cssi/SciSciGPT-SciSciNet huggingface dataset:
Benzotriazole: A novel synthetic auxiliary

Abstract Benzotriazole and aldehydes react reversibly to give addition products: in the presence of amines and other NH-compounds water can be eliminated to form products of type Bt-CHR-NR′R″. The latter are versatile intermediates for the preparation of primary, secondary, and tertiary amines and in the alkylation of hydroxylamines, hydrazines, amides, thioamides, and sulfonamides. Polyfunctional amines and other polyfunctional compounds can also be prepared, and they enable significant extending of Mannich reaction. Similar oxygen compounds Bt-CRR′-OR″ enable new syntheses of ethers and esters. Reactions in which benzotriazole is eliminated rather than substituted open up new pathways to enamines, enol ethers, and nitrones. The methodology is capable of extension to a variety of vinylogous systems including benzenoid and heteroaromatic derivatives. In addition to acting as a versatile leaving group, benzotriazolyl residues activate neighboring CH bonds to proton loss and a variety of such applications is described.
A Primeron Mineral Taxation

The paper discusses options available to tax mineral extraction projects particularly in developing countries. A desirable government share of the economic rent generated from mineral extraction can be achieved through different tax and non-tax instruments. This gives some room to design a fiscal regime that will be attractive to investors while providing the government with a fair share of the economic rent. However, achieving this will require a careful assessment of the appropriate distribution of risk and reward between the investor and the government. Moreover, there is growing pressure on countries to provide increasingly lenient fiscal terms so as to remain competitive as global investment destinations.
How broadly do product preannouncement performance effects generalize? Product life cycle and switching cost perspectives

Firms use preannouncements to inform customers about the impending introduction of a new product or service. These preannouncements are significant events because they provide customers with product specific information while signaling the health, strategic intent, and future of a company. One important area of research in this field investigates the performance consequences of product preannouncements (PPA). However, a notable gap in our knowledge exists because the focus of past research has been on studying wealth effects rather narrowly in certain industries, e.g., high-tech, or under certain contingencies. This restrictive approach is surprising because PPA are observed in a broad range of product categories. Moreover, product life cycle and consumer switching cost theories predict performance effects of PPA irrespective of category or context. We address this lack of generalizability by using switching cost and life cycle theories to hypothesize positive performance effects of PPA independent of context and contingencies. We use the event study method from finance to empirically test the relationship between PPA and stock prices in a broad sample of events comprising multiple product categories. Using events reported in the Wall Street Journal, we find evidence of a positive effect of PPA on stock prices irrespective of the type of product or context involved. We note several managerial implications of the study and outline avenues for further research.
Atypical Functional Connectivity in Tourette Syndrome Differs Between Children and Adults.

Abstract Background Tourette syndrome (TS) is a neuropsychiatric disorder with symptomatology that typically changes over development. Whether and how brain function in TS also differs across development has been largely understudied. Here, we used functional connectivity magnetic resonance imaging to examine whole-brain functional networks in children and adults with TS. Methods Multivariate classification methods were used to find patterns among functional connections that distinguish individuals with TS from control subjects separately for children and adults (N = 202). We tested whether the patterns of connections that classify diagnosis in one age group (e.g., children) could classify diagnosis in another age group (e.g., adults). We also tested whether the developmental trajectory of these connections was altered in TS. Results Diagnostic classification was successful in children and adults separately but expressly did not generalize across age groups, suggesting that the patterns of functional connections that best distinguished individuals with TS from control subjects were age specific. Developmental patterns among these functional connections used for diagnostic classification deviated from typical development. Brain networks in childhood TS appeared “older” and brain networks in adulthood TS appeared “younger” in comparison with typically developing individuals. Conclusions Our results demonstrate that brain networks are differentially altered in children and adults with TS. The observed developmental trajectory of affected connections is consistent with theories of accelerated and/or delayed maturation, but may also involve anomalous developmental pathways. These findings further our understanding of neurodevelopmental trajectories in TS and carry implications for future applications aimed at predicting the clinical course of TS in individuals over development.
Vacuum energy density and pressure of a massive scalar field

With a view toward application of the Pauli–Villars regularization method to the Casimir energy of boundaries, we calculate the expectation values of the components of the stress tensor of a confined massive field in space-time dimensions. Previous papers by Hays and Fulling are bridged and generalized. The Green function for the time-independent Schrodinger equation is constructed from the Green function for the whole line by the method of images; equivalently, the one-dimensional system is solved exactly in terms of closed classical paths and periodic orbits. Terms in the energy density and in the eigenvalue density attributable to the two boundaries individually and those attributable to the confinement of the field to a finite interval are distinguished so that their physical origins are clear. Then the pressure is found similarly from the cylinder kernel, the Green function associated most directly with an exponential frequency cutoff of the Fourier mode expansion. Finally, we discuss how the theory could be rendered finite by the Pauli–Villars method.
Factorization of banded permutations

