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Orbifold equivalence: structure and new examples
Orbifold equivalence is a notion of symmetry that does not rely on group actions. Among other applications, it leads to surprising connections between hitherto unrelated singularities. While the concept can be defined in a very general category-theoretic language, we focus on the most explicit setting in terms of mat...
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Efficient Attention using a Fixed-Size Memory Representation
The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is...
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Multiplicities of bifurcation sets of Pham singularities
The local multiplicities of the Maxwell sets in the spaces of versal deformations of Pham holomorphic function singularities are calculated. A similar calculation for some other bifurcation sets (generalized Stokes' sets) defined by more complicated relations between the critical values is given. Aplications to the c...
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Fast and scalable Gaussian process modeling with applications to astronomical time series
The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large datasets. Gaussian Processes are a popular class of models used for this purpose but, since the computational cost scales, in general, as the cube of the number o...
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Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs
Motivations Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their ful...
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Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning
Patient pain can be detected highly reliably from facial expressions using a set of facial muscle-based action units (AUs) defined by the Facial Action Coding System (FACS). A key characteristic of facial expression of pain is the simultaneous occurrence of pain-related AU combinations, whose automated detection woul...
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A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectori...
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Exact Affine Counter Automata
We introduce an affine generalization of counter automata, and analyze their ability as well as affine finite automata. Our contributions are as follows. We show that there is a language that can be recognized by exact realtime affine counter automata but by neither 1-way deterministic pushdown automata nor realtime ...
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Preorder characterizations of lower separation axioms and their applications to foliations and flows
In this paper, we characterize several lower separation axioms $C_0, C_D$, $C_R$, $C_N$, $\lambda$-space, nested, $S_{YS}$, $S_{YY}$, $S_{YS}$, and $S_{\delta}$ using pre-order. To analyze topological properties of (resp. dynamical systems) foliations, we introduce notions of topology (resp. dynamical systems) for fo...
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Convergence of row sequences of simultaneous Padé-Faber approximants
We consider row sequences of vector valued Padé-Faber approximants (simultaneous Padé-Faber approximants) and prove a Montessus de Ballore type theorem.
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A Deep Convolutional Neural Network for Background Subtraction
In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network parameters can be learned from data by training a single CNN that can handle...
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A second main theorem for holomorphic curve intersecting hypersurfaces
In this paper, we establish a second main theorem for holomorphic curve intersecting hypersurfaces in general position in projective space with level of truncation. As an application, we reduce the number hypersurfaces in uniqueness problem for holomorphic curve of authors before.
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Partially hyperbolic diffeomorphisms with one-dimensional neutral center on 3-manifolds
We prove that for any partially hyperbolic diffeomorphism with one dimensional neutral center on a 3-manifold, the center stable and center unstable foliations are complete; moreover, each leaf of center stable and center unstable foliations is a cylinder, a M$\ddot{o}$bius band or a plane. Further properties of the ...
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Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network
Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual ...
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Finding structure in the dark: coupled dark energy, weak lensing, and the mildly nonlinear regime
We reexamine interactions between the dark sectors of cosmology, with a focus on robust constraints that can be obtained using only mildly nonlinear scales. While it is well known that couplings between dark matter and dark energy can be constrained to the percent level when including the full range of scales probed ...
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Semantic Autoencoder for Zero-Shot Learning
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g.~attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g.~attribute prediction) or classification....
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An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide...
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Noise-synchronizability of opinion dynamics
With the analysis of noise-induced synchronization of opinion dynamics with bounded confidence (BC), a natural and fundamental question is what opinion structures can be synchronized by noise. In the traditional Hegselmann-Krause (HK) model, each agent examines the opinion values of all the other ones and then choose...
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Conformer-selection by matter-wave interference
We establish that matter-wave interference at near-resonant ultraviolet optical gratings can be used to spatially separate individual conformers of complex molecules. Our calculations show that the conformational purity of the prepared beam can be close to 100% and that all molecules remain in their electronic ground...
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Building a Neural Machine Translation System Using Only Synthetic Parallel Data
Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this study, we build NMT systems using only synthetic parallel data. As an efficient alternative to real parallel data, we also present a new type...
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Learning to Predict Indoor Illumination from a Single Image
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that ...
