cssi/SciSciGPT-SciSciNet huggingface dataset:
SPRK: A low-cost stewart platform for motion study in surgical robotics

To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research Kit) to translate and rotate phantom tissue. This platform is 20cm χ 20cm χ 10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot and costs $250, two orders of magnitude less than a commercial Stewart platform. The platform has a range of motion of ± 1.27 cm in translation along x, y, and z, and of ± 15o in roll, pitch, and yaw directions. The platform also has motion modes for sinusoidal motion, breathing-inspired motion, and multi-axis motion. Modular mounts facilitate pattern cutting and debridement experiments. The platform's positional controller has a time-constant of 0.2 seconds and the root-mean-square error is 1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. Construction directions, CAD models, and control software for the platform are available at github.com/BerkeleyAutomation/sprk.
Perverse sheaves and knot contact homology

Abstract In this paper, we give a new algebraic construction of knot contact homology in the sense of Ng [35] . For a link L in R 3 , we define a differential graded (DG) k-category A ˜ L with finitely many objects, whose quasi-equivalence class is a topological invariant of L. In the case when L is a knot, the endomorphism algebra of a distinguished object of A ˜ L coincides with the fully noncommutative knot DGA as defined by Ekholm, Etnyre, Ng, and Sullivan in [13] . The input of our construction is a natural action of the braid group B n on the category of perverse sheaves on a two-dimensional disk with singularities at n marked points, studied by Gelfand, MacPherson, and Vilonen in [19] . As an application, we show that the category of finite-dimensional representations of the link k-category A ˜ L = H 0 ( A ˜ L ) defined as the 0-th homology of A ˜ L is equivalent to the category of perverse sheaves on R 3 that are singular along the link L. We also obtain several generalizations of the category A ˜ L by extending the Gelfand–MacPherson–Vilonen braid group action. Detailed proofs of results announced in this paper will appear in [4] .
Dynamical modular curves for quadratic polynomial maps

Motivated by the dynamical uniform boundedness conjecture of Morton and Silverman, specifically in the case of quadratic polynomials, we give a formal construction of a certain class of dynamical analogues of classical modular curves. The preperiodic points for a quadratic polynomial map may be endowed with the structure of a directed graph satisfying certain strict conditions; we call such a graph admissible. Given an admissible graph $G$, we construct a curve $X_1(G)$ whose points parametrize quadratic polynomial maps -- which, up to equivalence, form a one-parameter family -- together with a collection of marked preperiodic points that form a graph isomorphic to $G$. Building on work of Bousch and Morton, we show that these curves are irreducible in characteristic zero, and we give an application of irreducibility in the setting of number fields. We end with a discussion of the Galois theory associated to the preperiodic points of quadratic polynomials, including a certain Galois representation that arises naturally in this setting.
QCD at imaginary chemical potential with Wilson fermions

We investigate the phase diagram in the temperature, imaginary chemical potential plane for QCD with three degenerate quark flavors using Wilson type fermions. While more expensive than the staggered fermions used in past studies in this area, Wilson fermions can be used safely to simulate systems with three quark flavors. In this talk, we focus on the (pseudo)critical line that extends from m = 0 in the imaginary chemical potential plane, trace it to the Roberge-Weiss line and determine its location relative to the Roberge-Weiss transition point. In order smoothly follow the (pseudo)critical line in this plane we perform a multi-histogram reweighting in both temperature and chemical potential. To perform reweighting in the chemical potential we use the compression formula to compute the determinants exactly. Our results are compatible with the standard scenario.
Geometry of the Pfaff Lattices

Pfaff lattice was introduced by Adler and van Moerbeke to describe the partition functions for the random matrix models of GOE and GSE type. The partition functions of those matrix models are given by the Pfaffians of certain skew-symmetric matrices called the moment matrices, and they are the $\tau$-functions of the Pfaff lattice. In this paper, we study a finite version of the Pfaff lattice equation as a Hamiltonian system. In particular, we prove the complete integrability in the sense of Arnold-Liouville, and using a moment map, we describe the real isospectral varieties of the Pfaff lattice. The image of the moment map is a convex polytope whose vertices are identified as the fixed points of the flow generated by the Pfaff lattice.
On the recovery of traveling water waves with vorticity from the pressure at the bed

