cssi/SciSciGPT-SciSciNet huggingface dataset:
Harish-Chandra bimodules over rational Cherednik algebras

Abstract We study Harish-Chandra bimodules over the rational Cherednik algebra H c ( W ) associated to a complex reflection group W with parameter c. Our results allow us to partially reduce the study of these bimodules to smaller algebras. We classify those pairs of parameters ( c , c ′ ) for which there exist fully supported Harish-Chandra bimodules, and give a description of the category of all Harish-Chandra bimodules modulo those without full support. When W is a symmetric group we are able to classify all irreducible Harish-Chandra bimodules. Our proofs are based on localization techniques, the action of the Namikawa–Weyl group on the set of parameters, and the study of partial KZ functors.
Abstract Syntax Networks for Code Generation and Semantic Parsing

Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.
Value Directed Exploration in Multi-Armed Bandits with Structured Priors.

Multi-armed bandits are a quintessential machine learning problem requiring the balancing of exploration and exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an algorithm for Bayesian multi-armed bandits that utilizes value-function-driven online planning techniques. Building on previous work on UCB and Gittins index, we introduce linearly-separable value functions that take both the expected return and the benefit of exploration into consideration to perform n-step lookahead. The algorithm enjoys a sub-linear performance guarantee and we present simulation results that confirm its strength in problems with structured priors. The simplicity and generality of our approach makes it a strong candidate for analyzing more complex multi-armed bandit problems.
Optimal evidence accumulation on social networks

A fundamental question in biology is how organisms integrate sensory and social evidence to make decisions. However, few models describe how both these streams of information can be combined to optimize choices. Here we develop a normative model for collective decision making in a network of agents performing a two-alternative forced choice task. We assume that rational (Bayesian) agents in this network make private measurements, and observe the decisions of their neighbors until they accumulate sufficient evidence to make an irreversible choice. As each agent communicates its decision to those observing it, the flow of social information is described by a directed graph. The decision-making process in this setting is intuitive, but can be complex. We describe when and how the absence of a decision of a neighboring agent communicates social information, and how an agent must marginalize over all unobserved decisions. We also show how decision thresholds and network connectivity affect group evidence accumulation, and describe the dynamics of decision making in social cliques. Our model provides a bridge between the abstractions used in the economics literature and the evidence accumulator models used widely in neuroscience and psychology.
On a strange invariant bilinear form on the space of automorphic forms

Let F be a global field and \(G:=SL(2)\). We study the bilinear form \({{\mathcal {B}}}\) on the space of K-finite smooth compactly supported functions on \(G({\mathbb {A}})/G(F)\) defined by $$\begin{aligned} {{\mathcal {B}}}(f_1,f_2):={{\mathcal {B}}}_{\mathrm {naive}}(f_1,f_2)-\langle M^{-1}{{\mathrm{{CT}}}}(f_1)\, ,{{\mathrm{{CT}}}}(f_2)\rangle , \end{aligned}$$ where \({{\mathcal {B}}}_{\mathrm {naive}}\) is the usual scalar product, \({{\mathrm{{CT}}}}\) is the constant term operator, and M is the standard intertwiner. This form is natural from the viewpoint of the geometric Langlands program. To justify this claim, we provide a dictionary between the classical and ‘geometric’ theory of automorphic forms. We also show that the form \({{\mathcal {B}}}\) is related to S. Schieder’s Picard–Lefschetz oscillators.
Electric field tuning of the anomalous Hall effect at oxide interfaces

Anomalous Hall effect is the phenomenon where the transport properties of the spin-polarized electrons are governed by the spin-orbit coupling that couples the orbital and spin degrees of freedom of the electron. Here we show that the anomalous Hall effect at a magnetic interface with strong spin-orbit coupling can be tuned with an external electric field. By altering the strength of the inversion symmetry breaking, the electric field changes the Rashba interaction, which in turn modifies the magnitude of the Berry curvature, the central quantity in determining the anomalous Hall conductivity. The effect is illustrated with a square lattice model, which yields a quadratic dependence of the anomalous Hall conductivity for small electric fields. Explicit density-functional calculations were performed for the recently grown iridate interface, viz., the (SrIrO3)1/(SrMnO3)1 (001) structure, both with and without an electric field, which show a strong electric field dependence. The effect may be potentially useful in spintronics applications.
REPRESENTATION OF SMALL INTEGERS BY BINARY FORMS

