cssi/SciSciGPT-SciSciNet huggingface dataset:
Evolution of metabolic novelty: A trichome-expressed invertase creates specialized metabolic diversity in wild tomato

Plants produce a myriad of taxonomically restricted specialized metabolites. This diversity—and our ability to correlate genotype with phenotype—makes the evolution of these ecologically and medicinally important compounds interesting and experimentally tractable. Trichomes of tomato and other nightshade family plants produce structurally diverse protective compounds termed acylsugars. While cultivated tomato (Solanum lycopersicum) strictly accumulates acylsucroses, the South American wild relative Solanum pennellii produces copious amounts of acylglucoses. Genetic, transgenic, and biochemical dissection of the S. pennellii acylglucose biosynthetic pathway identified a trichome gland cell–expressed invertase-like enzyme that hydrolyzes acylsucroses (Sopen03g040490). This enzyme acts on the pyranose ring–acylated acylsucroses found in the wild tomato but not on the furanose ring–decorated acylsucroses of cultivated tomato. These results show that modification of the core acylsucrose biosynthetic pathway leading to loss of furanose ring acylation set the stage for co-option of a general metabolic enzyme to produce a new class of protective compounds.
NOJAH: NOt Just Another Heatmap for genome-wide cluster analysis

Since their inception, several tools have been developed for cluster analysis and heatmap construction. The application of such tools to the number and types of genome-wide data available from next generation sequencing (NGS) technologies requires the adaptation of statistical concepts, such as in defining a most variable gene set, and more intricate cluster analyses method to address multiple omic data types. Additionally, the growing number of publicly available datasets has created the desire to estimate the statistical significance of a gene signature derived from one dataset to similarly group samples based on another dataset. The currently available number of tools and their combined use for generating heatmaps, along with the several adaptations of statistical concepts for addressing the higher dimensionality of genome-wide NGS-derived data, has created a further challenge in the ability to replicate heatmap results. We introduce NOJAH (NOt Just Another Heatmap), an interactive tool that defines and implements a workflow for genome-wide cluster analysis and heatmap construction by creating and combining several tools into a single user interface. NOJAH includes several newly developed scripts for techniques that though frequently applied are not sufficiently documented to allow for replicability of results. These techniques include: defining a most variable gene set (a.k.a., 'core genes'), estimating the statistical significance of a gene signature to separate samples into clusters, and performing a result merging integrated cluster analysis. With only a user uploaded dataset, NOJAH provides as output, among other things, the minimum documentation required for replicating heatmap results. Additionally, NOJAH contains five different existing R packages that are connected in the interface by their functionality as part of a defined workflow for genome-wide cluster analysis. The NOJAH application tool is available at http://bbisr.shinyapps.winship.emory.edu/NOJAH/ http://shinygispa.winship.emory.edu/shinyGISPA/ with corresponding source code available at https://github.com/bbisr-shinyapps/NOJAH/.
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy

We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.
Metal-Centered Deltahedral Zintl Ions: Synthesis of [Ni@Sn9]4– by Direct Extraction from Intermetallic Precursors and of the Vertex-Fused Dimer [{Ni@Sn8(μ-Ge)1/2}2]4–

Ni-centered deltahedral Sn9 clusters with a charge of 4–, i.e., [Ni@Sn9]4–, were extracted in ethylenediamine in high yield directly from intermetallic precursors with the nominal composition “K4Sn9Ni3”. The new endohedral clusters were crystallized and structurally characterized in K[K(18-crown-6)]3[Ni@Sn9]·3benzene (1a, triclinic, P1, a = 10.2754(5) A, b = 19.5442(9) A, and c = 20.5576(13) A, α = 73.927(3)°, β = 79.838(4)°, and γ = 84.389(3)°, V = 3899.6(4) A3, Z = 2) and K[K(2,2,2-crypt)]3[Ni@Sn9] (1b, triclinic, P1, a = 15.8028(8) A, b = 16.21350(9) A, and c = 20.1760(12) A, α = 98.71040(10)°, β = 104.4690(10)°, and γ = 118.3890(10)°, V = 4181.5(4) A3, Z = 2). The alternative method of a post-synthetic insertion of a Ni atom in empty Sn9 clusters by a reaction with Ni(cod)2 predominantly produces the more-oxidized clusters with a charge of 3–, i.e., the recently reported [Ni@Sn9]3–. Nonetheless, using substoichiometric amounts of 18-crown-6 as a cation sequestering agent, we also have been able to i...
Tracing the history of LINE and SINE extinction in sigmodontine rodents

