cssi/SciSciGPT-SciSciNet huggingface dataset:
En route to complete design of heterogeneous catalysts

Achieving complete control in the synthesis of the active center of a heterogeneous catalyst, which includes the active site, environment around the active site, and the access path is highly desirable but not attainable yet. However, there has been significant progress in recent years that permits a level of control of these properties that are unachievable before. Some illustrative examples are described to demonstrate this in the synthesis of active sites in metal and oxide catalysts and their surroundings. For the active site synthesis, examples include using polynuclear metal complexes as precursors, cluster beam deposition, and use of dendrimers to generate metallic active sites, protection of coordination unsaturation in the synthesis of oxide active sites, and employing silsesquioxane, and unit-by-unit approach. Exfoliation, use of inverse micelles, molecular and macromolecular templating as techniques to exert control of the environment of the active center are also discussed. The limitations of these synthetic methods and remaining challenges are examined.
TOTAL SYNTHESIS OF THE CYCLOPEPTIDE ALKALOID SANJOININE G1 AND ITS C-11 EPIMER

Abstract The naturally occurring cyclopeptide alkaloid sanjoinine G1 and its C-11 epimer were synthesized in 18 steps from D -serine. The key steps in the synthesis were the formation of the alkylaryl ether linkage via an SNAr reaction with 4-fluorobenzonitrile and the macrocyclization to form the 14-membered ring using a modification of the Schmidt protocol involving an activated pentafluorophenyl ester.
The 1,1-Dioxobenzo[b]thiophene-2-ylmethyloxycarbonyl (Bsmoc)† Amino-Protecting Group

Full details are presented for use of the Bsmoc amino-protecting group for both solid phase and rapid continuous solution syntheses. Application to the latter methodology represents a significant improvement over the corresponding Fmoc-based method for rapid solution synthesis due to the opportunity to use water or saturated sodium chloride solution rather than an acidic phosphate buffer to remove all byproducts, with consequent cleaner phase separation and higher yields of the growing peptide. Comparison of the Bsmoc and Bspoc functions showed that the former, because of steric hindrance, does not suffer from the competitive or premature deblocking observed with the Bspoc system. Because of its incorporation of a styrene chromophore, resin loading of Bsmoc amino acids could be followed as has previously been shown for the Fmoc analogues. Applications of Bsmoc chemistry to peptide sequences incorporating the base sensitive Asp-Gly unit gave less contamination due to aminosuccinimide formation than compara...
One-pot, three-component, domino Heck-aza-Michael approach to libraries of functionalized 1,1-dioxido-1,2-benzisothiazoline-3-acetic acids.

A sequential three-component synthesis of functionalized benzisothiazoline-3-acetic acid 1,1-dioxides utilizing a domino Heck-aza-Michael pathway is reported. This one-pot procedure rapidly assembles functionalized benzylsulfonamides, which undergo a palladium-catalyzed, domino, Heck-aza-Michael transformation in an experimentally straightforward manner. This attractive protocol has been utilized to synthesize three combinatorial sublibraries (I−III) comprising a total of 95 compounds in high purities (≥95% for 75 compounds), yield and quantities.
New oxidative demetalation protocol for molybdenum π-complexes: Enantiocontrolled synthesis of unsaturated ketones and lactones

An efficient and general oxidative demetalation of (η3-allyl)molybdenum complexes using pyridinium dichromate allows the introduction of a carbonyl group at an allylic terminus of the π-system. The...
Resolvent Analysis for Turbulent Channel Flow with Riblets

This paper extends the resolvent formalism for wall turbulence to account for the effect of streamwise-constant riblets. Under the resolvent formulation, the Navier–Stokes equations are interpreted...
Open source adoption strategy

Abstract Open innovation in the form of open-source software (OSS) has been a transformative force in the software industry and beyond. The growth of open source has created new ways to develop, distribute and adopt software in organizations. Despite the associated impressive growth of open source research, a rigorous analytical examination of open-source adoption in organizations constitutes a gap in the literature. This article fills this gap by providing insights toward a comprehensive open-source strategy. It develops a game-theoretic analytical model to explain when organizations adopt open source software applications and platforms, and what the implications are. The analysis characterizes conditions under which organizations adopt open source software, and examines whether these adoption patterns are socially beneficial. The article shows that open-source adoption depends crucially on organizational IT capabilities, network effects, and the fit of OSS with the organizations' application needs. The model predicts that firms may sometimes adopt a heterogeneous IT architecture that consists of open source and proprietary software. Moreover, the results suggest that open-source adoption is sometimes socially inefficient. Overall, this analysis contributes a nuanced understanding of the adoption of open innovation in the form of OSS that should be useful to managers and policy-makers involved in related decisions. The article concludes with practical managerial recommendations on formulating a comprehensive open-source strategy.
A Rab GTPase protein FvSec4 is necessary for fumonisin B1 biosynthesis and virulence in Fusarium verticillioides

