cssi/SciSciGPT-SciSciNet huggingface dataset:
General approach for the total synthesis of the sarpagine related indole alkaloids (+)-Na-methyl-16-epipericyclivine, (−)-alkaloid Q3 and (−)-panarine via the asymmetric Pictet–Spengler reaction

The stereospecific total synthesis of (+)-Na-methyl-16-epipericyclivine (1) was completed [from d-(+)-tryptophan methyl ester] in an overall yield of 42% (eight reaction vessels). The optical rotation {[α]D +22.8 (c 0.50, CHCl3)} obtained on this material confirmed that the reported optical rotation {[α]D 0 (c 0.50, CHCl3)} [] was biogenetically unreasonable. The first total synthesis of (−)-alkaloid Q3 (5) and (−)-panarine (6), via the intermediate vellosimine (18), is also described.
Structure, function, and regulation of renal organic anion transporters

Renal elimination of anionic drugs, xenobiotics, and toxins is necessary for the survival of mammalian species. This process is mediated by vectorial transport from blood to urine through the cooperative functions of specific transporters in the basolateral and apical membranes of the proximal tubule epithelium. The first step of this process is the extraction of organic anions from the peritubular blood plasma into proximal tubule cells largely through the organic anion transporter (OAT) pathway. Therefore, the OAT pathway is one of the major sites for body drug clearance/detoxification. As a result, it is also the site for drug-drug interaction and drug-induced nephrotoxicity. To maximize therapeutic efficacy and minimize toxicity, the structure-function relationships of OATs and their regulation must be defined. The recent cloning and identification of OATs have paved the way for such investigations. This review summarizes the available data on the general properties of OATs, focusing in particular on the recent progress made from the author's laboratory as well as from other's, on the molecular characterization of the structure-function relationships of OATs and their regulatory mechanisms.
Parallel Evolution of Sperm Hyper-Activation Ca2+ Channels.

Sperm hyper-activation is a dramatic change in sperm behavior where mature sperm burst into a final sprint in the race to the egg. The mechanism of sperm hyper-activation in many metazoans, including humans, consists of a jolt of Ca2+ into the sperm flagellum via CatSper ion channels. Surprisingly, all nine CatSper genes have been independently lost in several animal lineages. In Drosophila, sperm hyper-activation is performed through the cooption of the polycystic kidney disease 2 (pkd2) Ca2+ channel. The parallels between CatSpers in primates and pkd2 in Drosophila provide a unique opportunity to examine the molecular evolution of the sperm hyper-activation machinery in two independent, nonhomologous calcium channels separated by > 500 million years of divergence. Here, we use a comprehensive phylogenomic approach to investigate the selective pressures on these sperm hyper-activation channels. First, we find that the entire CatSper complex evolves rapidly under recurrent positive selection in primates. Second, we find that pkd2 has parallel patterns of adaptive evolution in Drosophila. Third, we show that this adaptive evolution of pkd2 is driven by its role in sperm hyper-activation. These patterns of selection suggest that the evolution of the sperm hyper-activation machinery is driven by sexual conflict with antagonistic ligands that modulate channel activity. Together, our results add sperm hyper-activation channels to the class of fast evolving reproductive proteins and provide insights into the mechanisms used by the sexes to manipulate sperm behavior.
Toward the synthesis of sparteine: Intramolecular Schmidt reactions on a norbornanone platform

Abstract The intramolecular Schmidt reaction was applied to the norbornane skeleton to examine an approach to sparteine. In so doing, the stereospecificity of intramolecular Schmidt reactions of e-azido ketones was demonstrated. The intramolecular Schmidt reaction was found to fail in a ketone containing a lactam moiety.
AN IMPROVED SYNTHESIS OF 3-CYANO-4-FLUOROBENZYL BROMIDE

(2000). AN IMPROVED SYNTHESIS OF 3-CYANO-4-FLUOROBENZYL BROMIDE. Organic Preparations and Procedures International: Vol. 32, No. 1, pp. 88-91.
Two-dimensional metallic chain compounds Y5M2Te2 (M = Fe, Co, Ni) that are related to Gd3MnI3. The hydride derivative Y5Ni2Te2D0.4.