Abstract We propose higher-order approximation formulae recovering the surface elevation from the pressure at the bed and the background shear flow, for small-amplitude Stokes and solitary water waves. They offer improvements over the pressure transfer function and the hydrostatic approximation. The formulae compare reasonably well with the asymptotic approximations of the exact relation between the pressure at the bed and the surface wave in the zero vorticity case, but they incorporate the effects of vorticity through the solutions of the Rayleigh equation. Several examples are discussed.
Compactons and their variational properties for degenerate KdV and NLS in dimension 1

We analyze the stationary and traveling wave solutions to a family of degenerate dispersive equations of KdV and NLS-type. In stark contrast to the standard soliton solutions for non-degenerate KdV and NLS equations, the degeneracy of the elliptic operators studied here allows for compactly supported steady or traveling states. As we work in $1$ dimension, ODE methods apply, however the models considered have formally conserved Hamiltonian, Mass and Momentum functionals, which allow for variational analysis as well.
The $p$-harmonic boundary and $D_p$-massive subsets of a graph of bounded degree

Let \(p\) be a real number greater than one and let \(\Gamma\) be a graph of bounded degree. We investigate links between the \(p\)-harmonic boundary of \(\Gamma\)and the \(D_p\)-massive subsets of \(\Gamma\). In particular, if there are \(n\) pairwise disjoint \(D_p\)-massive subsets of \(\Gamma\), then the \(p\)-harmonic boundary of \(\Gamma\) consists of at least \(n\) elements. We also show that the converse of this statement is also true. 10.1017/S0004972713000439
Optimal control of a nonconvex perturbed sweeping process

Abstract The paper concerns optimal control of discontinuous differential inclusions of the normal cone type governed by a generalized version of the Moreau sweeping process with control functions acting in both nonconvex moving sets and additive perturbations. This is a new class of optimal control problems in comparison with previously considered counterparts where the controlled sweeping sets are described by convex polyhedra. Besides a theoretical interest, a major motivation for our study of such challenging optimal control problems with intrinsic state constraints comes from the application to the crowd motion model in a practically adequate planar setting with nonconvex but prox-regular sweeping sets. Based on a constructive discrete approximation approach and advanced tools of first-order and second-order variational analysis and generalized differentiation, we establish the strong convergence of discrete optimal solutions and derive a complete set of necessary optimality conditions for discrete-time and continuous-time sweeping control systems that are expressed entirely via the problem data.
The Cohomology of the Full Directed Graph Complex

In his seminal paper “Formality conjecture”, M. Kontsevich introduced a graph complex GC1ve closely connected with the problem of constructing a formality quasi-isomorphism for Hochschild cochains. In this paper, we express the cohomology of the full directed graph complex dfGC explicitly in terms of the cohomology of GC1ve. Applications of our results include a recent work by the first author which completely characterizes homotopy classes of formality quasi-isomorphisms for Hochschild cochains in the stable setting.
Space-time fractional diffusions in Gaussian noisy environment

This paper studies the linear stochastic partial differential equation of fractional orders both in time and space variables , where is a general Gaussian noise and , . The existence and uniqueness of the solution, the moment bounds of the solution are obtained by using the fundamental solutions of the corresponding deterministic counterpart represented by the Fox H-functions. Along the way, we obtain some new properties of the fundamental solutions.
Lattice Erasure Codes of Low Rank with Noise Margins

Lattice codes of low rank are considered for an additive Gaussian noise channel with erasures. The objective is to minimize the error probability with no erasures subject to rate and power constraints while ensuring that the error probability under an allowable erasure pattern is bounded from above. Allowable erasure patterns considered here are those of fixed cardinality. Bounds on performance are derived, and several constructions are investigated for lattices in dimension four. It is shown that the problem can be viewed as a simultaneous ellipsoid packing problem, a generalization of the well known sphere packing problem.
Geodesics of learned representations

Abstract: We develop a new method for visualizing and refining the invariances of learned representations. Given two reference images (typically, differing by some transformation), we synthesize a sequence of images lying on a path between them that is of minimal length in the space of a representation (a "representational geodesic"). If the transformation relating the two reference images is an invariant of the representation, this sequence should follow the gradual evolution of this transformation. We use this method to assess the invariances of state-of-the-art image classification networks and find that surprisingly, they do not exhibit invariance to basic parametric transformations of translation, rotation, and dilation. Our method also suggests a remedy for these failures, and following this prescription, we show that the modified representation exhibits a high degree of invariance for a range of geometric image transformations.
Chronos: A Unifying Optimization Framework for Speculative Execution of Deadline-Critical MapReduce Jobs

