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Local minimax rates for closeness testing of discrete distributionsFeb 01 2019We consider the closeness testing (or two-sample testing) problem in the Poisson vector model - which is known to be asymptotically equivalent to the model of multinomial distributions. The goal is to distinguish whether two data samples are drawn from ... More
Hyperbox based machine learning algorithms: A comprehensive surveyJan 31 2019Feb 04 2019With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and ... More
The CM Algorithm for the Maximum Mutual Information Classifications of Unseen InstancesJan 28 2019The Maximum Mutual Information (MMI) criterion is different from the Least Error Rate (LER) criterion. It can reduce failing to report small probability events. This paper introduces the Channels Matching (CM) algorithm for the MMI classifications of ... More
Stopping Active Learning based on Predicted Change of F Measure for Text ClassificationJan 26 2019During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted Change of F Measure will be introduced that attempts to provide the users ... More
Embedding quadratization gadgets on Chimera and Pegasus graphsJan 23 2019We group all known quadratizations of cubic and quartic terms in binary optimization problems into six and seven unique graphs respectively. We then perform a minor embedding of these graphs onto the well-known Chimera graph, and the brand new Pegasus ... More
Pegasus: The second connectivity graph for large-scale quantum annealing hardwareJan 22 2019Pegasus is a graph which offers substantially increased connectivity between the qubits of quantum annealing hardware compared to the graph Chimera. It is the first fundamental change in the connectivity graph of quantum annealers built by D-Wave since ... More
Quadratization in discrete optimization and quantum mechanicsJan 14 2019A book about turning high-degree optimization problems into quadratic optimization problems that maintain the same global minimum (ground state). This book explores quadratizations for pseudo-Boolean optimization, perturbative gadgets used in QMA completeness ... More
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging DataJan 12 2019ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this paper presents and compares alternative approaches - timestamps and difference ... More
On Huang and Wong's Algorithm for Generalized Binary Split TreesJan 12 2019Huang and Wong [5] proposed a polynomial-time dynamic-programming algorithm for computing optimal generalized binary split trees. We show that their algorithm is incorrect. Thus, it remains open whether such trees can be computed in polynomial time. Spuler ... More
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning ProcessDec 20 2018We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods ... More
On balanced clustering with tree-like structures over clustersDec 09 2018The article addresses balanced clustering problems with an additional requirement as a tree-like structure over the obtained balanced clusters. This kind of clustering problems can be useful in some applications (e.g., network design, management and routing). ... More
A note on solving nonlinear optimization problems in variable precisionDec 09 2018Dec 11 2018This short note considers an efficient variant of the trust-region algorithm with dynamic accuracy proposed Carter (1993) and Conn, Gould and Toint (2000) as a tool for very high-performance computing, an area where it is critical to allow multi-precision ... More
Naive Dictionary On Musical Corpora: From Knowledge Representation To Pattern RecognitionNov 29 2018In this paper, we propose and develop the novel idea of treating musical sheets as literary documents in the traditional text analytics parlance, to fully benefit from the vast amount of research already existing in statistical text mining and topic modelling. ... More
The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecastsNov 21 2018We propose a multivariate elastic net regression forecast model for German quarter-hourly electricity spot markets. While the literature is diverse on day-ahead prediction approaches, both the intraday continuous and intraday call-auction prices have ... More
Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)Nov 14 2018Nov 26 2018In this paper we present a method for the accurate estimation of the derivative (aka.~sensitivity) of expectations of functions involving an indicator function by combining a stochastic algorithmic differentiation and a regression. The method is an improvement ... More
Time-interval balancing in multi-processor scheduling of composite modular jobs (preliminary description)Nov 11 2018The article describes a special time-interval balancing in multi-processor scheduling of composite modular jobs. This scheduling problem is close to just-in-time planning approach. First, brief literature surveys are presented on just-in-time scheduling ... More
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identificationNov 09 2018Nov 17 2018The task of multiple people tracking in monocular videos is challenging because of the numerous difficulties involved: occlusions, varying environments, crowded scenes, camera parameters and motion. In the tracking-by-detection paradigm, most approaches ... More
Deterministic and stochastic inexact regularization algorithms for nonconvex optimization with optimal complexityNov 09 2018Nov 12 2018A regularization algorithm using inexact function values and inexact derivatives is proposed and its evaluation complexity analyzed. This algorithm is applicable to unconstrained problems and to problems with inexpensive constraints (that is constraints ... More