We consider the factorization of permutations into bandwidth 1 permutations, which are products of mutually nonadjacent simple transpositions. We exhibit an upper bound on the minimal number of such factors and thus prove a conjecture of Gilbert Strang: a banded permutation of bandwidth $w$ can be represented as the product of at most $2w-1$ permutations of bandwidth 1. An analogous result holds also for infinite and cyclically banded permutations.
Triviality and Split of Vector Bundles on Rationally Connected Varieties

In this paper, we use the existence of a family of rational curves on a separably connected variety, which satisfies the Lefschetz condition, to give a simple proof of a triviality criterion due to I.Biswas and J.Pedro and P.Dos Santos. We also prove that a vector bundle on a homogenous space is trivial if and only if the restriction of the vector bundle to every Schubert line is trivial. Using this result and the theory of Chern classes of vector bundles, we give a general criterion for a uniform vector bundle on a homogenous space to be splitting. As an application, we prove that a uniform vector bundle on a classical Grassmannian of low rank is splitting.
On classification of four-dimensional nilpotent Leibniz algebras

ABSTRACTLeibniz algebras are certain generalization of Lie algebras. In this paper, we give the classification of four-dimensional non-Lie nilpotent Leibniz algebras. We use the canonical forms for the congruence classes of matrices of bilinear forms and some other techniques to obtain our result.
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs

The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm for analysing individual predictions made by standard LSTMs, without any changes to the underlying model. By decomposing the output of a LSTM, CD captures the contributions of combinations of words or variables to the final prediction of an LSTM. On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction. Using the phrase-level labels in SST, we also demonstrate that CD is able to successfully extract positive and negative negations from an LSTM, something which has not previously been done.
A polynomial chaos expansion in dependent random variables

Abstract This paper introduces a new generalized polynomial chaos expansion (PCE) comprising measure-consistent multivariate orthonormal polynomials in dependent random variables. Unlike existing PCEs, whether classical or generalized, no tensor-product structure is assumed or required. Important mathematical properties of the generalized PCE are studied by constructing orthogonal decomposition of polynomial spaces, explaining completeness of orthogonal polynomials for prescribed assumptions, exploiting whitening transformation for generating orthonormal polynomial bases, and demonstrating mean-square convergence to the correct limit. Analytical formulae are proposed to calculate the mean and variance of a truncated generalized PCE for a general output variable in terms of the expansion coefficients. An example derived from a stochastic boundary-value problem illustrates the generalized PCE approximation in estimating the statistical properties of an output variable for 12 distinct non-product-type probability measures of input variables.
A Provably Efficient Online Collaborative Caching Algorithm for Multicell-Coordinated Systems

Caching at the base stations brings the contents closer to the users, reduces the traffic through the backhaul links, and reduces the delay experienced by the cellular users. The cellular network operator may charge the content providers for caching their contents. Moreover, content providers may lose their users if the users are not getting their desired quality of service, such as maximum tolerable delay in Video on Demand services. In this paper, we study the collaborative caching problem for a multicell-coordinated system from the point of view of minimizing the total cost paid by the content providers. We formulate the problem as an Integer Linear Program and prove its NP-completeness. We also provide an online caching algorithm that does not require any knowledge about the contents popularities. We prove that the online algorithm achieves a competitive ratio of $\mathcal {O}(\log (n))$ , and we show that the best competitive ratio that any online algorithm can achieve is $\Omega (\frac{\log (n)}{\log \log (n)})$ . Therefore, our proposed caching algorithm is provably efficient. Through simulations, we show that our online algorithm performs very close to the optimal offline collaborative scheme, and can outperform it when contents popularities are not properly estimated.
Inroads Toward Robot-Assisted Internal Fixation of Bone Fractures Using a Bendable Medical Screw and the Curved Drilling Technique

