nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian constrained local models with depth data

This paper proposes an extended Constrained Local Model (CLM) formulation for aligning faces using depth information. The CLMs are popular methods that were initially designed to locate facial features in regular intensity images. Briefly, they combine a set of local detectors, one for each landmark, whose locations are regularized by a linear shape model. Fitting a CLM is usually framed as a two step approach: locally search, using the detectors, producing response maps (likelihood maps) followed by a global optimization strategy that jointly maximize all detection scores while enforcing an appropriate shape. Including depth data could be simply posed as adding additional likelihood sources to the main formulation. The paper discusses several likelihood fusion techniques and propose to jointly learn a multi-dimensional correlation filter as a more reliable solution. Moreover, we propose to learn the local detectors, in the Fourier domain, effectively augmenting the training set with virtual samples. Besides improving the detections reliability, this approach is particular important when applied to depth data, as no additional processing is required (such as fill missing information). The performance evaluation shows that our extended approach further increases the fitting performance (accuracy) effectively proving the benefit of using depth data in facial alignment tasks.
Overspread Digital Transmission Over Wireless Linear Time-Varying MIMO Systems

Accompanying the journey for higher spectral efficiency in mobile wireless communications, this paper studies multiple-input-multiple-output (MIMO) transmission in a unified framework that consolidates the phenomena responsible for frequency selectivity and time selectivity, i.e., delay overspreading and Doppler overspreading, respectively. Starting with a baseband description of the linear time-varying MIMO system, a novel approach using continuous, discrete, and hybrid linear operators leverages the process of channel orthonormalization and model discretization. Notably, this process leads to an optimal time-varying semiorthonormal matrix matched filter (the ORTHO-TS-MMF). Monte Carlo simulations test the error performance for typical wireless MIMO realizations. In particular, they unveil that combining the ORTHO-TS-MMF with optimal linear detection takes advantage of both delay and Doppler diversities, significantly reducing the symbol error probability.
A nonparametric Riemannian framework on tensor field with application to foreground segmentation

modelling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent research has focused on developing parametric methods on the tensor domain e.g. gaussians mixtures (GMM) . However, in some scenarios, simple parametric models do not accurately explain the physical processes. Kernel density estimators (KDE) have been successful to model, on Euclidean sample spaces the nonparametric nature of complex, time varying, and non-static backgrounds . Founded on the mathematically rigorous KDE paradigm on general Riemannian manifolds , we define a KDE specifically to operate on the tensor manifold. We present a mathematically-sound framework for nonparametric modeling on tensor field to foreground segmentation. We endow the tensor manifold with two well-founded Riemannian metrics, i.e. Affine-Invariant and Log-Euclidean. Theoretical aspects are defined and the metrics are compared experimentally. Theoretic analysis and experimental results demonstrate the promise/effectiveness of the framework.
A New Programming Model for Dependable Adaptive Real-Time Applications

The CORTEX project envisions a new class of applications comprising (possibly many) smart components, called sentient objects, that can sense their environment and interact with it and each other by accepting and producing events. These interactions might occur in ways that demand predictable and sometimes guaranteed quality of service (QoS). However, achieving predictability is difficult given the objects' changing environments dictated by unstable and mobile object populations, unpredictable network load, varying connectivity, and failed system components. We devised an innovative programming model that aims to reconcile uncertainty with an application's required predictability. The model is based on fulfilling two predicates - coverage stability and no-contamination - by programs or protocols subject to varying conditions of system timeliness.
Identity and expression recognition on low dimensional manifolds

A solution for identity and facial expression recognition is proposed using a two stage classifier approach using low dimensional representation of the geometry of the face. Face geometry is extracted from input images using Active Appearance Models (AAM) and low dimensional manifolds were then derived using Laplacian Eigen-Maps (LE) resulting in two types of manifolds, one for model identity and the other for person-specific facial expression. The first stage uses a multiclass Support Vector Machines (SVM) to establish identity across expression changes. The second stage deals with person-specific expression recognition, and is composed by a network of seven Hidden Markov Models (HMM) displaced in parallel, each one specialized on the several facial emotions analysed. The decision was made by the sequence that yielded the highest probability. For evaluation proposes a database was build consisting on 6770 images captured from 4 people exhibiting 7 different emotions. The identity overall recognition rate was 96.8%. Facial expression results are identity dependent, and the most expressive individual achieves 81.2% of overall recognition rate.
A Dynamic Voltage Restorer based on vector-switching matrix converters

