cssi/SciSciGPT-SciSciNet huggingface dataset:
Developmental Dieldrin Exposure Alters DNA Methylation at Genes Related to Dopaminergic Neuron Development and Parkinson's Disease in Mouse Midbrain.

Human and animal studies have shown that exposure to the organochlorine pesticide dieldrin is associated with increased risk of Parkinson’s disease (PD). Despite previous work showing a link between developmental dieldrin exposure and increased neuronal susceptibility to MPTP toxicity in male C57BL/6 mice, the mechanism mediating this effect has not been identified. Here, we tested the hypothesis that developmental exposure to dieldrin increases neuronal susceptibility via genome-wide changes in DNA methylation. Starting at 8 weeks of age and prior to mating, female C57BL/6 mice were exposed to 0.3 mg/kg dieldrin by feeding (every 3 days) throughout breeding, gestation, and lactation. At 12 weeks of age, pups were sacrificed and ventral mesencephalon, containing primarily substantia nigra, was microdissected. DNA was isolated and dieldrin-related changes in DNA methylation were assessed via reduced representation bisulfite sequencing. We identified significant, sex-specific differentially methylated CpGs (DMCs) and regions (DMRs) by developmental dieldrin exposure (false discovery rate < 0.05), including DMCs at the Nr4a2 and Lmx1b genes, which are involved in dopaminergic neuron development and maintenance. Developmental dieldrin exposure had distinct effects on the male and female epigenome. Together, our data suggest that developmental dieldrin exposure establishes sex-specific poised epigenetic states early in life. These poised epigenomes may mediate sensitivity to subsequent toxic stimuli and contribute to the development of late-life neurodegenerative disease, including PD.
Genome Analyses of a New Mycoplasma Species from the Scorpion Centruroides vittatus.

Arthropod Mycoplasma are little known endosymbionts in insects, primarily known as plant disease vectors. Mycoplasma in other arthropods such as arachnids are unknown. We report the first complete Mycoplasma genome sequenced, identified, and annotated from a scorpion, Centruroides vittatus, and designate it as Mycoplasma vittatus. We find the genome is at least a 683,827 bp single circular chromosome with a GC content of 42.7% and with 987 protein-coding genes. The putative virulence determinants include 11 genes associated with the virulence operon associated with protein synthesis or DNA transcription and ten genes with antibiotic and toxic compound resistance. Comparative analysis revealed that the M. vittatus genome is smaller than other Mycoplasma genomes and exhibits a higher GC content. Phylogenetic analysis shows M. vittatus as part of the Hominis group of Mycoplasma. As arthropod genomes accumulate, further novel Mycoplasma genomes may be identified and characterized.
The Hydrolysis of Chlorine Nitrate on Ice Is Autocatalytic

A key chemical reaction in stratospheric ozone depletion is the reaction of chlorine nitrate (ClONO2) with water. This reaction is known to be catalyzed in the presence of ice particles found in polar stratospheric clouds (PSCs). However, the mechanism and time scale of the reaction, and the role of the ice surface, are not well understood. This surface second harmonic generation study shows that the submonolayer ClONO2 hydrolysis on basal ice (Ih) surfaces at 185 K occurs autocatalytically, with the product molecule HOCl acting as the autocatalyst. The other product, HNO3, acts to delay the reaction. The hydrolysis reaction is surprisingly slow, with induction times that range from 100 to 1000 s, depending upon how much reactant is initially present. It is proposed that the ice surface serves as a reservoir, enabling the reaction to be acid catalyzed.
Operationally simple, efficient, and diastereoselective synthesis of cis-2,6-disubstituted-4-methylene tetrahydropyrans catalyzed by triflic acid.

A highly efficient (0.01 mol % of TfOH), operationally simple (room temperature, inexpensive, and commercially available catalyst), and diastereoselective (up to >98% de) method for Bronsted acid-catalyzed reaction of enol ethers to form cis-2,6-disubstituted tetrahydropyrans is disclosed.
Early genome activation in Drosophila is extensive with an initial tendency for aborted transcripts and retained introns

