cssi/SciSciGPT-SciSciNet huggingface dataset:
gem-Difluoropropargylation of aldehydes using cat. In/Zn in aqueous media

Abstract Zn (0.9 equiv) in combination with catalytic amounts of In (0.1 equiv) and I 2 (0.1 equiv) was found to effect the reaction of several difluoropropargyl bromide derivatives with aldehydes to produce gem -difluorohomopropargyl alcohols in aqueous media under conditions suitable for large scale applications.
PALLADIUM-CATALYZED ANNULATION OF ALLENES USING FUNCTIONALLY SUBSTITUTED VINYLIC HALIDES

Palladium catalyzes the regio- and stereoselective annulation of allenes by vinylic halides bearing alcohol-, amine-, sulfonamide-, carboxylic acid-, carboxamide- and carbanion-stabilizing groups to produce a variety of five- and six-membered-ring unsaturated heterocycles and carbocycles. The reaction appears to proceed by vinylic palladium formation and addition to the allene, followed by intramolecular nucleophilic displacement of the palladium. Six-membered rings are formed more readily than five-membered rings. The regioselectivity is generally high, with vinylic halides bearing alcohol, carboxylic acid, or carboxamide groups affording predominantly the product of intramolecular attack on the more substituted end of the π-allylpalladium intermediate, while amines and carbanions attack quite selectively at the less substituted end of the π-allylpalladium intermediate. Vinylic halides bearing tosylamides afford mixtures of regioisomers, where the predominant six-membered ring product arises by attack on...
Solvent effects, reaction coordinates, and reorganization energies on nucleophilic substitution reactions in aqueous solution☆

Publisher Summary This chapter reviews a combined quantum mechanical and molecular mechanical (QM/MM) method that couples molecular orbital and valence bond theories, called the MOVB method, to determine the free energy reaction profiles, or potentials of mean force (PMF), for chemical reactions in solution. The combined QM-MOVB/MM method is applied to three types of nucleophilic substitution reactions and the question of the dependence of the computed PMF on the choice of the reaction coordinate and its implications on the computed solvent reorganization energy have been addressed. A mixed MOVB model has been described for describing the potential energy surface of reactive systems, and results from applications to S N 2 reactions in aqueous solution have been presented. The MOVB model is based on a block-localized wave function method to define diabatic electronic state functions. A configuration interaction Hamiltonian has been constructed using these diabatic VB states as basis functions. The computed geometrical and energetic results for these systems are in accord with the experimental and theoretical studies. These studies show that the MOVB model can be adequately used as a mapping potential to derive solvent reaction coordinates for condensed phase processes.
Protein Phase Separation Provides Long-Term Memory of Transient Spatial Stimuli

Protein/RNA clusters arise frequently in spatially regulated biological processes, from the asymmetric distribution of P granules and PAR proteins in developing embryos to localized receptor oligomers in migratory cells. This co-occurrence suggests that protein clusters might possess intrinsic properties that make them a useful substrate for spatial regulation. Here, we demonstrate that protein droplets show a robust form of spatial memory, maintaining the spatial pattern of an inhibitor of droplet formation long after it has been removed. Despite this persistence, droplets can be highly dynamic, continuously exchanging monomers with the diffuse phase. We investigate the principles of biophysical spatial memory in three contexts: a computational model of phase separation; a novel optogenetic system where light can drive rapid, localized dissociation of liquid-like protein droplets; and membrane-localized signal transduction from clusters of receptor tyrosine kinases. Our results suggest that the persistent polarization underlying many cellular and developmental processes could arise through a simple biophysical process, without any additional biochemical feedback loops.
Total Synthesis of Vancomycin

A number of valuable new synthetic strategies, such as the triazene-driven biaryl ether synthesis, have been developed during the total synthesis of vancomycin. Modern catalytic asymmetric reactions were employed for the construction of the required amino acid building blocks, which were then assembled to the appropriate peptide fragments, whose cyclization in the order A-B/C-O-D/D-O-E led to the framework of vancomycin aglycon. Sequential attachment of the required sugar moieties onto a suitably protected aglycon derivative, followed by deprotection, allowed the stereoselective total synthesis of the glycopeptide antibiotic vancomycin.
TRIM5α Restricts Flavivirus Replication by Targeting the Viral Protease for Proteasomal Degradation.