L1 retrotransposons have co-evolved with their mammalian hosts for the entire history of mammals and currently compose ~ 20% of a mammalian genome. B1 retrotransposons are dependent on L1 for retrotransposition and span the evolutionary history of rodents since their radiation. L1s were found to have lost their activity in a group of South American rodents, the Sigmodontinae, and B1 inactivation preceded the extinction of L1 in the same group. Consequently, a basal group of sigmodontines have active L1s but inactive B1s and a derived clade have both inactive L1s and B1s. It has been suggested that B1s became extinct during a long period of L1 quiescence and that L1s subsequently reemerged in the basal group. Here we investigate the evolutionary histories of L1 and B1 in the sigmodontine rodents and show that L1 activity continued until after the L1-extinct clade and the basal group diverged. After the split, L1 had a small burst of activity in the former group, followed by extinction. In the basal group, activity was initially low but was followed by a dramatic increase in L1 activity. We found the last wave of B1 retrotransposition was large and probably preceded the split between the two rodent clades. Given that L1s had been steadily retrotransposing during the time corresponding to B1 extinction and that the burst of B1 activity preceding B1 extinction was large, we conclude that B1 extinction was not a result of L1 quiescence. Rather, the burst of B1 activity may have contributed to L1 extinction both by competition with L1 and by putting strong selective pressure on the host to control retrotransposition.
Hydrogen adsorption on supported cobalt, iron, and nickel

This paper emphasizes concepts and fundamentals relating to the kinetics, energetics, and stoichiometries of adsorption of hydrogen on supported cobalt, iron and nickel, with emphasis on nickel. Relationships between catalyst and adsorption properties and the application of hydrogen chemisorption to the measurement of metal surface areas are discussed. Evidence is presented for nonstoichiometric adsorption of hydrogen on supported metals and the results are interpreted in terms (i) reversibility of adsorption and (ii) interactions of hydrogen with metal oxides present on or near metal crystallites. Contamination of the metal surface by support moieties can cause (i) the appearance of new adsorption states of hydrogen at higher binding energies and (ii) an increase in the adsorption activation energy for hydrogen which can lead to severe kinetic limitations in the adsorption process. Precalcination treatments and promoters such as potassium also cause the appearance of new high temperature adsorption states and significantly increase the adsorption activation energy for hydrogen.
Carbohydrate Chains: Enzymatic and Chemical Synthesis

Carbohydrate chains play many important roles in biology, covering the surfaces of cells, mediating cell–cell recognition events, and forming major classes of biologically active molecules. Though carbohydrate chains are very important in biology, their study is frequently problematical because in vivo biosynthesis of carbohydrate chains often produces heterogeneous mixtures that are hard to purify in sufficient quantities for biochemical and structural studies. The in vitro enzymatic and chemical synthesis of carbohydrate chains offers another route to these interesting biomolecules that allows sufficient quantities of homogeneous oligosaccharides to be produced for biological studies.
CATALYSIS BY TEMPORARY COVALENT ACTIVATION. A NOVEL CATALYSIS OF UNACTIVATED DIELS-ALDER REACTIONS

Abstract Diels-Alder reactions of cis-2-chlorovinyltributylstannane are greatly accelerated by the presence of catalytic amounts of haloboranes. Mechanistic studies support a mechanism for the catalysis involving an intermediate boron-activated dienophile. A similar but simpler catalysis of Diels-Alder reactions of unactivated alkynes, via a hydroboration — Diels-Alder — dehydroboration mechanism, is also described.
Probing the Cybotactic Region in Gas-Expanded Liquids (GXLs)

Gas-expanded liquids (GXLs) are a new and benign class of liquid solvents, which may offer many advantages for separations, reactions, and advanced materials. GXLs are intermediate in properties between normal liquids and supercritical fluids, both in solvating power and in transport properties. Other advantages include benign nature, low operating pressures, and highly tunable properties by simple pressure variations. The chemical community has only just begun to exploit the advantages of these GXLs for industrial applications. This Account focuses on the synergism of experimental techniques with theoretical modeling resulting in a powerful combination for exploring chemical structure and transport in the cybotactic region of GXLs (at the nanometer lengthscale).
Preparation of thiochromans via thermal cyclization