Rab GTPases are responsible for a variety of membrane trafficking and vesicular transportation in fungi. But the role of Rab GTPases in Fusarium verticillioides, one of the key corn pathogens worldwide, remains elusive. These Small GTPases in fungi, particularly those homologous to Saccharomyces cerevisiae Sec4, are known to be associated with protein secretion, vesicular trafficking, secondary metabolism and pathogenicity. In this study, our aim was to investigate the molecular functions of FvSec4 in F. verticillioides associated with physiology and virulence. Interestingly, the FvSec4 null mutation did not impair the expression of key conidiation-related genes. Also, the mutant did not show any defect in sexual development, including perithecia production. Meanwhile, GFP-FvSec4 localized to growing hyphal tips and raised the possibility that FvSec4 is involved in protein trafficking and endocytosis. The mutant exhibited defect in corn stalk rot virulence and also significant alteration of fumonisin B1 production. The mutation led to higher sensitivity to oxidative and cell wall stress agents, and defects in carbon utilization. Gene complementation fully restored the defects in the mutant demonstrating that FvSec4 plays important roles in these functions. Taken together, our data indicate that FvSec4 is critical in F. verticillioides hyphal development, virulence, mycotoxin production and stress responses.
Success is good but failure is not so bad either: Serial entrepreneurs and venture capital contracting

Abstract I analyze prior entrepreneurship as a determinant of financial contracting with venture capitalists and find more company-favorable contracts in startups founded by serial entrepreneurs. Repeat founders and other insiders retain greater board control and also suffer less equity dilution in their dealings with VCs. Second, serial founders retain their CEO positions more often. Third, startups founded by serial entrepreneurs obtain higher valuations at VC funding although this finding is confined to previously successful founders who also obtain the best contracts. Interestingly these results obtain despite poorer performance of such startups and VCs funding them sooner. Overall, even previously unsuccessful serial entrepreneurs receive better deal terms than novice founders, consistent with entrepreneurial learning being an important factor in fostering future entrepreneurship.
Coincidental pattern avoidance

There are several versions of permutation pattern avoidance that have arisen in the literature, and some known examples of two different types of pattern avoidance coinciding. In this paper, we examine barred patterns and vincular patterns. Answering a question of Steingrimsson, we determine when barred pattern avoidance coincides with avoiding a finite set of vincular patterns, and when vincular pattern avoidance coincides with avoiding a finite set of barred patterns. There are 720 barred patterns with this property, each having between 3 and 7 letters, of which at most 2 are barred, and there are 48 vincular patterns with this property, each having between 2 and 4 letters and exactly one bond.
Stochastic deep learning in memristive networks

We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning efficiency of DNNs is the variability in its programming characteristics. DNNs with such memristive synapses, even with dynamic range as low as 15 and only 32 discrete levels, when trained based on stochastic updates suffer less than 3% loss in accuracy compared to floating point software baseline. We also study the performance of stochastic memristive DNNs when used as inference engines with noise corrupted data and find that if the device variability can be minimized, the relative degradation in performance for the Stochastic DNN is better than that of the software baseline. Hence, our study presents a new optimization corner for memristive devices for building large noise-immune deep learning systems.
Norm-Based Capacity Control in Neural Networks

We investigate the capacity, convexity and characterization of a general family of norm-constrained feed-forward networks.
A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior

Adaptable computing is an increasingly important paradigm that specializes system resources to variable application requirements, environmental conditions, or user requirements. Adapting computing resources to variable application requirements (or application phases) is otherwise known as phase-based optimization. Phase-based optimization takes advantage of application phases, or execution intervals of an application that behave similarly, to enable effective and beneficial adaptability. In order for phase-based optimization to be effective, the phases must first be classified to determine when application phases begin and end, and ensure that system resources are accurately specialized. In this paper, we present a survey of phase classification techniques that have been proposed to exploit the advantages of adaptable computing through phase-based optimization. We focus on recent techniques and classify these techniques with respect to several factors in order to highlight their similarities and differences. We divide the techniques by their major defining characteristics—online/offline and serial/parallel. In addition, we discuss other characteristics such as prediction and detection techniques, the characteristics used for prediction, interval type, etc. We also identify gaps in the state-of-the-art and discuss future research directions to enable and fully exploit the benefits of adaptable computing.
Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction

We present a framework for learning single-view shape and pose prediction without using direct supervision for either. Our approach allows leveraging multi-view observations from unknown poses as supervisory signal during training. Our proposed training setup enforces geometric consistency between the independently predicted shape and pose from two views of the same instance. We consequently learn to predict shape in an emergent canonical (view-agnostic) frame along with a corresponding pose predictor. We show empirical and qualitative results using the ShapeNet dataset and observe encouragingly competitive performance to previous techniques which rely on stronger forms of supervision. We also demonstrate the applicability of our framework in a realistic setting which is beyond the scope of existing techniques: using a training dataset comprised of online product images where the underlying shape and pose are unknown.
A topological classification of locally constant potentials via zero-temperature measures

We provide a topological classification of locally constant functions over subshifts of finite type via their zero-temperature measures. Our approach is to analyze the relationship between the distribution of the zero-temperature measures and the boundary of higher dimensional generalized rotation sets. We also discuss the regularity of the localized entropy function on the boundary of the generalized rotation sets.
Creating Spin-One Fermions in the Presence of Artificial Spin–Orbit Fields: Emergent Spinor Physics and Spectroscopic Properties

We propose the creation and investigation of a system of spin-one fermions in the presence of artificial spin–orbit coupling, via the interaction of three hyperfine states of fermionic atoms to Raman laser fields. We explore the emergence of spinor physics in the Hamiltonian described by the interaction between light and atoms, and analyze spectroscopic properties such as dispersion relation, Fermi surfaces, spectral functions, spin-dependent momentum distributions and density of states. Connections to spin-one bosons and SU(3) systems is made, as well relations to the Lifshitz transition and Pomeranchuk instability are presented.
Clutching and gluing in tropical and logarithmic geometry

Abstract The classical clutching and gluing maps between the moduli stacks of stable marked algebraic curves are not logarithmic, i.e. they do not induce morphisms over the category of logarithmic schemes, since they factor through the boundary. Using insight from tropical geometry, we enrich the category of logarithmic schemes to include so-called sub-logarithmic morphisms and show that the clutching and gluing maps are naturally sub-logarithmic. Building on the recent framework developed by Cavalieri, Chan, Wise, and the third author, we further develop a stack-theoretic counterpart of these maps in the tropical world and show that the resulting maps naturally commute with the process of tropicalization.
Simple and Nearly Optimal Polynomial Root-Finding by Means of Root Radii Approximation

We propose a new simple but nearly optimal algorithm for the approximation of all sufficiently well isolated complex roots and root clusters of a univariate polynomial. Quite typically the known root-finders at first compute some crude but reasonably good approximations to well-conditioned roots (that is, those well isolated from the other roots) and then refine the approximations very fast, by using Boolean time which is nearly optimal, up to a polylogarithmic factor. By combining and extending some old root-finding techniques, the geometry of the complex plane, and randomized parametrization, we accelerate the initial stage of obtaining crude approximations to all well-conditioned simple and multiple roots as well as to all isolated root clusters. Our algorithm performs this stage at a Boolean cost dominated by the nearly optimal cost of the subsequent refinement of these approximations, which we can perform concurrently, with minimum processor communication and synchronization. Our techniques are quite simple and elementary; their power and application range may increase in their combination with the known efficient root-finding methods.
Universal geometric cluster algebras

We consider, for each exchange matrix $$B$$ , a category of geometric cluster algebras over $$B$$ and coefficient specializations between the cluster algebras. The category also depends on an underlying ring $$R$$ , usually $$\mathbb {Z},\,\mathbb {Q}$$ , or $$\mathbb {R}$$ . We broaden the definition of geometric cluster algebras slightly over the usual definition and adjust the definition of coefficient specializations accordingly. If the broader category admits a universal object, the universal object is called the cluster algebra over $$B$$ with universal geometric coefficients, or the universal geometric cluster algebra over $$B$$ . Constructing universal geometric coefficients is equivalent to finding an $$R$$ -basis for $$B$$ (a “mutation-linear” analog of the usual linear-algebraic notion of a basis). Polyhedral geometry plays a key role, through the mutation fan $${\mathcal {F}}_B$$ , which we suspect to be an important object beyond its role in constructing universal geometric coefficients. We make the connection between $${\mathcal {F}}_B$$ and $$\mathbf{g}$$ -vectors. We construct universal geometric coefficients in rank $$2$$ and in finite type and discuss the construction in affine type.
Predicting native language from gaze

