Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation Paradox of the Dissociation Limit: Formal Discussion and Quantitative Resolution based on Quantum Information Theory

The interplay between electron interaction and geometry in a molecular system can lead to rather paradoxical situations. The prime example is the dissociation limit of the hydrogen molecule: While a significant increase of the distance $r$ between the two nuclei marginalizes the electron-electron interaction, the exact ground state does, however, not take the form of a single Slater determinant. By first reviewing and then employing concepts from quantum information theory, we resolve this paradox and its generalizations to more complex systems in a quantitative way. To be more specific, we illustrate and prove that thermal noise due to finite, possibly even just infinitesimally low, temperature $T$ will destroy the entanglement beyond a critical separation distance $r_{\mathrm{crit}}$($T$) entirely. Our analysis is comprehensive in the sense that we simultaneously discuss both total correlation and entanglement in the particle picture as well as in the orbital/mode picture. Our results reveal a conceptually new characterization of static and dynamical correlation in ground states by relating them to the (non)robustness of correlation with respect to thermal noise.
Bovine Serum Albumin Protein-Based Liquid Crystal Biosensors for Optical Detection of Toxic Heavy Metals in Water

A new methodology involving the use of Bovine Serum Albumin (BSA) as a probe and liquid crystal (LC) as a signal reporter for the detection of heavy metal ions in water at neutral pH was developed. BSA acted as a multi-dentate ligand for the detection of multiple metal ions. The LC sensor was fabricated by immobilizing 3 µg mL−1 BSA solution on dimethyloctadecyl-[3-(trimethoxysilyl)propyl]ammonium chloride (DMOAP)-coated glass slides. In the absence of heavy metal ions, a dark optical image was observed, while in their presence, a dark optical image turned to bright. The optical response was characterized by using a polarized optical microscope (POM). The BSA based LC sensor selectively detected toxic metal ions as compared to s block metal ions and ammonium ions in water. Moreover, the limit of detection was found to be very low (i.e., 1 nM) for the developed new biosensor in comparison to reported biosensors.
Deep Learning Stereo Vision at the edge

We present an overview of the methodology used to build a new stereo vision solution that is suitable for System on Chip. This new solution was developed to bring computer vision capability to embedded devices that live in a power constrained environment. The solution is constructured as a hybrid between classical Stereo Vision techniques and deep learning approaches. The stereoscopic module is composed of two separate modules: one that accelerates the neural network we trained and one that accelerates the front-end part. The system is completely passive and does not require any structured light to obtain very compelling accuracy. With respect to the previous Stereo Vision solutions offered by the industries we offer a major improvement is robustness to noise. This is mainly possible due to the deep learning part of the chosen architecture. We submitted our result to Middlebury dataset challenge. It currently ranks as the best System on Chip solution. The system has been developed for low latency applications which require better than real time performance on high definition videos.
Combining Textual and Visual Information for Typed and Handwritten Text Separation in Legal Documents

© 2019 The authors and IOS Press. A paginated legal bundle is an indexed version of all the evidence documents considered relevant to a court case. The pagination process requires all documents to be analysed by an expert and sorted accordingly. This is a time consuming and expensive task. Automated pagination is complicated by the fact that the constituent documents can contain both typed and handwritten texts. A successful auto-pagination system must recognise the different text types, and treat them accordingly. In this paper we compare methods for determining the type of text data contained within paginated bundle pages. Specifically, we classify pages as containing typed data only, handwritten data only, or a mixture of the two. For this purpose, we compare text classification methods, image classification methods, and ensemble methods using both textual and visual information. We find the text and image based approaches provide complimentary information, and that combining the two produces a powerful document classifier.
A panchromatic spatially resolved analysis of nearby galaxies – I. Sub-kpc-scale main sequence in grand-design spirals

We analyse the spatially resolved relation between stellar mass (M⋆) and star formation rate (SFR) in disc galaxies (i.e. the main sequence, MS). The studied sample includes eight nearby face-on grand-design spirals, e.g. the descendant of high-redshift, rotationally supported star-forming galaxies. We exploit photometric information over 23 bands, from the UV to the far-IR, from the publicly available DustPedia data base to build spatially resolved maps of stellar mass and SFRs on sub-galactic scales of 0.5–1.5 kpc, by performing a spectral energy distribution fitting procedure that accounts for both the observed and obscured star formation processes, over a wide range of internal galaxy environments (bulges, spiral arms, and outskirts). With more than 30 000 physical cells, we have derived a definition of the local spatially resolved MS per unit area for discs, log (ΣSFR) = 0.82log (Σ*) − 8.69. This is consistent with the bulk of recent results based on optical IFU, using the H α line emission as an SFR tracer. Our work extends the analysis at lower sensitivities in both M⋆ and SFR surface densities, up to a factor of ∼10. The self-consistency of the MS relation over different spatial scales, from sub-galactic to galactic, as well as with a rescaled correlation obtained for high-redshift galaxies, clearly proves its universality.
Quantity vs. size in representation theory