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$H^\infty$-calculus for semigroup generators on BMO
We prove that the negative infinitesimal generator $L$ of a semigroup of positive contractions on $L^\infty$ has a bounded $H^\infty(S_\eta^0)$-calculus on BMO$(\sqrt L)$ for any angle $\eta>\pi/2$, provided the semigroup satisfies Bakry-Emry's $\Gamma_2 $ criterion. Our arguments only rely on the properties of the u...
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A Bayesian Game without epsilon equilibria
We present a three player Bayesian game for which there is no epsilon equilibria in Borel measurable strategies for small enough epsilon, however there are non-measurable equilibria.
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Towards Robust Interpretability with Self-Explaining Neural Networks
Most recent work on interpretability of complex machine learning models has focused on estimating $\textit{a posteriori}$ explanations for previously trained models around specific predictions. $\textit{Self-explaining}$ models where interpretability plays a key role already during learning have received much less at...
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Deep Learning Scaling is Predictable, Empirically
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would ...
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Coupled Self-Organized Hydrodynamics and Stokes models for suspensions of active particles
We derive macroscopic dynamics for self-propelled particles in a fluid. The starting point is a coupled Vicsek-Stokes system. The Vicsek model describes self-propelled agents interacting through alignment. It provides a phenomenological description of hydrodynamic interactions between agents at high density. Stokes e...
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Tunable Spin-Orbit Torques in Cu-Ta Binary Alloy Heterostructures
The spin Hall effect (SHE) is found to be strong in heavy transition metals (HM), such as Ta and W, in their amorphous and/or high resistivity form. In this work, we show that by employing a Cu-Ta binary alloy as buffer layer in an amorphous Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructure with perpendicular magne...
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On Properties of Nests: Some Answers and Questions
By considering nests on a given space, we explore order-theoretical and topological properties that are closely related to the structure of a nest. In particular, we see how subbases given by two dual nests can be an indicator of how close or far are the properties of the space from the structure of a linearly ordere...
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Differential Characters of Drinfeld Modules and de Rham Cohomology
We introduce differential characters of Drinfeld modules. These are function-field analogues of Buium's p-adic differential characters of elliptic curves and of Manin's differential characters of elliptic curves in differential algebra, both of which have had notable Diophantine applications. We determine the structu...
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Strong submeasures and several applications
A strong submeasure on a compact metric space X is a sub-linear and bounded operator on the space of continuous functions on X. A strong submeasure is positive if it is non-decreasing. By Hahn-Banach theorem, a positive strong submeasure is the supremum of a non-empty collection of measures whose masses are uniformly...
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Understanding Career Progression in Baseball Through Machine Learning
Professional baseball players are increasingly guaranteed expensive long-term contracts, with over 70 deals signed in excess of \$90 million, mostly in the last decade. These are substantial sums compared to a typical franchise valuation of \$1-2 billion. Hence, the players to whom a team chooses to give such a contr...
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Testing the validity of the local and global GKLS master equations on an exactly solvable model
When deriving a master equation for a multipartite weakly-interacting open quantum systems, dissipation is often addressed \textit{locally} on each component, i.e. ignoring the coherent couplings, which are later added `by hand'. Although simple, the resulting local master equation (LME) is known to be thermodynamica...
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Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is ...
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Learning to Sequence Robot Behaviors for Visual Navigation
Recent literature in the robotics community has focused on learning robot behaviors that abstract out lower-level details of robot control. To fully leverage the efficacy of such behaviors, it is necessary to select and sequence them to achieve a given task. In this paper, we present an approach to both learn and seq...
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Evaluation complexity bounds for smooth constrained nonlinear optimisation using scaled KKT conditions, high-order models and the criticality measure $χ$
Evaluation complexity for convexly constrained optimization is considered and it is shown first that the complexity bound of $O(\epsilon^{-3/2})$ proved by Cartis, Gould and Toint (IMAJNA 32(4) 2012, pp.1662-1695) for computing an $\epsilon$-approximate first-order critical point can be obtained under significantly w...
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One level density of low-lying zeros of quadratic and quartic Hecke $L$-functions
In this paper, we prove some one level density results for the low-lying zeros of famliies of quadratic and quartic Hecke $L$-functions of the Gaussian field. As corollaries, we deduce that, respectively, at least $94.27 \%$ and $5\%$ of the members of the quadratic family and the quartic family do not vanish at the ...