We establish some upper bounds for the number of integer solutions to the Thue inequality $|F(x , y)| \leq m$, where $F$ is a binary form of degree $n \geq 3$ and with non-zero discriminant $D$, and $m$ is an integer. Our upper bounds are independent of $m$, when $m$ is smaller than $|D|^{\frac{1}{4(n-1)}}$. We also consider the Thue equation $|F(x , y)| = m$ and give some upper bounds for the number of its integral solutions. In the case of equation, our upper bounds will be independent of integer $m$, when $ m < |D|^{\frac{1}{2(n-1)}}$.
Resilience of ranks of higher inclusion matrices

Let $$n \ge r \ge s \ge 0$$ be integers and $$\mathcal {F}$$ a family of r-subsets of [n]. Let $$W_{r,s}^{\mathcal {F}}$$ be the higher inclusion matrix of the subsets in $${{\mathcal {F}}}$$ vs. the s-subsets of [n]. When $$\mathcal {F}$$ consists of all r-subsets of [n], we shall simply write $$W_{r,s}$$ in place of $$W_{r,s}^{\mathcal {F}}$$ . In this paper we prove that the rank of the higher inclusion matrix $$W_{r,s}$$ over an arbitrary field K is resilient. That is, if the size of $$\mathcal {F}$$ is “close” to $${n \atopwithdelims ()r}$$ then $${{\mathrm{rank}}}_{K}( W_{r,s}^{\mathcal {F}}) = {{\mathrm{rank}}}_{K}(W_{r,s})$$ , where K is an arbitrary field. Furthermore, we prove that the rank (over a field K) of the higher inclusion matrix of r-subspaces vs. s-subspaces of an n-dimensional vector space over $${\mathbb {F}}_q$$ is also resilient if $$\mathrm{char}(K)$$ is coprime to q.
Weighted distribution of points on cyclic covers of the projective line over finite fields

Abstract For any prescribed finite field F q and integers m ≥ 2 , we study the distribution of the number of F q -points on (possibly singular) affine curves C f : y m = f ( x ) , where f is randomly chosen from a fixed collection F ( F q ) of polynomials in F q [ x ] . These equations are affine models of cyclic m-covers of the projective line. Previously, different authors obtained asymptotic results about distributions of F q -points on curves associated to certain collections of polynomials f. We obtain analogous results for infinitely many new examples of collections F ( F q ) and study how the asymptotic distribution depends on the choice of the collection of polynomials F ( F q ) .
SYBILFUSE: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection

Sybil attacks are becoming increasingly widespread and pose a significant threat to online social systems; a single adversary can inject multiple colluding identities in the system to compromise security and privacy. Recent works have leveraged social network-based trust relationships to defend against Sybil attacks. However, existing defenses are based on oversimplified assumptions about network structure, which do not necessarily hold in real-world social networks. Recognizing these limitations, we propose SYBILFUSE, a defense-in-depth framework for Sybil detection when the oversimplified assumptions are relaxed. SYBILFUSE adopts a collective classification approach by first training local classifiers to compute local trust scores for nodes and edges, and then propagating the local scores through the global network structure via weighted random walk and loopy belief propagation mechanisms. We evaluate our framework on both synthetic and real-world network topologies, including a large-scale, labeled Twitter network comprising 20M nodes and 265M edges, and demonstrate that SYBILFUSE outperforms state-of-the-art approaches significantly. In particular, SYBILFUSE achieves 98% of Sybil coverage among top-ranked nodes.
Independence results around constructive ZF

Abstract CZF is an intuitionistic set theory that does not contain Power Set, substituting instead a weaker version, Subset Collection. In this paper a Kripke model of CZF is presented in which Power Set is false. In addition, another Kripke model is presented of CZF with Subset Collection replaced by Exponentiation, in which Subset Collection fails.
Partial quotients and representation of rational numbers

Abstract It is shown that there is an absolute constant C such that any rational b q ∈ ] 0 , 1 [ , ( b , q ) = 1 , admits a representation as a finite sum b q = ∑ α b α q α where ∑ α ∑ i a i ( b α q α ) C log q and { a i ( x ) } denotes the sequence of partial quotients of x.
On remote estimation with multiple communication channels

This paper considers a sequential estimation and sensor scheduling problem in the presence of multiple communication channels. As opposed to the classical remote estimation problem that involves one perfect (noiseless) channel and one extremely noisy channel (which corresponds to not transmitting the observed state), a more realistic additive noise channel with fixed power constraint along with a more costly perfect channel is considered. It is shown, via a counter-example, that the common folklore of applying symmetric threshold policy, which is well known to be optimal (for unimodal state densities) in the classical two-channel remote estimation problem, is no longer optimal for the setting considered. Next, in order to make the problem tractable, a side channel which signals the sign of the underlying state is considered. It is shown that, under some technical assumptions, threshold-in-threshold communication scheduling is optimal for this setting. The impact of the presence of a noisy channel is analyzed numerically based on dynamic programming. This numerical analysis uncovers some rather surprising results inheriting known properties from the noisy and noiseless settings.
Optimal Deterministic Mechanisms for an Additive Buyer