Y5M2Te2 (M = Fe, Co, Ni) have been prepared by high-temperature solid-state techniques and shown to be isostructural and orthorhombic Cmcm (No. 63), Z = 4. The structure was established by single crystal X-ray methods at 23 °C for M = Fe, with a = 3.9594(3) A, b = 15.057(1) A, and c = 15.216(1) A. The new structure contains zigzag chains of the late transition metal sheathed by a column of yttrium atoms that are in turn condensed through trans vertices on the latter to yield 2D bimetallic layers separated by single layers of tellurium atoms. Reaction of hydrogen with Y5Ni2Te2 causes a rumpling of the Y−Ni layers as determined by both single X-ray crystal means at 23 °C and neutron powder diffraction at −259 °C for Y5Ni2Te2D0.41(1), Pnma (No. 62), Z = 4. Lattice constants from the former study are a = 14.3678(7) A, b = 4.0173(2) A, and c = 15.8787(7) A. The hydrogen is accommodated in tetrahedral yttrium cavities generated by bending the formerly flat sheets at the trans Y vertices. A higher hydride versio...
A Versatile Enantioselective Strategy Toward L-C-Nucleosides: A Total Synthesis of L-Showdomycin.

Strategies for the synthesis of nucleosides that can provide either l or d isomers become more important as a result of the increasing number of such compounds that are therapeutically useful. The ...
Environmental perturbations lead to extensive directional shifts in RNA processing

Environmental perturbations have large effects on both organismal and cellular traits, including gene expression, but the extent to which the environment affects RNA processing remains largely uncharacterized. Recent studies have identified a large number of genetic variants associated with variation in RNA processing that also have an important role in complex traits; yet we do not know in which contexts the different underlying isoforms are used. Here, we comprehensively characterized changes in RNA processing events across 89 environments in five human cell types and identified 15,300 event shifts (FDR = 15%) comprised of eight event types in over 4,000 genes. Many of these changes occur consistently in the same direction across conditions, indicative of global regulation by trans factors. Accordingly, we demonstrate that environmental modulation of splicing factor binding predicts shifts in intron retention, and that binding of transcription factors predicts shifts in alternative first exon (AFE) usage in response to specific treatments. We validated the mechanism hypothesized for AFE in two independent datasets. Using ATAC-seq, we found altered binding of 64 factors in response to selenium at sites of AFE shift, including ELF2 and other factors in the ETS family. We also performed AFE QTL mapping in 373 individuals and found an enrichment for SNPs predicted to disrupt binding of the ELF2 factor. Together, these results demonstrate that RNA processing is dramatically changed in response to environmental perturbations through specific mechanisms regulated by trans factors.
Neural engagement with online educational videos predicts learning performance for individual students

Online educational materials are largely disseminated through videos, and yet there is little understanding of how these videos engage students and fuel academic success. We hypothesized that components of the electroencephalogram (EEG), previously shown to reflect video engagement, would be predictive of academic performance in the context of educational videos. Two groups of subjects watched educational videos in either an intentional learning paradigm, in which they were aware of an upcoming test, or in an incidental learning paradigm, in which they were unaware that they would be tested. "Neural engagement" was quantified by the inter-subject correlation (ISC) of the EEG that was evoked by the videos. In both groups, students with higher neural engagement retained more information. Neural engagement also discriminated between attentive and inattentive video viewing. These results suggest that this EEG metric is a marker of the stimulus-related attentional mechanisms necessary to retain information. In the future, EEG may be used as a tool to design and assess online educational content.
The new misfit compound (BiSe)1.15(TiSe2)2 and the role of dimensionality in the Cux(BiSe)1+δ(TiSe2)n series

Abstract The synthesis and physical properties of the new misfit compound (BiSe)1.15(TiSe2)2 are reported. Transmission electron microscopy and powder X-ray diffraction show that the structure consists of alternating rock-salt type BiSe layers and hexagonal (TiSe2)2 double layers. Resistivity, specific heat, and magnetization measurements show that it has metallic and diamagnetic behaviors. These results are interpreted and discussed in the context of the transition between single-layer (BiSe)1.13(TiSe2), which shows no charge density wave, and infinite-layered (bulk) 1T-TiSe2, which undergoes a charge density wave transition at T=202 K. Intercalation with copper, Cux(BiSe)1.15(TiSe2)2, (0≤x≤0.10) is also reported, but unlike CuxTiSe2, no superconductivity is observed down to T=0.05 K. Thus, the series Cux(BiSe)1+δ(TiSe2)n provides an effective approach to elucidate the impact of dimensionality on charge density wave formation and superconductivity.
Synthesis and transformations of some new 2,4-bismethylene-1,3-ditelluretanes

A novel four-membered 1,3-ditelluretane dialdehyde 8/9 was synthesized from trimethylsilylethynyl tellurolate. The dialdehyde was transformed into extended tetrathiafulvalenes (14 and 15). These are the first examples of ditelluretane spaced TTF derivatives.
Cytosine Methylation Within Marine Sediment Microbial Communities: Potential Epigenetic Adaptation to the Environment

