cssi/SciSciGPT-SciSciNet huggingface dataset:
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks

This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch-and-bound algorithm for learning Bayesian network structures, based on the minimum description length (MDL) principle, for a given (consistent) variable ordering. The algorithm exhaustively searches through all network structures and guarantees to find the network with the best MDL score. Preliminary experiments show that the algorithm is efficient, and that the time complexity grows slowly with the sample size. The algorithm is useful for empirically studying both the performance of suboptimal heuristic search algorithms and the adequacy of the MDL principle in learning Bayesian networks.
A Short Note on the Nested-Sweep Polarized Traces Method for the 2D Helmholtz Equation

We present a variant of the solver in Zepeda-Nunez and Demanet (2014), for the 2D high-frequency Helmholtz equation in heterogeneous acoustic media. By changing the domain decomposition from a layered to a grid-like partition, this variant yields improved asymptotic online and offline runtimes and a lower memory footprint. The solver has online parallel complexity that scales sublinearly as O (N P ) , where N is the number of volume unknowns, and P is the number of processors, provided that P = O(N1/5). The variant in Zepeda-Nunez and Demanet (2014) only afforded P = O(N1/8). Algorithmic scalability is a prime requirement for wave simulation in regimes of interest for geophysical imaging.
Symmetric 1-dependent colorings of the integers

In a recent paper, we constructed a stationary $1-$dependent $4-$coloring of the integers that is invariant under permutations of the colors. This was the first stationary $k-$dependent $q-$coloring for any $k$ and $q$. When the analogous construction is carried out for $q>4$ colors, the resulting process is not $k-$dependent for any $k$. We construct here a process that is symmetric in the colors and $1-$dependent for every $q\geq 4$. The construction uses a recursion involving Chebyshev polynomials evaluated at $\sqrt{q}/2$.
Comparing tamed and compatible symplectic cones and cohomological properties of almost complex manifolds

We introduce certain homology and cohomology subgroups for any almost complex structure and study their pureness, fullness and duality properties. Motivated by a question of Donaldson, we use these groups to relate J-tamed symplectic cones and J-compatible symplectic cones over a large class of almost complex manifolds, including all Kahler manifolds, almost Kahler 4-manifolds and complex surfaces.
Faster p-adic feasibility for certain multivariate sparse polynomials

We present algorithms revealing new families of polynomials admitting sub-exponential detection of p-adic rational roots, relative to the sparse encoding. For instance, we prove NP-completeness for the case of honest n-variate (n+1)-nomials and, for certain special cases with p exceeding the Newton polytope volume, constant-time complexity. Furthermore, using the theory of linear forms in p-adic logarithms, we prove that the case of trinomials in one variable can be done in NP. The best previous complexity upper bounds for all these problems were EXPTIME or worse. Finally, we prove that detecting p-adic rational roots for sparse polynomials in one variable is NP-hard with respect to randomized reductions. The last proof makes use of an efficient construction of primes in certain arithmetic progressions. The smallest n where detecting p-adic rational roots for n-variate sparse polynomials is NP-hard appears to have been unknown.
Gessel polynomials, rooks, and extended Linial arrangements

Abstract We study a family of polynomials associated with ascent–descent statistics on labeled rooted plane k-ary trees introduced by Gessel, from a rook-theoretic perspective. We generalize the excedance statistic on permutations to maximal nonattacking rook placements on certain rectangular boards by decomposing them into staircase boards. We then relate the number of maximal nonattacking rook placements on certain skew boards to the number of regions in extended Linial arrangements by establishing a relation between the factorial polynomial of those boards to the characteristic polynomial of extended Linial arrangements. Furthermore, we give a combinatorial interpretation of the number of bounded regions in extended Linial arrangements in terms of certain labeled rooted plane k-ary trees. Finally, using the work of Goldman–Joichi–White, we identify graphs whose chromatic polynomials equal the characteristic polynomials of extended Linial arrangements up to a straightforward normalization.
Optimal Control of Connected and Automated Vehicles at Roundabouts: An Investigation in a Mixed-Traffic Environment