Internal fixation is a common orthopedic procedure in which a rigid screw is used to fix fragments of a fractured bone together and expedite the healing process. However, the rigidity of the screw, geometry of the fractured anatomy (e.g. femur and pelvis), and patient's age can cause an array of complications during screw placement, such as improper fracture healing due to misalignment of the bone fragments, lengthy procedure time and subsequently high radiation exposure. To address these issues, we propose a minimally invasive robot-assisted procedure comprising of a continuum robot, called ortho-snake, together with a novel bendable medical screw (BMS) for fixating the fractures. We describe the implementation of a curved drilling technique and focus on the design, manufacturing, and evaluation of a novel BMS, which can passively morph into the drilled curved tunnels with various curvatures. We evaluate the performance and efficacy of the proposed BMS using both finite element simulations as well as experiments conducted on synthetic bone samples.
p-adic quotient sets II: Quadratic forms

Abstract For A ⊆ { 1 , 2 , … } , we consider R ( A ) = { a / a ′ : a , a ′ ∈ A } . If A is the set of nonzero values assumed by a quadratic form, when is R ( A ) dense in the p-adic numbers? We show that for a binary quadratic form Q, R ( A ) is dense in Q p if and only if the discriminant of Q is a nonzero square in Q p , and for a quadratic form in at least three variables, R ( A ) is always dense in Q p . This answers a question posed by several authors in 2017.
Coloring large complex networks

Given a large social or information network, how can we partition the vertices into sets (i.e., colors) such that no two vertices linked by an edge are in the same set while minimizing the number of sets used. Despite the obvious practical importance of graph coloring, existing works have not systematically investigated or designed methods for large complex networks. In this work, we develop a unified framework for coloring large complex networks that consists of two main coloring variants that effectively balances the tradeoff between accuracy and efficiency. Using this framework as a fundamental basis, we propose coloring methods designed for the scale and structure of complex networks. In particular, the methods leverage triangles, triangle-cores, and other egonet properties and their combinations. We systematically compare the proposed methods across a wide range of networks (e.g., social, web, biological networks) and find a significant improvement over previous approaches in nearly all cases. Additionally, the solutions obtained are nearly optimal and sometimes provably optimal for certain classes of graphs (e.g., collaboration networks). We also propose a parallel algorithm for the problem of coloring neighborhood subgraphs and make several key observations. Overall, the coloring methods are shown to be (1) accurate with solutions close to optimal, (2) fast and scalable for large networks, and (3) flexible for use in a variety of applications.
Excitation of epsilon-near-zero resonance in ultra-thin indium tin oxide shell embedded nanostructured optical fiber

We report a novel optical waveguide design of a hollow step index fiber modified with a thin layer of indium tin oxide (ITO). We show an excitation of highly confined waveguide mode in the proposed fiber near the wavelength where permittivity of ITO approaches zero. Due to the high field confinement within thin ITO shell inside the fiber, the epsilon-near-zero (ENZ) mode can be characterized by a peak in modal loss of the hybrid waveguide. Our results show that such in-fiber excitation of ENZ mode is due to the coupling of the guided core mode to the thin-film ENZ mode. We also show that the phase matching wavelength, where the coupling takes place, varies depending on the refractive index of the constituents inside the central bore of the fiber. These ENZ nanostructured optical fibers have many potential applications, for example, in ENZ nonlinear and magneto-optics, as in-fiber wavelength-dependent filters, and as subwavelength fluid channel for optical and bio-photonic sensing.
Equations in Algebras

We show that the Diophantine problem (decidability of equations) is undecidable in free associative algebras over any field and in the group algebras over any field of a wide variety of torsion fre...
The geometry of arithmetic noncommutative projective lines

Abstract Let k be a perfect field and let K / k be a finite extension of fields. An arithmetic noncommutative projective line is a noncommutative space of the form Proj S K ( V ) , where V be a k-central two-sided vector space over K of rank two and S K ( V ) is the noncommutative symmetric algebra generated by V over K defined by M. Van den Bergh [26] . We study the geometry of these spaces. More precisely, we prove they are integral, we classify vector bundles over them, we classify them up to isomorphism, and we classify isomorphisms between them. Using the classification of isomorphisms, we compute the automorphism group of an arithmetic noncommutative projective line.
Reconstructing three-dimensional helical structure with an X-ray free electron laser

Recovery of three-dimensional structure from single-particle X-ray scattering of completely randomly oriented diffraction patterns as predicted a few decades ago has been realized owing to the advent of the new emerging X-ray free electron laser (XFEL) technology. Since the world's first XFEL started operation in June 2009 at SLAC National Laboratory at Stanford, the first few experiments have been conducted on larger objects such as viruses. Many of the important structures of nature such as helical viruses or DNA consist of helical repetition of biological subunits. Hence development of a method for reconstructing helical structure from collected XFEL data has been a top research priority. This work describes the development of a method for solving helical structures such as tobacco mosaic virus from a set of randomly oriented simulated diffraction patterns exploiting the symmetry and Fourier space constraint of the diffraction volume.
A Neurobiological Evaluation Metric for Neural Network Model Search

