Miracle-dz/UniFAR-FTUs huggingface dataset:
Validating x-ray line-profile defect analysis using atomistic models of deformed material

The population of dislocation defects in a crystalline material strongly inﬂuences its properties, so the ability to analyse this population in experimental samples is of great utility. As a complement to direct counting in the transmission electron microscope, quantitative analysis of x-ray diﬀraction line proﬁles is an important tool. This is an indirect approach to quantiﬁcation and so requires careful validation of the physical models that underly the inferential process. Here we undertake to directly evaluate the ability of line proﬁle analysis to quantify aspects of the dislocation and stacking fault populations by exploiting atomistic models of deformed copper single crystals. We directly analyse these models to determine exact details of the defect content (our “ground truth”). We then generate theoretical line proﬁles for the models and analyse them using the same procedures used in experimental analysis. This leads to inferred measures of the defect content which we are able to compare with the exact data. We show that line proﬁle analysis is able to provide sound predictions of both dislocation density and stacking fault fraction across two orders of magnitude. We further show how the outer cut-oﬀ radius in the mean-square strain of a dislocation distribution invoked by Warren and Averbach corresponds to the cell size in an artiﬁcially constructed restrictedly-random distribution of dislocations according to the model of Wilkens. Overall, our results lend important new support to the use of line proﬁle analysis for the quantiﬁcation of line and planar defects in crystalline materials.
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use binocular data to construct a new benchmark for monocular textureless 3D object tracking. The proposed method requires no markers, and the cameras only need to be synchronous, relatively fixed as cross-view and calibrated. Based on our object-centered model, we jointly optimize the object pose by minimizing shape reprojection constraints in all views, which greatly improves the accuracy compared with the single-view approach, and is even more accurate than the depth-based method. Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes. The annotation error is guaranteed to be less than 2mm, according to both theoretical analysis and validation experiments. We reevaluate the state-of-the-art 3D object tracking methods with our dataset, reporting their performance ranking in real scenes. Our BCOT benchmark and code can be found at https://ar3dv.github. io/BCOT-Benchmark/.
Computing Optimal Location of Microphone for Improved Speech Recognition

—It was shown in our earlier work that the measure- ment error in the microphone position affected the room impulse response (RIR) which in turn affected the single-channel close microphone [1] and multi-channel distant microphone [2] speech recognition. In this paper, as an extension, we systematically study to identify the optimal location of the microphone, given an approximate and hence erroneous location of the microphone in 3D space. The primary idea is to use Monte-Carlo technique to generate a large number of random microphone positions around the erroneous microphone position and select the microphone position that results in the best performance of a general purpose automatic speech recognition ( gp-asr ). We experiment with clean and noisy speech and show that the optimal location of the microphone is unique and is affected by noise.
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning

We present the extension of the Remember and Forget for Experience Replay (ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL). ReF-ER was shown to outperform state of the art algorithms for continuous control in problems ranging from the OpenAI Gym to complex fluid flows. In MARL, the dependencies between the agents are included in the state-value estimator and the environment dynamics are modeled via the importance weights used by ReF-ER. In collaborative environments, we find the best performance when the value is estimated using individual rewards and we ignore the effects of other actions on the transition map. We benchmark the performance of ReF-ER MARL on the Stanford Intelligent Systems Laboratory (SISL) environments. We find that employing a single feed-forward neural network for the policy and the value function in ReF-ER MARL, outperforms state of the art algorithms that rely on complex neural network architectures.
Component-wise analysis of automatically designed multiobjective algorithms on constrained problems

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential in problems where MOEA/D performs the worst.
Gravity-constrained point cloud registration

Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other lower-rate modalities. Moreover, in the absence of constant acceleration, the gravity vector makes two attitude angles out of three observable in the global coordinate frame. In visual odometry, this is already being used to reduce the 6-Degrees Of Freedom (DOF) pose estimation problem to 4-DOF. In lidar SLAM, the gravity measurements are often used as a penalty in the back-end global map optimization to prevent map deformations. In this work, we propose an Iterative Closest Point (ICP)-based front-end which exploits the observable DOF and provides pose estimates aligned with the gravity vector. We believe that this front-end has the potential to support the loop closure identification, thus speeding up convergences of global map optimizations. The presented approach has been extensively tested against accurate ground-truth localization in large-scale outdoor environments as well as in the Subterranean Challenge organized by Defense Advanced Research Projects Agency (DARPA). We show that it can reduce the localization drift by 30% when compared to the standard 6-DOF ICP. Moreover, the code is readily available to the community as a part of the libpointmatcher library.
Calibration Error for Heterogeneous Treatment Effects