In an embodiment, a method includes presenting, on a display, sample text in a given language to a user. The method further includes recording eye fixation times for each word of the sample text for the user and recording saccade times between each pair of fixations of the sample text. The method further includes comparing features of the gaze pattern of the user to features of a gaze pattern of a plurality of training readers. Each training reader (e.g., training user) has a known native language. The method further generates a probability of at least one an estimated native language of the user based on the results of the comparison.
Calabi-Yau manifolds with isolated conical singularities

Let $X$ be a complex projective variety with only canonical singularities and with trivial canonical bundle. Let $L$ be an ample line bundle on $X$. Assume that the pair $(X,L)$ is the flat limit of a family of smooth polarized Calabi-Yau manifolds. Assume that for each singular point $x \in X$ there exist a Kahler-Einstein Fano manifold $Z$ and a positive integer $q$ dividing $K_Z$ such that $-\frac{1}{q}K_Z$ is very ample and such that the germ $(X,x)$ is locally analytically isomorphic to a neighborhood of the vertex of the blow-down of the zero section of $\frac{1}{q}K_{Z}$. We prove that up to biholomorphism, the unique weak Ricci-flat Kahler metric representing $2\pi c_1(L)$ on $X$ is asymptotic at a polynomial rate near $x$ to the natural Ricci-flat Kahler cone metric on $\frac{1}{q}K_Z$ constructed using the Calabi ansatz. In particular, our result applies if $(X, \mathcal{O}(1))$ is a nodal quintic threefold in $\mathbb{P}^4$. This provides the first known examples of compact Ricci-flat manifolds with non-orbifold isolated conical singularities.
Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs

We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs. The subgradient-push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out-degree at each time. Our main result is a convergence rate of $O((\ln t)/t)$ for strongly convex functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the $O((\ln t)/\sqrt{t})$ rate previously known for (general) convex functions.
Coagulation and universal scaling limits for critical Galton-Watson processes

The basis of this paper is the elementary observation that the n-step descendant distribution of any Galton–Watson process satisfies a discrete Smoluchowski coagulation equation with multiple coalescence. Using this we obtain simple necessary and sufficient criteria for the convergence of scaling limits of critical Galton–Watson processes in terms of scaled family-size distributions and a natural notion of convergence of Levy triples. Our results provide a clear and natural interpretation, and an alternate proof, of the fact that the Levy jump measure of certain continuous-state branching processes (CSBPs) satisfies a generalized Smoluchowski equation. (This result was previously proved by Bertoin and Le Gall (2006).) Our analysis shows that the nonlinear scaling dynamics of CSBPs become linear and purely dilatational when expressed in terms of the Levy triple associated with the branching mechanism. We prove a continuity theorem for CSBPs in terms of the associated Levy triples, and use our scaling analysis to prove the existence of universal critical Galton–Watson processes and CSBPs analogous to Doeblin's `universal laws'. Namely, these universal processes generate all possible critical and subcritical CSBPs as subsequential scaling limits. Our convergence results rely on a natural topology for Levy triples and a continuity theorem for Bernstein transforms (Laplace exponents) which we develop in a self-contained appendix.
Generalized r-Lah numbers

In this paper, we consider a two-parameter polynomial generalization, denoted by \(\mathcal {G}_{a,b}(n,k;r)\), of the r-Lah numbers which reduces to these recently introduced numbers when a = b = 1. We present several identities for \(\mathcal {G}_{a,b}(n,k;r)\) that generalize earlier identities given for the r-Lah and r-Stirling numbers. We also provide combinatorial proofs of some earlier identities involving the r-Lah numbers by defining appropriate sign-changing involutions. Generalizing these arguments yields orthogonality-type relations that are satisfied by \(\mathcal {G}_{a,b}(n,k;r)\).
Introspective Classification with Convolutional Nets

We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities. We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory. Our ICN tries to iteratively: (1) synthesize pseudo-negative samples; and (2) enhance itself by improving the classification. The single CNN classifier learned is at the same time generative --- being able to directly synthesize new samples within its own discriminative model. We conduct experiments on benchmark datasets including MNIST, CIFAR-10, and SVHN using state-of-the-art CNN architectures, and observe improved classification results.
Energy-Efficient Object Detection Using Semantic Decomposition

