cssi/SciSciGPT-SciSciNet huggingface dataset:
Synthesis of the C(29)-C(45) bis-pyran subunit (E-F) of spongistatin 1 (Altohyrtin A).

A synthesis of the C(29)−C(45) bis-pyran subunit 2 of spongistatin 1 (1a) is described. The synthesis proceeds in 19 steps from the chiral aldehyde ent-7, and features highly diastereoselective α-alkoxyallylation reactions using the γ-alkoxy substituted allylstannanes 17 and 19, as well as a thermodynamically controlled intramolecular Michael addition to close the F-ring pyran. The E ring was assembled via the Mukaiyama aldol reaction of F-ring methyl ketone 3 and the 2,3-syn aldehyde 4.
APPLICATIONS OF VINYLOGOUS MANNICH REACTIONS. CONCISE ENANTIOSPECIFIC TOTAL SYNTHESES OF (+)-CROOMINE

Because the vinylogous Mannich reaction of substituted furans with iminium ions is a useful construction in alkaloid synthesis, it is important to know what effects substituents on the two reacting partners have upon the stereoselectivity of the reaction. Toward this end, the additions of the methylated furans 9a−h to the iminium ion generated in situ from the ethoxy carbamate 10 were examined. Generally, mixtures (3−24:1) of the threo and erythro adducts 11a−h and 12a−h were obtained in 50−96% combined yields, with the threo isomers being the major products. Two extraordinarily concise asymmetric syntheses of (+)-croomine (1) have been completed using a novel strategy, highlighted by two vinylogous Mannich reactions as key constructions. The first such reaction involved the addition of 5-(4-bromobut-1-yl)-3-methyl-2-(triisopropylsilyloxy)furan to the N-acyliminium salt derived from the l-pyroglutamic acid derivative 17 to give the adduct [5(S),2‘(S),5‘(S)]-5-(4‘ ‘-bromobut-1‘ ‘yl)-5-[N-(tert-butoxycarbon...
Diels−Alder Reaction of 2-Amino-Substituted Furans as a Method for Preparing Substituted Anilines

5-Amino-2-furancarboxylic acid methyl ester undergoes a facile Diels−Alder cycloaddition with a variety of dienophiles to afford ring-opened cycloadducts that are readily dehydrated using BF3· OEt2 to give polysubstituted anilines. In each case, the cycloaddition proceeds with high regioselectivity, with the electron-withdrawing group being located ortho to the amino group. The most favorable FMO interaction is between the HOMO of the furanamine and the LUMO of the dienophile. The atomic coefficient at the ester carbon of the furan is larger than that at the amino center, and this nicely accommodates the observed regioselectivity. The [4 + 2]-cycloaddition of N-(5-nitrofuranyl)morpholine with methyl vinyl ketone affords a mixture of three phenols. One of the phenols is derived from a Diels−Alder reaction followed by nitro group ejection and subsequent aromatization. The remaining two phenols are the result of cleavage of the initially formed oxabicyclic intermediate with concomitant migration of the nitro...
Anodic coupling reactions: probing the stereochemistry of tetrahydrofuran formation. A short, convenient synthesis of linalool oxide.

Intramolecular coupling reactions between enol ether radical cations and oxygen nucleophiles are primarily governed by stereoelectronics. By taking advantage of this observation, a tetrahydrofuran building block for use in constructing (+)-linalool oxide and rotundisine has been synthesized in four steps from a commercially available starting material. The synthesis of (+)-linalool oxide has been completed.
Preparation and reactions of cyclic and acyclic allene complexes of zirconocene

Abstract The zirconocene π-complex of 3-methyl-1,2-butadiene(1,1-dimethylallene) cannot be prepared by simple ligand substitution due to rapid coupling of the initally formed complex with the unreacted allene. This problem was circumvented by generating the allene complex by β-hydrogen elimination from a pre-formed sigma complex. This appears to be a potentially general way to prepare allene complexes of zirconocene if the allene precursor has no cis-vinyl hydrogen. This method has been successfully used to prepare zirconocene complexes of 3-methyl-1,2-cycloheptadiene, 3-methyl-1,2-cyclohexadiene and 4-phenyl-1,2,3-cyclohexatriene. Attempts to prepare a complex of 3-methyl-1,2-cyclopentadiene failed. Selected reactions of two of these cumulene complexes and their coupling products are reported.
Snord116-dependent diurnal rhythm of DNA methylation in mouse cortex