Deep BV: A Fully Automated System for Brain Ventricle Localization and Segmentation in 3D Ultrasound Images of Embryonic MiceNov 05 2018Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging ... More
Sharp worst-case evaluation complexity bounds for arbitrary-order nonconvex optimization with inexpensive constraintsNov 03 2018We provide sharp worst-case evaluation complexity bounds for nonconvex minimization problems with general inexpensive constraints, i.e.\ problems where the cost of evaluating/enforcing of the (possibly nonconvex or even disconnected) constraints, if any, ... More
CMI: An Online Multi-objective Genetic Autoscaler for Scientific and Engineering Workflows in Cloud Infrastructures with Unreliable Virtual MachinesNov 02 2018Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic ... More
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural NetworksNov 01 2018Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals. Therefore, common performance evaluation toolkits are not applicable to real-world situations where the ground truth ... More
From the EM Algorithm to the CM-EM Algorithm for Global Convergence of Mixture ModelsOct 26 2018The Expectation-Maximization (EM) algorithm for mixture models often results in slow or invalid convergence. The popular convergence proof affirms that the likelihood increases with Q; Q is increasing in the M -step and non-decreasing in the E-step. The ... More
A minimax near-optimal algorithm for adaptive rejection samplingOct 22 2018Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample from distributions admitting a probability density function which can be evaluated exactly at any given point, albeit at a high computational cost. However, without proper tuning, ... More
An $O(1/\varepsilon)$-Iteration Triangle Algorithm for A Convex Hull MembershipOct 17 2018A fundamental problem in linear programming, machine learning, and computational geometry is the {\it Convex Hull Membership} (CHM): Given a point $p$ and a subset $S$ of $n$ points in $\mathbb{R}^m$, is $p \in conv(S)$? The {\it Triangle Algorithm} (TA) ... More
On the Properties of Simulation-based Estimators in High DimensionsOct 10 2018Oct 11 2018Considering the increasing size of available data, the need for statistical methods that control the finite sample bias is growing. This is mainly due to the frequent settings where the number of variables is large and allowed to increase with the sample ... More
Non-Line-of-Sight Reconstruction using Efficient Transient RenderingSep 21 2018Being able to see beyond the direct line of sight is an intriguing prospective and could benefit a wide variety of important applications. Recent work has demonstrated that time-resolved measurements of indirect diffuse light contain valuable information ... More
From Bayesian Inference to Logical Bayesian Inference: A New Mathematical Frame for Semantic Communication and Machine LearningSep 03 2018Bayesian Inference (BI) uses the Bayes' posterior whereas Logical Bayesian Inference (LBI) uses the truth function or membership function as the inference tool. LBI was proposed because BI was not compatible with the classical Bayes' prediction and didn't ... More
AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on DemandAug 14 2018Nov 06 2018Urban transportation of next decade is expected to be disrupted by Autonomous Mobility on Demand (AMoD): AMoD providers will collect ride requests from users and will dispatch a fleet of autonomous vehicles to satisfy requests in the most efficient way. ... More
Bringing Together Dynamic Geometry Software and the Graphics Processing UnitAug 14 2018We equip dynamic geometry software (DGS) with a user-friendly method that enables massively parallel calculations on the graphics processing unit (GPU). This interplay of DGS and GPU opens up various applications in education and mathematical research. ... More
Simulation using random numbersAug 03 2018This article is devoted to methods of construction and study of stochastic models based on Monte Carlo method. A model of Brownian motion, the construction and processing which brings to a world of random numbers and mathematical statistics, promotes ... More
Network-Coding Approach for Information-Centric NetworkingAug 01 2018Aug 09 2018The current internet architecture is inefficient in fulfilling the demands of newly emerging internet applications. To address this issue, several over-the-top (OTT) application-level solutions have been employed, making the overall architecture very ... More
Gaussian Process Landmarking for Three-Dimensional Geometric MorphometricsJul 31 2018Jan 08 2019We demonstrate applications of the Gaussian process-based landmarking algorithm proposed in [T. Gao, S.Z. Kovalsky, and I. Daubechies, SIAM Journal on Mathematics of Data Science (2019)] to geometric morphometrics, a branch of evolutionary biology centered ... More
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelJul 20 2018Sep 01 2018We present a novel framework that enables efficient probabilistic inference in large-scale scientific models by allowing the execution of existing domain-specific simulators as probabilistic programs, resulting in highly interpretable posterior inference. ... More
PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learningJul 20 2018Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform quantitative phase images (QPI) of labelfree tissue sections into images that are equivalent to brightfield microscopy images of the same ... More
Uncertainty quantification for an optical grating coupler with an adjoint-based Leja adaptive collocation methodJul 19 2018This paper addresses uncertainties arising in the nano-scale fabrication of optical devices. The stochastic collocation method is used to propagate uncertainties in material and geometry to the scattering parameters of the system. A dimension-adaptive ... More
