cssi/SciSciGPT-SciSciNet huggingface dataset:
Inference with Multivariate Heavy-Tails in Linear Models

Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which involve such heavy-tailed distributions. In this work, we propose a novel simple linear graphical model for independent latent random variables, called linear characteristic model (LCM), defined in the characteristic function domain. Using stable distributions, a heavy-tailed family of distributions which is a generalization of Cauchy, Levy and Gaussian distributions, we show for the first time, how to compute both exact and approximate inference in such a linear multivariate graphical model. LCMs are not limited to stable distributions, in fact LCMs are always defined for any random variables (discrete, continuous or a mixture of both). We provide a realistic problem from the field of computer networks to demonstrate the applicability of our construction. Other potential application is iterative decoding of linear channels with non-Gaussian noise.
Bounds on the roots of the Steiner polynomial

We consider the Steiner polynomial of a C^2 convex body K in R^n (n \leq 5). The opposites of the real parts of the roots of the Steiner polynomial are bounded below by the minimum value and above by the maximum value of the principal radii of curvature of the boundary of K.
On the postcritical set of a rational map

The postcritical set P(f) of a rational map $$f:{\mathbb P}^1\rightarrow {\mathbb P}^1$$ is the smallest forward invariant subset of $${\mathbb P}^1$$ that contains the critical values of f. In this paper we show that every finite set $$X\subset {\mathbb P}^1({\overline{{\mathbb Q}}})$$ can be realized as the postcritical set of a rational map. We also show that every map $$F:X\rightarrow X$$ defined on a finite set $$X\subset {\mathbb P}^1({\mathbb C})$$ can be realized by a rational map $$f:P(f)\rightarrow P(f)$$, provided we allow small perturbations of the set X. The proofs involve Belyi’s theorem and iteration on Teichmuller space.
Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings

We present WALKLETS, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multi-scale vertex relationships in a way that is analytically derivable. WALKLETS generates these multiscale relationships by sub-sampling short random walks on the vertices of a graph. By 'skipping' over steps in each random walk, our method generates a corpus of vertex pairs which are reachable via paths of a fixed length. This corpus can then be used to learn a series of latent representations, each of which captures successively higher order relationships from the adjacency matrix. We demonstrate the efficacy of WALKLETS's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, DBLP, Flickr, and YouTube. Our results show that WALKLETS outperforms new methods based on neural matrix factorization. Specifically, we outperform DeepWalk by up to 10% and LINE by 58% Micro-F1 on challenging multi-label classification tasks. Finally, WALKLETS is an online algorithm, and can easily scale to graphs with millions of vertices and edges.
Statistical mechanics algorithm for response to targets (SMART)

It is proposed to apply modern methods of nonlinear nonequilibrium statistical mechanics to develop software algorithms that will optimally respond to targets within short response times with minimal computer resources. This Statistical Mechanics Algorithm for Response to Targets (SMART) can be developed with a view towards its future implementation into a hardwired Statistical Algorithm Multiprocessor (SAM) to enhance the efficiency and speed of response to targets (SMART_SAM).
Control function assisted ipw estimation with a secondary outcome in case-control studies

Case-control studies are designed towards studying associations between risk factors and a single, primary outcome. Information about additional, secondary outcomes is also collected, but association studies targeting such secondary outcomes should account for the case-control sampling scheme, or otherwise results may be biased. Often, one uses inverse probability weighted (IPW) estimators to estimate population effects in such studies. IPW estimators are robust, as they only require correct specification of the mean regression model of the secondary outcome on covariates, and knowledge of the disease prevalence. However, IPW estimators are inefficient relative to estimators that make additional assumptions about the data generating mechanism. We propose a class of estimators for the effect of risk factors on a secondary outcome in case-control studies that combine IPW with an additional modeling assumption: specification of the disease outcome probability model. We incorporate this model via a mean zero control function. We derive the class of all regular and asymptotically linear estimators corresponding to our modeling assumption, when the secondary outcome mean is modeled using either the identity or the log link. We find the efficient estimator in our class of estimators and show that it reduces to standard IPW when the model for the primary disease outcome is unrestricted, and is more efficient than standard IPW when the model is either parametric or semiparametric.
A sufficient condition for strong $F$-regularity

Let $(R,\mathfrak{m},K)$ be an $F$-finite Noetherian local ring which has a canonical ideal $I \subsetneq R$. We prove that if $R$ is $S_2$ and $H^{d-1}_{\mathfrak{m}}(R/I)$ is a simple $R\{F\}$-module, then $R$ is a strongly $F$-regular ring. In particular, under these assumptions, $R$ is a Cohen-Macaulay normal domain.
I,F-partitions of sparse graphs

