cssi/SciSciGPT-SciSciNet huggingface dataset:
The CGRP receptor antagonist BIBN4096 inhibits prolonged meningeal afferent activation evoked by brief local K+ stimulation but not cortical spreading depression-induced afferent sensitization.

Introduction Cortical spreading depression (CSD) is believed to promote migraine headache by enhancing the activity and mechanosensitivity of trigeminal intracranial meningeal afferents. One putative mechanism underlying this afferent response involves an acute excitation of meningeal afferents by cortical efflux of K+ and the ensuing antidromic release of proinflammatory sensory neuropeptides, such as calcitonin gene-related peptide (CGRP). Objectives We sought to investigate whether (1) a brief meningeal K+ stimulus leads to CGRP-dependent enhancement of meningeal afferent responses and (2) CSD-induced meningeal afferent activation and sensitization involve CGRP receptor signaling. Methods Extracellular single-unit recording were used to record the activity of meningeal afferents in anesthetized male rats. Stimulations included a brief meningeal application of K+ or induction of CSD in the frontal cortex using pinprick. Cortical spreading depression was documented by recording changes in cerebral blood flow using laser Doppler flowmetery. Calcitonin gene-related peptide receptor activity was inhibited with BIBN4096 (333 μM, i.v.). Results Meningeal K+ stimulation acutely activated 86% of the afferents tested and also promoted in ∼65% of the afferents a 3-fold increase in ongoing activity, which was delayed by 23.3 ± 4.1 minutes and lasted for 22.2 ± 5.6 minutes. K+ stimulation did not promote mechanical sensitization. Pretreatment with BIBN4096 suppressed the K+-induced delayed afferent activation, reduced CSD-evoked cortical hyperemia, but had no effect on the enhanced activation or mechanical sensitization of meningeal afferents following CSD. Conclusion While CGRP-mediated activation of meningeal afferents evoked by cortical efflux of K+ could promote headache, acute activation of CGRP receptors may not play a key role in mediating CSD-evoked headache.
Concise total synthesis of (±)-palominol and (±)-dolabellatrienone via a dianion-accelerated oxy-cope rearrangement

Abstract A short synthesis of (±)-palominol (1) and (±)-dolabellatrienone (2) starting from farnesol is reported. Noteworthy steps include an intramolecular pinacol coupling to form a 15-membered carbocyclic diol and subsequent dianion-accelerated oxy-Cope rearrangement to form the 11,5-trans-fused ring system of the dolabellanes.
NF90/ILF3 is a transcription factor that promotes proliferation over differentiation by hierarchical regulation in K562 erythroleukemia cells

NF90 and splice variant NF110 are DNA- and RNA-binding proteins encoded by the Interleukin enhancer-binding factor 3 (ILF3) gene that have been established to regulate RNA splicing, stabilization and export. The roles of NF90 and NF110 in regulating transcription as chromatin-interacting proteins have not been comprehensively characterized. Here, chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) identified 9,081 genomic sites specifically occupied by NF90/NF110 in K562 cells. One third of NF90/NF110 peaks occurred at promoters of annotated genes. NF90/NF110 occupancy colocalized with chromatin marks associated with active promoters and strong enhancers. Comparison with 150 ENCODE ChIP-seq experiments revealed that NF90/NF110 clustered with transcription factors exhibiting preference for promoters over enhancers (POLR2A, MYC, YY1). Differential gene expression analysis following shRNA knockdown of NF90/NF110 in K562 cells revealed that NF90/NF110 activates transcription factors that drive growth and proliferation (EGR1, MYC), while attenuating differentiation along the erythroid lineage (KLF1). NF90/NF110 associates with chromatin to hierarchically regulate transcription factors that promote proliferation and suppress differentiation.
Facile synthesis and cleavage of imidazolidines in a novel protection strategy for the preparation of peptides containing a reduced amide bioisostere

Abstract Unsymmetrical imidazolidines were obtained in 75–91% yield by treating monoalkoxycarbonyl vicinal diamines at room temperature with aqueous 37% formaldehyde in the presence of Montmorillonite KSF as a solid catalyst. The imidazolidines were shown to be useful intermediates in a novel protection strategy for the synthesis of peptide analogues containing a reduced glycine amide bioisostere. The imidazolidine intermediate was cleaved conveniently and efficiently by 50% TFA in methylene chloride.
Agricultural fungicides inadvertently influence the fitness of Colorado potato beetles, Leptinotarsa decemlineata, and their susceptibility to insecticides

The Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say), is an agricultural pest of solanaceous crops which has developed insecticide resistance at an alarming rate. Up to this point, little consideration has been given to unintended, or inadvertent effects that non-insecticide xenobiotics may have on insecticide susceptibility in L. decemlineata. Fungicides, such as chlorothalonil and boscalid, are often used to control fungal pathogens in potato fields and are applied at regular intervals when L. decemlineata populations are present in the crop. In order to determine whether fungicide use may be associated with elevated levels of insecticide resistance in L. decemlineata, we examined phenotypic responses in L. decemlineata to the fungicides chlorothalonil and boscalid. Using enzymatic and transcript abundance investigations, we also examined modes of molecular detoxification in response to both insecticide (imidacloprid) and fungicide (boscalid and chlorothalonil) application to more specifically determine if fungicides and insecticides induce similar metabolic detoxification mechanisms. Both chlorothalonil and boscalid exposure induced a phenotypic, enzymatic and transcript response in L. decemlineata which correlates with known mechanisms of insecticide resistance.
Cyclic α-Acetoxynitrosamines: Mechanisms of Decomposition and Stability of α-Hydroxynitrosamine and Nitrosiminium Ion Reactive Intermediates

A study of the kinetics and mechanism of the decay of α-acetoxy-N-nitrosopyrrolidine and α-acetoxy-N-nitrosopiperidine are reported. The compounds differ in reactivity by more than 2 orders of magnitude at physiological pH. On the basis of thermodynamic parameters, common ion inhibition and azide ion trapping experiments, both compounds appear to decompose under these conditions by the formation of N-nitrosiminium ion intermediates. The differences in reactivity are rationalized on the basis of results from an ab initio study, described in the accompanying paper. The first direct study of the kinetics of decay of cyclic α-hydroxynitrosamines of nitrosopiperidine and nitrosopyrrolidine and a third compound, 2-hydroxy-2-methylnitrosopyrrolidine is also summarized. These prove to be highly unstable reactive intermediates, in contrast to what might be expected on the basis of earlier reports concerning cyclic α-hydroxynitrosamines.
A highly efficient, recyclable catalyst for C-C coupling reactions in ionic liquids: pyrazolyl-functionalized N-heterocyclic carbene complex of palladium(II).

A hemilabile pyrazolyl-functionalized N-heterocyclic carbene complex of palladium(II) has been synthesized. It is an excellent catalyst for Heck and Suzuki cross-coupling reactions in ionic liquids.
Saccades and handedness interact to affect scene memory.

Repetitive saccades benefit memory when executed before retrieval, with greatest effects for episodic memory in consistent-handers. Questions remain including how saccades affect scene memory, an important visual component of episodic memory. The present study tested how repetitive saccades affect working and recognition memory for novel scenes. Handedness direction (left-right) and degree (strong/consistent vs. mixed/inconsistent) was measured by raw and absolute laterality quotients respectively from an 8-question handedness inventory completed by 111 adults. Each then performed either 30 s of repetitive horizontal saccades or fixation before or after tasks of scene working memory and scene recognition. Regression with criterion variables of overall percent correct accuracy and d-prime sensitivity showed that when saccades were made before working memory, there was better overall accuracy as a function of increased direction but not degree of handedness. Subjects who made saccades before working memory also performed worse during subsequent recognition memory, while subjects who fixated or made saccades after the working memory task performed better. Saccades made before recognition resulted in recognition accuracy that was better (Cohen's d = 0.3729), but not significantly different from fixation before recognition. The results demonstrate saccades and handedness interact to affect scene memory with larger effects on encoding than recognition. Saccades before scene encoding in working memory are detrimental to short- and long-term memory, especially for those who are not consistently right-handed, while saccade execution before scene recognition does not appear to benefit recognition accuracy. The findings are discussed with respect to theories of interhemispheric interaction and control of visuospatial attention.
Adipose fin development and its relation to the evolutionary origins of median fins

The dorsal, anal and caudal fins of vertebrates are proposed to have originated by the partitioning and transformation of the continuous median fin fold that is plesiomorphic to chordates. Evaluating this hypothesis has been challenging, because it is unclear how the median fin fold relates to the adult median fins of vertebrates. To understand how new median fins originate, here we study the development and diversity of adipose fins. Phylogenetic mapping shows that in all lineages except Characoidei (Characiformes) adipose fins develop from a domain of the larval median fin fold. To inform how the larva's median fin fold contributes to the adipose fin, we study Corydoras aeneus (Siluriformes). As the fin fold reduces around the prospective site of the adipose fin, a fin spine develops in the fold, growing both proximally and distally, and sensory innervation, which appears to originate from the recurrent ramus of the facial nerve and from dorsal rami of the spinal cord, develops in the adipose fin membrane. Collectively, these data show how a plesiomorphic median fin fold can serve as scaffolding for the evolution and development of novel, individuated median fins, consistent with the median fin fold hypothesis.
Asymmetric synthesis of α-fluoro ketones using α-fluoro oxazolidinone carboximides