Marine sediments harbor a vast amount of Earth's microbial biomass, yet little is understood regarding how cells subsist in this low-energy, presumably slow-growth environment. Cells in marine sediments may require additional methods for genetic regulation, such as epigenetic modification via DNA methylation. We investigated this potential phenomenon within a shallow estuary sediment core spanning 100 years of age. Here, we provide evidence of dynamic community m5-cytosine methylation within estuarine sediment metagenomes. The methylation states of individual CpG sites were reconstructed and quantified across three depths within the sediment core. A total of 6,254 CpG sites were aligned for direct comparison of methylation states between samples, and 4,235 of these sites mapped to taxa and genes. Our results demonstrate the presence of differential methylation within environmental CpG sites across an age gradient of sediment. We show that epigenetic modification can be detected via Illumina sequencing within complex environmental communities. The change in methylation state of environmentally relevant genes across depths may indicate a dynamic role of DNA methylation in regulation of biogeochemical processes.
Stochastic spacetime and Brownian motion of test particles

The operational meaning of spacetime fluctuations is discussed. Classical spacetime geometry can be viewed as encoding the relations between the motions of test particles in the geometry. By analogy, quantum fluctuations of spacetime geometry can be interpreted in terms of the fluctuations of these motions. Thus, one can give meaning to spacetime fluctuations in terms of observables which describe the Brownian motion of test particles. We will first discuss some electromagnetic analogies, where quantum fluctuations of the electromagnetic field induce Brownian motion of test particles. We next discuss several explicit examples of Brownian motion caused by a fluctuating gravitational field. These examples include lightcone fluctuations, variations in the flight times of photons through the fluctuating geometry, and fluctuations in the expansion parameter given by a Langevin version of the Raychaudhuri equation. The fluctuations in this parameter lead to variations in the luminosity of sources. Other phenomena that can be linked to spacetime fluctuations are spectral line broadening and angular blurring of distant sources.
A regioselective Ru-catalyzed alkene-alkyne coupling to form (Z,Z)-1,3-dienes.

Generally, Ru-catalyzed alkene−alkyne coupling forms 1,4-dienes. A version of this process which generates a 1,3-diene as the Z,Z-isomer preferentially has now been observed. The scope of this new atom-economic process is described herein.
NSGA-Net: neural architecture search using multi-objective genetic algorithm

This paper introduces NSGA-Net --- an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a procedure considering multiple and conflicting objectives, (2) an efficient procedure balancing exploration and exploitation of the space of potential neural network architectures, and (3) a procedure finding a diverse set of trade-off network architectures achieved in a single run. NSGA-Net is a population-based search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures, and finally an exploitation step that utilizes the hidden useful knowledge stored in the entire history of evaluated neural architectures in the form of a Bayesian Network. Experimental results suggest that combining the dual objectives of minimizing an error metric and computational complexity, as measured by FLOPs, allows NSGA-Net to find competitive neural architectures. Moreover, NSGA-Net achieves error rate on the CIFAR-10 dataset on par with other state-of-the-art NAS methods while using orders of magnitude less computational resources. These results are encouraging and shows the promise to further use of EC methods in various deep-learning paradigms.
Interference Management in UAV-Assisted Integrated Access and Backhaul Cellular Networks

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of the next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance, and spatial dynamics of user distribution pose major challenges in the practical deployment of the IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of the UAVs. We consider two spatial configuration modes of the UAVs, distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs can not only be used for coverage improvement but also for capacity boosting in the IAB cellular networks.
Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies

Stories can have tremendous power – not only useful for entertainment, they can activate our interests and mobilize our actions. The degree to which a story resonates with its audience may be in part reflected in the emotional journey it takes the audience upon. In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement. The system is applied to Hollywood films and high quality shorts found on the web. We begin by using deep convolutional neural networks for audio and visual sentiment analysis. These models are trained on both new and existing large-scale datasets, after which they can be used to compute separate audio and visual emotional arcs. We then crowdsource annotations for 30-second video clips extracted from highs and lows in the arcs in order to assess the micro-level precision of the system, with precision measured in terms of agreement in polarity between the system's predictions and annotators' ratings. These annotations are also used to combine the audio and visual predictions. Next, we look at macro-level characterizations of movies by investigating whether there exist 'universal shapes' of emotional arcs. In particular, we develop a clustering approach to discover distinct classes of emotional arcs. Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives. These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement. Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other.
Injectivity and projectivity in p -multinormed spaces

We find large classes of injective and projective p-multinormed spaces. In fact, these classes are universal, in the sense that every p-multinormed space embeds into (is a quotient of) an injective (resp. projective) p-multinormed space. As a consequence, we show that any p-multinormed space has a canonical representation as a subspace of a quotient of a Banach lattice.
Differentiable Scheduled Sampling for Credit Assignment