Formation of the thiochroman ring system is achieved by a two step synthesis that involves heating 3-thiophenyl-1-propanols or 4-thiophenyl-2-butanols in toluene with catalytic amounts of p-toluenesulfonic acid. The propanols are made by the addition of sulfur stabilized carbanions to styrene oxide, ethylene oxide, propylene oxide, and isobutylene oxide. The carbanions are generated by treatment of either benzyl phenyl sulfide or thioanisole with butyllithium. The effect of substitution at the 1 and 3 positions of the propanols on the reaction yields is discussed. The mechanism of the reaction apparently involves intramolecular electrophilic aromatic substitution rather than a Claisen or thio-Claisen rearrangement.
The use of N,O-bis(tert-butoxycarbonyl)-hydroxylamine in the synthesis of N-hydroxylamines and hydroxamic acids

Abstract N,O-bis ( tert -butoxycarbonyl)-hydroxylamine has been used in the synthesis of 5-lipoxygenase inhibitor LY280810. In addition, this reagent has been utilized to synthesize a number of other hydroxylamine and hydroxamic acid derivatives in high yields.
Highly Enantioselective Aldol Reactions Catalyzed by a Recyclable Fluorous (S) Pyrrolidine Sulfonamide on Water

Fluorous (S) pyrrolidine sulfonamide serves as an efficient promoter for highly enantioselective aldol reactions of ketones and aldehydes with aromatic aldehydes on water. A notable feature of the organocatalyst is that it can be recovered from the reaction mixtures by simple fluorous solid-phase extraction and subsequently reused (up to seven cycles) without a significant loss of catalytic activity and stereoselectivity.
4,4-Diphenylcyclohexa-2,5-dienylidene: Rearrangement via an Isobenzene Pathway

Pyrolytic decompostition of the Li salt of the tosylhydrazone of 4,4-diphenyl-2,5-cyclohexadienone produces a mixture of products which includes biphenyl, p-terphenyl, the azine of 4,4-diphenyl-2,5-cyclohexadienone, and o-terphenyl. 13C labeling studies and computational results (semiempirical AM1 and density functional B3LYP/6-31G* molecular orbital calculations) elucidate the mechanistic pathway for the formation of o-terphenyl. A single mechanism is involved which proceeds through formation of an isobenzene species followed by subsequent phenyl and hydrogen migrations.
Oligomerization of the Thiophene-Based p-Quinodimethanes 2,5-Dimethylene-2,5-dihydrothiophene and 2-Ethylidene-5-methylene-2,5-dihydrothiophene

Flash vacuum pyrolysis (FVP) of (5-methyl-2-thiophene-yl)methyl benzoate (8) produces in ca. 75% yield 2,5-dimethylene-2,3-dihydrothiophene, S-monomer (3). S-Monomer 3 is relatively stable dissolved in carbon disulfide−chloroform at −78 °C. The structure of 3 is confirmed by its spectral properties. When a 0.17 M solution of S-monomer 3 was allowed to warm to room temperature, SS-dimer 5 ([2,2](2,5)thiophenophane, 14.7%), SSS-trimer 7 ([2,2,2](2,5)thiophenophane, 44.3%), and polymer were produced. A small amount (<1%) of an SSSS-tetramer was detected by GC/MS. The mechanism proposed for the formation of these oligomers involves the combination of two molecules of 3 to give an intermediate diradical (11) that can close to form dimer 5 or react with additional molecules of 3 to form the higher oligomers. Evidence for the trapping of diradical 11 by 2,5-dimethylene-2,5-dihydrofuran (O-monomer 2) was obtained. Co-oligomerization of S-monomer 3 and O-monomer 2 gave four compounds containing the thiophene moiet...
Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.
Allylic hydroxy phosphonates: versatile chiral building blocks

Abstract Allylic hydroxy phosphonates are converted into β and γ substituted amino phosphonates using a series of palladium-catalyzed reactions. The judicious selection of nitrogen nucleophile and palladium catalyst allow for excellent regio- and stereochemical control. Palladium(0)-catalyzed amine addition or tosyl carbamate rearrangement gives rise to the γ-substituted phosphonates, whereas, reaction of tosyl carbamates with palladium (II) and base gives oxazolidinones (β-substitution).
Synthesis and characterization of an N-acylsulfonamide inhibitor of human asparagine synthetase.