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An Online Convex Optimization Approach to Dynamic Network Resource Allocation
Existing approaches to online convex optimization (OCO) make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best ...
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Learning causal Bayes networks using interventional path queries in polynomial time and sample complexity
Causal discovery from empirical data is a fundamental problem in many scientific domains. Observational data allows for identifiability only up to Markov equivalence class. In this paper we first propose a polynomial time algorithm for learning the exact correctly-oriented structure of the transitive reduction of any...
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The Fredholm alternative for the $p$-Laplacian in exterior domains
We investigate the Fredholm alternative for the $p$-Laplacian in an exterior domain which is the complement of the closed unit ball in $\mathbb{R}^N$ ($N\geq 2$). By employing techniques of Calculus of Variations we obtain the multiplicity of solutions. The striking difference between our case and the entire space ca...
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Thermodynamic Stabilization of Precipitates through Interface Segregation: Chemical Effects
Precipitation hardening, which relies on a high density of intermetallic precipitates, is a commonly utilized technique for strengthening structural alloys. Structural alloys are commonly strengthened through a high density of small size intermetallic precipitates. At high temperatures, however, the precipitates coar...
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Wavelength Does Not Equal Pressure: Vertical Contribution Functions and their Implications for Mapping Hot Jupiters
Multi-band phase variations in principle allow us to infer the longitudinal temperature distributions of planets as a function of height in their atmospheres. For example, 3.6 micron emission originates from deeper layers of the atmosphere than 4.5 micron due to greater water vapor absorption at the longer wavelength...
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Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction
We introduce a new algorithm, called CDER, for supervised machine learning that merges the multi-scale geometric properties of Cover Trees with the information-theoretic properties of entropy. CDER applies to a training set of labeled pointclouds embedded in a common Euclidean space. If typical pointclouds correspond...
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Design Considerations for Proposed Fermilab Integrable RCS
Integrable optics is an innovation in particle accelerator design that provides strong nonlinear focusing while avoiding parametric resonances. One promising application of integrable optics is to overcome the traditional limits on accelerator intensity imposed by betatron tune-spread and collective instabilities. Th...
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Consistent Estimation in General Sublinear Preferential Attachment Trees
We propose an empirical estimator of the preferential attachment function $f$ in the setting of general preferential attachment trees. Using a supercritical continuous-time branching process framework, we prove the almost sure consistency of the proposed estimator. We perform simulations to study the empirical proper...
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Learned Watershed: End-to-End Learning of Seeded Segmentation
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is con- volutional (over space) and r...
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MAGIC Contributions to the 35th International Cosmic Ray Conference (ICRC2017)
MAGIC (Major Atmospheric Gamma Imaging Cherenkov) is a system of two 17 m diameter, F/1.03 Imaging Atmospheric Cherenkov Telescopes (IACT). They are dedicated to the observation of gamma rays from galactic and extragalactic sources in the very high energy range (VHE, 30 GeV to 100 TeV). This submission contains links...
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Efficient Hidden Vector Encryptions and Its Applications
Predicate encryption is a new paradigm of public key encryption that enables searches on encrypted data. Using the predicate encryption, we can search keywords or attributes on encrypted data without decrypting the ciphertexts. In predicate encryption, a ciphertext is associated with attributes and a token correspond...
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The Galaxy Clustering Crisis in Abundance Matching
Galaxy clustering on small scales is significantly under-predicted by sub-halo abundance matching (SHAM) models that populate (sub-)haloes with galaxies based on peak halo mass, $M_{\rm peak}$. SHAM models based on the peak maximum circular velocity, $V_{\rm peak}$, have had much better success. The primary reason $M...
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Supplying Dark Energy from Scalar Field Dark Matter
We consider the hypothesis that dark matter and dark energy consists of ultra-light self-interacting scalar particles. It is found that the Klein-Gordon equation with only two free parameters (mass and self-coupling) on a Schwarzschild background, at the galactic length-scales has the solution which corresponds to Bo...
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GHz-Band Integrated Magnetic Inductors
The demand on mobile electronics to continue to shrink in size while increase in efficiency drives the demand on the internal passive components to do the same. Power amplifiers require inductors with small form factors, high quality factors, and high operating frequency in the single-digit GHz range. This work explo...
