Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Audiovisual Speaker Tracking with Distributed Sensors Configuration

Smart spaces are environments equipped with a set of sensors with the main objective of understanding humans’ behavior within them, their interactions and to improve human-machine interfaces. Audiovisual tracking is used to know people’s position in the environment and if they are talking, through the use of cameras and microphones. In this work we present an audiovisual tracking solution with a single camera and microphone array in a distributed configuration. Our idea is to exploit the estimation of azimuth and elevation from audio information to be fused with the position estimation obtained from a Viola and Jones based observation model. The fact that the microphone array is not co-located with the camera will allow to reduce the distance estimation uncertainty from the video model and improve tracking accuracy. The system was evaluated on the AV16.3 database on single speaker sequences, outperforming results of state-of-the-art, under these conditions.
Advances and Challenges of UAV SFM MVS Photogrammetry and Remote Sensing: Short Review

Interest in Unnamed Aerial Vehicle (UAV)-sourced data and Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) photogrammetry has seen a dramatic expansion over the last decade, revolutionizing the fields of aerial remote sensing and mapping. This literature review provides a summary overview on the recent developments and applications of light-weight UA V s and on the widely-accepted SfM - MVS approach. Firstly, the advantages and limitations of UAV remote sensing systems are discussed, followed by an identification of the different UA V and miniaturised sensor models applied to numerous disciplines, showing the range of systems and sensor types utilised recently. Afterwards, a concise list of advantages and challenges of UAV SfM-MVS is provided and discussed. Overall, the accuracy and quality of the SfM-MVS-derived products (e.g. orthomosaics, digital surface model) depends on the quality of the UAV data set, characteristics of the study area and processing tools used. Continued development and investigation are necessary to better determine the quality, precision and accuracy of UAV SfM-MVS derived outputs.
Automatic Identification of Diatom Morphology using Deep Learning

This paper proposes a method to automatically identify diatom frustules using nine morphological categories. A total of 7092 images from NIWA and ADIAC with related taxa data were used to create training and test sets. Different augmentations and image processing methods were used on the training set to see if this would increase accuracy. Several CNNs were trained over a total of 50 epochs and the highest accuracy model was saved based on the validation set. Resnet-50 produced the highest accuracy of 94%, which is not as accurate as a similar study that achieved 99%, although this was for a slightly different classification problem.
A Small-Scale Network for Seismic Patterns Classification

Deep Convolutional Neural Networks (DCNNs) correspond to the state-of-art for image classification. However to train such systems it is necessary to have access to a large number of samples and powerful computational resources, given the huge number of involved parameters. In the field of seismic images, large and freely available databases are scarce due to their strategic interest. In this situation, large architectures lead to hardly tractable problems in terms of overfitting. In this paper, we propose a reduced-size CNN with low computational cost that allows high accuracy performance on two small seismic datasets. The results are compared with KNN, SVM and LeNet.
On Open-Set Classification with L3-Net Embeddings for Machine Listening Applications

Obtaining labeled data for machine listening applications is expensive because labeling audio data requires humans listening to recordings. However, state-of-the-art deep learning based systems usually require large amounts of labeled data to be trained with. A solution for this problem is to train a neural network with a large collection of unlabeled data to extract embeddings and then use these embeddings to train a shallow classifier on a small but labeled dataset suitable for the application. One example are Look, Listen, and Learn (L3-Net) embeddings, which are trained self-supervised to capture audio-visual correspondence in videos. Since shallow classifiers are trained discriminatively and thus tacitly assume a closed-set classification task, they do not perform well in open-set classification tasks. In this paper, a neural network that combines all L3-Net embeddings belonging to one recording into a single vector by using an x-vector mechanism as well as an open-set classification system based on that are presented. In experiments conducted on the open-set acoustic scene classification task belonging to the DCASE challenge 2019, the proposed system significantly outperforms a shallow discriminative classifier and all other previously published systems, while at the same time performing equally well as a shallow classifier on multiple closed-set machine listening datasets.
Black Hole Approach: A Systematic Review

The article deals with the factors contributing to the emergence of a visual illusion, the so-called black hole approach. Among all the visual illusions that can jeopardize the course of a flight in its particular phases, this illusion is quite specific. This illusion arises in cases of visual approach at night. The risks of this illusion, such as hitting the terrain, are known. However, the causes of this illusion and other influencing factors that may result in this illusion are not sufficiently investigated. Therefore, a systematic review was performed in order to summarize the existing knowledge on this issue. The results show that in the case of black hole approach, only basic research is carried out worldwide and there is a lack of studies. However, the studies carried out partially confirm the well-known hypotheses about the origin of this illusion.
Analysis of Race and Gender Bias in Deep Age Estimation Models