Meeting desired application deadlines in cloud processing systems such as MapReduce is crucial as the nature of cloud applications is becoming increasingly mission-critical and deadline-sensitive. It has been shown that the execution times of MapReduce jobs are often adversely impacted by a few slow tasks, known as stragglers, which result in high latency and deadline violations. While a number of strategies have been developed in existing work to mitigate stragglers by launching speculative or clone task attempts, none of them provide a quantitative framework that optimizes the speculative execution for offering guaranteed Service Level Agreements (SLAs) to meet application deadlines. In this paper, we bring several speculative scheduling strategies together under a unifying optimization framework, called Chronos, which defines a new metric, Probability of Completion before Deadlines (PoCD), to measure the probability that MapReduce jobs meet their desired deadlines. We systematically analyze PoCD for popular strategies including Clone, Speculative-Restart, and Speculative-Resume, and quantify their PoCD in closed-form. The results illuminate an important tradeoff between PoCD and the cost of speculative execution, measured by the total (virtual) machine time required under different strategies. We propose an optimization problem to jointly optimize PoCD and execution cost in different strategies, and develop an algorithmic solution that is guaranteed to be optimal. Chronos is prototyped on Hadoop MapReduce and evaluated against three baseline strategies using both experiments and trace-driven simulations, and achieves 50% net utility increase with up to 80% PoCD and 88% cost improvements.
Homogenization and boundary layers in domains of finite type

ABSTRACTThis paper is concerned with the homogenization of Dirichlet problem of elliptic systems in a bounded, smooth domain of finite type. Both the coeﬃcients of the elliptic operator and the Dir...
Metrics on visual boundaries of CAT(0) spaces

A famous open problem asks whether the asymptotic dimension of a CAT(0) group is necessarily finite. For hyperbolic G, it is known that $${\text {asdim}}\,G$$ is bounded above by $$\dim ~\partial G+1$$ , which is known to be finite. For CAT(0) G, the latter quantity is also known to be finite, so one approach is to try proving a similar inequality. So far those efforts have failed. Motivated by these questions we work toward understanding the relationship between large scale dimension of CAT(0) groups and small scale dimension of the group’s boundary by shifting attention to the linearly controlled dimension of the boundary. To do that, one must choose appropriate metrics for the boundaries. In this paper, we suggest two candidates and develop some basic properties. Under one choice, we show that linearly controlled dimension of the boundary remains finite; under another choice, we prove that macroscopic dimension of the group is bounded above by $$2\cdot \ell $$ - $$\dim \partial G+1$$ . Other useful results are established, some basic examples are analyzed, and a variety of open questions are posed.
REPRESENTATIONS OF UNIPOTENT GROUPS OVER LOCAL FIELDS AND GUTKIN'S CONJECTURE

Let F be a finite field or a local field of any characteristic. If A is a finite dimensional associative nilpotent algebra over F, the set 1 + A of all formal expressions of the form 1+x, where x 2 A, is a locally compact group with the topology induced by the standard one on F and the multiplication (1+x)·(1+y) = 1+(x+y+xy). We prove a result conjectured by E. Gutkin in 1973: every unitary irreducible representation of 1 + A can be obtained by unitary induction from a 1-dimensional unitary character of a subgroup of the form 1 + B, where B A is an F-subalgebra. In the case where F is local and nonarchimedean we also establish an analogous result for smooth irreducible representations of 1 + A over C and show that every such representation is admissible and carries an invariant Hermitian inner product.
A martingale approach and time-consistent sampling-based algorithms for risk management in stochastic optimal control

In this paper, we consider a class of stochastic optimal control problems with risk constraints that are expressed as bounded probabilities of failure for particular initial states. We present here a martingale approach that diffuses a risk constraint into a martingale to construct time-consistent control policies. The martingale stands for the level of risk tolerance that is contingent on available information over time. By augmenting the system dynamics with the controlled martingale, the original risk-constrained problem is transformed into a stochastic target problem. We extend the incremental Markov Decision Process (iMDP) algorithm to approximate arbitrarily well an optimal feedback policy of the original problem by sampling in the augmented state space and computing proper boundary conditions for the reformulated problem. We show that the algorithm is both probabilistically sound and asymptotically optimal. The performance of the proposed algorithm is demonstrated on motion planning and control problems subject to bounded probability of collision in uncertain cluttered environments.
On left-orderability and double branched covers of Kanenobu’s knots

