discounted cost function (or value function) for a given fixed policy without the model knowledge. In the distributed RL case each agent receives local reward through local processing. Information exchange over sparse communication network allows the agents to learn the global value function corresponding to a global reward, which is a sum of local rewards. In this paper, the problem is converted into a constrained convex optimization problem with a consensus constraint. We then propose a primal-dual distributed GTD algorithm and prove that it almost surely converges to a set of stationary points of the optimization problem.
Research and Development for a Gadolinium Doped Water Cherenkov Detector

Abstract The proposed introduction of a soluble gadolinium (Gd) compound into water Cherenkov detectors can result in a high efficiency for the detection of free neutrons capturing on the Gd. The delayed 8 MeV gamma cascades produced by these captures, in coincidence with a prompt positron signal, serve to uniquely identify electron antineutrinos interacting via inverse beta decay. Such coincidence detection can reduce backgrounds, allowing a large Gd-enhanced water Cherenkov detector to make the first observation of supernova relic neutrinos and high precision measurements of Japan's reactor antineutrino flux, while still allowing for all current physics studies to be continued. Now, a dedicated Gd test facility is operating in the Kamioka Mine. This new facility houses everything needed to successfully operate a Gd doped water Cherenkov detector. Successful running of this facility will demonstrate that adding Gd salt to SK is both safe for the detector and is capable of delivering the expected physics benefits
AN INDEX THEOREM ON ANTI-SELF-DUAL ORBIFOLDS

An index theorem for the anti-self-dual deformation complex on anti- self-dual orbifolds with singularities conjugate to ADE-type is proved. In 1988, Claude Lebrun gave examples of scalar-at Kahler ALE metrics with negative mass, on the total space of the bundleO( n) over S 2 . A corollary of this index theorem is that the moduli space of anti-self-dual ALE metrics near each of these metrics has dimension at least 4n 12, and thus for n 4 the LeBrun metrics admit a plethora of non-trivial anti-self-dual deformations.
Semiblind subgraph reconstruction in Gaussian graphical models

Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its customers' dependency subgraph: it is blinded to any external interactions of its customers and this blindness creates false edges in its subgraph. In this paper we address the semiblind scenario where the company has access to a noisy summary of the complementary subgraph connecting external agents, e.g., provided by a consolidator. The proposed framework applies to other applications as well, including field estimation from a network of awake and sleeping sensors and privacy-constrained information sharing over social subnetworks. We propose a penalized likelihood approach in the context of a graph signal obeying a Gaussian graphical models (GGM). We use a convex-concave iterative optimization algorithm to maximize the penalized likelihood. The effectiveness of our approach is demonstrated through numerical experiments and comparison with state-of-the-art GGM and latent-variable (LV-GGM) methods.
Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners

Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental classification results show that grammatical template features significantly improve text difficulty prediction accuracy over baseline readability features by 7.4%. Moreover,we build a simple and human-understandable text difficulty evaluation approach with 87.7% accuracy, using only 5 grammatical template features.
Multi-view task-driven recognition in visual sensor networks

Nowadays, distributed smart cameras are deployed for a wide set of tasks in several application scenarios, ranging from object recognition, image retrieval, and forensic applications. Due to limited bandwidth in distributed systems, efficient coding of local visual features has in fact been an active topic of research. In this paper, we propose a novel approach to obtain a compact representation of high-dimensional visual data using sensor fusion techniques. We convert the problem of visual analysis in resource-limited scenarios to a multiview representation learning, and we show that the key to finding properly compressed representation is to exploit the position of cameras with respect to each other as a norm-based regularization in the particular signal representation of sparse coding. Learning the representation of each camera is viewed as an individual task and a multi-task learning with joint sparsity for all nodes is employed. The proposed representation learning scheme is referred to as the multi-view task-driven learning for visual sensor network (MT-VSN). We demonstrate that MT-VSN outperforms state-of-the-art in various surveillance recognition tasks.
Impact of Random Receiver Orientation on Visible Light Communications Channel

