cssi/SciSciGPT-SciSciNet huggingface dataset:
Stable recovery of sparse vectors from random sinusoidal feature maps

Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimensionality reduction by first multiplying each data vector by a random Gaussian matrix, and then computing an element-wise sinusoid. Theoretical analysis shows that collecting a sufficient number of such features can be reliably used for subsequent inference in kernel classification and regression. In this work, we demonstrate that with a mild increase in the dimension of the embedding, it is also possible to reconstruct the data vector from such random sinusoidal features, provided that the underlying data is sparse enough. In particular, we propose a numerically stable algorithm for reconstructing the data vector given the nonlinear features, and analyze its sample complexity. Our algorithm can be extended to other types of structured inverse problems, such as demixing a pair of sparse (but incoherent) vectors. We support the efficacy of our approach via numerical experiments.
Volumes and Ehrhart polynomials of flow polytopes

The Lidskii formula for the type $$A_n$$ root system expresses the volume and Ehrhart polynomial of the flow polytope of the complete graph with nonnegative integer netflows in terms of Kostant partition functions. For every integer polytope the volume is the leading coefficient of the Ehrhart polynomial. The beauty of the Lidskii formula is the revelation that for these polytopes their Ehrhart polynomial function can be deduced from their volume function! Baldoni and Vergne generalized Lidskii’s result for flow polytopes of arbitrary graphs G. While their formulas are combinatorial in nature, their proofs are based on residue computations. In this paper we construct canonical polytopal subdivisions of flow polytopes which we use to prove the Baldoni–Vergne–Lidskii formulas. In contrast with the original computational proof of these formulas, our proof reveal their geometry and combinatorics. We conclude by exhibiting enumerative properties of the Lidskii formulas via our canonical polytopal subdivisions.
Overcomplete tensor decomposition via convex optimization

This work develops theories and computational methods for overcomplete, non-orthogonal tensor decomposition using convex optimization. Under an incoherence condition of the rank-one factors, we show that one can retrieve tensor decomposition by solving a convex, infinite-dimensional analog of l1 minimization on the space of measures. The optimal value of this optimization defines the tensor nuclear norm. Two computational schemes are proposed to solve the infinite-dimensional optimization: semidefinite programs based on sum-of-squares relaxations and nonlinear programs that are an exact reformulation of the tensor nuclear norm. The latter exhibits superior performance compared with the state-of-the-art tensor decomposition methods.
Cooperative data exchange with unreliable clients

Consider a set of clients in a broadcast network, each of which holds a subset of packets in the ground set X. In the (coded) cooperative data exchange problem, the clients need to recover all packets in X by exchanging coded packets over a lossless broadcast channel. Several previous works analyzed this problem under the assumption that each client initially holds a random subset of packets in X. In this paper we consider a generalization of this problem for settings in which an unknown (but of a certain size) subset of clients are unreliable and their packet transmissions are subject to arbitrary erasures. For the special case of one unreliable client, we derive a closed-form expression for the minimum number of transmissions required for each reliable client to obtain all packets held by other reliable clients (with probability approaching 1 as the number of packets tends to infinity). Furthermore, for the cases with more than one unreliable client, we provide an approximation solution in which the number of transmissions per packet is within an arbitrarily small additive factor from the value of the optimal solution.
Complete Test Sets And Their Approximations

We use testing to check if a combinational circuit N always evaluates to 0 (written as N ≡ 0). We call a set of tests proving N ≡ 0 a complete test set (CTS). The conventional point of view is that to prove N ≡ 0 one has to generate a trivial CTS. It consists of all 2|X| input assignments where X is the set of input variables of N. We use the notion of a Stable Set of Assignments (SSA) to show that one can build a non-trivial CTS consisting of less than 2|X| tests. Given an unsatisfiable CNF formula H(W ), an SSA of H is a set of assignments to W that proves unsatisfiability of H. A trivial SSA is the set of all 2|W| assignments to W. Importantly, real-life formulas can have non-trivial SSAs that are much smaller than 2|W|. In general, construction of even non-trivial CTSs is inefficient. We describe a much more efficient approach where tests are extracted from an SSA built for a projection of N on a subset of its variables. These tests can be viewed as an approximation of a CTS for N. We describe potential applications of our approach. We show experimentally that it can be used to facilitate hitting corner cases and expose bugs in sequential circuits overlooked due to checking "misdefined" properties.
The rigidity transition in random graphs