We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding in sequence-to-sequence (seq2seq) models. By incorporating this approximation into the scheduled sampling training procedure–a well-known technique for correcting exposure bias–we introduce a new training objective that is continuous and differentiable everywhere and can provide informative gradients near points where previous decoding decisions change their value. By using a related approximation, we also demonstrate a similar approach to sampled-based training. We show that our approach outperforms both standard cross-entropy training and scheduled sampling procedures in two sequence prediction tasks: named entity recognition and machine translation.
Generalization Error Bounds for Noisy, Iterative Algorithms

In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data. Recent work [Xu and Raginsky (2017)] has established a bound on the generalization error of empirical risk minimization based on the mutual information $I$ ( $S$ ; W) between the algorithm input $S$ and the algorithm output W, when the loss function is sub-Gaussian. We leverage these results to derive generalization error bounds for a broad class of iterative algorithms that are characterized by bounded, noisy updates with Markovian structure. Our bounds are very general and are applicable to numerous settings of interest, including stochastic gradient Langevin dynamics (SGLD) and variants of the stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm. Furthermore, our error bounds hold for any output function computed over the path of iterates, including the last iterate of the algorithm or the average of subsets of iterates, and also allow for non-uniform sampling of data in successive updates of the algorithm.
The resolution of the bracket powers of the maximal ideal in a diagonal hypersurface ring

Abstract Let k be a field. For each pair of positive integers ( n , N ) , we resolve Q = R / ( x N , y N , z N ) as a module over the ring R = k [ x , y , z ] / ( x n + y n + z n ) . Write N in the form N = a n + r for integers a and r, with r between 0 and n − 1 . If n does not divide N and the characteristic of k is fixed, then the value of a determines whether Q has finite or infinite projective dimension. If Q has infinite projective dimension, then value of r, together with the parity of a, determines the periodic part of the infinite resolution. When Q has infinite projective dimension we give an explicit presentation for the module of first syzygies of Q. This presentation is quite complicated. We also give an explicit presentation for the module of second syzygies for Q. This presentation is remarkably uncomplicated. We use linkage to find an explicit generating set for the grade three Gorenstein ideal ( x N , y N , z N ) : ( x n + y n + z n ) in the polynomial ring k [ x , y , z ] . The question “Does Q have finite projective dimension?” is intimately connected to the question “Does k [ X , Y , Z ] / ( X a , Y a , Z a ) have the Weak Lefschetz Property?”. The second question is connected to the enumeration of plane partitions. When the field k has positive characteristic, we investigate three questions about the Frobenius powers F t ( Q ) of Q. When does there exist a pair ( n , N ) so that Q has infinite projective dimension and F ( Q ) has finite projective dimension? Is the tail of the resolution of the Frobenius power F t ( Q ) eventually a periodic function of t (up to shift)? In particular, we exhibit a situation where the tail of the resolution of F t ( Q ) , after shifting, is periodic as a function of t, with an arbitrarily large period. Can one use socle degrees to predict that the tail of the resolution of F t ( Q ) is a shift of the tail of the resolution of Q?
DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval

Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels. Our system first splits the documents into topical segments, “visualizes” the matchings between the query and the segments, and then feeds an interaction matrix into a Neu-IR model, DeepTileBars, to obtain the final ranking scores. DeepTileBars models the relevance signals occurring at different granularities in a document’s topic hierarchy. It better captures the discourse structure of a document and thus the matching patterns. Although its design and implementation are light-weight, DeepTileBars outperforms other state-of-the-art Neu-IR models on benchmark datasets including the Text REtrieval Conference (TREC) 2010-2012 Web Tracks and LETOR 4.0.
Universal Abelian H-spaces

Abstract The question of the existence of universal homotopy commutative and homotopy associative H-spaces (called Abelian H-spaces) is studied. Such a space T ( X ) would prolong a map from X into an Abelian H-space to a unique H-map from T into X . Examples of such pairs ( X , T ) are given and conditions are discussed which limit the possible spaces X for which such a T can exist. Contrary to published assertions, the Anick spaces are shown not to be universal Abelian H-spaces for the corresponding Moore spaces; however conditions are discussed which could lead to a universal property with respect to a more limited range of targets, and a restricted universal property is proven.
INVERSE THEOREMS FOR SETS AND MEASURES OF POLYNOMIAL GROWTH