In this note, we survey two instances in the representation theory of finite-dimensional algebras where the quantity of a type of structures is intimately related to the size of those same structures. More explicitly, we review the fact that (1) a finite-dimensional algebra admits only finitely many indecomposable modules up to isomorphism if and only if every indecomposable module is finite-dimensional; (2) the category of modules over a finite-dimensional algebra admits only finitely many torsion classes if and only if every torsion class is generated by a finite-dimensional module.
Neural Human Video Rendering: Joint Learning of Dynamic Textures and Rendering-to-Video Translation

—Synthesizing realistic videos of humans using neural networks has been a popular alternative to the conventional graphics-based rendering pipeline due to its high efﬁciency. Existing works typically formulate this as an image-to-image translation problem in 2D screen space, which leads to artifacts such as over-smoothing, missing body parts, and temporal instability of ﬁne-scale detail, such as pose-dependent wrinkles in the clothing. In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent ﬁne-scale details from the embedding of the human in 2D screen space. More speciﬁcally, our method relies on the combination of two convolutional neural networks (CNNs). Given the pose information, the ﬁrst CNN predicts a dynamic texture map that contains time-coherent high-frequency details, and the second CNN conditions the generation of the ﬁnal video on the temporally coherent output of the ﬁrst CNN. We demonstrate several applications of our approach, such as human reenactment and novel view synthesis from monocular video, where we show signiﬁcant improvement over the state of the art both qualitatively and quantitatively.
Analytical probabilistic modeling of dose-volume histograms.

PURPOSE Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, e. g. , dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data. METHODS We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, i. e. , α-DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and singlefraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling. RESULTS The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90% of all computed expected DVH-points and their standard deviations agree within 1% volume with their empirical counterpart from sampling computations, for both fractionated and single fraction treatments. When computing α-DVHs, the assumption of a beta distribution achieved better agreement with empirical percentiles than the assumption of a normal distribution: While in both cases probabilities locally showed large deviations (up to ±0.2), the respective α -DVHs for α = {0:05; 0:5; 0:95} only showed small deviations in respective volume (up to ±5% volume for a normal distribution, and up to 2% for a beta distribution). A previously published model from literature, which was included for comparison, exhibited substantially larger deviations. CONCLUSIONS With APM we could derive a mathematically exact description of moments of probability distributions over DVH-points given a probability distribution over dose. The model generalizes previous attempts and performs well for both choices of probability distributions, i. e. , normal or beta distributions, over DVH-points.
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems

On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions. However, algorithms can fail to solve the problems they were designed for if they conflict with the values of communities who use them. In this study, we take a Value-Sensitive Algorithm Design approach to understanding a community-created and -maintained machine learning-based algorithm called the Objective Revision Evaluation System (ORES)---a quality prediction system used in numerous Wikipedia applications and contexts. Five major values converged across stakeholder groups that ORES (and its dependent applications) should: (1) reduce the effort of community maintenance, (2) maintain human judgement as the final authority, (3) support differing peoples' differing workflows, (4) encourage positive engagement with diverse editor groups, and (5) establish trustworthiness of people and algorithms within the community. We reveal tensions between these values and discuss implications for future research to improve algorithms like ORES.
Weakly Supervised One-Shot Classification Using Recurrent Neural Networks with Attention: Application to Claim Acceptance Detection

Determining if a claim is accepted given judge arguments is an important non-trivial task in court decisions analyses. Application of recent efficient machine learning techniques may however be inappropriate for tackling this problem since, in the Legal domain, labelled datasets are most often small, scarce and expensive. This paper presents a deep learning model and a methodology for solving such complex classification tasks with only few labelled examples. We show in particular that mixing one-shot learning with recurrent neural networks and an attention mechanism enables obtaining efficient models while preserving some form of inter-pretability and limiting potential overfit. Results obtained on several types of claims in French court decisions, using different vectorization processes, are presented.
Development of an On-Board Measurement System for Railway Vehicle Wheel Flange Wear

The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of wheel flange thickness to an extreme level increases the probability of train accidents. To avoid the unsafe operation of a rail vehicle, it is important to stay continuously up to date on the status of the wheel flange thickness dimensions by using precise and accurate measurement tools. The wheel wear measurement tools that are based on laser and vision technology are quite expensive to implement in railway lines of developing countries. Alternatively significant measurement errors can result from using imprecise measurement tools such as the hand tools, which are currently utilized by the railway companies such as Addis Ababa Light Rail Transit Service (AALRTS). Thus, the objective of this research is to propose and test a new measurement tool that uses an inductive displacement sensor. The proposed system works in both static and dynamic state of the railway vehicle and it is able to save the historical records of the wheel flange thickness for further analysis. The measurement system is fixed on the bogie frame. The fixture was designed using dimensions of the bogie and wheelset structure of the trains currently used by AALRTS. Laboratory experiments and computer simulations for of the electronic system were carried out to assess the feasibility of the data acquisition and analysis method. The noises and unwanted signals due to the dynamics of the system are filtered out from the sensor readings. The results show that the implementation of the proposed measurement system can accurately measure the wheel flange wear. Also, the faulty track section can be identified using the system recorded data and the operational control center data.
Defeasible Systems in Legal Reasoning: A Comparative Assessment