Visible light communications (VLC) has been studied thoroughly in recent years as an alternative or complementary technology to radio frequency communications. The reliability of VLC channels highly depends on the availability and alignment of line of sight links. In this paper, we study the effect of random receiver orientation for mobile users over VLC downlink channels, which affects the existence and quality of line of sight links. Based on the statistics of the receiver location and relative orientation with respect to the transmitter LED, we develop an analytical framework to characterize the statistical distribution of VLC downlink channels, which is then utilized to obtain the outage probability and the bit error rate. Our analysis is generalized for arbitrary distributions of relative orientation and location for a single transmitter, and extended to multiple transmitter case for some certain scenarios. Extensive Monte Carlo simulations show a perfect match between the analytical and the simulation data in terms of both the statistical channel distribution and the resulting bit error rate. Our results also characterize the channel attenuation due to random receiver orientation and location for various scenarios of interest.
AN INDEX FORMULA FOR THE EXTENDED HEISENBERG ALGEBRA OF EPSTEIN, MELROSE AND MENDOZA

The extended Heisenberg algebra for a contact manifold has a symbolic calculus that accommodates both Heisenberg pseudodifferential operators as well as classical pseudodifferential operators. We derive here a formula for the index of Fredholm operators in this extended calculus. This formula incorporates in a single expression the Atiyah–Singer formula for elliptic operators, as well as Boutet de Monvel's Toeplitz index formula.
Triple correlations of Fourier coefficients of cusp forms

We treat an unbalanced shifted convolution sum of Fourier coefficients of cusp forms. As a consequence, we obtain an upper bound for correlation of three Hecke eigenvalues of holomorphic cusp forms \(\sum _{H\le h\le 2H}W\left( \frac{h}{H}\right) \sum _{X\le n\le 2X}\lambda _{1}(n-h)\lambda _{2}(n)\lambda _{3}(n+h)\), which is nontrivial provided that \(H\ge X^{2/3+\varepsilon }\). The result can be viewed as a cuspidal analogue of a recent result of Blomer on triple correlations of divisor functions.
Crosscorrelation of Rudin–Shapiro-like polynomials

Abstract We consider the class of Rudin–Shapiro-like polynomials, whose L 4 norms on the complex unit circle were studied by Borwein and Mossinghoff. The polynomial f ( z ) = f 0 + f 1 z + ⋯ + f d z d is identified with the sequence ( f 0 , f 1 , … , f d ) of its coefficients. From the L 4 norm of a polynomial, one can easily calculate the autocorrelation merit factor of its associated sequence, and conversely. In this paper, we study the crosscorrelation properties of pairs of sequences associated to Rudin–Shapiro-like polynomials. We find an explicit formula for the crosscorrelation merit factor. A computer search is then used to find pairs of Rudin–Shapiro-like polynomials whose autocorrelation and crosscorrelation merit factors are simultaneously high. Pursley and Sarwate proved a bound that limits how good this combined autocorrelation and crosscorrelation performance can be. We find infinite families of polynomials whose performance approaches quite close to this fundamental limit.
Normal equivalencies for eventually periodic basic sequences

Abstract Researchers such as W.M. Schmidt, A.D. Pollington, and F. Schweiger have studied when normality with respect to one expansion is equivalent to normality with respect to another expansion. Following in their footsteps, we show that when Q is an eventually periodic basic sequence, that Q -normality and Q -distribution normality are equivalent to normality in base b where b is dependent on Q . We also show that boundedness of the basic sequence is not sufficient for this equivalence.
Multi-megawatt, self-seeded Mamyshev oscillator

We demonstrate a fiber oscillator that is environmentally stable and self-seeded. The oscillator generates 190 nJ, linearly chirped pulses that can be compressed to 35 fs resulting in 3 MW peak power.
On Ribet's isogeny for $J_0(65)$

Let $J^{65}$ be the Jacobian of the Shimura curve attached to the indefinite quaternion algebra over $\mathbb{Q}$ of discriminant $65$. We study the isogenies $J_0(65)\rightarrow J^{65}$ defined over $\mathbb{Q}$, whose existence was proved by Ribet. We prove that there is an isogeny whose kernel is supported on the Eisenstein maximal ideals of the Hecke algebra acting on $J_0(65)$, and moreover the odd part of the kernel is generated by a cuspidal divisor of order $7$, as is predicted by a conjecture of Ogg.
Multi-Objective Evolution for 3D RTS Micro