Due to advances in deep learning and convolutional neural networks (CNNs) there has been significant progress in the field of visual age estimation from face images over recent years. While today’s models are able to achieve considerable age estimation accuracy, their behaviour, especially with respect to specific demographic groups is still not well understood. In this paper, we take a deeper look at CNN-based age estimation models and analyze their performance across different race and gender groups. We use two publicly available off-the-shelf age estimation models, i.e., FaceNet and WideResNet, for our study and analyze their performance on the UTKFace and APPA-REAL datasets. We partition face images into sub-groups based on race, gender and combinations of race and gender. We then compare age estimation results and find that there are noticeable differences in performance across demographics. Specifically, our results show that age estimation accuracy is consistently higher for men than for women, while race does not appear to have consistent effects on the tested models across different test datasets.
Machine Learning with Synthetic Data – a New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time

The idea of Augmented Reality (AR) appeared in the early 60s, which recently received a large amount of public attention. AR allows us to work, learn, play, and connect with the world around us both virtually and physically in real-time. However, picking the AR marker to match the users’ needs is one of the most challenging tasks due to different marker encryption/decryption methods and essential requirements. Barcode AR cards are fast and efficient, but they do not contain much visual information; pictorial coloured AR card, on the other hand, is slow and not reliable. This paper proposes a solution to obtain detectable arbitrary pictorial/colour AR cards in real-time by applying the benefit of machine learning and the power of synthetic data generation techniques. This technique solves the issue of labour-intensive tasks of manual annotations when building a massive training dataset of deep-learning. Thus, with a small number of input of the AR-enhanced target figures (as few as one for each coloured card), the synthetic data generated process will produce a deep-learning trainable dataset using computer-graphic rendering techniques (ten of thousands from just one image). Second, the generated dataset is then trained with a chosen object recognition convolutional neural network, acting as the AR marker tracking functionality. Our proposed idea works effectively well without modifying the original contents (of the chosen AR card). The benefits of using synthetic data generated techniques help us to improve the AR marker recognition accuracy and reduce the marker registration time. The trained model is capable of processing video sequences at approximately 25 frames per second without GPU Acceleration, which is suitable for AR experience on the mobile/web platform. We believed that it could be a promising low-cost AR approach in many areas, such as education and gaming.
Molecular dynamics simulations of chloroform on icePresented at the 81st International Bunsen Discussion Meeting on ?Interfacial Water in Chemistry and Biology?, Velen, Germany, September 19?23, 2003.

The adsorption of chloroform molecules on an ice Ih(0001) crystal surface was studied using classical molecular dynamics simulations as a function of the coverage, in the temperature range of [35–200 K]. The configurations of the molecules in their adsorption sites and the corresponding adsorption energies have been determined, and support previous conclusions on the chloroform/ice interactions based on the interpretation of experimental data. Constrained molecular dynamics simulations have also been performed in order to study the incorporation process of a chloroform molecule into the top layers of the ice crystal.
A residual U-Net network with image prior for 3D image denoising

Denoising algorithms via sparse representation are among the state-of-the art for image restoration. On previous work, we proposed SPADE - a sparse- and prior-based method for 3D-image denoising. In this work, we extend this idea to learning approaches and propose a novel residual-U-Net prior-based (ResPrU-Net) method that exploits a prior image. The proposed ResPrU-Net architecture has two inputs, the noisy image and the prior image, and a residual connection that connects the prior image to the output of the network. We compare ResPrU-Net to U-Net and SPADE on human knee data acquired on a spectral computerized tomography scanner. The prior image is built from the noisy image by combining information from neighbor slices and it is the same for both SPADE and ResPrU-Net. For deep learning approaches, we use four knee samples and data augmentation for training, one knee for validation and two for test. Results show that for high noise, U-Net leads to worst results, with images that are excessively blurred. Prior-based methods, SPADE and ResPrU-Net, outperformed U-Net, leading to restored images that present similar image quality than the target. ResPrU-Net provides slightly better results than SPADE. For low noise, methods present similar results.
Decoupled Direction-of-Arrival Estimations Using Relative Harmonic Coefficients

Traditional source direction-of-arrival (DOA) estimation algorithms generally localize the elevation and azimuth simultaneously, requiring an exhaustive search over the two-dimensional (2-D) space. By contrast, this paper presents two decoupled source DOA estimation algorithms using a recently introduced source feature called the relative harmonic coefficients. They are capable to recover the source's elevation and azimuth separately, since the elevation and azimuth components in the relative harmonic coefficients are decoupled. The proposed algorithms are highlighted by a large reduction of computational complexity, thus enable a direct application for sound source tracking. Simulation results, using both a static and moving sound source, confirm the proposed methods are computationally efficient while achieving competitive localization accuracy.
Solute‐enriched surface layers and X‐ray microanalysis of thin foils of a commercial aluminium alloy

