cssi/SciSciGPT-SciSciNet huggingface dataset:
A probabilistic data-driven model for planar pushing

This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) [1] that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.
Discovering Blind Spots in Reinforcement Learning

Agents trained in simulation may make errors in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult to discover because the agent cannot predict them a priori. We propose using oracle feedback to learn a predictive model of these blind spots to reduce costly errors in real-world applications. We focus on blind spots in reinforcement learning (RL) that occur due to incomplete state representation: The agent does not have the appropriate features to represent the true state of the world and thus cannot distinguish among numerous states. We formalize the problem of discovering blind spots in RL as a noisy supervised learning problem with class imbalance. We learn models to predict blind spots in unseen regions of the state space by combining techniques for label aggregation, calibration, and supervised learning. The models take into consideration noise emerging from different forms of oracle feedback, including demonstrations and corrections. We evaluate our approach on two domains and show that it achieves higher predictive performance than baseline methods, and that the learned model can be used to selectively query an oracle at execution time to prevent errors. We also empirically analyze the biases of various feedback types and how they influence the discovery of blind spots.
Mixed Volumes of Hypersimplices

In this paper we consider mixed volumes of combinations of hypersimplices. These numbers, called "mixed Eulerian numbers", were first considered by A. Postnikov and were shown to satisfy many properties related to Eulerian numbers, Catalan numbers, binomial coefficients, etc. We give a general combinatorial interpretation for mixed Eulerian numbers and prove the above properties combinatorially. In particular, we show that each mixed Eulerian number enumerates a certain set of permutations in $S_n$. We also prove several new properties of mixed Eulerian numbers using our methods. Finally, we consider a type B analogue of mixed Eulerian numbers and give an analogous combinatorial interpretation for these numbers.
The ABC of simulation estimation with auxiliary statistics

Abstract The frequentist method of simulated minimum distance (SMD) is widely used in economics to estimate complex models with an intractable likelihood. In other disciplines, a Bayesian approach known as Approximate Bayesian Computation (ABC) is far more popular. This paper connects these two seemingly related approaches to likelihood-free estimation by means of a Reverse Sampler that uses both optimization and importance weighting to target the posterior distribution. Its hybrid features enable us to analyze an ABC estimate from the perspective of SMD. We show that an ideal ABC estimate can be obtained as a weighted average of a sequence of SMD modes, each being the minimizer of the deviations between the data and the model. This contrasts with the SMD, which is the mode of the average deviations. Using stochastic expansions, we provide a general characterization of frequentist estimators and those based on Bayesian computations including Laplace-type estimators. Their differences are illustrated using analytical examples and a simulation study of the dynamic panel model.
On the extreme values of the Riemann zeta function on random intervals of the critical line

In the present paper, we show that under the Riemann hypothesis, and for fixed $$h, \epsilon > 0$$ , the supremum of the real and the imaginary parts of $$\log \zeta (1/2 + it)$$ for $$t \in [UT -h, UT + h]$$ are in the interval $$[(1-\epsilon ) \log \log T, (1+ \epsilon ) \log \log T]$$ with probability tending to 1 when T goes to infinity, U being a uniform random variable in [0, 1]. This proves a weak version of a conjecture by Fyodorov, Hiary and Keating, which has recently been intensively studied in the setting of random matrices. We also unconditionally show that the supremum of $$\mathfrak {R}\log \zeta (1/2 + it)$$ is at most $$\log \log T + g(T)$$ with probability tending to 1, g being any function tending to infinity at infinity.
Bessenrodt-Stanley Polynomials and the Octahedron Recurrence

We show that a family of multivariate polynomials recently introduced by Bessenrodt and Stanley can be expressed as solution of the octahedron recurrence with suitable initial data. This leads to generalizations and explicit expressions as path or dimer partition functions.
Divergence-based characterization of fundamental limitations of adaptive dynamical systems

Adaptive dynamical systems arise in a multitude of contexts, e.g., optimization, control, communications, signal processing, and machine learning. A precise characterization of their fundamental limitations is therefore of paramount importance. In this paper, we consider the general problem of adaptively controlling and/or identifying a stochastic dynamical system, where our a priori knowledge allows us to place the system in a subset of a metric space (the uncertainty set). We present an information-theoretic meta-theorem that captures the trade-off between the metric complexity (or richness) of the uncertainty set, the amount of information acquired online in the process of controlling and observing the system, and the residual uncertainty remaining after the observations have been collected. Following the approach of Zames, we quantify a priori information by the Kolmogorov (metric) entropy of the uncertainty set, while the information acquired online is expressed as a sum of information divergences. The general theory is used to derive new minimax lower bounds on the metric identification error, as well as to give a simple derivation of the minimum time needed to stabilize an uncertain stochastic linear system.
Constructive harmonic conjugates

