cssi/SciSciGPT-SciSciNet huggingface dataset:
Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image

We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera. Our method images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. We train a CNN to regress an SVBRDF and surface normals from this image. Our network is trained using a large-scale SVBRDF dataset and designed to incorporate physical insights for material estimation, including an in-network rendering layer to model appearance and a material classifier to provide additional supervision during training. We refine the results from the network using a dense CRF module whose terms are designed specifically for our task. The framework is trained end-to-end and produces high quality results for a variety of materials. We provide extensive ablation studies to evaluate our network on both synthetic and real data, while demonstrating significant improvements in comparisons with prior works.
Mathematics of a Sudo-Kurve

We investigate a type of a Sudoku variant called Sudo-Kurve, which allows bent rows and columns, and develop a new, yet equivalent, variant we call a Sudo-Cube. We examine the total number of distinct solution grids for this type with or without symmetry. We study other mathematical aspects of this puzzle along with the minimum number of clues needed and the number of ways to place individual symbols.
A primal-dual weak Galerkin finite element method for second order elliptic equations in non-divergence form

This article proposes a new numerical algorithm for second order elliptic equations in non-divergence form. The new method is based on a discrete weak Hessian operator locally constructed by following the weak Galerkin strategy. The numerical solution is characterized as a minimization of a non-negative quadratic functional with constraints that mimic the second order elliptic equation by using the discrete weak Hessian. The resulting Euler-Lagrange equation offers a symmetric finite element scheme involving both the primal and a dual variable known as the Lagrange multiplier, and thus the name of primal-dual weak Galerkin finite element method. Optimal order error estimates are derived for the finite element approximations in a discrete $H^2$-norm, as well as the usual $H^1$- and $L^2$-norms. Some numerical results are presented for smooth and non-smooth coefficients on convex and non-convex domains.
Asymptotics of principal evaluations of Schubert polynomials for layered permutations

Denote by $u(n)$ the largest principal specialization of the Schubert polynomial: $ u(n) := \max_{w \in S_n} \mathfrak{S}_w(1,\ldots,1) $ Stanley conjectured in [arXiv:1704.00851] that there is a limit $\lim_{n\to \infty} \, \frac{1}{n^2} \log u(n), $ and asked for a limiting description of permutations achieving the maximum $u(n)$. Merzon and Smirnov conjectured in [arXiv:1410.6857] that this maximum is achieved on layered permutations. We resolve both Stanley's problems restricted to layered permutations.
Geometry of Higher-Order Markov Chains

We determine an explicit Grobner basis, consisting of linear forms and determinantal quadrics, for the prime ideal of Raftery's mixture transition distribution model for Markov chains. When the states are binary, the corresponding projective variety is a linear space, the model itself consists of two simplices in a cross-polytope, and the likelihood function typically has two local maxima. In the general non-binary case, the model corresponds to a cone over a Segre variety.
Spectral partitioning with blends of eigenvectors

Many common methods for data analysis rely on linear algebra. We provide new results connecting data analysis error to numerical accuracy, which leads to the first meaningful stopping criterion for two way spectral partitioning. More generally, we provide pointwise convergence guarantees so that blends (linear combinations) of eigenvectors can be employed to solve data analysis problems with confidence in their accuracy. We demonstrate this theory on an accessible model problem, the Ring of Cliques, by deriving the relevant eigenpairs and comparing the predicted results to numerical solutions. These results bridge the gap between linear algebra based data analysis methods and the convergence theory of iterative approximation methods.
Epistemic Logic for Communication Chains.

The paper considers epistemic properties of linear communication chains. It describes a sound and complete logical system that, in addition to the standard axioms of S5 in a multi-modal language, contains two non-trivial axioms that capture the linear structure of communication chains.
Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability.

This paper proposes a technique for automatically learning semantic malware signatures for Android from very few samples of a malware family. The key idea underlying our technique is to look for a maximally suspicious common subgraph (MSCS) that is shared between all known instances of a malware family. An MSCS describes the shared functionality between multiple Android applications in terms of inter-component call relations and their semantic metadata (e.g., data-flow properties). Our approach identifies such maximally suspicious common subgraphs by reducing the problem to maximum satisfiability. Once a semantic signature is learned, our approach uses a combination of static analysis and a new approximate signature matching algorithm to determine whether an Android application matches the semantic signature characterizing a given malware family. We have implemented our approach in a tool called ASTROID and show that it has a number of advantages over state-of-the-art malware detection techniques. First, we compare the semantic malware signatures automatically synthesized by ASTROID with manually-written signatures used in previous work and show that the signatures learned by ASTROID perform better in terms of accuracy as well as precision. Second, we compare ASTROID against two state-of-the-art malware detection tools and demonstrate its advantages in terms of interpretability and accuracy. Finally, we demonstrate that ASTROID's approximate signature matching algorithm is resistant to behavioral obfuscation and that it can be used to detect zero-day malware. In particular, we were able to find 22 instances of zero-day malware in Google Play that are not reported as malware by existing tools.
Automata and graph compression