A star k -coloring is a proper k -coloring where the union of two color classes induces a star forest. While every planar graph is 4-colorable, not every planar graph is star 4-colorable. One method to produce a star 4-coloring is to partition the vertex set into a 2-independent set and a forest; such a partition is called an I,F-partition. We use a combination of potential functions and discharging to prove that every graph with maximum average degree less than 5 2 has an I,F-partition, which is sharp and answers a question of Cranston and West (0000). This result implies that planar graphs of girth at least 10 are star 4-colorable, improving upon previous results of Bu etźal. (2009).
Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context

The problem of accurately predicting relative reading difficulty across a set of sentences arises in a number of important natural language applications, such as finding and curating effective usage examples for intelligent language tutoring systems. Yet while significant research has explored document- and passage-level reading difficulty, the special challenges involved in assessing aspects of readability for single sentences have received much less attention, particularly when considering the role of surrounding passages. We introduce and evaluate a novel approach for estimating the relative reading difficulty of a set of sentences, with and without surrounding context. Using different sets of lexical and grammatical features, we explore models for predicting pairwise relative difficulty using logistic regression, and examine rankings generated by aggregating pairwise difficulty labels using a Bayesian rating system to form a final ranking. We also compare rankings derived for sentences assessed with and without context, and find that contextual features can help predict differences in relative difficulty judgments across these two conditions.
Systems of Dilated Functions: completeness, minimality, basisness

The completeness, minimality, and basis property in L 2[0, π] and L p[0, π], p ≠ 2, are considered for systems of dilated functions u n (x) = S(nx), n ∈ N, where S is the trigonometric polynomial S(x) = Σ k=0 m a k sin(kx), a 0 a m ≠ 0. A series of results are presented and several unanswered questions are mentioned.
A Fourier transform for the quantum Toda lattice

We answer a question of V. Drinfeld by constructing an ‘algebraic Fourier transform’ for the quantum Toda lattice of a complex reductive algebraic group G, which extends the classical ‘algebraic Fourier transform’ for its subalgebra $$D(T)^W$$ of Weyl group invariant differential operators on a maximal torus. The proof is contained in Sect. 2 and relies on a result of Bezrukavnikov–Finkelberg realizing the quantum Toda lattice as the equivariant homology of the dual affine Grassmannian; the Fourier transform boils down to nothing more than the duality between homology and cohomology. In Sect. 3, we compare our result with a related result of V. Ginzburg, and explain the apparent discrepancy by showing that W-equivariant quasicoherent sheaves on $${{\mathrm{\mathfrak {t}}}}^*$$ descend to $${{\mathrm{\mathfrak {t}}}}^*//W$$ if they descend to $${{\mathrm{\mathfrak {t}}}}^*/\langle s_i\rangle $$ for every simple reflection $$s_i$$ of W.
Cutting plane methods can be extended into nonconvex optimization

We show that it is possible to obtain an $O(\epsilon^{-4/3})$ expected runtime --- including computational cost --- for finding $\epsilon$-stationary points of smooth nonconvex functions using cutting plane methods. This improves on the best-known epsilon dependence achieved by cubic regularized Newton of $O(\epsilon^{-3/2})$ as proved by Nesterov and Polyak (2006). Our techniques utilize the convex until proven guilty principle proposed by Carmon, Duchi, Hinder, and Sidford (2017).
Discriminative segmental cascades for feature-rich phone recognition

Discriminative segmental models, such as segmental conditional random fields (SCRFs) and segmental structured support vector machines (SSVMs), have had success in speech recognition via both lattice rescoring and first-pass decoding. However, such models suffer from slow decoding, hampering the use of computationally expensive features, such as segment neural networks or other high-order features. A typical solution is to use approximate decoding, either by beam pruning in a single pass or by beam pruning to generate a lattice followed by a second pass. In this work, we study discriminative segmental models trained with a hinge loss (i.e., segmental structured SVMs). We show that beam search is not suitable for learning rescoring models in this approach, though it gives good approximate decoding performance when the model is already well-trained. Instead, we consider an approach inspired by structured prediction cascades, which use max-marginal pruning to generate lattices. We obtain a high-accuracy phonetic recognition system with several expensive feature types: a segment neural network, a second-order language model, and second-order phone boundary features.
On Greedy Routing in Dynamic UAV Networks