We study revenue maximization by deterministic mechanisms for the simplest case for which Myerson's characterization does not hold: a single seller selling two items, with independently distributed values, to a single additive buyer. We prove that optimal mechanisms are submodular and hence monotone. Furthermore, we show that in the IID case, optimal mechanisms are symmetric. Our characterizations are surprisingly non-trivial, and we show that they fail to extend in several natural ways, e.g. for correlated distributions or more than two items. In particular, this shows that the optimality of symmetric mechanisms does not follow from the symmetry of the IID distribution.
A lower bound on the canonical height for polynomials

We prove a lower bound on the canonical height associated to polynomials over number fields evaluated at points with infinite forward orbit. The lower bound depends only on the degree of the polynomial, the degree of the number field, and the number of places of bad reduction.
Refractors in anisotropic media associated with norms

Abstract We show existence of interfaces between two anisotropic materials so that light is refracted in accordance with a given pattern of energy. To do this we formulate a vector Snell law for anisotropic media when the wave fronts are given by norms for which the corresponding unit spheres are strictly convex.
Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games

Regret minimization is a powerful tool for solving large-scale extensive-form games. State-of-the-art methods rely on minimizing regret locally at each decision point. In this work we derive a new framework for regret minimization on sequential decision problems and extensive-form games with general compact convex sets at each decision point and general convex losses, as opposed to prior work which has been for simplex decision points and linear losses. We call our framework laminar regret decomposition. It generalizes the CFR algorithm to this more general setting. Furthermore, our framework enables a new proof of CFR even in the known setting, which is derived from a perspective of decomposing polytope regret, thereby leading to an arguably simpler interpretation of the algorithm. Our generalization to convex compact sets and convex losses allows us to develop new algorithms for several problems: regularized sequential decision making, regularized Nash equilibria in zero-sum extensive-form games, and computing approximate extensive-form perfect equilibria. Our generalization also leads to the first regret-minimization algorithm for computing reduced-normal-form quantal response equilibria based on minimizing local regrets. Experiments show that our framework leads to algorithms that scale at a rate comparable to the fastest variants of counterfactual regret minimization for computing Nash equilibrium, and therefore our approach leads to the first algorithm for computing quantal response equilibria in extremely large games. Our algorithms for (quadratically) regularized equilibrium finding are orders of magnitude faster than the fastest algorithms for Nash equilibrium finding; this suggests regret-minimization algorithms based on decreasing regularization for Nash equilibrium finding as future work. Finally we show that our framework enables a new kind of scalable opponent exploitation approach.
Statistical challenges of big brain network data

Abstract We explore the main characteristics of big brain network data that offer unique statistical challenges. The brain networks are biologically expected to be both sparse and hierarchical. Such unique characterizations put specific topological constraints onto statistical approaches and models we can use effectively. We explore the limitations of the current models used in the field and offer alternative approaches and explain new challenges.
Polynomial Space Randomness in Analysis

We study the interaction between polynomial space randomness and a fundamental result of analysis, the Lebesgue differentiation theorem. We generalize Ko's framework for polynomial space computability in R^n to define weakly pspace-random points, a new variant of polynomial space randomness. We show that the Lebesgue differentiation theorem characterizes weakly pspace random points. That is, a point x is weakly pspace random if and only if the Lebesgue differentiation theorem holds for a point x for every pspace L_1-computable function.
Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ.

Scattertext is an open source tool for visualizing linguistic variation between document categories in a language-independent way. The tool presents a scatterplot, where each axis corresponds to the rank-frequency a term occurs in a category of documents. Through a tie-breaking strategy, the tool is able to display thousands of visible term-representing points and find space to legibly label hundreds of them. Scattertext also lends itself to a query-based visualization of how the use of terms with similar embeddings differs between document categories, as well as a visualization for comparing the importance scores of bag-of-words features to univariate metrics.
A Bayesian Perspective on the Deep Image Prior