Neuroscience theory posits that the brain's visual system coarsely identifies broad object categories via neural activation patterns, with similar objects producing similar neural responses. Artificial neural networks also have internal activation behavior in response to stimuli. We hypothesize that networks exhibiting brain-like activation behavior will demonstrate brain-like characteristics, e.g., stronger generalization capabilities. In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior. To calculate HMS, representational dissimilarity matrices (RDMs) are created as abstractions of activation behavior, measured by the correlations of activations to stimulus pairs. HMS is then the correlation between the fMRI RDM and the neural network RDM across all stimulus pairs. We test the metric on unsupervised predictive coding networks, which specifically model visual perception, and assess the metric for statistical significance over a large range of hyperparameters. Our experiments show that networks with increased human-model similarity are correlated with better performance on two computer vision tasks: next frame prediction and object matching accuracy. Further, HMS identifies networks with high performance on both tasks. An unexpected secondary finding is that the metric can be employed during training as an early-stopping mechanism.
Scalable quantum computing architecture with mixed species ion chains

We describe our work on trapping, cooling and detecting mixed ion species for a scalable ion trap quantum information processing architecture. These mixed species chains in linear RF traps may help solve several problems with scaling ion trap quantum computation to large numbers of qubits. Initial temperature measurements of linear Coulomb crystals containing barium and ytterbium ions indicate that the mass difference does not significantly impede sympathetic cooling of normal modes that couple well to the coolant ions (Ba in our case). Average motional occupation numbers are estimated to be 10 to 20 quanta per mode for these well cooled modes for chains with small numbers of ions, consistent with the Doppler limit temperature. For normal modes that do not couple significantly to the coolant atoms, the occupation numbers are significantly higher, of order several thousand. Strategies for better cooling of these modes are discussed. Further, we are working to implement these techniques in microfabricated surface traps in order to exercise greater control over ion chain ordering and positioning.
Coordination of multi-agent systems via asynchronous cloud communication

In this work we study a multi-agent coordination problem in which agents are only able to communicate with each other intermittently through a cloud server. To reduce the amount of required communication, we develop a self-triggered algorithm that allows agents to communicate with the cloud only when necessary rather than at some fixed period. Unlike the vast majority of similar works that propose distributed event- and/or self-triggered control laws, this work doesn't assume agents can be “listening” continuously. In other words, when an event is triggered by one agent, neighboring agents will not be aware of this until the next time they establish communication with the cloud themselves. Using a notion of “promises” about future control inputs, agents are able to keep track of higher quality estimates about their neighbors allowing them to stay disconnected from the cloud for longer periods of time while still guaranteeing a positive contribution to the task. We show that our self-triggered coordination algorithm guarantees that the system asymptotically reaches the desired state. Simulations illustrate our results.
Universal and Near-Universal Cycles of Set Partitions

We study universal cycles of the set $\mathcal{P}(n,k)$ of $k$-partitions of the set $[n]:=\{1,2,\ldots,n\}$ and prove that the transition digraph associated with $\mathcal{P}(n,k)$ is Eulerian. But this does not imply that universal cycles (or ucycles) exist, since vertices represent equivalence classes of partitions. We use this result to prove, however, that ucycles of $\mathcal{P}(n,k)$ exist for all $n \geq 3$ when $k=2$. We reprove that they exist for odd $n$ when $k = n-1$ and that they do not exist for even $n$ when $k = n-1$. An infinite family of $(n,k)$ for which ucycles do not exist is shown to be those pairs for which ${{n-2}\brace{k-2}}$ is odd ($3 \leq k < n-1$). We also show that there exist universal cycles of partitions of $[n]$ into $k$ subsets of distinct sizes when $k$ is sufficiently smaller than $n$, and therefore that there exist universal packings of the partitions in $\mathcal{P}(n,k)$. An analogous result for coverings completes the investigation.
Introduction to scattering for radial 3D NLKG below energy norm

Abstract We prove scattering for some radial 3D semilinear Klein–Gordon equations with rough data. First we prove Strichartz-type estimates in mixed norm spaces. Then by using these decays we establish some local bounds. By combining these results to a Morawetz-type estimate and a radial Sobolev inequality we control the variation of an almost conserved quantity on arbitrary large intervals. Once we have showed that this quantity is controlled, we prove that some of these local bounds can be upgraded to global bounds. This is enough to establish scattering. All the estimates involved require a delicate analysis due to the nature of the nonlinearity and the lack of scaling.
Coloring Sparse Hypergraphs