As we add rigid bars between points in the plane, at what point is there a giant (linear-sized) rigid component, which can be rotated and translated, but which has no internal flexibility? If the points are generic, this depends only on the combinatorics of the graph formed by the bars. We show that if this graph is an Erdos-Renyi random graph G(n, c/n), then there exists a sharp threshold for a giant rigid component to emerge. For c c2, w.h.p. there is a giant rigid component. The constant c2 a 3.588 is the threshold for 2-orientability, discovered independently by Fernholz and Ramachandran and Cain, Sanders, and Wormald in SODA'07. We also give quantitative bounds on the size of the giant rigid component when it emerges, proving that it spans a (1 − o(1))-fraction of the vertices in the (3 + 2)-core. Informally, the (3 + 2)-core is maximal induced subgraph obtained by starting from the 3-core and then inductively adding vertices with 2 neighbors in the graph obtained so far.
Extremal functions of forbidden multidimensional matrices

Abstract Pattern avoidance is a central topic in graph theory and combinatorics. Pattern avoidance in matrices has applications in computer science and engineering, such as robot motion planning and VLSI circuit design. A d -dimensional zero–one matrix A avoids another d -dimensional zero–one matrix P if no submatrix of A can be transformed to P by changing some ones to zeros. A fundamental problem is to study the maximum number of nonzero entries in a d -dimensional n × ⋯ × n matrix that avoids P . This maximum number, denoted by f ( n , P , d ) , is called the extremal function. We advance the extremal theory of matrices in two directions. The methods that we use come from combinatorics, probability, and analysis. Firstly, we obtain non-trivial lower and upper bounds on f ( n , P , d ) when n is large for every d -dimensional block permutation matrix P . We establish the tight bound Θ ( n d − 1 ) on f ( n , P , d ) for every d -dimensional tuple permutation matrix P . This tight bound has the lowest possible order that an extremal function of a nontrivial matrix can ever achieve. Secondly, we show that the limit inferior of the sequence { f ( n , P , d ) n d − 1 } has a lower bound 2 Ω ( k 1 ∕ 2 ) for a family of k × ⋯ × k permutation matrices P . We also improve the upper bound on the limit superior from 2 O ( k log k ) to 2 O ( k ) for all k × ⋯ × k permutation matrices and show that the new upper bound also holds for tuple permutation matrices.
THE MODULI SPACE OF 1|1-DIMENSIONAL COMPLEX ASSOCIATIVE ALGEBRAS

In this paper, we study the moduli space of 1|1-dimensional complex associative algebras. We give a complete calculation of the cohomology of every element in the moduli space, as well as compute their versal deformations.
Two weight estimates with matrix measures for well localized operators

In this paper, we give necessary and sufficient conditions for weighted $L^2$ estimates with matrix-valued measures of well localized operators. Namely, we seek estimates of the form: \[ \| T(\mathbf{W} f)\|_{L^2(\mathbf{V})} \le C\|f\|_{L^2(\mathbf{W})} \] where $T$ is formally an integral operator with additional structure, $\mathbf{W}, \mathbf{V}$ are matrix measures, and the underlying measure space possesses a filtration. The characterization we obtain is of Sawyer-type; in particular we show that certain natural testing conditions obtained by studying the operator and its adjoint on indicator functions suffice to determine boundedness. Working in both the matrix weighted setting and the setting of measure spaces with arbitrary filtrations requires novel modifications of a T1 proof strategy; a particular benefit of this level of generality is that we obtain polynomial estimates on the complexity of certain Haar shift operators.
Generic representation theory of quivers with relations

Abstract The irreducible components of varieties parametrizing the finite dimensional representations of a finite dimensional algebra Λ are explored, with regard to both their geometry and the structure of the modules they encode. Provided that the base field is algebraically closed and has infinite transcendence degree over its prime field, we establish the existence and uniqueness (not up to isomorphism, but in a strong sense to be specified) of a generic module for any irreducible component C , that is, of a module which displays all categorically defined generic properties of the modules parametrized by C ; the crucial point of the existence statement – a priori almost obvious – lies in the description of such a module in a format accessible to representation-theoretic techniques. Our approach to generic modules over path algebras modulo relations, by way of minimal projective resolutions, is largely constructive. It is explicit for large classes of algebras of wild type. We follow with an investigation of the properties of such generic modules in terms of quiver and relations. The sharpest specific results on all fronts are obtained for truncated path algebras, that is, for path algebras of quivers modulo ideals generated by all paths of a fixed length; this class of algebras extends the comparatively thoroughly studied hereditary case, displaying many novel features.
Remarks on Springer’s representations

We give an apriori description of a set of irreducible representations of a Weyl group which parametrize the nilpotent orbits in the Lie algebra of a connected reductive group in arbitrary characteristic. We also answer a question of Serre concerning the conjugacy class of a power of a unipotent element in a connected reductive group.
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts

Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English during in-person meetings with hearing individuals, we have augmented a transcript of the Switchboard conversational dialogue corpus with an overlay of word-importance annotations, with a numeric score for each word, to indicate its importance to the meaning of each dialogue turn. Further, we demonstrate the utility of this corpus by training an automatic word importance labeling model; our best performing model has an F-score of 0.60 in an ordinal 6-class word-importance classification task with an agreement (concordance correlation coefficient) of 0.839 with the human annotators (agreement score between annotators is 0.89). Finally, we discuss our intended future applications of this resource, particularly for the task of evaluating ASR performance, i.e. creating metrics that predict ASR-output caption text usability for DHH users better thanWord Error Rate (WER).
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

The amount of data available in the world is growing faster than our ability to deal with it. However, if we take advantage of the internal structure, data may become much smaller for machine learning purposes. In this paper we focus on one of the fundamental machine learning tasks, empirical risk minimization (ERM), and provide faster algorithms with the help from the clustering structure of the data. We introduce a simple notion of raw clustering that can be efficiently computed from the data, and propose two algorithms based on clustering information. Our accelerated algorithm ClusterACDM is built on a novel Haar transformation applied to the dual space of the ERM problem, and our variance-reduction based algorithm ClusterSVRG introduces a new gradient estimator using clustering. Our algorithms outperform their classical counterparts ACDM and SVRG respectively.
Some results for impulsive problems via Morse theory

Abstract We use Morse theory to study impulsive problems. First we consider asymptotically piecewise linear problems with superlinear impulses, and prove a new existence result for this class of problems using the saddle point theorem. Next we compute the critical groups at zero when the impulses are asymptotically linear near zero, in particular, we identify an important resonance set for this problem. As an application, we finally obtain a nontrivial solution for asymptotically piecewise linear problems with impulses that are asymptotically linear at zero and superlinear at infinity. Our results here are based on the simple observation that the underlying Sobolev space naturally splits into a certain finite dimensional subspace where all the impulses take place and its orthogonal complement that is free of impulsive effects.
Volume computation for sparse Boolean quadric relaxations

Abstract Motivated by understanding the quality of tractable convex relaxations of intractable polytopes, Ko et al. gave a closed-form expression for the volume of a standard relaxation Q ( G ) of the Boolean quadric polytope (also known as the (full) correlation polytope) P ( G ) of the complete graph G = K n . We extend this work to structured sparse graphs. In particular, we (i) demonstrate the existence of an efficient algorithm for vol ( Q ( G ) ) when G has bounded treewidth, (ii) give closed-form expressions (and asymptotic behaviors) for vol ( Q ( G ) ) for all stars, paths, and cycles, and (iii) give a closed-form expression for vol ( P ( G ) ) for all cycles. Further, we demonstrate that when G is a cycle, the simple relaxation Q ( G ) is a very close model for the much more complicated P ( G ) . Additionally, we give some computational results demonstrating that this behavior of the cycle seems to extend to more complicated graphs. Finally, we speculate on the possibility of extending some of our results to cactii or even series–parallel graphs.
Compactness of Hankel operators and analytic discs in the boundary of pseudoconvex domains

Abstract Using several complex variables techniques, we investigate the interplay between the geometry of the boundary and compactness of Hankel operators. Let β be a function smooth up to the boundary on a smooth bounded pseudoconvex domain Ω ⊂ C n . We show that, if Ω is convex or the Levi form of the boundary of Ω is of rank at least n − 2 , then compactness of the Hankel operator H β implies that β is holomorphic “along” analytic discs in the boundary. Furthermore, when Ω is convex in C 2 we show that the condition on β is necessary and sufficient for compactness of H β .
A game-theoretic approach to Robust Fusion and Kalman filtering under unknown correlations

This work addresses the problem of fusing two random vectors with unknown cross-correlations. We present a formulation and a numerical method for computing the optimal estimate in the minimax sense. We extend our formulation to linear measurement models that depend on two random vectors with unknown cross-correlations. As an application we consider the problem of decentralized state estimation. The proposed estimator takes cross-correlations into account while being less conservative than the widely used Covariance Intersection. We demonstrate the superiority of the proposed method compared to Covariance Intersection with numerical examples and simulations within the specific application of decentralized state estimation.
Global Duality, Signature Calculus and the Discrete Logarithm Problem