The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For “inference”, gradient descent is performed to adjust network parameters to make the output match observations. This approach yields good performance on a range of image reconstruction tasks. We show that the deep image prior is asymptotically equivalent to a stationary Gaussian process prior in the limit as the number of channels in each layer of the network goes to infinity, and derive the corresponding kernel. This informs a Bayesian approach to inference. We show that by conducting posterior inference using stochastic gradient Langevin dynamics we avoid the need for early stopping, which is a drawback of the current approach, and improve results for denoising and impainting tasks. We illustrate these intuitions on a number of 1D and 2D signal reconstruction tasks.
Predictors of short-term decay of cell phone contacts in a large scale communication network

a b s t r a c t Under what conditions is an edge present in a social network at time t likely to decay or persist by some future time t + � t? Previous research addressing this issue suggests that the network range of the people involved in the edge, the extent to which the edge is embedded in a surrounding structure, and the age of the edge all play a role in edge decay. This paper uses weighted data from a large-scale social network built from cell-phone calls in an 8-week period to determine the importance of edge weight for the decay/persistence process. In particular, we study the relative predictive power of directed weight, embeddedness, newness, and range (measured as outdegree) with respect to edge decay and assess the effectiveness with which a simple decision tree and logistic regression classifier can accurately predict whether an edge that was active in one time period continues to be so in a future time period. We find that directed edge weight, weighted reciprocity and time-dependent measures of edge longevity are highly predictive of whether we classify an edge as persistent or decayed, relative to the other types of factors at the dyad and neighborhood level.
Mind control as a guide for the mind

The human brain is a complex network that supports mental function. The nascent field of network neuroscience applies tools from mathematics to neuroimaging data in the hope of shedding light on cognitive function. A critical question arising from these empirical studies is how to modulate a human brain network to treat cognitive deficits or enhance mental abilities. While historically a number of tools have been employed to modulate mental states (such as cognitive behavioural therapy and brain stimulation), theoretical frameworks to guide these interventions—and to optimize them for clinical use—are fundamentally lacking. One promising and as yet under-explored approach lies in a subdiscipline of engineering known as network control theory. Here, we posit that network control fundamentally relates to mind control, and that this relationship highlights important areas for future empirical research and opportunities to translate knowledge into practical domains. We clarify the conceptual intersection between neuroanatomy, cognition, and control engineering in the context of network neuroscience. Finally, we discuss the challenges, ethics, and promises of mind control. Medaglia et al. explore how network control theory — a subdiscipline of engineering — could guide interventions that modulate mental states in order to treat cognitive deficits or enhance mental abilities.
Evaluating Methods for End-User Creation of Robot Task Plans

How can we enable users to create effective, perception-driven task plans for collaborative robots? We conducted a 35-person user study with the Behavior Tree-based CoSTAR system to determine which strategies for end user creation of generalizable robot task plans are most usable and effctive. CoSTAR allows domain experts to author complex, perceptually grounded task plans for collaborative robots. As a part of CoSTAR's wide range of capabilities, it allows users to specify SmartMoves: abstract goals such as “pick up component A from the right side of the table.” Users were asked to perform pick-and-place assembly tasks with either SmartMoves or one of three simpler baseline versions of CoSTAR. Overall, participants found CoSTAR to be highly usable, with an average System Usability Scale score of 73.4 out of 100. SmartMove also helped users perform tasks faster and more effectively; all SmartMove users completed the first two tasks, while not all users completed the tasks using the other strategies. SmartMove users showed better performance for incorporating perception across all three tasks.
The cup subalgebra has the absorbing amenability property

Consider an inclusion of diffuse von Neumann algebras A ⊂ M. We say that A ⊂ M has the absorbing amenability property (AAP) if for any diffuse subalgebra B ⊂ A and any amenable intermediate algebra B ⊂ D ⊂ M we have that D is contained in A. We prove that the cup subalgebra associated to any subfactor planar algebra has the AAP.
Convex Chance Constrained Model Predictive Control

We consider the Chance Constrained Model Predictive Control problem for polynomial systems subject to disturbances. In this problem, we aim at finding optimal control input for given disturbed dynamical system to minimize a given cost function subject to probabilistic constraints, over a finite horizon. The control laws provided have a predefined (low) risk of not reaching the desired target set. Building on the theory of measures and moments, a sequence of finite semidefinite programmings are provided, whose solution is shown to converge to the optimal solution of the original problem. Numerical examples are presented to illustrate the computational performance of the proposed approach.
Optimal rates for first-order stochastic convex optimization under Tsybakov noise condition

We focus on the problem of minimizing a convex function f over a convex set S given T queries to a stochastic first order oracle. We argue that the complexity of convex minimization is only determined by the rate of growth of the function around its minimizer xf,S*, as quantified by a Tsybakov-like noise condition. Specifically, we prove that if f grows at least as fast as ||x - xf,S*||κ around its minimum, for some κ > 1, then the optimal rate of learning f(xf,S*) is Θ(T- κ/2κ-2). The classic rate Θ(1/T) for convex functions and Θ(1/T) for strongly convex functions are special cases of our result for κ → ∞ and κ = 2, and even faster rates are attained for κ < 2. We also derive tight bounds for the complexity of learning xf,S*, where the optimal rate is Θ(T-1/2κ-2). Interestingly, these precise rates for convex optimization also characterize the complexity of active learning and our results further strengthen the connections between the two fields, both of which rely on feedback-driven queries.
Resource Constrained Structured Prediction