Abstract Addition of Grignard reagents to enantiopure α-fluoro N-methoxy-N-methylamides 5 affords α-fluoro ketones (R)- 6 in >96% ee.
Joint Face Detection and Facial Motion Retargeting for Multiple Faces

Facial motion retargeting is an important problem in both computer graphics and vision, which involves capturing the performance of a human face and transferring it to another 3D character. Learning 3D morphable model (3DMM) parameters from 2D face images using convolutional neural networks is common in 2D face alignment, 3D face reconstruction etc. However, existing methods either require an additional face detection step before retargeting or use a cascade of separate networks to perform detection followed by retargeting in a sequence. In this paper, we present a single end-to-end network to jointly predict the bounding box locations and 3DMM parameters for multiple faces. First, we design a novel multitask learning framework that learns a disentangled representation of 3DMM parameters for a single face. Then, we leverage the trained single face model to generate ground truth 3DMM parameters for multiple faces to train another network that performs joint face detection and motion retargeting for images with multiple faces. Experimental results show that our joint detection and retargeting network has high face detection accuracy and is robust to extreme expressions and poses while being faster than state-of-the-art methods.
Noise-cancelling repeat finder: uncovering tandem repeats in error-prone long-read sequencing data.

Summary Tandem DNA repeats can be sequenced with long-read technologies, but cannot be accurately deciphered due to the lack of computational tools taking high error rates of these technologies into account. Here we introduce Noise-Cancelling Repeat Finder (NCRF) to uncover putative tandem repeats of specified motifs in noisy long reads produced by Pacific Biosciences and Oxford Nanopore sequencers. Using simulations, we validated the use of NCRF to locate tandem repeats with motifs of various lengths and demonstrated its superior performance as compared to two alternative tools. Using real human whole-genome sequencing data, NCRF identified long arrays of the (AATGG)n repeat involved in heat shock stress response. Availability and implementation NCRF is implemented in C, supported by several python scripts, and is available in bioconda and at https://github.com/makovalab-psu/NoiseCancellingRepeatFinder. Supplementary information Supplementary data are available at Bioinformatics online.
Network-targeted approach and postoperative resting-state functional magnetic resonance imaging are associated with seizure outcome.

OBJECTIVE Postoperative resting-state functional magnetic resonance imaging (MRI) in children with intractable epilepsy has not been quantified in relation to seizure outcome. Therefore, its value as a biomarker for epileptogenic pathology is not well understood. METHODS In a sample of children with intractable epilepsy who underwent prospective resting-state seizure onset zone (SOZ)-targeted epilepsy surgery, postoperative resting-state functional MRI (rs-fMRI) was performed 6 to 12 months later. Graded normalization of the postoperative resting-state SOZ was compared to seizure outcomes, patient, surgery, and anatomical MRI characteristics. RESULTS A total of 64 cases were evaluated. Network-targeted surgery, followed by postoperative rs-fMRI normalization was significantly (p < 0.001) correlated with seizure reduction, with a Spearman rank correlation coefficient of 0.83. Of 39 cases with postoperative rs-fMRI SOZ normalization, 38 (97%) became completely seizure free. In contrast, of the 25 cases without complete rs-fMRI SOZ normalization, only 3 (5%) became seizure free. The accuracy of rs-fMRI as a biomarker predicting seizure freedom is 94%, with 96% sensitivity and 93% specificity. INTERPRETATION Among seizure localization techniques in pediatric epilepsy, network-targeted surgery, followed by postoperative rs-fMRI normalization, has high correlation with seizure freedom. This study shows that rs-fMRI SOZ can be used as a biomarker of the epileptogenic zone, and postoperative rs-fMRI normalization is a biomarker for SOZ quiescence. ANN NEUROL 2019;86:344-356.
Determining phonon mean free path spectrum by ballistic phonon resistance within a nanoslot-patterned thin film

Abstract At microscales to nanoscales, classical size effects in heat conduction play an important role in suppressing the thermal transport process. Such effects occur when the characteristic lengths become commensurate to the mean free paths (MFPs) of heat carriers that are mainly phonons for non-metallic crystals. Beyond existing experimental efforts on thin films using laser-induced thermal gratings, this work provides the theoretical analysis for a new approach to extract the effective phonon MFP distribution for the in-plane heat conduction within a thin film or flake-like sample. In this approach, nanoslots are patterned on a suspended thin film. Phonons will transport ballistically through the neck region between adjacent nanoslots if the phonon MFPs are much longer than the neck width. The associated ‘ballistic thermal resistance’ for varied neck dimensions can then be used to reconstruct the phonon MFP distribution within the film. The technique can be further extended to two-dimensional materials when the relaxation time approximation is reasonably accurate.
Multilingual topic models for unaligned text

