cssi/SciSciGPT-SciSciNet huggingface dataset:
Multi-scale responses of scattering layers to environmental variability in Monterey Bay, California

Abstract A 38 kHz upward-facing echosounder was deployed on the seafloor at a depth of 875 m in Monterey Bay, CA, USA (36° 42.748′N, 122° 11.214′W) from 27 February 2009 to 18 August 2010. This 18-month record of acoustic backscatter was compared to oceanographic time series from a nearby data buoy to investigate the responses of animals in sound-scattering layers to oceanic variability at seasonal and sub-seasonal time scales. Pelagic animals, as measured by acoustic backscatter, moved higher in the water column and decreased in abundance during spring upwelling, attributed to avoidance of a shoaling oxycline and advection offshore. Seasonal changes were most evident in a non-migrating scattering layer near 500 m depth that disappeared in spring and reappeared in summer, building to a seasonal maximum in fall. At sub-seasonal time scales, similar responses were observed after individual upwelling events, though they were much weaker than the seasonal relationship. Correlations of acoustic backscatter with oceanographic variability also differed with depth. Backscatter in the upper water column decreased immediately following upwelling, then increased approximately 20 days later. Similar correlations existed deeper in the water column, but at increasing lags, suggesting that near-surface productivity propagated down the water column at 10–15 m d −1 , consistent with sinking speeds of marine snow measured in Monterey Bay. Sub-seasonal variability in backscatter was best correlated with sea-surface height, suggesting that passive physical transport was most important at these time scales.
Palladium/copper (I) halide catalyzed stereospecific couplings of 1,2-difluorovinylstannanes with aryl iodides and vinyl halides

Abstract Palladium/copper (I) halide catalyzed cross couplings of (E)- and (Z)-1,2-difluorovinylstannanes with aryl iodides and vinyl halides proceeded at room temperature to stereospecifically give 1,2-disubstituted-1,2-difluoroolefins and 1,2-difluoro or 1,2,3,4-tetrafluorosubstituted dienes in good to excellent yields.
Could a Neuroscientist Understand a Microprocessor

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.
Intramolecular 1,4-addition of nitrogen nucleophile and bromine electrophile to conjugated 1,3-enyne

Abstract We have discovered a series of N-bromosuccinimide-promoted 1,4-bromocyclization reactions for conjugated 1,3-enynes. Various nitrogen nucleophiles could be added to conjugated enynes to generate bromoallenyl substituted nitrogen heterocycles. These processes led to simultaneous formation of a highly functionalized axially chiral allene and a stereogenic center under mild conditions.
Liquid Metallic Hydrogen II. A Critical Assessment of Current and Primordial Helium Levels in the Sun

Before a solar model becomes viable in astrophysics, one must consider how the elemental constitution of the Sun was ascertained, especially relative to its principle components: hydrogen and helium. Liquid metallic hydrogen has been proposed as a solar structural material for models based on condensed matter (e.g. Robitaille P.-M. Liquid Metallic Hydrogen: A Building Block for the Liquid Sun. Progr. Phys., 2011, v. 3, 60‐74). There can be little doubt that hydrogen plays a dominant role in the universe and in the stars; the massive abundance of hydrogen in the Sun was established long ago. Today, it can be demonstrated that the near isointense nature of the Sun’s Balmer lines provides strong confirmatory evidence for a dis tinct solar surface. The situation relative to helium remains less conclusive. Stil l, helium occupies a prominent role in astronomy, both as an element associated with cosmology and as a byproduct of nuclear energy generation, though its abundances within the Sun cannot be reliably estimated using theoretical approaches. With respect to the determination of helium levels, the element remains spectroscopically silent at the le vel of the photosphere. While helium can be monitored with ease in the chromosphere and the prominences of the corona using spectroscopic methods, these measures are highly variable and responsive to elevated solar activity and nuclear fragmentation. Dire ct assays of the solar winds are currently viewed as incapable of providing definitive in formation regarding solar helium abundances. As a result, insight relative to helium remains strictly based on theoretical estimates which couple helioseismological approaches to metrics derived from solar models. Despite their “state of the art” nature, heliu m estimates based on solar models and helioseismology are suspect on several fronts, including their reliance on solar opacities. The best knowledge can only come from the solar winds which, though highly variable, provide a wealth of data. Evaluations of primordial helium levels based on 1) the spectroscopic study of H-II regions and 2) microwave anisotropy data, remain highly questionable. Current helium levels, both within the stars (Robitaille J. C. and Robitaille P.-M. Liquid Metallic Hydrogen III. Intercalation and Lattice Exclusion versus Gravitational Settling, and Their Consequences Relative to Internal Structure, Surface Activity, and Solar Winds in the Sun. Progr. Phys., 2013, v. 2, in press) and the universe at large, appear to be overstated. A careful consideration of available observational data suggests that helium abundances are considerably lower than currently believed.
Adaptive feature detection from differential processing in parallel retinal pathways.