In the synthetic study of the real projective plane, harmonic conjugates have an essential role, with applications to projectivities, involutions, and polarity. The construction of a harmonic conjugate requires the selection of auxiliary elements; it must be verified, with an invariance theorem, that the result is independent of the choice of these auxiliary elements. A constructive proof of the invariance theorem is given here; the methods used follow principles put forth by Errett Bishop.
Continuity of translation operators

For a Radon measure $\mu$ on $\bbR,$ we show that $L^{\infty}(\mu)$ is invariant under the group of translation operators $T_t(f)(x) = {$f(x-t)$}\ (t \in \bbR)$ if and only if $\mu$ is equivalent to Lebesgue measure $m$. We also give necessary and sufficient conditions for $L^p(\mu),\1 \leq p < \infty,$ to be invariant under the group $\{T_t\}$ in terms of the Radon-Nikodym derivative w.r.t. $m$.
Bilinear Forms on Grothendieck Groups of Triangulated Categories

We extend the theory of bilinear forms on the Green ring of a finite group developed by Benson and Parker to the context of the Grothendieck group of a triangulated category with Auslander–Reiten triangles, taking only relations given by direct sum decompositions. We examine the non-degeneracy of the bilinear form given by dimensions of homomorphisms, and show that the form may be modified to give a Hermitian form for which the standard basis given by indecomposable objects has a dual basis given by Auslander–Reiten triangles. An application is given to the homotopy category of perfect complexes over a symmetric algebra, with a consequence analogous to a result of Erdmann and Kerner.
Twisting of paramodular vectors

Let F be a non-Archimedean local field of characteristic zero, let (π, V) be an irreducible, admissible representation of GSp(4, F) with trivial central character, and let χ be a quadratic character of F× with conductor c(χ) > 1. We define a twisting operator Tχ from paramodular vectors for π of level n to paramodular vectors for χ ⊗ π of level max(n + 2c(χ), 4c(χ)), and prove that this operator has properties analogous to the well-known GL(2) twisting operator.
Positive scalar curvature, higher rho invariants and localization algebras

Abstract In this paper, we use localization algebras to study higher rho invariants of closed spin manifolds with positive scalar curvature metrics. The higher rho invariant is a secondary invariant and is closely related to positive scalar curvature problems. The main result of the paper connects the higher index of the Dirac operator on a spin manifold with boundary to the higher rho invariant of the Dirac operator on the boundary, where the boundary is endowed with a positive scalar curvature metric. Our result extends a theorem of Piazza and Schick [27, Theorem 1.17] .
Kondo resonances in molecular devices

Molecular electronic devices currently serve as a platform for studying a variety of physical phenomena only accessible at the nanometer scale. One such phenomenon is the highly correlated electronic state responsible for the Kondo effect, manifested here as a "Kondo resonance" in the conductance. Because the Kondo effect results from strong electron-electron interactions, it is not captured by the usual quantum chemistry approaches traditionally applied to understand chemical electron transfer. In this review, we will discuss the origins and phenomenology of Kondo resonances observed in single-molecule devices, focusing primarily on the spin-1/2 Kondo state arising from a single unpaired electron. We explore the rich physical system of a single-molecule device, which offers a unique spectroscopic tool for investigating the interplay of emergent Kondo behavior and such properties as molecular orbital transitions and vibrational modes. We will additionally address more exotic systems, such as higher spin states in the Kondo regime, and we will review recent experimental advances in the ability to manipulate and exert control over these nanoscale devices.
Clustering Algorithms for the Centralized and Local Models

We revisit the problem of finding a minimum enclosing ball with differential privacy: Given a set of $n$ points in the Euclidean space $\mathbb{R}^d$ and an integer $t\leq n$, the goal is to find a ball of the smallest radius $r_{opt}$ enclosing at least $t$ input points. The problem is motivated by its various applications to differential privacy, including the sample and aggregate technique, private data exploration, and clustering. Without privacy concerns, minimum enclosing ball has a polynomial time approximation scheme (PTAS), which computes a ball of radius almost $r_{opt}$ (the problem is NP-hard to solve exactly). In contrast, under differential privacy, until this work, only a $O(\sqrt{\log n})$-approximation algorithm was known. We provide new constructions of differentially private algorithms for minimum enclosing ball achieving constant factor approximation to $r_{opt}$ both in the centralized model (where a trusted curator collects the sensitive information and analyzes it with differential privacy) and in the local model (where each respondent randomizes her answers to the data curator to protect her privacy). We demonstrate how to use our algorithms as a building block for approximating $k$-means in both models.
When size matters: Subshifts and their related tiling spaces