Control of metazoan embryogenesis shifts from maternal to zygotic gene products as the zygotic genome becomes transcriptionally activated. In Drosophila, zygotic genome activation (ZGA) has been thought to occur in two phases, starting with a minor wave, in which a small number of genes become expressed, and progressing to the major wave, in which many more genes are activated. However, technical challenges have hampered the identification of early transcripts or obscured the onset of their transcription. Here, we develop an approach to isolate transcribed mRNAs and apply it over the course of Drosophila early genome activation. Our results increase by 10-fold the genes reported to be activated during what has been thought of as the minor wave and show that early genome activation is continuous and gradual. Transposable-element mRNAs are also produced, but discontinuously. Genes transcribed in the early and middle part of ZGA are short with few if any introns, and their transcripts are frequently aborted and tend to have retained introns, suggesting that inefficient splicing as well as rapid cell divisions constrain the lengths of early transcripts.
Refocusing on the Success Enabling Factors in Mergers and Acquisitions

Mergers and acquisitions have continued to serve as a primary financing tool undertaken by organizations to achieve corporate objectives. Despite the increased popularity of the mergers and acquisitions phenomenon, determining acceptable metrics for identifying successful mergers and acquisitions continue to pose challenges to investors, financial analysts and other stakeholders involved with mergers and acquisitions. Mergers and acquisitions activities have presented mixed outcomes to different organizations with high failure rates recorded in some and less-significant successes reported in others. Consequently, understanding acceptable metrics for determining a successful merger or acquisition becomes paramount given the challenges experienced by players in that industry. Therefore, a thorough review of the literature is made in this study to identify factors that improve the chances of mergers and acquisitions success. The unique features of successful and unsuccessful mergers and acquisitions are itemized to provide a premise for assessing and evaluating the essential characteristics that make mergers and acquisitions successful. The importance of due diligence, low acquisition purchase premiums, and related business acquisitions in the mergers and acquisitions process were fully explicated. Low acquisition purchase premiums, timing of mergers and acquisitions and related business acquisitions were found to tremendously enhance the success of mergers and acquisitions.
Geometry of Schreieder's varieties and some elliptic and K3 moduli curves

We study the geometry of a class of n-dimensional smooth projective varieties constructed by Schreieder for their noteworthy Hodge-theoretic properties. In particular, we realize Schreieder’s surfaces as elliptic modular surfaces and Schreieder’s threefolds as one-dimensional families of Picard rank 19 K3 surfaces.
A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models

This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. We aim to address the following problems: (1) estimation of systematic model errors in output quantities of interest at future times and (2) identification of specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. To address these problems, we employ simple machine learning algorithms and perform numerical experiments with Weather Research and Forecasting (WRF) model and the results show a reduction of forecast errors by an order of magnitude.
Translators asymptotic to cylinders

We show that the Bowl soliton in $\mathbb{R}^3$ is the unique translating solutions of the mean curvature flow which has the family of shrinking cylinders as an asymptotic shrinker at $-\infty$. As an application, we show that for a generic mean curvature flow, all (non-static) translating limit flows are the bowl soliton. The crucial point is that we do not make any global convexity assumption, while as the same time, the asymptotic requirement is very weak.
On the global synchronization of pulse-coupled oscillators interacting on chain and directed tree graphs

Abstract Driven by increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators (PCOs) has gained increased popularity. However, most existing results address the local synchronization of PCOs with initial phases constrained in a half cycle, and results on global synchronization from any initial condition are very sparse. In this paper, we address global PCO synchronization from an arbitrary phase distribution under chain or directed tree graphs. Our results differ from existing global synchronization studies on decentralized PCO networks in two key aspects: first, our work allows heterogeneous coupling functions, and we analyze the behavior of oscillators with perturbations on their natural frequencies; second, rather than requiring a large enough coupling strength, our results hold under any coupling strength between zero and one, which is crucial because a large coupling strength has been shown to be detrimental to the robustness of PCO synchronization to disturbances.
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion.

We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations. Specifically, we propose to use text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding significant improvement in word error rate with better conversational-context representation. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.
Quantum locally compact metric spaces

Abstract We introduce the notion of a quantum locally compact metric space, which is the noncommutative analogue of a locally compact metric space, and generalize to the non-unital setting the notion of quantum metric spaces introduced by Rieffel. We then provide several examples of such structures, including the Moyal plane, compact quantum metric spaces and locally compact metric spaces. This paper provides an answer to the question raised in the literature about the proper notion of a quantum metric space in the non-unital setup and offers important insights into noncommutative geometry for non-compact quantum spaces.
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is added to an unbiased estimate of the gradient at each iteration. This modest change allows SGLD to escape local minima and suffices to guarantee asymptotic convergence to global minimizers for sufficiently regular non-convex objectives (Gelfand and Mitter, 1991). The present work provides a nonasymptotic analysis in the context of non-convex learning problems, giving finite-time guarantees for SGLD to find approximate minimizers of both empirical and population risks. As in the asymptotic setting, our analysis relates the discrete-time SGLD Markov chain to a continuous-time diffusion process. A new tool that drives the results is the use of weighted transportation cost inequalities to quantify the rate of convergence of SGLD to a stationary distribution in the Euclidean $2$-Wasserstein distance.
End-to-end Multimodal Speech Recognition