We develop a formalism for studying the discrete logarithm problem for the multiplicative group and for elliptic curves over finite fields by lifting the respective group to an algebraic number field and using global duality. One of our main tools is the signature of a Dirichlet character (in the multiplicative group case) or principal homogeneous space (in the elliptic curve case), which is a measure of its ramification at certain places. We then develop signature calculus, which generalizes and refines the index calculus method. Finally, using some heuristics, we show the random polynomial time equivalence for these two cases between the problem of computing signatures and the discrete logarithm problem. This relates the discrete logarithm problem to some very well-known problems in algebraic number theory and arithmetic geometry.
Viable phenomenologies of the normal state of cuprates

We revisit the problem of constructing an elusive scaling theory of the strange-metal phase of the cuprates. By using the four robust experimentally established temperature dependences as the constitutive relations we then predict the scaling behaviors of a number of other observables, all those for which the reliable data are available being in agreement with experiment. Such predictions are also contrasted against the recent proposal inspired by the holographic approach, thus allowing one to critically assess the status of the latter.
UNIRATIONALITY OF CUBIC HYPERSURFACES

Segre proved that a smooth cubic surface over is unirational if and only if it has a rational point. We prove that the result also holds for cubic hypersurfaces over any field, including finite fields.AMS 2000 Mathematics subject classification: Primary 14G05; 14G15. Secondary 11G25; 11D25
Stable homology of automorphism groups of free groups

Homology of the group Aut(F_n) of automorphisms of a free group on n generators is known to be independent of n in a certain stable range. Using tools from homotopy theory, we prove that in this range it agrees with homology of symmetric groups. In particular, Aut(F_n) has no stable rational homology, settling a conjecture of Hatcher and Vogtmann.
The Quillen model category of topological spaces

Abstract We give a complete and careful proof of Quillen’s theorem on the existence of the standard model category structure on the category of topological spaces. We do not assume any familiarity with model categories.
The algebraic geometry of Kazhdan–Lusztig–Stanley polynomials

Kazhdan-Lusztig-Stanley polynomials are a combinatorial generalization of Kazhdan-Lusztig polynomials of for Coxeter groups that include g-polynomials of polytopes and Kazhdan-Lusztig polynomials of matroids. In the cases of Weyl groups, rational polytopes, and realizable matroids, one can count points over finite fields on flag varieties, toric varieties, or reciprocal planes to obtain cohomological interpretations of these polynomials. We survey these results and unite them under a single geometric framework.
von Neumann's problem and extensions of non-amenable equivalence relations

The goals of this paper are twofold. First, we generalize the result of Gaboriau and Lyons [GL07] to the setting of von Neumann's problem for equivalence relations, proving that for any non-amenable ergodic probability measure preserving (pmp) equivalence relation $\mathcal{R}$, the Bernoulli extension over a non-atomic base space $(K, \kappa)$ contains the orbit equivalence relation of a free ergodic pmp action of $\mathbb{F}_2$. Moreover, we provide conditions which imply that this holds for any non-trivial probability space $K$. Second, we use this result to prove that any non-amenable unimodular locally compact second countable group admits uncountably many free ergodic pmp actions which are pairwise not von Neumann equivalent (hence, pairwise not orbit equivalent).
A COMPLETE SET OF INTERTWINERS FOR ARBITRARY TENSOR PRODUCT REPRESENTATIONS VIA CURRENT ALGEBRAS

Let $\mathfrak{g}$ be a reductive Lie algebra and let $\vec{V}(\vec{\lambda})$ be a tensor product of $k$ copies of finite dimensional irreducible $\mathfrak{g}$-modules. Choosing $k$ points in $\mathbb{C}$, $\vec{V}(\vec{\lambda})$ acquires a natural structure of the current algebra $\mathfrak{g}\otimes \mathbb{C}[t]$-module. Following a work of Rao [R], we produce an explicit and complete set of $\mathfrak{g}$-module intertwiners of $\vec{V}(\vec{\lambda})$ in terms of the action of the current algebra.
Discriminative Cross-View Binary Representation Learning

Learning compact representation is vital and challenging for large scale multimedia data. Cross-view/crossmodal hashing for effective binary representation learning has received significant attention with exponentially growing availability of multimedia content. Most existing crossview hashing algorithms emphasize the similarities in individual views, which are then connected via cross-view similarities. In this work, we focus on the exploitation of the discriminative information from different views, and propose an end-to-end method to learn semantic-preserving and discriminative binary representation, dubbed Discriminative Cross-View Hashing (DCVH), in light of learning multitasking binary representation for various tasks including cross-view retrieval, image-to-image retrieval, and image annotation/tagging. The proposed DCVH has the following key components. First, it uses convolutional neural network (CNN) based nonlinear hashing functions and multilabel classification for both images and texts simultaneously. Such hashing functions achieve effective continuous relaxation during training without explicit quantization loss by using Direct Binary Embedding (DBE) layers. Second, we propose an effective view alignment via Hamming distance minimization, which is efficiently accomplished by bit-wise XOR operation. Extensive experiments on two image-text benchmark datasets demonstrate that DCVH outperforms state-of-the-art cross-view hashing algorithms as well as single-view image hashing algorithms. In addition, DCVH can provide competitive performance for image annotation/tagging.
Local-global principles in circle packings