We give a structural description of the finite subsets $A$ of an arbitrary group $G$ which obey the polynomial growth condition $|A^n| \leq n^d |A|$ for some bounded $d$ and sufficiently large $n$ , showing that such sets are controlled by (a bounded number of translates of) a coset nilprogression in a certain precise sense. This description recovers some previous results of Breuillard–Green–Tao and Breuillard–Tointon concerning sets of polynomial growth; we are also able to describe the subsequent growth of $|A^m|$ fairly explicitly for $m \geq n$ , at least when $A$ is a symmetric neighbourhood of the identity. We also obtain an analogous description of symmetric probability measures $\mu $ whose $n$ -fold convolutions $\mu ^{*n}$ obey the condition $\| \mu ^{*n} \|_{\ell ^2}^{-2} \leq n^d \|\mu \|_{\ell ^2}^{-2}$ . In the abelian case, this description recovers the inverse Littlewood–Offord theorem of Nguyen–Vu, and gives a ‘symmetrized’ variant of a recent non-abelian inverse Littlewood–Offord theorem of Tiep–Vu. Our main tool to establish these results is the inverse theorem of Breuillard, Green, and the author that describes the structure of approximate groups.
Communication Cost in Parallel Query Processing

Fix a full, conjunctive query, and consider the following problem: what is the amount of communication required to compute the query in parallel, on p servers, over a large database instance? We define the Massively Parallel Communication (MPC) model, where the computation proceeds in rounds consisting of local computations followed by a global reshuffling of the data. Servers have unlimited computational power and are allowed to exchange any data, the only cost parameters are the number of rounds and the maximum amount of communication per server. I will describe tight bounds on the amount of communication for the case of a single round and data without skew, then discuss extensions to skewed data and multiround. This is joint work with Paul Beame and Paris Koutris
Linear Contextual Bandits with Knapsacks

We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm. The budget/capacity constraints require that the sum of these vectors doesn't exceed the budget in each dimension. The objective is once again to maximize the total reward. This problem turns out to be a common generalization of classic linear contextual bandits (linContextual), bandits with knapsacks (BwK), and the online stochastic packing problem (OSPP). We present algorithms with near-optimal regret bounds for this problem. Our bounds compare favorably to results on the unstructured version of the problem, where the relation between the contexts and the outcomes could be arbitrary, but the algorithm only competes against a fixed set of policies accessible through an optimization oracle. We combine techniques from the work on linContextual, BwK and OSPP in a nontrivial manner while also tackling new difficulties that are not present in any of these special cases.
A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization

In this paper we improve upon the running time for finding a point in a convex set given a separation oracle. In particular, given a separation oracle for a convex set K a#x2282; Rn that is contained in a box of radius R we show how to either compute a point in K or prove that K does not contain a ball of radius a#x03B5; using an expected O(n log(nR=a#x03B5;)) evaluations of the oracle and additional time O(n3 logO(1)(nR=a#x03B5;)). This matches the oracle complexity and improves upon the O(na#x03C9;+1 log(nR=a#x03B5;)) additional time of the previous fastest algorithm achieved over 25 years ago by Vaidya [91] for the current value of the matrix multiplication constant a#x03C9; < 2:373 [98], [36] when R=a#x03B5; = O(poly(n)). Using a mix of standard reductions and new techniques we show how our algorithm can be used to improve the running time for solving classic problems in continuous and combinatorial optimization. In particular we provide the following running time improvements: a#x03B5; Sub modular Function Minimization: n is the size of the ground set, M is the maximum absolute value of function values and EO is the time for function evaluation. Our weakly and strongly polynomial time algorithms have a running time of O(n2 lognM EO+n3 logO(1) nM) and O(n3 log2 n EO+n4 logO(1) n), improving upon the previous best of O((n4 · EO+n5) logM) and O(n5 · EO + n6) respectively. a#x03B5; Sub modular Flow: n = |V|, m = |E|, C is the maximum edge cost in absolute value and U is maximum edge capacity in absolute value. We obtain a faster weakly polynomial running time of O(n2 log nCU · EO + n3 logO(1) nCU), improving upon the previous best of O(mn5 log nU · EO) and O(n4h min {log C, log U}) from 15 years ago by a factor of O(n4). We also achieve faster strongly polynomial time algorithms as a consequence of our result on sub modular minimization. a#x03B5; Matroid Intersection: n is the size of the ground set, r is the maximum size of independent sets, M is the maximum absolute value of element weight, Trank and Tind are the time for each rank and independence oracle query. We obtain a running time of O((nr log2 nTrank+n3 logO(1) n) lognM) and O((n2 log nTind+n3 logO(1) n) lognM), achieving the first quadratic bound on the query complexity for the independence and rank oracles. In the unweighted case, this is the first improvement since 1986 for independence oracle. a#x03B5; Semi definite Programming: n is the number of constraints, m is the number of dimensions and S is the total number of non-zeros in the constraint matrices. We obtain a running time of O(n(n2 +ma#x03C9; +S)), improving upon the previous best of O(n(na#x03C9; +ma#x03C9; +S)) for the regime S is small.
Robust shortest path planning and semicontractive dynamic programming