Rhythmic oscillations of physiological processes depend on integrating the circadian clock and diurnal environment. DNA methylation is epigenetically responsive to daily rhythms, as a subset of CpG dinucleotides in brain exhibit diurnal rhythmic methylation. Here, we show a major genetic effect on rhythmic methylation in a mouse Snord116 deletion model of the imprinted disorder Prader-Willi syndrome (PWS). More than 23,000 diurnally rhythmic CpGs are identified in wild-type cortex, with nearly all lost or phase-shifted in PWS. Circadian dysregulation of a second imprinted Snord cluster at the Temple/Kagami-Ogata syndrome locus is observed at the level of methylation, transcription, and chromatin, providing mechanistic evidence of cross-talk. Genes identified by diurnal epigenetic changes in PWS mice overlapped rhythmic and PWS-specific genes in human brain and are enriched for PWS-relevant phenotypes and pathways. These results support the proposed evolutionary relationship between imprinting and sleep, and suggest possible chronotherapy in the treatment of PWS and related disorders.
4,4'-Disubstituted BINAPs for highly enantioselective Ru-catalyzed asymmetric hydrogenation of ketones.

A family of tunable precatalysts Ru(4,4‘-BINAP)(chiral diamine)Cl2 was synthesized and used for highly enantioselective hydrogenation of aromatic ketones. This result differs from previous chiral diphosphines that rely on the bis(xylyl)phosphino groups to control enantioselectivity. An X-ray structural study reveals that the bulky substituents on the 4,4‘-positions of BINAP can effectively create a suitable chiral pocket in the transition state and thus provide a new mechanism for the enantiocontrol in such a remarkable asymmetric catalytic process.
Synthesis and anticonvulsant activity of 3-alkyl-3,4-dihydro-2(1H)-quinazolinones

Previously we reported the potent anticonvulsant activity of 3-dimethylamino-3,4-dihydro-2(1H)-quinazolinone. In this report, a series of 3-alkyl-3,4-dihydro-2(1H)-quinazolinones was synthesized in three steps from isatoic anhydrides or an anthranilic acid. Structure/activity investigations revealed optimal activity for the 3-isopropyl and 3-cyciohexyl analogs. These compounds were effective against seizures induced by maximal electroshock.
Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.

One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in vivo. Accumulated evidence suggests that in vivo activity is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of protein–ligand interactions (PLIs). However, existing experimental and computational techniques are insufficient in studying the molecular details of kinetics processes of PLIs on a large scale. Consequently, we not only have limited mechanistic understanding of the kinetic processes but also lack a practical platform for high-throughput screening and optimization of drug leads on the basis of their kinetic properties. For the first time, we address this unmet need by integrating coarse-grained normal mode analysis with multitarget machine learning (MTML). To test our method, HIV-1 protease is used as a model system. We find that computational models based on the residue normal mode directionality displacemen...
Identifying the Biosynthetic Gene Cluster for Triacsins with an N-Hydroxytriazene Moiety

Triacsins are a family of natural products having in common an N-hydroxytriazene moiety not found in any other known secondary metabolites. Though many studies have examined the biological activity of triacsins in lipid metabolism, their biosynthesis has remained unknown. Here we report the identification of the triacsin biosynthetic gene cluster in Streptomyces aureofaciens ATCC 31442. Bioinformatic analysis of the gene cluster led to the discovery of the tacrolimus producer Streptomyces tsukubaensis NRRL 18488 as a new triacsin producer. In addition to targeted gene disruption to identify necessary genes for triacsin production, stable isotope feeding was performed in vivo to advance the understanding of N-hydroxytriazene biosynthesis.
Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares

Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git.
A Novel Pyrrolidine Imide Catalyzed Direct Formation of α,β-Unsaturated Ketones from Unmodified Ketones and Aldehydes

A method for direct, stereoselective preparation of (E)-α,β-unsaturated ketones from ketones and aldehydes, promoted by a novel pyrrolidine imide organocatalyst, has been developed in moderate to high yields. Unlike the Claisen−Schmidt condensation and Lewis acid catalyzed tandem aldol−dehydration processes, this method provides mild reaction conditions to access α,β-unsaturated ketones from simple, unmodified ketones.
The Colored Refresh Server for DRAM

Bounding each task's worst-case execution time (WCET) accurately is essential for real-time systems to determine if all deadlines can be met. Yet, access latencies to Dynamic Random Access Memory (DRAM) vary significantly due to DRAM refresh, which blocks access to memory cells. Variations further increase as DRAM density grows. This work contributes the “Colored Refresh Server” (CRS), a uniprocessor scheduling paradigm that partitions DRAM in two distinctly colored groups such that refreshes of one color occur in parallel to the execution of real-time tasks of the other color. By executing tasks in phase with periodic DRAM refreshes with opposing colors, memory requests no longer suffer from refresh interference. Experimental results confirm that refresh overhead is completely hidden and memory throughput enhanced.
GENERALIZED ITERATED WREATH PRODUCTS OF CYCLIC GROUPS AND ROOTED TREES CORRESPONDENCE

