Evaluating the performance of personal social health related biomarker and genetic data for predicting an individuals future health using machine learning A longitudinal analysis

The study compares the performance of five types of measures: age, sex, social, health-related, biomarker and genetic single nucleotidepolymorphisms (SNPs) The predicted outcome variable was limiting long-termillness one and five years from baseline . Health-related measures had the strongest prediction of future health status, with genetic data performing poorly .…

Unified Spatio Temporal Modeling for Traffic Forecasting using Graph Neural Network

Research in deep learning models to forecast traffic intensities has gainedgreat attention in recent years due to their capability to capture the complexspatio-temporal relationships within the traffic data . We propose a UnifiedSpatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that performs both spatial and temporal aggregation through direct informationpropagation across different timestamp nodes .…

Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations

We present a framework that allows for the non-asymptotic study of the $2$-Wasserstein distance between the invariant distribution of an ergodicstochastic differential equation and the distribution of its numericalapproximation in the strongly log-concave case . This allows us to study in aunified way a number of different integrators proposed in the literature for the overdamped and underdamped Langevin dynamics .…

Syft 0 5 A Platform for Universally Deployable Structured Transparency

Syft is a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems . This framework is demonstrated through the design and implementation of a novel privacy-preserving inference information flow wherewe pass homomorphically encrypted activation signals through a split neuralnetwork for inference .…

Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes

This work studies finite sample approximations of the exact and entropic regularized Wasserstein distances between centered Gaussian processes and, moregenerally, covariance operators of functional random processes . We first show that these distances/divergences are fully represented by reproducing kernelHilbert space (RKHS) covariance and cross-covariance operators associated with the corresponding covariance functions .…

One parameter family of acquisition functions for efficient global optimization

Bayesian optimization (BO) with Gaussian processes is a powerful methodology to optimize an expensive black-box function . The expected improvement (EI) and probability of improvement (PI) are among the most widely used schemes for BO . The proposed method isnumerically inexpensive, is easy to implement, can be easily parallelized, and on benchmark tasks shows a performance superior to EI and GP-UCB .…

Improved Bounded Model Checking of Timed Automata

Timed Automata (TA) are a very popular modeling formalism for systems with time-sensitive properties . A common task is to verify if a network of TAsatisfies a given property, usually expressed in Linear Temporal Logic (LTL) The produced CLTLoc formula can then be solved by toolssuch as Zot, which transforms CLTLOC properties into the input logics of SMT solvers .…

Towards Knowledge Graphs Validation through Weighted Knowledge Sources

The performance of applications rely on high-quality knowledge bases, a.k.a. Knowledge Graphs . To ensure their quality one important task is KnowledgeValidation, which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct . We propose and implement a validation approach that computes a confidence score for everytriple and instance in a KG .…

DABT A Dependency aware Bug Triaging Method

The Dependency-aware Bug Triaging (DABT) leverages natural language processing and integer programming to assign bugs to developers . DABT is able to reduce the number of overdue bugs up to 12\% . It also decreases the average fixing time of the bugs by half .…

Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging

The consideration of predictive uncertainty in medical imaging with deeplearning is of utmost importance . We apply $ \sigma $ scaling with a single scalarvalue; a simple, yet effective calibration method for both types ofuncertainty . The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-artconvolutional network architectures .…

dualFace Two Stage Drawing Guidance for Freehand Portrait Sketching

dualFace consists of two-stage drawing assistance toprovide global and local visual guidance: global guidance, which helps users draw contour lines of portraits, and local guidance, inspired by traditional artist workflows in portrait drawing . In the stage of local guidance users synthesize detailed portrait images with a deep generative model, but use the synthesized results as detailed drawing guidance .…

Reconfigurable Adaptive Channel Sensing

Channel sensing consists of probing the channel from time to time to check whether or not it is active – say of an incoming message . When communication is sparse with information being sent once in a long while,channel sensing becomes a significant source of energy consumption .…

Algorithmic Solution for Non Square Dense Systems of Linear Equations with applications in Feature Selection

We present a novel algorithm attaining excessively fast, the sought solutionof linear systems of equations . The execution time is very short compared with state-of-the-art methods, exhibiting up to $10^3 speed-up and lowmemory allocation demands . The accuracy is high and straightforwardly controlled, and the numerical results highlight the efficiency of the proposed algorithm, in terms of computation time, solution accuracy and solution accuracy .…

Bijective proofs for Eulerian numbers in types B and D

We give bijective proofs of the identity of Stembridge’s identity . We also establish abijective correspondence between even signed permutations and pairs of pairs of $w, E)$ with $([n, E), a threshold graph and $w$ a degree ordering of $($n), $(w) and $(W)$ (W)) The bijectives rely on a representation ofsigned permutations as paths .…

Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Classical approaches in cluster analysis are typically based on a featurespace analysis . Many applications lead to datasets with additionalspatial information and ground truth with spatially coherent classes . We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent postprocessing step or included into the clustering algorithm .…