In this article, we consider shortest path problems in a directed graph where the transitions between nodes are subject to uncertainty. We use a minimax formulation, where the objective is to guarantee that a special destination state is reached with a minimum cost path under the worst possible instance of the uncertainty. Problems of this type arise, among others, in planning and pursuit-evasion contexts, and in model predictive control. Our analysis makes use of the recently developed theory of abstract semicontractive dynamic programming models. We investigate questions of existence and uniqueness of solution of the optimality equation, existence of optimal paths, and the validity of various algorithms patterned after the classical methods of value and policy iteration, as well as a Dijkstra-like algorithm for problems with nonnegative arc lengths.© 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016
ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES

We consider a nonparametric Bayesian model for conditional densities. The model is a finite mixture of normal distributions with covariate dependent multinomial logit mixing probabilities. A prior for the number of mixture components is specified on positive integers. The marginal distribution of covariates is not modeled. We study asymptotic frequentist behavior of the posterior in this model. Specifically, we show that when the true conditional density has a certain smoothness level, then the posterior contraction rate around the truth is equal up to a log factor to the frequentist minimax rate of estimation. An extension to the case when the covariate space is unbounded is also established. As our result holds without a priori knowledge of the smoothness level of the true density, the established posterior contraction rates are adaptive. Moreover, we show that the rate is not affected by inclusion of irrelevant covariates in the model. In Monte Carlo simulations, a version of the model compares favorably to a cross-validated kernel conditional density estimator.
A Method of Solving Certain Nonlinear Diophantine Equations

In this paper we propose a method of solving a Nonlinear Diophantine Equation by converting it into a System of Diophantine Linear Equations.
Wireless network simplification: Beyond diamond networks

We consider an arbitrary layered Gaussian relay network with L layers of N relays each, from which we select subnetworks with K relays per layer. We prove that: (i) For arbitrary L;N and K = 1, there always exists a subnetwork that approximately achieves 2/(L−1)N+4 (resp. 2/LN+2) of the network capacity for odd L (resp. even L), (ii) For L = 2; N = 3; K = 2, there always exists a subnetwork that approximately achieves 1/2 of the network capacity. We also provide example networks where even the best subnetworks achieve exactly these fractions (up to additive gaps). Along the way, we derive some results on MIMO antenna selection and capacity decomposition that may also be of independent interest.
PRODUCTIVITY INDEX FOR DARCY AND PRE-/POST-DARCY FLOW (ANALYTICAL APPROACH)

We investigate the impact of nonlinearity of high and low velocity flows on the well productivity index (PI). Experimental data shows the departure from the linear Darcy relation for high and low velocities. High-velocity (post-Darcy) flow occurring near wells and fractures is described by Forchheimer equations and is relatively well-studied. While low velocity flow receives much less attention, there is multiple evidence suggesting the existence of pre-Darcy effects for slow flows far away from the well. This flow is modeled via pre-Darcy equation. We combine all three flow regimes, pre-Darcy, Darcy and post-Darcy, under one mathematical formulation dependent on the critical transitional velocities. This allows to use our previously developed framework to obtain the analytical formulas for the PI for the cylindrical reservoir. We study the impact of pre-Darcy effect on the PI of steady-state flow depending on the well-flux and the parameters of the equations.
The weakly coupled Pfaffian as a type I quantum hall liquid

Abstract The Pfaffian phase of electrons in the proximity of a half-filled Landau level is understood to be a p + ip superconductor of composite fermions. We consider the properties of this paired quantum Hall phase when the pairing scale is small, i.e. in the weak coupling, BCS, limit, where the coherence length is much larger than the charge screening length. We find that, as in a Type I superconductor, vortices attract so that, upon varying the magnetic field from its magic value at ν = 5 / 2 , the system exhibits Coulomb frustrated phase separation. We propose that the weakly and strongly coupled Pfaffian states exemplify a general dichotomy between Type I and Type II quantum Hall fluids.
The character and the wave front set correspondence in the stable range

Abstract We relate the distribution characters and the wave front sets of unitary representation for real reductive dual pairs of type I in the stable range.
Oblivious Query Processing.