We develop the multilingual topic model for unaligned text (MuTo), a probabilistic model of text that is designed to analyze corpora composed of documents in two languages. From these documents, MuTo uses stochastic EM to simultaneously discover both a matching between the languages and multilingual latent topics. We demonstrate that MuTo is able to find shared topics on real-world multilingual corpora, successfully pairing related documents across languages. MuTo provides a new framework for creating multilingual topic models without needing carefully curated parallel corpora and allows applications built using the topic model formalism to be applied to a much wider class of corpora.
Derivation and Extensions of the Linear Feedback Particle Filter Based on Duality Formalisms

This paper is concerned with a duality-based approach to derive the linear feedback particle filter (FPF). The FPF is a controlled interacting particle system where the control law is designed to provide an exact solution for the nonlinear filtering problem. For the linear Gaussian special case, certain simplifications arise whereby the linear FPF is identical to the square-root form of the ensemble Kalman filter. For this and for the more general nonlinear non-Gaussian case, it has been an open problem to derive/interpret the FPF control law as a solution of an optimal control problem. In this paper, certain duality-based arguments are employed to transform the filtering problem into an optimal control problem. Its solution is shown to yield the deterministic form of the linear FPF. An extension is described to incorporate stochastic effects due to noise leading to a novel homotopy of exact ensemble Kalman filters. All the derivations are based on duality formalisms.
Shape transitions in hyperbolic non-Euclidean plates

A non-Euclidean plate is a thin elastic object whose intrinsic geometry is not flat and hence has residual stresses arising from being embedded in three dimensional space. Recently, there has been interest in using localized swelling to induce residual stresses that shape flat objects into desired three dimensional structures. A fundamental question is whether we can use the mathematical theory of non-Euclidean plates to deduce the three dimensional configuration of the swelling sheet given the exact knowledge of the imposed geometry. We present and summarize the results of recent mathematical studies on non-Euclidean plates with imposed constant negative Gaussian curvature in both annular and disc geometries. We show in the Foppl–von Karman approximation to the elastic energy there are only two types of global minimizers – flat and saddle shaped deformations – with localized regions of stretching near the boundary of the domain. We also show that there exist n-wave local minimizers that closely resemble experimental observations and have additional regions of stretching near lines of inflection. Furthermore, in the Kirchhoff approximation to the elastic energy, we show that there exists exact isometric immersions with periodic profiles. The number of waves in these configurations is set by the condition that the bending energy remains finite and grows approximately exponentially with the radius of the annulus. For large radii, these shape are energetically favorable over saddle shapes and could explain why wavy shapes are selected in crochet models of the hyperbolic plane. The predicted morphologies however differ from what is observed in experiments on hydrogel disks highlighting the need for further theoretical studies.
Mask iterative hard thresholding algorithms for sparse image reconstruction of objects with known contour

We develop mask iterative hard thresholding algorithms (mask IHT and mask DORE) for sparse image reconstruction of objects with known contour. The measurements follow a noisy underdetermined linear model common in the compressive sampling literature. Assuming that the contour of the object that we wish to reconstruct is known and that the signal outside the contour is zero, we formulate a constrained residual squared error minimization problem that incorporates both the geometric information (i.e. the knowledge of the object's contour) and the signal sparsity constraint. We first introduce a mask IHT method that aims at solving this minimization problem and guarantees monotonically non-increasing residual squared error for a given signal sparsity level. We then propose a double overrelaxation scheme for accelerating the convergence of the mask IHT algorithm. We also apply convex mask reconstruction approaches that employ a convex relaxation of the signal sparsity constraint. In X-ray computed tomography (CT), we propose an automatic scheme for extracting the convex hull of the inspected object from the measured sinograms; the obtained convex hull is used to capture the object contour information. We compare the proposed mask reconstruction schemes with the existing large-scale sparse signal reconstruction methods via numerical simulations and demonstrate that, by exploiting both the geometric contour information of the underlying image and sparsity of its wavelet coefficients, we can reconstruct this image using a significantly smaller number of measurements than the existing methods.
Statistically Efficient Thinning of a Markov Chain Sampler

ABSTRACTIt is common to subsample Markov chain output to reduce the storage burden. Geyer shows that discarding k − 1 out of every k observations will not improve statistical efficiency, as quantified through variance in a given computational budget. That observation is often taken to mean that thinning Markov chain Monte Carlo (MCMC) output cannot improve statistical efficiency. Here, we suppose that it costs one unit of time to advance a Markov chain and then θ > 0 units of time to compute a sampled quantity of interest. For a thinned process, that cost θ is incurred less often, so it can be advanced through more stages. Here, we provide examples to show that thinning will improve statistical efficiency if θ is large and the sample autocorrelations decay slowly enough. If the lag l ⩾ 1 autocorrelations of a scalar measurement satisfy ρl > ρl + 1 > 0, then there is always a θ < ∞ at which thinning becomes more efficient for averages of that scalar. Many sample autocorrelation functions resemble first ord...
Statistical Channel Model with Multi-Frequency and Arbitrary Antenna Beamwidth for Millimeter-Wave Outdoor Communications