This paper introduces a novel Dynamic Voltage Restorer (DVR) based on a reduced-order matrix converter capable of compensating for balanced as well as unbalanced voltage sags. The DVR has the ability of independently and selectively generating positive as well as negative sequence voltages, and hence it can compensate for different kind of sags. Operating principles, equivalent circuits, and illustrative examples in terms of computer simulations are presented in order to validate the approach.
Device-Independent Middleware for Industrial Wireless Sensor Networks

Wireless sensor networks (WSNs) are deployed to sense, monitor and act on the environment. Deployments in scenarios such as industrial sense and react environments require a set of functionality, and for both ease and reconfiguration capabilities it is important to offer an appropriate framework. We propose a framework for interaction with real-world devices by abstracting proprietary protocols, allowing the interaction between client applications and heterogeneous sensor network platforms and protocols. The framework allows the (re)configuration of alarms, actions or closed-loop techniques, offering flexibility and the possibility to modify for providing performance control guarantees. It allows users to configure and apply various operations, including complex closed-loop techniques that monitor and act over any actuator in the WSN, independently of the underlying WSN infrastructure. The framework is being deployed in a real scenario in the context of European FP7 GINSENG project (wireless networks with performance control guarantees).
Likelihood-enhanced Bayesian constrained local models

This paper addresses to the problem of aligning images in unseen faces. The Constrained Local Models (CLM) are popular methods that combine a set of local landmark detectors whose locations are constrained to lie in a subspace spanned by a linear shape model. The CLM fitting is usually based on a two step approach: locally search, using the detectors, producing response maps (likelihood) followed by a global optimization strategy that jointly maximize all detections at once. In this paper, we mainly focus on the first stage: improving the detectors reliability. Usually the local landmarks detectors are far from perfect. Most often are designed to be fast, having a small support region and are learnt from limited data. As consequence, they will suffer from detection ambiguities. Here we propose to improve the detectors performance by considering multiple detection per landmark. In particular, we propose a joint learning of the detectors by clustering of their training data. Afterwards, the multiple likelihoods are combined using a nonlinear fusion approach. The performance evaluation shows that our (extended) approach further increases the fitting performance of the CLM formulation, when compared with recent state-of-the-art methods.
A Neural Network-Based Dynamic Cost Function for the Implementation of a Predictive Current Controller

Predictive current controllers based on finite control set-model predictive control (FCS-MPC) have been extensively used in power converters. One of the clear advantages of FCS-MPC is that several control targets and constraints can be included in a cost function and simultaneously controlled. In order to establish the importance of one controlled target in relation to the others, weighting factors are used. Once the weighting factors have been tuned, they remain unchanged. This paper presents a neural network-based novel approach to the problem, in which weighting factors are tuned online as a function of several merit figures and references. This adaptive method updates online the weighting factors in the cost function when either the merit figures or the references change to boost the performance of the controller. This strategy is validated through simulations and experiments are carried out on a three-level neutral point clamped converter. The results are compared with the conventional FCS-MPC, which is based on static cost functions.
Non-parametric Bayesian Constrained Local Models

This work presents a novel non-parametric Bayesian formulation for aligning faces in unseen images. Popular approaches, such as the Constrained Local Models (CLM) or the Active Shape Models (ASM), perform facial alignment through a local search, combining an ensemble of detectors with a global optimization strategy that constraints the facial feature points to be within the subspace spanned by a Point Distribution Model (PDM). The global optimization can be posed as a Bayesian inference problem, looking to maximize the posterior distribution of the PDM parameters in a maximum a posteriori (MAP) sense. Previous approaches rely exclusively on Gaussian inference techniques, i.e. both the likelihood (detectors responses) and the prior (PDM) are Gaussians, resulting in a posterior which is also Gaussian, whereas in this work the posterior distribution is modeled as being non-parametric by a Kernel Density Estimator (KDE). We show that this posterior distribution can be efficiently inferred using Sequential Monte Carlo methods, in particular using a Regularized Particle Filter (RPF). The technique is evaluated in detail on several standard datasets (IMM, BioID, XM2VTS, LFW and FGNET Talking Face) and compared against state-of-the-art CLM methods. We demonstrate that inferring the PDM parameters non-parametrically significantly increase the face alignment performance.
Dependable adaptive real-time applications in wormhole-based systems