Motivated by cloud security concerns, there is an increasing interest in database systems that can store and support queries over encrypted data. A common architecture for such systems is to use a trusted component such as a cryptographic co-processor for query processing that is used to securely decrypt data and perform computations in plaintext. The trusted component has limited memory, so most of the (input and intermediate) data is kept encrypted in an untrusted storage and moved to the trusted component on “demand.” In this setting, even with strong encryption, the data access pattern from untrusted storage has the potential to reveal sensitive information; indeed, all existing systems that use a trusted component for query processing over encrypted data have this vulnerability. In this paper, we undertake the first formal study of secure query processing, where an adversary having full knowledge of the query (text) and observing the query execution learns nothing about the underlying database other than the result size of the query on the database. We introduce a simpler notion, oblivious query processing, and show formally that a query admits secure query processing iff it admits oblivious query processing. We present oblivious query processing algorithms for a rich class of database queries involving selections, joins, grouping and aggregation. For queries not handled by our algorithms, we provide some initial evidence that designing oblivious (and therefore secure) algorithms would be hard via reductions from two simple, well-studied problems that are generally believed to be hard. Our study of oblivious query processing also reveals interesting connections to database join theory.
Can chemotaxis speed up or slow down the spatial spreading in parabolic–elliptic Keller–Segel systems with logistic source?

The current paper is concerned with the spatial spreading speed and minimal wave speed of the following Keller–Segel chemoattraction system, 0.1 $$\begin{aligned} \left\{ \begin{aligned}&u_t=u_{xx}-\chi (uv_x)_x +u(a-bu),\quad x\in {{\mathbb {R}}},\\&0=v_{xx}- \lambda v+\mu u,\quad x\in {{\mathbb {R}}}, \ \end{aligned}\right. \end{aligned}$$ where $$\chi $$ , a, b, $$\lambda $$ , and $$\mu $$ are positive constants. Assume $$b>\chi \mu $$ . Then if in addition $$\big (1+\frac{1}{2}\frac{(\sqrt{a}-\sqrt{\lambda })_+}{(\sqrt{a}+\sqrt{\lambda })}\big )\chi \mu { \le } b$$ holds, it is proved that $$c_0^*=2\sqrt{a}$$ is the spreading speed of the solutions of (0.1) with nonnegative continuous initial function $$u_0$$ with nonempty compact support, that is, $$\begin{aligned} \limsup _{|x|\ge ct, t\rightarrow \infty }u(t,x;u_0)=0\quad \forall \, c>c_0^* \end{aligned}$$ and $$\begin{aligned} \liminf _{|x|\le ct,t\rightarrow \infty } u(t,x;u_0)>0\quad \forall \, 0<c<c_0^*, \end{aligned}$$ where $$(u(t,x;u_0),v(t,x;u_0))$$ is the unique global classical solution of (0.1) with $$u(0,x;u_0)=u_0(x)$$ . It is also proved that, if $$b>2\chi \mu $$ and $$\lambda \ge a$$ holds, then $$c_0^*=2\sqrt{a}$$ is the minimal speed of the traveling wave solutions of (0.1) connecting (0, 0) and $$(\frac{a}{b},\frac{\mu }{\lambda }\frac{a}{b})$$ , that is, for any $$c\ge c_0^*$$ , (0.1) has a traveling wave solution connecting (0, 0) and $$(\frac{a}{b},\frac{\mu }{\lambda }\frac{a}{b})$$ with speed c, and (0.1) has no such traveling wave solutions with speed less than $$c_0^*$$ . Note that $$c_0^*=2\sqrt{a}$$ is the spatial spreading speed as well as the minimal wave speed of the following Fisher-KPP equation, 0.2 $$\begin{aligned} u_t=u_{xx}+u(a-bu),\quad x\in {{\mathbb {R}}}. \end{aligned}$$ Hence, if $$\lambda \ge a$$ and $$b>\chi \mu $$ , or $$\lambda <a$$ and $$b\ge \big (1+\frac{1}{2}\frac{(\sqrt{a}-\sqrt{\lambda })}{(\sqrt{a}+\sqrt{\lambda })}\big )\chi \mu $$ , then the chemotaxis neither speeds up nor slows down the spatial spreading in (0.1).
Sculpting quantum speedups

Given a problem which is intractable for both quantum and classical algorithms, can we find a sub-problem for which quantum algorithms provide an exponential advantage? We refer to this problem as the "sculpting problem." In this work, we give a full characterization of sculptable functions in the query complexity setting. We show that a total function f can be restricted to a promise P such that Q(f|P) = O(polylogN) and R(f|P) = NΩ(1), if and only if f has a large number of inputs with large certificate complexity. The proof uses some interesting techniques: for one direction, we introduce new relationships between randomized and quantum query complexity in various settings, and for the other direction, we use a recent result from communication complexity due to Klartag and Regev. We also characterize sculpting for other query complexity measures, such as R(f) vs. R0(f) and R0(f) vs. D(f). Along the way, we prove some new relationships for quantum query complexity: for example, a nearly quadratic relationship between Q(f) and D(f) whenever the promise of f is small. This contrasts with the recent super-quadratic query complexity separations, showing that the maximum gap between classical and quantum query complexities is indeed quadratic in various settings -- just not for total functions! Lastly, we investigate sculpting in the Turing machine model. We show that if there is any BPP-bi-immune language in BQP, then every language outside BPP can be restricted to a promise which places it in PromiseBQP but not in PromiseBPP. Under a weaker assumption, that some problem in BQP is hard on average for P/poly, we show that every paddable language outside BPP is sculptable in this way.
IPA in the loop: Control design for throughput regulation in computer processors