To transmit information efficiently in a changing environment, the retina adapts to visual contrast by adjusting its gain, latency and mean response. Additionally, the temporal frequency selectivity, or bandwidth changes to encode the absolute intensity when the stimulus environment is noisy, and intensity differences when noise is low. We show that the On pathway of On-Off retinal amacrine and ganglion cells is required to change temporal bandwidth but not other adaptive properties. This remarkably specific adaptive mechanism arises from differential effects of contrast on the On and Off pathways. We analyzed a biophysical model fit only to a cell’s membrane potential, and verified pharmacologically that it accurately revealed the two pathways. We conclude that changes in bandwidth arise mostly from differences in synaptic threshold in the two pathways, rather than synaptic release dynamics as has previously been proposed to underlie contrast adaptation. Different efficient codes are selected by different thresholds in two independently adapting neural pathways.
Low temperature synthesis of transition metal sulfides

The various techniques for the low temperature synthesis of transition metal sulfides are discussed. The advantages of methods using precursors which already contain metal sulfur linkages are treated, and the properties of the sulfides produced are compared with those prepared by high temperature methods.
An inhibitor of apoptosis (SfIAP) interacts with SQUAMOSA promoter-binding protein (SBP) transcription factors that exhibit pro-cell death characteristics.

Despite the importance of proper cell death regulation across broad evolutionary distances, an understanding of the molecular machinery underpinning this fundamental process in plants remains largely elusive. This is despite its critical importance to development, homeostasis, and proper responses to stress. The identification of endogenous plant regulators of cell death has been hindered by the fact that many core regulators of cell death in animals are absent in plant genomes. Remarkably, numerous studies have shown that the ectopic expression of animal prosurvival genes in plants can suppress cell death imposed by many stresses. In this study, we capitalize on the ectopic expression of one of these animal prosurvival genes, an inhibitor of apoptosis from Spodoptera frugiperda (SfIAP), to identify novel cell death regulators in plants. A yeast two-hybrid assay was conducted using SfIAP as bait to screen a tomato cDNA library. This screen identified several transcription factors of the SQUAMOSA promoter-binding protein (SBP) family as potential SfIAP binding partners. We confirmed this interaction in vivo for our top two interactors, SlySBP8b and SlySBP12a, using coimmunoprecipitation. Interestingly, overexpression of SlySBP8b and SlySBP12a induced cell death in Nicotiana benthamiana leaves. Overexpression of these two transcription factors also induced the accumulation of reactive oxygen species and enhanced the growth of the necrotrophic pathogen Alternaria alternata. Fluorescence microscopy confirmed the nuclear localization of both SlySBP8b and SlySBP12a, while SlySBP12a was also localized to the ER membrane. These results suggest a prodeath role for SlySBP8b and SlySBP12a and implicate ER membrane tethering as a means of regulating SlySBP12a activity.
Asymmetric Synthesis of α-Amino 1,3-Dithioketals from Sulfinimines (N-Sulfinyl Imines). Synthesis of (2S,3R)-(−)-3-Hydroxy-3-methylproline

N-Sulfinyl α-amino 1,3-dithioketals are prepared in high de and good yield by treating sulfinimines with lithio-1,3-dithianes. Selective removal of the N-sulfinyl or the thioketal groups affords stable α-amino 1,3-dithioketals and N-sulfinyl α-amino ketones, respectively. This new sulfinimine-derived chiral building block is employed in the asymmetric synthesis of polyoxypeptin amino acid (2S,3R)-(−)-3-hydroxy-3-methylproline.
Synthesis of two hyaluronan trisaccharides

The synthesis of two hyaluronan trisaccharides, methyl O-(β-d-glucopyranosyluronic acid)-(1,3)-O-(2-acetamido-2-deoxy-β-d-glucopyranosyl)-(1,4)-O-β-d-glucopyranosiduronic acid and methyl O-(2-acetamido-2-deoxy-β-d-glucopyranosyl)-(1,4)-O-β-d-glycopyranosyluronic acid)-(1,3)-O-(2-acetamido-2-deoxy-β-d-glucopyranoside, are described. Construction of the target molecules was achieved though a combination of the phenyl sulfoxide and trichloroacetimidate glycosylation methodologies. This is the first report on the synthesis of the β-methyl derivatives, which represent the smallest fragments that incorporate all the structural features of polymeric hyaluronan.
Explicit Control of Step Timing During Split-Belt Walking Reveals Interdependent Recalibration of Movements in Space and Time.