We generalize work by Bourgain and Kontorovich [On the local-global conjecture for integral Apollonian gaskets, Invent. Math. 196 (2014), 589–650] and Zhang [On the local-global principle for integral Apollonian 3-circle packings, J. Reine Angew. Math. 737, (2018), 71–110], proving an almost local-to-global property for the curvatures of certain circle packings, to a large class of Kleinian groups. Specifically, we associate in a natural way an infinite family of integral packings of circles to any Kleinian group satisfying certain conditions, where is an imaginary quadratic field, and show that the curvatures of the circles in any such packing satisfy an almost local-to-global principle. A key ingredient in the proof is that possesses a spectral gap property, which we prove for any infinite-covolume, geometrically finite, Zariski dense Kleinian group in containing a Zariski dense subgroup of .
The theory of prime ideals of Leavitt path algebras over arbitrary graphs

Abstract Given an arbitrary graph E and a field K, the prime ideals as well as the primitive ideals of the Leavitt path algebra L K ( E ) are completely characterized in terms of their generators. The stratification of the prime spectrum of L K ( E ) is indicated with information on its individual stratum. Necessary and sufficient conditions are given on the graph E under which every prime ideal of the Leavitt path algebra L K ( E ) is primitive. Leavitt path algebras with Krull dimension zero are characterized and those with various prescribed Krull dimension are constructed. The minimal prime ideals of L K ( E ) are described in terms of the graphical properties of E and using this, complete descriptions of the height one as well as the co-height one prime ideals of L K ( E ) are given.
Images of nowhere differentiable Lipschitz maps of $[0,1]$ into $L_1[0,1]$

The main result: for every sequence $\{\omega_m\}_{m=1}^\infty$ of positive numbers ($\omega_m>0)$ there exists an isometric embedding $F:[0,1]\to L_1[0,1]$ which is nowhere differentiable, but for each $t\in [0,1]$ the image $F_t$ is infinitely differentiable on $[0,1]$ with bounds $\max_{x\in[0,1]}|F_t^{(m)}(x)|\le\omega_m$ and has an analytic extension to the complex plane which is an entire function.
Cross-graph learning of multi-relational associations

Cross-graph Relational Learning (CGRL) refers to the problem of predicting the strengths or labels of multi-relational tuples of heterogeneous object types, through the joint inference over multiple graphs which specify the internal connections among each type of objects. CGRL is an open challenge in machine learning due to the daunting number of all possible tuples to deal with when the numbers of nodes in multiple graphs are large, and because the labeled training instances are extremely sparse as typical. Existing methods such as tensor factorization or tensor-kernel machines do not work well because of the lack of convex formulation for the optimization of CGRL models, the poor scalability of the algorithms in handling combinatorial numbers of tuples, and/or the non-transductive nature of the learning methods which limits their ability to leverage unlabeled data in training. This paper proposes a novel framework which formulates CGRL as a convex optimization problem, enables transductive learning using both labeled and unlabeled tuples, and offers a scalable algorithm that guarantees the optimal solution and enjoys a linear time complexity with respect to the sizes of input graphs. In our experiments with a subset of DBLP publication records and an Enzyme multi-source dataset, the proposed method successfully scaled to the large cross-graph inference problem, and outperformed other representative approaches significantly.
Distribution-free detection of a submatrix

Abstract We consider the problem of detecting the presence of a submatrix with larger-than-usual values in a large data matrix. This problem was considered in Butucea and Ingster (2013) under a one-parameter exponential family, and one of the procedures they analyzed is the scan test. Taking a nonparametric stance, we show that a calibration by permutation leads to the same (first-order) asymptotic performance. This is true for the two types of permutations we consider. We also study the corresponding rank-based variants and quantify precisely the loss in asymptotic power.
Steroid receptors and vertebrate evolution