A new technique for performance regulation in event-driven systems, recently proposed by the authors, consists of an adaptive-gain integral control. The gain is adjusted in the control loop by a real-time estimation of the derivative of the plant-function with respect to the control input. This estimation is carried out by Infinitesimal Perturbation Analysis (IPA). The main motivation comes from applications to throughput regulation in computer processors, where to-date, the testing of the proposed control technique has been assessed by simulation. The purpose of this paper is to report on its implementation on Intel's Haswell microprocessor, and compare its performance to that obtained from cycle-level, full system simulation environment. The intrinsic contribution of the paper to the Workshop on Discrete Event Systems is in describing the process of taking an IPA-based design and simulation to a concrete implementation, thereby providing a bridge between theory and applications.
Lifts, derandomization, and diameters of Schreier graphs of Mealy automata

It is known that random 2-lifts of graphs give rise to expander graphs. We present a new conjectured derandomization of this construction based on certain Mealy automata. We verify that these graphs have polylogarithmic diameter, and present a class of automata for which the same is true. However, we also show that some automata in this class do not give rise to expander graphs.
RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best'' lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated'' items and create a "representative'' subset of the data, comprising the "best items'' in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be a large portion of data. A much smaller representative can be found if we relax the requirement to include the best item for each user, and instead just limit the users' "regret''. Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, we consider the position of the items in the ranked list for defining the regret and propose the \em rank-regret representative as the minimal subset of the data containing at least one of the top-k of any possible ranking function. This problem is NP-complete. We use a geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets.
AptRank: an adaptive PageRank model for protein function prediction on bi-relational graphs.

Motivation Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. Results We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. Availability and Implementation The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . Contact gribskov@purdue.edu. Supplementary information Supplementary data are available at Bioinformatics online.
Incremental truncated LSTD

Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning. Temporal difference (TD) learning algorithms stochastically update the value function, with a linear time complexity in the number of features, whereas least-squares temporal difference (LSTD) algorithms are sample efficient but can be quadratic in the number of features. In this work, we develop an efficient incremental low-rank LSTD(λ) algorithm that progresses towards the goal of better balancing computation and sample efficiency. The algorithm reduces the computation and storage complexity to the number of features times the chosen rank parameter while summarizing past samples efficiently to nearly obtain the sample efficiency of LSTD. We derive a simulation bound on the solution given by truncated low-rank approximation, illustrating a bias-variance trade-off dependent on the choice of rank. We demonstrate that the algorithm effectively balances computational complexity and sample efficiency for policy evaluation in a benchmark task and a high-dimensional energy allocation domain.
Symmetry gaps in Riemannian geometry and minimal orbifolds

We study the size of the isometry group $\mathrm{Isom}(M,g)$ of Riemannian manifolds $(M,g)$ as $g$ varies. For $M$ not admitting a circle action, we show that the order of $\mathrm{Isom}(M,g)$ can be universally bounded in terms of the bounds on Ricci curvature, diameter, and injectivity radius of $M$. This generalizes results known for negative Ricci curvature to all manifolds. More generally we establish a similar universal bound on the index of the deck group $\pi_1 (M)$ in the isometry group $\mathrm{Isom}(\widetilde{M},\widetilde{g})$ of the universal cover $\widetilde{M}$ in the absence of suitable actions by connected groups. We apply this to characterize locally symmetric spaces by their symmetry in covers. This proves a conjecture of Farb andWeinberger with the additional assumption of bounds on curvature, diameter, and injectivity radius. Further we generalize results of KazhdannMargulis and Gromov on minimal orbifolds of nonpositively curved manifolds to arbitrary manifolds with only a purely topological assumption.
Finite time blow up in the hyperbolic Boussinesq system

Abstract In recent work of Luo and Hou [10] , a new scenario for finite time blow up in solutions of 3D Euler equation has been proposed. The scenario involves a ring of hyperbolic points of the flow located at the boundary of a cylinder. In this paper, we propose a two dimensional model that we call “hyperbolic Boussinesq system”. This model is designed to provide insight into the hyperbolic point blow up scenario. The model features an incompressible velocity vector field, a simplified Biot–Savart law, and a simplified term modeling buoyancy. We prove that finite time blow up happens for a natural class of initial data.