Abstract Connectivity and automation in vehicles provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. This paper investigates the implications of optimally coordinating vehicles that are wirelessly connected to each other in roundabouts to achieve a smooth traffic flow. We apply an optimization framework and an analytical solution that allows optimal coordination of vehicles for merging in such traffic scenario. The effectiveness of the proposed approach is validated through simulation and it is shown that fully coordinated vehicles reduce total travel time by 51% and fuel consumption by 35%. Furthermore, we study the influence of vehicle coordination in a mixed-traffic environment and compare the network performance under different market penetration rates of connected and automated vehicles (CAVs). For this particular study with near-capacity demand, high penetration of CAVs is necessary to ensure an efficient and effective control, due to extremely unstable traffic.
Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning

Recent studies have shown that attackers can force deep learning models to misclassify so-called “adversarial examples:” maliciously generated images formed by making imperceptible modifications to pixel values. With growing interest in deep learning for security applications, it is important for security experts and users of machine learning to recognize how learning systems may be attacked. Due to the complex nature of deep learning, it is challenging to understand how deep models can be fooled by adversarial examples. Thus, we present a web-based visualization tool, Adversarial-Playground, to demonstrate the efficacy of common adversarial methods against a convolutional neural network (CNN) system. Adversarial-Playground is educational, modular and interactive. (1) It enables non-experts to compare examples visually and to understand why an adversarial example can fool a CNN-based image classifier. (2) It can help security experts explore more vulnerability of deep learning as a software module. (3) Building an interactive visualization is challenging in this domain due to the large feature space of image classification (generating adversarial examples is slow in general and visualizing images are costly). Through multiple novel design choices, our tool can provide fast and accurate responses to user requests. Empirically, we find that our client-server division strategy reduced the response time by an average of 1.5 seconds per sample. Our other innovation, a faster variant of JSMA evasion algorithm, empirically performed twice as fast as JSMA and yet maintains a comparable evasion rate1.
Variance of the number of zeroes of shift-invariant Gaussian analytic functions

Following Wiener, we consider the zeroes of Gaussian analytic functions in a strip in the complex plane, with translation-invariant distribution. We show that the variance of the number of zeroes in a long horizontal rectangle [−T,T] × [a, b] is asymptotically between cT and CT2, with positive constants c and C. We also supply with conditions (in terms of the spectral measure) under which the variance grows asymptotically linearly with T, as a quadratic function of T, or has intermediate growth. The results are compared with known results for real stationary Gaussian processes and other models.
Mechanisms for Global Warming Impacts on Madden–Julian Oscillation Precipitation Amplitude

AbstractMechanisms that cause changes in Madden–Julian oscillation (MJO) precipitation amplitude under global warming are examined in models from phase 5 of the Coupled Model Intercomparison Projec...
R A Fisher, design theory, and the Indian connection

Design Theory, a branch of mathematics, was born out of the experimental statistics research of the population geneticist R A Fisher and of Indian mathematical statisticians in the 1930s. The field combines elements of combinatorics, finite projective geometries, Latin squares, and a variety of further mathematical structures, brought together in surprising ways. This essay will present these structures and ideas as well as how the field came together, in itself an interesting story.
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies

We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Based on the Lagrangian Relaxation technique in Adelman & Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multiarmed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other stateof- arts and performs close to optimal solution.
Diffusivity and hydrodynamic drag of nanoparticles at a vapor-liquid interface

Measurements of the surface diffusivity of colloidal spheres translating along a vapor/liquid inter- face show an unexpected decrease in diffusivity, or increase in surface drag (from the Stokes-Einstein relation) when the particles situate further into the vapor phase. However, direct measurements of the surface drag from the colloid velocity due to an external force find the expected decrease with deeper immersion into the vapor. The paradoxical drag increase observed in diffusion experiments has been attributed to the attachment of the fluid interface to heterogeneities on the colloid surface, which causes the interface, in response to thermal fluctuations, to either jump or remain pinned, creating added drag. We have performed molecular dynamics simulations of the diffusivity and force experiments for a nanoparticle with a rough surface at a vapor/liquid interface to examine the effect of contact line fluctuations. The drag calculated from both experiments agree and decrease as the particle positions further into the vapor. The surface drag is smaller than the bulk liquid drag due to the partial submersion into the liquid, and the finite thickness of the interfacial zone relative to the nanoparticle size. Contact line fluctuations do not give rise to an anomalous increase in drag.
Sharp Bounds for Mutual Covering