Considering that life on earth evolved about 3.7 billion years ago, vertebrates are young, appearing in the fossil record during the Cambrian explosion about 542 to 515 million years ago. Results from sequence analyses of genomes from bacteria, yeast, plants, invertebrates and vertebrates indicate that receptors for adrenal steroids (aldosterone, cortisol), and sex steroids (estrogen, progesterone, testosterone) also are young, with an estrogen receptor and a 3-ketosteroid receptor first appearing in basal chordates (cephalochordates: amphioxus), which are close ancestors of vertebrates. Duplication and divergence of the 3-ketosteroid receptor yielded an ancestral progesterone receptor and an ancestral corticoid receptor, the common ancestor of the glucocorticoid and mineralocorticoid receptors, in jawless vertebrates (cyclostomes: lampreys, hagfish). This was followed by evolution of an androgen receptor, distinct glucocorticoid and mineralocorticoid receptors and estrogen receptor-α and -β in cartilaginous fishes (Chondrichthyes: sharks). Further evolution of mineralocorticoid signaling occurred with the evolution of aldosterone synthase in lungfish, a forerunner of terrestrial vertebrates. Adrenal and sex steroid receptors are not found in echinoderms and hemichordates, which are ancestors in the lineage of cephalochordates and vertebrates. The evolution of steroid receptors at key nodes in the evolution of vertebrates, in which steroid receptors act as master switches to regulate differentiation, development, reproduction, immune responses, electrolyte homeostasis and stress responses, suggests an important role for steroid receptors in the evolutionary success of vertebrates, considering that the human genome contains about 22,000 genes, which is not much larger than genomes of invertebrates, such as Caenorhabditis elegans (~18,000 genes) and Drosophila (~14,000 genes).
Knock Intensity Distribution and a Stochastic Control Framework for Knock Control

One of the main factors limiting the efficiency of spark-ignited engines is the occurrence of engine knock. In high temperature and high pressure in-cylinder conditions, the fuel-air mixture auto-ignites creating pressure shock waves in the cylinder. Knock can significantly damage the engine and hinder its performance; as such, conservative knock control strategies are generally implemented that avoid such operating conditions at the cost of lower thermal efficiencies. Significant improvements in the performance of conventional knock controllers are possible if the properties of the knock process are better characterized and exploited in knock controller designs. One of the methods undertaken to better characterize knocking instances is to employ a probabilistic approach, in which the likelihood of knock is derived from the statistical distribution of knock intensity. In this paper, it is shown that knock intensity values at a fixed operating point for single fuel and dual fuel engines are accurately described using a mixed lognormal distribution. The fitting accuracy is compared against those for a randomly generated mixed-lognormally distributed data set, and shown to exceed a 95% accuracy threshold for almost all of the operating points tested. Additionally, this paper discusses a stochastic knock control approach that leverages the mixed lognormal distribution to adjust spark timing based on knock intensity measurements. This more informed knock control strategy would allow for improvements in engine performance and fuel efficiency by minimizing knock occurrences.
Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

Abstract ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase-field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHAD-based elastochemical phase-field model. We motivate our work by investigating the microstructure evolution in Mg2SixSn 1 − x thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase-field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database ( OPMD ), available at http://microstructures.net .
SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.

Summary Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
Estimation of acoustic impedance from seismic data using temporal convolutional network

In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data. Knowledge of physical properties can prove helpful in identifying key structures in the subsurface for hydrocarbon exploration. In this work, we propose a workflow for predicting acoustic impedance (AI) from seismic data using a network architecture based on Temporal Convolutional Network by posing the problem as that of sequence modeling. The proposed workflow overcomes some of the problems that other network architectures usually face, like gradient vanishing in Recurrent Neural Networks, or overfitting in Convolutional Neural Networks. The proposed workflow was used to predict AI on Marmousi 2 dataset with an average $r^{2}$ coefficient of $91\%$ on a hold-out validation set.
Autonomous Exploration, Reconstruction, and Surveillance of 3D Environments Aided by Deep Learning

We propose a greedy and supervised learning approach for visibility-based exploration, reconstruction and surveillance. Using a level set representation, we train a convolutional neural network to determine vantage points that maximize visibility. We show that this method drastically reduces the on-line computational cost and determines a small set of vantage points that solve the problem. This enables us to efficiently produce highly-resolved and topologically accurate maps of complex 3D environments. Unlike traditional next-best-view and frontier-based strategies, the proposed method accounts for geometric priors while evaluating potential vantage points. While existing deep learning approaches focus on obstacle avoidance and local navigation, our method aims at finding near-optimal solutions to the more global exploration problem. We present realistic simulations on 2D and 3D urban environments.
On the transfer of technology from universities: The impact of the Bayh–Dole Act of 1980 on the institutionalization of university research