We investigate the dynamics of substitution subshifts and their associated tiling spaces. For a given subshift, the associated tiling spaces are all homeomorphic, but their dynamical properties may differ. We give criteria for such a tiling space to be weakly mixing, and for the dynamics of two such spaces to be topologically conjugate.
Multimodal Speaker Segmentation and Diarization Using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks.

While there has been substantial amount of work in speaker diarization recently, there are few efforts in jointly employing lexical and acoustic information for speaker segmentation. Towards that, we investigate a speaker diarization system using a sequence-to-sequence neural network trained on both lexical and acoustic features. We also propose a loss function that allows for selecting not only the speaker change points but also the best speaker at any time by allowing for different speaker groupings. We incorporate Mel Frequency Cepstral Coefficients (MFCC) as an acoustic feature alongside lexical information that are obtained from conversations from the Fisher dataset. Thus, we show that acoustics provide complementary information to the lexical modality. The experimental results show that sequence-to-sequence system trained on both word sequences and MFCC can improve on speaker diarization result compared to the system that only relies on lexical modality or the baseline MFCC-based system. In addition, we test the performance of our proposed method with Automatic Speech Recognition (ASR) transcripts. While the performance on ASR transcripts drops, the Diarization Error Rate (DER) of our proposed method still outperforms the traditional method based on Bayesian Information Criterion (BIC).
Sequence assembly from corrupted shotgun reads

The prevalent technique for DNA sequencing consists of two main steps: shotgun sequencing, where many randomly located fragments, called reads, are extracted from the overall sequence, followed by an assembly algorithm that aims to reconstruct the original sequence. There are many different technologies that generate the reads: widely-used second-generation methods create short reads with low error rates, while emerging third-generation methods create long reads with high error rates. Both error rates and error profiles differ among methods, so reconstruction algorithms are often tailored to specific shotgun sequencing technologies. As these methods change over time, a fundamental question is whether there exist reconstruction algorithms which are robust, i.e., which perform well under a wide range of error distributions. Here we study this question of sequence assembly from corrupted reads. We make no assumption on the types of errors in the reads, but only assume a bound on their magnitude. More precisely, for each read we assume that instead of receiving the true read with no errors, we receive a corrupted read which has edit distance at most e times the length of the read from the true read. We show that if the reads are long enough and there are sufficiently many of them, then approximate reconstruction is possible: we construct a simple algorithm such that for almost all original sequences the output of the algorithm is a sequence whose edit distance from the original one is at most O(e) times the length of the original sequence.
Integral trace forms associated to cubic extensions

Given a nonzero integer $d$, we know by Hermite's Theorem that there exist only finitely many cubic number fields of discriminant $d$. However, it can happen that two non-isomorphic cubic fields have the same discriminant. It is thus natural to ask whether there are natural refinements of the discriminant which completely determine the isomorphism class of the cubic field. Here we consider the trace form $q_K:\text{tr}_{K/\mathbb{Q}}(x^2)|_{O^{0}_{K}}$ as such a refinement. For a cubic field of fundamental discriminant $d$ we show the existence of an element $T_K$ in Bhargava's class group $\Cl(\mathbb{Z}^{2}\otimes\mathbb{Z}^{2}\otimes\mathbb{Z}^{2}; -3d)$ such that $q_K$ is completely determined by $T_K$. By using one of Bhargava's composition laws, we show that $q_K$ is a complete invariant whenever $K$ is totally real and of fundamental discriminant
Variational Formula for the Time Constant of First‐Passage Percolation

Consider first-passage percolation with positive, stationary-ergodic weights on the square lattice in d-dimensions. Let T(x) be the first-passage time from the origin to x in Z^d. The convergence of T([nx])/n to the time constant as n tends to infinity is a consequence of the subadditive ergodic theorem. This convergence can be viewed as a problem of homogenization for a discrete Hamilton-Jacobi-Bellman (HJB) equation. By borrowing several tools from the continuum theory of stochastic homogenization for HJB equations, we derive an exact variational formula (duality principle) for the timeconstant. Under a symmetry assumption, we will use the variational formula to construct an explicit iteration that produces the limit shape.
Tracking Emerges by Colorizing Videos