Consider the generalized iterated wreath product \(\mathbb {Z}_{r_1}\wr \mathbb {Z}_{r_2}\wr \ldots \wr \mathbb {Z}_{r_k}\) where \(r_i \in \mathbb {N}\). We prove that the irreducible representations for this class of groups are indexed by a certain type of rooted trees. This provides a Bratteli diagram for the generalized iterated wreath product, a simple recursion formula for the number of irreducible representations, and a strategy to calculate the dimension of each irreducible representation. We calculate explicitly fast Fourier transforms (FFT) for this class of groups, giving the literature’s fastest FFT upper bound estimate.
Stochastic mass-reconstruction: a new technique to reconstruct resonance masses of heavy particles decaying into tau lepton pairs

The invariant mass of tau lepton pairs turns out to be smaller than the resonant mass of their mother particle and the invariant mass distribution is stretched wider than the width of the resonant mass as signicant fraction of tau lepton momenta are carried away by neutrinos escaping undetected at collider experiments. This paper describes a new approach to reconstruct resonant masses of heavy particles decaying to tau leptons at such experiments. A typical example is a Z or Higgs boson decaying to a tau pair. Although the new technique can be used for each tau lepton separately, I combine two tau leptons to improve mass resolution by requiring the two tau leptons are lined up in a transverse plane. The method is simple to implement and complementary to the collinear approximation technique that works well when tau leptons are not lined up in a transverse plane. The reconstructed mass can be used as another variable in analyses that already use a visible tau pair mass and missing transverse momentum as these variables are not explicitly used in the stochastic mass-reconstruction to select signal-like events.
Analysis of university workplace building surfaces reveals usage-specific chemical signatures

Abstract Western people now spend close to 90% of their time indoors, one-quarter of which occurs at their place of employment. As such, interactions between employees and the workplace built environment have significant potential impact on employee health and safety. However, the range of workers’ daily chemical exposures is still poorly understood. Likewise, the influence of workers themselves and of worker behavior on the chemical profile of workplace surfaces is still unknown. In this case study, we used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to compare the chemical signatures of three different university workplaces: scientific research buildings, office buildings, and one mixed-purpose, high-traffic building housing teaching areas, student training laboratories, and offices. Our results identified differential signatures of public building surfaces based on building purpose and occupant behavior, sampling location and surface materials. Detected chemicals include occupant-derived molecules and molecules linked to human behavior such as cleaning products, medication, and food-derived molecules. The latter were particularly common in areas associated with food consumption such as refrigerators, microwaves and garbage cans. We also observed higher chemical diversity on metal surfaces, likely because these represent frequently-touched building areas such as door handles. Overall, these results are helping define the influence of human behavior on the workplace chemical environment and identify workplace daily chemical exposures.
On modules over Laurent polynomial rings

A finitely generated ℤ[t, t-1]-module without ℤ-torsion and having nonzero order Δ(M) of degree d is determined by a pair of sub-lattices of ℤd. Their indices are the absolute values of the leading and trailing coefficients of Δ(M). This description has applications in knot theory.
Unsupervised Learning using Pretrained CNN and Associative Memory Bank

Deep Convolutional features extracted from a comprehensive labeled dataset, contain substantial representations which could be effectively used in a new domain. Despite the fact that generic features achieved good results in many visual tasks, fine-tuning is required for pretrained deep CNN models to be more effective and provide state-of-the-art performance. Fine tuning using the backpropagation algorithm in a supervised setting, is a time and resource consuming process. In this paper, we present a new architecture and an approach for unsupervised object recognition that addresses the above mentioned problem with fine tuning associated with pretrained CNN-based supervised deep learning approaches while allowing automated feature extraction. Unlike existing works, our approach is applicable to general object recognition tasks. It uses a pretrained (on a related domain) CNN model for automated feature extraction pipelined with a Hopfield network based associative memory bank for storing patterns for classification purposes. The use of associative memory bank in our framework allows eliminating backpropagation while providing competitive performance on an unseen dataset.
QoE-Aware Resource Allocation for Small Cells