This paper presents a 3-dimensional millimeter- wave statistical channel impulse response model from 28 GHz and 73 GHz ultrawideband propagation measurements. An accurate 3GPP-like channel model that supports arbitrary carrier frequency, RF bandwidth, and antenna beamwidth (for both omnidirectional and arbitrary directional antennas), is provided. Time cluster and spatial lobe model parameters are extracted from empirical distributions from field measurements. A step-by- step modeling procedure for generating channel coefficients is shown to agree with statistics from the field measurements, thus confirming that the statistical channel model faithfully recreates spatial and temporal channel impulse responses for use in millimeter-wave 5G air interface designs.
Multi-vehicle collision avoidance via hamilton-jacobi reachability and mixed integer programming

Multi-agent differential games are important and useful tools for analyzing many practical problems. With the recent surge of interest in using UAVs for civil purposes, the importance and urgency of developing tractable multiagent analysis techniques that provide safety and performance guarantees is at an all-time high. Hamilton-Jacobi (HJ) reachability has successfully provided safety guarantees to smallscale systems and is flexible in terms of system dynamics. However, the exponential complexity scaling of HJ reachability prevents its direct application to large scale problems when the number of vehicles is greater than two. In this paper, we overcome the scalability limitations of HJ reachability by using a mixed integer program that exploits the properties of HJ solutions to provide higher-level control logic. Our proposed method provides safety guarantee for three-vehicle systems - a previously intractable task for HJ reachability - without incurring significant additional computation cost. Furthermore, our method is scalable beyond three vehicles and performs significantly better by several metrics than an extension of pairwise collision avoidance to multi-vehicle collision avoidance. We demonstrate our proposed method in simulations.
Simplicity, primitivity and semiprimitivity of étale groupoid algebras with applications to inverse semigroup algebras

This paper studies simplicity, primitivity and semiprimitivity of algebras associated to etale groupoids. Applications to inverse semigroup algebras are presented. The results also recover the semiprimitivity of Leavitt path algebras and can be used to recover the known primitivity criterion for Leavitt path algebras.
Extensions of probability-preserving systems by measurably-varying homogeneous spaces and applications

We study a generalized notion of a homogeneous skew-product extension of a probability-preserving system in which the homogeneous space fibres are allowed to vary over the ergodic decomposition of the base. The construction of such extensions rests on a simple notion of `direct integral' for a `measurable family' of homogeneous spaces, which has a number of precedents in older literature. The main contribution of the present paper is the systematic development of a formalism for handling such extensions, including non-ergodic versions of the results of Mackey describing ergodic components of such extensions, of the Furstenberg-Zimmer Structure Theory and of results of Mentzen describing the structure of automorphisms of such extensions when they are relatively ergodic. We then offer applications to two structural results for actions of several commuting transformations: firstly to describing the possible joint distributions of three isotropy factors corresponding to three commuting transformations; and secondly to describing the characteristic factors for a system of double nonconventional ergodic averages. Although both applications are modest in themselves, we hope that they point towards a broader usefulness of this formalism in ergodic theory.
Introducing the Polylogarithmic Hardy Space

In this paper we investigate the reproducing kernel Hilbert space where the polylogarithm appears as the kernel function. This investigation begins with the properties of functions in this space, and here a connection to the classical Hardy space is shown through the Bose–Einstein integral equation. Next we consider function theoretic operators over the polylogarithmic Hardy space, such as multiplication and Toeplitz operators. It is determined that there are only trivial densely defined multiplication operators (and therefore only trivial bounded multipliers) over this space, which makes this space the first for which this has been found to be true. In the case of Toeplitz operators, a connection between a certain subset of these operators and the number theoretic divisor function is established. Finally, the paper concludes with an operator theoretic proof of the divisibility of the divisor function.
Optimal amortized regret in every interval

Consider the classical problem of predicting the next bit in a sequence of bits. A standard performance measure is regret (loss in payoff) with respect to a set of experts. For example if we measure performance with respect to two constant experts one that always predicts 0's and another that always predicts 1's it is well known that one can get regret O(√T) with respect to the best expert by using, say, the weighted majority algorithm [LW89]. But this algorithm does not provide performance guarantee in any interval. There are other algorithms (see [BM07, FSSW97, Vov99]) that ensure regret O(√x log T) in any interval of length x. In this paper we show a randomized algorithm that in an amortized sense gets a regret of O(√x) for any interval when the sequence is partitioned into intervals arbitrarily. We empirically estimated the constant in the O() for T upto 2000 and found it to be small - around 2.1. We also experimentally evaluate the efficacy of this algorithm in predicting high frequency stock data.
Trellis Networks for Sequence Modeling