Different formalisms for defeasible reasoning have been used to represent legal knowledge and to reason with it. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: Defeasible Logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches from three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software). On this basis, we identify and apply criteria for assessing their suitability for legal applications. We discuss the different approaches through a legal running example.
Balancing the composition of word embeddings across heterogenous data sets

Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications. Notably these relationships are solely learned from the data and subsequently the data composition impacts the semantic of embeddings which arguably can lead to biased word vectors. Given qualitatively different data subsets, we aim to align the influence of single subsets on the resulting word vectors, while retaining their quality. In this regard we propose a criteria to measure the shift towards a single data subset and develop approaches to meet both objectives. We find that a weighted average of the two subset embeddings balances the influence of those subsets while word similarity performance decreases. We further propose a promising optimization approach to balance influences and quality of word embeddings.
Icosahedral order in liquid and glassy phases of cyclohexane

ABSTRACT We performed all-atom molecular dynamics simulations for bulk cyclohexane and analysed the short- and medium-range structures in supercooled and glassy states by using the Voronoi tessellation technique. From the analyses of both the potential energy of the system and the radial distribution function of molecules, cyclohexane was found to be vitrified as the temperature decreased. Furthermore, the icosahedral-like local structures are dominant at all temperatures and grow in a supercooled liquid, whereas the face-centred cubic structures do not grow when the temperature decreases. It was also ascertained that the icosahedral-like structure is more dominant than the full-icosahedral one. The network of the distorted icosahedron spreads throughout the system at low temperatures. Our simulation demonstrates the stability of the icosahedral local structure even in a non-spherical molecule such as cyclohexane.
Interpreting Observations of Absorption Lines in the Circumgalactic Medium with a Turbulent Medium

Single-phase photoionization equilibrium (PIE) models are often used to infer the underlying physical properties of galaxy halos probed in absorption with ions at different ionization potentials. To incorporate the effects of turbulence, we use the Models of Agitated and Illuminated Hindering and Emitting Media (MAIHEM) code to model an isotropic turbulent medium exposed to a redshift-zero metagalactic UV background, while tracking the ionizations, recombinations, and species-by-species radiative cooling for a wide range of ions. By comparing observations and simulations over a wide range of turbulent velocities, densities, and metallicity with a Markov chain Monte Carlo technique, we find that MAIHEM models provide an equally good fit to the observed low-ionization species compared to PIE models, while reproducing at the same time high-ionization species such as Si iv and O vi. By including multiple phases, MAIHEM models favor a higher metallicity (Z/Z⊙ ≈ 40%) for the circumgalactic medium compared to PIE models. Furthermore, all of the solutions require some amount of turbulence (σ3D 26 km s−1). Correlations between turbulence, metallicity, column density, and impact parameter are discussed alongside mechanisms that drive turbulence within the halo.
A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach

Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and synchronization steps. To reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can be applied, where instead of the full gradient, only a small subset of important elements of the gradient is communicated. Existing work on GS uses a fixed degree of gradient sparsity for i.i. d. -distributed data within a datacenter. In this paper, we consider adaptive degree of sparsity and non-i.i. d. local datasets. We first present a fairness-aware GS method which ensures that different clients provide a similar amount of updates. Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity. The online learning algorithm uses an estimated sign of the derivative of the objective function, which gives a regret bound that is asymptotically equal to the case where exact derivative is available. Experiments with real datasets confirm the benefits of our proposed approaches, showing up to 40% improvement in model accuracy for a finite training time.
A Computer Vision-Based Navigation and Localization Method for Station-Moving Aircraft Transport Platform with Dual Cameras

In order to develop equipment adapted to the aircraft pulse final assembly line, a vision-based aircraft transport platform system is developed. This article explores a guiding method between assembly stations which is low-cost and easy to change routes by using two-dimensional code and two complementary metal oxide semiconductor (CMOS) cameras. The two cameras installed on the front and back of the platform read the two-dimensional code containing station information to guide the platform. In the process of guiding, the theoretical position and posture of the platform at each assembly station are known, but there is a difference between the actual and theoretical values due to motion errors. To reduce the influence of the deviation on the navigation route, a localization method is proposed based on the two-dimensional images captured by the cameras. Canny edge detection is applied to the processed image to obtain the position of the two-dimensional code in the image, which can measure the angle/distance deviation of the platform. Then, the computer can locate the platform precisely by the information in the two-dimensional code and the deviation measured by the image. To verify the feasibility of the proposed method, experiments have been performed on the developed platform system. The results show that the distance and angle errors of the platform are within ±10 mm and ±0.15° respectively.