A fundamental tool in network information theory is the covering lemma, which lower bounds the probability that there exists a pair of random variables; among a given number of independently generated candidates, falling within a given set. We use a weighted sum trick and Talagrand’s concentration inequality to prove new mutual covering bounds. We identify two interesting applications: 1) when the probability of the set under the given joint distribution is bounded away from 0 and 1, the covering probability converges to 1 doubly exponentially fast in the blocklength, which implies that the covering lemma does not induce penalties on the error exponents in the applications to coding theorems; and 2) using Hall’s marriage lemma, we show that the maximum difference between the probability of the set under the joint distribution and the covering probability equals half the minimum total variation distance between the joint distribution and any distribution that can be simulated by selecting a pair from the candidates. Thus we use the mutual covering bound to derive the exact error exponent in the joint distribution simulation problem. In both applications, the determination of the exact exponential (or double exponential) behavior relies crucially on the sharp concentration inequality used in the proof of the mutual covering lemma.
On polytopes with congruent projections or sections

Abstract Let 2 ≤ k ≤ d − 1 and let P and Q be two convex polytopes in E d . Assume that their projections, P | H , Q | H , onto every k-dimensional subspace H, are congruent. In this paper we show that P and Q or P and −Q are translates of each other. We also prove an analogous result for sections by showing that P = Q or P = − Q , provided the polytopes contain the origin in their interior and their sections, P ∩ H , Q ∩ H , by every k-dimensional subspace H, are congruent.
Similarity problems in noncommutative polydomains

Abstract In this paper we consider several problems of joint similarity to tuples of bounded linear operators in noncommutative polydomains and varieties associated with sets of noncommutative polynomials. We obtain analogues of classical results such as Rota's model theorem for operators with spectral radius less than one, Sz.-Nagy characterization of operators similar to isometries (or unitary operators), and the refinement obtained by Foias and by de Branges and Rovnyak for strongly stable contractions. We also provide analogues of these results in the context of joint similarity of commuting tuples of positive linear maps on the algebra of bounded linear operators on a separable Hilbert space. An important role in this paper is played by a class of noncommutative cones associated with positive linear maps, the Fourier type representation of their elements, and the constrained noncommutative Berezin transforms associated with these elements. It is shown that there is an intimate relation between the similarity problems and the existence of positive invertible elements in these noncommutative cones and the corresponding Berezin kernels.
The asymptotically sharp Korn interpolation and second inequalities for shells

Abstract We consider shells in three-dimensional Euclidean space that have bounded principal curvatures. We prove Korn's interpolation (or the so-called first and a half 1 ) and the second inequalities on that kind of shells for u ∈ W 1 , 2 vector fields, imposing no boundary or normalization conditions on u. The constants in the estimates are optimal in terms of the asymptotics in the shell thickness h, having the scalings h or O ( 1 ) . The Korn interpolation inequality reduces the problem of deriving any linear Korn type estimate for shells to simply proving a Poincare-type estimate with the symmetrized gradient on the right-hand side. In particular, this applies to linear geometric rigidity estimates for shells, i.e. Korn's fist inequality without boundary conditions.
Optimal Placement of a UAV to Maximize the Lifetime of Wireless Devices

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider downlink scenarios from an aerial base station to ground users. In this paper, we consider an uplink scenario under disaster situations (such as earthquakes or floods), when cellular networks are down. We formulate the problem of optimal UAV placement, where the objective is to determine the placement of a single UAV such that the sum of time durations of uplink transmissions is maximized. We prove that the constraint sets of problem can be represented by the intersection of half spheres and the region formed by this intersection is a convex set in terms of two variables. This proof enables us to transform our problem to an optimization problem with two variables. We also prove that the objective function of the transformed problem is a concave function under a restriction on the minimum altitude of the UAV and propose a gradient projection-based algorithm to find the optimal location of the UAV. We validate the analysis by simulations and demonstrate the effectiveness of the proposed algorithm under different cases.
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics.