Recently, many researchers have advanced data-driven methods for modeling heterogeneous treatment eﬀects (HTEs). Even still, estimation of HTEs is a diﬃcult task—these methods frequently over- or under-estimate the treatment eﬀects, lead-ing to poor calibration of the resulting models. However, while many methods exist for evaluating the calibration of prediction and classiﬁcation models, formal approaches to assess the calibration of HTE models are limited to the calibration slope. In this paper, we deﬁne an analogue of the ( (cid:96) 2 ) expected calibration error for HTEs, and propose a robust estimator. Our approach is motivated by doubly robust treatment eﬀect estimators, making it unbiased, and resilient to confounding, overﬁtting, and high-dimensionality issues. Fur-thermore, our method is straightforward to adapt to many structures under which treatment eﬀects can be identiﬁed, including randomized trials, observational studies, and survival analysis. We illustrate how to use our proposed metric to evaluate the calibration of learned HTE models through the application to the CRITEO-UPLIFT Trial.
Voltage-Controlled Skyrmionic Interconnect with Multiple Magnetic Information Carriers

Magnetic skyrmions have been in the spotlight since they were observed in technologically relevant systems at room temperature. More recently, there has been increasing interest in additional quasiparticles that may exist as stable/metastable spin textures in magnets, such as the skyrmionium and the antiskyrmionite (i.e., a skyrmion bag with two skyrmions inside) that have distinct topological characteristics. The next challenge and opportunity, at the same time, is to investigate the use of multiple magnetic quasiparticles as information carriers in a single device for next-generation nanocomputing. In this paper, we propose a spintronic interconnect device where multiple sequences of information signals are encoded and transmitted simultaneously by skyrmions, skyrmioniums, and antiskyrmionites. The proposed spintronic interconnect device can be pipelined via voltage-controlled magnetic anisotropy (VCMA) gated synchronizers that behave as intermediate registers. We demonstrate theoretically that the interconnect throughput and transmission energy can be effectively tuned by the VCMA gate voltage and appropriate electric current pulses. By carefully adjusting the device structure characteristics, our spintronic interconnect device exhibits comparable energy efficiency with copper interconnects in mainstream CMOS technologies. This study provides fresh insight into the possibilities of skyrmionic devices in future spintronic applications.
Nonlinear Systems Negative Imaginary via State Feedback

This paper provides a state feedback stabilization approach for nonlinear systems of relative degree less than or equal to two by rendering them nonlinear negative imaginary (NI) systems. Conditions are provided under which a nonlinear system can be made a nonlinear NI system or a nonlinear output strictly negative imaginary (OSNI) system. Roughly speaking, an affine nonlinear system that has a normal form with relative degree less than or equal to two, after possible output transformation, can be rendered nonlinear NI and nonlinear OSNI. In addition, if the internal dynamics of the normal form are input-to-state stable, then there exists a state feedback input that stabilizes the system. This stabilization result is then extended to achieve stability for systems with a nonlinear NI uncertainty.
On the Correlation between Angle and Distance Distributions in Finite Wireless Networks

—Directional beamforming will play a paramount role in 5G and beyond networks in order to combat the higher path losses incurred at millimeter wave bands. Appropriate modeling and analysis of the angles and distances between transmitters and receivers in these networks are thus essential to understand performance and limiting factors. Most existing literature considers either inﬁnite and uniform networks, where nodes are drawn according to a Poisson point process, or ﬁnite networks with the reference receiver placed at the origin of a disk. Under either of these assumptions, the distance and azimuth angle between transmitter and receiver are independent, and the angle follows a uniform distribution between 0 and 2 𝜋 . Here, we consider a more realistic case of ﬁnite networks where the reference node is placed at any arbitrary location. We obtain the joint distribution between the distance and azimuth angle and demonstrate that these random variables do exhibit certain correlation, which depends on the shape of the region and the location of the reference node. To conduct the analysis, we present a general mathematical framework which is specialized to exemplify the case of a rectangular region. We then also derive the statistics for the 3D case where, considering antenna heights, the joint distribution of distance, azimuth and zenith angles is obtained. Finally, we describe some immediate applications of the present work, including the analysis of directional beamforming, the design of analog codebooks and wireless routing algorithms.
On Exploiting Layerwise Gradient Statistics for Effective Training of Deep Neural Networks