The synthesis of N-acylsulfonamide 6, which is an analogue of β-aspartyl-AMP, is described. This compound appears to be the first and only potent inhibitor of human asparagine synthetase that has been described to date. The N-acylsulfonamide 6 exhibits slow-onset inhibition kinetics, with a Ki* of 728 nM. Preparation and characterization of two additional N-acylsulfonamide analogues has also demonstrated the importance of hydrogen-bonding interactions in the recognition of the AS inhibitor with the enzyme. These observations provide the basis for the discovery of new compounds with application in the treatment of drug-resistant leukemia.
Scale-Up Methodology of a Modular Multilevel Converter for HVdc Applications

Modular multilevel converters (MMCs) are a realistic alternative to the conventional voltage source converters for medium-voltage (MV) and high-voltage direct current (HVdc) applications. The number of submodules (SMs) per arm of the MMC can be as high as 512 to achieve desired high dc voltage levels required for HVdc with a very low total harmonic distortion (THD) (e.g., <0.1%) of the MMC ac-side interface voltage. Although the low THD of the MMC output voltage with a high number of SMs is desirable, the MMC control implementation and complexity is also important to be considered for the high number of SMs. The MMC control complexity significantly increases as the number of SMs increases. Redesigning the number of SMs in MMCs also becomes quite difficult and may require significant control upgrade, which in turn also needs additional tests and validations. This paper presents an MMC scale-up control methodology applicable for MV and HVdc applications. The number of SMs can be conveniently increased or reduced without any significant control modifications. The proposed control method and capacitor voltage balancing algorithm are implemented in the real-time digital simulator and MMC support units based on field-programmable gate array boards. The performance of the proposed MMC control method is investigated for a point-to-point MMC-based HVdc system under various operating conditions.
Addressing the role of working memory in mathematical word-problem solving when designing intervention for struggling learners

The focus of this article is the well documented association between low working memory capacity and difficulty with mathematical word-problem solving. We begin by describing a model that specifies how various cognitive resources, including working memory, contribute to individual differences in word-problem solving and by then summarizing findings on the relation between working memory and word-problem solving. This sets the context for the article’s main purpose and major section: to describe the findings of research studies that take one of two approaches for addressing the needs of students with low working memory within word-problem solving intervention. One approach focuses on compensating for working memory limitations; the other on building working memory capacity. We then suggest the need for research on integrating the two approaches by embedding working memory training within explicit word-problem solving intervention.
Who benefits from selective education? Evidence from elite boarding school admissions

Abstract Existing research finds minimal gains from attending elite US secondary schools. This paper estimates the causal effect of attending a selective public boarding school, an institutional model increasingly used by states to serve academically gifted students. Regression discontinuity estimates using multiple admissions thresholds show math score gains and college application and enrollment patterns that shift away from less competitive colleges. Effects are concentrated among minorities, students with lower prior individual achievement, from rural neighborhoods, or lower-achieving sending schools. The opportunity to attend selective boarding schools reduces the tendency of disadvantaged or under-represented students to attend a less selective college by at least one-quarter.
An analogue of the Kac–Weisfeiler conjecture

Abstract In this paper we discuss an analogue of the Kac–Weisfeiler conjecture for a certain class of almost commutative algebras. In particular, we prove a Kac–Weisfeiler type statement for rational Cherednik algebras.
Compressible strips , chiral luttinger liquids , and all that jazz

When the quantum Hall effect occurs in a two-dimensional electron gas, all low-energy elementary excitations are localized near the system edge. The edge acts in many ways like a one-dimensional ring of electrons, except that a finite current flows around the ring in equilibrium. This article is a brief and informal review of some of the physics of quantum Hall system edges. We discuss the implications of macroscopic {\em compressible strip} models for microscopic {chiral Luttinger liquid} models and make an important distinction between the origin of non-Fermi-liquid behavior in fractional quantum Hall edges and in usual one-dimensional electron gas systems.
Almost Primes in Thin Orbits of Pythagorean Triangles

Let $F=x^2+y^2-z^2$, $x_0 \in \mathbb{Z}^3$ primitive with $F(x_0)=0$, and $\Gamma \leq SO_F(\mathbb{Z})$ be a finitely generated thin subgroup. We consider the resulting thin orbits of Pythagorean triples $x_0 \cdot \Gamma$ - specifically which hypotenuses, areas, and products of all three coordinates arise. We produce infinitely many $R$-almost primes in these three cases whenever $\Gamma$ has exponent $\delta_\Gamma>\delta_0(R)$ for explicit $R$, $\delta_0$.
New curvature flows in complex geometry

This is a survey of some of the recent developments on the geometric and analytic aspects of the Anomaly flow. It is a flow of $(2,2)$-forms on a $3$-fold which was originally motivated by string theory and the need to preserve the conformally balanced property of a Hermitian metric in the absence of a $\partial\bar\partial$-Lemma. It has revealed itself since to be a remarkable higher order extension of the Ricci flow. It has also led to several other curvature flows which may be interesting from the point of view of both non-Kahler geometry and the theory of non-linear partial differential equations.
Hydrodynamic shocks in microroller suspensions

Suspensions of rotating particles near a floor can generate different types of density waves. Freely propagating waves and traveling fronts are observed and studied, and it is shown that the nonlocal nature of hydrodynamic interactions is responsible for the dispersive behavior of the system.
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances.