This paper describes and discusses the work carried on in the context of the CORTEX project, for the development of adaptive real-time applications in wormhole based systems. The architecture of CORTEX relies on the existence of a timeliness wormhole, called timely computing base (TCB), which we have described in previous papers. Here we focus on the practical demonstration of the wormhole concept, through a demo with two complementary facets. The objective is to illustrate the effectiveness of the concept from a practical, yet rigorous, perspective, which is done with the help of an emulation framework that we present in the paper. Furthermore, the paper also describes two different ways of implementing timeliness wormholes on top of both wired and wireless infrastructures.
Fully remote rapid multiheterodyne spectroscopy based on the remote detection of an optical frequency comb through optical injection locking and electro-optic comb generation

The potential of Optical Frequency Combs (OFC) as wide bandwidth, stable, coherent, multimode sources is widely recognized and has applications in various fields, such as metrology, spectroscopy, optical communications or biomedicine. The dual optical frequency comb approach relies on the use of a second local oscillator (LO) comb with slightly different repetition frequency. This LO is then heterodyned with the primary comb to generate a down-converted RF comb from which the spectral information is retrieved. The dual-comb approach results in ultra-fast measurement times while maintaining the OFC capabilities concerning resolution, bandwidth and stability .
Piezoelectric relaxation in bismuth titanate based ferroelectric ceramics

The dynamic piezoelectric behavior of bismuth titanate based ferroelectric ceramics has been characterized as a function of the frequency and temperature. Both increase and decrease of the piezoelectric coefficient with the frequency have been observed. The influence of the microstructure and the anisotropy of properties on relaxation are discussed. A model based on the properties of a composite of two materials connected in series is proposed to explain the relaxation.
Implant shape influence on the mechanical behavior of breast implants

In the last few decades, breast reconstruction has evolved significantly due to improvement in surgical techniques and development of new materials. Safety issues of the materials are biocompatibility (especially of the gel) and bio durability of the shell. Reviewed literature indicates that biocompatibility in general is not an issue with the current generation of the breast implants. However, bio durability is. The deformation of breast prosthesis was simulated using the Finite Element Method (FEM). Elastic mechanical behaviour was considered for the silicone implant - filler and shell. The deformed geometry obtained from finite element analysis was compared with the real breast implant deformation made in laboratory. The aim of this research is to analyse the stresses and strains that occur in the breast implant shell and filler, and to analyse implant shape influence on its mechanical behaviour. This simulation is performed using the finite element package ABAQUS.
Hausdorff distance applied on real data experiment for underwater localization

This paper addresses the problem of localizing and tracking a surface or underwater vessel with the technique called as Hausdorff Distance. Two proposed approaches, based on TDOAs comparison, were used for 2-D localization, in range and depth, with one sensor only, and have been successfully applied to localize a motionless unknown target in a tank's experiment. Results in terms of the localization accuracy have been obtained with real signal and the performance of the proposed localization techniques have been demonstrated and confirmed by simulation with respect of signal-to-noise ratio and compared with the correlation techniques used nowadays for single hydrophones.
Integrated Power and Natural Gas Model for Energy Adequacy in Short-Term Operation

The significant growth in gas-fired units worldwide has increased the grade of interdependency between power and natural gas networks. Since these units are usually required to ramp up during the peak and backup intermittent renewable generation and contingencies, the power system tends to demand more flexibility and reliability from the gas system. This paper contributes with a novel mixed-integer linear programming (MILP) formulation that couples power and gas networks taking into account the gas traveling velocity and compressibility. As a result, the model accounts for the gas adequacy needed to assure the power system reliability in the short term. The robustness of the MILP formulation allows guaranteeing global optimality within predefined tolerances. Case studies integrate the IEEE 24-bus system and Belgian high-calorific gas network for validating the formulation.
Design Space Exploration for the Implementation of a Predictive Current Controller Based on FPGA

Predictive algorithms are increasingly being proposed for implementing current controllers in power converters. However, the computational burden associated with this technique has hindered its widespread use compared to classic control schemes. This paper presents a new approach to the implementation of a predictive current controller for a multilevel converter. This approach takes advantage of the computing capability of FPGA devices with the aim of speeding up the predictive current controller implementation. A processing element is defined and optimized to evaluate a cost function for each future state of the system.
Security-constrained unit commitment with dynamic gas constraints