Split-belt treadmills that move the legs at different speeds are thought to update internal representations of the environment, such that this novel condition generates a new locomotor pattern with distinct spatio-temporal features compared to those of regular walking. It is unclear the degree to which such recalibration of movements in the spatial and temporal domains is interdependent. In this study, we explicitly altered subjects’ limb motion in either space or time during split-belt walking to determine its impact on the adaptation of the other domain. Interestingly, we observed that motor adaptation in the spatial domain was susceptible to altering the temporal domain, whereas motor adaptation in the temporal domain was resilient to modifying the spatial domain. This nonreciprocal relation suggests a hierarchical organization such that the control of timing in locomotion has an effect on the control of limb position. This is of translational interest because clinical populations often have a greater deficit in one domain compared to 14 the other. Our results suggest that explicit changes to temporal deficits cannot occur without modifying the spatial control of the limb.
Synthesis of a novel ring‐expanded nucleoside analogue containing the imidazo[4,5‐e][1,3]diazepine ring system with a guanidinocarbamoyl‐substituted cyclopropylidene group in place of a sugar moiety

The synthesis of a novel ring-expanded nucleoside analogue, (Z)-1-((2-Guanidinocarbamoyl-cyclopropylidene)methyl)-4,5,7,8-tetrahydro-6H-6-iminoimidazo[4,5-e][1,3]diazepine-4,8-dione (1) has been reported. It was prepared starting from methyl imidazole-4,5-dicarboxylate by sequential condensations with 2-bromo-2-bromomethylcyclopropane-1-carboxylate and guanidine. The overall yield for the two-step synthesis is 46%.
An Asymmetric Aminohydroxylation Approach to the Azepine Core of (−)-Balanol

An efficient formal synthesis of the potent protein kinase C inhibitor (−)-balanol that relies on a modified asymmetric aminohydroxylation of the α,β-unsaturated aryl ester (1) is reported. The aryl ester functionality and the dihydroquinyl alkaloid ligand system (DHQ)2−AQN are used to control the regio- and enantioselectivity of the process.
The chemistry of substituted pyrazolidinones; applications to the synthesis of bicyclic derivatives

Abstract Methodology for the selective chemical derivatizations of substituted pyrazolidinones is described. The application of these methods to the preparation of [4.3.0] and [3.3.0] bicyclic systems is also discussed. The importance of these latter systems as nuclei of antibacterial agents with potential utility in the treatment of infectious disease provides the motivation for these investigations.
Light thoughts on dark energy

Abstract The physical process leading to the acceleration of the expansion of the universe is unknown. It may involve new high energy physics or extensions to gravitation. Calling this generically dark energy, we examine the consistencies and relations between these two approaches, showing that an effective equation of state function w ( z ) is broadly useful in describing the properties of the dark energy. A variety of cosmological observations can provide important information on the dynamics of dark energy and the future looks bright for constraining dark energy, though both the measurements and the interpretation will be challenging. We also discuss a more direct relation between the spacetime geometry and acceleration, via “geometric dark energy” from the Ricci scalar, and superacceleration or phantom energy where the fate of the universe may be more gentle than the Big Rip.
2-Methyl-2-propanol as solvent for o-iodoxybenzoic acid (IBX) oxidation of 1˚ alcohols to aldehydes

Abstract 2-Methyl-2-propanol has been demonstrated to be an effective solvent for IBX oxidation of 1˚ alcohols to the corresponding aldehydes. IBX has a measurable, but low, solubility in 2-methyl-2-propanol and thus partially avoids the drawbacks inherent in the solubility problems associated with IBX without resorting to less desirable solvents. This may be particularly useful where solubility of the alcohol is also at issue.
Lithium niobate piezo-optomechanical crystals