Machine learning offers novel ways and means to design personalized learning systems wherein each student’s educational experience is customized in real time depending on their background, learning goals, and performance to date. SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner’s knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts. SPARFA jointly learns the associations among the questions and the concepts, learner concept knowledge profiles, and the underlying question difficulties, solely based on the correct/incorrect graded responses of a population of learners to a collection of questions. In this paper, we extend the SPARFA framework significantly to enable: (i) the analysis of graded responses on an ordinal scale (partial credit) rather than a binary scale (correct/incorrect); (ii) the exploitation of tags/labels for questions that partially describe the question–concept associations. The resulting Ordinal SPARFA-Tag framework greatly enhances the interpretability of the estimated concepts. We demonstrate using real educational data that Ordinal SPARFA-Tag outperforms both SPARFA and existing collaborative filtering techniques in predicting missing learner responses.
PIRC Net : Using Proposal Indexing, Relationships and Context for Phrase Grounding

Phrase Grounding aims to detect and localize objects in images that are referred to and are queried by natural language phrases. Phrase grounding finds applications in tasks such as Visual Dialog, Visual Search and Image-text co-reference resolution. In this paper, we present a framework that leverages information such as phrase category, relationships among neighboring phrases in a sentence and context to improve the performance of phrase grounding systems. We propose three modules: Proposal Indexing Network (PIN); Inter-phrase Regression Network (IRN) and Proposal Ranking Network (PRN) each of which analyze the region proposals of an image at increasing levels of detail by incorporating the above information. Also, in the absence of ground-truth spatial locations of the phrases (weakly-supervised), we propose knowledge transfer mechanisms that leverages the framework of PIN module. We demonstrate the effectiveness of our approach on the Flickr 30k Entities and ReferItGame datasets, for which we achieve improvements over state-of-the-art approaches in both supervised and weakly-supervised variants.
On primitive elements in finite fields of low characteristic

We prove new character sum bounds over short intervals in a function field.We give applications in the context of primitive elements in finite fields.We show low degree polynomials have the expected number of primitive elements. We discuss the problem of constructing a small subset of a finite field containing primitive elements of the field. Given a finite field, F q n , small q and large n, we show that the set of all low degree polynomials contains the expected number of primitive elements.The main theorem we prove is a bound for character sums over short intervals in function fields. Our result is unconditional and slightly better than what is known (conditionally under GRH) in the integer case and might be of independent interest.
Factorial Hidden Markov Models for Learning Representations of Natural Language

Most representation learning algorithms for language and image processing are local, in that they identify features for a data point based on surrounding points. Yet in language processing, the correct meaning of a word often depends on its global context. As a step toward incorporating global context into representation learning, we develop a representation learning algorithm that incorporates joint prediction into its technique for producing features for a word. We develop efficient variational methods for learning Factorial Hidden Markov Models from large texts, and use variational distributions to produce features for each word that are sensitive to the entire input sequence, not just to a local context window. Experiments on part-of-speech tagging and chunking indicate that the features are competitive with or better than existing state-of-the-art representation learning methods.
Limit theorems for maximum flows on a lattice

We independently assign a non-negative value, as a capacity for the quantity of flows per unit time, with a distribution F to each edge on the $$\mathbf{Z}^d$$ lattice. We consider the maximum flows through the edges from a source to a sink in a large cube. In this paper, we show that the ratio of the maximum flow and the size of the source is asymptotic to a constant. This constant is denoted by the flow constant. By the max-flow and min-cut theorem, this is equivalent to a statement about the asymptotic behavior of the minimal value assigned to any surface on the large cube. We can also show that there exists such a surface that is proportional to the size of the faces of the cube.
Complex Monge Ampere Equations

This is a survey of some of the recent developments in the theory of complex Monge-Ampere equations. The topics discussed include refinements and simplifications of classical a priori estimates, methods from pluripotential theory, variational methods for big cohomology classes, semiclassical constructions of solutions of homogeneous equations, and envelopes.
Near-optimal Irrevocable Sample Selection for Periodic Data Streams with Applications to Marine Robotics