We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that this task causes the model to automatically learn to track visual regions. Although the model is trained without any ground-truth labels, our method learns to track well enough to outperform the latest methods based on optical flow. Moreover, our results suggest that failures to track are correlated with failures to colorize, indicating that advancing video colorization may further improve self-supervised visual tracking.
Confluent operator algebras and the closability property

Abstract Certain operator algebras A on a Hilbert space have the property that every densely defined linear transformation commuting with A is closable. Such algebras are said to have the closability property. They are important in the study of the transitive algebra problem. More precisely, if A is a two-transitive algebra with the closability property, then A is dense in the algebra of all bounded operators, in the weak operator topology. In this paper we focus on algebras generated by a completely nonunitary contraction, and produce several new classes of algebras with the closability property. We show that this property follows from a certain strict cyclicity property, and we give very detailed information on the class of completely nonunitary contractions satisfying this property, as well as a stronger property which we call confluence.
Co-evolving Tracing and Fault Injection with Box of Pain

Distributed systems are hard to reason about largely because of uncertainty about what may go wrong in a particular execution, and about whether the system will mitigate those faults. Tools that perturb executions can help test whether a system is robust to faults, while tools that observe executions can help better understand their system-wide effects. We present Box of Pain, a tracer and fault injector for unmodified distributed systems that addresses both concerns by interposing at the system call level and dynamically reconstructing the partial order of communication events based on causal relationships. Box of Pain's lightweight approach to tracing and focus on simulating the effects of partial failures on communication rather than the failures themselves sets it apart from other tracing and fault injection systems. We present evidence of the promise of Box of Pain and its approach to lightweight observation and perturbation of distributed systems.
On the structure of level sets of uniform and Lipschitz quotient mappings from \( \mathbb{R}^n \) to \( \mathbb{R} \)

We study two questions posed by Johnson, Lindenstrauss, Preiss, and Schechtman, concerning the structure of level sets of uniform and Lipschitz quotient mappings from \( \mathbb{R}^n \rightarrow \mathbb{R} \). We show that if \( f:\mathbb{R}^n \rightarrow \mathbb{R}, n\geq 2 \), is a uniform quotient mapping then for every \( t \in \mathbb{R}, f^{-1}(t) \) has a bounded number of components, each component of \( f^{-1}(t) \) separates \( \mathbb{R}^n \) and the upper bound of the number of components depends only on \( n \) and the moduli of co-uniform and uniform continuity of \( f \).
Statistical Learning under Nonstationary Mixing Processes

We study a special case of the problem of statistical learning without the i.i.d. assumption. Specifically, we suppose a learning method is presented with a sequence of data points, and required to make a prediction (e.g., a classification) for each one, and can then observe the loss incurred by this prediction. We go beyond traditional analyses, which have focused on stationary mixing processes or nonstationary product processes, by combining these two relaxations to allow nonstationary mixing processes. We are particularly interested in the case of $\beta$-mixing processes, with the sum of changes in marginal distributions growing sublinearly in the number of samples. Under these conditions, we propose a learning method, and establish that for bounded VC subgraph classes, the cumulative excess risk grows sublinearly in the number of predictions, at a quantified rate.
Swan conductors for p-adic differential modules, II: Global variation

Using a local construction from a previous paper, we exhibit a numerical invariant, the differential Swan conductor, for an isocrystal on a variety over a perfect field of positive characteristic overconvergent along a boundary divisor; this leads to an analogous construction for certain p-adic and l-adic representations of the etale fundamental group of a variety. We then demonstrate some variational properties of this definition for overconvergent isocrystals, paying special attention to the case of surfaces.
Eigenvector Distribution of Wigner Matrices

We consider N × N Hermitian or symmetric random matrices with independent entries. The distribution of the (i, j)-th matrix element is given by a probability measure ν ij whose first two moments coincide with those of the corresponding Gaussian ensemble. We prove that the joint probability distribution of the components of eigenvectors associated with eigenvalues close to the spectral edge agrees with that of the corresponding Gaussian ensemble. For eigenvectors associated with bulk eigenvalues, the same conclusion holds provided the first four moments of the distribution ν ij coincide with those of the corresponding Gaussian ensemble. More generally, we prove that the joint eigenvector–eigenvalue distributions near the spectral edge of two generalized Wigner ensembles agree, provided that the first two moments of the entries match and that one of the ensembles satisfies a level repulsion estimate. If in addition the first four moments match then this result holds also in the bulk.
Asymptotic Expansion of Spherical Integral