—Adam and AdaBelief compute and make use of elementwise adaptive stepsizes in training deep neural networks (DNNs) by tracking the exponential moving average (EMA) of the squared-gradient g 2 t and the squared prediction error ( m t − g t ) 2 , respectively, where m t is the ﬁrst momentum at iteration t and can be viewed as a prediction of g t . In this work, we investigate if layerwise gradient statistics can be expoited in Adam and AdaBelief to allow for more effective training of DNNs. We address the above research question in two steps. Firstly, we slightly modify Adam and AdaBelief by introducing layerwise adaptive stepsizes in their update procedures via either pre- or post-processing. Our empirical results indicate that the slight modiﬁcation produces comparable performance for training VGG and ResNet models over CIFAR10 and CIFAR100, suggesting that layer-wise gradient statistics play an important role towards the success of Adam and AdaBelief for at least certian DNN tasks. In the second step, we propose Aida , a new optimisation method, with the objective that the elementwise stepsizes within each layer have signiﬁcantly smaller statistical variances, and the layerwise average stepsizes are much more compact across all the layers. Motivated by the fact that ( m t − g t ) 2 in AdaBelief is conservative in comparison to g 2 t in Adam in terms of layerwise statistical averages and variances, Aida is designed by tracking a more conservative function of m t and g t than ( m t − g t ) 2 via layerwise vector projections. Experimental results show that Aida produces either competitive or better performance with respect to a number of existing methods including Adam and AdaBelief for a set of challenging DNN tasks.
Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning

Graph convolution networks (GCNs) are currently mainstream in learning with irregular data. These models rely on message passing and attention mechanisms that capture context and node-to-node relationships. With multi-head attention, GCNs become highly accurate but oversized, and their deployment on cheap devices requires their pruning. However, pruning at high regimes usually leads to topologically inconsistent networks with weak generalization. In this paper, we devise a novel method for lightweight GCN design. Our proposed approach parses and selects subnetworks with the highest magnitudes while guaranteeing their topological consistency. The latter is obtained by selecting only accessible and co-accessible connections which actually contribute in the evaluation of the selected subnetworks. Experiments conducted on the challenging FPHA dataset show the substantial gain of our topologically consistent pruning method especially at very high pruning regimes.
Human Gait Recognition Using Bag of Words Feature Representation Method

In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method. The algorithm is trained, tested and evaluated on a unique human gait data consisting of 93 individuals who walked with comfortable pace between two end-points during two different sessions. To evaluate the effectiveness of the proposed model, the results are compared with the outputs of the classification using extracted features. As it is presented, the proposed method results in significant improvement accuracy compared to using common statistical features, in all the used classifiers.
Diverse surface waves supported by bianisotropic metasurfaces

: Surface waves supported by structured metallic surfaces, i.e. metasurfaces, have drawn wide attention recently. They are promising for various applications ranging from integrated photonic circuits to imaging and bio-sensing in various frequency regimes. In this work, we show that surface states with diverse polarization configurations can be supported by a metasurface consisting of a single layer of bianisotropic metamaterial elements. The structure possesses D 2d symmetry, which includes mirror symmetry in the xz and yz plane, and C 2 rotational symmetry along y =  x axis. Due to this unique symmetry, the metasuface supports both transverse electric (TE) and transverse magnetic (TM) waves along k x and k y directions, while a purely longitudinal mode and an elliptically polarized transverse electromagnetic (TEM) mode along k y =  k x directions. The versatility of the surface modes on the metasurface may lead to new surface wave phenomena and
Development of a repair cost calculation model for DAF Trucks N.V. using the CRISP-DM framework

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
Independent set reconfiguration on directed graphs