We consider the task of monitoring spatiotemporal phenomena in real-time by deploying limited sampling resources at locations of interest irrevocably and without knowledge of future observations. This task can be modeled as an instance of the classical secretary problem. Although this problem has been studied extensively in theoretical domains, existing algorithms require that data arrive in random order to provide performance guarantees. These algorithms will perform arbitrarily poorly on data streams such as those encountered in robotics and environmental monitoring domains, which tend to have spatiotemporal structure. We focus on the problem of selecting representative samples from phenomena with periodic structure and introduce a novel sample selection algorithm that recovers a near-optimal sample set according to any monotone submodular utility function. We evaluate our algorithm on a seven-year environmental dataset collected at the Martha's Vineyard Coastal Observatory and show that it selects phytoplankton sample locations that are nearly optimal in an information-theoretic sense for predicting phytoplankton concentrations in locations that were not directly sampled. The proposed periodic secretary algorithm can be used with theoretical performance guarantees in many real-time sensing and robotics applications for streaming, irrevocable sample selection from periodic data streams.
Neutron electric polarizability

We use the background field method to extract the “connected” piece of the neutron electric polarizability. We present results for quenched simulations using both clover andWilson fermions and discuss our experience in extracting the mass shifts and the challenges we encountered when we lowered the quark mass. For the neutron we find that as the pion mass is lowered below 500MeV, the polarizability starts rising in agreement with predictions from chiral perturbation theory. For our lowest pion mass, mπ = 320MeV, we find that αn = 3.8(1.3)×10−4 fm3, which is still only one third of the experimental value. We also present results for the neutral pion; we find that its polarizability turns negative for pion masses smaller than 500MeV which is puzzling.
Reflection arrangements and ribbon representations

Ehrenborg and Jung (2011) recently related the order complex for the lattice of d-divisible partitions with the simplicial complex of pointed ordered set partitions via a homotopy equivalence. The latter has top homology naturally identified as a Specht module. Their work unifies that of Calderbank, Hanlon, Robinson (1986), and Wachs (1996). By focusing on the underlying geometry, we strengthen and extend these results from type A to all real reflection groups and the complex reflection groups known as Shephard groups.
Coded convolution for parallel and distributed computing within a deadline

We consider the problem of computing the convolution of two long vectors using parallel processors in the presence of “stragglers”. Stragglers refer to the small fraction of faulty or slow processors that delays the entire computation in time-critical distributed systems. We first show that splitting the vectors into smaller pieces and using a linear code to encode these pieces provides improved resilience against stragglers than replication-based schemes under a simple, worst-case straggler analysis. We then demonstrate that under commonly used models of computation time, coding can dramatically improve the probability of finishing the computation within a target “deadline” time. As opposed to the more commonly used technique of expected computation time analysis, we quantify the exponents of the probability of failure in the limit of large deadlines. Our exponent metric captures the probability of failing to finish before a specified deadline time, i.e., the behavior of the “tail”. Moreover, our technique also allows for simple closed form expressions for more general models of computation time, e.g. shifted Weibull models instead of only shifted exponentials. Thus, through this problem of coded convolution, we establish the utility of a novel asymptotic failure exponent analysis for distributed systems.
On Some Conjectures Concerning Critical Independent Sets of a Graph

Let $G$ be a simple graph with vertex set $V(G)$. A set $S\subseteq V(G)$ is independent if no two vertices from $S$ are adjacent. For $X\subseteq V(G)$, the difference of $X$ is $d(X) = |X|-|N(X)|$ and an independent set $A$ is critical if $d(A) = \max \{d(X): X\subseteq V(G) \text{ is an independent set}\}$ (possibly $A=\emptyset$). Let $\text{nucleus}(G)$ and $\text{diadem}(G)$ be the intersection and union, respectively, of all maximum size critical independent sets in $G$. In this paper, we will give two new characterizations of Konig-Egervary graphs involving $\text{nucleus}(G)$ and $\text{diadem}(G)$. We also prove a related lower bound for the independence number of a graph. This work answers several conjectures posed by Jarden, Levit, and Mandrescu.
Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts.
ON THE ANNIHILATOR OF A DOLBEAULT GROUP

We show that any Dolbeault cohomology group Hp,q(D), p ≥ 0, q ≥ 1, of an open subset D of a closed finite codimensional complex Hilbert submanifold of l2 is either zero or infinite dimensional. We also show that any continuous character of the algebra of holomorphic functions of a closed complex Hilbert submanifold M of l2 is induced by its evaluation at a point of M. Lastly, we prove that any closed split infinite dimensional complex Banach submanifold of l2 admits a nowhere critical holomorphic function.
Non-symmetric Convex Polytope and Gabor Orthonormal Bases