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Acylindrical actions on projection complexes
We simplify the construction of projection complexes due to Bestvina-Bromberg-Fujiwara. To do so, we introduce a sharper version of the Behrstock inequality, and show that it can always be enforced. Furthermore, we use the new setup to prove acylindricity results for the action on the projection complexes. We also tr...
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Introducing Geometric Algebra to Geometric Computing Software Developers: A Computational Thinking Approach
Designing software systems for Geometric Computing applications can be a challenging task. Software engineers typically use software abstractions to hide and manage the high complexity of such systems. Without the presence of a unifying algebraic system to describe geometric models, the use of software abstractions a...
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Recovering Nonuniform Planted Partitions via Iterated Projection
In the planted partition problem, the $n$ vertices of a random graph are partitioned into $k$ "clusters," and edges between vertices in the same cluster and different clusters are included with constant probability $p$ and $q$, respectively (where $0 \le q < p \le 1$). We give an efficient spectral algorithm that rec...
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Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory
Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on inter...
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Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability pr...
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Phase-type distributions in population genetics
Probability modelling for DNA sequence evolution is well established and provides a rich framework for understanding genetic variation between samples of individuals from one or more populations. We show that both classical and more recent models for coalescence (with or without recombination) can be described in ter...
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Robust Wald-type test in GLM with random design based on minimum density power divergence estimators
We consider the problem of robust inference under the important generalized linear model (GLM) with stochastic covariates. We derive the properties of the minimum density power divergence estimator of the parameters in GLM with random design and used this estimator to propose a robust Wald-type test for testing any g...
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Locally Smoothed Neural Networks
Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel l...
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Asymptotic properties of a componentwise ARH(1) plug-in predictor
This paper presents new results on prediction of linear processes in function spaces. The autoregressive Hilbertian process framework of order one (ARH(1) process framework) is adopted. A componentwise estimator of the autocorrelation operator is formulated, from the moment-based estimation of its diagonal coefficien...
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The Effect of Population Control Policies on Societal Fragmentation
Population control policies are proposed and in some places employed as a means towards curbing population growth. This paper is concerned with a disturbing side-effect of such policies, namely, the potential risk of societal fragmentation due to changes in the distribution of family sizes. This effect is illustrated...
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Optimizing Beam Transport in Rapidly Compressing Beams on the Neutralized Drift Compression Experiment - II
The Neutralized Drift Compression Experiment-II (NDCX-II) is an induction linac that generates intense pulses of 1.2 MeV helium ions for heating matter to extreme conditions. Here, we present recent results on optimizing beam transport. The NDCX-II beamline includes a 1-meter-long drift section downstream of the last...
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Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of describing an image, multiple ways of translating a sentence; however, exhaustively annotating the applicability of all possible outputs is in...
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Direct and indirect seismic inversion: interpretation of certain mathematical theorems
Quantitative methods are more familiar to most geophysicists with direct inversion or indirect inversion. We will discuss seismic inversion in a high level sense without getting into the actual algorithms. We will stay with meta-equations and argue pros and cons based on certain mathematical theorems.
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Kan's combinatorial spectra and their sheaves revisited
We define a right Cartan-Eilenberg structure on the category of Kan's combinatorial spectra, and the category of sheaves of such spectra, assuming some conditions. In both structures, we use the geometric concept of homotopy equivalence as the strong equivalence. In the case of sheaves, we use local equivalence as th...
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Adiabatic Quantum Computing for Binary Clustering
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerica...
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Similarity Search Over Graphs Using Localized Spectral Analysis
This paper provides a new similarity detection algorithm. Given an input set of multi-dimensional data points, where each data point is assumed to be multi-dimensional, and an additional reference data point for similarity finding, the algorithm uses kernel method that embeds the data points into a low dimensional ma...
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Reminiscences of Julian Schwinger: Late Harvard, Early UCLA Years (1968-1981)
These are reminiscences of my interactions with Julian Schwinger from 1968 through 1981 and beyond.
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Site-resolved imaging of a bosonic Mott insulator using ytterbium atoms
We demonstrate site-resolved imaging of a strongly correlated quantum system without relying on laser-cooling techniques during fluorescence imaging. We observed the formation of Mott shells in the insulating regime and realized thermometry on the atomic cloud. This work proves the feasibility of the noncooled approa...
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A unified continuum and variational multiscale formulation for fluids, solids, and fluid-structure interaction
We develop a unified continuum modeling framework for viscous fluids and hyperelastic solids using the Gibbs free energy as the thermodynamic potential. This framework naturally leads to a pressure primitive variable formulation for the continuum body, which is well-behaved in both compressible and incompressible reg...