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach based on selectively acquiring computationally costly features during test-time in order to reduce the computational cost of pre- diction with minimal performance degradation. We formulate a novel empirical risk minimization (ERM) for policy learning. We show that policy learning can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition and dependency parsing and show significant reduction in the feature costs without degrading accuracy.
Link diagrams with low Turaev genus

We classify link diagrams with Turaev genus one and two in terms of an alternating tangle structure of the link diagram. The proof involves surgery along simple closed curves on the Turaev surface, called cutting loops, which have corresponding cutting arcs that are visible on the planar link diagram. These also provide new obstructions for a link diagram on a surface to come from the Turaev surface algorithm.
Neural Probabilistic Model for Non-projective MST Parsing

In this paper, we propose a probabilistic parsing model that defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTMCNNs, which automatically benefits from both word- and character-level representations, by using a combination of bidirectional LSTMs and CNNs. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. By exploiting Kirchhoff’s Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straightforward end-to-end model training procedure via back-propagation. We evaluate our model on 17 different datasets, across 14 different languages. Our parser achieves state-of-the-art parsing performance on nine datasets.
Some non-pseudoconvex domains with explicitly computable non-Hausdorff Dolbeault cohomology

We explicitly compute the Dolbeault cohomologies of certain domains in complex space generalizing the classical Hartogs figure. The cohomology groups are non-Hausdorff topological vector spaces, and it is possible to identify the reduced (Hausdorff) and the indiscrete part of the cohomology.
Semidefinite relaxations in optimal experiment design with application to substrate injection for hyperpolarized MRI

We consider the problem of optimal input design for estimating uncertain parameters in a discrete-time linear state space model, subject to simultaneous amplitude and l1/l2-norm constraints on the admissible inputs. We formulate this problem as the maximization of a (non-concave) quadratic function over the space of inputs, and use semidefinite relaxation techniques to efficiently find the global solution or to provide an upper bound. This investigation is motivated by a problem in medical imaging, specifically designing a substrate injection profile for in vivo metabolic parameter mapping using magnetic resonance imaging (MRI) with hyperpolarized carbon-13 pyruvate. In the l2-norm-constrained case, we show that the relaxation is tight, allowing us to efficiently compute a globally optimal injection profile. In the l1-norm-constrained case the relaxation is no longer tight, but can be used to prove that the boxcar injection currently used in practice achieves at least 98.7% of the global optimum.
Hand-Held Video Deblurring Via Efficient Fourier Aggregation

Videos captured with hand-held cameras often suffer from a significant amount of blur, mainly caused by the inevitable natural tremor of the photographer’s hand. In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video. The dynamic nature of typical videos with the presence of multiple moving objects and occlusions makes this problem of camera shake removal extremely challenging, in particular when low complexity is needed. Given an input video frame, we first create a consistent registered version of temporally adjacent frames. Then, the set of consistently registered frames is block-wise fused in the Fourier domain with weights depending on the Fourier spectrum magnitude. The method is motivated from the physiological fact that camera shake blur has a random nature; therefore, nearby video frames are generally blurred differently. Experiments with numerous videos recorded in the wild, along with extensive comparisons, show that the proposed algorithm achieves state-of-the-art results while at the same time being much faster than its competitors.
An Empirical Evaluation of Current Convolutional Architectures’ Ability to Manage Nuisance Location and Scale Variability

We conduct an empirical study to test the ability of convolutional neural networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. In theory, averaging the latter should yield inferior performance compared to proper marginalization. Yet empirical evidence suggests the converse, leading us to conclude that – at the current level of complexity of convolutional architectures and scale of the data sets used to train them – CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on a classification task using the ImageNet Challenge benchmark and on a wide-baseline matching task using the Oxford and Fischer's datasets.
Matching and Independence Complexes Related to Small Grids

The topology of the matching complex for the $2\times n$ grid graph is mysterious. We describe a discrete Morse matching for a family of independence complexes $\mathrm{Ind}(\Delta_n^m)$ that include these matching complexes. Using this matching, we determine the dimensions of the chain spaces for the resulting Morse complexes and derive bounds on the location of non-trivial homology groups for certain $\mathrm{Ind}(\Delta_n^m)$. Further, we determine the Euler characteristic of $\mathrm{Ind}(\Delta_n^m)$ and prove that several homology groups of $\mathrm{Ind}(\Delta_n^m)$ are non-zero.
Specification and towers in shift spaces