Demonstrating a device that efficiently connects light, motion, and microwaves is an outstanding challenge in classical and quantum photonics. We make significant progress in this direction by demonstrating a photonic crystal resonator on thin-film lithium niobate (LN) that simultaneously supports high-Q optical and mechanical modes, and where the mechanical modes are coupled piezoelectrically to microwaves. For optomechanical coupling, we leverage the photoelastic effect in LN by optimizing the device parameters to realize coupling rates g0/2π≈120 kHz. An optomechanical cooperativity C>1 is achieved leading to phonon lasing. Electrodes on the nanoresonator piezoelectrically drive mechanical waves on the beam that are then read out optically allowing direct observation of the phononic bandgap. Quantum coupling efficiency of η≈10−8 from the input microwave port to the localized mechanical resonance is measured. Improvements of the microwave circuit and electrode geometry can increase this efficiency and bring integrated ultra-low-power modulators and quantum microwave-to-optical converters closer to reality.
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions

This paper studies classification with an abstention option in the online setting. In this setting, examples arrive sequentially, the learner is given a hypothesis class H, and the goal of the learner is to either predict a label on each example or abstain, while ensuring that it does not make more than a pre-specified number of mistakes when it does predict a label. Previous work on this problem has left open two main challenges. First, not much is known about the optimality of algorithms, and in particular, about what an optimal algorithmic strategy is for any individual hypothesis class. Second, while the realizable case has been studied, the more realistic non-realizable scenario is not well-understood. In this paper, we address both challenges. First, we provide a novel measure, called the Extended Littlestone’s Dimension, which captures the number of abstentions needed to ensure a certain number of mistakes. Second, we explore the non-realizable case, and provide upper and lower bounds on the number of abstentions required by an algorithm to guarantee a specified number of mistakes.
High-dimensional stochastic optimal control using continuous tensor decompositions

Motion planning and control problems are embedded and essential in almost all robotics applications. These problems are often formulated as stochastic optimal control problems and solved using dynamic programming algorithms. Unfortunately, most existing algorithms that guarantee convergence to optimal solutions suffer from the curse of dimensionality: the run time of the algorithm grows exponentially with the dimension of the state space of the system. We propose novel dynamic programming algorithms that alleviate the curse of dimensionality in problems that exhibit certain low-rank structure. The proposed algorithms are based on continuous tensor decompositions recently developed by the authors. Essentially, the algorithms represent high-dimensional functions e.g. the value function in a compressed format, and directly perform dynamic programming computations e.g. value iteration, policy iteration in this format. Under certain technical assumptions, the new algorithms guarantee convergence towards optimal solutions with arbitrary precision. Furthermore, the run times of the new algorithms scale polynomially with the state dimension and polynomially with the ranks of the value function. This approach realizes substantial computational savings in źcompressibleź problem instances, where value functions admit low-rank approximations. We demonstrate the new algorithms in a wide range of problems, including a simulated six-dimensional agile quadcopter maneuvering example and a seven-dimensional aircraft perching example. In some of these examples, we estimate computational savings of up to 10 orders of magnitude over standard value iteration algorithms. We further demonstrate the algorithms running in real time on board a quadcopter during a flight experiment under motion capture.
Anisotropic magnetic properties of the triangular plane lattice material TmMgGaO4

Abstract The crystal growth, structure, and basic magnetic properties of TmMgGaO 4 are reported. The Tm ions are located in a planar triangular lattice consisting of distorted TmO 6 octahedra, while the Mg and Ga atoms randomly occupy intermediary bilayers of M-O triangular bipyramids. The Tm ions are positionally disordered. The material displays an antiferromagnetic Curie Weiss theta of ∼−20 −25 K, with no clear ordering visible in the magnetic susceptibility down to 1.8 K; the structure and magnetic properties suggest that ordering of the magnetic moments is frustrated by both structural disorder and the triangular magnetic motif. Single crystal magnetization measurements indicate that the magnetic properties are highly anisotropic, with large moments measured perpendicular to the triangular planes. At 2 K, a broad step-like feature is seen in the field-dependent magnetization perpendicular to the plane on applied field near 2 T.
A simple and strongly-local flow-based method for cut improvement

Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We introduce and analyze a new method for locally-biased graph-based learning called SimpleLocal, which finds good conductance cuts near a set of seed vertices. An important feature of our algorithm is that it is strongly-local, meaning it does not need to explore the entire graph to find cuts that are locally optimal. This method solves the same objective as existing strongly-local flow-based methods, but it enables a simple implementation. We also show how it achieves localization through an implicit l1-norm penalty term. As a flow-based method, our algorithm exhibits several advantages in terms of cut optimality and accurate identification of target regions in a graph. We demonstrate the power of SimpleLocal by solving problems on a 467 million edge graph based on an MRI scan.
Nodal sets of Laplace eigenfunctions: polynomial upper estimates of the Hausdorff measure