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Time Assignment System and Its Performance aboard the Hitomi Satellite
Fast timing capability in X-ray observation of astrophysical objects is one of the key properties for the ASTRO-H (Hitomi) mission. Absolute timing accuracies of 350 micro second or 35 micro second are required to achieve nominal scientific goals or to study fast variabilities of specific sources. The satellite carri...
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Common fixed points via $λ$-sequences in $G$-metric spaces
In this article, we use $\lambda$-sequences to derive common fixed points for a family of self-mappings defined on a complete $G$-metric space. We imitate some existing techniques in our proofs and show that the tools emlyed can be used at a larger scale. These results generalise well known results in the literature....
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Bidirectional Nested Weighted Automata
Nested weighted automata (NWA) present a robust and convenient automata-theoretic formalism for quantitative specifications. Previous works have considered NWA that processed input words only in the forward direction. It is natural to allow the automata to process input words backwards as well, for example, to measur...
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Uniform Shapiro-Lopatinski conditions and boundary value problems on manifolds with bounded geometry
We study the regularity of the solutions of second order boundary value problems on manifolds with boundary and bounded geometry. We first show that the regularity property of a given boundary value problem $(P, C)$ is equivalent to the uniform regularity of the natural family $(P_x, C_x)$ of associated boundary valu...
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A Fourier-Chebyshev Spectral Method for Cavitation Computation in Nonlinear Elasticity
A Fourier-Chebyshev spectral method is proposed in this paper for solving the cavitation problem in nonlinear elasticity. The interpolation error for the cavitation solution is analyzed, the elastic energy error estimate for the discrete cavitation solution is obtained, and the convergence of the method is proved. An...
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Learning Criticality in an Embodied Boltzmann Machine
Many biological and cognitive systems do not operate deep into one or other regime of activity. Instead, they exploit critical surfaces poised at transitions in their parameter space. The pervasiveness of criticality in natural systems suggests that there may be general principles inducing this behaviour. However, th...
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A contemporary look at Hermann Hankel's 1861 pioneering work on Lagrangian fluid dynamics
The present paper is a companion to the paper by Villone and Rampf (2017), titled "Hermann Hankel's On the general theory of motion of fluids, an essay including an English translation of the complete Preisschrift from 1861" together with connected documents. Here we give a critical assessment of Hankel's work, which...
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A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense. The model achieved superior link prediction accuracy on multiple data sets when compared to a dynamic variant of the blockmodel, and is able to e...
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Current-mode Memristor Crossbars for Neuromemristive Systems
Motivated by advantages of current-mode design, this brief contribution explores the implementation of weight matrices in neuromemristive systems via current-mode memristor crossbar circuits. After deriving theoretical results for the range and distribution of weights in the current-mode design, it is shown that any ...
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Incomplete Gauss sums modulo primes
We obtain a new bound for incomplete Gauss sums modulo primes. Our argument falls under the framework of Vinogradov's method which we use to reduce the problem under consideration to bounding the number of solutions to two distinct systems of congruences. The first is related to Vinogradov's mean value theorem, altho...
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FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI
Functional Magnetic Resonance Imaging is a noninvasive tool used to study brain function. Detecting activation is challenged by many factors, and even more so in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated and fast adaptive smoothing and thresholding ...
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Improper multiferroicity and colossal dielectric constants in Bi$_{2}$CuO$_{4}$
The layered cuprate Bi$_{2}$CuO$_{4}$ is investigated using magnetic, dielectric and pyroelectric measurements. This system is observed to be an improper multiferroic, with a robust ferroelectric state being established near the magnetic transition. Magnetic and dielectric measurements indicate the presence of a regi...
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Suppression of plasma echoes and Landau damping in Sobolev spaces by weak collisions in a Vlasov-Fokker-Planck equation
In this paper, we study Landau damping in the weakly collisional limit of a Vlasov-Fokker-Planck equation with nonlinear collisions in the phase-space $(x,v) \in \mathbb T_x^n \times \mathbb R^n_v$. The goal is four-fold: (A) to understand how collisions suppress plasma echoes and enable Landau damping in agreement w...
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Deep Learning for Semantic Segmentation on Minimal Hardware
Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable out...