Unmanned aerial vehicles (UAVs), commonly known as drones, are becoming increasingly popular for various applications. Freely flying drones create highly dynamic environments, where conventional routing algorithms which rely on stationary network contact graphs fail to perform efficiently. Also, link establishment through exploring optimal paths using hello messages (as is used in AODV algorithm) deems extremely inefficient and costly for rapidly changing network topologies. In this paper, we present a distance-based greedy routing algorithm for UAV networks solely based on UAVs' local observations of their surrounding subnetwork. Thereby, neither a central decision maker nor a time consuming route setup and maintenance mechanism is required. To evaluate the proposed method, we derive an analytical bound for the expected number of hops that a packet traverses. Also, we find the expected end-to-end distance traveled by each packet as well as the probability of successful delivery. The simulation results verify the accuracy of the developed analytical expressions and show considerable improvement compared to centralized shortest path routing algorithms.
A link surgery spectral sequence in monopole Floer homology

Abstract To a link L ⊂ S 3 , we associate a spectral sequence whose E 2 page is the reduced Khovanov homology of L and which converges to a version of the monopole Floer homology of the branched double cover. The pages E k for k ⩾ 2 depend only on the mutation equivalence class of L. We define a mod 2 grading on the spectral sequence which interpolates between the δ-grading on Khovanov homology and the mod 2 grading on Floer homology. We also derive a new formula for link signature that is well adapted to Khovanov homology. More generally, we construct new bigraded invariants of a framed link in a 3-manifold as the pages of a spectral sequence modeled on the surgery exact triangle. The differentials count monopoles over families of metrics parameterized by permutohedra. We utilize a connection between the topology of link surgeries and the combinatorics of graph-associahedra. This also yields simple realizations of permutohedra and associahedra, as refinements of hypercubes.
Asymmetric Shapes of Radio Recombination Lines from Ionized Stellar Winds

Recombination line profile shapes are derived for ionized spherical stellar winds at radio wavelengths. It is assumed that the wind is optically thick owing to free-free opacity. Emission lines of arbitrary optical depth are obtained assuming that the free-free photosphere forms in the outer, constant expansion portion of the wind. Previous works have derived analytic results for isothermal winds when the line and continuum source functions are equal. Here, semi-analytic results are derived for when the source functions are not equal to reveal that line shapes can be asymmetric about line center. A parameter study is presented and applications discussed.
A note on quasi-alternating Montesinos links

Quasi-alternating links are a generalization of alternating links. They are homologically thin for both Khovanov homology and knot Floer homology. Recent work of Greene and joint work of the first author with Kofman resulted in the classification of quasi-alternating pretzel links in terms of their integer tassel parameters. Replacing tassels by rational tangles generalizes pretzel links to Montesinos links. In this paper we establish conditions on the rational parameters of a Montesinos link to be quasi-alternating. Using recent results on left-orderable groups and Heegaard Floer L-spaces, we also establish conditions on the rational parameters of a Montesinos link to be non-quasi-alternating. We discuss examples which are not covered by the above results.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering

How to cluster event sequences generated via different point processes is an interesting and important problem in statistical machine learning. To solve this problem, we propose and discuss an effective model-based clustering method based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process. The proposed model generates the event sequences with different clusters from the Hawkes processes with different parameters, and uses a Dirichlet process as the prior distribution of the clusters. We prove the identifiability of our mixture model and propose an effective variational Bayesian inference algorithm to learn our model. An adaptive inner iteration allocation strategy is designed to accelerate the convergence of our algorithm. Moreover, we investigate the sample complexity and the computational complexity of our learning algorithm in depth. Experiments on both synthetic and real-world data show that the clustering method based on our model can learn structural triggering patterns hidden in asynchronous event sequences robustly and achieve superior performance on clustering purity and consistency compared to existing methods.
Bertrand's postulate and subgroup growth

Abstract In this article we investigate the L 1 -norm of certain functions on groups called divisibility functions. Using these functions, their connection to residual finiteness, and integration theory on profinite groups, we define the residual average of a finitely generated group. One of the main results in this article is the finiteness of residual averages on finitely generated linear groups. Whether or not the residual average is finite depends on growth rates of indices of finite index subgroups. Our results on index growth rates are analogous to results on gaps between primes, and provide a variant of the subgroup growth function, which may be of independent interest.
Robust surface reconstruction from gradients via adaptive dictionary regularization