We show that a shift space on a finite alphabet with a non-uniform specification property can be modeled by a strongly positive recurrent countable-state Markov shift to which every equilibrium state lifts. In addition to uniqueness of the equilibrium state, this gives strong statistical properties including the Bernoulli property, exponential decay of correlations, central limit theorem, and analyticity of pressure, which are new even for uniform specification. We give applications to shifts of quasi-finite type, synchronised and coded shifts, and factors of $${\beta}$$ -shifts and S-gap shifts.
The asymptotic couple of the field of logarithmic transseries

Abstract The derivation on the differential-valued field T log of logarithmic transseries induces on its value group Γ log a certain map ψ. The structure ( Γ log , ψ ) is a divisible asymptotic couple. We prove that the theory T log = Th ( Γ log , ψ ) admits elimination of quantifiers in a natural first-order language. All models ( Γ , ψ ) of T log have an important discrete subset Ψ : = ψ ( Γ ∖ { 0 } ) . We give explicit descriptions of all definable functions on Ψ and prove that Ψ is stably embedded in Γ.
Pisces: Anonymous Communication Using Social Networks

The architectures of deployed anonymity systems such as Tor suffer from two key problems that limit user’s trust in these systems. First, paths for anonymous communication are built without considering trust relationships between users and relays in the system. Second, the network architecture relies on a set of centralized servers. In this paper, we propose Pisces, a decentralized protocol for anonymous communications that leverages users’ social links to build circuits for onion routing. We argue that such an approach greatly improves the system’s resilience to attackers. A fundamental challenge in this setting is the design of a secure process to discover peers for use in a user’s circuit. All existing solutions for secure peer discovery leverage structured topologies and cannot be applied to unstructured social network topologies. In Pisces, we discover peers by using random walks in the social network graph with a bias away from highly connected nodes to prevent a few nodes from dominating the circuit creation process. To secure the random walks, we leverage the reciprocal neighbor policy: if malicious nodes try to exclude honest nodes during peer discovery so as to improve the chance of being selected, then honest nodes can use a tit-for-tat approach and reciprocally exclude the malicious nodes from their routing tables. We describe a fully decentralized protocol for enforcing this policy, and use it to build the Pisces anonymity
Spatio-Temporal Action Detection with Cascade Proposal and Location Anticipation

In this work, we address the problem of spatio-temporal action detection in temporally untrimmed videos. It is an important and challenging task as finding accurate human actions in both temporal and spatial space is important for analyzing large-scale video data. To tackle this problem, we propose a cascade proposal and location anticipation (CPLA) model for frame-level action detection. There are several salient points of our model: (1) a cascade region proposal network (casRPN) is adopted for action proposal generation and shows better localization accuracy compared with single region proposal network (RPN); (2) action spatio-temporal consistencies are exploited via a location anticipation network (LAN) and thus frame-level action detection is not conducted independently. Frame-level detections are then linked by solving an linking score maximization problem, and temporally trimmed into spatio-temporal action tubes. We demonstrate the effectiveness of our model on the challenging UCF101 and LIRIS-HARL datasets, both achieving state-of-the-art performance.
Minimax Density Estimation for Growing Dimension.

This paper presents minimax rates for density estimation when the data dimension $d$ is allowed to grow with the number of observations $n$ rather than remaining fixed as in previous analyses. We prove a non-asymptotic lower bound which gives the worst-case rate over standard classes of smooth densities, and we show that kernel density estimators achieve this rate. We also give oracle choices for the bandwidth and derive the fastest rate $d$ can grow with $n$ to maintain estimation consistency.
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions

We consider the problem of optimizing an approximately convex function over a bounded convex set in R n using only function evaluations. The problem is reduced to sampling from an approximately log-concave distribution using the Hit-and-Run method, with query complexity of O ⁄ (n 4.5 ). In the context of zeroth order stochastic convex optimization, the proposed method produces an †minimizer after O ⁄ (n 7.5 † i2 ) noisy function evaluations by inducing a O (†/n)-approximately log concave distribution. We also consider the case when the “amount of non-convexity” decays towards the optimum of the function. Other applications of the random walk method include private computation of empirical risk minimizers, two-stage stochastic programming, and approximate dynamic programming for online learning.
Non-left-orderable surgeries on L-space twisted torus knots

We show that if $K$ is an L-space twisted torus knot $T^{l,m}_{p,pk \pm 1}$ with $p \ge 2$, $k \ge 1$, $m \ge 1$ and $1 \le l \le p-1$, then the fundamental group of the $3$-manifold obtained by $\frac{r}{s}$-surgery along $K$ is not left-orderable whenever $\frac{r}{s} \ge 2 g(K) -1$, where $g(K)$ is the genus of $K$.
Wrong way maps in uniformly finite homology and homology of groups