In this brief, we present a new approach to optimize energy efficiency of object detection tasks using semantic decomposition to build a hierarchical classification framework. We observe that certain semantic information like color/texture is common across various images in real-world data sets for object detection applications. We exploit these common semantic features to distinguish the objects of interest from the remaining inputs (nonobjects of interest) in a data set at a lower computational effort. We propose a 2-stage hierarchical classification framework, with increasing levels of complexity, wherein the first stage is trained to recognize the broad representative semantic features relevant to the object of interest. The first stage rejects the input instances that do not have the representative features and passes only the relevant instance to the second stage. Our methodology thus allows us to reject certain information at lower complexity and utilize the full computational effort of a network only on a smaller fraction of inputs resulting in energy-efficient detection.
Optimal event-driven multi-agent persistent monitoring of a finite set of targets

We consider the problem of controlling the movement of multiple cooperating agents so as to minimize an uncertainty metric associated with a finite number of targets. In a one-dimensional mission space, we adopt an optimal control framework and show that the solution is reduced to a simpler parametric optimization problem: determining a sequence of locations where each agent may dwell for a finite amount of time and then switch direction. This amounts to a hybrid system which we analyze using Infinitesimal Perturbation Analysis (IPA) to obtain a complete on-line solution through an event-driven gradient-based algorithm which is also robust with respect to the uncertainty model used. The resulting controller depends on observing the events required to excite the gradient-based algorithm, which cannot be guaranteed. We solve this problem by proposing a new metric for the objective function which creates a potential field guaranteeing that gradient values are non-zero. This approach is compared to an alternative graph-based task scheduling algorithm for determining an optimal sequence of target visits. Simulation examples are included to demonstrate the proposed methods.
Triadic consensus: a randomized algorithm for voting in a crowd

Typical voting rules do not work well in settings with many candidates. If there are even several hundred candidates, then a simple task such as evaluating and choosing a top candidate becomes impractical. Motivated by the hope of developing group consensus mechanisms over the internet, where the numbers of candidates could easily number in the thousands, we study an urn-based voting rule where each participant acts as a voter and a candidate. We prove that when participants lie in a one-dimensional space, this voting protocol finds a $(1-\epsilon/\sqrt{n})$ approximation of the Condorcet winner with high probability while only requiring an expected $O(\frac{1}{\epsilon^2}\log^2 \frac{n}{\epsilon^2})$ comparisons on average per voter. Moreover, this voting protocol is shown to have a quasi-truthful Nash equilibrium: namely, a Nash equilibrium exists which may not be truthful, but produces a winner with the same probability distribution as that of the truthful strategy.
Comparison between Grating Imaging and Transient Grating Techniques on Measuring Carrier Diffusion in Semiconductor

Optical grating technique, where optical gratings are generated via light inference, has been widely used to measure charge carrier and phonon transport in semiconductors. In this paper, compared a...
An analytic heuristic for multiplicity computation for Zaremba's Conjecture

Abstract Zaremba's Conjecture concerns the formation of continued fractions with partial quotients restricted to a given alphabet. In order to answer the numerous questions that arrive from this conjecture, it is best to consider a semi-group, often denoted Γ A , which arises naturally as a subset of S L 2 ( Z ) when considering finite continued fractions. To translate back from this semi-group into rational numbers, we select a projection mapping satisfying certain criteria to recover the numerator and denominator of the continued fractions in rational form. The central question of our research is to determine the multiplicity of a given denominator. To this end, we develop a heuristic method similar to the Hardy–Littlewood Circle Method. We compare this theoretical model to the exact data, gleaned by simulation, and demonstrate that our formula appears to be asymptotically valid. We then evaluate different aspects of the accuracy of our formula.
Effective field theory, past and future

I reminisce about the early development of effective field theories of the strong interactions, comment briefly on some other applications of effective field theories, and then take up the idea that the Standard Model and General Relativity are the leading terms in an effective field theory. Finally, I cite recent calculations that suggest that the effective field theory of gravitation and matter is asymptotically safe.
Transposing Noninvertible Polynomials