Summary Tripartite motif-containing protein 5α (TRIM5α) is a cellular antiviral restriction factor that prevents early events in retrovirus replication. The activity of TRIM5α is thought to be limited to retroviruses as a result of highly specific interactions with capsid lattices. In contrast to this current understanding, we show that both human and rhesus macaque TRIM5α suppress replication of specific flaviviruses. Multiple viruses in the tick-borne encephalitis complex are sensitive to TRIM5α-dependent restriction, but mosquito-borne flaviviruses, including yellow fever, dengue, and Zika viruses, are resistant. TRIM5α suppresses replication by binding to the viral protease NS2B/3 to promote its K48-linked ubiquitination and proteasomal degradation. Importantly, TRIM5α contributes to the antiviral function of IFN-I against sensitive flaviviruses in human cells. Thus, TRIM5α possesses remarkable plasticity in the recognition of diverse virus families, with the potential to influence human susceptibility to emerging flaviviruses of global concern.
Evaluation of strategies for the assembly of diverse bacterial genomes using MinION long-read sequencing

Short-read sequencing technologies have made microbial genome sequencing cheap and accessible. However, closing genomes is often costly and assembling short reads from genomes that are repetitive and/or have extreme %GC content remains challenging. Long-read, single-molecule sequencing technologies such as the Oxford Nanopore MinION have the potential to overcome these difficulties, although the best approach for harnessing their potential remains poorly evaluated. We sequenced nine bacterial genomes spanning a wide range of GC contents using Illumina MiSeq and Oxford Nanopore MinION sequencing technologies to determine the advantages of each approach, both individually and combined. Assemblies using only MiSeq reads were highly accurate but lacked contiguity, a deficiency that was partially overcome by adding MinION reads to these assemblies. Even more contiguous genome assemblies were generated by using MinION reads for initial assembly, but these assemblies were more error-prone and required further polishing. This was especially pronounced when Illumina libraries were biased, as was the case for our strains with both high and low GC content. Increased genome contiguity dramatically improved the annotation of insertion sequences and secondary metabolite biosynthetic gene clusters, likely because long-reads can disambiguate these highly repetitive but biologically important genomic regions. Genome assembly using short-reads is challenged by repetitive sequences and extreme GC contents. Our results indicate that these difficulties can be largely overcome by using single-molecule, long-read sequencing technologies such as the Oxford Nanopore MinION. Using MinION reads for assembly followed by polishing with Illumina reads generated the most contiguous genomes with sufficient accuracy to enable the accurate annotation of important but difficult to sequence genomic features such as insertion sequences and secondary metabolite biosynthetic gene clusters. The combination of Oxford Nanopore and Illumina sequencing can therefore cost-effectively advance studies of microbial evolution and genome-driven drug discovery.
Chiral 1,3-cyclobutane amino acids: Syntheses and extended conformations

Abstract Optically active samples of the N-protected 1,3-cyclobutane amino acids 1 and 2 were prepared from α-pinene. The synthesis of 1 is enantiodivergent insofar as both optical antipodes of the product can be prepared from the same α-pinene enantiomer. Single crystal X-ray diffraction studies of derivatives of 1 reveal these compounds can have extended conformations.
Emergence of an Adaptive Command for Orienting Behavior in Premotor Brainstem Neurons of Barn Owls.

The midbrain map of auditory space commands sound-orienting responses in barn owls. Owls precisely localize sounds in frontal space but underestimate the direction of peripheral sound sources. This bias for central locations was proposed to be adaptive to the decreased reliability in the periphery of sensory cues used for sound localization by the owl. Understanding the neural pathway supporting this biased behavior provides a means to address how adaptive motor commands are implemented by neurons. Here we find that the sensory input for sound direction is weighted by its reliability in premotor neurons of the midbrain tegmentum of owls (male and female), such that the mean population firing rate approximates the head-orienting behavior. We provide evidence that this coding may emerge through convergence of upstream projections from the midbrain map of auditory space. We further show that manipulating the sensory input yields changes predicted by the convergent network in both premotor neural responses and behavior. This work demonstrates how a topographic sensory representation can be linearly read out to adjust behavioral responses by the reliability of the sensory input. SIGNIFICANCE STATEMENT This research shows how statistics of the sensory input can be integrated into a behavioral command by readout of a sensory representation. The firing rate of midbrain premotor neurons receiving sensory information from a topographic representation of auditory space is weighted by the reliability of sensory cues. We show that these premotor responses are consistent with a weighted convergence from the topographic sensory representation. This convergence was also tested behaviorally, where manipulation of stimulus properties led to bidirectional changes in sound localization errors. Thus a topographic representation of auditory space is translated into a premotor command for sound localization that is modulated by sensory reliability.
APLIDIAMINE, A UNIQUE ZWITTERIONIC BENZYL HYDROXYADENINE FROM THE WESTERN AUSTRALIAN MARINE ASCIDIAN APLIDIOPSIS SP.