In this paper, we study the problem of Quality of Experience $(\mathbf{QoE})$ aware resource allocation in wireless systems. In particular, we consider application-aware joint Bandwidth-Power allocation for a small cell. We optimize a QoE metric for multi-user video streaming in a small cell that maintains a trade-off between maximizing the playback rate of each user and ensuring proportional fairness (PF) among users. We formulate the application-driven joint bandwidth-power allocation as a non-convex optimization problem. However, we develop a polynomial complexity algorithm, and we show that the proposed algorithm achieves the optimal solution of the proposed optimization problem. Simulation results show that the proposed QoE-aware algorithm significantly improves the average QoE. Moreover, it outperforms the weighted sum rate allocation which is the state-of-the-art physical resource allocation scheme.
Ordered set partition statistics and the Delta Conjecture

Abstract The Delta Conjecture of Haglund, Remmel, and Wilson is a recent generalization of the Shuffle Conjecture in the field of diagonal harmonics. In this paper we give evidence for the Delta Conjecture by proving a pair of conjectures of Wilson and Haglund–Remmel–Wilson which give equidistribution results for statistics related to inversion count and major index on objects related to ordered set partitions. Our results generalize the famous result of MacMahon that major index and inversion number share the same distribution on permutations.
Reconstruction of Word Embeddings from Sub-Word Parameters

Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size. We propose to benefit from this resource without paying the cost by operating strictly at the sub-lexical level. Our approach is quite simple: before task-specific training, we first optimize sub-word parameters to reconstruct pre-trained word embeddings using various distance measures. We report interesting results on a variety of tasks: word similarity, word analogy, and part-of-speech tagging.
Enhanced algebraic error control for random linear network coding

Error control is significant to network coding, since when unchecked, errors greatly deteriorate the throughput gains of network coding and seriously undermine both reliability and security of data. Two families of codes, subspace and rank metric codes, have been used to provide error control for random linear network coding. In this paper, we enhance the error correction capability of these two families of codes by using a novel two-tier decoding scheme. While the decoding of subspace and rank metric codes serves a second-tier decoding, we propose to perform a first-tier decoding on the packet level by taking advantage of Hamming distance properties of subspace and rank metric codes. This packet-level decoding can also be implemented by intermediate nodes to reduce error propagation. To support the first-tier decoding, we also investigate Hamming distance properties of three important families of subspace and rank metric codes, Gabidulin codes, Ko¨tter-Kschischang codes, and Mahdavifar-Vardy codes. Both the two-tier decoding scheme and the Hamming distance properties of these codes are novel to the best of our knowledge.
Supercharacters, elliptic curves, and the sixth moment of Kloosterman sums

Abstract We use supercharacter theory to connect the sixth power moment of Kloosterman sums to elliptic curves. This yields an elementary proof that Kloosterman sums K u with modulus p ∤ u are O ( p 2 / 3 ) .
q-DEFORMED SPIN NETWORKS, KNOT POLYNOMIALS AND ANYONIC TOPOLOGICAL QUANTUM COMPUTATION

We review the q-deformed spin network approach to Topological Quantum Field Theory and apply these methods to produce unitary representations of the braid groups that are dense in the unitary groups. Our methods are rooted in the bracket state sum model for the Jones polynomial. We give our results for a large class of representations based on values for the bracket polynomial that are roots of unity. We make a separate and self-contained study of the quantum universal Fibonacci model in this framework. We apply our results to give quantum algorithms for the computation of the colored Jones polynomials for knots and links, and the Witten–Reshetikhin–Turaev invariant of three manifolds.
Discovering sound concepts and acoustic relations in text

In this paper we describe approaches for discovering acoustic concepts and relations in text. The first major goal is to be able to identify text phrases which contain a notion of audibility and can be termed as a sound or an acoustic concept. We also propose a method to define an acoustic scene through a set of sound concepts. We use pattern matching and parts of speech tags to generate sound concepts from large scale text corpora. We use dependency parsing and LSTM recurrent neural network to predict a set of sound concepts for a given acoustic scene. These methods are not only helpful in creating an acoustic knowledge base but in the future can also directly help acoustic event and scene detection research.
Bootstrap Model Aggregation for Distributed Statistical Learning

In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation. A simple method is to linearly average the parameters of the local models, which, however, tends to be degenerate or not applicable on non-convex models, or models with different parameter dimensions. One more practical strategy is to generate bootstrap samples from the local models, and then learn a joint model based on the combined bootstrap set. Unfortunately, the bootstrap procedure introduces additional noise and can significantly deteriorate the performance. In this work, we propose two variance reduction methods to correct the bootstrap noise, including a weighted M-estimator that is both statistically efficient and practically powerful. Both theoretical and empirical analysis is provided to demonstrate our methods.
Joint Online Spoken Language Understanding and Language Modeling With Recurrent Neural Networks