We consider the spherical integral of real symmetric or Hermitian matrices when the rank of one matrix is one. We prove the existence of the full asymptotic expansions of these spherical integrals and derive the first and the second term in the asymptotic expansion.
GLOBAL RATES OF CONVERGENCE OF THE MLES OF LOG-CONCAVE AND S CONCAVE DENSITIES

We establish global rates of convergence for the Maximum Likelihood Estimators (MLEs) of log-concave and $s$-concave densities on $\mathbb{R}$. The main finding is that the rate of convergence of the MLE in the Hellinger metric is no worse than $n^{-2/5}$ when $-1<s<\infty$ where $s=0$ corresponds to the log-concave case. We also show that the MLE does not exist for the classes of $s$-concave densities with $s<-1$.
Principal minors and rhombus tilings

The algebraic relations between the principal minors of a generic n × n matrix are somewhat mysterious, see e.g. Lin and Sturmfels (2009 J. Algebra 322 4121–31). We show, however, that by adding in certain almost principal minors, the ideal of relations is generated by translations of a single relation, the so-called hexahedron relation, which is a composition of six cluster mutations. We give in particular a Laurent-polynomial parameterization of the space of n × n matrices, whose parameters consist of certain principal and almost principal minors. The parameters naturally live on vertices and faces of the tiles in a rhombus tiling of a convex -gon. A matrix is associated to an equivalence class of tilings, all related to each other by Yang–Baxter-like transformations. By specializing the initial data we can similarly parameterize the space of Hermitian symmetric matrices over or the quaternions. Moreover by further specialization we can parametrize the space of positive definite matrices over these rings.This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to 'Cluster algebras mathematical physics'.
On the derived DG functors

Assume that abelian categories A, B over a field admit countable direct limits and that these limits are exact. Let F : D dg(A)→ D + dg(B) be a DG quasi-functor such that the functor Ho(F) : D+(A)→ D+(B) carries D≥0(A) to D≥0(B) and such that, for every i > 0, the functor HiF : A → B is effaceable. We prove that F is canonically isomorphic to the right derived DG functor RH0(F). We also prove a similar result for bounded derived DG categories and a formula that expresses Hochschild cohomology of the categories Db dg(A), D + dg(A) as the Ext groups in the abelian category of left exact functors A → IndA . The proofs are based on a description of Drinfeld’s category of quasi-functors as the derived category of a category of sheaves.
Coercive region-level registration for multi-modal images

We propose a coercive approach to simultaneously register and segment multi-modal images which share similar spatial structure. Registration is done at the region level to facilitate data fusion while avoiding the need for interpolation. The algorithm performs alternating minimization of an objective function informed by statistical models for pixel values in different modalities. Hypothesis tests are developed to determine whether to refine segmentations by splitting regions. We demonstrate that our approach has significantly better performance than the state-of-the-art registration and segmentation methods on microscopy images.
Missing Value Imputation with Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in impute the missing values. In this article, we present a technique for unsupervised learning called unsupervised backpropagation UBP, which trains a multilayer perceptron to fit to the manifold sampled by a set of observed point vectors. We evaluate UBP with the task of imputing missing values in data sets and show that UBP is able to predict missing values with significantly lower sum of squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 data sets and nine supervised learning algorithms that classification accuracy is usually higher when randomly withheld values are imputed using UBP, rather than with other methods.
Channel estimation techniques for quantized distributed reception in MIMO systems

The Internet of Things (IoT) could enable the development of cloud multiple-input multiple-output (MIMO) systems where internet-enabled devices can work as distributed transmission/reception entities. We expect that spatial multiplexing with distributed reception using cloud MIMO would be a key factor of future wireless communication systems. In this paper, we first review practical receivers for distributed reception of spatially multiplexed transmit data where the fusion center relies on quantized received signals conveyed from geographically separated receive nodes. Using the structures of these receivers, we propose practical channel estimation techniques for the blockfading scenario. The proposed channel estimation techniques rely on very simple operations at the received nodes while achieving near-optimal channel estimation performance as the training length becomes large.
W-algebras and chiral differential operators at the critical level