We show that the fundamental group of the double branched cover of an infinite family of homologically thin, non-quasi-alternating knots is not left-orderable, giving further support for a conjecture of Boyer, Gordon, and Watson that an irreducible rational homology 3-sphere is an L-space if and only if its fundamental group is not left-orderable.
Local commensurability graphs of solvable groups

ABSTRACTThe commensurability index between two subgroups A,B of a group G is [A:A∩B][B:A∩B]. This gives a notion of distance among finite index subgroups of G, which is encoded in the p-local commensurability graphs of G. We show that for any metabelian group, any component of the p-local commensurabilty graph of G has diameter bounded above by 4. However, no universal upper bound on diameters of components exists for the class of finite solvable groups. In the appendix we give a complete classification of components for upper triangular matrix groups in GL(2,&#x1d53d;q).
The Affine Sieve Beyond Expansion I: Thin Hypotenuses

We study an instance of the Affine Sieve, producing a level of distribution beyond that which can be obtained from current techniques, even assuming a Selberg/Ramanujan-type spectral gap. In particular, we consider the set of hypotenuses in a thin orbit of Pythagorean triples. Previous work [Kon07, Kon09, KO12] gave an exponent of distribution alpha < 1/12 coming from Gamburd's [Gam02] gap theta = 5/6, thereby producing R = 13 almost primes in this linear sieve problem (see Sec. 1 for definitions). If conditioned on a best possible gap theta = 1/2, the known method would give an exponent alpha < 1/4, and R = 5 almost primes. The exponent 1/4 is the natural analogue of the "Bombieri-Vinogradov" range of distribution for this problem, see Remark 1.19. In this paper, we unconditionally prove the exponent alpha < 7/24 (in the "Elliott-Halberstam" range), thereby producing R = 4 almost primes. The main tools involve developing bilinear forms and the dispersion method in the range of incomplete sums for this Affine Sieve problem.
Tell Me Where to Look: Guided Attention Inference Network

Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks such as object localization and semantic segmentation. In one common framework we address three shortcomings of previous approaches in modeling such attention maps: We (1) make attention maps an explicit and natural component of the end-to-end training for the first time, (2) provide self-guidance directly on these maps by exploring supervision from the network itself to improve them, and (3) seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. We clearly surpass the state-of-the-art on PASCAL VOC 2012 test and val. sets. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing weakly supervised learners to improve their generalization performance.
A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

Transaction data obtained by Personal Financial Management (PFM) services from financial institutes such as banks and credit card companies contain a description string from which the merchant identity and an encoded store identifier may be parsed. However, the physical location of the purchase is absent from this description. In this paper we present a method designed to recover this valuable spatial information and map merchant and identifier tuples to physical map locations. We begin by constructing a graph of customer sharing between businesses, and based on a small set of known "seed" locations we formulate this task as a maximum likelihood problem using a model of customer sharing between nearby businesses. We test our method extensively on real world data and provide statistics on the displacement error in many cities.
On the isospectral orbifold–manifold problem for nonpositively curved locally symmetric spaces

An old problem asks whether a Riemannian manifold can be isospectral to a Riemannian orbifold with nontrivial singular set. In this short note we show that under the assumption of Schanuel’s conjecture in transcendental number theory, this is impossible whenever the orbifold and manifold in question are length-commensurable compact locally symmetric spaces of nonpositive curvature associated to simple Lie groups.
Hierarchical compound Poisson factorization

Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scalable recommendation systems with extreme sparsity. HPF, however, suffers from a tight coupling of sparsity model (absence of a rating) and response model (the value of the rating), which limits the expressiveness of the latter. Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices. More importantly, HCPF decouples the sparsity model from the response model, allowing us to choose the most suitable distribution for the response. HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses. We compare HCPF with HPF on nine discrete and three continuous data sets and conclude that HCPF captures the relationship between sparsity and response better than HPF.
Centers of Mass of Poncelet Polygons, 200 Years After

The locus of the centers of mass of the family of Poncelet polygons, inscribed into a conic $\Gamma$ and circumscribed about a conic $\gamma$, is a conic homothetic to $\Gamma$.
Spectral reciprocity and matrix representations of unbounded operators