Momentum methods such as Polyak's heavy ball (HB) method, Nesterov's accelerated gradient (AG) as well as accelerated projected gradient (APG) method have been commonly used in machine learning practice, but their performance is quite sensitive to noise in the gradients. We study these methods under a first-order stochastic oracle model where noisy estimates of the gradients are available. For strongly convex problems, we show that the distribution of the iterates of AG converges with the accelerated $O(\sqrt{\kappa}\log(1/\varepsilon))$ linear rate to a ball of radius $\varepsilon$ centered at a unique invariant distribution in the 1-Wasserstein metric where $\kappa$ is the condition number as long as the noise variance is smaller than an explicit upper bound we can provide. Our analysis also certifies linear convergence rates as a function of the stepsize, momentum parameter and the noise variance; recovering the accelerated rates in the noiseless case and quantifying the level of noise that can be tolerated to achieve a given performance. In the special case of strongly convex quadratic objectives, we can show accelerated linear rates in the $p$-Wasserstein metric for any $p\geq 1$ with improved sensitivity to noise for both AG and HB through a non-asymptotic analysis under some additional assumptions on the noise structure. Our analysis for HB and AG also leads to improved non-asymptotic convergence bounds in suboptimality for both deterministic and stochastic settings which is of independent interest. To the best of our knowledge, these are the first linear convergence results for stochastic momentum methods under the stochastic oracle model. We also extend our results to the APG method and weakly convex functions showing accelerated rates when the noise magnitude is sufficiently small.
Phantom Inflated Planets in Occurrence Rate Based Samples

The recently launched Transiting Exoplanet Survey Satellite (TESS) is expected to produce many new exoplanet discoveries which will be especially amenable to follow-up study. Assessments of the planet discovery yield of TESS, such as Sullivan et al. (2015) and Barclay et al. (2018), will be important for planning follow-up work. Analyzing these predicted planet samples, however, we find that giant planet radii derived from the current bulk transiting planet sample have been used at all potential orbits without accounting for the temperature dependence of radius inflation. These phantom inflated planets (PIPs) make up just over 1.1% of the Sullivan et al. (2015) predicted population and about 8% of the Barclay et al. (2018) sample. Similar population predictions for direct imaging studies should likewise take care not to include inflated planets at large separations as such planets will appear larger and, depending on assumed albedo, brighter and more easily detectable than physically possible. Despite their high false positive rate, giant planets are the testbeds for atmospheric characterization techniques and care should be taken to understand and account for potential contaminating factors in this population.
Low-Degree Factors of Random Polynomials

We study the probability that a monic polynomial with integer coefficients has a low-degree factor over the integers, which is equivalent to having a low-degree algebraic root. It is known in certain cases that random polynomials with integer coefficients are very likely to be irreducible, and our project can be viewed as part of a general program of testing whether this is a universal behavior exhibited by many random polynomial models. Our main result shows that pointwise delocalization of the roots of a random polynomial can be used to imply that the polynomial is unlikely to have a low-degree factor over the integers. We apply our main result to a number of models of random polynomials, including characteristic polynomials of random matrices, where strong delocalization results are known.
Schubert calculus on the Grassmannian of hermitian lagrangian spaces

Abstract With an eye towards index theoretic applications we describe a Schubert like stratification on the Grassmannian of hermitian lagrangian spaces in C n ⊕ C n . This is a natural compactification of the space of hermitian n × n matrices. The closures of the strata define integral cycles, and we investigate their intersection theoretic properties. We achieve this by blending Morse theoretic ideas, with techniques from o-minimal (or tame) geometry and geometric integration theory.
A heat flow approach to Onsager's conjecture for the Euler equations on manifolds

We give a simple proof of Onsager's conjecture concerning energy conservation for weak solutions to the Euler equations on any compact Riemannian manifold, extending the results of Constantin-E-Titi and Cheskidov-Constantin-Friedlander-Shvydkoy in the flat case. When restricted to $\mathbb{T}^{d}$ or $\mathbb{R}^{d}$, our approach yields an alternative proof of the sharp result of the latter authors. Our method builds on a systematic use of a smoothing operator defined via a geometric heat flow, which was considered by Milgram-Rosenbloom as a means to establish the Hodge theorem. In particular, we present a simple and geometric way to prove the key nonlinear commutator estimate, whose proof previously relied on a delicate use of convolutions.
Semi-Supervised Multi-Task Learning for Lung Cancer Diagnosis