We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. Thus trellis networks with general weight matrices generalize truncated recurrent networks. We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models. Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention. The code is available at this https URL .
Existence and Blow-Up Behavior for Solutions of the Generalized Jang Equation

The generalized Jang equation was introduced in an attempt to prove the Penrose inequality in the setting of general initial data for the Einstein equations. In this paper we give an extensive study of this equation, proving existence, regularity, and blow-up results. In particular, precise asymptotics for the blow-up behavior are given, and it is shown that blow-up solutions are not unique.
If it ain't broke, don't fix it: Sparse metric repair

Many modern data-intensive computational problems either require, or benefit from distance or similarity data that adhere to a metric. The algorithms run faster or have better performance guarantees. Unfortunately, in real applications, the data are messy and values are noisy. The distances between the data points are far from satisfying a metric. Indeed, there are a number of different algorithms for finding the closest set of distances to the given ones that also satisfy a metric (sometimes with the extra condition of being Euclidean). These algorithms can have unintended consequences; they can change a large number of the original data points, and alter many other features of the data. The goal of sparse metric repair is to make as few changes as possible to the original data set or underlying distances so as to ensure the resulting distances satisfy the properties of a metric. In other words, we seek to minimize the sparsity (or the l 0 “norm”) of the changes we make to the distances subject to the new distances satisfying a metric. We give three different combinatorial algorithms to repair a metric sparsely. In one setting the algorithm is guaranteed to return the sparsest solution and in the other settings, the algorithms repair the metric. Without prior information, the algorithms run in time proportional to the cube of the number of input data points and, with prior information we can reduce the running time considerably.
Smith normal form in combinatorics

This paper surveys some combinatorial aspects of Smith normal form, and more generally, diagonal form. The discussion includes general algebraic properties and interpretations of Smith normal form, critical groups of graphs, and Smith normal form of random integer matrices. We then give some examples of Smith normal form and diagonal form arising from (1) symmetric functions, (2) a result of Carlitz, Roselle, and Scoville, and (3) the Varchenko matrix of a hyperplane arrangement.
Multiple conceptual coherences in the speed tutorial: micro-processes of local stability

Researchers working within knowledge-in-pieces traditions have often employed observational approaches to investigate micro-processes of learning. There is growing evidence from this line of work that students' intuitive thinking about physical phenomena is characterized more so by its diversity and flexibility than its uniformity and robustness. This characterization implies that much of the dynamics of students' thinking over short timescales involve processes that stabilize local patterns of thinking, later destabilize them, and allow other patterns to form. This kind of "change" may only involve dynamics by which the system of intuitive knowledge settles into various states without changing the system structure itself. I describe a case study in which a group of college students shift their thinking about motion several times during a collaborative learning activity. Instead of focusing on micro-processes of change, I describe these dynamics in terms of mechanisms that contribute to local stability of students' conceptual coherences.
Snake Graph Calculus and Cluster Algebras from Surfaces

Abstract Snake graphs appear naturally in the theory of cluster algebras. For cluster algebras from surfaces, each cluster variable is given by a formula which is parametrized by the perfect matchings of a snake graph. In this paper, we identify each cluster variable with its snake graph, and interpret relations among the cluster variables in terms of these graphs. In particular, we give a new proof of skein relations of two cluster variables.
Rigidity in automorphic representations and local systems

We introduce the notion of rigidity for automorphic repre- sentations of groups over global function fields. We construct the Lang- lands parameters of rigid automorphic representations explicitly as local systems over open curves. We expect these local systems to be rigid. Examples of rigid automorphic representations from previous work are reviewed and more examples for GL2 are discussed in details.
Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important information about surrounding environment. In this paper, we collected trajectory data on a two lane urban road using a Velodyne VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the sensor, some of these trajectories have missing points or gaps. In this paper, we propose a novel method for recovery of missing vehicle trajectory data points using microscopic traffic flow models. While short gaps (less than 5 seconds) can be recovered with simple linear regression, and longer gaps are recovered with the proposed method that makes use of car following models calibrated by assigning weights to known points based on proximity to the gaps. Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps' calibrated model yielded the best result.
Small-Bias Sets for Nonabelian Groups: Derandomizing the Alon-Roichman Theorem