Abstract We study a family of unbounded Hermitian operators in Hilbert space which generalize the usual graph-theoretic discrete Laplacian. For an infinite discrete set X , we consider operators acting on Hilbert spaces of functions on X , and their representations as infinite matrices; the focus is on l 2 ( X ) , and the energy space H E . In particular, we prove that these operators are always essentially self-adjoint on l 2 ( X ) , but may fail to be essentially self-adjoint on H E . In the general case, we examine the von Neumann deficiency indices of these operators and explore their relevance in mathematical physics. Finally we study the spectra of the H E operators with the use of a new approximation scheme.
An Atomistic-to-Continuum Analysis of Crystal Cleavage in a Two-Dimensional Model Problem

A two-dimensional atomic mass spring system is investigated for critical fracture loads and its crack path geometry. We rigorously prove that, in the discrete-to-continuum limit, the minimal energy of a crystal under uniaxial tension leads to a universal cleavage law and energy minimizers are either homogeneous elastic deformations or configurations that are completely cracked and do not store elastic energy. Beyond critical loading, the specimen generically cleaves along a unique optimal crystallographic hyperplane. For specific symmetric crystal orientations, however, cleavage might fail. In this case a complete characterization of possible limiting crack geometries is obtained.
Neighbor-Neighbor Correlations Explain Measurement Bias in Networks

In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node's nearest neighbors. However, a node's local view of its neighbors may systematically bias perceptions of network connectivity or the prevalence of certain traits. We investigate the strong friendship paradox, which occurs when the majority of a node's neighbors have more neighbors than does the node itself. We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in real-world networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.
On the triangle space of a random graph

Settling a first case of a conjecture of M. Kahle on the homology of the clique complex of the random graph $G=G_{n,p}$, we show, roughly speaking, that (with high probability) the triangles of $G$ span its cycle space whenever each of its edges lies in a triangle (which happens (w.h.p.) when $p$ is at least about $\sqrt{(3/2)\ln n/n}$, and not below this unless $p$ is very small.) We give two related proofs of this statement, together with a relatively simple proof of a fundamental "stability" theorem for triangle-free subgraphs of $G_{n,p}$, originally due to Kohayakawa, \L uczak and R\"odl, that underlies the first of our proofs.
Investigating the Working of Text Classifiers

Text classification is one of the most widely studied tasks in natural language processing. Motivated by the principle of compositionality, large multilayer neural network models have been employed for this task in an attempt to effectively utilize the constituent expressions. Almost all of the reported work train large networks using discriminative approaches, which come with a caveat of no proper capacity control, as they tend to latch on to any signal that may not generalize. Using various recent state-of-the-art approaches for text classification, we explore whether these models actually learn to compose the meaning of the sentences or still just focus on some keywords or lexicons for classifying the document. To test our hypothesis, we carefully construct datasets where the training and test splits have no direct overlap of such lexicons, but overall language structure would be similar. We study various text classifiers and observe that there is a big performance drop on these datasets. Finally, we show that even simple models with our proposed regularization techniques, which disincentivize focusing on key lexicons, can substantially improve classification accuracy.
On a conjecture of Kashiwara

Kashiwara conjectured that the hard Lefshetz theorem and the semisimplicity theorem hold for any semisimple perverse sheaf M on a variety over a field of characteristic 0. He also conjectured that if you apply to such M the nearby cycle functor corresponding to some function then the successive quotients of the monodromy filtration are semisimple. We prove that these conjectures would follow from de Jong's conjecture on representations modulo l of the arithmetic fundamental group of a variety over a finite field.
Alternating Anderson–Richardson method: An efficient alternative to preconditioned Krylov methods for large, sparse linear systems