Transcription or sub-titling of open-domain videos is still a challenging domain for Automatic Speech Recognition (ASR) due to the data's challenging acoustics, variable signal processing and the essentially unrestricted domain of the data. In previous work, we have shown that the visual channel - specifically object and scene features - can help to adapt the acoustic model (AM) and language model (LM) of a recognizer, and we are now expanding this work to end-to-end approaches. In the case of a Connectionist Temporal Classification (CTC)-based approach, we retain the separation of AM and LM, while for a sequence-to-sequence (S2S) approach, both information sources are adapted together, in a single model. This paper also analyzes the behavior of CTC and S2S models on noisy video data (How-To corpus), and compares it to results on the clean Wall Street Journal (WSJ) corpus, providing insight into the robustness of both approaches.
Text extraction and retrieval from smartphone screenshots: building a repository for life in media

Daily engagement in life experiences is increasingly interwoven with mobile device use. Screen capture at the scale of seconds is being used in behavioral studies and to implement "just-in-time" health interventions. The increasing psychological breadth of digital information will continue to make the actual screens that people view a preferred if not required source of data about life experiences. Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data. In this paper, we present the experimental workflow we exploited to: (i) pre-process a unique collection of screen captures, (ii) extract unstructured text embedded in the images, (iii) organize image text and metadata based on a structured schema, (iv) index the resulting document collection, and (v) allow for Image Retrieval through a dedicated vertical search engine application. The adopted procedure integrates different open source libraries for traditional image processing, Optical Character Recognition (OCR), and Image Retrieval. Our aim is to assess whether and how state-of-the-art methodologies can be applied to this novel data set. We show how combining OpenCV-based pre-processing modules with a Long short-term memory (LSTM) based release of Tesseract OCR, without ad hoc training, led to a 74% character-level accuracy of the extracted text. Further, we used the processed repository as baseline for a dedicated Image Retrieval system, for the immediate use and application for behavioral and prevention scientists. We discuss issues of Text Information Extraction and Retrieval that are particular to the screenshot image case and suggest important future work.
Polyhedral computational geometry for averaging metric phylogenetic trees

This paper investigates the computational geometry relevant to calculations of the Frechet mean and variance for probability distributions on the phylogenetic tree space of Billera, Holmes and Vogtmann, using the theory of probability measures on spaces of nonpositive curvature developed by Sturm. We show that the combinatorics of geodesics with a specified fixed endpoint in tree space are determined by the location of the varying endpoint in a certain polyhedral subdivision of tree space. The variance function associated to a finite subset of tree space has a fixed C ∞ algebraic formula within each cell of the corresponding subdivision, and is continuously differentiable in the interior of each orthant of tree space. We use this subdivision to establish two iterative methods for producing sequences that converge to the Frechet mean: one based on Sturm's Law of Large Numbers, and another based on descent algorithms for finding optima of smooth functions on convex polyhedra. We present properties and biological applications of Frechet means and extend our main results to more general globally nonpositively curved spaces composed of Euclidean orthants.
GROUP EXTENSIONS AND AUTOMORPHISM GROUP RINGS

We use extensions to study the semi-simple quotient of the group ring FpAut(P) of a finite p-group P. For an extension E : N → P → Q, our results involve relations between Aut(N), Aut(P), Aut(Q) and the extension class [E] ∈ H 2 (Q, ZN). One novel feature is the use of the intersection orbit group ([E]), defined as the intersection of the orbits Aut(N) � [E] and Aut(Q) � [E] in H 2 (Q, ZN). This group is useful in computing |Aut(P)|. In case N, Q are elementary Abelian 2-groups our results involve the theory of quadratic forms and the Arf invariant.
Sum-product estimates via directed expanders

Let $\F_q$ be a finite field of order $q$ and $P$ be a polynomial in $\F_q[x_1, x_2]$. For a set $A \subset \F_q$, define $P(A):=\{P(x_1, x_2) | x_i \in A \}$. Using certain constructions of expanders, we characterize all polynomials $P$ for which the following holds \vskip2mm \centerline{\it If $|A+A|$ is small (compared to $|A|$), then $|P(A)|$ is large.} \vskip2mm \noindent The case $P=x_1x_2$ corresponds to the well-known sum-product problem.
Effect of size on necking of dynamically loaded notched bars