We attack the problem of controlling teams of autonomous units during skirmishes in real-time strategy games. Earlier work had shown promise in evolving control algorithm parameters that lead to high performance team behaviors similar to those favored by good human players in real-time strategy games like Starcraft. This algorithm specifically encoded parameterized kiting and fleeing behaviors and the genetic algorithm evolved these parameter values. In this paper we investigate using influence maps and potential fields alone to compactly represent and control real-time team behavior for entities that can maneuver in three dimensions. A two-objective fitness function that maximizes damage done and minimizes damage taken guides our multi-objective evolutionary algorithm. Preliminary results indicate that evolving friend and enemy unit potential field parameters for distance, weapon characteristics, and entity health suffice to produce complex, high performing, three-dimensional, team tactics.
On the regularity of CR mappings between CR manifolds of hypersurface type

We prove smooth and analytic versions of the classical Schwarz reflection principle for transversal CR mappings between two Levi-nondegenerate CR manifolds of hypersurface type.
Continued fraction normality is not preserved along arithmetic progressions

It is well known that if \({0.a_1a_2a_3\ldots}\) is the base-\({b}\) expansion of a number normal to base-\({b}\), then the numbers \({0.a_ka_{m+k}a_{2m+k} \ldots}\) for \({m\ge 2}\), \({k\ge 1}\) are all normal to base-\({b}\) as well. In contrast, given a continued fraction expansion \({\langle a_1,a_2,a_3,\ldots\rangle}\) that is normal (now with respect to the continued fraction expansion), we show that for any integers \({m\ge 2}\), \({k\ge 1}\), the continued fraction \({\langle a_k, a_{m+k},a_{2m+k},a_{3m+k},\ldots\rangle}\) will never be normal.
Two-stage hybrid day-ahead solar forecasting

Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.
Generalized rectifiability of measures and the identification problem

One goal of geometric measure theory is to understand how measures in the plane or a higher dimensional Euclidean space interact with families of lower dimensional sets. An important dichotomy arises between the class of rectifiable measures, which give full measure to a countable union of the lower dimensional sets, and the class of purely unrectifiable measures, which assign measure zero to each distinguished set. There are several commonly used definitions of rectifiable and purely unrectifiable measures in the literature (using different families of lower dimensional sets such as Lipschitz images of subspaces or Lipschitz graphs), but all of them can be encoded using the same framework. In this paper, we describe a framework for generalized rectifiability, review a selection of classical results on rectifiable measures in this context, and survey recent advances on the identification problem for Radon measures that are carried by Lipschitz or Holder or $$C^{1,\alpha }$$ images of Euclidean subspaces, including theorems of Azzam–Tolsa, Badger–Schul, Badger–Vellis, Edelen–Naber–Valtorta, Ghinassi, and Tolsa–Toro.
Generalizing Gillespie's direct method to enable network-free simulations

Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.
Load-Balanced Fractional Repetition Codes

We introduce load-balanced fractional repetition (LBFR) codes, which are a strengthening of fractional repetition (FR) codes. LBFR codes have the additional property that multiple node failures can be sequentially repaired by downloading no more than one block from any other node. This allows for better use of the network, and can additionally reduce the number of disk reads necessary to repair multiple nodes. We characterize LBFR codes in terms of their adjacency graphs, and use this characterization to present explicit constructions of LBFR codes with storage capacity comparable to existing FR codes. Surprisingly, in some parameter regimes, our constructions of LBFR codes match the parameters of the best constructions of FR codes.
The sad state of entrepreneurship in America: What educators can do about it

Abstract Today's entrepreneurial scene suffers from dysfunctional startups, a sick venture capital industry, possibly misplaced adulation from the public, and other troubling developments. This Perspective documents a number of current issues in technology entrepreneurship in the USA (many of which apply in other countries as well), offers prescriptions for what higher education institutions can do before, during, and after students' periods of study, and notes some recent and forthcoming events that may, in conjunction with universities' efforts, ameliorate the situation.
Constructing equivalence-preserving Dirac variational integrators with forces

The dynamical motion of mechanical systems possesses underlying geometric structures, and preserving these structures in numerical integration improves the qualitative accuracy and reduces the long-time error of the simulation. For a single mechanical system, structure preservation can be achieved by adopting the variational integrator construction. This construction has been generalized to more complex systems involving forces or constraints as well as to the setting of Dirac mechanics. Variational integrators have recently been applied to interconnected systems in Parks and Leok (2017), which are an important class of practically useful mechanical systems whose description in terms of Dirac structures and Dirac mechanical systems was elucidated in Jacobs and Yoshimura (2014). Since these interconnected systems are modeled as a collection of subsystems with forces of interconnection, we revisit some of the properties of forced variational integrators. In particular, we derive a class of Dirac variational integrators with forces that exhibit preservation properties that are critical when applying variational integrators to the discretization of interconnected Dirac systems. We close with a discussion of ongoing and future research based on these findings.
Min–max theory for constant mean curvature hypersurfaces