We present a theoretical framework for the compression of automata, which are widely used representations in speech processing, natural language processing and many other tasks. As a corollary, our framework further covers graph compression. We introduce a probabilistic process of graph and automata generation that is similar to stationary ergodic processes and that covers real-world phenomena. We also introduce a universal compression scheme LZA for this probabilistic model and show that LZA significantly outperforms other compression techniques such as gzip and the UNIX compress command for several synthetic and real data sets.
Vertex Isoperimetry and Independent Set Stability for Tensor Powers of Cliques.

The tensor power of the clique on $t$ vertices (denoted by $K_t^n$) is the graph on vertex set $\{1, ..., t\}^n$ such that two vertices $x, y \in \{1, ..., t\}^n$ are connected if and only if $x_i \neq y_i$ for all $i \in \{1, ..., n\}$. Let the density of a subset $S$ of $K_t^n$ to be $\mu(S) := \frac{|S|}{t^n}$, and let the vertex boundary of a set $S$ to be vertices which are incident to some vertex of $S$, perhaps including points of $S$. We investigate two similar problems on such graphs. First, we study the vertex isoperimetry problem. Given a density $\nu \in [0, 1]$ what is the smallest possible density of the vertex boundary of a subset of $K_t^n$ of density $\nu$? Let $\Phi_t(\nu)$ be the infimum of these minimum densities as $n \to \infty$. We find a recursive relation allows one to compute $\Phi_t(\nu)$ in time polynomial to the number of desired bits of precision. Second, we study given an independent set $I \subseteq K_t^n$ of density $\mu(I) = \frac{1}{t}(1-\epsilon)$, how close it is to a maximum-sized independent set $J$ of density $\frac{1}{t}$. We show that this deviation (measured by $\mu(I \setminus J)$) is at most $4\epsilon^{\frac{\log t}{\log t - \log(t-1)}}$ as long as $\epsilon < 1 - \frac{3}{t} + \frac{2}{t^2}$. This substantially improves on results of Alon, Dinur, Friedgut, and Sudakov (2004) and Ghandehari and Hatami (2008) which had an $O(\epsilon)$ upper bound. We also show the exponent $\frac{\log t}{\log t - \log(t-1)}$ is optimal assuming $n$ tending to infinity and $\epsilon$ tending to $0$. The methods have similarity to recent work by Ellis, Keller, and Lifshitz (2016) in the context of Kneser graphs and other settings. The author hopes that these results have potential applications in hardness of approximation, particularly in approximate graph coloring and independent set problems.
Low subpacketization schemes for coded caching

Coded caching is a technique that generalizes conventional caching and promises significant reductions in traffic over caching networks. However, the basic coded caching scheme requires that each file hosted in the server be partitioned into a large number (called the subpacketization level) of non-overlapping subfiles. From a practical perspective, this is problematic as it means that prior schemes are only applicable when the size of the files is extremely large. In this work, we propose coded caching schemes based on combinatorial structures called resolvable designs. These structures can be obtained in a natural manner from linear block codes whose generator matrices possess certain rank properties. We demonstrate that several schemes with subpacketization levels that are exponentially smaller than the basic scheme can be obtained.
Statistics with Set-Valued Functions: Applications to Inverse Approximate Optimization

Much of statistics relies upon four key elements: a law of large numbers, a calculus to operationalize stochastic convergence, a central limit theorem, and a framework for constructing local approximations. These elements are well-understood for objects in a vector space (e.g., points or functions); however, much statistical theory does not directly translate to sets because they do not form a vector space. Building on probability theory for random sets, this paper uses variational analysis to develop operational tools for statistics with set-valued functions. These tools are first applied to nonparametric estimation (kernel regression of set-valued functions). The second application is to the problem of inverse approximate optimization, in which approximate solutions (corrupted by noise) to an optimization problem are observed and then used to estimate the amount of suboptimality of the solutions and the parameters of the optimization problem that generated the solutions. We show that previous approaches to this problem are statistically inconsistent when the data is corrupted by noise, whereas our approach is consistent under mild conditions.
A 9 7 -approximation algorithm for Graphic TSP in cubic bipartite graphs