Abstract The influence of material inertia on neck development in a notched round bar is analyzed numerically. Dynamic axisymmetric calculations are carried out for isotropically hardening elastic-viscoplastic solids so that both material strain rate sensitivity and material inertia are accounted for. The focus is on the effect of bar size on whether the notch triggers necking or necking initiates away from the notch. The governing equations are presented in non-dimensional form and two key non-dimensional groups that involve both material and loading parameters are identified. For both non-dimensional groups, with all parameters fixed except for bar size, it is found that for sufficiently small bars, the notch triggers necking, whereas for sufficiently large bars necking ultimately occurs away from the notch. With material properties fixed, for one non-dimensional group the transition to necking away from the notch corresponds to increasing imposed velocity whereas for the other non-dimensional group this transition takes place for decreasing imposed strain rate. Both these transitions correspond to increasing bar size. The results indicate that this transition is governed by material inertia but the bar size at which it occurs depends on the material properties, particularly strain hardening and strain rate hardening.
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling

Abstract: Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a `wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels ($54^3$) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks.
Training variance and performance evaluation of neural networks in speech

In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well known, this is, to the best of our knowledge, the first paper that performs an extensive empirical study on its effects in speech recognition. We view training as sampling from a distribution and show that these distributions can have a substantial variance. These results show the urgent need to rethink the way in which results in the literature are reported and interpreted.
The Impact of Bundling Licensed and Unlicensed Wireless Service

Unlicensed spectrum has been viewed as a way to increase competition in wireless access and promote innovation in new technologies and business models. However, several recent papers have shown that the openness of such spectrum can also lead to it becoming over congested when used by competing wireless service providers (SPs). This in turn can result in the SPs making no profit and may deter them from entering the market. However, this prior work assumes that unlicensed access is a separate service from any service offered using licensed spectrum. Here, we instead consider the more common case were service providers bundle both licensed and unlicensed spectrum as a single service and offer this with a single price. We analyze a model for such a market and show that in this case SPs are able to gain higher profit than the case without bundling. It is also possible to get higher social welfare with bundling. Moreover, we explore the case where SPs are allowed to manage the customers' average percentage of time they receive service on unlicensed spectrum and characterize the social welfare gap between the profit maximizing and social welfare maximizing setting.
Random sampling in computational algebra: Helly numbers and violator spaces

This paper transfers a randomized algorithm, originally used in geometric optimization, to computational problems in commutative algebra. We show that Clarkson's sampling algorithm can be applied to two problems in computational algebra: solving large-scale polynomial systems and finding small generating sets of graded ideals. The cornerstone of our work is showing that the theory of violator spaces of Gartner et al. applies to polynomial ideal problems. To show this, one utilizes a Helly-type result for algebraic varieties. The resulting algorithms have expected runtime linear in the number of input polynomials, making the ideas interesting for handling systems with very large numbers of polynomials, but whose rank in the vector space of polynomials is small (e.g., when the number of variables and degree is constant).
End-To-End Optimized Speech Coding with Deep Neural Networks

Modern compression algorithms are often the result of laborious domain-specific research; industry standards such as MP3, JPEG, and AMR-WB took years to develop and were largely hand-designed. We present a deep neural network model which optimizes all the steps of a wideband speech coding pipeline (compression, quantization, entropy coding, and decompression) end-to-end directly from raw speech data - no manual feature engineering necessary, and it trains in hours. In testing, our DNN-based coder performs on par with the AMR -WB standard at a variety of bitrates (~9kbps up to ~24kbps). It also runs in realtime on a 3.8GhZ Intel CPU.
#Anorexia, #anarexia, #anarexyia: Characterizing online community practices with orthographic variation