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural DecodingJul 17 2018Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state ... More
Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and ControlJun 21 2018This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method ... More
Homonym Detection in Curated Bibliographies: Learning from dblp's Experience (full version)Jun 15 2018Identifying (and fixing) homonymous and synonymous author profiles is one of the major tasks of curating personalized bibliographic metadata repositories like the dblp computer science bibliography. In this paper, we present and evaluate a machine learning ... More
q-Space Novelty Detection with Variational AutoencodersJun 08 2018Oct 25 2018In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose ... More
Deep Bayesian regression modelsJun 06 2018Jun 07 2018Regression models are used for inference and prediction in a wide range of applications providing a powerful scientific tool for researchers and analysts from different fields. In many research fields the amount of available data as well as the number ... More
Path Throughput Importance WeightsJun 04 2018Sep 06 2018Many Monte Carlo light transport simulations use multiple importance sampling (MIS) to weight between different path sampling strategies. We propose to use the path throughput to compute the MIS weights instead of the commonly used probability density ... More
Holographic Neural ArchitecturesJun 04 2018Representation learning is at the heart of what makes deep learning effective. In this work, we introduce a new framework for representation learning that we call "Holographic Neural Architectures" (HNAs). In the same way that an observer can experience ... More
Optimal Clustering under UncertaintyJun 02 2018Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing ... More
Bayesian Learning with Wasserstein BarycentersMay 28 2018Dec 27 2018We introduce a novel paradigm for Bayesian learning based on optimal transport theory. Namely, we propose to use the Wasserstein barycenter of the posterior law on models as a predictive posterior, thus introducing an alternative to classical choices ... More
Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image ReconstructionMay 23 2018We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely ... More
A Compositional Approach to Network AlgorithmsMay 19 2018We present elements of a typing theory for flow networks, where "types", "typings", and "type inference" are formulated in terms of familiar notions from polyhedral analysis and convex optimization. Based on this typing theory, we develop an alternative ... More
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworksMay 17 2018We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor edge in predictive ... More
Intracranial Error Detection via Deep LearningMay 04 2018Nov 02 2018Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated ... More
Semantic Channel and Shannon's Channel Mutually Match for Multi-Label ClassificationMay 02 2018A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. Label learning is to let semantic channels match Shannon's channels and label selection is to let Shannon's channels match ... More
Modelling Bitcoin in AgdaApr 17 2018We present two models of the block chain of Bitcoin in the interactive theorem prover Agda. The first one is based on a simple model of bank accounts, while having transactions with multiple inputs and outputs. The second model models transactions, which ... More
Mitigating Docker Security IssuesApr 13 2018It is very easy to run applications in Docker. Docker offers an ecosystem that offers a platform for application packaging, distributing and managing within containers. However, Docker platform is yet not matured. Presently, Docker is less secured as ... More
Experimental similarity assessment for a collection of fragmented artifactsApr 11 2018In the Visual Heritage domain, search engines are expected to support archaeologists and curators to address cross-correlation and searching across multiple collections. Archaeological excavations return artifacts that often are damaged with parts that ... More
Edge-based LBP description of surfaces with colorimetric patternsApr 11 2018In this paper we target the problem of the retrieval of colour patterns over surfaces. We generalize to surface tessellations the well known Local Binary Pattern (LBP) descriptor for images. The key concept of the LBP is to code the variability of the ... More
Learning tensors from partial binary measurementsMar 31 2018In this paper we generalize the 1-bit matrix completion problem to higher order tensors. We prove that when $r=O(1)$ a bounded rank-$r$, order-$d$ tensor $T$ in $\mathbb{R}^{N} \times \mathbb{R}^{N} \times \cdots \times \mathbb{R}^{N}$ can be estimated ... More
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissueMar 30 2018Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious preparation, which stains the tissue to visualize different histological ... More
Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio OptimizationMar 29 2018Sep 11 2018The least squares Monte Carlo algorithm has become popular for solving portfolio optimization problems. A simple approach is to approximate the value functions on a discrete grid of portfolio weights, then use control regression to generalize the discrete ... More
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-EncodersMar 02 2018Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. ... More
Multiclass Weighted Loss for Instance Segmentation of Cluttered CellsFeb 21 2018We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T-cells which might help us in understanding ... More