This paper introduces a novel approach to robust surface reconstruction from photometric stereo normal vector maps that is particularly well-suited for reconstructing surfaces from noisy gradients. Specifically, we propose an adaptive dictionary learning based approach that attempts to simultaneously integrate the gradient fields while sparsely representing the spatial patches of the reconstructed surface in an adaptive dictionary domain. We show that our formulation learns the underlying structure of the surface, effectively acting as an adaptive regularizer that enforces a smoothness constraint on the reconstructed surface. Our method is general and may be coupled with many existing approaches in the literature to improve the integrity of the reconstructed surfaces. We demonstrate the performance of our method on synthetic data as well as real photometric stereo data and evaluate its robustness to noise.
Stable Restoration and Separation of Approximately Sparse Signals

Abstract This paper develops new theory and algorithms to recover signals that are approximately sparse in some general dictionary (i.e., a basis, frame, or over-/incomplete matrix) but corrupted by a combination of interference having a sparse representation in a second general dictionary and measurement noise. The algorithms and analytical recovery conditions consider varying degrees of signal and interference support-set knowledge. Particular applications covered by the proposed framework include the restoration of signals impaired by impulse noise, narrowband interference, or saturation/clipping, as well as image in-painting, super-resolution, and signal separation. Two application examples for audio and image restoration demonstrate the efficacy of the approach.
A Hierarchical WDM-Based Scalable Data Center Network Architecture

Massive data centers are at the heart of the Internet. The rapid growth of Internet traffic and the abundance of rich data-driven applications have raised the need for enormous network bandwidth. Towards meeting this growing traffic demand, optical interconnects have gained significant attention, as they can provide high throughput, low latency, and scalability. In particular, optical Wavelength Division Multiplexing (WDM) provides the possibility to build data centers comprising of millions of servers, while providing hundreds of terabits per second bandwidth. In this paper, we propose a WDM-based Reconfigurable Hierarchical Optical Data Center Architecture (RHODA) that can satisfy future Internet traffic demands. To improve scalability, our DCN architecture is hierarchical, as it groups server racks into clusters. Cluster membership is reconfigurable through the use of optical switches. Each cluster enables heavy-traffic communication among the racks within. To support varying traffic patterns, the inter-cluster network topology and link capacities are also reconfigurable, which is achieved through the use of optical space switches and Wavelength Selective Switches (WSSs). Our simulation results demonstrate that in terms of average hop distance, RHODA outperforms OSA, FatTree and WaveCube by up to 81&#x0025;, 66&#x0025; and 60&#x0025;, respectively.
Concordant chemical reaction networks and the Species-Reaction Graph.

In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network’s Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams.
An optimal randomized incremental gradient method

In this paper, we consider a class of finite-sum convex optimization problems whose objective function is given by the average of $$m\, ({\ge }1)$$ smooth components together with some other relatively simple terms. We first introduce a deterministic primal–dual gradient (PDG) method that can achieve the optimal black-box iteration complexity for solving these composite optimization problems using a primal–dual termination criterion. Our major contribution is to develop a randomized primal–dual gradient (RPDG) method, which needs to compute the gradient of only one randomly selected smooth component at each iteration, but can possibly achieve better complexity than PDG in terms of the total number of gradient evaluations. More specifically, we show that the total number of gradient evaluations performed by RPDG can be $${{\mathcal {O}}} (\sqrt{m})$$ times smaller, both in expectation and with high probability, than those performed by deterministic optimal first-order methods under favorable situations. We also show that the complexity of the RPDG method is not improvable by developing a new lower complexity bound for a general class of randomized methods for solving large-scale finite-sum convex optimization problems.
Co-production of cement and carbon nanotubes with a carbon negative footprint

Abstract C2CNT (CO 2 to carbon nanotube) cement plants are introduced and analyzed with a significant economic incentive to eliminate current plant CO 2 greenhouse gas emissions. The exhaust from partial and full oxy-fuel cement plant configurations are coupled to the inlet of a C2CNT chamber in which CO 2 is transformed by electrolysis in a molten carbonate electrolyte into CNTs at a steel cathode and pure oxygen at a nickel anode that is looped back in improving cement line energy efficiency and production rate. A partial oxy-fuel process looping O 2 back through the plant calcinator has been compared to a full process in which it enters through the plant kiln. The first provides easier retrofit; the second maximizes efficiency by minimizing N 2 throughput. Providing an economic impetus to carbon dioxide removal, per ton CO 2 avoided the C2CNT cement plant consumes $50 electricity, emits no CO 2 , and produces $100 cement and ∼$60,000 of CNTs.
Expressive recommender systems through normalized nonnegative models