Distinctive linguistic practices help communities build solidarity and differentiate themselves from outsiders. In an online community, one such practice is variation in orthography, which includes spelling, punctuation, and capitalization. Using a dataset of over two million Instagram posts, we investigate orthographic variation in a community that shares pro-eating disorder (pro-ED) content. We find that not only does orthographic variation grow more frequent over time, it also becomes more profound or “deep,” with variants becoming increasingly distant from the original: as, for example, #anarexyia is more distant than #anarexia from the original spelling #anorexia. We find that the these changes are driven by newcomers, who adopt the most extreme linguistic practices as they enter the community. Moreover, this behavior correlates with engagement with the community: the newcomers that adopt deeper variant orthography tend to remain active for longer in the community, and posts with deeper variation receive more positive feedback in the form of “likes.” Previous work has linked community membership change with language change, and our work casts this connection in a new light, with newcomers driving an evolving practice rather than adapting to it. We also demonstrate the utility of orthographic variation as a new lens to study sociolinguistic change in online communities, particularly when the change results from an exogenous force such as a content ban.
Using Structurally Well-Defined Norbornyl-Bridged Acene Dimers to Map a Mechanistic Landscape for Correlated Triplet Formation in Singlet Fission.

Structurally well-defined TIPS-acetylene substituted tetracene (TIPS-BT1′) and pentacene (TIPS-BP1′) dimers utilizing a [2.2.1] bicyclic norbornyl bridge have been studied—primarily using time-resolved spectroscopic methods—to uncover mechanistic details about primary steps in singlet fission leading to formation of the biexcitonic 1TT state as well as decay pathways to the ground state. For TIPS-BP1′ in room-temperature toluene, 1TT formation is rapid and complete, occurring in 4.4 ps. Decay to the ground state in 100 ns is the primary loss pathway for 1TT in this system. For TIPS-BT1′, the 1TT is also observed to form rapidly (with a time constant of 5 ps), but in this case it occurs in concert with establishment of an excited-state equilibrium (K ∼ 1) with the singlet exciton state S1 at an energy of 2.3 eV above the ground state. The equilibrated states survive for 36 ns and are lost to ground state through both radiative and nonradiative pathways via the S1 and nonradiative pathways via the 1TT. The ...
Szegö Kernels and a Theorem of Tian

We give a simple proof of Tian's theorem that the Kodaira embeddings associated to a positive line bundle over a compact complex manifold are asymptotically isometric. The proof is based on the diagonal asymptotics of the Szego kernel (i.e. the orthogonal projection onto holomorphic sections). In deriving these asymptotics we use the Boutet de Monvel-Sjostrand parametrix for the Szego kernel.
k -Best Enumeration

We survey k-best enumeration problems and the algorithms for solving them, including in particular the problems of finding the k shortest paths, k smallest spanning trees, and k best matchings in weighted graphs.
A Stackelberg Game Approach for Two-Level Distributed Energy Management in Smart Grids

The pursuit of sustainability motivates microgrids that depend on distributed resources to produce more renewable energies. An efficient operation and planning relies on a holistic framework that takes into account the interdependent decision-making of the generators of the existing power grids and the distributed resources of the microgrid in the integrated system. To this end, we use a Stackelberg game-theoretic framework to study the interactions between generators (leaders) and microgrids (followers). Entities on both sides make strategic decisions on the amount of power generation to maximize their payoffs. Our framework not only takes into account the economic factors but also incorporates the stability and efficiency of the smart grid, such as the power flow constraints and voltage angle regulations. We present three update schemes for microgrids. In addition, we develop three other algorithms for generators, and among which a fully distributed algorithm enabled by phasor measurement units is proposed. The distributed algorithm merely requires the information of voltage angles at local buses for updates, and its convergence to the unique equilibrium is shown. We further develop the implementation architectures of the update schemes in the smart grid. Finally, case studies are used to corroborate the effectiveness of the proposed algorithms.
Renormalization and Power Counting of Chiral Nuclear Forces

I discuss the progress we have made on modifying Weinberg's prescription for chiral nuclear forces, using renormalization group invariance as the guideline. Some of the published results are presented.
An observation on generalized Hilbert-Kunz functions

We prove that, under certain assumptions, generalized Hilbert-Kunz multiplicities can be expressed as linear combinations of classical Hilbert-Kunz multiplicities.
Blue) taxi destination and trip time prediction from partial trajectories

Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems. We present an approach based on trip matching and ensemble learning, in which we leverage the patterns observed in a dataset of roughly 1.7 million taxi journeys to predict the corresponding final destination and travel time for ongoing taxi trips, as a solution for the ECML/PKDD Discovery Challenge 2015 competition. The results of our empirical evaluation show that our approach is effective and very robust, which led our team - BlueTaxi - to the 3rd and 7th position of the final rankings for the trip time and destination prediction tasks, respectively. Given the fact that the final rankings were computed using a very small test set (with only 320 trips) we believe that our approach is one of the most robust solutions for the challenge based on the consistency of our good results across the test sets.
Faster Projection-free Convex Optimization over the Spectrahedron