Landau-Ginzburg mirror symmetry predicts isomorphisms between graded Frobenius algebras (denoted A and B) that are constructed from a nondegenerate quasihomogeneous polynomial W and a related group of symmetries G. Duality between A and B models has been conjectured for particular choices of W and G. These conjectures have been proven in many instances where W is restricted to having the same number of monomials as variables (called invertible). Some conjectures have been made regarding isomorphisms between A and B models when W is allowed to have more monomials than variables. In this paper we show these conjectures are false; that is, the conjectured isomorphisms do not exist. Insight into this problem will not only generate new results for Landau-Ginzburg mirror symmetry, but will also be interesting from a purely algebraic standpoint as a result about groups acting on graded algebras. Acknowledgements: The author would like to thank Dr. Tyler Jarvis of Brigham Young University for his mentoring during this project. This research was also partially supported by grants from the BYU Harold B. Lee Library and the BYU Office of Research and Creative Activities. Page 54 RHIT Undergrad. Math. J., Vol. 16, No. 2
A QUALITATIVE DESCRIPTION OF BOUNDARY LAYER WIND SPEED RECORDS

The complexity of the atmosphere endows it with the property of turbulence by virtue of which, wind speed variations in the atmospheric boundary layer (ABL) exhibit highly irregular fluctuations that persist over a wide range of temporal and spatial scales. Despite the large and significant body of work on microscale turbulence, understanding the statistics of atmospheric wind speed variations has proved to be elusive and challenging. Knowledge about the nature of wind speed at ABL has far reaching impact on several fields of research such as meteorology, hydrology, agriculture, pollutant dispersion, and more importantly wind energy generation. In the present study, temporal wind speed records from twenty eight stations distributed through out the state of North Dakota (ND, USA), (~ 70,000 square-miles) and spanning a period of nearly eight years are analyzed. We show that these records exhibit a characteristic broad multifractal spectrum irrespective of the geographical location and topography. The rapid progression of air masses with distinct qualitative characteristics originating from Polar regions, Gulf of Mexico and Northern Pacific account for irregular changes in the local weather system in ND. We hypothesize that one of the primary reasons for the observed multifractal structure could be the irregular recurrence and confluence of these three air masses.
Subelliptic resolvent estimates for non-self-adjoint semiclassical Schrödinger operators

In this paper we prove a subelliptic resolvent estimate for a class of semiclassical non-self-adjoint Schrodinger operators with purely imaginary potentials when the spectral parameter is in a parabolic neighborhood of the imaginary axis.
Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network

Recent work by Cohen et al. has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups.
Complete dictionary recovery over the sphere

We consider the problem of recovering a complete (i.e., square and invertible) dictionary A 0 , from Y = A 0 X 0 with Y ∊ Rn×p. This recovery setting is central to the theoretical understanding of dictionary learning. We give the first efficient algorithm that provably recovers A 0 when X 0 has O (n) nonzeros per column, under suitable probability model for X 0 . Prior results provide recovery guarantees when X 0 has only O (√n) nonzeros per column. Our algorithm is based on nonconvex optimization with a spherical constraint, and hence is naturally phrased in the language of manifold optimization. Our proofs give a geometric characterization of the high-dimensional objective landscape, which shows that with high probability there are no spurious local minima. This invited talk summarizes these results, presented in [1]. It also presents numerical experiments demonstrating their implications for practical problems in representation learning and the more general algorithmic problem of recovering matrix decompositions with structured factors.
Links and Planar Diagram Codes

In this paper we formalize a combinatorial object for describing link diagrams called a Planar Diagram Code (PD-Code). PD-codes are used by the KnotTheory Mathematica package developed by Bar-Natan et al. We present the set of PD-codes as a standalone object and discuss its relationship with link diagrams. We give an explicit algorithm for reconstructing a knot diagram on a surface from a PD-code. We also discuss the intrinsic symmetries of PD-codes (i.e. invertibility and chirality). The moves analogous to the Reidemeister moves are also explored, and we show that the given set of PD-codes modulo these combinatorial Reidemeister moves is equivalent to classical link theory.
Clinical patient tracking in the presence of transient and permanent occlusions via geodesic feature

This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clinicians must remain close to the patient during training sessions, the patient is usually under permanent and transient occlusions due to the training equipment and the movements of the attending clinicians. These occlusions can significantly degrade the accuracy of existing human tracking methods. To improve the data association problem in these circumstances, we present a new global feature based on the geodesic distances of surface mesh points to a set of anchor points. Transient occlusions are handled via a multi-hypothesis tracking framework. To evaluate the method, we simulated different occlusion sizes on a data set captured from a human in varying movement patterns, and compared the proposed feature with other tracking methods. The results show that the proposed method achieves robustness to both surface deformations and transient occlusions.
Efficient counting of degree sequences