Early detection of lung nodules is of great importance in lung cancer screening. Existing research recognizes the critical role played by CAD systems in early detection and diagnosis of lung nodules. However, many CAD systems, which are used as cancer detection tools, produce a lot of false positives (FP) and require a further FP reduction step. Furthermore, guidelines for early diagnosis and treatment of lung cancer are consist of different shape and volume measurements of abnormalities. Segmentation is at the heart of our understanding of nodules morphology making it a major area of interest within the field of computer aided diagnosis systems. This study set out to test the hypothesis that joint learning of false positive (FP) nodule reduction and nodule segmentation can improve the computer aided diagnosis (CAD) systems’ performance on both tasks. To support this hypothesis we propose a 3D deep multi-task CNN to tackle these two problems jointly. We tested our system on LUNA16 dataset and achieved an average dice similarity coefficient (DSC) of 91% as segmentation accuracy and a score of nearly 92% for FP reduction. As a proof of our hypothesis, we showed improvements of segmentation and FP reduction tasks over two baselines. Our results support that joint training of these two tasks through a multi-task learning approach improves system performance on both. We also showed that a semi-supervised approach can be used to overcome the limitation of lack of labeled data for the 3D segmentation task.
Landmark Placement for Localization in a GPS-denied Environment

Path planning algorithms for unmanned aerial or ground vehicles, in many surveillance applications, rely on Global Positioning System (GPS) information for localization. However, disruption of GPS signals, by intention or otherwise, can render these plans and algorithms ineffective. This article provides a way of addressing this issue by utilizing stationary landmarks to aid localization in such GPS-disrupted or GPS-denied environment. In particular, given the vehicle's path, we formulate a landmark-placement problem and present algorithms to place the minimum number of landmarks while satisfying the localization, sensing, and collision-avoidance constraints. The performance of such a placement is also evaluated via extensive simulations on ground robots.
A module-theoretic approach to matroids

Abstract Speyer recognized that matroids encode the same data as a special class of tropical linear spaces and Shaw interpreted tropically certain basic matroid constructions; additionally, Frenk developed the perspective of tropical linear spaces as modules over an idempotent semifield. All together, this provides bridges between the combinatorics of matroids, the algebra of idempotent modules, and the geometry of tropical linear spaces. The goal of this paper is to strengthen and expand these bridges by systematically developing the idempotent module theory of matroids. Applications include a geometric interpretation of strong matroid maps and the factorization theorem; a generalized notion of strong matroid maps, via an embedding of the category of matroids into a category of module homomorphisms; a monotonicity property for the stable sum and stable intersection of tropical linear spaces; a novel perspective of fundamental transversal matroids; and a tropical analogue of reduced row echelon form.
Limitless HTTP in an HTTPS World: Inferring the Semantics of the HTTPS Protocol without Decryption

We present new analytic techniques for inferring HTTP semantics from passive observations of HTTPS that can infer the value of important fields including the status-code, Content-Type, and Server, and the presence or absence of several additional HTTP header fields, e.g., Cookie and Referer. Our goals are to improve the understanding of the confidentiality limitations of HTTPS, and to explore benign uses of traffic analysis that could replace HTTPS interception and static private keys in some scenarios. We found that our techniques increase the efficacy of malware detection, but they do not enable more powerful website fingerprinting attacks against Tor. Our broader set of results raises concerns about the confidentiality goals of TLS relative to a user's expectation of privacy, warranting future research. We apply our methods to the semantics of both HTTP/1.1 and HTTP/2 on data collected from automated runs of Firefox 58.0, Chrome 63.0, and Tor Browser 7.0.11 in a lab setting, and from applications running in a malware sandbox. We obtain ground truth plaintext for a diverse set of applications from the malware sandbox by extracting the key material needed for decryption from RAM post-execution. We developed an iterative approach to simultaneously solve several multi-class (field values) and binary (field presence) classification problems, and we show that our inference algorithm achieves an unweighted $F_1$ score greater than 0.900 for most HTTP fields examined.
The category of singularities as a crystal and global Springer fibers

We prove the "Gluing Conjecture" on the spectral side of the categorical geo- metric Langlands conjecture. The key tool is the structure of crystal on the category of singularities, which allows to reduce the conjecture to the question of homological triviality of certain homotopy types. These homotopy types are obtained by gluing from a global version of Springer fibers.
Randomized block Krylov methods for stronger and faster approximate singular value decomposition