Minimizing a convex function over the spectrahedron, i.e., the set of all $d\times d$ positive semidefinite matrices with unit trace, is an important optimization task with many applications in optimization, machine learning, and signal processing. It is also notoriously difficult to solve in large-scale since standard techniques require to compute expensive matrix decompositions. An alternative, is the conditional gradient method (aka Frank-Wolfe algorithm) that regained much interest in recent years, mostly due to its application to this specific setting. The key benefit of the CG method is that it avoids expensive matrix decompositions all together, and simply requires a single eigenvector computation per iteration, which is much more efficient. On the downside, the CG method, in general, converges with an inferior rate. The error for minimizing a $\beta$-smooth function after $t$ iterations scales like $\beta/t$. This rate does not improve even if the function is also strongly convex. In this work we present a modification of the CG method tailored for the spectrahedron. The per-iteration complexity of the method is essentially identical to that of the standard CG method: only a single eigenvecor computation is required. For minimizing an $\alpha$-strongly convex and $\beta$-smooth function, the \textit{expected} error of the method after $t$ iterations is: $O\left({\min\{\frac{\beta{}}{t} ,\left({\frac{\beta\sqrt{\rank(\X^*)}}{\alpha^{1/4}t}}\right)^{4/3}, \left({\frac{\beta}{\sqrt{\alpha}\lambda_{\min}(\X^*)t}}\right)^{2}\}}\right)$. Beyond the significant improvement in convergence rate, it also follows that when the optimum is low-rank, our method provides better accuracy-rank tradeoff than the standard CG method. To the best of our knowledge, this is the first result that attains provably faster convergence rates for a CG variant for optimization over the spectrahedron. We also present encouraging preliminary empirical results.
Diagonal rnns in symbolic music modeling

In this paper, we use Diagonal Recurrent Neural Networks on a sequence prediction task. The modification from standard RNN is simple: Diagonal recurrent matrices are used instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures as well as with the vanilla RNN architecture, on four standard symbolic music datasets.
The optimal trilinear restriction estimate for a class of hypersurfaces with curvature

Abstract In [4] nearly optimal L 1 trilinear restriction estimates in R n + 1 are established under transversality assumptions only. In this paper we show that the curvature improves the range of exponents, by establishing L p estimates, for any p > 2 ( n + 4 ) 3 ( n + 2 ) in the case of double-conic surfaces. The exponent 2 ( n + 4 ) 3 ( n + 2 ) is shown to be the universal threshold for the trilinear estimate.
Covers of elliptic curves with unique, totally ramified branch points

A well-known and difficult problem in computational number theory and algebraic geometry is to write down equations for branched covers of algebraic curves with specified monodromy type. In this article, we present a technique for computing such covers in the case of covers of elliptic curves with unique, totally ramified branch points.
Rotational Unit of Memory

The concepts of unitary evolution matrices and associative memory have boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art performance in a variety of sequential tasks. However, RNN still have a limited capacity to manipulate long-term memory. To bypass this weakness the most successful applications of RNN use external techniques such as attention mechanisms. In this paper we propose a novel RNN model that unifies the state-of-the-art approaches: Rotational Unit of Memory (RUM). The core of RUM is its rotational operation, which is, naturally, a unitary matrix, providing architectures with the power to learn long-term dependencies by overcoming the vanishing and exploding gradients problem. Moreover, the rotational unit also serves as associative memory. We evaluate our model on synthetic memorization, question answering and language modeling tasks. RUM learns the Copying Memory task completely and improves the state-of-the-art result in the Recall task. RUM's performance in the bAbI Question Answering task is comparable to that of models with attention mechanism. We also improve the state-of-the-art result to 1.189 bits-per-character (BPC) loss in the Character Level Penn Treebank (PTB) task, which is to signify the applications of RUM to real-world sequential data. The universality of our construction, at the core of RNN, establishes RUM as a promising approach to language modeling, speech recognition and machine translation.
Law invariant risk measures and information divergences