Speaker intent detection and semantic slot filling are two critical tasks in spoken language understanding (SLU) for dialogue systems. In this paper, we describe a recurrent neural network (RNN) model that jointly performs intent detection, slot filling, and language modeling. The neural network model keeps updating the intent estimation as word in the transcribed utterance arrives and uses it as contextual features in the joint model. Evaluation of the language model and online SLU model is made on the ATIS benchmarking data set. On language modeling task, our joint model achieves 11.8% relative reduction on perplexity comparing to the independent training language model. On SLU tasks, our joint model outperforms the independent task training model by 22.3% on intent detection error rate, with slight degradation on slot filling F1 score. The joint model also shows advantageous performance in the realistic ASR settings with noisy speech input.
SDN based Network Function Parallelism in Cloud

Network function virtualization (NFV) based service function chaining (SFC) allows the provisioning of various security and traffic engineering applications in a cloud network. Inefficient deployment of network functions can lead to security violations and performance overhead. In an OpenFlow enabled cloud, the key problem with current mechanisms is that several packet field match and flow rule action sets associated with the network functions are non-overlapping and can be parallelized for performance enhancement. We introduce Network Function Parallelism (NFP) SFC-NFP for OpenFlow network. Our solution utilizes network function parallelism over the OpenFlow rules to improve SFC performance in the cloud network. We have utilized the DPDK platform with an OpenFlow switch (OVS) for experimental analysis. Our solution achieves a 1.40-1.90x reduction in latency for SFC in an OpenStack cloud network managed by the SDN framework.
A semiparametric spatio-temporal model for solar irradiance data

Abstract We evaluate semiparametric spatio-temporal models for global horizontal irradiance at high spatial and temporal resolution. These models represent the spatial domain as a lattice and are capable of predicting irradiance at lattice points, given data measured at other lattice points. Using data from a 1.2 MW PV plant located in Lanai, Hawaii, we show that a semiparametric model can be more accurate than simple interpolation between sensor locations. We investigate spatio-temporal models with separable and nonseparable covariance structures and find no evidence to support assuming a separable covariance structure. Our results indicate a promising approach for modeling irradiance at high spatial resolution consistent with available ground-based measurements. Such modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.
On graded characterizations of finite dimensionality for algebraic algebras

We observe that a finitely generated algebraic algebra R (over a field) is finite dimensional if and only if the associated graded ring $${{\text {gr}}}{R}$$ is right noetherian, if and only if $${{\text {gr}}}{R}$$ has right Krull dimension, if and only if $${{\text {gr}}}{R}$$ satisfies a polynomial identity.
Interpolating local constants in families

We extend the theory of local constants to l-adic families of representations of GL_n(F) where F is a p-adic field with l not equal to p. We construct zeta integrals and gamma factors for representations coming from the conjectural "local Langlands correspondence in families" of Emerton-Helm, proving a rationality result and functional equation. We also construct a universal gamma factor with coefficients in the integral Bernstein center.
Distributed estimation of a parametric field using sparse noisy data

The problem of distributed estimation of a parametric physical field is stated as a maximum likelihood estimation problem. Sensor observations are distorted by additive white Gaussian noise. Prior to data transmission, each sensor quantizes its observation to M levels. The quantized data are then communicated over parallel additive white Gaussian channels to a fusion center for a joint estimation. An iterative expectation-maximization (EM) algorithm to estimate the unknown parameter is formulated, and its linearized version is adopted for numerical analysis. The numerical examples are provided for the case of the field modeled as a Gaussian bell. The dependence of the integrated mean-square error on the number of quantization levels, the number of sensors in the network and the SNR in observation and transmission channels is analyzed.
Multichannel Sparse Blind Deconvolution on the Sphere

Multichannel blind deconvolution is the problem of recovering an unknown signal f and multiple unknown channels x i from convolutional measurements y i = x i ⊛ f (i = 1, 2, …, N). We consider the case where the x i ’s are sparse, and convolution with f is invertible. Our nonconvex optimization formulation solves for a filter h on the unit sphere that produces sparse output y i ⊛ h. Under some technical assumptions, we show that all local minima of the objective function correspond to the inverse filter of f up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of f and x i using a simple manifold gradient descent algorithm with random initialization. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods.
There may be no minimal non-$\sigma$-scattered linear orders

In this paper we demonstrate that it is consistent, relative to the existence of a supercompact cardinal, that there is no linear order which is minimal with respect to being non $\sigma$-scattered. This shows that a theorem of Laver, which asserts that the class of $\sigma$-scattered linear orders is well quasi-ordered, is sharp. We also prove that PFA${}^+$ implies that every non $\sigma$-scattered linear order either contains a real type, an Aronszajn type, or a ladder system indexed by a stationary subset of $\omega_1$, equipped with either the lexicographic or reverse lexicographic order. Our work immediately implies that CH is consistent with "no Aronszajn tree has a base of cardinality $\aleph_1$." This gives an affirmative answer to a problem due to Baumgartner.
A construction of distance cospectral graphs