Given a non-compact Riemannian manifold M and a submanifold $$N \hookrightarrow M$$ of codimension q, we will construct under certain assumptions on both M and N a wrong way map $$H^\mathrm {uf}_*(M) \rightarrow H^\mathrm {uf}_{*-q}(N)$$ in uniformly finite homology. Using an equivariant version of the construction and applying it to universal covers, we will construct a map $$H_*(B\pi _1 M) \rightarrow H_{*-q}(B\pi _1 N)$$ . As applications we discuss obstructions to the existence of positive scalar curvature metrics and to inessentialness.
Integer invariants of abelian Cayley graphs

Let G be a finite abelian group, let E be a subset of G, and form the Cayley (directed) graph of G with connecting set E. We explain how, for various matrices associated to this graph, the spectrum can be used to give information on the Smith normal form. This technique is applied to several interesting examples, including matrices in the Bose–Mesner algebra of the Hamming association scheme H(n,q). We also recover results of Bai and Jacobson–Niedermaier–Reiner on the critical group of a Cartesian product of complete graphs.
Fast and robust compressive phase retrieval with sparse-graph codes

In this paper, we tackle the compressive phase retrieval problem in the presence of noise. The noisy compressive phase retrieval problem is to recover a K-sparse complex signal s ∈ ℂn, from a set of m noisy quadratic measurements: y i = |a i Hs|2 + w i ; where a i H ∈ ℂn is the ith row of the measurement matrix A ∈ ℂm×n, and w i is the additive noise to the ith measurement. We consider the regime where K = βnδ, δ ∈ (0; 1). We use the architecture of PhaseCode algorithm [1], and robustify it using two schemes: the almost-linear scheme and the sublinear scheme. We prove that with high probability, the almost-linear scheme recovers s with sample complexity1 Θ(K log(n)) and computational complexity Θ(n log(n)), and the sublinear scheme recovers s with sample complexity Θ(K log3(n)) and computational complexity Θ(K log3(n)). To the best of our knowledge, this is the first scheme that achieves sublinear computational complexity for compressive phase retrieval problem. Finally, we provide simulation results that support our theoretical contributions.
METRIC DIOPHANTINE APPROXIMATION FOR SYSTEMS OF LINEAR FORMS VIA DYNAMICS

The goal of this paper is to generalize the main results of [21] and subsequent papers on metric with dependent quantities to the set-up of systems of linear forms. In particular, we establish "joint strong extremality" of arbitrary finite collection of smooth non-degenerate submanifolds of ℝn. The proofs are based on generalized quantitative non-divergence estimates for translates of measures on the space of lattices.
Conformal classes realizing the Yamabe invariant

We give a characterization of conformal classes realizing a compact manifold's Yamabe invariant. This characterization is the analogue of an observation of Nadirashvili for metrics realizing the maximal first eigenvalue, and of Fraser and Schoen for metrics realizing the maximal first Steklov eigenvalue.
Parameter variation in the 'log t' convergence test

This article estimates a local linear version of the model used in the ‘log t’ convergence test. It documents the economically and statistically significant within-sample variation in the estimated...
Bayesian Graphical Compositional Regression for Microbiome Data

An important task in microbiome studies is to test the existence of and give characterization to differences in the microbiome composition across groups of samples. Important challenges of this pro...
Reach for your cell phone at your own risk: The cognitive costs of media choice for breaks.

IntroductionSince there is steady increase in cell phone addiction, the act of reaching for a phone between tasks, or even mid-task, is becoming more commonplace, without a true understanding about...
Unifying computational entropies via Kullback-Leibler divergence

We introduce hardness in relative entropy, a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible entropy, two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding commitment schemes, respectively. Thus, hardness in relative entropy unifies the latter two notions of computational entropy and sheds light on the apparent “duality” between them. Additionally, it yields a more modular and illuminating proof that one-way functions imply next-block inaccessible entropy, similar in structure to the proof that one-way functions imply next-block pseudoentropy (Vadhan and Zheng, STOC ‘12).
Subnanometer imaging and controlled dynamical patterning of thermocapillary driven deformation of thin liquid films