Fix $k \geq 3$, and let $G$ be a $k$-uniform hypergraph with maximum degree $\Delta$. Suppose that for each $l = 2, \dots, k-1$, every set of $l$ vertices of $G$ is in at most $\Delta^{\frac{k-l}{k-1}}/f$ edges. Then the chromatic number of $G$ is $O((\frac{\Delta}{\log f})^{\frac{1}{k-1}})$. This extends results of Frieze and the second author [J. Combin. Theory. Ser. B, 103 (2013), pp. 767--794] and Bennett and Bohman [arXiv:1308:3732, 2013]. A similar result is proved for 3-uniform hypergraphs where every vertex lies in few triangles. This generalizes a result of Alon, Krivelevich, and Sudakov [J. Combin. Theory Ser. B, 77 (1999), pp. 73--82], who proved the result for graphs. Our main new technical contribution is a deviation inequality for positive random variables with expectation less than $1$. This may be of independent interest and have further applications.
Evaluations of Hecke Algebra Traces at Kazhdan-Lusztig Basis Elements

For irreducible characters $\{ \chi_q^\lambda \,|\, \lambda \vdash n \}$, induced sign characters $\{ \epsilon_q^\lambda \,|\, \lambda \vdash n \}$, and induced trivial characters $\{ \eta_q^\lambda \,|\, \lambda \vdash n \}$ of the Hecke algebra $H_n(q)$, and Kazhdan-Lusztig basis elements $C'_w(q)$ with $w$ avoiding the patterns 3412 and 4231, we combinatorially interpret the polynomials $\smash{\chi_q^\lambda(q^{\ell(w)/2} C'_w(q))}$, $\smash{\epsilon_q^\lambda(q^{\ell(w)/2} C'_w(q))}$, and $\smash{\eta_q^\lambda(q^{\ell(w)/2} C'_w(q))}$. This gives a new algebraic interpretation of chromatic quasisymmetric functions of Shareshian and Wachs, and a new combinatorial interpretation of special cases of results of Haiman. We prove similar results for other $H_n(q)$-traces, and confirm a formula conjectured by Haiman.
Triangular decomposition of skein algebras

By introducing a finer version of the Kauffman bracket skein algebra, we show how to decompose the Kauffman bracket skein algebra of a surface into elementary blocks corresponding to the triangles in an ideal triangulation of the surface. The new skein algebra of an ideal triangle has a simple presentation. This gives an easy proof of the existence of the quantum trace map of Bonahon and Wong. We also explain the relation between our skein algebra and the one defined by Muller, and use it to show that the quantum trace map can be extended to the Muller skein algebra.
An Optimal Control Approach to Sequential Machine Teaching

Given a sequential learning algorithm and a target model, sequential machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate sequential machine teaching as a time-optimal control problem. This allows us to solve sequential teaching by leveraging key theoretical and computational tools developed over the past 60 years in the optimal control community. Specifically, we study the Pontryagin Maximum Principle, which yields a necessary condition for opti- mality of a training sequence. We present analytic, structural, and numerical implica- tions of this approach on a case study with a least-squares loss function and gradient de- scent learner. We compute optimal train- ing sequences for this problem, and although the sequences seem circuitous, we find that they can vastly outperform the best available heuristics for generating training sequences.
Additivity of information in multilayer networks via additive Gaussian noise transforms

Multilayer (or deep) networks are powerful probabilistic models based on multiple stages of a linear transform followed by a non-linear (possibly random) function. In general, the linear transforms are defined by matrices and the nonlinear functions are defined by information channels. These models have gained great popularity due to their ability to characterize complex probabilistic relationships arising in a wide variety of inference problems. The contribution of this paper is a new method for analyzing the fundamental limits of statistical inference in settings where the model is known. The validity of our method can be established in a number of settings and is conjectured to hold more generally. A key assumption made throughout is that the matrices are drawn randomly from orthogonally invariant distributions. Our method yields explicit formulas for 1) the mutual information; 2) the minimum mean-squared error (MMSE); 3) the existence and locations of certain phase-transitions with respect to the problem parameters; and 4) the stationary points for the state evolution of approximate message passing algorithms. When applied to the special case of models with multivariate Gaussian channels our method is rigorous and has close connections to free probability theory for random matrices. When applied to the general case of non-Gaussian channels, our method provides a simple alternative to the replica method from statistical physics. A key observation is that the combined effects of the individual components in the model (namely the matrices and the channels) are additive when viewed in a certain transform domain.
Equivariant Hilbert series of monomial orbits