Abstract We present the Alternating Anderson–Richardson (AAR) method: an efficient and scalable alternative to preconditioned Krylov solvers for the solution of large, sparse linear systems on high performance computing platforms. Specifically, we generalize the recently proposed Alternating Anderson–Jacobi (AAJ) method (Pratapa et al., 2016) to include preconditioning, discuss efficient parallel implementation, and provide serial MATLAB and parallel C/C++ implementations. In serial applications to nonsymmetric systems, we find that AAR is comparably robust to GMRES, using the same preconditioning, while often outperforming it in time to solution; and find AAR to be more robust than Bi-CGSTAB for the problems considered. In parallel applications to the Helmholtz and Poisson equations, we find that AAR shows superior strong and weak scaling to GMRES, Bi-CGSTAB, and Conjugate Gradient (CG) methods, using the same preconditioning, with consistently shorter times to solution at larger processor counts. Finally, in massively parallel applications to the Poisson equation, on up to 110,592 processors, we find that AAR shows superior strong and weak scaling to CG, with shorter minimum time to solution. We thus find that AAR offers a robust and efficient alternative to current state-of-the-art solvers, with increasing advantages as the number of processors grows. Program summary Program Title: AAR Program Files doi: http://dx.doi.org/10.17632/669xrdcwry.1 Licensing provisions: GNU General Public License 3 (GPL) Programming language: MATLAB for the MATLAB version. C/C++ for the PETSc version. C/C++ for the standalone version. External routines/libraries: MATLAB 2014a or later for the MATLAB version. PETSc 3.5.3 ( http://www.mcs.anl.gov/petsc ) or later and MVAPICH2 2.1 ( http://mvapich.cse.ohio-state.edu/ ) or later for the PETSc version. MVAPICH2 2.1 or later for the standalone version. Nature of problem: Linear systems of equations. Solution method: Anderson extrapolation at periodic intervals within a preconditioned Richardson iteration. Restrictions: Jacobi preconditioning for the standalone version.
Multi-dimensional geometric complexity in urban transportation systems

Transportation networks serve as windows into the complex world of urban systems. By properly characterizing a road network, one can better understand its encompassing urban system. This study offers a geometrical approach toward capturing inherent properties of urban road networks. It offers a robust and efficient methodology toward defining and extracting three relevant indicators of road networks—area, line, and point thresholds—through measures of their grid equivalents. By applying the methodology to 50 U.S. urban systems, one can successfully observe differences between eastern versus western, coastal versus inland, and old versus young cities. Moreover, we show that many socioeconomic characteristics, as well as travel patterns, within urban systems are directly correlated with their corresponding area, line, and point thresholds.
The Emergence of Spectral Universality in Deep Networks

Recent work has shown that tight concentration of the entire spectrum of singular values of a deep network's input-output Jacobian around one at initialization can speed up learning by orders of magnitude. Therefore, to guide important design choices, it is important to build a full theoretical understanding of the spectra of Jacobians at initialization. To this end, we leverage powerful tools from free probability theory to provide a detailed analytic understanding of how a deep network's Jacobian spectrum depends on various hyperparameters including the nonlinearity, the weight and bias distributions, and the depth. For a variety of nonlinearities, our work reveals the emergence of new universal limiting spectral distributions that remain concentrated around one even as the depth goes to infinity.
Charmanteau: Character Embedding Models For Portmanteau Creation.

Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.
Species Tree Inference from Gene Splits by Unrooted STAR Methods

The $\text{NJ}_{st}$ method was proposed by Liu and Yu to infer a species tree topology from unrooted topological gene trees. While its statistical consistency under the multispecies coalescent model was established only for a four-taxon tree, simulations demonstrated its good performance on gene trees inferred from sequences for many taxa. Here, we prove the statistical consistency of the method for an arbitrarily large species tree. Our approach connects $\text{NJ}_{st}$ to a generalization of the STAR method of Liu, Pearl, and Edwards, and a previous theoretical analysis of it. We further show $\text{NJ}_{st}$ utilizes only the distribution of splits in the gene trees, and not their individual topologies. Finally, we discuss how multiple samples per taxon per gene should be handled for statistical consistency.
Inversions of Semistandard Young Tableaux

A tableau inversion is a pair of entries from the same column of a row-standard tableau that lack the relative ordering necessary to make the tableau column-standard. An $i$-inverted Young tableau is a row-standard tableau with precisely $i$ inversion pairs, and may be interpreted as a generalization of (column-standard) Young tableaux. Inverted Young tableaux that lack repeated entries were introduced by Fresse to calculate the Betti numbers of Springer fibers in Type A, and were later developed as combinatorial objects in their own right by Beagley and Drube. This paper generalizes earlier notions of tableau inversions to row-standard tableaux with repeated entries, yielding an interesting new generalization of semistandard (as opposed to merely standard) Young tableaux. We develop a closed formula for the maximum numbers of inversion pairs for a row-standard tableau with a specific shape and content, and show that the number of $i$-inverted tableaux of a given shape is invariant under permutation of content. We then enumerate $i$-inverted Young tableaux for a variety of shapes and contents, and generalize an earlier result that places $1$-inverted Young tableaux of a general shape in bijection with $0$-inverted Young tableaux of a variety of related shapes.
A problem on completeness of exponentials