A one-to-one correspondence is drawn between law invariant risk measures and divergences, which we define as functionals of pairs of probability measures on arbitrary standard Borel spaces satisfying a few natural properties. Divergences include many classical information divergence measures, such as relative entropy and $f$-divergences. Several properties of divergence and their duality with law invariant risk measures are developed, most notably relating their chain rules or additivity properties with certain notions of time consistency for dynamic law invariant risk measures known as acceptance and rejection consistency. These properties are linked also to a peculiar property of the acceptance sets on the level of distributions, analogous to results of Weber on weak acceptance and rejection consistency. Finally, the examples of shortfall risk measures and optimized certainty equivalents are discussed in some detail, and it is shown that the relative entropy is essentially the only divergence satisfying the chain rule.
Optimal Analysis of an Online Algorithm for the Bipartite Matching Problem on a Line

In the online metric bipartite matching problem, we are given a set S of server locations in a metric space. Requests arrive one at a time, and on its arrival, we need to immediately and irrevocably match it to a server at a cost which is equal to the distance between these locations. A alpha-competitive algorithm will assign requests to servers so that the total cost is at most alpha times the cost of M_{Opt} where M_{Opt} is the minimum cost matching between S and R. We consider this problem in the adversarial model for the case where S and R are points on a line and |S|=|R|=n. We improve the analysis of the deterministic Robust Matching Algorithm (RM-Algorithm, Nayyar and Raghvendra FOCS'17) from O(log^2 n) to an optimal Theta(log n). Previously, only a randomized algorithm under a weaker oblivious adversary achieved a competitive ratio of O(log n) (Gupta and Lewi, ICALP'12). The well-known Work Function Algorithm (WFA) has a competitive ratio of O(n) and Omega(log n) for this problem. Therefore, WFA cannot achieve an asymptotically better competitive ratio than the RM-Algorithm.
Nucleon Charges, Form-factors and Neutron EDM

We present an update of our analysis of statistical and systematic errors in the calculation of iso-vector scalar, axial and tensor charges of the nucleon. The calculations are done using Nf = 2+ 1+ 1 flavor HISQ ensembles generated by the MILC Collaboration at three values of the lattice spacing (a = 0:12; 0:09; and 0:06 fm) and three values of the quark mass (Mp 310; 220 and 130 MeV); and clover fermions for calculating the correlation functions, i.e., we use a cloveron-HISQ lattice formulation. The all-mode-averaging method allows us to increase the statistics by a factor of eight for the same computational cost leading to a better understanding of and control over excited state contamination. Our current results, after extrapolation to the continuum limit and physical pion mass are g u d A = 1:21(3), g u d T = 1:005(59) and g
Lower bounds for non-Archimedean Lyapunov exponents

Let $K$ be a complete, algebraically closed, non-Archimedean valued field, and let $\textbf{P}^1$ denote the Berkovich projective line over $K$. The Lyapunov exponent for a rational map $\phi\in K(z)$ of degree $d\geq 2$ measures the exponential rate of growth along a typical orbit of $\phi$. When $\phi$ is defined over $\mathbb{C}$, the Lyapunov exponent is bounded below by $\frac{1}{2}\log d$. In this article, we give a lower bound for $L(\phi)$ for maps $\phi$ defined over non-Archimedean fields $K$. The bound depends only on the degree $d$ and the Lipschitz constant of $\phi$. For maps $\phi$ whose Julia sets satisfy a certain boundedness condition, we are able to remove the dependence on the Lipschitz constant.
On finite time BV blow-up for the p-system

AbstractThe paper studies the possible blowup of the total variation for entropy weak solutions of the p-system, modeling isentropic gas dynamics. It is assumed that the density remains uniformly p...
On groups with quasidiagonal C*-algebras

Abstract We examine the question of quasidiagonality for C*-algebras of discrete amenable groups from a variety of angles. We give a quantitative version of Rosenbergʼs theorem via paradoxical decompositions and a characterization of quasidiagonality for group C*-algebras in terms of embeddability of the groups. We consider several notable examples of groups, such as topological full groups associated with Cantor minimal systems and Abelsʼ celebrated example of a finitely presented solvable group that is not residually finite, and show that they have quasidiagonal C*-algebras. Finally, we study strong quasidiagonality for group C*-algebras, exhibiting classes of amenable groups with and without strongly quasidiagonal C*-algebras.
Dynamic Routing Between Capsules

A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.
Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed:

The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehic...
Overview of the incompressible Navier–Stokes simulation capabilities in the MOOSE framework

Abstract The Multiphysics Object Oriented Simulation Environment (MOOSE) framework is a high-performance, open source, C++ finite element toolkit developed at Idaho National Laboratory. MOOSE was created with the aim of assisting domain scientists and engineers in creating customizable, high-quality tools for multiphysics simulations. While the core MOOSE framework itself does not contain code for simulating any particular physical application, it is distributed with a number of physics “modules” which are tailored to solving e.g. heat conduction, phase field, and solid/fluid mechanics problems. In this report, we describe the basic equations, finite element formulations, software implementation, and regression/verification tests currently available in MOOSE’s navier_stokes module for solving the Incompressible Navier-Stokes (INS) equations.
Polaronic behavior in a weak-coupling superconductor.