In this paper, we show that the non-symmetric convex polytope cannot be served as a window function to produce a Gabor orthonormal basis by any time-frequency sets.
An Elementary Proof for the Krull Dimension of a Polynomial Ring

It is proved that, for every infinite field F, the polynomial ring F[t1, …, tn] has Krull dimension n. The proof uses only “high school algebra” and the rudiments of undergraduate “abstract algebra.”
Finite Sample Differentially Private Confidence Intervals

We study the problem of estimating finite sample confidence intervals of the mean of a normal population under the constraint of differential privacy. We consider both the known and unknown variance cases and construct differentially private algorithms to estimate confidence intervals. Crucially, our algorithms guarantee a finite sample coverage, as opposed to an asymptotic coverage. Unlike most previous differentially private algorithms, we do not require the domain of the samples to be bounded. We also prove lower bounds on the expected size of any differentially private confidence set showing that our the parameters are optimal up to polylogarithmic factors.
Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering

Understanding a user's motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review texts. Unfortunately matrix factorization approaches to recommendation result in large, complex models that are difficult to interpret. In this paper, we attack this problem through succinct additive co-clustering on both ratings and reviews. Our model yields accurate and interpretable recommendations.
A higher Boltzmann distribution

We characterize the classical Boltzmann distribution as the unique solution to a combinatorial Hodge theory problem in homological degree zero on a finite graph. By substituting for the graph a CW complex X and a choice of degree $$d \le \dim X$$ , we define by direct analogy a higher dimensional Boltzmann distribution $$\rho ^B$$ as a certain real-valued cellular $$(d-1)$$ -cycle. We then give an explicit formula for $$\rho ^B$$ . We explain how these ideas relate to the Higher Kirchhoff Network Theorem of Catanzaro et al. (Homol Homotopy Appl 17:165–189, 2015). We also deduce an improved version of the Higher Matrix-Tree Theorems of Catanzaro et al. (Homol Homotopy Appl 17:165–189, 2015).
DISPOT: a simple knowledge-based protein domain interaction statistical potential.

MOTIVATION The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task. RESULTS Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. AVAILABILITY AND IMPLEMENTATION DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Efficient Algorithms for Approximate Smooth Selection

In this paper, we provide efficient algorithms for approximate $${\mathcal {C}}^m({\mathbb {R}}^n, {\mathbb {R}}^D)-$$ selection. In particular, given a set E, a constant $$M_0 > 0$$ , and convex sets $$K(x) \subset {\mathbb {R}}^D$$ for $$x \in E$$ , we show that an algorithm running in $$C(\tau ) N \log N$$ steps is able to solve the smooth selection problem of selecting a point $$y \in (1+\tau )\blacklozenge K(x)$$ for $$x \in E$$ for an appropriate dilation of K(x), $$(1+\tau )\blacklozenge K(x)$$ , and guaranteeing that a function interpolating the points (x, y) will be $${\mathcal {C}}^m({\mathbb {R}}^n, {\mathbb {R}}^D)$$ with norm bounded by CM.
Driving an Ornstein-Uhlenbeck Process to Desired First-Passage Time Statistics

First-passage time (FPT) of an Ornstein-Uhlenbeck (OU) process is of interest in various contexts. This paper investigates tuning of FPT moments of an OU process. The region of interest is defined by two boundaries, out of which at least one is absorbing. We find that the FPT distribution of an OU process is scale invariant with respect to the drift parameter, i.e., the drift parameter just controls the mean FPT and does not affect the shape of the distribution. This facilitates independent control of the mean and the coefficient of variation (CV) of the FPT. We also explore the effect of control parameters on the FPT distribution, and find parameters that minimize the distance between the FPT distribution and a desired distribution.
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poorly or not at all, and the systematic differences between simulation and the real world are typically impossible to eliminate. In this work, we investigate how data from both simulation and the real world can be combined in a hybrid deep reinforcement learning algorithm. Our method uses real-world data to learn about the dynamics of the system, and simulated data to learn a generalizable perception system that can enable the robot to avoid collisions using only a monocular camera. We demonstrate our approach on a real-world nano aerial vehicle collision avoidance task, showing that with only an hour of real-world data, the quadrotor can avoid collisions in new environments with various lighting conditions and geometry. Code, instructions for building the aerial vehicles, and videos of the experiments can be found at github.com/gkahn13/GtS
Effects of Irritable Bowel Syndrome on Daily Activities Vary Among Subtypes Based on Results From the IBS in America Survey