Abstract Aplidiamine (1), a unique zwitterionic benzyl substituted hydroxyadenine, has been isolated from the marine ascidian Aplidiopsis sp. collected in Western Australia. The structure of aplidiamine was determined by spectral analysis of the natural product and of several derivatives produced by diazomethane methylation.
Bismuth nitrate-catalyzed Michael reactions of indoles in water

A remarkable and simple bismuth nitrate-catalyzed Michael reaction of indoles with 1,2-unsaturated ketones in water has been developed to reduce the complications that characterize the current standard conditions.
Synthesis, Characterization, Electronic Structure, and Bonding of Heteroatomic Deltahedral Clusters: Na49Cd58.5Sn37.5, A Network Structure Containing the First Empty Icosahedron without a Group 13 Element and the Largest closo-Deltahedron

The intermetallic compound Na49Cd58.5Sn37.5 was obtained in nearly 100% yield after fusion of the elements in stoichiometric proportions in a Nb container and quenching of the mixture. The structure was determined by single-crystal X-ray diffraction (rhombohedral, R3m, Z = 3, a = 16.034(1) A, c = 50.64(1) A; R, Rw(F) = 3.3%, 4.2%). The covalently-bonded part of the structure is a network of cadmium and tin that contains the following: (a) the first empty icosahedra built without atoms of an element of group 13; (b) 12-bonded 18-atom closo-deltahedra, the largest closo-deltahedron ever made; (c) 20-atom spacers. The sodium cations are located between the clusters and also center the 18-atom cluster. Band calculations on the network and molecular orbital calculations on the 12-bonded 18-atom cluster show that the latter is hyperelectronic with 2n + 4 skeletal electrons, and also that its six lone-pair orbitals are not occupied. Magnetic measurements show small temperature-independent paramagnetism.
Oligonucleotide Incorporation of 8-Thio-2‘-deoxyguanosine

8-Thio-2‘-deoxyguanosine (SdG) is a useful analogue of the abundant promutagen 8-oxo-2‘-deoxyguanosine (OdG). Its synthesis and DNA incorporation using standard phosphoramidite chemistry is reported. To prevent oxidation during DNA synthesis, the sulfur was protected as a 2-(trimethylsilyl)ethyl sulfide. Subsequent treatment with TBAF yielded the desired 8-thiocarbonyl functionality. Melting studies with SdG revealed almost equal stabilities of SdG:dC and SdG:dA base pairs, lending insight into the base-pairing preferences of OdG.
DockerizeMe: automatic inference of environment dependencies for python code snippets

Platforms like Stack Overflow and GitHub's gist system promote the sharing of ideas and programming techniques via the distribution of code snippets designed to illustrate particular tasks. Python, a popular and fast-growing programming language, sees heavy use on both sites, with nearly one million questions asked on Stack Overflow and 400 thousand public gists on GitHub. Unfortunately, around 75% of the Python example code shared through these sites cannot be directly executed. When run in a clean environment, over 50% of public Python gists fail due to an import error for a missing library. We present DockerizeMe, a technique for inferring the dependencies needed to execute a Python code snippet without import error. DockerizeMe starts with offline knowledge acquisition of the resources and dependencies for popular Python packages from the Python Package Index (PyPI). It then builds Docker specifications using a graph-based inference procedure. Our inference procedure resolves import errors in 892 out of nearly 3,000 gists from the Gistable dataset for which Gistable's baseline approach could not find and install all dependencies.
Improved charge transfer multiplet method to simulate M- and L-edge X-ray absorption spectra of metal-centered excited states

Charge transfer multiplet (CTM) theory is a computationally undemanding and highly mature method for simulating the soft X-ray spectra of first-row transition metal complexes. However, CTM theory has seldom been applied to the simulation of excited-state spectra. In this article, the CTM4XAS software package is extended to simulate M2,3- and L2,3-edge spectra for the excited states of first-row transition metals and also interpret CTM eigenfunctions in terms of Russell–Saunders term symbols. These new programs are used to reinterpret the recently reported excited-state M2,3-edge difference spectra of photogenerated ferrocenium cations and to propose alternative assignments for the electronic state of these cations responsible for the spectroscopic features. These new programs were also used to model the L2,3-edge spectra of FeII compounds during nuclear relaxation following photoinduced spin crossover and to propose spectroscopic signatures for their vibrationally hot states.
Multiple realizations of varieties as ball quotient compactifications

We study the number of distinct ways in which a smooth projective surface X can be realized as a smooth toroidal compactification of a ball quotient. It follows from work of Hirzebruch that there are infinitely many distinct ball quotients with birational smooth toroidal compactifications. We take this to its natural extreme by constructing arbitrarily large families of distinct ball quotients with biholomorphic smooth toroidal compactifications.
Single-Shot MeV Transmission Electron Microscopy with Picosecond Temporal Resolution