We prove new results for approximating the Graphic TSP. Specifically, we provide a polynomial-time 9 7 -approximation algorithm for cubic bipartite graphs and a ( 9 7 + 1 21 ( k 2 ) ) -approximation algorithm for k -regular bipartite graphs, both of which are improved approximation factors compared to previous results. Our approach involves finding a cycle cover with relatively few cycles, which we are able to do by leveraging the fact that all cycles in bipartite graphs are of even length along with our knowledge of the structure of cubic graphs.
Analysis and Design of a Passive Switched-Capacitor Matrix Multiplier for Approximate Computing

A switched-capacitor matrix multiplier is presented for approximate computing and machine learning applications. The multiply-and-accumulate operations perform discrete-time charge-domain signal processing using passive switches and 300 aF unit capacitors. The computation is digitized with a 6 b asynchronous successive approximation register analog-to-digital converter. The analyses of incomplete charge accumulation and thermal noise are discussed. The design was fabricated in 40 nm CMOS, and experimental measurements of multiplication are illustrated using matched filtering and image convolutions to analyze noise and offset. Two applications are highlighted: 1) energy-efficient feature extraction layer performing both compression and classification in a neural network for an analog front end and 2) analog acceleration for solving optimization problems that are traditionally performed in the digital domain. The chip obtains measured efficiencies of 8.7 TOPS/W at 1 GHz for the first application and 7.7 TOPS/W at 2.5 GHz for the second application.
A Theory of Truncated Inverse Sampling

In this paper, we have established a new theory of truncated inverse sampling for estimating mean values of non-negative random variables such as binomial, Poisson, hyper-geometrical, and bounded v...
More on gauge theory and geometric Langlands

Abstract The geometric Langlands correspondence was described some years ago in terms of S-duality of N = 4 super Yang–Mills theory. Some additional matters relevant to this story are described here. The main goal is to explain directly why an A-brane of a certain simple kind can be an eigenbrane for the action of 't Hooft operators. To set the stage, we review some facts about Higgs bundles and the Hitchin fibration. We consider only the simplest examples, in which many technical questions can be avoided.
The median largest prime factor

Abstract Let M ( x ) denote the median largest prime factor of the integers in the interval [ 1 , x ] . We prove that M ( x ) = x 1 e exp ( − li f ( x ) / x ) + O ϵ ( x 1 e e − c ( log x ) 3 / 5 − ϵ ) , where li f ( x ) = ∫ 2 x { x / t } log t d t . From this, we obtain the asymptotic M ( x ) = e γ − 1 e x 1 e ( 1 + O ( 1 log x ) ) , where γ is the Euler–Mascheroni constant. This answers a question posed by Martin [3] , and improves a result of Selfridge and Wunderlich [7] .
Addressee and Response Selection in Multi-Party Conversations With Speaker Interaction RNNs

In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex speaker interactions: multiple speakers exchange messages with each other, playing different roles (sender, addressee, observer), and these roles vary across turns. To tackle this challenge, we propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the previous state-of-the-art system updated speaker embeddings only for the sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a role-sensitive way. Additionally, unlike the previous work that selected the addressee and response separately, SI-RNN selects them jointly by viewing the task as a sequence prediction problem. Experimental results show that SI-RNN significantly improves the accuracy of addressee and response selection, particularly in complex conversations with many speakers and responses to distant messages many turns in the past.
Far-infrared and Nebular Star Formation Rates of Dusty Star-forming Galaxies from Herschel and 3D-HST at z ∼ 1

We present results of a multi-band Spectral Energy Distribution (SED) and nebular emission line analysis of a sample of 1147 spectroscopically identified dusty star-forming galaxies at 0.49 < z < 2.24 from Herschel/SPIRE and HST/WFC3 grism observations in the five CANDELS fields: AEGIS, GOODS-N, GOODS-S, COSMOS, and UDS. We use the spectroscopic redshifts measured from nebular lines to construct the SEDs of galaxies from the optical to the infrared using HST and Herschel photometry. We further utilize the 3D-HST grism Ha line flux measurements to measure the nebular star-formation rates after correcting for attenuation. We compare this with direct observations of the SFR measurements in the far-infrared from Herschel. Observation of the infrared excess (IRX) in this sample as a function of the UV spectral slope reveals that these DSFGs deviate toward bluer colors, thus sitting well above the expected relation for normal star-forming galaxies. The high-z dusty galaxies have a stellar mass distribution that is skewed towards larger masses, with $M_{med} = 2.6 \times 10^{10} M_{\odot}$. However this population has star-formation rates consistent with the most massive tail of the main sequence, showing that these are both the most massive and the most star-forming galaxies during the peak epoch of formation.
Note on the index conjecture in zero-sum theory and its connection to a Dedekind-type sum☆