Background & Aims Irritable bowel syndrome (IBS) is associated with significant disease burden and decreased quality of life (QOL). We investigated the effects of IBS on different areas of daily function and compared these among disease subtypes. Methods The Life with IBS survey was conducted by Gfk Public Affairs & Corporate Communications from September through October 2015. Respondents met Rome III criteria for constipation-predominant or diarrhea-predominant IBS (IBS-C and IBS-D, respectively). Data were collected from 3254 individuals (mean age, 47 years; 81% female; and 90% Caucasian) who met IBS criteria. Results Respondents who were employed or in school (n = 1885) reported that IBS symptoms affected their productivity an average of 8.0 days out of the month and they missed approximately 1.5 days of work/school per month because of IBS. More than half the individuals reported that their symptoms were very bothersome. Individuals with IBS-C were more likely than with IBS-D to report avoiding sex, difficulty concentrating, and feeling self-conscious. Individuals with IBS-D reported more avoidance of places without bathrooms, difficulty making plans, avoiding leaving the house, and reluctance to travel. These differences remained when controlling for symptom bothersomeness, age, sex, and employment status. In exchange for 1 month of relief from IBS, more than half of the sample reported they would be willing to give up caffeine or alcohol, 40% would give up sex, 24.5% would give up cell phones, and 21.5% would give up the internet for 1 month. Conclusions Although the perceived effects of IBS symptoms on productivity are similar among its subtypes, patients with IBS-C and IBS-D report differences in specific areas of daily function.
Certified Robustness to Adversarial Word Substitutions

State-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of possible transformations scales exponentially with text length, so data augmentation cannot cover all transformations of an input. This paper considers one exponentially large family of label-preserving transformations, in which every word in the input can be replaced with a similar word. We train the first models that are provably robust to all word substitutions in this family. Our training procedure uses Interval Bound Propagation (IBP) to minimize an upper bound on the worst-case loss that any combination of word substitutions can induce. To evaluate models’ robustness to these transformations, we measure accuracy on adversarially chosen word substitutions applied to test examples. Our IBP-trained models attain 75% adversarial accuracy on both sentiment analysis on IMDB and natural language inference on SNLI; in comparison, on IMDB, models trained normally and ones trained with data augmentation achieve adversarial accuracy of only 12% and 41%, respectively.
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have made a theory of learning dynamics elusive. In this work, we show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian neural networks and Gaussian processes, gradient-based training of wide neural networks with a squared loss produces test set predictions drawn from a Gaussian process with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions.
Parameter-free Sentence Embedding via Orthogonal Basis.

We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word’s novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks

While Nash equilibrium in extensive-form games is well understood, very little is known about the properties of extensive-form correlated equilibrium (EFCE), both from a behavioral and from a computational point of view. In this setting, the strategic behavior of players is complemented by an external device that privately recommends moves to agents as the game progresses; players are free to deviate at any time, but will then not receive future recommendations. Our contributions are threefold. First, we show that an EFCE can be formulated as the solution to a bilinear saddle-point problem. To showcase how this novel formulation can inspire new algorithms to compute EFCEs, we propose a simple subgradient descent method which exploits this formulation and structural properties of EFCEs. Our method has better scalability than the prior approach based on linear programming. Second, we propose two benchmark games, which we hope will serve as the basis for future evaluation of EFCE solvers. These games were chosen so as to cover two natural application domains for EFCE: conflict resolution via a mediator, and bargaining and negotiation. Third, we document the qualitative behavior of EFCE in our proposed games. We show that the social-welfare-maximizing equilibria in these games are highly nontrivial and exhibit surprisingly subtle sequential behavior that so far has not received attention in the literature.
Genetic Selection for Alcohol Preference in Mice Alters Dorsal Striatum Neurotransmission.