In this paper, we develop a min–max theory for the construction of constant mean curvature (CMC) hypersurfaces of prescribed mean curvature in an arbitrary closed manifold. As a corollary, we prove the existence of a nontrivial, smooth, closed, almost embedded, CMC hypersurface of any given mean curvature c. Moreover, if c is nonzero then our min–max solution always has multiplicity one.
Jet Lag Recovery: Synchronization of Circadian Oscillators as a Mean Field Game

A mean field game is proposed for the synchronization of oscillators facing conflicting objectives. Our motivation is to offer an alternative to recent attempts to use dynamical systems to illustrate some of the idiosyncrasies of jet lag recovery. Our analysis is driven by two goals: (1) to understand the long time behavior of the oscillators when an individual remains in the same time zone, and (2) to quantify the costs from jet lag recovery when the individual has traveled across time zones. Finite difference schemes are used to find numerical approximations to the mean field game solutions. They are benchmarked against explicit solutions derived for a special case. Numerical results are presented and conjectures are formulated. The numerics suggest that the cost the oscillators accrue while recovering is larger for eastward travel which is consistent with the widely admitted wisdom that jet lag is worse after traveling east than west.
On the synthesis of acoustic sources with controllable near fields

Abstract In this paper we present a strategy for the synthesis of acoustic sources with controllable near fields in free space and constant depth homogeneous ocean environments. We first present the theoretical results at the basis of our discussion and then, to illustrate our findings we focus on the following three particular examples: 1. acoustic source approximating a prescribed field pattern in a given bounded sub-region of its near field. 2. acoustic source approximating different prescribed field patterns in given disjoint bounded near field sub-regions. 3. acoustic source approximating a prescribed back-propagating field in a given bounded near field sub-region while maintaining a very low far field signature. For each of these three examples, we discuss the optimization scheme used to approximate their solutions and support our claims through relevant numerical simulations.
Supporting Ruled Polygons.

We explore several problems related to ruled polygons. Given a ruling of a polygon $P$, we consider the Reeb graph of $P$ induced by the ruling. We define the Reeb complexity of $P$, which roughly equates to the minimum number of points necessary to support $P$. We give asymptotically tight bounds on the Reeb complexity that are also tight up to a small additive constant. When restricted to the set of parallel rulings, we show that the Reeb complexity can be computed in polynomial time.
RF-PUF: IoT security enhancement through authentication of wireless nodes using in-situ machine learning

Physical unclonable functions (PUF) in silicon exploit die-to-die manufacturing variations during fabrication for uniquely identifying each die. Since it is practically a hard problem to recreate exact silicon features across dies, a PUF-based authentication system is robust, secure and cost-effective, as long as bias removal and error correction are taken into account. In this work, we utilize the effects of inherent process variation on analog and radio-frequency (RF) properties of multiple wireless transmitters (Tx) in a sensor network, and detect the features at the receiver (Rx) using a deep neural network based framework. The proposed mechanism/ framework, called RF-PUF, harnesses already-existing RF communication hardware and does not require any additional PUF-generation circuitry in the Tx for practical implementation. Simulation results indicate that the RF-PUF framework can distinguish up to 10000 transmitters (with standard foundry defined variations for a 65 nm process, leading to non-idealities such as LO offset and I-Q imbalance) under varying channel conditions, with a probability of false detection < 10−3.
On the law of the iterated logarithm for Brownian motion on compact manifolds

By taking a functional analytic point of view, we consider a family of distributions (continuous linear functionals on smooth functions), denoted by $\{\mu_t,t>0\}$, associated to the law of the iterated logarithm for Brownian motion on a compact manifold. We give a complete characterization of the collection of limiting distributions of $\{\mu_t,~t>0\}$.
Micky: A Cheaper Alternative for Selecting Cloud Instances