Abstract The distance matrix of a connected graph is the symmetric matrix with columns and rows indexed by the vertices and entries that are the pairwise distances between the corresponding vertices. We give a construction for graphs which differ in their edge counts yet are cospectral with respect to the distance matrix. Further, we identify a subgraph switching behavior which constructs additional distance cospectral graphs. The proofs for both constructions rely on a perturbation of (most of) the distance eigenvectors of one graph to yield the distance eigenvectors of the other.
DRAPS: Dynamic and resource-aware placement scheme for docker containers in a heterogeneous cluster

Virtualization is a promising technology that has facilitated cloud computing to become the next wave of the Internet revolution. Adopted by data centers, millions of applications that are powered by various virtual machines improve the quality of services. Although virtual machines are well-isolated among each other, they suffer from redundant boot volumes and slow provisioning time. To address limitations, containers were born to deploy and run distributed applications without launching entire virtual machines. As a dominant player, Docker is an open-source implementation of container technology. When managing a cluster of Docker containers, the management tool, Swarmkit, does not take the heterogeneities in both physical nodes and virtualized containers into consideration. The heterogeneity lies in the fact that different nodes in the cluster may have various configurations, concerning resource types and availabilities, etc., and the demands generated by services are varied, such as CPU-intensive (e.g. Clustering services) as well as memory-intensive (e.g. Web services). In this paper, we target on investigating the Docker container cluster and developed, DRAPS, a resource-aware placement scheme to boost the system performance in a heterogeneous cluster.
Algebraic independence of values of Goss L-functions at s=1

Abstract We investigate special values of Goss L -functions for Dirichlet characters at s = 1 over rings of class number one and prove results on their transcendence and algebraic independence.
Hierarchical animal movement models for population‐level inference

New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal movement models are still predominantly developed for individual-level inference. Most efforts to upscale the inference to the population-level are either post hoc or complicated enough that only the developer can implement the model. Hierarchical Bayesian models provide an ideal platform for the development of population-level animal movement models but can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical animal movement models to telemetry data. The two-stage approach is statistically rigorous and allows one to fit individual-level movement models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for a completely automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models. The first application involves a spatial point process approach to modeling telemetry data and the second involves a more complicated continuous-time discrete-space animal movement model. We fit these models to simulated data and real telemetry data arising from a population of monitored Canada lynx in Colorado, USA.
The exact information-based complexity of smooth convex minimization

Abstract We obtain a new lower bound on the information-based complexity of first-order minimization of smooth and convex functions. We show that the bound matches the worst-case performance of the recently introduced Optimized Gradient Method (Drori and Teboulle, 2013; Kim and Fessler, 2015), thereby establishing that the bound is tight and can be realized by an efficient algorithm. The proof is based on a novel construction technique of smooth and convex functions.
Unexpected secondary flows in reverse nonequilibrium shear flow simulations

Surprising secondary flows develop in reverse nonequilibrium simulations when the simulation box is elongated in the direction of flow.
Batch Spreadsheet for C Programmers.

A computing environment is proposed, based on batch spreadsheet processing, which produces a spreadsheet display from plain text input files of commands, similar to the way documents are created using LaTeX. In this environment, besides the usual spreadsheet rows and columns of cells, variables can be defined and are stored in a separate symbol table. Cell and symbol formulas may contain cycles, and cycles which converge can be used to implement iterative algorithms. Formulas are specified using the syntax of the C programming language, and all of C's numeric operators are supported, with operators such as ++, +=, etc. being implicitly cyclic. User-defined functions can be written in C and are accessed using a dynamic link library. The environment can be combined with a GUI front-end processor to enable easier interaction and graphics including plotting.
A_{infinity}-structures, Brill-Noether loci and the Fourier-Mukai transform

We use the -formalism to study variation in cohomology spaces under formal deformations of coherent sheaves on projective varieties. As an application we describe formal neighborhoods of some singular points in twisted Brill–Noether loci in the moduli spaces of vector bundles on a curve. Another application is a computation of the Fourier–Mukai transform of some natural line bundles on symmetric powers of a curve.
Developments toward a 250-nm, Fully Planarized Fabrication Process with Ten Superconducting Layers and Self-Shunted Josephson Junctions