Abstract Let A crit be the chiral algebra corresponding to the affine Kac–Moody algebra at the critical level g ˆ crit . Let Z crit be the center of A crit . The commutative chiral algebra Z crit admits a canonical deformation into a non-commutative chiral algebra W ℏ . In this paper we will express the resulting first order deformation via the chiral algebra D crit of chiral differential operators on G ( ( t ) ) at the critical level.
Difference dimension quasi-polynomials

Abstract We consider Hilbert-type functions associated with difference (not necessarily inversive) field extensions and systems of algebraic difference equations in the case when the translations are assigned some integer weights. We will show that such functions are quasi-polynomials, which can be represented as alternating sums of Ehrhart quasi-polynomials associated with rational conic polytopes. In particular, we obtain generalizations of main theorems on difference dimension polynomials and their invariants to the case of weighted basic difference operators.
Intel RealSense Stereoscopic Depth Cameras

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and the Intel RealSense D400 (formally RS400).
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares

Sparse recovery aims to reconstruct sparse signals from compressed linear measurements. In this paper, we propose a sparse recovery algorithm called multiple orthogonal least squares (MOLS), which extends the well-known orthogonal least squares (OLS) algorithm by allowing multiple $L$ indices to be selected per iteration. Owing to its ability to catch multiple support indices in each selection, MOLS often converges in fewer iterations and hence improves the computational efficiency over the conventional OLS algorithm. Theoretical analysis shows that MOLS ( $L > 1$ ) performs exact recovery of $K$ -sparse signals ( $K > 1$ ) in at most $K$ iterations, provided that the sensing matrix obeys the restricted isometry property with isometry constant $\delta _{LK} When $L = 1,$ MOLS reduces to the conventional OLS algorithm and our analysis shows that exact recovery is guaranteed under $\delta_{K +1} . This condition is nearly optimal with respect to $\delta _{K+1}$ in the sense that, even with a small relaxation (e.g., $\delta_{K + 1} = 1 / \sqrt{K}$ ), exact recovery with OLS may not be guaranteed. The recovery performance of MOLS in the noisy scenario is also studied. It is shown that stable recovery of sparse signals can be achieved with the MOLS algorithm when the signal-to-noise ratio scales linearly with the sparsity level of input signals.
Multiplex Influence Maximization in Online Social Networks With Heterogeneous Diffusion Models

Motivated by online social networks that are linked together through overlapping users, we study the influence maximization problem on a multiplex, with each layer endowed with its own model of influence diffusion. This problem is a novel version of the influence maximization problem that necessitates new analysis incorporating the type of propagation on each layer of the multiplex. We identify a new property, generalized deterministic submodular, which when satisfied by the propagation in each layer, ensures that the propagation on the multiplex overall is submodular–for this case, we formulate influential seed finder (ISF), the greedy algorithm with approximation ratio $(1-1/e)$ . Since the size of a multiplex comprising multiple OSNs may encompass billions of users, we formulate an algorithm knapsack seeding of network (KSN) that runs on each layer of the multiplex in parallel. KSN takes an $\alpha $ -approximation algorithm $A$ for the influence maximization problem on a single network as input, and has approximation ratio $({(1 - \epsilon) \alpha })/({(o + 1)k})$ for arbitrary $\epsilon > 0$ , $o$ is the number of overlapping users, and $k$ is the number of layers in the multiplex. Experiments on real and synthesized multiplexes validate the efficacy of the proposed algorithms for the problem of influence maximization in the heterogeneous multiplex. Implementations of ISF and KSN are available at http://www.alankuhnle.com/papers/mim/mim.html .
Gistable: Evaluating the Executability of Python Code Snippets on GitHub

Software developers create and share code online to demonstrate programming language concepts and programming tasks. Code snippets can be a useful way to explain and demonstrate a programming concept, but may not always be directly executable. A code snippet can contain parse errors, or fail to execute if the environment contains unmet dependencies. This paper presents an empirical analysis of the executable status of Python code snippets shared through the GitHub gist system, and the ability of developers familiar with software configuration to correctly configure and run them. We find that 75.6% of gists require non-trivial configuration to overcome missing dependencies, configuration files, reliance on a specific operating system, or some other environment configuration. Our study also suggests the natural assumption developers make about resource names when resolving configuration errors is correct less than half the time. We also present Gistable, a database and extensible framework built on GitHub's gist system, which provides executable code snippets to enable reproducible studies in software engineering. Gistable contains 10,259 code snippets, approximately 5,000 with a Dockerfile to configure and execute them without import error. Gistable is publicly available at https://github.com/gistable/gistable.
You are who you know and how you behave: attribute inference attacks via users' social friends and behaviors