A radical change to the electron source could improve by orders of magnitude the combined spatiotemporal resolution of ultrafast electron microscopy. The authors design and evaluate an instrument featuring a high-brightness MeV electron beam from an rf photoinjector. Being able to take snapshots at 1000 times the formerly highest rate would enable researchers to study nanoscale dynamical processes in real time.
Extending chip-based Kerr-comb to visible spectrum by dispersive wave engineering

Anomalous group velocity dispersion is a key parameter for generating bright solitons, and thus wideband Kerr frequency combs. Extension of the frequency combs spectrum to visible wavelengths has been a major challenge because of the strong normal dispersion of conventional photonic materials at these wavelengths. In this paper, we numerically demonstrate a wideband frequency comb extending from near-infrared to visible wavelengths (∼1200 nm to 650 nm). The proposed frequency comb micro-resonator takes advantage of a wideband blue-shifted anomalous dispersion, achieved in an optimized over-etched silicon nitride waveguide and strong power transfer to shorter wavelengths through radiative dispersive waves, achieved by modulating the dispersion in a coupled resonator architecture. We show the possibility of obtaining a close to visible dispersive Cherenkov radiation peak that is only 10 dB below the overall comb peak and can be tuned by adjusting the coupling structure in the coupled resonator architecture.
TimeWeaver: Opportunistic One Way Delay Measurement Via NTP

One-way delay (OWD) between end hosts has important implications for Internet applications, protocols, and measurement-based analyses. We describe a new approach for identifying OWDs via passive measurement of Network Time Protocol (NTP) traffic. NTP traffic offers the opportunity to measure OWDs accurately and continuously from hosts throughout the Internet. Based on detailed examination of NTP implementations and in-situ behavior, we develop an analysis tool that we call TimeWeaver, which enables assessment of precision and accuracy of OWD measurements from NTP. We apply TimeWeaver to a ~1TB corpus of NTP traffic collected from 19 servers located in the US and report on the characteristics of hosts and their associated OWDs, which we classify in a precision/accuracy hierarchy. To demonstrate the utility of these measurements, we apply iterative hard-threshold singular value decomposition to estimate the missing OWDs between arbitrary hosts from the highest tier in the hierarchy. We show that this approach results in highly accurate estimates of missing OWDs, with average error rates on the order of less than 2%.
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. Crowds of smart devices offer opportunities to collectively sense and perform computing tasks at an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowd sensing data with differential privacy guarantees. Crowd-ML endows a crowd sensing system with the ability to learn classifiers or predictors online from crowd sensing data privately with minimal computational overhead on devices and servers, suitable for practical large-scale use of the framework. We analyze the performance and scalability of Crowd-ML and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions.
Involutive operator algebras

Examples of operator algebras with involution include the operator $$*$$-algebras occurring in noncommutative differential geometry studied recently by Mesland, Kaad, Lesch, and others, several classical function algebras, triangular matrix algebras, (complexifications) of real operator algebras, and an operator algebraic version of the complex symmetric operators studied by Garcia, Putinar, Wogen, Zhu, and others. We investigate the general theory of involutive operator algebras, and give many applications, such as a characterization of the symmetric operator algebras introduced in the early days of operator space theory.
TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech Separation

Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation. In addition, time-frequency decomposition results in inherent problems such as phase/magnitude decoupling and long time window which is required to achieve sufficient frequency resolution. We propose Time-domain Audio Separation Network (TasNet) to overcome these limitations. We directly model the signal in the time-domain using an encoder-decoder framework and perform the source separation on nonnegative encoder outputs. This method removes the frequency decomposition step and reduces the separation problem to estimation of source masks on encoder outputs which is then synthesized by the decoder. Our system outperforms the current state-of-the-art causal and noncausal speech separation algorithms, reduces the computational cost of speech separation, and significantly reduces the minimum required latency of the output. This makes TasNet suitable for applications where low-power, real-time implementation is desirable such as in hearable and telecommunication devices.
Graph Isomorphism and Circuit Size

We show that the Graph Automorphism problem is ZPP-reducible to MKTP, the problem of minimizing time-bounded Kolmogorov complexity. MKTP has previously been studied in connection with the Minimum Circuit Size Problem (MCSP) and is often viewed as essentially a different encoding of MCSP. All prior reductions to MCSP have applied equally well to MKTP, and vice-versa, and all such reductions have relied on the fact that functions computable in polynomial time can be inverted with high probability relative to MCSP and MKTP. Our reduction uses a different approach, and consequently yields the first example of a problem in ZPP that is not known to lie in NP ∩ coNP. We also show that this approach can be used to provide a reduction of the Graph Isomorphism problem to MKTP.
Faster Distributed Shortest Path Approximations via Shortcuts