While the academic and policy literature has focused on patent counts and patent quality as possible outcome measures to evaluate the impact of the U.S. Bayh–Dole Act of 1980, we argue that the impact of the Act on university effort to transfer its technology to the private sector might be seen more accurately by examining the trend in the initial establishment of technology transfer offices (TTOs). Using an econometric framework to identify the presence of multiple structural breaks in data on the annual number of university TTOs, we find multiple break dates over the period 1925 to 2014. One break date was in the late-1960s and a second break date occurred about 1982. We suggest, in contrast to previous findings in the literature, that the Act did have an impact on the formal internal transfer of technology from universities through patenting by providing an incentive for universities to invest in a TTO research infrastructure. We also suggest that our empirical methodology is applicable to an assessment of the impact of legislation similar to the Bayh–Dole Act in the many countries with such legislation.
Atrocity Prevention in the New Media Landscape

Journalists have traditionally played a crucial role in building public pressure on government officials to uphold their legal obligations under the 1948 UN Convention on the Prevention and Punishment of Genocide. But over the past twenty years there has been radical change in the media landscape: foreign bureaus have been shuttered, young freelance journalists have taken over some of the work traditionally done by experienced foreign correspondents, and, more recently, the advent of social media has enabled people in conflict-affected areas to tell their own stories to the world. This essay assesses the impact of these changes on atrocity prevention across the different stages of the policy process. It concludes that the new media landscape is comparatively poorly equipped to raise an early warning alarm in a way that will spur preventive action, but that it is well-positioned to sustain attention to ongoing atrocities. Unfortunately, such later stages of a crisis generally provide the most limited policy options for civilian protection.
Steadiface: Real-Time Face-Centric Stabilization On Mobile Phones

We present Steadiface, a new real-time face-centric video stabilization method that simultaneously removes hand shake and keeps subject’s head stable. We use a CNN to estimate the face landmarks and use them to optimize a stabilized head center. We then formulate an optimization problem to find a virtual camera pose that locates the face to the stabilized head center while retains smooth rotation and translation transitions across frames. We test the proposed method on fieldtest videos and show it stabilizes both the head motion and background. It is robust to large head pose, occlusion, facial appearance variations, and different kinds of camera motions. We show our method advances the state of art in selfie video stabilization by comparing against alternative methods. The whole process runs very efficiently on a modern mobile phone (8.1 ms/frame).
Crowder-Induced Conformational Ensemble Shift in Escherichia coli Prolyl-tRNA Synthetase

Abstract The effect of molecular crowding on the structure and function of Escherichia coli prolyl-transfer RNA synthetase (Ec ProRS), a member of the aminoacyl-transfer RNA synthetase family, has been investigated using a combined experimental and theoretical method. Ec ProRS is a multidomain enzyme; coupled-domain dynamics are essential for efficient catalysis. To gain insight into the mechanistic detail of the crowding effect, kinetic studies were conducted with varying concentrations and sizes of crowders. In parallel, spectroscopic and quantum chemical studies were employed to probe the “soft interactions” between crowders and protein side chains. Finally, the dynamics of the dimeric protein was examined in the presence of crowders using a long-duration (70 ns) classical molecular dynamic simulations. The results of the simulations revealed a shift in the conformational ensemble, which is consistent with the preferential exclusion of cosolutes. The “soft interactions” model of the crowding effect also explained the alteration in kinetic parameters. In summary, the study found that the effects of molecular crowding on both conformational dynamics and catalytic function are correlated in the multidomain Ec ProRS, an enzyme that is central to protein synthesis in all living cells. This study affirmed that large and small cosolutes have considerable impacts on the structure, dynamics, and function of modular proteins and therefore must be considered for stabilizing protein-based pharmaceuticals and industrial enzymes.
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud. Furthermore, we propose a training method for compensating for the potential accuracy loss due to the lossy compression of features before transmitting them to the cloud. BottleNet achieves on average 5.1× improvement in end-to-end latency and 6.9× improvement in mobile energy consumption compared to the cloud-only approach with no accuracy loss.
A collision resolution algorithm for clump-free fast moving cloth

Cloth animation is an important area of computer graphics due to its numerous applications. However, so far a fast moving cloth with multiple wrinkles has been difficult to animate because of the cloth clump problem. Cloth clumps are the frozen areas where cloth pieces are clustered unnaturally — an obstacle in making a realistic cloth animation. Hence we present a novel cloth collision resolution algorithm that prevents clump formation during fast cloth motions. The goal of our resolution algorithm is to make cloth move swiftly without having any unnatural frozen cloth clumps, while preventing any cloth-cloth and rigid-cloth penetrations at any moment of a simulation. The non-penetration status of cloth is maintained without the formation of cloth clumps regardless of the speed of cloth motion. Our algorithm is based on a particular order that we found in the resolution of cloth collisions, and can be used with any structural modeling approaches such as spring-masses or finite elements. This paper includes several realistic simulation examples involving fast motions that are clump-free.
Gravity surveys using a mobile atom interferometer