Most cloud computing optimizers explore and improve one workload at a time. When optimizing many workloads, the single-optimizer approach can be prohibitively expensive. Accordingly, we examine "collective optimizer" that concurrently explore and improve a set of workloads significantly reducing the measurement costs. Our large-scale empirical study shows that there is often a single cloud configuration which is surprisingly near-optimal for most workloads. Consequently, we create a collective-optimizer, MICKY, that reformulates the task of finding the near-optimal cloud configuration as a multi-armed bandit problem. MICKY efficiently balances exploration (of new cloud configurations) and exploitation (of known good cloud configuration). Our experiments show that MICKY can achieve on average 8.6 times reduction in measurement cost as compared to the state-of-the-art method while finding near-optimal solutions. Hence we propose MICKY as the basis of a practical collective optimization method for finding good cloud configurations (based on various constraints such as budget and tolerance to near-optimal configurations)
Inequality constraints in causal models with hidden variables

We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.
Spectral sequences for the cohomology rings of a smash product

Abstract Stefan and Guichardet have provided Lyndon–Hochschild–Serre type spectral sequences which converge to the Hochschild cohomology and Ext groups of a smash product. We show that these spectral sequences carry natural multiplicative structures, and that these multiplicative structures can be used to calculate the cup product on Hochschild cohomology and the Yoneda product on an Ext algebra.
Forbidden Induced Subgraphs

Abstract In descending generality I survey: five partial orderings of graphs, the induced-subgraph ordering, and examples like perfect, threshold, and mock threshold graphs. The emphasis is on how the induced subgraph ordering differs from other popular orderings and leads to different basic questions.
Multi-way Interacting Regression via Factorization Machines

We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables. Our model makes use of a factorization mechanism for representing the regression coefficients of interactions among the predictors, while the interaction selection is guided by a prior distribution on random hypergraphs, a construction which generalizes the Finite Feature Model. We present a posterior inference algorithm based on Gibbs sampling, and establish posterior consistency of our regression model. Our method is evaluated with extensive experiments on simulated data and demonstrated to be able to identify meaningful interactions in applications in genetics and retail demand forecasting.
DFCV: A Novel Approach for Message Dissemination in Connected Vehicles Using Dynamic Fog

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities.
Learning to poke by poking: experiential learning of intuitive physics

We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods.
Domain decomposition for stochastic optimal control

This work proposes a method for solving linear stochastic optimal control (SOC) problems using sum of squares and semidefinite programming. Previous work had used polynomial optimization to approximate the value function, requiring a high polynomial degree to capture local phenomena. To improve the scalability of the method to problems of interest, a domain decomposition scheme is presented. By using local approximations, lower degree polynomials become sufficient, and both local and global properties of the value function are captured. The domain of the problem is split into a non-overlapping partition, with added constraints ensuring C1 continuity. The Alternating Direction Method of Multipliers (ADMM) is used to optimize over each domain in parallel and ensure convergence on the boundaries of the partitions. This results in improved conditioning of the problem and allows for much larger and more complex problems to be addressed with improved performance.
Optimal price management in retail energy markets: an impulse control problem with asymptotic estimates

We consider a retailer who buys energy in the wholesale market and resells it to final consumers. The retailer has to decide when to intervene to change the price he asks to his customers, in order to maximize his income. We model the problem as an infinite-horizon stochastic impulse control problem. We characterize an optimal price strategy and provide analytical existence results for the equations involved. We then investigate the dependence on the intervention cost. In particular, we prove that the measure of the continuation region is asymptotic to the fourth root of the cost. Finally, we provide some numerical results and consider a suitable extension of the model.
Schatten class Fourier integral operators

Abstract Fourier integral operators with sufficiently smooth phase act on the time-frequency content of functions. However time-frequency analysis has only recently been used to analyze these operators. In this paper, we show that if a Fourier integral operator has a smooth phase function and its symbol is well localized in time and frequency, then the operator is Schatten p-class for p ∈ [ 1 , 2 ] , with inclusion of the symbol in mixed modulation spaces serving as the appropriate measure of time-frequency localization. Our main results are sharp in the sense that larger mixed modulation spaces necessarily contain symbols of Fourier integral operators that are not Schatten p-class.
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences. Rather than plugging a consistent density estimate (which requires k → ∞ as the sample size n → ∞) into the functional of interest, the estimators we consider fix k and perform a bias correction. This can be more efficient computationally, and, as we show, statistically, leading to faster convergence rates. Our framework unifies several previous estimators, for most of which ours are the first finite sample guarantees.
Derivation degree sequences of non-free arrangements

In this note, we study the logarithmic derivation module of a non-free arrangement. We prove a generalized addition theorem for all arrangements. This addition theorem allows us to find various rel...
Targeting by Transnational Terrorist Groups