A PGAS Communication Library for Heterogeneous Clusters

This work presents a heterogeneous communication library for clusters ofprocessors and FPGAs . This library, Shoal, supports the Partitioned GlobalAddress Space (PGAS) memory model for applications . PGAS is a shared memorymodel for clusters that creates a distinction between local and remote memory access .…

Efficient Hyperparameter Optimization for Physics based Character Animation

Physics-based character animation has seen significant advances in recent years with the adoption of Deep Reinforcement Learning (DRL) However,DRL-based learning methods are usually computationally expensive . Tuninghyperparameters for these methods often requires repetitive training of controlpolicies . In this work, we propose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework .…

baller2vec A Look Ahead Multi Entity Transformer For Modeling Coordinated Agents

In many multi-agent spatiotemporal systems, the trajectories of the agents are oftenstatistically dependent at any given time step . In this paper, we introduce baller2vec++, amulti-entity Transformer that can effectively model coordinated agents . We show that,unlike the previous model, baller 2vec++ can learn toemulate the behavior of perfectly coordinated agents in a simulated toydataset .…

A Session Subtyping Tool Extended Version

Session types are becoming popular and have been integrated in several programming languages . The notion of subtyping used in session type implementations is the one defined by Gay and Hole for synchronous communication . The aim of this paper is to make the growing body of knowledge about asynchronous session subtypp more accessible to non-experts and promote its integration in practical applications of sessiontypes .…

Communication Efficient and Personalized Federated Lottery Ticket Learning

LotteryFL relies on unicasttransmission on the downlink, and ignores mitigating stragglers, questioningscalability . CELL is a federated lottery ticket learning algorithm, coinedCELL, which exploits downlink broadcast for communication efficiency. CELL achieves up to 3.6% higher personalized task classification accuracy with 4.3x smaller total communication cost until convergence under the CIFAR-10 dataset .…

Game Theoretic Mode Scheduling for Dynamic TDD in 5G Systems

Dynamic time-division duplexing (TDD) enables independent uplink/downlinkmode scheduling at each cell, based on the local traffic . However, this createscross-interference among cells. Thus, the joint power allocation and scheduling problem becomes mixed-integer non-convex and turns out to be NP-hard. Wepropose a low-complexity and decentralized solution .…

Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems based on Sample Identifying Complexity and Deciphering Time

There has been no systematic methodology of constructing cyber-physical systems that can achieve desired control performance while being protected against eavesdropping attacks . We propose a systematic method for designing the both of an optimal key length and an optimal controller to maximize both of the controlperformance and the difficulty of the identification .…

Adaptive Encoding for Constrained Video Delivery in HEVC VP9 AV1 and VVC Compression Standards and Adaptation to Video Content

The dissertation proposes the use of a multi-objective optimization framework for designing and selecting among enhanced GOP configurations in videocompression standards . The proposed methods achieve fine optimization over aset of general modes that include: (i) maximum video quality, (ii) minimumbitrate, (iii) maximum encoding rate (previously minimum encoding time mode) and (iv) can be shown to improve upon the YouTube/Netflix default encoder modesettings over a set of opposing constraints to guarantee satisfactoryperformance .…

Efficient Evolutionary Models with Digraphons

We present two main contributions which help us in leveraging the theory ofgraphons for modeling evolutionary processes . We show a generative model fordigraphons using a finite basis of subgraphs, which is representative ofbiological networks with evolution by duplication . We also show an efficient implementation to do simulations on finite basis segmentations of digraphons .…

Spherical formulation of geometric motion segmentation constraints in fisheye cameras

We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving . Three commonly used geometric constraints in pin-hole imagery are reformulated to spherical coordinates . A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near parallel motion with respect to the hostvehicle .…

HyperRNN Deep Learning Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO

In frequencydivision duplex systems, full channel reciprocity does not hold, and CSIacquisition generally requires downlink pilot transmission followed by uplinkfeedback . The proposed method is based on a novel deep learningarchitecture — HyperRNN — that combines hypernetworks and recurrent neuralnetworks (RNNs) to optimize the transfer of long-term channel features fromuplink to downlink .…

International Migration in Academia and Citation Performance An Analysis of German Affiliated Researchers by Gender and Discipline Using Scopus Publications 1996 2020

Germany has become a major country of immigration, as well as a researchpowerhouse in Europe . As Germany spends a higher fraction of its GDP onresearch and development than most countries with advanced economies, there is an expectation that Germany should be able to attract and retain internationalscholars who have high citation performance .…

Dynamic Degradation for Image Restoration and Fusion

The DDRF-Net is capable of solving twoproblems, i.e., static restoration and fusion, dynamic degradation . In order to solve the static fusion problem of existing methods, dynamic convolution is introduced . In addition, adynamic degradation kernel is proposed to improve the robustness of imagerestoration and fusion .…

Variational Pedestrian Detection

Pedestrian detection in a crowd is a challenging task due to a high number of human instances, which brings ambiguity and optimizationdifficulties to the current IoU-based ground truth assignment procedure inclassical object detection methods . The proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stagedetectors .…