Background Although it is widely acknowledged that the risk of developing an alcohol use disorder (AUD) is strongly influenced by genetic factors, very little is known about how this genetic predisposition may alter neurotransmission in a way that promotes AUD susceptibility. The dorsal striatum has garnered increased attention as a brain region of interest in AUD development given its significant roles in goal-directed and habitual behavior. Methods In the present work, dorsal striatal neurotransmission parameters were measured in preclinical mouse models of high and low AUD risk. We performed brain slice whole-cell patch clamp electrophysiological recordings from medium spiny neurons (MSNs) in the dorsomedial (DMS) and dorsolateral (DLS) striatum of naive adult male and female selectively bred high- and low-alcohol-preferring lines of mice (HAP and LAP). Results We found that MSNs of HAP mice were significantly more excitable than those of LAP mice, specifically in the DLS. Additionally, the frequencies of spontaneous glutamate- and GABA-mediated currents were both elevated in HAP mice relative to LAP mice in both dorsal striatal subregions, whereas amplitude differences were more variable between lines and subregions. AMPAR/NMDAR current ratios were significantly lower in HAP mice in both DLS and DMS. Conclusions Collectively, these results suggest that genetic predisposition for high or low alcohol consumption produces significantly different basal functional states within both DLS and DMS which may be important factors in the behavioral phenotypes of HAP and LAP mice.
Lorentz Symmetry from Multifractal Scaling

We show that relativistic invariance is encoded in the multifractal structure of the Standard Model near the electroweak scale. The approximate scale invariance of this structure accounts for the flavor hierarchy and chiral symmetry breaking in the electroweak sector. Surprisingly, it also accounts for breaking of conformal symmetry in General Relativity and the emergence of a non-vanishing cosmological constant.
Symplectic embeddings of products

McDuff and Schlenk determined when a four-dimensional ellipsoid can be symplectically embedded into a four-dimensional ball, and found that when the ellipsoid is close to round, the answer is given by an "infinite staircase" determined by the odd-index Fibonacci numbers. We show that this result still holds in higher dimensions when we "stabilize" the embedding problem.
Frequency estimation derived from a dynamical system analysis

The main concern of this paper is to build a method of characterization and identification of a dynamical system with several degrees of freedom when only one observation record is available. The analysis of the number of degrees of freedom provided by performing a principal component analysis leads to an eigendecomposition-based method. The link between the proposed method and the MUSIC algorithm is then drawn. This approach gives a new insight on the use of this type of methods.
Efficient Collection of Connected Vehicles Data With Precision Guarantees

Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and control of transportation systems. The outstanding challenge for such analysis is how to continuously collect and process extremely large volumes of data. To address this challenge, efficient collection of data is critical to prevent overburdening the communication systems and overreaching computational and memory capacity. In this work, we propose an efficient data collection scheme that selects and transmits only a small subset of the data to alleviate data transmission burden. As a demonstration, we have used the proposed approach to select the data points to be transmitted from 10,000 connected vehicles trips available in the Safety Pilot Model Deployment dataset. The presented results show that collection ratio can be as small as 0.05 depending on the required precision. Moreover, a simulation study was performed to evaluate the travel time estimation accuracy using the proposed data collection approach. Results show that the proposed data collection approach can significantly improve travel time estimation accuracy.
Employment accessibility and rising seas

Abstract Recent projections suggest worst-case scenarios of more than six ft (1.8 m) of global mean sea-level rise by end of century, progressively making coastal flood events more frequent and more severe. The impact on transportation systems along coastal regions is likely to be substantial. An analysis of impacts for Atlantic and Cape May counties in southern New Jersey is conducted. The impact on accessibility to employment is analyzed using a dataset of sea-level increases merged with road network (TIGER) data and Census data on population and employment. Using measures of accessibility, it is shown how access will be reduced at the block-group level. An additional analysis of low and high income quartiles suggest that lower-income block groups will have greater reductions in accessibility. The implication is that increasing sea levels will have large impacts on people and the economy, and large populations will have access to employment disrupted well before their own properties or places of employment may begin to flood (assuming no adaptation).