We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy - private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users; via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.
DTM: Deformable template matching

A novel template matching algorithm that can incorporate the concept of deformable parts, is presented in this paper. Unlike the deformable part model (DPM) employed in object recognition, the proposed template-matching approach called Deformable Template Matching (DTM) does not require a training step. Instead, deformation is achieved by a set of predefined basic rules (e.g. the left sub-patch cannot pass across the right patch). Experimental evaluation of this new method using the PASCAL VOC 07 dataset demonstrated substantial performance improvement over conventional template matching algorithms. Additionally, to confirm the applicability of DTM, the concept is applied to the generation of a rotation-invariant SIFT descriptor. Experimental evaluation employing deformable matching of SIFT features shows an increased number of matching features compared to a conventional SIFT matching.
When Exceptions Are the Norm: Exploring the Role of Consent in HRI

HRI researchers have made major strides in developing robotic architectures that are capable of reading a limited set of social cues and producing behaviors that enhance their likeability and feeling of comfort amongst humans. However, the cues in these models are fairly direct and the interactions largely dyadic. To capture the normative qualities of interaction more robustly, we propose “consent” as a distinct, critical area for HRI research. Convening important insights in existing HRI work around topics like touch, proxemics, gaze, and moral norms, the notion of consent reveals key expectations that can shape how a robot acts in social spaces. Consent need not be limited to just an explicit permission given in ethically charged or normatively risky scenarios. Instead, it is a richer notion, one that covers even implicit acquiescence in scenarios that otherwise seem normatively neutral. By sorting various kinds of consent through social and legal doctrine, we delineate empirical and technical questions to meet consent challenges faced in major application domains and robotic roles. Attention to consent could show, for example, how extraordinary, norm-violating actions can be justified by agents and accepted by those around them. We argue that operationalizing ideas from legal scholarship can better guide how robotic systems might cultivate and sustain proper forms of consent.
Duality Approach to Bilevel Programs with a Convex Lower Level

Bilevel programs are optimization problems where some variables are solutions to optimization problems themselves, and they arise in a variety of control applications, including: control of vehicle traffic networks, inverse reinforcement learning and inverse optimization, and robust control for human-automation systems. This paper develops a duality-based approach to solving bilevel programs where the lower level problem is convex. Our approach is to use partial dualization to construct a new dual function that is differentiable, unlike the Lagrangian dual that is only directionally differentiable. We use our dual to define a duality-based reformulation of bilevel programs, prove equivalence of our reformulation with the original bilevel program, and then introduce regularization to ensure constraint qualification holds. These technical results about our new dual and regularized duality-based reformulation are used to provide theoretical justification for an algorithm we construct for solving bilevel programs with a convex lower level, and we conclude by demonstrating the efficacy of our algorithm by solving two practical instances of bilevel programs.
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.
Evolutionary neural AutoML for deep learning

Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a configuration is difficult and as a result, DNNs are often not used to their full potential. In addition, DNNs in commercial applications often need to satisfy real-world design constraints such as size or number of parameters. To make configuration easier, automatic machine learning (AutoML) systems for deep learning have been developed, focusing mostly on optimization of hyperparameters. This paper takes AutoML a step further. It introduces an evolutionary AutoML framework called LEAF that not only optimizes hyperparameters but also network architectures and the size of the network. LEAF makes use of both state-of-the-art evolutionary algorithms (EAs) and distributed computing frameworks. Experimental results on medical image classification and natural language analysis show that the framework can be used to achieve state-of-the-art performance. In particular, LEAF demonstrates that architecture optimization provides a significant boost over hyperparameter optimization, and that networks can be minimized at the same time with little drop in performance. LEAF therefore forms a foundation for democratizing and improving AI, as well as making AI practical in future applications.
Conjugacy growth of commutators

Abstract For the free group F r on r > 1 generators (respectively, the free product G 1 ⁎ G 2 of two nontrivial finite groups G 1 and G 2 ), we obtain the asymptotic for the number of conjugacy classes of commutators in F r (respectively, G 1 ⁎ G 2 ) with a given word length in a fixed set of free generators (respectively, the set of generators given by the nontrivial elements of G 1 and G 2 ). Our result is proven by using the classification of commutators in free groups and in free products by Wicks, and builds on the works of Rivin and Sharp, who asymptotically counted the conjugacy classes of commutator-subgroup elements in F r with a given word length.
The Existence of a Maximal Green Sequence is not Invariant under Quiver Mutation