We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ideally satisfy: high predictive power, computational tractability, and expressive representations of users and items. Expressive user and item representations are important in practice to succinctly summarize the pool of customers and the pool of items. In NNMs, user representations are expressive because each user's preference can be regarded as normalized mixture of preferences of stereotypical users. The interpretability of item and user representations allow us to arrange properties of items (e.g., genres of movies or topics of documents) or users (e.g., personality traits) hierarchically.
A stratified homotopy hypothesis

We show that conically smooth stratified spaces embed fully faithfully into $\infty$-categories. This articulates a stratified generalization of the homotopy hypothesis proposed by Grothendieck. As such, each $\infty$-category defines a stack on conically smooth stratified spaces, and we identify the descent conditions it satisfies. These include $\mathbb{R}^1$-invariance and descent for open covers and blow-ups, analogous to sheaves for the h-topology in $\mathbb{A}^1$-homotopy theory. In this way, we identify $\infty$-categories as striation sheaves, which are those sheaves on conically smooth stratified spaces satisfying the indicated descent. We use this identification to construct by hand two remarkable examples of $\infty$-categories: $\mathcal{B}{\sf un}$, an $\infty$-category classifying constructible bundles; and $\mathcal{E}{\sf xit}$, the absolute exit-path $\infty$-category. These constructions are deeply premised on stratified geometry, the key geometric input being a characterization of conically smooth stratified maps between cones and the existence of pullbacks for constructible bundles.
VUNet: Dynamic Scene View Synthesis for Traversability Estimation Using an RGB Camera

We present VUNet, a novel view(VU) synthesis method for mobile robots in dynamic environments, and its application to the estimation of future traversability. Our method predicts future images for given virtual robot velocity commands using only RGB images at previous and current time steps. The future images result from applying two types of image changes to the previous and current images: first, changes caused by different camera pose. Second, changes due to the motion of the dynamic obstacles. We learn to predict these two types of changes disjointly using two novel network architectures, SNet and DNet. We combine SNet and DNet to synthesize future images that we pass to our previously presented method GONet [N. Hirose, A. Sadeghian, M. Vazquez, P. Goebel, and S. Savarese, “Gonet: A semi-supervised deep learning approach for traversability estimation,” in Proc. IEEE International Conference on Intelligent Robots and Systems, 2018, pp. 3044–3051] to estimate the traversable areas around the robot. Our quantitative and qualitative evaluation indicate that our approach for view synthesis predicts accurate future images in both static and dynamic environments. We also show that these virtual images can be used to estimate future traversability correctly. We apply our view synthesis-based traversability estimation method to two applications for assisted teleoperation.
Now that I Have a Good Theory of Uncertainty, What Else Do I Need.

Abstract Rather than discussing the isolated merits of a normative theory of uncertainty, this paper focuses on a class of problems, referred to as Dynamic Classification Problem (DCP), which requires the integration of many theories, including a prescriptive theory of uncertainty. We start by analyzing the Dynamic Classification Problem and by defining its induced requirements on a supporting (plausible) reasoning system. We provide a summary of the underlying theory (based on the semantics of many-valed logics) and illustrate the constraints imposed upon it to ensure the modularity and computational performance required by the applications. We describe the technologies used for knowledge engineering (such as object-based simulator to exercise requirements, and development tools to build the Knowledge Base and functionally validate it). We emphasize the difference between development environment and run-time system, describe the rule cross-compiler, and the real-time inference engine with meta-reasoning capabilities. Finally, we illustrate how our proposed technology satisfies the DCP's requirements and analyze some of the lessons learned from its applications to situation assessment problems for Pilot's Associate and Submarine Commander Associate.
Local search is better than random assignment for bounded occurrence Ordering k-CSPs

We prove that the Bounded Occurrence Ordering k-CSP Problem is not approximation resistant. We give a very simple local search algorithm that always performs better than the random assignment algorithm (unless, the number of satisfied constraints does not depend on the ordering). Specifically, the expected value of the solution returned by the algorithm is at least ALG >= AVG + alpha(B,k)(OPT-AVG), where OPT is the value of the optimal solution; AVG is the expected value of the random solution; and alpha(B,k) = Omega_k(B^{-(k+O(1))}) is a parameter depending only on k (the arity of the CSP) and B (the maximum number of times each variable is used in constraints). The question whether bounded occurrence ordering k-CSPs are approximation resistant was raised by Guruswami and Zhou (2012), who recently showed that bounded occurrence 3-CSPs and "monotone" k-CSPs admit a non-trivial approximation.
Approximate capacity of index coding for some classes of graphs