Let $\mathbb{M}$ be a compact $C^\infty$-smooth Riemannian manifold of dimension $n$, $n\geq 3$, and let $\varphi_\lambda: \Delta_M \varphi_\lambda + \lambda \varphi_\lambda = 0$ denote the Laplace eigenfunction on $\mathbb{M}$ corresponding to the eigenvalue $\lambda$. We show that $$H^{n-1}(\{ \varphi_\lambda=0\}) \leq C \lambda^{\alpha},$$ where $\alpha>1/2$ is a constant, which depends on $n$ only, and $C>0$ depends on $\mathbb{M}$ . This result is a consequence of our study of zero sets of harmonic functions on $C^\infty$-smooth Riemannian manifolds. We develop a technique of propagation of smallness for solutions of elliptic PDE that allows us to obtain local bounds from above for the volume of the nodal sets in terms of the frequency and the doubling index.
DKVF: a framework for rapid prototyping and evaluating distributed key-value stores

We present our framework DKVF that enables one to quickly prototype and evaluate new consistency protocols for key-value stores. DKVF is designed based on the separation of concerns in creating distributed data stores. This separation of concerns allows the designers of consistency protocols to only focus on the high-level consistency protocols which gives them the opportunity to quickly deploy a consistency protocol and evaluate its performance. Moreover, the loose coupling of the different components allows us to easily change different components (e.g. storage engine) of an implementation. We demonstrate DKVF by implementing four existing protocols --eventual consistency, COPS, GentleRain and CausalSpartan-- with it. The implementation of these protocols was very convenient with DKVF, as it only required to write a piece of code for the consistency component that is very close to the pseudocode of the original papers. Hence, it was possible to achieve this in just 1-2 days per protocol. DKVF also comes with a toolset that facilitates running clusters and performing experiments. Tutorial video: https://www.youtube.com/watch?v=MFJQzsJkwfc&list=PLErtSVEHsnBJvoQQI6iqGn61oNrUVVuST
On Riemannian submersions

We prove that the image of a real analytic Riemannian manifold under a smooth Riemannian submersion is necessarily real analytic.
Outside Insiders: Understanding the Role of Contracting in the Careers of Managerial Workers

We explore the role that contracting plays within the careers of managerial workers. Contracting distances workers from organizational coordination and politics, aspects of organizational life that...
Lipschitz regularity of solutions to two-phase free boundary problems

We prove Lipschitz continuity of viscosity solutions to a class of two-phase free boundary problems governed by fully nonlinear operators.
Characters of independent Stanley sequences

Abstract Odlyzko and Stanley introduced a greedy algorithm for constructing infinite sequences with no 3-term arithmetic progressions when beginning with a finite set with no 3-term arithmetic progressions. The sequences constructed from this procedure are known as Stanley sequences and appear to have two distinct growth rates which dictate whether the sequences are structured or chaotic. A large subclass of sequences of the former type is independent sequences, which have a self-similar structure. An attribute of interest for independent sequences is the character. In this paper, building on recent progress, we prove that every nonnegative integer λ ∉ { 1 , 3 , 5 , 9 , 11 , 15 } is attainable as the character of an independent Stanley sequence, thus resolving a conjecture of Rolnick.
Nonabelian Fourier transforms for spherical representations

Braverman and Kahzdan have introduced an influential conjecture on local functional equations for general Langlands $L$-functions. It is related to L. Lafforgue's equally influential conjectural construction of kernels for functorial transfers. We formulate and prove a version of Braverman and Kazhdan's conjecture for spherical representations over an archimedean field that is suitable for application to the trace formula. We then give a global application related to Langlands' beyond endoscopy proposal. It is motivated by Ngo's suggestion that one combine nonabelian Fourier transforms with the trace formula in order to prove the functional equations of Langlands $L$-functions in general.
Graph autoencoder-based unsupervised feature selection with broad and local data structure preservation

Abstract Feature selection is a dimensionality reduction technique that selects a subset of representative features from high-dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method.
Silicon photonic crystal cavity enhanced second-harmonic generation from monolayer WSe 2

We demonstrate a silicon photonic crystal cavity enhanced second-harmonic generation (SHG) in tungsten diselenide. The observed SHG is enhanced by a factor of ∼200 compared to a bare monolayer on silicon.
Convergence to SPDE of the Schrödinger equation with large, random potential