This note provides a quiver which does not admit a maximal green sequence, but which is mutation-equivalent to a quiver which does admit a maximal green sequence. The proof uses the `scattering diagrams' of Gross-Hacking-Keel-Kontsevich to show that a maximal green sequence for a quiver determines a maximal green sequence for any induced subquiver.
Effect of gene-expression bursts on stochastic timing of cellular events

Gene expression is inherently a noisy process which manifests as cell-to-cell variability in time evolution of proteins. Consequently, events that trigger at critical threshold levels of regulatory proteins exhibit stochasticity in their timing. An important contributor to the noise in gene expression is translation bursts phenomena, which corresponds to randomness in number of proteins produced in a single mRNA lifetime. Modeling timing of an event as a first-passage time (FPT) problem, we explore the effect of burst size distribution on event timing. Towards this end, the probability density function of FPT is computed for a gene expression model where the burst size is drawn from a generic non-negative distribution. Analytical formulas for FPT moments are provided in terms of known vectors and inverse of a known matrix. The effect of burst size distribution is investigated by looking at how the feedback regulation strategy that minimizes noise in timing around a given time deviates from the strategy when the burst size is deterministic. Interestingly, results show that the feedback strategy for deterministic burst case is quite robust to change in burst size distribution, and deviations from it are confined to about 20% of the optimal value. These findings facilitate an improved understanding of noise regulation in event timing.
Sharp regularity for the integrability of elliptic structures

Abstract As part of his celebrated Complex Frobenius Theorem, Nirenberg showed that given a smooth elliptic structure (on a smooth manifold), the manifold is locally diffeomorphic to an open subset of R r × C n (for some r and n) in such a way that the structure is locally the span of ∂ ∂ t 1 , … , ∂ ∂ t r , ∂ ∂ z ‾ 1 , … , ∂ ∂ z ‾ n ; where R r × C n has coordinates ( t 1 , … , t r , z 1 , … , z n ) . In this paper, we give optimal regularity for the coordinate charts which achieve this realization. Namely, if the manifold has Zygmund regularity of order s + 2 and the structure has Zygmund regularity of order s + 1 (for some s > 0 ), then the coordinate charts may be taken to have Zygmund regularity of order s + 2 . We do this by generalizing Malgrange's proof of the Newlander-Nirenberg Theorem to this setting.
A Relaxed Optimization Approach for Cardinality-Constrained Portfolios

A cardinality-constrained portfolio caps the number of stocks to be traded across and within groups or sectors. These limitations arise from real-world scenarios faced by fund managers, who are constrained by transaction costs and client preferences as they seek to maximize return and limit risk. We develop a new approach to solve cardinality-constrained portfolio optimization problems, extending both Markowitz and conditional value at risk (CVaR) optimization models with cardinality constraints. We derive a continuous relaxation method for the NP-hard objective, which allows for very efficient algorithms with standard convergence guarantees for nonconvex problems. For smaller cases, where brute force search is feasible to compute the globally optimal cardinality-constrained portfolio, the new approach finds the best portfolio for the cardinality-constrained Markowitz model and a very good local minimum for the cardinality-constrained CVaR model. For higher dimensions, where brute-force search is prohibitively expensive, we find feasible portfolios that are nearly as efficient as their non-cardinality constrained counterparts.
The group of boundary fixing homeomorphisms of the disc is not left-orderable

A left-order on a group $G$ is a total order $<$ on $G$ such that for any $f$, $g$ and $h$ in $G$ we have $f < g \Leftrightarrow hf < hg$. We construct a finitely generated subgroup $G$ of $\operatorname{Homeo} (I^2;\delta I^2)$, the group of those homeomorphisms of the disc that fix the boundary pointwise, and show $G$ does not admit a left-order. Since any left-order on $\operatorname{Homeo} (I^2;\delta I^2)$ would restrict to a left-order on $G$ this shows that $\operatorname{Homeo} (I^2;\delta I^2)$ does not admit a left-order. Since $\operatorname{Homeo} (I;\delta I)$ admits a left-order it follows that neither $G$ nor $\operatorname{Homeo} (I^2;\delta I^2)$ embed in $\operatorname{Homeo} (I;\delta I)$.