Since being analyzed by Rokhlin, Szlam, and Tygert [1] and popularized by Halko, Martinsson, and Tropp [2], randomized Simultaneous Power Iteration has become the method of choice for approximate singular value decomposition. It is more accurate than simpler sketching algorithms, yet still converges quickly for any matrix, independently of singular value gaps. After O(1/∊) iterations, it gives a low-rank approximation within (1 + ∊) of optimal for spectral norm error. We give the first provable runtime improvement on Simultaneous Iteration: a randomized block Krylov method, closely related to the classic Block Lanczos algorithm, gives the same guarantees in just O(1/√∊) iterations and performs substantially better experimentally. Our analysis is the first of a Krylov subspace method that does not depend on singular value gaps, which are unreliable in practice. Furthermore, while it is a simple accuracy benchmark, even (1 + ∊) error for spectral norm low-rank approximation does not imply that an algorithm returns high quality principal components, a major issue for data applications. We address this problem for the first time by showing that both Block Krylov Iteration and Simultaneous Iteration give nearly optimal PCA for any matrix. This result further justifies their strength over non-iterative sketching methods.
Accelerating PageRank using Partition-Centric Processing

PageRank is a fundamental link analysis algorithm that also functions as a key representative of the performance of Sparse Matrix-Vector (SpMV) multiplication. The traditional PageRank implementation generates fine granularity random memory accesses resulting in large amount of wasteful DRAM traffic and poor bandwidth utilization. In this paper, we present a novel Partition-Centric Processing Methodology (PCPM) to compute PageRank, that drastically reduces the amount of DRAM communication while achieving high sustained memory bandwidth. PCPM uses a Partition-centric abstraction coupled with the Gather-Apply-Scatter (GAS) programming model. By carefully examining how a PCPM based implementation impacts communication characteristics of the algorithm, we propose several system optimizations that improve the execution time substantially. More specifically, we develop (1) a new data layout that significantly reduces communication and random DRAM accesses, and (2) branch avoidance mechanisms to get rid of unpredictable data-dependent branches. We perform detailed analytical and experimental evaluation of our approach using 6 large graphs and demonstrate an average 2.7x speedup in execution time and 1.7x reduction in communication volume, compared to the state-of-the-art. We also show that unlike other GAS based implementations, PCPM is able to further reduce main memory traffic by taking advantage of intelligent node labeling that enhances locality. Although we use PageRank as the target application in this paper, our approach can be applied to generic SpMV computation.
Global-and-local attention networks for visual recognition

Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived "top-down" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than "bottom-up" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.
How to categorify one-half of quantum gl(1|2)

We describe a collection of differential graded rings that categorify weight spaces of the positive half of the quantized universal enveloping algebra of the Lie superalgebra gl(1|2).
Contracting orbits in Outer space

We show that strongly contracting geodesics in Outer space project to parameterized quasigeodesics in the free factor complex. This result provides a converse to a theorem of Bestvina–Feighn, and is used to give conditions for when a subgroup of $${{\,\mathrm{Out}\,}}(\mathbb {F})$$ has a quasi-isometric orbit map into the free factor complex. It also allows one to construct many new examples of strongly contracting geodesics in Outer space.
Whittaker Models for Depth Zero Representations of Covering Groups

We study the dimension of the space of Whittaker functionals for depth zero representations of covering groups. In particular, we determine such dimensions for arbitrary Brylinski-Deligne coverings of the general linear group. The results in the paper are motivated by and compatible with the work of Howard and the second author, and earlier work by Blondel.
Autofocus Layer for Semantic Segmentation

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale-invariant, with only a modest increase in the number of parameters. The proposed autofocus layer can be easily integrated into existing networks to improve a model’s representational power. We evaluate our mod els on the challenging tasks of multi-organ segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very promising performance.
Two-User Erasure Interference Channels With Local Delayed CSIT

We study the capacity region of two-user erasure interference channels with local delayed channel state information at the transmitters. In our model, transmitters have local mismatched outdated knowledge of the channel gains. We propose a transmission strategy that only relies on the delayed knowledge of the outgoing links at each transmitter and achieves the outer bound for the scenario in which transmitters learn the entire channel state with delay. Our result reveals the subset of the channel state information that affects the capacity region the most. We also identify cases in which local delayed knowledge of the channel state does not provide any gain over the zero knowledge assumption. To do so, we revisit a long-known intuition about interference channels that as long as the marginal distributions at the receivers are conserved, the capacity remains the same. We take this intuition and impose a certain spatial correlation among channel gains such that the marginal distributions remain unchanged. Then, we provide an outer bound on the capacity region of the channel with correlation that matches the capacity region when transmitters do not have access to channel state information.
The Second Moment of the Siegel Transform in the Space of Symplectic Lattices

Using results from spectral theory of Eisenstein series, we prove a formula for the second moment of the Siegel transform when averaged over the subspace of symplectic lattices. This generalizes the classical formula of Rogers for the second moment in the full space of unimodular lattices. Using this new formula we give very strong bounds for the discrepancy of the number of lattice points in an Borel set, which hold for generic symplectic lattices.
The Price of Differential Privacy for Online Learning.