In analogy with e-biased sets over \({\mathbb Z}_2^n\), we construct explicit e-biased sets over nonabelian finite groups G. That is, we find sets S ⊂ G such that \(\parallel{{\mathbb E}_{x \in S} \rho(x)} \parallel \leq \epsilon\) for any nontrivial irreducible representation ρ. Equivalently, such sets make G’s Cayley graph an expander with eigenvalue |λ| ≤ e. The Alon-Roichman theorem shows that random sets of size O(log|G| / e 2) suffice. For groups of the form G = G 1 × ⋯ ×G n , our construction has size poly( max i |G i |, n, e − 1), and we show that a specific set S ⊂ G n considered by Meka and Zuckerman that fools read-once branching programs over G is also e-biased in this sense. For solvable groups whose abelian quotients have constant exponent, we obtain e-biased sets of size (log|G|)1 + o(1) poly(e − 1). Our techniques include derandomized squaring (in both the matrix product and tensor product senses) and a Chernoff-like bound on the expected norm of the product of independently random operators that may be of independent interest.
Bounds on the largest Kronecker and induced multiplicities of finite groups

We give new bounds and asymptotic estimates on the largest Kronecker and induced multiplicities of finite groups. The results apply to large simple groups of Lie type and other groups with few conj...
Compactification of the moduli of polarized abelian varieties and mirror symmetry

We show that Martin Olsson’s compactification of moduli space of polarized abelian varieties in [Ols08] can be interpreted in terms of KSBA stable pairs. We find that there is a canonical set of divisors S(K2) associated with each cusp. Near the cusp, a polarized semiabelic scheme (X , G,L) is the canonical degeneration given by the compactification if and only if (X , G,Θ) is an object in A Pg,d for any Θ ∈ S(K2). Moreover, we give an alternative construction of the compactification by using mirror symmetry. We construct a toroidal compactification A m g,δ that is isomorphic to Olsson’s compactification over characteristic zero. The data needed for a toroidal compactification is a collection of fans. We obtain the collection of fans from the Mori fans of the minimal models of the mirror families.
No topological condition implies equality of polynomial and rational hulls

It is shown that no purely topological condition implies the equality of the polynomial and rational hulls of a set: For any compact subset $K$ of a Euclidean space, there exists a set $X$, in some ${\mathbb C}^N$, that is homeomorphic to $K$ and is rationally convex but not polynomially convex. In addition, it is shown that for the surfaces in ${\mathbb C}^3$ constructed by Izzo and Stout, whose polynomial hulls are nontrivial but contain no analytic discs, the polynomial and rational hulls coincide, thereby answering a question of Gupta. Equality of polynomial and rational hulls is shown also for $m$-dimensional manifolds ($m\geq 2$) with polynomial hulls containing no analytic discs constructed by Izzo, Samuelsson Kalm, and Wold and by Arosio and Wold.
NavREn-Rl: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images

We present NavREn-Rl, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning (RL). A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities. Collection of small number of expert data and knowledge based data aggregation is integrated into the RL process to aid convergence. Experimentation is carried out on a Parrot AR drone in different indoor arenas and the results are compared with other baseline technologies. We demonstrate how the drone successfully avoids obstacles and navigates across different arenas. Video of the drone navigating using the proposed approach can be seen at https://youtu.be/yOTkTHUPNVY
The Feller property for graphs

The Feller property concerns the preservation of the space of functions vanishing at infinity by the semigroup generated by an operator. We study this property in the case of the Laplacian on infinite graphs with arbitrary edge weights and vertex measures. In particular, we give conditions for the Feller property involving curvature-type quantities for general graphs, characterize the property in the case of model graphs, and give some comparison results to the model case.
Reasoning in Vector Space: An Exploratory Study of Question Answering

Abstract: Question answering tasks have shown remarkable progress with distributed vector representation. In this paper, we investigate the recently proposed Facebook bAbI tasks which consist of twenty different categories of questions that require complex reasoning. Because the previous work on bAbI are all end-to-end models, errors could come from either an imperfect understanding of semantics or in certain steps of the reasoning. For clearer analysis, we propose two vector space models inspired by Tensor Product Representation (TPR) to perform knowledge encoding and logical reasoning based on common-sense inference. They together achieve near-perfect accuracy on all categories including positional reasoning and path finding that have proved difficult for most of the previous approaches. We hypothesize that the difficulties in these categories are due to the multi-relations in contrast to uni-relational characteristic of other categories. Our exploration sheds light on designing more sophisticated dataset and moving one step toward integrating transparent and interpretable formalism of TPR into existing learning paradigms.
Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images. We propose an unsupervised learning technique called spectral-spatial diffusion learning (DLSS) that combines a geometric estimation of class modes with a diffusion-inspired labeling that incorporates both spectral and spatial information. The mode estimation incorporates the geometry of the hyperspectral data by using diffusion distance to promote learning a unique mode from each class. These class modes are then used to label all the points by a joint spectral-spatial nonlinear diffusion process. A related variation of DLSS is also discussed, which enables active learning by requesting labels for a very small number of well-chosen pixels, dramatically boosting overall clustering results. Extensive experimental analysis demonstrates the efficacy of the proposed methods against benchmark and state-of-the-art hyperspectral analysis techniques on a variety of real data sets, their robustness to choices of parameters, and their low computational complexity.
Emergence and Evolution of Hierarchical Structure in Complex Systems

It is well known that many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. What is not well understood however is how this hierarchical structure (which is fundamentally a network property) emerges, and how it evolves over time.
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation.