The equivariant Hilbert series of an ideal generated by an orbit of a monomial under the action of the monoid $\mbox{Inc}(\mathbb{N})$ of strictly increasing functions is determined. This is used to find the dimension and degree of such an ideal. The result also suggests that the description of the denominator of an equivariant Hilbert series of an arbitrary $\mbox{Inc}(\mathbb{N})$-invariant ideal as given by Nagel and Romer is rather efficient.
The geometry of right angled Artin subgroups of mapping class groups

We describe sucient conditions which guarantee that a nite set of mapping classes generate a right-angled Artin group quasi-isometrically embedded in the mapping class group. Moreover, under these conditions, the orbit map to Teichmuller space is a quasi- isometric embedding for both of the standard metrics. As a consequence, we produce innitely many genus h surfaces (for any h at least 2) in the moduli space of genus g surfaces (for any g at least 3) for which the universal covers are quasi-isometrically embedded in the Teichmuller space.
Measure equipartitions via finite Fourier analysis

Applications of harmonic analysis on finite groups are introduced to measure partition problems, with equipartitions obtained as the vanishing of prescribed Fourier transforms. For elementary abelian groups \(Z_p^k\), \(p\) an odd prime, equipartitions are by \(k\)-tuples of complex regular \(p\)-fans in \({\mathbb {C}}^d\), analogues of the famous Grunbaum problem on equipartitions in \({\mathbb {R}}^d\) by \(k\)-tuples of hyperplanes (i.e., regular 2-fans). Here the number of regions is a prime power, as usual in topological applications to combinatorial geometry. For general abelian groups, however, the Fourier perspective yields new classes of equipartitions by families of complex regular fans \(F_{q_1},\ldots , F_{q_k}\) (such as those of a “Makeev-type”), including when the number of regions is not a prime power.
ListOps: A Diagnostic Dataset for Latent Tree Learning

Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.
How Will the Presence of Autonomous Vehicles Affect the Equilibrium State of Traffic Networks

It is known that connected and autonomous vehicles are capable of maintaining shorter headway and distances when they form platoons of vehicles. Thus, such technologies can potentially increase the road capacities of traffic networks. Consequently, it is envisioned that their deployment will also increase the overall network mobility. In this paper, we examine the validity of this expected impact, assuming that drivers select their routes selfishly, in traffic networks with mixed vehicle autonomy, that is, traffic networks with both regular and autonomous vehicles. We consider a nonatomic routing game on a network with inelastic (fixed) demands for a set of network origin destination (O/D) pairs, and study how replacing a fraction of regular vehicles by autonomous vehicles will affect the mobility of the network. Using well-known U.S. Bureau of Public Roads traffic delay models, we show that the resulting Wardrop equilibrium is not necessarily unique for networks with mixed autonomy. Then, we state the conditions under which the total network delay at equilibrium is guaranteed to not increase as the fraction of autonomous vehicles increases. However, we show that when these conditions do not hold, counterintuitive behaviors may occur—the total network delay can grow as the fraction of autonomous cars increases. In particular, we prove that for networks with a single O/D pair, if the road degrees of capacity asymmetry (i.e., the ratio between the road capacity when all vehicles are regular and the road capacity when all vehicles are autonomous) are homogeneous, the total delay is: 1) unique and 2) a nonincreasing continuous function of the fraction of autonomous vehicles in the network. We show that for heterogeneous degrees of capacity asymmetry, the total delay is not unique, and it can further grow as the fraction of autonomous vehicles increases. We demonstrate that similar behaviors may be observed in networks with multiple O/D pairs. We further bound such performance degradations due to the introduction of autonomy in general homogeneous networks.
Higher dimensional distortion of random complexes

Using the random complexes of Linial and Meshulam, we exhibit a large family of simplicial complexes for which, whenever affinely embedded into Euclidean space, the filling areas of simplicial cycles is greatly distorted. This phenomenon can be regarded as a higher order analogue of the metric distortion of embeddings of random graphs.
Measuring sample quality with Stein's method