Thin foils of the commercial Al alloy 7075 were subjected to X‐ray microanalysis in a Jeol 100 CX TEMSCAN using a modified double‐tilting stage. The ratio of the Cu Kx and Al Kx peaks, Icu/IAl, was found to decrease rapidly with increasing foil thickness, t, in regions thinner than about 200 nm. This was attributed to the formation of surface layers enriched in Cu during electropolishing, consistent with the findings of other investigators. An equation predicting the variation of IB/IA with t in an A‐rich alloy in which B (or additional solute elements) is dilute is derived for a sample with surface layers enriched in B. The derivation predicts that IB/IA should vary linearly with t−1, and that the intercept of such a plot should be positive. It is suggested that consistency with this prediction serves as a criterion for true surface enrichment. Our data on Cu obey these predictions, and further analysis indicates that the layers on both surfaces are enriched by a factor of about 5.6. The ratio of the intensities of the Zn Kx and Al Kx X‐ray peaks was found to be nearly independent of foil thickness to t≅400 nm. This result, combined with additional analysis, indicates that the apparent enrichment observed by other investigators is most likely due to the influence of instrumental factors.
Rhinoplasty in secondary nasal deformities: Subjective and objective outcome evaluation

Introduction: Secondary nasal deformities are associated with trauma and secondary cleft nose (after primary cleft nose surgery). Nasal deformities affect esthetic, function, and psychological status of the patient. The goal of the secondary rhinoplasty is to correct both form and function, so that this positively impacts on their facial appearance. Aims: The study aimed to evaluate the patient satisfaction (subjective outcome) by rhinoplasty outcome evaluation questionnaire (ROEQ) preoperatively and postoperatively and esthetic outcome (objective outcome) by surgical team in patients with secondary nasal deformities. Materials and Methods: Secondary rhinoplasty was done in 13 patients of traumatic and unilateral secondary cleft nose through the external approach. Objective outcome was assessed by surgical team with clinical measurement, radiograph (lateral cephalometric), and photographic documentation pre- and postoperatively. Clinical measurements include nasolabial and nasofrontal angle. The patients completed the ROEQ for the subjective outcome evaluation. Results: There was significant improvement of subjective outcome (83.30%) based on the ROEQ and objective outcome based on the clinical measurement. Conclusion: Our study suggests that secondary rhinoplasty in trauma and cleft patients leads to both subjective and objective improvement of the facial appearance.
The Serum Prolidase Enzyme Activity as a Biomarker for Evaluation of the Subclinical Vascular Damage in Children with Epilepsy

Backgroud: Epilepsy is a chronic medical condition requiring long term or even lifelong therapy. Various researches have shown that epilepsy patients have vascular risk factors such as abnormal lipids, insulin, elevated oxidative stress, chronic inflammation, and subclinical atherosclerosis. Objectives: The purpose of the present study was to determine serum prolidase enzyme activity as a biomarker in children taking antiepileptic drug treatment through comparison with control cases. Materials and Methods: The present study group consists of 61 children (20 females, 41 males) with epilepsy and a control group was formed of 32 healthy individuals (14 females, 18 males). Aspectrophotometric method was used to measure serum prolidase enzyme activity. Results: The epilepsy group demonstrated statistically significantly higher prolidase enzyme activity values when compared with the control group (P = 0.003). It was measured that the serum TOS and OSI values were significantly elevated in patients with epilepsy compared to controls (P < 0.001). However, serum TAS values were significantly lower in the epilepsy group than in the control group (P = 0.032). Conclusions: These results supported that epileptic patients taking the antiepileptic treatment had increased serum prolidase enzyme activity, suggesting that it may show an increased risk of subclinical vascular damage related to both chronic inflammation and fibrotic process associated with degenerated collagen turnover. Therefore, serum prolidase enzyme activity could be considered a useful biomarker for evaluation of the subclinical vascular damage in children with epilepsy on some antiepileptic drugs.
Diagnostic Value of Electroencephalogram in Creutzfeldt-Jakob Disease