Directed Token Sliding asks, given a directed graph and two sets of pairwise non-adjacent vertices, whether one can reach from one set to the other by repeatedly applying a local operation that exchanges a vertex in the current set with one of its out-neighbors, while keeping the nonadjacency. It can be seen as a reconﬁguration process where a token is placed on each vertex in the current set, and the local operation slides a token along an arc respect-ing its direction. Previously, such a problem was extensively studied on undirected graphs, where the edges have no directions and thus the local operation is symmetric. Directed Token Sliding is a generalization of its undirected variant since an undirected edge can be simulated by two arcs of opposite directions. In this paper, we initiate the algorithmic study of Directed Token Sliding . We ﬁrst observe that the problem is PSPACE-complete even if we forbid parallel arcs in opposite directions and that the problem on directed acyclic graphs is NP-complete and W[1]-hard parameterized by the size of the sets in consideration. We then show our main result: a linear-time algorithm for the problem on directed graphs whose underlying undirected graphs are trees, which are called polytrees. Such a result is also known for the undirected variant of the problem on trees [Demaine et al. TCS 2015], but the techniques used here are quite diﬀerent because of the asymmetric nature of the directed problem. We present a characterization of yes-instances based on the existence of a certain set of directed paths, and then derive simple equivalent conditions from it by some observations, which admits an eﬃcient algorithm. For the polytree case, we also present a quadratic-time algorithm that outputs, if the input is a yes-instance, one of the shortest reconﬁguration sequences.
Parkinson’s Disease Treatment as Seen from a Mechanical Point of View

Tremor is considered as the most common faced abnormal involuntary movement disorder and the source of functional disability. Parkinson disease (PD) is a slowly progressive degenerative disorder of the central nervous system caused by the lack in the level of dopamine. Levodopa is the most effective dopaminergic medication used to manage Parkinson symptoms. However, it will be the source of the motor fluctuation after several years. An uncommon type of medication is suggested to suppress the resting tremor of PD patients. In this paper, a vibration absorber is used as a mechanical treatment and designed to reduce critical angular displacement amplitude at the resonance frequency. Human hand is modeled dynamically at the musculoskeletal level to reflect Parkinsonism. Motion is considered due to shoulder, elbow, Biceps brachii and wrist muscles activation. Absorber’s geometry, materials properties and parameters are well chosen to satisfy the tuning condition. The solution to the equation of motion for the hand is shown in the frequency and time domains to check the performance of the absorber in reducing the flexion angular motion at the wrist joint. Results show that the absorber was very effective over a good frequency bandwidth. It was able to reduce 93% of tremors amplitude at the wrist joint in the frequency domain. This type of absorber has low cost, can operate without power requirements, and has a simple design. Since its effectiveness was proved when tested numerically, it is recommended to proceed to the manufacturing process and the experimental study.
On forward sufficient dimension reduction for categorical and ordinal responses

We present a forward sufficient dimension reduction method for categorical or ordinal responses by extending the outer product of gradients and minimum average variance estimator to multinomial generalized linear model. Previous work in this direction extend forward regression to binary responses, and are applied in a pairwise manner to multinomial data, which is less efficient than our approach. Like other forward regression-based sufficient dimension reduction methods, our approach avoids the relatively stringent distributional requirements necessary for inverse regression alternatives. We show consistency of our proposed estimator and derive its convergence rate. We develop an algorithm for our methods based on repeated applications of available algorithms for forward regression. We also propose a clustering-based tuning procedure to estimate the tuning parameters. The effectiveness of our estimator and related algorithms is demonstrated via simulations and applications.
Transcultural Validation of the Nordic Age Discrimination Scale for the Spanish-Speaking Working Populations

Abstract Negative stereotypes about older workers can result in different types of age discrimination. The aim of this study was to run a transcultural adaptation and validation of the Nordic Age Discrimination Scale (NADS) into Spanish. Three independent samples of Chilean (N = 301), Colombian (N = 150), and Spanish (N = 209) workers over the age of 45, from different sectors and professional categories, answered a questionnaire including the NADS scale, measures of perceptions of inequality, workplace harassment and several scales related to outcome variables to test criterion and construct validity. The reliability index for the NADS was . 85, a similar value for both Cronbach’s alpha (α) and McDonald’s omega (ω). CFA by country suggest good fit of this single-dimension structure in a final version of 5 items, and it presents scalar invariance; using the modification indices, partial invariance is achieved at the level of the variance of the errors. Both criterion and construct validity were verified, with strong evidence for criterion validity, and moderate results for construct validity. Therefore, the Spanish version of NADS had a single-dimension structure and adequate psychometric properties being a useful tool in measuring perceptions of age discrimination in different countries.