RoboChain: A Secure Data-Sharing Framework for Human-Robot InteractionFeb 13 2018Mar 26 2018Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need ... More
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary ResultsFeb 10 2018A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining ... More
Gaussian Process Landmarking on ManifoldsFeb 09 2018Jan 08 2019As a means of improving analysis of biological shapes, we propose an algorithm for sampling a Riemannian manifold by sequentially selecting points with maximum uncertainty under a Gaussian process model. This greedy strategy is known to be near-optimal ... More
Uncertainty Quantification for Maxwell's Eigenproblem using Isogeometric AnalysisFeb 08 2018May 30 2018The electromagnetic field distribution as well as the resonating frequency of various modes in superconducting cavities used in particle accelerators for example are sensitive to small geometry deformations. The occurring variations are motivated by measurements ... More
Uncertainty Quantification for Geometry Deformations of Superconducting Cavities using Eigenvalue TrackingFeb 08 2018The electromagnetic field distribution as well as the resonating frequency of various modes in superconducting cavities are sensitive to small geometry deformations. The occurring variations are motivated by measurements of an available set of resonators ... More
Robust Vertex Enumeration for Convex Hulls in High DimensionsFeb 05 2018Sep 24 2018Computation of the vertices of the convex hull of a set $S$ of $n$ points in $\mathbb{R} ^m$ is a fundamental problem in computational geometry, optimization, machine learning and more. We present "All Vertex Triangle Algorithm" (AVTA), a robust and efficient ... More
Robust Vertex Enumeration for Convex Hulls in High DimensionsFeb 05 2018Computation of the vertices of the convex hull of a set $S$ of $n$ points in $\mathbb{R} ^m$ is a fundamental problem in computational geometry, optimization, machine learning and more. We present "All Vertex Triangle Algorithm" (AVTA), a robust and efficient ... More
STEAM: A Hierarchical Co-Simulation Framework for Superconducting Accelerator Magnet CircuitsJan 26 2018Simulating the transient effects occurring in superconducting accelerator magnet circuits requires including the mutual electro-thermo-dynamic interaction among the circuit elements, such as power converters, magnets, and protection systems. Nevertheless, ... More
Clustering with Deep Learning: Taxonomy and New MethodsJan 23 2018Sep 13 2018Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. ... More
Combinatorial framework for planning in geological explorationJan 22 2018The paper describes combinatorial framework for planning of geological exploration for oil-gas fields. The suggested scheme of the geological exploration involves the following stages: (1) building of special 4-layer tree-like model (layer of geological ... More
A Workload Analysis of NSF's Innovative HPC Resources Using XDMoDJan 12 2018Workload characterization is an integral part of performance analysis of high performance computing (HPC) systems. An understanding of workload properties sheds light on resource utilization and can be used to inform performance optimization both at the ... More
A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience ApplicationsJan 11 2018Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data ... More
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse modelsJan 03 2018Oct 17 2018The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our ... More
Coupling of Magneto-Thermal and Mechanical Superconducting Magnet Models by Means of Mesh-Based InterpolationDec 29 2017In this paper we present an algorithm for the coupling of magneto-thermal and mechanical finite element models representing superconducting accelerator magnets. The mechanical models are used during the design of the mechanical structure as well as the ... More
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific SimulatorsDec 21 2017We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed ... More
Deep learning enhanced mobile-phone microscopyDec 12 2017Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy ... More
Deep Transfer Learning for Error Decoding from Non-Invasive EEGOct 25 2017Jan 10 2018We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning for error decoding with deep convolutional neural networks (deep ConvNets). ... More
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for PredictionOct 13 2017Mar 09 2018In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addressed ... More
Multiple domination models for placement of electric vehicle charging stations in road networksOct 03 2017Electric and hybrid vehicles play an increasing role in the road transport networks. Despite their advantages, they have a relatively limited cruising range in comparison to traditional diesel/petrol vehicles, and require significant battery charging ... More
LoIDE: a web-based IDE for Logic Programming - Preliminary Technical ReportSep 15 2017Logic-based paradigms are nowadays widely used in many different fields, also thank to the availability of robust tools and systems that allow the development of real-world and industrial applications. In this work we present LoIDE, an advanced and modular ... More
Towards combinatorial modeling of wireless technology generationsAug 29 2017Sep 02 2017The paper addresses the following problems: (1) a brief survey on wireless mobile communication technologies including evolution, history evolution (e.g., chain of system generations 0G, 1G, 2G, 3G, 4G, 5G, 6G, 7G); (2) using a hierarchical structural ... More