A long series of recent results and breakthroughs have led to faster and better distributed approximation algorithms for single source shortest paths (SSSP) and related problems in the CONGEST model. The runtime of all these algorithms, however, is Omega~(sqrt{n}), regardless of the network topology, even on nice networks with a (poly)logarithmic network diameter D. While this is known to be necessary for some pathological networks, most topologies of interest are arguably not of this type. We give the first distributed approximation algorithms for shortest paths problems that adjust to the topology they are run on, thus achieving significantly faster running times on many topologies of interest. The running time of our algorithms depends on and is close to Q, where Q is the quality of the best shortcut that exists for the given topology. While Q = Theta~(sqrt{n} + D) for pathological worst-case topologies, many topologies of interest have Q = Theta~(D), which results in near instance optimal running times for our algorithm, given the trivial Omega(D) lower bound. The problems we consider are as follows: - an approximate shortest path tree and SSSP distances, - a polylogarithmic size distance label for every node such that from the labels of any two nodes alone one can determine their distance (approximately), and - an (approximately) optimal flow for the transshipment problem. Our algorithms have a tunable tradeoff between running time and approximation ratio. Our fastest algorithms have an arbitrarily good polynomial approximation guarantee and an essentially optimal O~(Q) running time. On the other end of the spectrum, we achieve polylogarithmic approximations in O~(Q * n^epsilon) rounds for any epsilon > 0. It seems likely that eventually, our non-trivial approximation algorithms for the SSSP tree and transshipment problem can be bootstrapped to give fast Q * 2^O(sqrt{log n log log n}) round (1+epsilon)-approximation algorithms using a recent result by Becker et al.
Reactive Control Improvisation.

Reactive synthesis is a paradigm for automatically building correct-by-construction systems that interact with an unknown or adversarial environment. We study how to do reactive synthesis when part of the specification of the system is that its behavior should be random. Randomness can be useful, for example, in a network protocol fuzz tester whose output should be varied, or a planner for a surveillance robot whose route should be unpredictable. However, existing reactive synthesis techniques do not provide a way to ensure random behavior while maintaining functional correctness. Towards this end, we generalize the recently-proposed framework of control improvisation (CI) to add reactivity. The resulting framework of reactive control improvisation provides a natural way to integrate a randomness requirement with the usual functional specifications of reactive synthesis over a finite window. We theoretically characterize when such problems are realizable, and give a general method for solving them. For specifications given by reachability or safety games or by deterministic finite automata, our method yields a polynomial-time synthesis algorithm. For various other types of specifications including temporal logic formulas, we obtain a polynomial-space algorithm and prove matching \(\mathsf {PSPACE}\)-hardness results. We show that all of these randomized variants of reactive synthesis are no harder in a complexity-theoretic sense than their non-randomized counterparts.
Directed immersions of closed manifolds

To every .C/ immersion f W M !R of a closed oriented n–manifold M , there corresponds a unit normal vector field or Gauss map Gf W M ! S , which generates a set Gf .M / S known as the spherical image of f . Conversely, one may ask (see Gromov [8, page 3]): for which sets A S is there an immersion f W M ! R such that Gf .M / A? Such a mapping would be called an A–directed immersion of M ; see Eliashberg and Mishachev [1], Gromov [7], Rourke and Sanderson [13] and Spring [14]. It is well-known that when A ¤ S , f must have double points (Section 4.1), and M must be parallelizable, eg, M can only be the torus T when n D 2 (Section 4.2). Furthermore, the only known necessary condition on A is the elementary observation that A[ AD S , while there is also a sufficient condition due to Gromov [7, Theorem .D/, page 186]:
Relative error tensor low rank approximation