Mobile gravimetry is important in metrology, navigation, geodesy, and geophysics. Atomic gravimeters could be among the most accurate mobile gravimeters but are currently constrained by being complex and fragile. Here, we demonstrate a mobile atomic gravimeter, measuring tidal gravity variations in the laboratory and surveying gravity in the field. The tidal gravity measurements achieve a sensitivity of 37 μGal/ Hz (1 μGal = 10 nm/s2) and a long-term stability of better than 2 μGal, revealing ocean tidal loading effects and recording several distant earthquakes. We survey gravity in the Berkeley Hills with an uncertainty of around 0.04 mGal and determine the density of the subsurface rocks from the vertical gravity gradient. With simplicity and sensitivity, our instrument paves the way for bringing atomic gravimeters to field applications.
A screen of 1,049 schizophrenia and 30 Alzheimer's‐associated variants for regulatory potential

Recent genome-wide association studies (GWAS) identified numerous schizophrenia (SZ) and Alzheimer's disease (AD) associated loci, most outside protein-coding regions and hypothesized to affect gene transcription. We used a massively parallel reporter assay to screen, 1,049 SZ and 30 AD variants in 64 and nine loci, respectively for allele differences in driving reporter gene expression. A library of synthetic oligonucleotides assaying each allele five times was transfected into K562 chronic myelogenous leukemia lymphoblasts and SK-SY5Y human neuroblastoma cells. One hundred forty eight variants showed allelic differences in K562 and 53 in SK-SY5Y cells, on average 2.6 variants per locus. Nine showed significant differences in both lines, a modest overlap reflecting different regulatory landscapes of these lines that also differ significantly in chromatin marks. Eight of nine were in the same direction. We observe no preference for risk alleles to increase or decrease expression. We find a positive correlation between the number of SNPs in linkage disequilibrium and the proportion of functional SNPs supporting combinatorial effects that may lead to haplotype selection. Our results prioritize future functional follow up of disease associated SNPs to determine the driver GWAS variant(s), at each locus and enhance our understanding of gene regulation dynamics.
Robust Inference Using Inverse Probability Weighting

Inverse probability weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of “small denominators,” trimming ...
Building Efficient Deep Neural Networks With Unitary Group Convolutions

We propose unitary group convolutions (UGConvs), a building block for CNNs which compose a group convolution with unitary transforms in feature space to learn a richer set of representations than group convolution alone. UGConvs generalize two disparate ideas in CNN architecture, channel shuffling (i.e. ShuffleNet) and block-circulant networks (i.e. CirCNN), and provide unifying insights that lead to a deeper understanding of each technique. We experimentally demonstrate that dense unitary transforms can outperform channel shuffling in DNN accuracy. On the other hand, different dense transforms exhibit comparable accuracy performance. Based on these observations we propose HadaNet, a UGConv network using Hadamard transforms. HadaNets achieve similar accuracy to circulant networks with lower computation complexity, and better accuracy than ShuffleNets with the same number of parameters and floating-point multiplies.
Linking individuation and organizational identification: Mediation through psychological safety

This paper examines how individuation, a view that organizational members are all unique individuals, induces a perception of psychological safety and how perception of psychological safety, in turn, increases one's organizational identification. Results from 66 respondents in Study 1 provided first support for the proposed mechanism. In Study 2, data collected from 176 employees in work organizations also provided evidence for this mediation model. It was found in both studies that individuation has a significantly positive association with the perception of psychological safety such that the more employees view individual members of the organization as unique individuals, the more likely they perceive that their organization is a safe environment for self-expression. Furthermore, perception of psychological safety was found to serve as a mediator linking individuation and organizational identification.
Finite Horizon Backward Reachability Analysis and Control Synthesis for Uncertain Nonlinear Systems

We present a method for synthesizing controllers to steer trajectories from an initial set to a target set on a finite time horizon. The proposed control synthesis problem is decomposed into two steps. The first step under-approximates the backward reachable set (BRS) from the target set, using level sets of storage functions. The storage function is constructed with an iterative algorithm to maximize the volume of the under-approximated BRS. The second step obtains a control law by solving a pointwise min-norm optimization problem using the pre-computed storage function. A closed-form solution of this min-norm optimization can be computed through the KKT conditions. This control synthesis framework is then extended to uncertain nonlinear systems with parametric uncertainties and $\mathcal{L}_{2}$ disturbances. The computation algorithm for all cases is derived using sum-of-squares (SOS) programming and the S-procedure. The proposed method is applied to several examples, including robotic systems.