For a class of index coding problems with side information graph having the Ramsey number R(i, j) upper bounded by ciajb, it is shown that the clique covering scheme approximates the broadcast rate within a multiplicative factor of O(a+b/na+b+1), where n is the number of messages. Based on this result and known bounds on Ramsey numbers, it is demonstrated that the broadcast rate of planar graphs, line graphs, and fuzzy circular interval graphs can be approximated within a factor of (2n)2/3.
Elliptic systems of variable order

The general theory of boundary value problems for linear elliptic wedge operators (on smooth manifolds with boundary) leads naturally, even in the scalar case, to the need to consider vector bundles over the boundary together with general smooth fiberwise multiplicative group actions. These actions, essentially trivial (and therefore invisible) in the case of regular boundary value problems, are intimately connected with what passes for Poisson and trace operators, and to pseudodifferential boundary conditions in the more general situation. Here the part of the theory pertaining to pseudodifferential operators is presented in its entirety. The symbols for these are defined with the aid of an intertwining of the actions. Also presented here are the ancillary Sobolev spaces, an index theorem for the elliptic elements of the pseudodifferential calculus, and essential ingredients for analyzing boundary conditions of Atiyah–Patodi–Singer type in the more general theory.
Distributional Adversarial Networks

We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measures and two-sample tests between probability distributions, we propose two such distributional adversaries that operate and predict on samples, and show how they can be easily implemented on top of existing models. Various experimental results show that generators trained with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with pointwise prediction discriminators. The application of our framework to domain adaptation also results in considerable improvement over recent state-of-the-art.
Noncommutative plurisubharmonic polynomials part II: Local assumptions

Abstract We say that a symmetric noncommutative (nc) polynomial is nc plurisubharmonic (nc plush) on an nc open set if it has an nc complex hessian that is positive semidefinite when evaluated on open sets of matrix tuples of sufficiently large size. In this paper, we show that if an nc polynomial is nc plurisubharmonic on an nc open set then the polynomial is actually nc plurisubharmonic everywhere and has the form (0.1) p = ∑ f j T f j + ∑ k j k j T + F + F T where the sums are finite and f j , k j , F are all nc analytic. Greene et al. (2011) [1] has shown that if p is nc plurisubharmonic everywhere then p has the form in Eq. (0.1) . In other words, [1] makes a global assumption while the current paper makes a local assumption, but both reach the same conclusion. We show that if p is nc plurisubharmonic on an nc “open set” (local) then p is, in fact, nc plurisubharmonic everywhere (global) and has the form expressed in Eq. (0.1) . This paper requires a technique that is not used in [1] . We use a Gram-like vector and matrix representation (called the border vector and middle matrix) for homogeneous degree 2 nc polynomials. We then analyze this representation for the nc complex hessian on an nc open set and positive semidefiniteness forces a very rigid structure on the border vector and middle matrix. This rigid structure plus the theorems in [1] ultimately force the form in Eq. (0.1) .
A WITT–BURNSIDE RING ATTACHED TO A PRO-DIHEDRAL GROUP

The ring of p-typical Witt vectors is an indispensable tool in number theory and mixed characteristic commutative algebra. Witt vectors were significantly generalized by Dress and Siebeneicher by producing for any profinite group G, a ring-valued functor WG. The p-typical Witt vectors are recovered as the example G = Zp. This paper explores the structure of the ring WD2∞(k) where k is a field of characteristic 2 and .
Model-Based Optimization of Compressive Antennas for High-Sensing-Capacity Applications

This letter presents a novel, model-based compressive antenna design method for high-sensing-capacity imaging applications. Given a set of design constraints, the method maximizes the capacity of the compressive antenna by varying the constitutive properties of scatterers distributed along the antenna. Preliminary two-dimensional design results demonstrate the new method's ability to produce antenna configurations with enhanced imaging capabilities.
Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups

In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics. Instead of merging displacements at each data point, our model calculates the velocities induced by the component transformations, via a geodesic interpolation on a Lie transformation group. Such velocities are later summed up to produce a global transformation that is guaranteed to be diffeomorphic. Consequently, pair-wise distances computed this way conform to a smooth and spatially varying metric, which can greatly benefit k-NN classification. Experiments on synthetic and real datasets demonstrate the effectiveness of our model.
Fluctuating phases and fluctuating relaxation times in glass forming liquids

The presence of fluctuating local relaxation times, τr→(t) has been used for some time as a conceptual tool to describe dynamical heterogeneities in glass-forming systems. However, until now no general method is known to extract the full space and time dependent τr→(t) from experimental or numerical data. Here we report on a new method for determining a local phase field, ϕr→(t)≡∫tdt′τr→(t′) from snapshots {r→(ti)}i=1…M of the positions of the particles in a system, and we apply it to extract ϕr→(t) and τr→(t) from numerical simulations. By studying how this phase field depends on the number of snapshots, we find that it is a well defined quantity. By studying fluctuations of the phase field, we find that they describe heterogeneities well at long distance scales.
Inverting Learned Dynamics Models for Aggressive Multirotor Control.