We consider relative error low rank approximation of tensors with respect to the Frobenius norm. Namely, given an order- q tensor A ∈ RΠqi=1ni, output a rank-k tensor B for which ||A − B||2F ≤ (1 + ϵ) OPT, where OPT = infrank-kA' ||A − A'||2F. Despite much success on obtaining relative error low rank approximations for matrices, no such results were known for tensors for arbitrary (1 + ϵ)-approximations. One structural issue is that there may be no rank-k tensor Ak achieving the above infinum. Another, computational issue, is that an efficient relative error low rank approximation algorithm for tensors would allow one to compute the rank of a tensor, which is NP-hard. We bypass these two issues via (1) bicriteria and (2) parameterized complexity solutions:1. We give an algorithm which outputs a rank k' = O((k/ϵ)q − 1) tensor B for which ||A − B||2F ≤ (1 + ϵ) OPT in nnz(A) + n · poly(k/ϵ) time in the real RAM model, whenever either Ak exists or OPT > 0. Here nnz(A) denotes the number of non-zero entries in A. If both Ak does not exist and OPT = 0, then B instead satisfies ||A − B||2F 0 which outputs a rank k tensor B for which ||A − B||2F ≤ (1 + ϵ) OPT and runs in (nnz(A)+ n poly(k/ϵ)+exp(k2/ϵ))·nδ time in the unit cost RAM model, whenever OPT > 2−O(nδ) and there is a rank-k tensor B = ∑ki = 1ui ⊗ ui ⊗ wi for which ||A − B||2F ≤ (1 + ϵ/2) OPT and ||ui ||2, ||ui ||2, ||wi||2 ≤ 2O(nδ). If OPT ≤ 2-Ω(nδ), then B instead satisfies ||A − B||2F ≤ 2-Ω(nδ).Our first result is polynomial time, and in fact input sparsity time, in n, k, and 1/ϵ, for any k ≥ 1 and any 0 1, we show a 2Ω(k1 − o(1)) time lower bound under the Exponential Time Hypothesis.Our results are based on an "iterative existential argument", and also give the first relative error low rank approximations for tensors for a large number of error measures for which nothing was known. In particular, we give the first relative error approximation algorithms on tensors for: column row and tube subset selection, entrywise ep-low rank approximation for 1 ≤ p
The Shape of an Image - A Study of Mapper on Images

We study the topological construction called Mapper in the context of simply connected domains, in particular on images. The Mapper construction can be considered as a generalization for contour, split, and joint trees on simply connected domains. A contour tree on an image domain assumes the height function to be a piecewise linear Morse function. This is a rather restrictive class of functions and does not allow us to explore the topology for most real world images. The Mapper construction avoids this limitation by assuming only continuity on the height function allowing this construction to robustly deal with a significant larger set of images. We provide a customized construction for Mapper on images, give a fast algorithm to compute it, and show how to simplify the Mapper structure in this case. Finally, we provide a simple procedure that guarantees the equivalence of Mapper to contour, join, and split trees on a simply connected domain.
Size of Interventional Markov Equivalence Classes in random DAG models

Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We investigate the size of MECs for random DAG models generated by uniformly sampling and ordering an Erd\H{o}s-R\'{e}nyi graph. For constant density, we show that the expected $\log$ observational MEC size asymptotically (in the number of vertices) approaches a constant. We characterize I-MEC size in a similar fashion in the above settings with high precision. We show that the asymptotic expected number of interventions required to fully identify a DAG is a constant. These results are obtained by exploiting Meek rules and coupling arguments to provide sharp upper and lower bounds on the asymptotic quantities, which are then calculated numerically up to high precision. Our results have important consequences for experimental design of interventions and the development of algorithms for causal inference.
Finite decomposition rank for virtually nilpotent groups

We show that inductive limits of virtually nilpotent groups have strongly quasidiagonal C*-algebras, extending results of the first author on solvable virtually nilpotent groups. We use this result to show that the decomposition rank of the group C*-algebra of a finitely generated virtually nilpotent group $G$ is bounded by $2\cdot h(G)!-1$, where $h(G)$ is the Hirsch length of $G.$ This extends and sharpens results of the first and third authors on finitely generated nilpotent groups. It then follows that if a C*-algebra generated by an irreducible representation of a virtually nilpotent group satisfies the universal coefficient theorem, it is classified by its Elliott invariant.
SEQUENCE MODELING VIA SEGMENTATIONS

In neural-network-based approaches to sequence modeling, an output sequence may be modeled via segmentations, the probability of the output sequence being constructed as a sum of products of output-segment probabilities, taken over all valid output-sequence segmentations. A set of artificial neural networks may model the distribution of the output-sequence probability with a recurrent neural network modeling the distributions of the individual output-segment probabilities, optionally in conjunction with a second recurrent neural network modeling concatenations of output segments. In various embodiments, this approach is applied to neural phrase-based machine translation.
Variable synaptic strengths controls the firing rate distribution in feedforward neural networks

Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.
Higher spin six vertex model and symmetric rational functions