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Overpartition $M2$-rank differences, class number relations, and vector-valued mock Eisenstein series
We prove that the generating function of overpartition $M2$-rank differences is, up to coefficient signs, a component of the vector-valued mock Eisenstein series attached to a certain quadratic form. We use this to compute analogs of the class number relations for $M2$-rank differences. As applications we split the K...
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Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
We study the decentralized machine learning scenario where many users collaborate to learn personalized models based on (i) their local datasets and (ii) a similarity graph over the users' learning tasks. Our approach trains nonlinear classifiers in a multi-task boosting manner without exchanging personal data and wi...
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On the well-posedness of SPDEs with singular drift in divergence form
We prove existence and uniqueness of strong solutions for a class of second-order stochastic PDEs with multiplicative Wiener noise and drift of the form $\operatorname{div} \gamma(\nabla \cdot)$, where $\gamma$ is a maximal monotone graph in $\mathbb{R}^n \times \mathbb{R}^n$ obtained as the subdifferential of a conv...
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Trace norm regularization and faster inference for embedded speech recognition RNNs
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique...
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Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs
Sparse subspace clustering (SSC) is one of the current state-of-the-art methods for partitioning data points into the union of subspaces, with strong theoretical guarantees. However, it is not practical for large data sets as it requires solving a LASSO problem for each data point, where the number of variables in ea...
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Deep Health Care Text Classification
Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working note presents the Recurrent neural network (RNN) and Long short-term memory (LST...
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Study of cost functionals for ptychographic phase retrieval to improve the robustness against noise, and a proposal for another noise-robust ptychographic phase retrieval scheme
Recently, efforts have been made to improve ptychography phase retrieval algorithms so that they are more robust against noise. Often the algorithm is adapted by changing the cost functional that needs to be minimized. In particular, it has been suggested that the cost functional should be obtained using a maximum-li...
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Predicting Native Language from Gaze
A fundamental question in language learning concerns the role of a speaker's first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first ti...
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A form of Schwarz's lemma and a bound for the Kobayashi metric on convex domains
We present a form of Schwarz's lemma for holomorphic maps between convex domains $D_1$ and $D_2$. This result provides a lower bound on the distance between the images of relatively compact subsets of $D_1$ and the boundary of $D_2$. This is a natural improvement of an old estimate by Bernal-González that takes into ...
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The COM-negative binomial distribution: modeling overdispersion and ultrahigh zero-inflated count data
In this paper, we focus on the COM-type negative binomial distribution with three parameters, which belongs to COM-type $(a,b,0)$ class distributions and family of equilibrium distributions of arbitrary birth-death process. Besides, we show abundant distributional properties such as overdispersion and underdispersion...
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Efficient Contextual Bandits in Non-stationary Worlds
Most contextual bandit algorithms minimize regret against the best fixed policy, a questionable benchmark for non-stationary environments that are ubiquitous in applications. In this work, we develop several efficient contextual bandit algorithms for non-stationary environments by equipping existing methods for i.i.d...
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Search Engine Drives the Evolution of Social Networks
The search engine is tightly coupled with social networks and is primarily designed for users to acquire interested information. Specifically, the search engine assists the information dissemination for social networks, i.e., enabling users to access interested contents with keywords-searching and promoting the proce...
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End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-t...
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Von Neumann Regular Cellular Automata
For any group $G$ and any set $A$, a cellular automaton (CA) is a transformation of the configuration space $A^G$ defined via a finite memory set and a local function. Let $\text{CA}(G;A)$ be the monoid of all CA over $A^G$. In this paper, we investigate a generalisation of the inverse of a CA from the semigroup-theo...
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A Distance Between Filtered Spaces Via Tripods
We present a simplified treatment of stability of filtrations on finite spaces. Interestingly, we can lift the stability result for combinatorial filtrations from [CSEM06] to the case when two filtrations live on different spaces without directly invoking the concept of interleaving. We then prove that this distance ...
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Robust Shape Estimation for 3D Deformable Object Manipulation
Existing shape estimation methods for deformable object manipulation suffer from the drawbacks of being off-line, model dependent, noise-sensitive or occlusion-sensitive, and thus are not appropriate for manipulation tasks requiring high precision. In this paper, we present a real-time shape estimation approach for a...
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Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning
In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go, and Poker. However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations,...
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