We present a control strategy that applies inverse dynamics to a learned acceleration error model for accurate multirotor control input generation. This allows us to retain accurate trajectory and control input generation despite the presence of exogenous disturbances and modeling errors. Although accurate control input generation is traditionally possible when combined with parameter learning-based techniques, we propose a method that can do so while solving the relatively easier non-parametric model learning problem. We show that our technique is able to compensate for a larger class of model disturbances than traditional techniques can and we show reduced tracking error while following trajectories demanding accelerations of more than 7 m/s^2 in multirotor simulation and hardware experiments.
Why a Constant Number of Vertebrae? Digital Control of Segmental Identity during Vertebrate Development

It is not understood how the numbers and identities of vertebrae are controlled during mammalian development. The remarkable robustness and conservation of segmental numbers may suggest the digital nature of the underlying process. The study proposes a mechanism that allows cells to obtain and store the segmental information in digital form, and to produce a pattern of chromatin accessibility that in turn regulates Hox gene expression specific to the metameric segment. The model requires that a regulatory element be present such that the number of occurrences of the motif between two consecutive Hox genes equals the number of segments under the control of the anterior gene. This is true for the recently discovered hydroxyl radical cleavage 3bp-periodic (HRC3) motif, associated with histone modifications and developmental genes. The finding not only allows the correct prediction of the numbers of segments using only sequence information, but also resolves the 40-year-old enigma of the function of temporal and spatial collinearity of Hox genes. The logic of the mechanism is illustrated in the attached animated video. How different aspects of the proposed mechanism can be tested experimentally is also discussed.
TACIT (High Sensitivity Troponin T Rules Out Acute Cardiac Insufficiency Trial).

Background: Identifying low-risk acute heart failure patients safe for discharge from the emergency department is a major unmet need. Methods AND RESULTS: A prospective, observational, multicenter ...
Greater Disease Burden, Greater Risk? Exploring Cognitive Change and Health Status Among Older Blacks.

Objective: The objective of study is to examine the relationships between health status and changes in cognition over time among middle to older aged Blacks. Method: Data come from the Baltimore St...
Workflow-Driven Distributed Machine Learning in CHASE-CI: A Cognitive Hardware and Software Ecosystem Community Infrastructure

The advances in data, computing and networking over the last two decades led to a shift in many application domains that includes machine learning on big data as a part of the scientific process, requiring new capabilities for integrated and distributed hardware and software infrastructure. This paper contributes a workflow-driven approach for dynamic data-driven application development on top of a new kind of networked Cyberinfrastructure called CHASE-CI. In particular, we present: 1) The architecture for CHASE-CI, a network of distributed fast GPU appliances for machine learning and storage managed through Kubernetes on the high-speed (10-100Gbps) Pacific Research Platform (PRP); 2) A machine learning software containerization approach and libraries required for turning such a network into a distributed computer for big data analysis; 3) An atmospheric science case study that can only be made scalable with an infrastructure like CHASE-CI; 4) Capabilities for virtual cluster management for data communication and analysis in a dynamically scalable fashion, and visualization across the network in specialized visualization facilities in near real-time; and, 5) A step-by-step workflow and performance measurement approach that enables taking advantage of the dynamic architecture of the CHASE-CI network and container management infrastructure.
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives. However, in certain cases a practitioner might desire to identify Pareto optimal points only in a subset of the Pareto front due to external considerations. In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem. Our approach is able to flexibly sample from desired regions of the Pareto front and, computationally, is considerably cheaper than most approaches for MOO. We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret. We experiment with both synthetic and real-life problems, and demonstrate superior performance of our proposed algorithm in terms of the flexibility and regret.
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model

Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to meet a given energy constraint while minimizing accuracy loss. The key idea of ECC is to model the DNN energy consumption via a novel bilinear regression function. The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint. The optimization problem, however, has nontrivial constraints. Therefore, existing deep learning solvers do not apply directly. We propose an optimization algorithm that combines the essence of the Alternating Direction Method of Multipliers (ADMM) framework with gradient-based learning algorithms. The algorithm decomposes the original constrained optimization into several subproblems that are solved iteratively and efficiently. ECC is also portable across different hardware platforms without requiring hardware knowledge. Experiments show that ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art resource-constrained DNN compression techniques.
ReLaDec: Reliable Latency Decision Algorithm for Connected Vehicle Applications