Abstract Let S = ( a 1 ) ⋯ ( a k ) be a minimal zero-sum sequence over a finite cyclic group G. The index conjecture states that if k = 4 and gcd ⁡ ( | G | , 6 ) = 1 , then S has index 1. In this note we study the index conjecture and connect it to a Dedekind-type sum. In particular we reprove a special case of the conjecture when | G | is prime.
Passive Crowd Speed Estimation in Adjacent Regions With Minimal WiFi Sensing

In this paper, we propose a methodology for estimating the crowd speed using WiFi devices without relying on people to carry any device. Our approach not only enables speed estimation in the region where WiFi links are, but also in the adjacent possibly WiFi-free regions. More specifically, we use a pair of WiFi links in one region, whose RSSI measurements are then used to estimate the crowd speed, not only in this region, but also in adjacent WiFi-free regions. We first prove how the cross-correlation and the probability of crossing the two links implicitly carry key information about the pedestrian speeds and develop a mathematical model to relate them to pedestrian speeds. We then validate our approach with 108 experiments, in both indoor and outdoor, where up to 10 people walk in two adjacent areas, with a variety of speeds per region, showing that our framework can accurately estimate these speeds with only a pair of WiFi links in one region. For instance, the NMSE over all experiments is 0.18. We also evaluate our framework in a museum-type setting and estimate the popularity of different exhibits. We finally run experiments in an aisle in Costco, estimating key attributes of buyers’ behaviors.
Faster Algorithms for High-Dimensional Robust Covariance Estimation

We study the problem of estimating the covariance matrix of a high-dimensional distribution when a small constant fraction of the samples can be arbitrarily corrupted. Recent work gave the first polynomial time algorithms for this problem with near-optimal error guarantees for several natural structured distributions. Our main contribution is to develop faster algorithms for this problem whose running time nearly matches that of computing the empirical covariance. Given $N = \tilde{\Omega}(d^2/\epsilon^2)$ samples from a $d$-dimensional Gaussian distribution, an $\epsilon$-fraction of which may be arbitrarily corrupted, our algorithm runs in time $\tilde{O}(d^{3.26})/\mathrm{poly}(\epsilon)$ and approximates the unknown covariance matrix to optimal error up to a logarithmic factor. Previous robust algorithms with comparable error guarantees all have runtimes $\tilde{\Omega}(d^{2 \omega})$ when $\epsilon = \Omega(1)$, where $\omega$ is the exponent of matrix multiplication. We also provide evidence that improving the running time of our algorithm may require new algorithmic techniques.
The derived category of a locally complete intersection ring

Abstract In this paper, we answer a question of Dwyer, Greenlees, and Iyengar by proving a local ring R is a complete intersection if and only if every complex of R-modules with finitely generated homology is proxy small. Moreover, we establish that a commutative noetherian ring R is locally a complete intersection if and only if every complex of R-modules with finitely generated homology is virtually small.
Feature Forwarding for Efficient Single Image Dehazing

Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three variants of our architecture to explore the dependency of dehazed image quality on parameter count and model design. The first two variants presented, a small and big version, make use of a single efficient encoder–decoder convolutional feature extractor. The final variant utilizes a pair of encoder–decoders for atmospheric light and transmission map estimation. Each variant ends with an image refinement pyramid pooling network to form the final dehazed image. For the big variant of the single-encoder network, we demonstrate state-of-the-art performance on the NYU Depth dataset. For the small variant, we maintain competitive performance on the super-resolution O/I-HAZE datasets without the need for image cropping. Finally, we examine some challenges presented by the Dense-Haze dataset when leveraging CNN architectures for dehazing of dense haze imagery and examine the impact of loss function selection on image quality. Benchmarks are included to show the feasibility of introducing this approach into real-time systems.
Security in the Presence of Key Reuse: Context-Separable Interfaces and Their Applications

Key separation is often difficult to enforce in practice. While key reuse can be catastrophic for security, we know of a number of cryptographic schemes for which it is provably safe. But existing formal models, such as the notions of joint security (Haber-Pinkas, CCS ’01) and agility (Acar et al., EUROCRYPT ’10), do not address the full range of key-reuse attacks—in particular, those that break the abstraction of the scheme, or exploit protocol interactions at a higher level of abstraction. This work attends to these vectors by focusing on two key elements: the game that codifies the scheme under attack, as well as its intended adversarial model; and the underlying interface that exposes secret key operations for use by the game. Our main security experiment considers the implications of using an interface (in practice, the API of a software library or a hardware platform such as TPM) to realize the scheme specified by the game when the interface is shared with other unspecified, insecure, or even malicious applications. After building up a definitional framework, we apply it to the analysis of two real-world schemes: the EdDSA signature algorithm and the Noise protocol framework. Both provide some degree of context separability, a design pattern for interfaces and their applications that aids in the deployment of secure protocols.
Selection and Genome Plasticity as the Key Factors in the Evolution of Bacteria