Abstract We present formulas to count the set D 0 ( n ) of degree sequences of simple graphs of order n , the set D ( n ) of degree sequences of those graphs with no isolated vertices, and the set D k _ c o n ( n ) of degree sequences of those graphs that are k -connected for fixed k . The formulas all use a function of Barnes and Savage and the associated dynamic programming algorithms all run in time polynomial in n and are asymptotically faster than previous known algorithms for these problems. We also show that | D ( n ) | and | D 1 _ c o n ( n ) | are asymptotically equivalent but | D ( n ) | and | D 0 ( n ) | as well as | D ( n ) | and | D k _ c o n ( n ) | with fixed k ≥ 2 are asymptotically inequivalent. Finally, we explain why we are unable to obtain the absolute asymptotic orders of these functions from their formulas.
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection

This paper describes the Duluth system that participated in SemEval-2017 Task 6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks A and B using N-gram language models, ranking highly in the task evaluation. This paper discusses the results of our system in the development and evaluation stages and from two post-evaluation runs.
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior

Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this inferred intent to predict, imitate, or assist the human user. However, a central assumption in inverse reinforcement learning is that the demonstrator is close to optimal. While models of suboptimal behavior exist, they typically assume that suboptimal actions are the result of some type of random noise or a known cognitive bias, like temporal inconsistency. In this paper, we take an alternative approach, and model suboptimal behavior as the result of internal model misspecification: the reason that user actions might deviate from near-optimal actions is that the user has an incorrect set of beliefs about the rules -- the dynamics -- governing how actions affect the environment. Our insight is that while demonstrated actions may be suboptimal in the real world, they may actually be near-optimal with respect to the user's internal model of the dynamics. By estimating these internal beliefs from observed behavior, we arrive at a new method for inferring intent. We demonstrate in simulation and in a user study with 12 participants that this approach enables us to more accurately model human intent, and can be used in a variety of applications, including offering assistance in a shared autonomy framework and inferring human preferences.
Harmonic measure and approximation of uniformly rectifiable sets

Let E⊂Rn+1, n≥1, be a uniformly rectifiable set of dimension nn. We show E that has big pieces of boundaries of a class of domains which satisfy a 2-sided corkscrew condition, and whose connected components are all chord-arc domains (with uniform control of the various constants). As a consequence, we deduce that E has big pieces of sets for which harmonic measure belongs to weak-A∞.
Equilibrium ion distribution in the presence of clearing electrodes and its influence on electron dynamics

Here we compute the ion distribution produced by an electron beam when ion-clearing electrodes are installed. This ion density is established as an equilibrium between gas ionization and ion clearing. The transverse ion distributions are shown to strongly peak in the beam's center, producing very nonlinear forces on the electron beam. We will analyze perturbations to the beam properties by these nonlinear fields. To obtain reasonable simulation speeds, we develop fast algorithms that take advantage of adiabatic invariants and scaling properties of Maxwell's equations and the Lorentz force. Our results are very relevant for high current energy recovery linacs, where ions are produced relatively quickly, and where clearing gaps in the electron beam cannot easily be used for ion elimination. The examples in this paper therefore use parameters of the Cornell Energy Recovery Linac project. For simplicity we only consider the case of a circular electron beam of changing diameter. However, we parametrize this model to approximate nonround beams well. We find suitable places for clearing electrodes and compute the equilibrium ion density and its effect on electron-emittance growth and halo development. We find that it is not sufficient to place clearing electrodes only at the minimum of the electron beam potential where ions are accumulated.
Large values of modular forms

We show that there are primitive holomorphic modular forms f of weight two and arbitrary large level N such that $|f(z)| \gg N^{1/4}$ for some point z. Thereby we disprove a folklore conjecture that the sup-norm of such forms would be as small as $N^{o(1)}$.
On integrals of eigenfunctions over geodesics

If $(M,g)$ is a compact Riemannian surface then the integrals of $L^2(M)$-normalized eigenfunctions $e_j$ over geodesic segments of fixed length are uniformly bounded. Also, if $(M,g)$ has negative curvature and $\gamma(t)$ is a geodesic parameterized by arc length, the measures $e_j(\gamma(t))\, dt$ on $\R$ tend to zero in the sense of distributions as the eigenvalue $\la_j\to \infty$, and so integrals of eigenfunctions over periodic geodesics tend to zero as $\la_j\to \infty$. The assumption of negative curvature is necessary for the latter result.
Rank Gradient and p-gradient of Amalgamated Free Products and HNN Extensions

We calculate the rank gradient and p-gradient of free products with amalgamation over an amenable subgroup and HNN extensions with an amenable associated subgroup. The notion of cost is used to compute the rank gradient of amalgamated free products and HNN extensions. For the p-gradient the Kurosh subgroup theorems for amalgamated free products and HNN extensions will be used.
Bi-interpretability of some monoids with the arithmetic and applications