741 Creutzfeldt‐Jakob disease (CJD) is an inexorably progressive and consistently fatal transmissible spongiform encephalopathy, characterized by the accumulation of an abnormal isoform of the host‐encoded cellular prion protein in the brain, resulting in rapidly progressive dementia, cerebellar and extrapyramidal signs, and myoclonus and visual symptoms. [1] CJD is a rare disease occurring with an incidence rate of 1 to 2 cases per million population per year. [2] Based on the etiopathogenesis, CJD is classified into sporadic (sCJD), familial (fCJD), iatrogenic (iCJD), and variant (vCJD) subtypes; of these, sCJD is the most common subtype accounting for around 85% of CJD cases reported worldwide. [1,3] sCJD typically occurs later in life with a mean age of 67 years with a postdiagnosis survival of around four months; however, there is marked heterogeneity in the clinical presentation. [1,3] Emerging understanding of the molecular mechanisms of sCJD has underscored the influence of methionine (M)/valine (V) polymorphisms involving codon 129 of the prion protein gene PRNP, located on the short arm of chromosome 20, on the electroclinical characteristics and course of the disease. [1,3] While M homozygous polymorphism (MM) occurs in more than half of the individuals, heterozygous polymorphism (MV) is encountered in just over one‐third and V homozygous polymorphism (VV) is the least common. [1,3] The clinical presentation of sCJD can imitate that of the other dementias, extrapyramidal disorders, metabolic and toxic encephalopathies, autoimmune encephalitis, and nonconvulsive status epilepticus (NCSE). [1,3‐5]
Asphaltene Self-Association: Modeling and Effect of Fractionation With A Polar Solvent

Abstract The self-association of asphaltenes in toluene is believed to occur step-wise, rather than by the formation of micelles. A number of step-wise models have been used to fit the calorimetric titration of asphaltenes in dried toluene solutions, with excellent results. All the models are based on chemical reactions equivalent to the ones found in polymerization. The study shows that the choice of the average properties of asphaltenes, such as the molecular weight, is critical in the final value of the parameter of interest, namely the average heat of self-association ΔH a . The low values of ΔH a obtained suggest that a fraction of asphaltenes is not active in the calorimetric experiments. Asphaltenes from Venezuela (LM1) and Mexico (KU) have been fractionated by precipitation with a mixture of acetone and toluene. It is considered that the most polar compounds are collected in the soluble fraction. A calorimetry study was performed on the two fractions, and the results show that the soluble fraction (SOL) has a much higher heat developed than the insoluble fraction (INS). This suggests again that a fraction of asphaltenes is not active in the calorimetric experiments, either because it does not self-associate or because the dilution effect is not strong enough to break the aggregates. Fluorescence and IR spectroscopy experiments confirm there is self-association in INS fraction, leading to the conclusion that asphaltene aggregates are formed by bonds of different strengths. The stronger aggregates would be predominantly in INS fraction and would be inactive in the calorimetric experiments.
Local‐global equivalence in voting models: A characterization and applications

The paper considers a voting model where each voter's type is her preference. The type graph for a voter is a graph whose vertices are the possible types of the voter. Two vertices are connected by an edge in the graph if the associated types are “neighbors. ” A social choice function is locally strategy‐proof if no type of a voter can gain by misrepresentation to a type that is a neighbor of her true type. A social choice function is strategy‐proof if no type of a voter can gain by misrepresentation to an arbitrary type. Local‐global equivalence (LGE) is satisfied if local strategy‐proofness implies strategy‐proofness. The paper identifies a condition on the graph that characterizes LGE. Our notion of “localness” is perfectly general. We use this feature of our model to identify notions of localness according to which various models of multidimensional voting satisfy LGE. Finally, we show that LGE for deterministic social choice functions does not imply LGE for random social choice functions.
Measurement techniques for piezoelectric nanogenerators

Electromechanical energy harvesting converts mechanical energy from the environment, such as vibration or human activity, into electrical energy that can be used to power a low power electronic device. Nanostructured piezoelectric energy harvesting devices, often termed nanogenerators, have rapidly increased in measured output over recent years. With these improvements nanogenerators have the potential to compete with more traditional micro- or macroscopic energy harvesting devices based on piezoelectric ceramics such as lead zirconate titanate (PZT), polymers such as polyvinylidene fluoride (PVDF) or electrostatic, electret or electromagnetic kinetic energy harvesters. Power output from a nanogenerator is most commonly measured through open-circuit voltage and/or short-circuit current, where power may be estimated from the product of these values. Here we show that such measures do not provide a complete picture of the output of these devices, and can be misleading when attempting to compare alternative designs. In order to compare the power output from a nanogenerator, techniques must be improved in line with those used for more established technologies. We compare ZnO nanorod/poly(methyl methacrylate) (PMMA) and ZnO nanorod/poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS) devices, and show that despite an open-circuit voltage nearly three times lower the ZnO/PEDOT:PSS device generates 150 times more power on an optimum load. In addition, it is shown that the peak voltage and current output can be increased by straining the device more rapidly and therefore time-averaged power, or time-integrated measures of output such as total energy or total charge should be calculated. Finally, the internal impedance of the devices is characterised to develop an understanding of their behaviour and shows a much higher internal resistance but lower capacitive impedance for the ZnO/PMMA device. It is hoped that by following more rigorous testing procedures the performance of nanostructured piezoelectric devices can be compared more realistically to other energy harvesting technologies and improvements can be rapidly driven by a more complete understanding of their behaviour.