Let be a nite positive measure on the real line. For a > 0, denote by Ea the family of exponential functions Ea =fe ist j s2 [0;a]g:
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.
Examining the Viability of Phantom Dark Energy

Kevin J. Ludwick ∗ Department of Physics, University of Virginia, Charlottesville, VA 22904-4714, USA Abstract In the standard cosmological framework of the 0th-order FLRW metric and the use of perfect fluids in the stress-energy tensor, dark energy with an equation-of-state parameter w −1) do not necessarily have positive kinetic energy categorically. Staying within the confines of observational constraints and general relativity, for which there is good experimental validation, we consider a few reasonable departures from the standard 0th-order framework in an attempt to see if negative kinetic energy can be avoided in these settings despite an apparent w < −1. We consider a more accurate description of the universe through the perturbing of the isotropic and homogeneous FLRW metric and the components of the stress-energy tensor, and we consider dynamic w and primordial isocurvature and adiabatic perturbations. We find that phantom dark energy does not necessarily have negative kinetic energy for all relevant length scales at all times, and we also find that, by the same token, quintessence dark energy does not necessarily have positive kinetic energy for all relevant length scales at all times.
High-order computational fluid dynamics simulations of a spinning golf ball

This paper presents the first high-order computational fluid dynamics (CFD) simulations of static and spinning golf balls at realistic flow conditions. The present results are shown to capture the complex fluid dynamics inside the dimples which lead to drag reduction versus a smooth sphere, and compare well to previous experimental and computational studies. The high-order flux reconstruction method has been paired with the artificial boundary overset method to enable simplified mesh generation and grid motion. The compressible Navier–Stokes equations are modeled using a scale-resolving large eddy simulation (LES) approach with no sub-grid models. The codes implementing these methods have been implemented for NVIDIA graphical processing units (GPUs), enabling large speedups over traditional computer hardware. The new method allows for the simulation of golf balls, and other objects at moderate Reynolds numbers, to be simulated in a matter of days on large computing clusters.
Feature selection via block-regularized regression

Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one typically encounters in problems such as whole genome association (WGA) mapping, remains an open problem in statistical learning. We propose a block-regularized regression model for sparse variable selection in a high-dimensional space where the covariates are linearly ordered, and are possibly subject to local statistical linkages (e.g., block structures) due to spacial or temporal proximity of the features. Our goal is to identify a small subset of relevant covariates that are not merely from random positions in the ordering, but grouped as contiguous blocks from large number of ordered covariates. Following a typical linear regression framework between the features and the response, our proposed model employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a 1st-order Markovian process along the feature sequence that "activates" the regression coefficients in a coupled fashion. We describe a sampling-based learning algorithm and demonstrate the performance of our method on simulated and biological data for marker identification under WGA.
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization

We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.
A Separation Principle for Control in the Age of Deep Learning

We review the problem of defining and inferring a state for a control system based on complex, high-dimensional, highly uncertain measurement streams, such as videos. Such a state, or representation, should contain all and only the information needed for control and discount nuisance variability in the data. It should also have finite complexity, ideally modulated depending on available resources. This representation is what we want to store in memory in lieu of the data, as it separates the control task from the measurement process. For the trivial case with no dynamics, a representation can be inferred by minimizing the information bottleneck Lagrangian in a function class realized by deep neural networks. The resulting representation has much higher dimension than the data (already in the millions) but is smaller in the sense of information content, retaining only what is needed for the task. This process also yields representations that are invariant to nuisance factors and have maximally independent ...
Dynamic jamming of dense suspensions under tilted impact

High-speed ultrasound imaging is used to reconstruct the three-dimensional flow field of dense cornstarch suspensions under tilted impact. Experiments show how jamming fronts propagate and get deformed in a nonaxisymmetric manner under different impact angles.
High-Frequency Bidirectional Resonant Converter for High Conversion Ratio and Variable Load Operation