One of the main factors impacting the system reliability nowadays is the growing interdependence between electricity and gas networks due to the increase in the installation of gas units. Security-constrained unit commitment (SCUC) models are utilized to economically schedule generating units without compromising the system reliability. This paper proposes a novel SCUC formulation that includes dynamic gas constraints, such as the line pack. A Benders' decomposition with mixed-integer programming techniques is developed to solve the problem efficiently. The master problem calculates the unit commitment guaranteeing gas transmission feasibility. Hourly subproblems minimize network violations of the power system for pre- and post-contingency conditions. Including dynamic gas networks into the SCUC allows accounting for the flexibility and reliability that power systems demand from gas systems in the short term. The IEEE 118-bus system is integrated with the Belgian high-calorific gas network for validating the formulation.
Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem

In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds []. A popular framework, valid over any Riemannian manifold, was proposed in [32] for binary classification. Once moving from binary to multi-class classification this paradigm is not valid anymore, due to the spread of multiple positive classes on the manifold [28]. It is then natural to ask whether the multi-class paradigm could be extended to operate on a large class of Riemannian manifolds. We propose a mathematically well-founded classification paradigm that allows to extend the work in [32] to multi-class models, taking into account the structure of the space. The idea is to project all the data from the manifold onto an affine tangent space at a particular point. To mitigate the distortion induced by local diffeomorphisms, we introduce for the first time in the computer vision community a well-founded mathematical concept, so-called Rolling map [22, 17]. The novelty in this alternate school of thought is that the manifold will be firstly rolled (without slipping or twisting) as a rigid body, then the given data is unwrapped onto the affine tangent space, where the classification is performed.
Multi-source domain adaptation using C⁁1-smooth subspaces interpolation

Manifold-based domain adaptation algorithms are receiving increasing attention in computer vision to model distribution shifts between source and target domain. In contrast to early works, that mainly explore intermediate subspaces along geodesics, in this work we propose to interpolate subspaces through C1-smooth curves on the Grassmann manifold. The new methodis based on the geometric Casteljau algorithm that is used to generate smooth interpolating polynomial curves on non-euclidean spaces and can be extended to generate polynomial splines that interpolate a given set of data on the Grassmann manifold. To evaluate the usefulness of the proposed interpolating curves on vision related problems, several experiments were conducted. We show the advantage of using smooth subspaces interpolation in multi-source unsupervised domain adaptation problems and in object recognition problems across datasets.
Reinforcement learning of sensor-based reaching strategies for a two-link manipulator

This paper presents a neural controller that learns goal-oriented obstacle-avoiding reaction strategies for a multilink robot arm. It acquires these strategies through reinforcement learning from local sensory data. The robot arm has rings of range sensors placed along its links. The neural controller achieves a good performance quite rapidly and shows good generalization abilities in the face of new environments. Suitable input and output codification schemes help greatly to attain these aims. The input codification exploits the inherent symmetry of the robot kinematics and the action given by the controller is interpreted with regard to the shortest path vector (SPV) to the closest goal in the configuration space. In order to avoid the SPV computation for multilink manipulators, we put forward the use of a module for differential inverse kinematics based on the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics. The use of this module does not only get round the SPV calculation, but also speeds up the learning process.
Direct Load Control Decision Model for Aggregated EV Charging Points

This paper details a decision model to implement direct load control (DLC) on battery charging processes at electric vehicle charging points located at parking areas. The programming model combines optimally three types of energy management decisions: grid-to-vehicle charges, vehicle-to-grid discharges, and novel vehicle-to-vehicle energy exchanges. The objective function maximizes the net energy supplied to batteries minimizing simultaneously the global energy cost. A 50-plug-in vehicle park case is analyzed for three possible mobility patterns: household, commercial and mixed. Outputs from the DLC model are compared with the ones using a dumb charging policy from the service quality and economic points of view. Finally, a sensitivity analysis has been done to evaluate the economic impact of the Depth of Discharge condition to preserve battery lifecycle of electric vehicles.
Security-Constrained Optimal Power and Natural-Gas Flow

Continuous liberalization and interconnection of energy markets worldwide has raised concerns about the inherent interdependency between primary energy supply and electric systems. With the growing interaction among energy carriers, limitations on the fuel delivery are becoming increasingly relevant to the operation of power systems. This paper contributes with a novel formulation of a mixed-integer linear programing (MILP) security-constrained optimal power and gas flow. To this end, an iterative methodology, based on development of linear sensitivity factors, determines the stabilized post-contingency condition of the integrated network. The proposed model allows system operators not only to perform security analysis but also to adjust in advance state variables of the integrated system optimally and fast, so that n-1 contingencies do not result in violations. Case studies integrate the IEEE 24-bus system and a modified Belgian high-calorific gas network for analyzing the performance of the formulation and solution methodology.