A simple mathematical model for bacterial genome evolution provides a universal framework for understanding scaling laws among functional classes of genes.
Aggregation of Nontuberculous Mycobacteria Is Regulated by Carbon-Nitrogen Balance

ABSTRACT Nontuberculous mycobacteria (NTM) are emerging opportunistic pathogens that colonize household water systems and cause chronic lung infections in susceptible patients. The ability of NTM to form surface-attached biofilms in the nonhost environment and corded aggregates in vivo is important to their ability to persist in both contexts. Underlying the development of these multicellular structures is the capacity of mycobacterial cells to adhere to one another. Unlike most other bacteria, NTM spontaneously and constitutively aggregate in vitro, hindering our ability to understand the transition between planktonic and aggregated cells. While culturing a model NTM, Mycobacterium smegmatis, in rich medium, we fortuitously discovered that planktonic cells accumulate after ∼3 days of growth. By providing selective pressure for bacteria that disperse earlier, we isolated a strain with two mutations in the oligopeptide permease operon (opp). A mutant lacking the opp operon (Δopp) disperses earlier than wild type (WT) due to a defect in nutrient uptake. Experiments with WT M. smegmatis revealed that growth as aggregates is favored when carbon is replete, but under conditions of low available carbon relative to available nitrogen, M. smegmatis grows as planktonic cells. By adjusting carbon and nitrogen sources in defined medium, we tuned the cellular C/N ratio such that M. smegmatis grows either as aggregates or as planktonic cells. C/N-mediated aggregation regulation is widespread among NTM with the possible exception of rough-colony Mycobacterium abscessus isolates. Altogether, we show that NTM aggregation is a controlled process that is governed by the relative availability of carbon and nitrogen for metabolism. IMPORTANCE Free-living bacteria can assemble into multicellular structures called biofilms. Biofilms help bacteria tolerate multiple stresses, including antibiotics and the host immune system. Nontuberculous mycobacteria are a group of emerging opportunistic pathogens that utilize biofilms to adhere to household plumbing and showerheads and to avoid phagocytosis by host immune cells. Typically, bacteria regulate biofilm formation by controlling expression of adhesive structures to attach to surfaces and other bacterial cells. Mycobacteria harbor a unique cell wall built chiefly of long-chain mycolic acids that confers hydrophobicity and has been thought to cause constitutive aggregation in liquid media. Here we show that aggregation is instead a regulated process dictated by the balance of available carbon and nitrogen. Understanding that mycobacteria utilize metabolic cues to regulate the transition between planktonic and aggregated cells reveals an inroad to controlling biofilm formation through targeted therapeutics.
Antibody-Mediated Immobilization of Virions in Mucus.

Antibodies have been shown to hinder the movement of herpes simplex virus virions in cervicovaginal mucus, as well as other viruses in other mucus secretions. However, it has not been possible to directly observe the mechanisms underlying this phenomenon, so the nature of virion–antibody–mucin interactions remain poorly understood. In this work, we analyzed thousands of virion traces from single particle tracking experiments to explicate how antibodies must cooperate to immobilize virions for relatively long time periods. First, using a clustering analysis, we observed a clear separation between two classes of virion behavior: freely diffusing and immobilized. While the proportion of freely diffusing virions decreased with antibody concentration, the magnitude of their diffusivity did not, implying an all-or-nothing dichotomy in the pathwise effect of the antibodies. Proceeding under the assumption that all binding events are reversible, we used a novel switch-point detection method to conclude that there are very few, if any, state switches on the experimental timescale of 20 s. To understand this slow state switching, we analyzed a recently proposed continuous-time Markov chain model for binding kinetics and virion movement. Model analysis implied that virion immobilization requires cooperation by multiple antibodies that are simultaneously bound to the virion and mucin matrix and that there is an entanglement phenomenon that accelerates antibody–mucin binding when a virion is immobilized. In addition to developing a widely applicable framework for analyzing multistate particle behavior, this work substantially enhances our mechanistic understanding of how antibodies can reinforce a mucus barrier against passive invasive species.
A hybrid approach to persistent coverage in stochastic environments

Abstract This paper considers the persistent coverage of a 2-D manifold that has been embedded in 3-D space. The manifold is subject to continual impact by intruders which travel at constant velocities along arbitrarily oriented straight-line trajectories. The trajectories of intruders are estimated online with an extended Kalman filter and their predicted impact points contribute normally distributed decay terms to the coverage level. A formal hybrid control strategy is presented that allows for power-constrained 3-D free-flyer agents to persistently monitor the domain, track and intercept intruders, and periodically deploy from and return to a single charging station on the manifold. Guarantees on intruder interception with respect to agent power lifespans are formally proven. The efficacy of the algorithm is demonstrated through simulation.
Accurate Inference of Tree Topologies from Multiple Sequence Alignments Using Deep Learning

Reconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and empirical studies have identified several "zones" of parameter space where accuracy of some methods can plummet, even for four-taxon trees. Further, some methods can have undesirable statistical properties such as statistical inconsistency and/or the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning techniques have made inroads on a number of both new and longstanding problems in biological research. In this study, we designed a deep convolutional neural network (CNN) to infer quartet topologies from multiple sequence alignments. This CNN can readily be trained to make inferences using both gapped and ungapped data. We show that our approach is highly accurate on simulated data, often outperforming traditional methods, and is remarkably robust to bias-inducing regions of parameter space such as the Felsenstein zone and the Farris zone. We also demonstrate that the confidence scores produced by our CNN can more accurately assess support for the chosen topology than bootstrap and posterior probability scores from traditional methods. Although numerous practical challenges remain, these findings suggest that the deep learning approaches such as ours have the potential to produce more accurate phylogenetic inferences.
Is coal-fired power generation associated with rural electrification? A global analysis

Abstract Using comprehensive data on power generation and energy access, we explore associations between coal-fired power generation and household electrification. We find that, although there is a strong between-country correlation between coal-fired power generation capacity and electricity access, the correlation disappears when we focus on within-country variation and control for secular trends. Further, statistical analyses using dynamic panel models show no evidence for the effect of coal-fired generation capacity on electrification rates. These results suggest that increases in coal-fired power generation may not have played an important role in promoting rural electrification in recent decades, calling into question the relationship between coal-fired power generation and energy access. The findings also imply that rural electrification can advance without the greenhouse gas emissions from coal combustion in electricity generation.
A machine learning approach to improving occupational income scores

Abstract Historical studies of labor markets frequently lack data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes. We consider the consequences of using OCCSCORE when researchers are interested in earnings regressions. We estimate race and gender earnings gaps in modern decennial Censuses as well as the 1915 Iowa State Census. Using OCCSCORE biases results towards zero and can result in estimated gaps of the wrong sign. We use a machine learning approach to construct a new adjusted score based on industry, occupation, and demographics. The new income score provides estimates closer to earnings regressions. Lastly, we consider the consequences for estimates of intergenerational mobility elasticities.
Characterizations and enumerations of patterns of signed shifts

Abstract Shift maps can be interpreted as maps from the unit interval to itself sending x to the fractional part of N x , whose graph consists of N segments of positive slope. Signed shifts generalize shift maps by allowing some of these segments to have negative slope. Permutations realized by the relative order of the elements in the orbits of these maps have been studied recently by Amigo, Archer and Elizalde. In this paper, we give a complete characterization of the permutations (also called patterns) realized by signed shifts. In the case of the negative shift, which is the signed shift having only negative slopes, we use our characterization to give an exact enumeration of these patterns. Finally, we improve the best known bounds for the number of patterns realized by the tent map, and calculate the topological entropy of signed shifts using these combinatorial methods.
Strobe -- Towards Low Cost Soil Sensing Using Wi-Fi

A farm's soil moisture and soil electrical conductivity (EC)readings are extremely valuable for a farmer. They can help her reduce water use and improve productivity. However, the high cost of commercial soil moisture sensors and the inaccuracy of sub-1000 dollar EC sensors have limited their adoption. In this paper, we present the design and implementation of a system, called Strobe, that senses soil moisture and soil EC using RF propagation in existing Wi-Fi bands. Strobe overcomes the key challenge of limited bandwidth availability in the 2.4 GHz unlicensed spectrum using a novel multi-antenna technique. It maps the propagation time and amplitude of Wi-Fi signals received by different antennas to the soil permittivity and EC, which in turn depend on soil moisture and salinity. Our experiments with USRP, WARP, and commodity Wi-Fi cards show that Strobe can accurately estimate soil moisture and EC using Wi-Fi, thereby showing the potential of a future where a farmer can sense soil in their farm without investing 1000s of dollars in sensing tools.
Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking

We present a novel algorithm utilizing a deep Siamese neural network as a general object similarity function in combination with a Bayesian optimization (BO) framework to encode spatio-temporal information for efficient object tracking in video. In particular, we treat the video tracking problem as a dynamic (i.e. temporally-evolving) optimization problem. Using Gaussian Process priors, we model a dynamic objective function representing the location of a tracked object in each frame. By exploiting temporal correlations, the proposed method queries the search space in a statistically principled and efficient way, offering several benefits over current state of the art video tracking methods.
MemSTATS: A Benchmark Set of Membrane Protein Symmetries and Pseudosymmetries

Abstract In membrane proteins, symmetry and pseudosymmetry often have functional or evolutionary implications. However, available symmetry detection methods have not been tested systematically on this class of proteins because of the lack of an appropriate benchmark set. Here we present MemSTATS, a publicly available benchmark set of both quaternary- and internal-symmetries in membrane protein structures. The symmetries are described in terms of order, repeated elements, and orientation of the axis with respect to the membrane plane. Moreover, using MemSTATS, we compare the performance of four widely used symmetry detection algorithms and highlight specific challenges and areas for improvement in the future.
Credible Information, Allowable Information and Belief Revision - Extended Abstract.