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational speed. The reasoning is sound: a reduction in variance due to more rapid sampling can outweigh the bias introduced. However, the inexactness creates new challenges for sampler and parameter selection, since standard measures of sample quality like effective sample size do not account for asymptotic bias. To address these challenges, we introduce a new computable quality measure based on Stein's method that quantifies the maximum discrepancy between sample and target expectations over a large class of test functions. We use our tool to compare exact, biased, and deterministic sample sequences and illustrate applications to hyperparameter selection, convergence rate assessment, and quantifying bias-variance tradeoffs in posterior inference.
Towards Universal Paraphrastic Sentence Embeddings

Abstract: We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We compare six compositional architectures, evaluating them on annotated textual similarity datasets drawn both from the same distribution as the training data and from a wide range of other domains. We find that the most complex architectures, such as long short-term memory (LSTM) recurrent neural networks, perform best on the in-domain data. However, in out-of-domain scenarios, simple architectures such as word averaging vastly outperform LSTMs. Our simplest averaging model is even competitive with systems tuned for the particular tasks while also being extremely efficient and easy to use. In order to better understand how these architectures compare, we conduct further experiments on three supervised NLP tasks: sentence similarity, entailment, and sentiment classification. We again find that the word averaging models perform well for sentence similarity and entailment, outperforming LSTMs. However, on sentiment classification, we find that the LSTM performs very strongly-even recording new state-of-the-art performance on the Stanford Sentiment Treebank. We then demonstrate how to combine our pretrained sentence embeddings with these supervised tasks, using them both as a prior and as a black box feature extractor. This leads to performance rivaling the state of the art on the SICK similarity and entailment tasks. We release all of our resources to the research community with the hope that they can serve as the new baseline for further work on universal sentence embeddings.
Some cut-generating functions for second-order conic sets

Abstract In this paper, we study cut generating functions for conic sets. Our first main result shows that if the conic set is bounded, then cut generating functions for integer linear programs can easily be adapted to give the integer hull of the conic integer program. Then we introduce a new class of cut generating functions which are non-decreasing with respect to second-order cone. We show that, under some minor technical conditions, these functions together with integer linear programming-based functions are sufficient to yield the integer hull of intersections of conic sections in R 2 .
A Stochastic Diffusion Process for the Dirichlet Distribution

The method of potential solutions of Fokker-Planck equations is used to develop a transport equation for the joint probability of N coupled stochastic variables with the Dirichlet distribution as its asymptotic solution. To ensure a bounded sample space, a coupled nonlinear diffusion process is required: the Wiener processes in the equivalent system of stochastic differential equations are multiplicative with coefficients dependent on all the stochastic variables. Individual samples of a discrete ensemble, obtained from the stochastic process, satisfy a unit-sum constraint at all times. The process may be used to represent realizations of a fluctuating ensemble of N variables subject to a conservation principle. Similar to the multivariate Wright-Fisher process, whose invariant is also Dirichlet, the univariate case yields a process whose invariant is the beta distribution. As a test of the results, Monte Carlo simulations are used to evolve numerical ensembles toward the invariant Dirichlet distribution.
Design of defect spins in piezoelectric aluminum nitride for solid-state hybrid quantum technologies

Spin defects in wide-band gap semiconductors are promising systems for the realization of quantum bits, or qubits, in solid-state environments. To date, defect qubits have only been realized in materials with strong covalent bonds. Here, we introduce a strain-driven scheme to rationally design defect spins in functional ionic crystals, which may operate as potential qubits. In particular, using a combination of state-of-the-art ab-initio calculations based on hybrid density functional and many-body perturbation theory, we predicted that the negatively charged nitrogen vacancy center in piezoelectric aluminum nitride exhibits spin-triplet ground states under realistic uni- and bi-axial strain conditions; such states may be harnessed for the realization of qubits. The strain-driven strategy adopted here can be readily extended to a wide range of point defects in other wide-band gap semiconductors, paving the way to controlling the spin properties of defects in ionic systems for potential spintronic technologies.
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series.

Time series and signals are attracting more attention across statistics, machine learning and pattern recognition as it appears widely in the industry， especially in sensor and IoT related research and applications, but few advances has been achieved in effective time series visual analytics and interaction due to its temporal dimensionality and complex dynamics. Inspired by recent effort on using network metrics to characterize time series for classification, we present an approach to visualize time series as complex networks based on the first order Markov process in its temporal ordering. In contrast to the classical bar charts, line plots and other statistics based graph, our approach delivers more intuitive visualization that better preserves both the temporal dependency and frequency structures. It provides a natural inverse operation to map the graph back to raw signals, making it possible to use graph statistics to characterize time series for better visual exploration and statistical analysis. Our experimental results suggest the effectiveness on various tasks such as pattern discovery and classification on both synthetic and the real time series and sensor data.
Quantum invariants of periodic links and periodic 3-manifolds