On the approximation by single hidden layer feedforward neural networks with fixed weightsAug 21 2017Feedforward neural networks have wide applicability in various disciplines of science due to their universal approximation property. Some authors have shown that single hidden layer feedforward neural networks (SLFNs) with fixed weights still possess ... More
Agent-based computing from multi-agent systems to agent-based Models: a visual surveyAug 19 2017Agent-Based Computing is a diverse research domain concerned with the building of intelligent software based on the concept of "agents". In this paper, we use Scientometric analysis to analyze all sub-domains of agent-based computing. Our data consists ... More
A discrete event system specification (DEVS)-based model of consanguinityAug 10 2017Consanguinity or inter-cousin marriage is a phenomenon quite prevalent in certain regions around the globe. Consanguineous parents have a higher risk of having offspring with congenital disorders. It is difficult to model large scale consanguineous parental ... More
A histogram-free multicanonical Monte Carlo algorithm for the basis expansion of density of statesJul 21 2017We report a new multicanonical Monte Carlo (MC) algorithm to obtain the density of states (DOS) for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain an analytical form for the DOS expressed in ... More
Automatic Backward Differentiation for American Monte-Carlo Algorithms (Conditional Expectation)Jul 16 2017In this note we derive the backward (automatic) differentiation (adjoint [automatic] differentiation) for an algorithm containing a conditional expectation operator. As an example we consider the backward algorithm as it is used in Bermudan product valuation, ... More
Deepest Neural NetworksJul 09 2017This paper shows that a long chain of perceptrons (that is, a multilayer perceptron, or MLP, with many hidden layers of width one) can be a universal classifier. The classification procedure is not necessarily computationally efficient, but the technique ... More
Like trainer, like bot? Inheritance of bias in algorithmic content moderationJul 05 2017The internet has become a central medium through which `networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this ... More
A Vision for Health Informatics: Introducing the SKED Framework.An Extensible Architecture for Scientific Knowledge Extraction from DataJun 24 2017The goals of the Triple Aim of health care and the goals of P4 medicine outline objectives that require a significant health informatics component. However, the goals do not provide specifications about how all of the new individual patient data will ... More
Deep Interest Network for Click-Through Rate PredictionJun 21 2017Sep 13 2018Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding\&MLP paradigm. In these methods large scale sparse input ... More
Parallel-In-Time Simulation of Eddy Current Problems Using PararealJun 19 2017Feb 07 2018In this contribution the usage of the Parareal method is proposed for the time-parallel solution of the eddy current problem. The method is adapted to the particular challenges of the problem that are related to the differential algebraic character due ... More
Towards balanced clustering - part 1 (preliminaries)Jun 09 2017The article contains a preliminary glance at balanced clustering problems. Basic balanced structures and combinatorial balanced problems are briefly described. A special attention is targeted to various balance/unbalance indices (including some new versions ... More
On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application PerspectiveJun 01 2017We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today's and future HPC systems, we present a scaling law ... More
A general theory of singular values with applications to signal denoisingMay 30 2017We study the Pareto frontier for two competing norms $\|\cdot\|_X$ and $\|\cdot\|_Y$ on a vector space. For a given vector $c$, the pareto frontier describes the possible values of $(\|a\|_X,\|b\|_Y)$ for a decomposition $c=a+b$. The singular value decomposition ... More
Note on Evolution and Forecasting of Requirements: Communications ExampleMay 22 2017Combinatorial evolution and forecasting of system requirements is examined. The morphological model is used for a hierarchical requirements system (i.e., system parts, design alternatives for the system parts, ordinal estimates for the alternatives). ... More
Deep Learning MicroscopyMay 12 2017We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular ... More
Forecasting using incomplete modelsMay 12 2017Nov 29 2018We consider the task of forecasting an infinite sequence of future observations based on some number of past observations, where the probability measure generating the observations is "suspected" to satisfy one or more of a set of incomplete models, i.e. ... More
Information Criterion for Minimum Cross-Entropy Model SelectionApr 14 2017Oct 27 2018This paper considers the problem of approximating a density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in machine learning, such as approximating a posterior density ... More
Application of the Waveform Relaxation Technique to the Co-Simulation of Power Converter Controller and Electrical Circuit ModelsApr 10 2017In this paper we present the co-simulation of a PID class power converter controller and an electrical circuit by means of the waveform relaxation technique. The simulation of the controller model is characterized by a fixed-time stepping scheme reflecting ... More
Probabilistic Mid- and Long-Term Electricity Price ForecastingMar 31 2017May 17 2018The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing ... More
Probabilistic Mid- and Long-Term Electricity Price ForecastingMar 31 2017The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing ... More