Applying RF circuit design techniques to dc–dc resonant converters has enabled switching frequencies well beyond 10 MHz, leading to higher power densities and faster transient response. Synchronous rectification is advantageous for low-voltage high-power applications but is challenging to implement in a high-frequency (HF) dc–dc converter. This article proposes an HF/very HF (VHF) resonant converter structure in which the rectifier and the inverter switches can be driven with the same gate signal. The presented structure enables efficient bidirectional power delivery in the HF/VHF range for applications that require a large voltage conversion ratio. A 64-MHz, 12-W, 36–12-V prototype converter verifies the operation of the structure. This article further explores an interleaved architecture of the same circuit to improve efficiency and transient performance. Finally, a 13.56-MHz 210–30-V prototype converter with 90% peak efficiency at 200 W demonstrates the advantages of the interleaved architecture.
An experiment on network density and sequential learning

Abstract We conduct a sequential social-learning experiment where subjects each guess a hidden state based on private signals and the guesses of a subset of their predecessors. A network determines the observable predecessors, and we compare subjects' accuracy on sparse and dense networks. Accuracy gains from social learning are twice as large on sparse networks compared to dense networks. Models of naive inference where agents ignore correlation between observations predict this comparative static in network density, while the finding is difficult to reconcile with rational-learning models.
Spatial offsets between interstellar bone-like filaments, radio masers, and cold diffuse CO gas, in the Scutum spiral arm

The advent of more precise measurements of distance estimates for some objects (Bayesian estimates for filaments, trigonometric estimates for masers) permits a better comparison of their relative locations, and a comparison with a most recent spiral arm model fitted to the diffuse CO 1-0 gas. Our results support the idea that some ‘bone-like’ filaments, greater than 10 pc (labeled elsewhere as ‘bones’, ‘mst’, ‘herschel’) and smaller than 100 pc, are observed near the potential minimum of the Scutum spiral arm (bone-like filaments are offset outward from masers by about 200 pc).
Hierarchical and dynamic relations of language and cognitive skills to reading comprehension: Testing the direct and indirect effects model of reading (DIER).

We investigated 2 hypotheses of a recently proposed integrative theoretical model of reading, the direct and indirect effects model of reading (DIER; Kim, 2017b, 2019): (a) hierarchical relations and (b) dynamic relations (or differential relations) of skills to reading comprehension. Students were assessed on reading comprehension, word reading, listening comprehension, working memory, attention, vocabulary, grammatical knowledge, perspective taking (theory of mind), knowledge-based inference, and comprehension monitoring in Grade 2 and again in Grade 4. Structural equation model results supported the hierarchical relations hypothesis of DIER. When a nonhierarchical, direct relations model was fitted, primarily the upper level skills (i.e., word reading and listening comprehension) were statistically significant. When hierarchical, direct, and indirect relations models were fitted, lower level skills (e.g., working memory, vocabulary) and higher order cognitive skills (e.g., perspective taking) were indirectly related to reading comprehension via multiple pathways, whereas word reading and listening comprehension remained directly related to reading comprehension. Furthermore, the magnitudes of relations varied in Grade 2 versus Grade 4 such that perspective taking (as measured by theory of mind), vocabulary, and working memory had larger effects whereas comprehension monitoring and grammatical knowledge had smaller effects on reading comprehension in Grade 4 than in Grade 2. These results provide some support for the dynamic relations hypothesis of DIER, but also suggest the complex nature of the dynamic relations as a function of development and text characteristics. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Socialism and populism

Since 1981, the rise of Solidarity, and even before, socialism has been in crisis in both its authoritarian and welfare state variants. More recently however, populism has been on the rise, fueled by both the serious deficits of liberal democracy, and the weakness of social democratic alternatives. It has become highly tempting for socialists and ex-socialists to call on the aid of populism, in part to counter nationalist populisms on the right. At the same time, on the right, democratic socialism is repeatedly denounced by referring to disasters caused by left populism. With that said, the relationship of populism and socialism remains a very difficult one, both in theory and in practice. While alliances have emerged, generally populism rather than socialism benefited, with the combination becoming invariably authoritarian. One serious consequence has been continued avoidance of the fundamental question “what is alive and what is dead in the tradition of socialism” in other words the reconstruction of the idea of socialism.
Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation

Trade-offs constrain the improvement of performance of multiple traits simultaneously. Such trade-offs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of trade-offs at least in the short term. Here we densely sample adaptive mutations in Saccharomyces cerevisiae to ask whether first-step adaptive mutations result in trade-offs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify trade-offs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected trade-offs. Finally, we sequenced hundreds of these adaptive clones, revealing new targets of adaptation and defining the genetic basis of the identified trade-offs.