We consider a fully inhomogeneous stochastic higher spin six vertex model in a quadrant. For this model we derive concise integral representations for multi-point q-moments of the height function and for the q-correlation functions. At least in the case of the step initial condition, our formulas degenerate in appropriate limits to many known formulas of such type for integrable probabilistic systems in the (1 $$+$$ 1)d KPZ universality class, including the stochastic six vertex model, ASEP, various q-TASEPs, and associated zero range processes. Our arguments are largely based on properties of a family of symmetric rational functions that can be defined as partition functions of the inhomogeneous higher spin six vertex model for suitable domains. In the homogeneous case, such functions were previously studied in Borodin (On a family of symmetric rational functions, 2014); they also generalize classical Hall–Littlewood and Schur polynomials. A key role is played by Cauchy-like summation identities for these functions, which are obtained as a direct corollary of the Yang–Baxter equation for the higher spin six vertex model.
New topological recursion relations

Simple boundary expressions for the k-th power of the cotangent line class on the moduli space of stable 1-pointed genus g curves are found for k >= 2g. The method is by virtual localization on the moduli space of maps to the projective line. As a consequence, nontrivial tautological classes in the kernel of the push-forward map associated to the irreducible boundary divisor of the moduli space of stable g+1 curves are constructed. The geometry of genus g+1 curves then provides universal equations in genus g Gromov-Witten theory. As an application, we prove all the Gromov-Witten identities conjectured recently by K. Liu and H. Xu.
EXMOVES: Classifier-based Features for Scalable Action Recognition

Abstract: This paper introduces EXMOVES, learned exemplar-based features for efficient recognition of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input sequence. Each movement classifier is a simple exemplar-SVM trained on low-level features, i.e., an SVM learned using a single annotated positive space-time volume and a large number of unannotated videos. Our representation offers two main advantages. First, since our mid-level features are learned from individual video exemplars, they require minimal amount of supervision. Second, we show that simple linear classification models trained on our global video descriptor yield action recognition accuracy approaching the state-of-the-art but at orders of magnitude lower cost, since at test-time no sliding window is necessary and linear models are efficient to train and test. This enables scalable action recognition, i.e., efficient classification of a large number of different actions even in large video databases. We show the generality of our approach by building our mid-level descriptors from two different low-level feature representations. The accuracy and efficiency of the approach are demonstrated on several large-scale action recognition benchmarks.
The cohomology of motivic $A(2)$

Working over an algebraically closed field of characteristic zero, we compute the cohomology of the subalgebra A(2) of the motivic Steenrod algebra that is generated by Sq^1, Sq^2, and Sq^4. The method of calculation is a motivic version of the May spectral sequence. Speculatively assuming that there is a "motivic modular forms" spectrum with certain properties, we use an Adams-Novikov spectral sequence to compute the homotopy of such a spectrum at the prime 2.
Optimizing Simulations with Noise-Tolerant Structured Exploration

We propose a simple drop-in noise-tolerant replacement for the standard finite difference procedure used ubiquitously in blackbox optimization. In our approach, parameter perturbation directions are defined by a family of structured orthogonal matrices. We show that at the small cost of computing a Fast Walsh-Hadamard/Fourier Transform (FWHT/FFT), such structured finite differences consistently give higher quality approximation of gradients and Jacobians in comparison to vanilla approaches that use coordinate directions or random Gaussian perturbations. We find that trajectory optimizers like Iterative LQR and Differential Dynamic Programming require fewer iterations to solve several classic continuous control tasks when our methods are used to linearize noisy, blackbox dynamics instead of standard finite differences. By embedding structured exploration in a quasi-Newton optimizer (LBFGS), we are able to learn agile walking and turning policies for quadruped locomotion, that successfully transfer from simulation to actual hardware. We theoretically justify our methods via bounds on the quality of gradient reconstruction and provide a basis for applying them also to nonsmooth problems.
Dislocation pattern formation in finite deformation crystal plasticity

Abstract Stressed dislocation pattern formation in crystal plasticity at finite deformation is demonstrated for the first time. Size effects are also demonstrated within the same mathematical model. The model involves two extra material parameters beyond the requirements of standard classical crystal plasticity theory. The dislocation microstructures shown are decoupled from deformation microstructures, and emerge without any consideration of latent hardening or constitutive assumptions related to cross-slip. Crystal orientation effects on the pattern formation and mechanical response are also demonstrated. The manifest irrelevance of the necessity of a multiplicative decomposition of the deformation gradient, a plastic distortion tensor, and the choice of a reference configuration in our model to describe the micromechanics of plasticity as it arises from the existence and motion of dislocations is demonstrated.
Time‐dependent global sensitivity analysis with active subspaces for a lithium ion battery model