Author(s): Bonanno, G | Abstract: In an earlier paper [Rational choice and AGM belief revision, Artificial Intelligence, 2009] a correspondence was established between the choice structures of revealed-preference theory (developed in economics) and the syntactic belief revision functions of the AGM theory (developed in philosophy and computer science). In this paper we extend the re-interpretation of (a generalized notion of) choice structure in terms of belief revision by adding: (1) the possibility that an item of “information” might be discarded as not credible (thus dropping the AGM success axiom) and (2) the possibility that an item of information, while not accepted as fully credible, may still be “taken seriously” (we call such items of information “allowable”). We establish a correspondence between generalized choice structures (GCS) and AGM belief revision; furthermore, we provide a syntactic analysis of the proposed notion of belief revision, which we call filtered belief revision.
A Classical Field Theory Formulation for the Numerical Solution of Time Harmonic Electromagnetic Fields

Finite element representations of Maxwell's equations pose unusual challenges inherent to the variational representation of the “curl-curl” equation for the fields. We present a variational formulation which solves for the four-potential instead, based on classical field theory. Borrowing from quantum electrodynamics, we modify the Lagrangian by adding an implicit gauge-fixing term. This reformulation explicitly accounts for Gauss' law through the coupling between φ and ρ, and enables the use of nodal basis functions instead of edge elements for time-harmonic problems. We demonstrate how this formulation, adhering to the deeper underlying symmetries of the four-dimensional covariant field description, provides a highly general, robust numerical framework.
Performance Differences on a Measure of Mathematics Vocabulary for English Learners and Non-English Learners with and without Mathematics Difficulty

The purpose of this study was to measure the mathematics-vocabulary performance of 3rd graders to determine if performance differences existed among English learners (ELs) and non-ELs with and with...
Mechanistic Understanding of Plasmon-Enhanced Electrochemistry

Noble metals are prime candidates for electrocatalysts for electrochemical energy conversions. In a separate realm, nanostructured noble metals are found to exhibit photocatalytic activity under pl...
Do Global Audit Firm Networks Apply Consistent Audit Methodologies across Jurisdictions? Evidence from Financial Reporting Comparability

Brand name audit firms are global networks of local audit firms. These networks claim to enforce consistent audit methodologies across their member firms, which if true, should systematically affec...
A single-cell transcriptome atlas for zebrafish development.

Abstract The ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome atlas encompasses transcriptional profiles from 44,102 ​cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting atlas is a resource for biologists to generate hypotheses for functional analysis, which we hope integrates with existing efforts to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.
Beating Optical-Turbulence Limits Using High-Peak-Power Lasers

Long-range propagation of a laser is critical for applications such as free-space optical communication and power transmission, and is often limited by beam distortions caused by atmospheric turbulence. Through the use of high-power laser pulses, this study illustrates how a nonlinear self-channeling beam can resist such distortions. This approach yields an increase in power delivery by a factor of 7 compared to linear propagation, even in the strongest turbulence produced by a nearly 1-km-long generator built for this study. These results will have an immediate impact on potential solutions to the limitations of optical propagation in realistic atmospheric turbulence.
Effect of Advanced Glycation End-Products (AGE) Lowering Drug ALT-711 on Biochemical, Vascular, and Bone Parameters in a Rat Model of CKD-MBD