The nature of superconductivity in the dilute semiconductor SrTiO 3 has remained an open question for more than 50 y. The extremely low carrier densities ( 10 18 – 10 20 cm −3 ) at which superconductivity occurs suggest an unconventional origin of superconductivity outside of the adiabatic limit on which the Bardeen–Cooper–Schrieffer (BCS) and Migdal–Eliashberg (ME) theories are based. We take advantage of a newly developed method for engineering band alignments at oxide interfaces and access the electronic structure of Nb-doped SrTiO 3 , using high-resolution tunneling spectroscopy. We observe strong coupling to the highest-energy longitudinal optic (LO) phonon branch and estimate the doping evolution of the dimensionless electron–phonon interaction strength ( 𝝀 ). Upon cooling below the superconducting transition temperature ( 𝑻 𝐜 ), we observe a single superconducting gap corresponding to the weak-coupling limit of BCS theory, indicating an order of magnitude smaller coupling ( 𝝀 𝐁𝐂𝐒 ≈ 0.1 ). These results suggest that despite the strong normal state interaction with electrons, the highest LO phonon does not provide a dominant contribution to pairing. They further demonstrate that SrTiO 3 is an ideal system to probe superconductivity over a wide range of carrier density, adiabatic parameter, and electron–phonon coupling strength.
Evolution of the Structure Factors in Pure Su(n) Lattice Gauge Theory and Effective Spin Models

We consider model A dynamics for a quench from the disordered into the ordered phase of SU(3) lattice gauge theory and the analogue 3d 3-state Potts model. For the gauge model this corresponds to a rapid heating from the confined to the deconfined phase. The exponential growth factors of low-lying structure function modes are numerically calculated. The linear theory of spinodal decomposition is used to determine the critical modes. This allows for the Debye screening mass estimation in an effective phenomeno-logical model. The quench leads to competing vacuum domains, which make the equilibration of the QCD vacuum after the heating non-trivial. The influence of such domains on the gluonic energy density is studied.
Discrete adaptive second order sliding mode controller design with application to automotive control systems with model uncertainties

Sliding mode control (SMC) is a robust and computationally efficient solution for tracking control problems of highly nonlinear systems with a great deal of uncertainty. High frequency oscillations due to chattering phenomena and sensitivity to data sampling imprecisions limit the digital implementation of conventional first order continuous-time SMC. Higher order discrete SMC is an effective solution to reduce the chattering during the controller software implementation, and also overcome imprecisions due to data sampling. In this paper, a new adaptive second order discrete sliding mode control (DSMC) formulation is presented to mitigate data sampling imprecisions and uncertainties within the modeled plant's dynamics. The adaptation mechanism is derived based on a Lyapunov stability argument which guarantees asymptotic stability of the closed-loop system. The proposed controller is designed and tested on a highly nonlinear combustion engine tracking control problem. The simulation test results show that the second order DSMC can improve the tracking performance up to 80% compared to a first order DSMC under sampling and model uncertainties.
Learning scalable deep kernels with recurrent structure

Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure, and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.
Weighted difference approximation of value functions for slow-discounting Markov Decision Processes

Modern applications of the theory of Markov Decision Processes (MDPs) often require frequent decision making, that is, taking an action every microsecond, second, or minute. Infinite horizon discount reward formulation is still relevant for a large portion of these applications, because actual time span of these problems can be months or years, during which discounting factors due to e.g. interest rates are of practical concern. In this paper, we show that, for such MDPs with discount rate α close to 1, under a common ergodicity assumption, a weighted difference between two successive value function estimates obtained from the classical value iteration (VI) is a better approximation than the value function obtained directly from VI. Rigorous error bounds are established which in turn show that the approximation converges to the actual value function in a rate (αβ)k with β < 1. This indicates a geometric convergence even if discount factor α → 1. Furthermore, we explicitly link the convergence speed to the system behaviors of the MDP using the notion of e-mixing time and extend our result to Q-functions. Numerical experiments are conducted to demonstrate the convergence properties of the proposed approximation scheme.