We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our results demonstrate that $\epsilon$-differential privacy may be ensured for free -- in particular, the regret bounds scale as $O(\sqrt{T})+\tilde{O}\left(\frac{1}{\epsilon}\right)$. For bandit linear optimization, and as a special case, for non-stochastic multi-armed bandits, the proposed algorithm achieves a regret of $\tilde{O}\left(\frac{1}{\epsilon}\sqrt{T}\right)$, while the previously known best regret bound was $\tilde{O}\left(\frac{1}{\epsilon}T^{\frac{2}{3}}\right)$.
EXPOSE the Line Failures Following a Cyber-Physical Attack on the Power Grid

Recent attacks on power grids demonstrated their vulnerability to cyber and physical attacks. To analyze this vulnerability, we study cyber-physical attacks that affect both the power grid physical infrastructure and its underlying supervisory control and data acquisition system. We assume that an adversary attacks an area by : (i) disconnecting some lines within that area, and (ii) obstructing the information (e.g., status of the lines and voltage measurements) from within the area to reach the control center. We leverage the algebraic properties of the ac power flows to introduce the efficient expose algorithm for detecting line failures and recovering voltages inside that attacked area after such an attack. The expose algorithm outperforms the state-of-the-art algorithm for detecting line failures using partial information under the ac power flow model in terms of scalability and accuracy. The main advantages of the expose algorithm are that its running time is independent of the size of the grid and number of line failures, and that it provides accurate information recovery under some conditions on the attacked area. Moreover, it approximately recovers the information and provides the confidence of the solution when these conditions do not hold.
Inverse design and demonstration of a compact on-chip narrowband three-channel wavelength demultiplexer

In wavelength division multiplexing schemes, splitters must be used to combine and separate different wavelengths. Conventional splitters are fairly large with footprints in hundreds to thousands of square microns, and experimentally demonstrated multimode-interference-based and inverse-designed ultracompact splitters operate with only two channels and large channel spacing (>100 nm). Here we inverse design and experimentally demonstrate a three-channel wavelength demultiplexer with 40 nm spacing (1500, 1540, and 1580 nm) with a footprint of 24.75 μm2. The splitter has a simulated peak insertion loss of −1.55 dB with under −15 dB crosstalk and a measured peak insertion loss of −2.29 dB with under −10.7 dB crosstalk.
Conformal immersions of prescribed mean curvature in R^3

Abstract We prove the existence of (branched) conformal immersions F : S 2 → R 3 with mean curvature H > 0 arbitrarily prescribed up to a 3-dimensional affine indeterminacy. A similar result is proved for the space forms S 3 , H 3 and partial results for surfaces of higher genus.
Concurrent transitions in wear rate and surface microstructure in nanocrystalline Ni-W

Abstract Nanocrystalline metals are promising materials for wear-resistant applications due to their superior strength and hardness, but prior work has shown that cyclic loading can lead to coarsening. In this study, scratch wear tests were carried out on nanocrystalline Ni-19 at.% W films with an as-deposited grain size of 3 nm, with systematic characterization performed after different wear cycles. A new gradient nanograined microstructure is observed and a direct connection between wear rate and subsurface microstructure is discovered. A second Ni-W specimen with the same composition and a 45 nm average grain size is produced by annealing the original specimen. Subsequent wear testing shows that an identical subsurface microstructure is produced in this sample, emphasizing the importance of the cross-over in deformation mechanisms for determining the steady-state grain size during wear.
Adaptive multigroup confidence intervals with constant coverage

SUMMARYCommonly used interval procedures for multigroup data attain their nominal coverage rates across a population of groups on average, but their actual coverage rate for a given group will be above or below the nominal rate, depending on the group mean. While correct coverage for a given group can be achieved with a standard $t$-interval, this approach is not adaptive to the available information about the distribution of group-specific means. In this article we construct confidence intervals that have a constant frequentist coverage rate and that make use of information about across-group heterogeneity, resulting in constant-coverage intervals that are narrower than standard $t$-intervals on average across groups. Such intervals are constructed by inverting biased Bayes-optimal tests for the mean of each group, where the prior distribution for a given group is estimated with data from the other groups.