Low latency is required for connected and intelligent vehicle applications. For instance, safety applications have strict latency requirements. Mobile edge computing (MEC) improves the latency by bringing the computational resources closer to the data source. However, despite the improvements in latency by MEC, the latency will vary based on traffic load and signal conditions. We seek to answer the question: is the current latency environment acceptable for our application? This paper presents our Reliable Latency Decision (ReLaDec) algorithm. ReLaDec uses prior information and the last latency samples to decide whether the latency will be acceptable to our application. The decisions are done with predictable confidence and with ReLaDec the false positive decision rate never exceeds the set maximum value. We demonstrate the performance of the algorithm using 860000 latency samples that include stationary and driving data over multiple USA states.
SWiM: Secure Wildcard Pattern Matching from OT Extension

Suppose a server holds a long text string and a receiver holds a short pattern string. Secure pattern matching allows the receiver to learn the locations in the long text where the pattern appears, while leaking nothing else to either party besides the length of their inputs. In this work we consider secure wildcard pattern matching (WPM), where the receiver’s pattern is allowed to contain wildcards that match to any character.
Acoustic Model Optimization Based on Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition.

Evolutionary stochastic gradient descent (ESGD) was proposed as a population-based approach that combines the merits of gradient-aware and gradient-free optimization algorithms for superior overall optimization performance. In this paper we investigate a variant of ESGD for optimization of acoustic models for automatic speech recognition (ASR). In this variant, we assume the existence of a well-trained acoustic model and use it as an anchor in the parent population whose good "gene" will propagate in the evolution to the offsprings. We propose an ESGD algorithm leveraging the anchor models such that it guarantees the best fitness of the population will never degrade from the anchor model. Experiments on 50-hour Broadcast News (BN50) and 300-hour Switchboard (SWB300) show that the ESGD with anchors can further improve the loss and ASR performance over the existing well-trained acoustic models.
Distributed Heavy-Ball: A Generalization and Acceleration of First-Order Methods With Gradient Tracking

We study distributed optimization to minimize a sum of smooth and strongly-convex functions. Recent work on this problem uses gradient tracking to achieve linear convergence to the exact global minimizer. However, a connection among different approaches has been unclear. In this paper, we first show that many of the existing first-order algorithms are related with a simple state transformation, at the heart of which lies a recently introduced algorithm known as $\mathcal {AB}$ . We then present distributed heavy-ball , denoted as $\mathcal {AB}m$ , that combines $\mathcal {AB}$ with a momentum term and uses nonidentical local step-sizes. By simultaneously implementing both row- and column-stochastic weights, $\mathcal {AB}m$ removes the conservatism in the related work due to doubly stochastic weights or eigenvector estimation. $\mathcal {AB}m$ thus naturally leads to optimization and average consensus over both undirected and directed graphs. We show that $\mathcal {AB}m$ has a global $R$ -linear rate when the largest step-size and momentum parameter are positive and sufficiently small. We numerically show that $\mathcal {AB}m$ achieves acceleration, particularly when the objective functions are ill-conditioned.
Multi-objective optimization of composite angle grid plates for maximum buckling load and minimum weight using genetic algorithms and neural networks

Abstract The present work describes an optimization process based on the e-constraint method to find an optimum design to maximize the critical buckling load and minimize the structural weight of an angle grid plate. A comprehensive geometrical model is considered including all geometrical design variables of the grid. In order to achieve a precise and effective approximation of the buckling load, an artificial neural network (ANN) is employed. Training data for ANN is obtained by the Mindlin plate theory as well as the Ritz method. The ANN is combined with genetic algorithms (GA) to find the optimized design variables for an angle grid structure. The results provide a wide range of geometrical data for designers to choose the maximum buckling load at the minimum structural weight.
Classification of reconfiguration graphs of shortest path graphs with no induced 4-cycles

Abstract For any graph G with a , b ∈ V ( G ) , a shortest path reconfiguration graph can be formed with respect to a and b ; we denote such a graph as S ( G , a , b ) . The vertex set of S ( G , a , b ) is the set of all shortest paths from a to b in G while two vertices U , W in V ( S ( G , a , b ) ) are adjacent if and only if the vertex sets of the paths that represent U and W differ in exactly one vertex. In a recent paper (Asplund et al., 2018), it was shown that shortest path graphs with girth five or greater are exactly disjoint unions of even cycles and paths. In this paper, we extend this result by classifying all shortest path graphs with no induced 4-cycles.