Abstract Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods, thus affect disambiguation performance. In this work, we have evaluated several types of features derived from the context of the ambiguous word and we have explored as well more global features derived from MEDLINE using word embeddings. Results show that word embeddings improve the performance of more traditional features and allow as well using recurrent neural network classifiers based on Long-Short Term Memory (LSTM) nodes. The combination of unigrams and word embeddings with an SVM sets a new state of the art performance with a macro accuracy of 95.97 in the MSH WSD data set.
Renormalization towers and their forcing

A cyclic permutation $\pi:\{1, \dots, N\}\to \{1, \dots, N\}$ has a \emph{block structure} if there is a partition of $\{1, \dots, N\}$ into $k\notin\{1,N\}$ segments (\emph{blocks}) permuted by $\pi$; call $k$ the \emph{period} of this block structure. Let $p_1<\dots <p_s$ be periods of all possible block structures on $\pi$. Call the finite string $(p_1/1,$ $p_2/p_1,$ $\dots,$ $p_s/p_{s-1}, N/p_s)$ the {\it renormalization tower of $\pi$}. The same terminology can be used for \emph{patterns}, i.e., for families of cycles of interval maps inducing the same (up to a flip) cyclic permutation. A renormalization tower $\mathcal M$ \emph{forces} a renormalization tower $\mathcal N$ if every continuous interval map with a cycle of pattern with renormalization tower $\mathcal M$ must have a cycle of pattern with renormalization tower $\mathcal N$. We completely characterize the forcing relation among renormalization towers. Take the following order among natural numbers: $ 4\gg 6\gg 3\gg \dots \gg 4n\gg 4n+2\gg 2n+1\gg\dots \gg 2\gg 1 $ understood in the strict sense. We show that the forcing relation among renormalization towers is given by the lexicographic extension of this order. Moreover, for any tail $T$ of this order there exists an interval map for which the set of renormalization towers of its cycles equals $T$.
Elliptic surfaces and linear systems with fat points

We investigate the expected dimensionality of linear systems with general fat points on certain surfaces using an approach by specialization to elliptic surfaces. For the projectivization of the Atiyah bundle over an elliptic curve with a certain polarization, we observe that the special case of only one fat point implies the general case of arbitrarily many fat points, as well as results concerning other surfaces. We conjecture that this special case holds in characteristic 0, but prove that it fails in any positive characteristic.
Volume effects on the method of extracting form factors at zero momentum

The Rome method allows one to extract form factors using lattice computations performed strictly at zero momentum. We investigate the size of finite volume effects resulting from this method. As a test case, we focus on the pion charge radius and show how to ascertain the finite volume effect with the aid of chiral perturbation theory. The framework developed can easily be generalized to account for modified infrared physics of other low-energy matrix elements extracted at zero momentum.
Regularized estimation and testing for high-dimensional multi-block vector-autoregressive models

Dynamical systems comprising of multiple components that can be partitioned into distinct blocks originate in many scientific areas. A pertinent example is the interactions between financial assets and selected macroeconomic indicators, which has been studied at aggregate level--e.g. a stock index and an employment index--extensively in the macroeconomics literature. A key shortcoming of this approach is that it ignores potential influences from other related components (e.g. Gross Domestic Product) that may impact the system's dynamics and structure and thus produces incorrect results. To mitigate this issue, we consider a multi-block linear dynamical system with Granger-causal ordering between blocks, wherein the blocks' temporal dynamics are described by vector autoregressive processes and are influenced by blocks higher in the system hierarchy. We derive the maximum likelihood estimator for the posited model for Gaussian data in the high-dimensional setting based on appropriate regularization schemes for the parameters of the block components. To optimize the underlying non-convex likelihood function, we develop an iterative algorithm with convergence guarantees. We establish theoretical properties of the maximum likelihood estimates, leveraging the decomposability of the regularizers and a careful analysis of the iterates. Finally, we develop testing procedures for the null hypothesis of whether a block "Granger-causes" another block of variables. The performance of the model and the testing procedures are evaluated on synthetic data, and illustrated on a data set involving log-returns of the US S&P100 component stocks and key macroeconomic variables for the 2001-16 period.