We are developing a superconductor electronics fabrication process with up to nine planarized superconducting layers, stackable stud vias, self-shunted Nb/AlOx-Al/Nb Josephson junctions, and one layer of MoNx kinetic inductors. The minimum feature size of resistors and inductors in the process is 250 nm. We present data on the mutual inductance of Nb stripline and microstrip inductors with linewidth and spacing from 250 nm to 1 μm made on the same or adjacent Nb layers, as well as the data on the linewidth and resistance uniformity.
Constraint solving for finite model finding in SMT solvers

Satisfiability modulo theories (SMT) solvers have been used successfully as reasoning engines for automated verification and other applications based on automated reasoning. Current techniques for dealing with quantified formulas in SMT are generally incomplete, forcing SMT solvers to report “unknown” when they fail to prove the unsatisfiability of a formula with quantifiers. This inability to return counter models limits their usefulness in applications that produce queries involving quantified formulas. In this paper, we reduce these limitations by integrating finite model finding techniques based on constraint solving into the architecture used by modern SMT solvers. This approach is made possible by a novel solver for cardinality constraints, as well as techniques for on-demand instantiation of quantified formulas. Experiments show that our approach is competitive with the state of the art in SMT, and orthogonal to approaches in automated theorem proving.
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning

We have created a large diverse set of cars from overhead images (Data sets, annotations, networks and scripts are available from http://gdo-datasci.ucllnl.org/cowc/), which are useful for training a deep learner to binary classify, detect and count them. The dataset and all related material will be made publically available. The set contains contextual matter to aid in identification of difficult targets. We demonstrate classification and detection on this dataset using a neural network we call ResCeption. This network combines residual learning with Inception-style layers and is used to count cars in one look. This is a new way to count objects rather than by localization or density estimation. It is fairly accurate, fast and easy to implement. Additionally, the counting method is not car or scene specific. It would be easy to train this method to count other kinds of objects and counting over new scenes requires no extra set up or assumptions about object locations.
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems

Self-localization during navigation with noisy sensors in an ambiguous world is computationally challenging, yet animals and humans excel at it. In robotics, {\em Simultaneous Location and Mapping} (SLAM) algorithms solve this problem through joint sequential probabilistic inference of their own coordinates and those of external spatial landmarks. We generate the first neural solution to the SLAM problem by training recurrent LSTM networks to perform a set of hard 2D navigation tasks that require generalization to completely novel trajectories and environments. Our goal is to make sense of how the diverse phenomenology in the brain's spatial navigation circuits is related to their function. We show that the hidden unit representations exhibit several key properties of hippocampal place cells, including stable tuning curves that remap between environments. Our result is also a proof of concept for end-to-end-learning of a SLAM algorithm using recurrent networks, and a demonstration of why this approach may have some advantages for robotic SLAM.
Net2Net: Accelerating Learning via Knowledge Transfer

Abstract: We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from scratch. Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network. Our techniques are based on the concept of function-preserving transformations between neural network specifications. This differs from previous approaches to pre-training that altered the function represented by a neural net when adding layers to it. Using our knowledge transfer mechanism to add depth to Inception modules, we demonstrate a new state of the art accuracy rating on the ImageNet dataset.
Adaptive Multiple-Arm Identification

We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game. This problem has a wide range of applications, e.g., A/B testing, crowdsourcing, simulation optimization. Our goal is to develop a PAC algorithm, which, with probability at least $1-\delta$, identifies a set of $K$ arms with the aggregate regret at most $\epsilon$. The notion of aggregate regret for multiple-arm identification was first introduced in \cite{Zhou:14} , which is defined as the difference of the averaged expected rewards between the selected set of arms and the best $K$ arms. In contrast to \cite{Zhou:14} that only provides instance-independent sample complexity, we introduce a new hardness parameter for characterizing the difficulty of any given instance. We further develop two algorithms and establish the corresponding sample complexity in terms of this hardness parameter. The derived sample complexity can be significantly smaller than state-of-the-art results for a large class of instances and matches the instance-independent lower bound upto a $\log(\epsilon^{-1})$ factor in the worst case. We also prove a lower bound result showing that the extra $\log(\epsilon^{-1})$ is necessary for instance-dependent algorithms using the introduced hardness parameter.
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions

We study connections between Dykstra's algorithm for projecting onto an intersection of convex sets, the augmented Lagrangian method of multipliers or ADMM, and block coordinate descent. We prove that coordinate descent for a regularized regression problem, in which the penalty is a separable sum of support functions, is exactly equivalent to Dykstra's algorithm applied to the dual problem. ADMM on the dual problem is also seen to be equivalent, in the special case of two sets, with one being a linear subspace. These connections, aside from being interesting in their own right, suggest new ways of analyzing and extending coordinate descent. For example, from existing convergence theory on Dykstra's algorithm over polyhedra, we discern that coordinate descent for the lasso problem converges at an (asymptotically) linear rate. We also develop two parallel versions of coordinate descent, based on the Dykstra and ADMM connections.
Cross-Discourse and Multilingual Exploration of Textual Corpora with the DualNeighbors Algorithm