We study the asymptotic behavior of solutions to the Schr{\"o}dinger equation with large-amplitude, highly oscillatory, random potential. In dimension $d \mathfrak{m}$, the solution to the Schr{\"o}dinger equation is shown to converge in $L^2(\Omega\times\mathbb{R}^d)$ to a deterministic Schr{\"o}dinger solution in \cite{ZB-12}.
Behaviour of Entropy Under Bounded and Integrable Orbit Equivalence

Let G and H be infinite finitely generated amenable groups. This paper studies two notions of equivalence between actions of such groups on standard Borel probability spaces. They are defined as stable orbit equivalences in which the associated cocycles satisfy certain tail bounds. In ‘integrable stable orbit equivalence’, the length in H of the cocycle-image of an element of G must have finite integral over its domain (a subset of the G-system), and similarly for the reverse cocycle. In ‘bounded stable orbit equivalence’, these functions must be essentially bounded in terms of the length in G. ‘Integrable’ stable orbit equivalence arises naturally in the study of integrable measure equivalence of groups themselves, as introduced recently by Bader, Furman and Sauer. The main result is a formula relating the Kolmogorov–Sinai entropies of two actions which are equivalent in one of these ways. Under either of these tail assumptions, the entropies stand in a proportion given by the compression constant of the stable orbit equivalence. In particular, in the case of full orbit equivalence subject to such a tail bound, entropy is an invariant. This contrasts with the case of unrestricted orbit equivalence, under which all free ergodic actions of countable amenable groups are equivalent. The proof uses an entropy-bound based on graphings for orbit equivalence relations, and in particular on a new notion of cost which is weighted by the word lengths of group elements.
Linear-complexity relaxed word Mover's distance with GPU acceleration

The amount of unstructured text-based data is growing every day. Querying, clustering, and classifying this big data requires similarity computations across large sets of documents. Whereas low-complexity similarity metrics are available, attention has been shifting towards more complex methods that achieve a higher accuracy. In particular, the Word Mover's Distance (WMD) method proposed by Kusner et al. is a promising new approach, but its time complexity grows cubically with the number of unique words in the documents. The Relaxed Word Mover's Distance (RWMD) method, again proposed by Kusner et al., reduces the time complexity from qubic to quadratic and results in a limited loss in accuracy compared with WMD. Our work contributes a low-complexity implementation of the RWMD that reduces the average time complexity to linear when operating on large sets of documents. Our linear-complexity RWMD implementation, henceforth referred to as LC-RWMD, maps well onto GPUs and can be efficiently distributed across a cluster of GPUs. Our experiments on real-life datasets demonstrate 1) a performance improvement of two orders of magnitude with respect to our GPU-based distributed implementation of the quadratic RWMD, and 2) a performance improvement of three to four orders of magnitude with respect to our distributed WMD implementation that uses GPU-based RWMD for pruning.
On Non-localization of Eigenvectors of High Girth Graphs

We prove improved bounds on how localized an eigenvector of a high girth regular graph can be, and present examples showing that these bounds are close to sharp. This study was initiated by Brooks and Lindenstrauss (2009) who relied on the observation that certain suitably normalized averaging operators on high girth graphs are hyper-contractive and can be used to approximate projectors onto the eigenspaces of such graphs. Informally, their delocalization result in the contrapositive states that for any $\varepsilon \in (0,1)$ and positive integer $k,$ if a $(d+1)-$regular graph has an eigenvector which supports $\varepsilon$ fraction of the $\ell_2^2$ mass on a subset of $k$ vertices, then the graph must have a cycle of size $\tilde{O}(\log_{d}(k)/\varepsilon^2)$, suppressing logarithmic terms in $1/\varepsilon$. In this paper, we improve the upper bound to $\tilde{O}(\log_{d}(k)/\varepsilon)$ and present a construction showing a lower bound of $\Omega(\log_d(k)/\varepsilon)$. Our construction is probabilistic and involves gluing together a pair of trees while maintaining high girth as well as control on the eigenvectors and could be of independent interest.
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

We study instancewise feature importance scoring as a method for model interpretation. Any such method yields, for each predicted instance, a vector of importance scores associated with the feature vector. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions of this kind, but incur an exponential complexity in the number of features. This combinatorial explosion arises from the definition of the Shapley value and prevents these methods from being scalable to large data sets and complex models. We focus on settings in which the data have a graph structure, and the contribution of features to the target variable is well-approximated by a graph-structured factorization. In such settings, we develop two algorithms with linear complexity for instancewise feature importance scoring. We establish the relationship of our methods to the Shapley value and another closely related concept known as the Myerson value from cooperative game theory. We demonstrate on both language and image data that our algorithms compare favorably with other methods for model interpretation.
Improving the scalability of optimal Bayesian network learning with external-memory frontier breadth-first branch and bound search

Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memory-efficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadth-first branch and bound search that leverages the layered structure of the search graph of this problem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithm stores most of the graph in external memory, such as hard disk, when it does not fit in RAM. Experimental results show that the resulting algorithm solves significantly larger problems than the current state of the art.
THE UNIVERSAL CHARACTER RING OF THE (-2, 2m + 1, 2n)-PRETZEL LINK

We explicitly calculate the universal character ring of the (-2, 2m + 1, 2n)-pretzel link and show that it is reduced for all integers m and n.
GP-SUM. Gaussian Process Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted Sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions.
Power of ordered hypothesis testing

Ordered testing procedures are multiple testing procedures that exploit a pre-specified ordering of the null hypotheses, from most to least promising. We analyze and compare the power of several recent proposals using the asymptotic framework of Li & Barber (2015). While accumulation tests including ForwardStop can be quite powerful when the ordering is very informative, they are asymptotically powerless when the ordering is weaker. By contrast, Selective SeqStep, proposed by Barber & Candes (2015), is much less sensitive to the quality of the ordering. We compare the power of these procedures in different regimes, concluding that Selective SeqStep dominates accumulation tests if either the ordering is weak or non-null hypotheses are sparse or weak. Motivated by our asymptotic analysis, we derive an improved version of Selective SeqStep which we call Adaptive SeqStep, analogous to Storey's improvement on the Benjamini-Hochberg procedure. We compare these methods using the GEO-Query data set analyzed by (Li & Barber, 2015) and find Adaptive SeqStep has favorable performance for both good and bad prior orderings.
Source-LDA: Enhancing Probabilistic Topic Models Using Prior Knowledge Sources

Topic modeling has increasingly attracted interests from researchers. Common methods of topic modeling usually produce a collection of unlabeled topics where each topic is depicted by a distribution of words. Associating semantic meaning with these word distributions is not always straightforward. Traditionally, this task is left to human interpretation. Manually labeling the topics is unfortunately not always easy, as topics generated by unsupervised learning methods do not necessarily align well with our prior knowledge in the subject domains. Currently, two approaches to solve this issue exist. The first is a post-processing procedure that assigns each topic with a label from the prior knowledge base that is semantically closest to the word distribution of the topic. The second is a supervised topic modeling approach that restricts the topics to a predefined set whose word distributions are provided beforehand. Neither approach is ideal, as the former may produce labels that do not accurately describe the word distributions, and the latter lacks the ability to detect unknown topics that are crucial to enrich our knowledge base. Our goal in this paper is to introduce a semisupervised Latent Dirichlet allocation (LDA) model, Source-LDA, which incorporates prior knowledge to guide the topic modeling process to improve both the quality of the resulting topics and of the topic labeling. We accomplish this by integrating existing labeled knowledge sources representing known potential topics into a probabilistic topic model. These knowledge sources are translated into a distribution and used to set the hyperparameters of the Dirichlet generated distribution over words. This approach ensures that the topic inference process is consistent with existing knowledge, and simultaneously, allows for discovery of new topics. The results show improved topic generation and increased accuracy in topic labeling when compared to those obtained using various labeling approaches based off LDA.
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients

Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more "legitimate," interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural networks.
Decomposing Treatment Effect Variation

Understanding and characterizing treatment effect variation in randomized experiments has become essential for going beyond the “black box” of the average treatment effect. Nonetheless, traditional...
Dirac-Maxwell correspondence: Spin-1 bosonic topological insulator

We introduce photonic Dirac monopoles and strings, proving the existence of a spin-1 bosonic topological phase for light. Fundamentally different from pseudo-spin-1/2 based photonic crystals, we discover quantized spin in symmetry protected helical edge states of continuous media.
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction

We consider the problem of approximately reconstructing a partially-observed, approximately low-rank matrix. This problem has received much attention lately, mostly using the trace-norm as a surrogate to the rank. Here we study low-rank matrix reconstruction using both the trace-norm, as well as the less-studied max-norm, and present reconstruction guarantees based on existing analysis on the Rademacher complexity of the unit balls of these norms. We show how these are superior in several ways to recently published guarantees based on specialized analysis.
Far-Field Compression for Fast Kernel Summation Methods in High Dimensions