We give criteria for framed links and 3-manifolds to be periodic of prime order. As applications we show that the Poincare sphere is of periodicity 2, 3, 5 only and the Brieskorn sphere (2,3,7) is of periodicity 2, 3, 7 only.
Indices of inseparability for elementary abelian p-extensions

Abstract Let K be a local field whose residue field K ¯ is a finite field of characteristic p and let L / K be a finite totally ramified Galois extension. Fried [3] and Heiermann [5] defined the “indices of inseparability” of L / K , a refinement of the ramification data of L / K . We give a method for computing the indices of inseparability of the extension L / K in terms of the norm group N L / K ( L × ) in the case where K has characteristic p and Gal ( L / K ) is an elementary abelian p-group with a single ramification break. In some cases our methods lead to simple formulas for the indices of inseparability.
A multi-batch L-BFGS method for machine learning

The question of how to parallelize the stochastic gradient descent (SGD) method has received much attention in the literature. In this paper, we focus instead on batch methods that use a sizeable fraction of the training set at each iteration to facilitate parallelism, and that employ second-order information. In order to improve the learning process, we follow a multi-batch approach in which the batch changes at each iteration. This can cause difficulties because L-BFGS employs gradient differences to update the Hessian approximations, and when these gradients are computed using different data points the process can be unstable. This paper shows how to perform stable quasi-Newton updating in the multi-batch setting, illustrates the behavior of the algorithm in a distributed computing platform, and studies its convergence properties for both the convex and nonconvex cases.
Multi-task neural networks for personalized pain recognition from physiological signals

Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.
Spontaneous Hall effects in the electron system at the SmTiO3/EuTiO3 interface

Magnetotransport and magnetism of epitaxial SmTiO3/EuTiO3 heterostructures grown by molecular beam epitaxy are investigated. It is shown that the polar discontinuity at the interface introduces ∼3.9 × 1014 cm−2 carriers into the EuTiO3. The itinerant carriers exhibit two distinct contributions to the spontaneous Hall effect. The anomalous Hall effect appears despite a very small magnetization, indicating a non-collinear spin structure, and the second contribution resembles a topological Hall effect. Qualitative differences exist in the temperature dependence of both Hall effects when compared to uniformly doped EuTiO3. In particular, the topological Hall effect contribution appears at higher temperatures and the anomalous Hall effect shows a sign change with temperature. The results suggest that interfaces can be used to tune topological phenomena in itinerant magnetic systems.
Approximating Sampled Sinusoids and Multiband Signals Using Multiband Modulated DPSS Dictionaries

Many signal processing problems—such as analysis, compression, denoising, and reconstruction—can be facilitated by expressing the signal as a linear combination of atoms from a well-chosen dictionary. In this paper, we study possible dictionaries for representing the discrete vector one obtains when collecting a finite set of uniform samples from a multiband analog signal. By analyzing the spectrum of combined time- and multiband-limiting operations in the discrete-time domain, we conclude that the information level of the sampled multiband vectors is essentially equal to the time–frequency area. For representing these vectors, we consider a dictionary formed by concatenating a collection of modulated discrete prolate spheroidal sequences (DPSS’s). We study the angle between the subspaces spanned by this dictionary and an optimal dictionary, and we conclude that the multiband modulated DPSS dictionary—which is simple to construct and more flexible than the optimal dictionary in practical applications—is nearly optimal for representing multiband sample vectors. We also show that the multiband modulated DPSS dictionary not only provides a very high degree of approximation accuracy in an MSE sense for multiband sample vectors (using a number of atoms comparable to the information level), but also that it can provide high-quality approximations of all sampled sinusoids within the bands of interest.
Optimization as Estimation with Gaussian Processes in Bandit Settings

Recently, there has been rising interest in Bayesian optimization ‐ the optimization of an unknown function with assumptions usually expressed by a Gaussian Process (GP) prior. We study an optimization strategy that directly uses an estimate of the argmax of the function. This strategy offers both practical and theoretical advantages: no tradeoff parameter needs to be selected, and, moreover, we establish close connections to the popular GP-UCB and GP-PI strategies. Our approach can be understood as automatically and adaptively trading off exploration and exploitation in GP-UCB and GP-PI. We illustrate the effects of this adaptive tuning via bounds on the regret as well as an extensive empirical evaluation on robotics and vision tasks, demonstrating the robustness of this strategy for a range of performance criteria.