Word choice is dependent on the cultural context of writers and their subjects. Different words are used to describe similar actions, objects, and features based on factors such as class, race, gender, geography and political affinity. Exploratory techniques based on locating and counting words may, therefore, lead to conclusions that reinforce culturally inflected boundaries. We offer a new method, the DualNeighbors algorithm, for linking thematically similar documents both within and across discursive and linguistic barriers to reveal cross-cultural connections. Qualitative and quantitative evaluations of this technique are shown as applied to two cultural datasets of interest to researchers across the humanities and social sciences. An open-source implementation of the DualNeighbors algorithm is provided to assist in its application.
Controlling a robotic stereo camera under image quantization noise

In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through...
Rigidity phenomena involving scalar curvature

We give a survey of various rigidity results involving scalar curvature. Many of these results are inspired by the positive mass theorem in general relativity. In particular, we discuss the recent solution of Min-Oo's Conjecture for the hemisphere (cf. [13]). We also analyze the case of equality in Bray's volume comparison theorem.
A canonical lift of Frobenius in Morava E-theory

We prove that the $p$th Hecke operator on the Morava $E$-cohomology of a space is congruent to the Frobenius mod $p$. This is a generalization of the fact that the $p$th Adams operation on the complex $K$-theory of a space is congruent to the Frobenius mod $p$. The proof implies that the $p$th Hecke operator may be used to test Rezk's congruence criterion.
Quantum Mechanics, Gravity, and the Multiverse

The discovery of accelerating expansion of the universe has led us to take the dramatic view that our universe may be one of the many universes in which low energy physical laws take different forms: the multiverse. I explain why/how this view is supported both observationally and theoretically, especially by string theory and eternal inflation. I then describe how quantum mechanics plays a crucial role in understanding the multiverse, even at the largest distance scales. The resulting picture leads to a revolutionary change of our view of spacetime and gravity, and completely unifies the paradigm of the eternally inflating multiverse with the many worlds interpretation of quantum mechanics. The picture also provides a solution to a long-standing problem in eternal inflation, called the measure problem, which I briefly describe.
Functional co-optimization of articulated robots

We present parametric trajectory optimization, a method for simultaneously computing physical parameters, actuation requirements, and robot motions for more efficient robot designs. In this scheme, robot dimensions, masses, and other physical parameters are solved for concurrently with traditional motion planning variables, including dynamically consistent robot states, actuation inputs, and contact forces. Our method requires minimal user domain knowledge, requiring only a coarse guess of the target robot configuration sequence and a parameterized robot topology as input. We demonstrate our results on four simulated robots, one of which we physically fabricated in order to demonstrate physical consistency. We demonstrate that by optimizing robot body parameters alongside robot trajectories, motion planning problems which would otherwise be infeasible can be made feasible, and actuation requirements can be significantly reduced.
Image Segmentation Using Sparse Subset Selection

In this paper, we present a new image segmentation method based on the concept of sparse subset selection. Starting with an over-segmentation, we adopt local spectral histogram features to encode the visual information of the small segments into high-dimensional vectors, called superpixel features. Then, the superpixel features are fed into a novel convex model which efficiently leverages the features to group the superpixels into a proper number of coherent regions. Our model automatically determines the optimal number of coherent regions and superpixels assignment to shape final segments. To solve our model, we propose a numerical algorithm based on the alternating direction method of multipliers (ADMM), whose iterations consist of two highly parallelizable sub-problems. We show each sub-problem enjoys closed-form solution which makes the ADMM iterations computationally very efficient. Extensive experiments on benchmark image segmentation datasets demonstrate that our proposed method in combination with an over-segmentation can provide high quality and competitive results compared to the existing state-of-the-art methods.
Density Estimation via Discrepancy Based Adaptive Sequential Partition.

Given $iid$ observations from an unknown continuous distribution defined on some domain $\Omega$, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function. Our density estimate is a piecewise constant function defined on a binary partition of $\Omega$. The key ingredient of the algorithm is to use discrepancy, a concept originates from Quasi Monte Carlo analysis, to control the partition process. The resulting algorithm is simple, efficient, and has provable convergence rate. We demonstrate empirically its efficiency as a density estimation method. We also show how it can be utilized to find good initializations for k-means.