Abstract We consider fast kernel summations in high dimensions: given a large set of points in d dimensions (with d ≫ 3 ) and a pair-potential function (the kernel function), we compute a weighted sum of all pairwise kernel interactions for each point in the set. Direct summation is equivalent to a (dense) matrix–vector multiplication and scales quadratically with the number of points. Fast kernel summation algorithms reduce this cost to log-linear or linear complexity. Treecodes and Fast Multipole Methods (FMMs) deliver tremendous speedups by constructing approximate representations of interactions of points that are far from each other. In algebraic terms, these representations correspond to low-rank approximations of blocks of the overall interaction matrix. Existing approaches require an excessive number of kernel evaluations with increasing d and number of points in the dataset. To address this issue, we use a randomized algebraic approach in which we first sample the rows of a block and then construct its approximate, low-rank interpolative decomposition. We examine the feasibility of this approach theoretically and experimentally. We provide a new theoretical result showing a tighter bound on the reconstruction error from uniformly sampling rows than the existing state-of-the-art. We demonstrate that our sampling approach is competitive with existing (but prohibitively expensive) methods from the literature. We also construct kernel matrices for the Laplacian, Gaussian, and polynomial kernels—all commonly used in physics and data analysis. We explore the numerical properties of blocks of these matrices, and show that they are amenable to our approach. Depending on the data set, our randomized algorithm can successfully compute low rank approximations in high dimensions. We report results for data sets with ambient dimensions from four to 1,000.
Almost flat K-theory of classifying spaces

We give a rigorous account and prove continuity properties for the correspondence between almost at bundles on a triangularizable compact connected space and the quasi-representations of its fundamental group. For a discrete countable group with nite classifying space B, we study a correspondence between between almost at K-theory classes on B and group homomorphism K0(C ()) ! Z that are implemented by pairs of discrete asymptotic homomorphisms from C () to matrix algebras.
A Review on Applications of Two-Dimensional Materials in Surface-Enhanced Raman Spectroscopy

Two-dimensional (2D) materials, such as graphene and MoS2, have been attracting wide interest in surface enhancement Raman spectroscopy. This perspective gives an overview of recent developments in 2D materials’ application in surface-enhanced Raman spectroscopy. This review paper focuses on the applications of using bare 2D materials and metal/2D material hybrid substrate for Raman enhancement. The Raman enhancing mechanism of 2D materials will also be discussed. The progress covered herein shows great promise for widespread adoption of 2D materials in SERS application.
Kolmogorov Complexity of Categories

Kolmogorov complexity theory is used to tell what the algorithmic informational content of a string is. It is defined as the length of the shortest program that describes the string. We present a programming language that can be used to describe categories, functors, and natural transformations. With this in hand, we define the informational content of these categorical structures as the shortest program that describes such structures. Some basic consequences of our definition are presented including the fact that equivalent categories have equal Kolmogorov complexity. We also prove different theorems about what can and cannot be described by our programming language.
Efficient Construction of Probabilistic Tree Embeddings.

In this paper we describe an algorithm that embeds a graph metric $(V,d_G)$ on an undirected weighted graph $G=(V,E)$ into a distribution of tree metrics $(T,D_T)$ such that for every pair $u,v\in V$, $d_G(u,v)\leq d_T(u,v)$ and ${\bf{E}}_{T}[d_T(u,v)]\leq O(\log n)\cdot d_G(u,v)$. Such embeddings have proved highly useful in designing fast approximation algorithms, as many hard problems on graphs are easy to solve on tree instances. For a graph with $n$ vertices and $m$ edges, our algorithm runs in $O(m\log n)$ time with high probability, which improves the previous upper bound of $O(m\log^3 n)$ shown by Mendel et al.\,in 2009. The key component of our algorithm is a new approximate single-source shortest-path algorithm, which implements the priority queue with a new data structure, the "bucket-tree structure". The algorithm has three properties: it only requires linear time in the number of edges in the input graph; the computed distances have a distance preserving property; and when computing the shortest-paths to the $k$-nearest vertices from the source, it only requires to visit these vertices and their edge lists. These properties are essential to guarantee the correctness and the stated time bound. Using this shortest-path algorithm, we show how to generate an intermediate structure, the approximate dominance sequences of the input graph, in $O(m \log n)$ time, and further propose a simple yet efficient algorithm to converted this sequence to a tree embedding in $O(n\log n)$ time, both with high probability. Combining the three subroutines gives the stated time bound of the algorithm. Then we show that this efficient construction can facilitate some applications. We proved that FRT trees (the generated tree embedding) are Ramsey partitions with asymptotically tight bound, so the construction of a series of distance oracles can be accelerated.