Chronic kidney disease-mineral bone disorder (CKD-MBD) is a systemic disorder that affects blood measures of bone and mineral homeostasis, vascular calcification, and bone. We hypothesized that the accumulation of advanced glycation end-products (AGEs) in CKD may be responsible for the vascular and bone pathologies via alteration of collagen. We treated a naturally occurring model of CKD-MBD, the Cy/+ rat, with a normal and high dose of the AGE crosslink breaker alagebrium (ALT-711), or with calcium in the drinking water to mimic calcium phosphate binders for 10 weeks. These animals were compared to normal (NL) untreated animals. The results showed that CKD animals, compared to normal animals, had elevated blood urea nitrogen (BUN), PTH, FGF23 and phosphorus. Treatment with ALT-711 had no effect on kidney function or PTH, but 3 mg/kg lowered FGF23 whereas calcium lowered PTH. Vascular calcification of the aorta assessed biochemically was increased in CKD animals compared to NL, and decreased by the normal, but not high dose of ALT-711, with parallel decreases in left ventricular hypertrophy. ALT-711 (3 mg/kg) did not alter aorta AGE content, but reduced aorta expression of receptor for advanced glycation end products (RAGE) and NADPH oxidase 2 (NOX2), suggesting effects related to decreased oxidative stress at the cellular level. The elevated total bone AGE was decreased by 3 mg/kg ALT-711 and both bone AGE and cortical porosity were decreased by calcium treatment, but only calcium improved bone properties. In summary, treatment of CKD-MBD with an AGE breaker ALT-711, decreased FGF23, reduced aorta calcification, and reduced total bone AGE without improvement of bone mechanics. These results suggest little effect of ALT-711 on collagen, but potential cellular effects. The data also highlights the need to better measure specific types of AGE proteins at the tissue level in order to fully elucidate the impact of AGEs on CKD-MBD. © 2019 American Society for Bone and Mineral Research.
Geometric Brain Surface Network for Brain Cortical Parcellation

A large number of surface-based analyses on brain imaging data adopt some specific brain atlases to better assess structural and functional changes in one or more brain regions. In these analyses, it is necessary to obtain an anatomically correct surface parcellation scheme in an individual brain by referring to the given atlas. Traditional ways to accomplish this goal are through a designed surface-based registration or hand-crafted surface features, although both of them are time-consuming. A recent deep learning approach depends on a regular spherical parameterization of the mesh, which is computationally prohibitive in some cases and may also demand further post-processing to refine the network output. Therefore, an accurate and fully-automatic cortical surface parcellation scheme directly working on the original brain surfaces would be highly advantageous. In this study, we propose an end-to-end deep brain cortical parcellation network, called DBPN. Through intrinsic and extrinsic graph convolution kernels, DBPN dynamically deciphers neighborhood graph topology around each vertex and encodes the deciphered knowledge into node features. Eventually, a non-linear mapping between the node features and parcellation labels is constructed. Our model is a two-stage deep network which contains a coarse parcellation network with a U-shape structure and a refinement network to fine-tune the coarse results. We evaluate our model in a large public dataset and our work achieves superior performance than state-of-the-art baseline methods in both accuracy and efficiency.
Predicting Auditory Spatial Attention from EEG using Single- and Multi-task Convolutional Neural Networks

Recent behavioral and electroencephalography (EEG) studies have defined ways that auditory spatial attention can be allocated over large regions of space. As with most experimental studies, behavior and EEG were averaged over 10s of minutes because identifying abstract feature spatial codes from raw EEG data is extremely challenging. The goal of this study is to design a deep learning model that can learn from raw EEG data and predict auditory spatial information on a trial-by-trial basis. We designed a convolutional neural network (CNN) model to predict the attended location or other stimulus locations relative to the attended location. A multi-task model was also used to predict the attended and stimulus locations at the same time. Based on the visualization of our models, we investigated features of individual classification tasks and joint feature of the multi-task model. Our model achieved an average 72.4% in relative location prediction and 90.0% in attended location prediction individually (AUROC’s). The multi-task model improved the performance of attended location prediction by 3%. Our results show that deep learning methods are able to define abstract neural codes in EEG thought to neural mechanisms of human spatial cognition and attention.
Rl-Ncs: Reinforcement Learning Based Data-Driven Approach For Nonuniform Compressed Sensing

A reinforcement-learning-based non-uniform compressed sensing (NCS) framework for time-varying signals is introduced. The proposed scheme, referred to as RL-NCS, aims to boost the performance of signal recovery through an optimal and adaptive distribution of sensing energy among two groups of coefficients of the signal, referred to as region of interest (ROI) coefficients and non-ROI coefficients. The coefficients in ROI usually have greater importance and need to be reconstructed with higher accuracy compared to non-ROI coefficients. In order to accomplish this task, the ROI is predicted at each time-step using two specific approaches. One of these approaches incorporates a long short-term memory (LSTM) network for the prediction. The other approach employs the previous ROI information for predicting the next step ROI. Using the exploration-exploitation technique, a Q-network learns to choose the best approach for designing the measurement matrix. Furthermore, a joint loss function is introduced for the efficient training of the Q-network as well as the LSTM network. The result indicates a significant performance gain for our proposed method, even for rapidly varying signals and reduced number of measurements.
A generalization of a power-conjugacy problem in torsion-free negatively curved groups

Let H and K be quasiconvex subgroups of a negatively curved torsion-free group G . We give an algorithm which decides whether an element of H is conjugate in G to an element of K .