Renewable energy researchers use computer simulation to aid the design of lithium ion storage devices. The underlying models contain several physical input parameters that affect model predictions. Effective design and analysis must understand the sensitivity of model predictions to changes in model parameters, but global sensitivity analyses become increasingly challenging as the number of input parameters increases. Active subspaces are part of an emerging set of tools for discovering and exploiting low-dimensional structures in the map from high-dimensional inputs to model outputs. We extend linear and quadratic model-based heuristics for active subspace discovery to time-dependent processes and apply the resulting technique to a lithium ion battery model. The results reveal low-dimensional structure and sensitivity metrics that a designer may exploit to study the relationship between parameters and predictions.
A multidomain model for ionic electrodiffusion and osmosis with an application to cortical spreading depression

Abstract Ionic electrodiffusion and osmotic water flow are central processes in many physiological systems. We formulate a system of partial differential equations that governs ion movement and water flow in biological tissue. A salient feature of this model is that it satisfies a free energy identity, ensuring the thermodynamic consistency of the model. A numerical scheme is developed for the model in one spatial dimension and is applied to a model of cortical spreading depression, a propagating breakdown of ionic and cell volume homeostasis in the brain.
Rapid and quantitative chemical exchange saturation transfer (CEST) imaging with magnetic resonance fingerprinting (MRF).

PURPOSE To develop a fast magnetic resonance fingerprinting (MRF) method for quantitative chemical exchange saturation transfer (CEST) imaging. METHODS We implemented a CEST-MRF method to quantify the chemical exchange rate and volume fraction of the Nα -amine protons of L-arginine (L-Arg) phantoms and the amide and semi-solid exchangeable protons of in vivo rat brain tissue. L-Arg phantoms were made with different concentrations (25-100 mM) and pH (pH 4-6). The MRF acquisition schedule varied the saturation power randomly for 30 iterations (phantom: 0-6 μT; in vivo: 0-4 μT) with a total acquisition time of ≤2 min. The signal trajectories were pattern-matched to a large dictionary of signal trajectories simulated using the Bloch-McConnell equations for different combinations of exchange rate, exchangeable proton volume fraction, and water T1 and T2 relaxation times. RESULTS The chemical exchange rates of the Nα -amine protons of L-Arg were significantly (P < 0.0001) correlated with the rates measured with the quantitation of exchange using saturation power method. Similarly, the L-Arg concentrations determined using MRF were significantly (P < 0.0001) correlated with the known concentrations. The pH dependence of the exchange rate was well fit (R2 = 0.9186) by a base catalyzed exchange model. The amide proton exchange rate measured in rat brain cortex (34.8 ± 11.7 Hz) was in good agreement with that measured previously with the water exchange spectroscopy method (28.6 ± 7.4 Hz). The semi-solid proton volume fraction was elevated in white (12.2 ± 1.7%) compared to gray (8.1 ± 1.1%) matter brain regions in agreement with previous magnetization transfer studies. CONCLUSION CEST-MRF provides a method for fast, quantitative CEST imaging.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk

ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Sub-graph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
Contraction of the proximal map and generalized convexity of the Moreau-Yosida regularization in the 2-Wasserstein metric

We investigate the Moreau-Yosida regularization and the associated proximal map in the context of discrete gradient flow for the 2-Wasserstein metric. Our main results are a stepwise contraction property for the proximal map and an "above the tangent line" inequality for the regularization. Using the latter, we prove a Talagrand inequality and an HWI inequality for the regularization, under appropriate hypotheses. In the final section, the results are applied to study the discrete gradient flow for Renyi entropies. As Otto showed, the gradient flow for these entropies in the 2 Wasserstein metric is a porous medium flow or a fast diffusion flow, depending on the exponent of the entropy. We show that a striking number of the remarkable features of the porous medium and fast diffusion flows are present in the discrete gradient flow and do not simply emerge in the limit as the time-step goes to zero.
Finite generation of the cohomology of quotients of PBW algebras

Abstract In this article we prove finite generation of the cohomology of quotients of a PBW algebra A by relating it to the cohomology of quotients of a quantum symmetric algebra S which is isomorphic to the associated graded algebra of A. The proof uses a spectral sequence argument and a finite generation lemma adapted from Friedlander and Suslin.
Clustering with t-SNE, Provably

t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten & Hinton in 2008, has rapidly become a standard tool in a number of natural sciences. Despite its overwhelming success, there is a distinct lack of mathematical foundations and the inner workings of the algorithm are not well understood. The purpose of this paper is to prove that t-SNE is able to recover well-separated clusters; more precisely, we prove that t-SNE in the 'early exaggeration' phase, an optimization technique proposed by van der Maaten & Hinton (2008) and van der Maaten (2014), can be rigorously analyzed. As a byproduct, the proof suggests novel ways for setting the exaggeration parameter α and step size h. Numerical examples illustrate the effectiveness of these rules: in particular, the quality of embedding of topological structures (e.g. the swiss roll) improves. We also discuss a connection to spectral clustering methods.