We will prove bi-interpretability of the arithmetic $${\mathbb {N}}= \langle N, +,\cdot , 0, 1\rangle $$ and the weak second order theory of $${\mathbb {N}}$$ with the free monoid $$\mathbb {M}_X$$ of finite rank greater than 1 and with a non-trivial partially commutative monoid with trivial center. This bi-interpretability implies that finitely generated submonoids of these monoids are definable. Moreover, any recursively enumerable language in the alphabet X is definable in $$\mathbb {M}_X$$ . Primitive elements, and, therefore, free bases are definable in the free monoid. It has the so-called QFA property, namely there is a sentence $$\phi $$ such that every finitely generated monoid satisfying $$\phi $$ is isomorphic to $$\mathbb {M}_X$$ . The same is true for a partially commutative monoid without center. We also prove that there is no quantifier elimination in the theory of any structure that is bi-interpretable with $$\mathbb {N}$$ to any boolean combination of formulas from $$\Pi _n$$ or $$\Sigma _n$$ .
Analytically Modeling Unmanaged Intersections with Microscopic Vehicle Interactions

With the emergence of autonomous vehicles, it is important to understand their impact on the transportation system. However, conventional traffic simulations are time-consuming. This paper introduces an analytical traffic model for unmanaged intersections accounting for microscopic vehicle interactions. The macroscopic property, i.e., delay at the intersection, is modeled as an event-driven stochastic dynamic process, whose dynamics encode the microscopic vehicle behaviors. The distribution of macroscopic properties can be obtained through either direct analysis or event-driven simulation. They are more efficient than conventional (time-driven) traffic simulation, and capture more microscopic details compared to conventional macroscopic flow models. We illustrate the efficiency of this method by delay analyses under two different policies at a two-lane intersection. The proposed model allows for 1) efficient and effective comparison among different policies, 2) policy optimization, 3) traffic prediction, and 4) system optimization (e.g., infrastructure and protocol).
CoQA: A Conversational Question Answering Challenge

Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa
Advances of machine learning in molecular modeling and simulation

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool.
Neutron-Antineutron Operator Renormalization

Baryon number symmetry violating theories beyond the standard model with suppressed proton decay rates can be experimentally constrained by data on neutron-antineutron transition rates. In order to apply this constraints, theoretical predictions for the neutron-antineutron transition rates in various models must be available for comparison. Reliable predictions of transition rates between hadronic states must include non-perturbative quantum chromodynamic effects. These can be calculated in a model independent way by calculating six-quark operator matrix elements with lattice quantum chromodynamics. Preliminary lattice calculations have been performed, but operator renormalization effects must be included in order to match beyond the standard model calculations performed in MS renormalized perturbation theory with lattice regularized matrix element results. In particular, a perturbative calculation of the two-loop anomalous dimensions and one-loop renormalization scheme matching coefficients of these six-quark operators is necessary in order to determine leading order corrections at lattice matching scales. This describes our ongoing calculation of these perturbative operator renormalization effects.
Spectral Clustering Methods for Multiplex Networks

Abstract Clustering nodes of a network into communities is a basic tool for identifying structure in complex system. We extend spectral clustering to multiplex structures and discuss some of the difficulties that arise in defining natural generalizations of the single layer case. To analyze the effectiveness of our approaches, we construct three simple families of synthetic multiplex networks and compare the performance of two different versions of multiplex spectral clustering. Our two clustering models – one based on the diagonal supra-Laplacian and the other based on a different diffusive model – perform differently on our tests. The first essentially interpolates between splitting the layers of the networks into different clusters and finding the layer clusters while the second interpolates between the layer clusterings, often finding clusters that span the different layers which reflect multiplex characteristics of the network.
Mixed tensors of the general linear supergroup

Abstract We describe the image of the canonical tensor functor from Deligne's interpolating category Re _ p ( G L m − n ) to R e p ( G L ( m | n ) ) attached to the standard representation. This implies explicit tensor product decompositions between any two projective modules and any two Kostant modules of G L ( m | n ) , covering the decomposition between any two irreducible G L ( m | 1 ) -representations. We also obtain character and dimension formulas. For m > n we classify the mixed tensors with non-vanishing superdimension. For m = n we characterize the maximally atypical mixed tensors and show some applications regarding tensor products.
A Neural Model of Adaptation in Reading

It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.