Many successful terrorist groups operate across international borders where different countries host different stages of terrorist operations. Often the recruits for the group come from one country or countries, while the targets of the operations are in another. Stopping such attacks is difficult because intervention in any region or route might merely shift the terrorists elsewhere. Here, we propose a model of transnational terrorism based on the theory of activity networks. The model represents attacks on different countries as paths in a network. The group is assumed to prefer paths of lowest cost (or risk) and maximal yield from attacks. The parameters of the model are computed for the Islamist–Salafi terrorist movement based on open source data and then used for estimation of risks of future attacks. The central finding is that the United States (US) has an enduring appeal as a target, due to lack of other nations of matching geopolitical weight or openness. It is also shown that countries in Africa and Asia that have been overlooked as terrorist bases may become highly significant threats in the future. The model quantifies the dilemmas facing countries in the effort to cut terror networks, and points to a limitation of deterrence against transnational terrorists.
Hilbert-Schmidt Hankel operators with anti-holomorphic symbols on complete pseudoconvex Reinhardt domains

On complete pseudoconvex Reinhardt domains in ℂ2, we show that there is no nonzero Hankel operator with anti-holomorphic symbol that is Hilbert-Schmidt. In the proof, we explicitly use the pseudoconvexity property of the domain. We also present two examples of unbounded non-pseudoconvex domains in ℂ2 that admit nonzero Hilbert-Schmidt Hankel operators with anti-holomorphic symbols. In the first example the Bergman space is finite dimensional. However, in the second example the Bergman space is infinite dimensional and the Hankel operator $${H_{{{\bar z}_1}{{\bar z}_2}}}$$ is Hilbert-Schmidt.
The Debt-Equity Choice When Regulatory Thresholds are Based on Equity Values: Evidence from SOX 404

ABSTRACT When larger market values of equity result in being subject to costly regulation, firms have incentives to shift their sources of financing toward debt and away from equity. We use the Sar...
Topics of concern: identifying user issues in reviews of IoT apps and devices

Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers. They might require innovative solutions to support the development and integration of such systems. In this paper, we analyze more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered while using these systems. Our results indicate that issues with connectivity, timing, and updates are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.
Access to Liver Transplantation and Patient Survival among Asian Populations: Pre-Share 35 vs Post-Share 35.

Background: Studies addressing ethnic disparities and trends in liver transplantation for Asian population are scant. Objective: To examine the impact of Share 35 policy on Asian patients’ access to liver transplantation and outcomes since its implementation in June 2013. Methods: A total of 11,910 adult white and Asian patients who were registered for deceased donor liver transplantation between 2012 and 2015, was identified from the United Network for Organ Sharing database. Logistic regression and proportional hazard models with adjustment for demographic, clinical and geographic factors were used to model the access to liver transplantation and patient survival. Stratification on pre- and post-Share 35 periods was performed to compare the first 18 months of Share 35 policy to an equivalent period. Results: Comparison of the pre- and post-Share 35 periods showed a significant decrease in time on waiting list and higher proportions of patients receiving liver transplantation for Asian patients. Asians shared similar transplant rates as whites (OR: 1.15, 95% CI: 0.80–1.67) but experienced significantly longer waiting time (HR: 0.56, 95% CI: 0.34–0.92) before they received liver transplantation after Share 35 policy took effect. No significant post-transplantation survival difference was observed between Asians and whites at the 18-month outcome. Conclusion: Although benefited from the Share 35 policy, Asian patients are still at greater risk of disparities in access to liver transplantation.
Reduced bias nonparametric lifetime density and hazard estimation

Kernel-based nonparametric hazard rate estimation is considered with a special class of infinite-order kernels that achieves favorable bias and mean square error properties. A fully automatic and adaptive implementation of a density and hazard rate estimator is proposed for randomly right censored data. Careful selection of the bandwidth in the proposed estimators yields estimates that are more efficient in terms of overall mean square error performance, and in some cases, a nearly parametric convergence rate is achieved. Additionally, rapidly converging bandwidth estimates are presented for use in second-order kernels to supplement such kernel-based methods in hazard rate estimation. Simulations illustrate the improved accuracy of the proposed estimator against other nonparametric estimators of the density and hazard function. A real data application is also presented on survival data from 13,166 breast carcinoma patients.
Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.