Exploring and controlling the physical factors that determine the topography of thin liquid dielectric films are of interest in manifold fields of research in physics, applied mathematics, and engineering and have been a key aspect of many technological advancements. Visualization of thin liquid dielectric film topography and local thickness measurements are essential tools for characterizing and interpreting the underlying processes. However, achieving high sensitivity with respect to subnanometric changes in thickness via standard optical methods is challenging. We propose a combined imaging and optical patterning projection platform that is capable of optically inducing dynamical flows in thin liquid dielectric films and plasmonically resolving the resulting changes in topography and thickness. In particular, we employ the thermocapillary effect in fluids as a novel heat-based method to tune plasmonic resonances and visualize dynamical processes in thin liquid dielectric films. The presented results indicate that light-induced thermocapillary flows can form and translate droplets and create indentation patterns on demand in thin liquid dielectric films of subwavelength thickness and that plasmonic microscopy can image these fluid dynamical processes with a subnanometer sensitivity along the vertical direction. A technique that induces and maps tiny topographical changes in thin liquid dielectric films could have applications in interfacial science and industry. Shimon Rubin, Brandon Hong and Yeshaiahu Fainman of the University of California, San Diego used a heating laser to induce thermocapillary flows in thin liquid dielectric films. These films are used, among various things, for UV photolithography of semiconductors and microelectronics applications. The team mapped the changes caused by the flows using surface plasmon resonance microscopy, a non-contact imaging method that measures changes of the collective oscillations of surface electromagnetic waves and electrons in the metal substrate. The technique successfully identified subnanometer-scale topographical changes caused by the thermocapillary flow. It overcomes problems in currently used techniques, such as white light interferometry, which is not sensitive enough to detect small, local thickness variations. The technique has many potential applications, including the study of light-induced thickness changes in photosensitive materials.
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions. Modern datasets can have millions of correlated time-series that evolve together, i.e they are extremely high dimensional (one dimension for each individual time-series). There is a need for exploiting global patterns and coupling them with local calibration for better prediction. However, most recent deep learning approaches in the literature are one-dimensional, i.e, even though they are trained on the whole dataset, during prediction, the future forecast for a single dimension mainly depends on past values from the same dimension. In this paper, we seek to correct this deficiency and propose DeepGLO, a deep forecasting model which thinks globally and acts locally. In particular, DeepGLO is a hybrid model that combines a global matrix factorization model regularized by a temporal convolution network, along with another temporal network that can capture local properties of each time-series and associated covariates. Our model can be trained effectively on high-dimensional but diverse time series, where different time series can have vastly different scales, without a priori normalization or rescaling. Empirical results demonstrate that DeepGLO can outperform state-of-the-art approaches; for example, we see more than 25% improvement in WAPE over other methods on a public dataset that contains more than 100K-dimensional time series.
Pricing and Routing Mechanisms for Differentiated Services in an Electric Vehicle Public Charging Station Network

We consider a Charging Network Operator (CNO) that owns a network of Electric Vehicle (EV) public charging stations and wishes to offer a menu of differentiated service options for access to its stations. This involves designing optimal pricing and routing schemes for the setting where users cannot directly choose which station they use. Instead, they choose their priority level and energy request amount from the differentiated service menu, and then the CNO directly assigns them to a station on their path. This allows higher priority users to experience lower wait times at stations, and allows the CNO to directly manage demand, exerting a higher level of control that can be used to manage the effect of EV on the grid and control station wait times. We consider the scenarios where the CNO is a social welfare-maximizing or a profit-maximizing entity, and in both cases, design pricing-routing policies that ensure users reveal their true parameters to the CNO.
Operationalizing relevance in physics education: Using a systems view to expand our conception of making physics relevant

It is useful to move away from a deficit view of students' attitudes and beliefs in order to make physics more relevant for all students.
(Nearly) Sample-Optimal Sparse Fourier Transform in Any Dimension; RIPless and Filterless

In this paper, we consider the extensively studied problem of computing a k-sparse approximation to the d-dimensional Fourier transform of a length n signal. Our algorithm uses O(k log k log n) samples, is dimension-free, operates for any universe size, and achieves the strongest l_∞/l_2 guarantee, while running in a time comparable to the Fast Fourier Transform. In contrast to previous algorithms which proceed either via the Restricted Isometry Property or via filter functions, our approach offers a fresh perspective to the sparse Fourier Transform problem.
Credit Misallocation and Economic Growth in Vietnam

The legacy of non-performing loans and high opportunity cost of government financing of bank recapitalization impeded the efficiency of financial intermediation and are an important policy issue in Vietnam. This paper presents a theoretical and empirical analysis of the issue. An empirical analysis using corporate data indicates credit misallocation between state owned enterprises and private firms in Vietnam. On the theoretical side, a micro-founded banking model is embedded in a political economy setting to assess the factors determining the size of bank recapitalization and its effects on the efficiency of financial intermediation, economic growth and welfare. The analysis suggests that recapitalization depends on an array of factors, including the tightness of the government budget and the decision maker’s concern for the favored sector.