Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiphase Local Mean Geodesic Active Regions

This paper presents two variational multiphase segmentation methods for recovery of segments in weakly structured images, presenting local and global intensity bias fields, as often is the case in micro-tomography. The proposed methods assume a fixed number of classes. They use local image averages as discriminative features and binary labelling for class membership and their relaxation to per pixel/voxel posterior probabilities, Hidden Markov Measure Field Models (HMMFM). The first model uses a Total Variation weighted semi-norm (wTV) for label field regularization, similar to Geodesic Active Contours, but with a different and possibly richer representation. The second model uses a weighted Dirichlet (squared gradient) regularization. Both problems are solved by alternating minimization on computation of local class averages and label fields. The quadratic problem is essentially smooth, except for HMMFM constraints. The wTV problem uses a Chambolle-Pock scheme for label field updates. We demonstrate on synthetic examples the capabilities of the approaches, and illustrate it on a real examples.
Handwritten Digit String Recognition using Convolutional Neural Network

String recognition is one of the most important tasks in computer vision applications. Recently the combinations of convolutional neural network (CNN) and recurrent neural network (RNN) have been widely applied to deal with the issue of string recognition. However RNNs are not only hard to train but also time-consuming. In this paper, we propose a new architecture which is based on CNN only, and apply it to handwritten digit string recognition (HDSR). This network is composed of three parts from bottom to top: feature extraction layers, feature dimension transposition layers and an output layer. Motivated by its super performance of DenseNet, we utilize dense blocks to conduct feature extraction. At the top of the network, a CTC (connectionist temporal classification) output layer is used to calculate the loss and decode the feature sequence, while some feature dimension transposition layers are applied to connect feature extraction and output layer. The experiments have demonstrated that, compared to other methods, the proposed method obtains significant improvements on ORAND-CAR-A and ORAND-CAR-B datasets with recognition rates 92.2% and 94.02%, respectively.
On Deep Learning for Inverse Problems

This paper analyses the generalization behaviour of a deep neural networks with a focus on their use in inverse problems. In particular, by leveraging the robustness framework by Xu and Mannor, we provide deep neural network based regression generalization bounds that are also specialized to sparse approximation problems. The proposed bounds show that the sparse approximation performance of deep neural networks can be potentially superior to that of classical sparse reconstruction algorithms, with reconstruction errors limited only by the noise level independently of the underlying data.
CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark

Multi-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of pose estimation in crowded scenes while it remains challenging and inevitable in many scenarios. Moreover, current benchmarks cannot provide an appropriate evaluation for such cases. In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms. Our model consists of two key components: joint-candidate single person pose estimation (SPPE) and global maximum joints association. With multi-peak prediction for each joint and global association using the graph model, our method is robust to inevitable interference in crowded scenes and very efficient in inference. The proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method.
Towards reliable IoT: Testing LoRa communication

In the last decade, the application of Internet of things expanded to many fields. The whole new range of possibilities to analyze data gathered from connected devices have appeared. However, one of the main problems of IoT is still communication between devices, especially on long range. Reliability of sensor network greatly affects reliability of analysis of collected data, and consequently actions to the analysis results. Therefore, continuous reliable communication in sensor network is necessity. It seems that LoRa is lately recognized as promising communication technology, due to its properties such as low power, long range and high reliability. It is stated that the range of LoRa is up to 10km, but it is not clearly in what measure does the environmental conditions, parameters of devices etc. affects communication distance. This paper aims to test the small sensor network prototype in real-life environment with real-time communication that has long range and ensures high reliability. Several indoor and outdoor experiments were made with same equipment but different distance, modulation, and packet size. The presented results show how changes in parameters such as packet size and techniques such as retransmission significantly affects communication reliability.
TEM based dislocation auto analysis flow of advanced logic devices

Automatic flow of transmission electron microscopy (TEM)-based dislocation analysis on Source/Drain (S/D) and contact formation process is developed. Based on the previously developed model of dislocation stress, an automated methodology is implemented that allows fast and human-error-free extraction of dislocation core position and its impact on the device channel stress and the electrical performance. This approach enables us to analyze the impact of dislocations in S/D of advanced logic devices and to optimize structure and process conditions.
Effective angular constrained trajectory generation for thrust-propelled vehicles

This paper proposes a novel approach to impose constraints on angles and angular velocities for the trajectory generation of thrust-propelled underactuated vehicles. The con- straints formulation requires only the translational acceleration and jerk values, without requiring the vehicle's attitude. Hence, the proposed angular constraints are satisfied even if the direction angle is designedly modified or it varies uncontrollably under unexpected events. Discussions over simulation results and comparisons with the existing methods in the literature highlight the benefits of our approach.
Identification and Grading of Freehand Sketches Using Deep Learning Techniques

A great deal of work has been done in the zone of computer vision on images. Be that as it may, the region of freehand sketches stays to be an unexplored zone. Freehand sketches fluctuate in light of masterful style. In spite of the fact that they contain minimum details, people can predict the class to which the sketch has a place. Concentrating on such negligibly itemized sketches can help us in understanding the neurobiological procedures that occur in people. In this paper, a Convolutional Neural Network (CNN) has been developed which can classify the freehand sketches in view of specific highlights. Classified freehand sketches are graded relative to the prototype being considered. Publicly available dataset of Eitz et al. is considered for identification and grading. Evaluating the sketches will help in surveying the advance of a client who is figuring out how to draw outlines.
Cathodoluminescence Characterization of Semiconductor Doping at the Nanoscale

Semiconductor nanostructures open new perspectives for light trapping, lattice-mismatched crystal growth (III-V on Si for instance) and novel transport phenomenon for next generation photovoltaics. The characterization of material properties at the nanoscale remains challenging. In particular, doping is a key parameter in the design and fabrication of solar cells. Here, we show that cathodoluminescence mapping can be used to determine both n-type and p-type doping levels of GaAs with nanometer resolution. n-type semiconductor shows characteristic blueshift emission from the electron filling, while ptype semiconductor exhibits redshift emission due to dominant bandgap narrowing at high concentrations. The generalized Planck’s law is used to fit the whole spectra and extract electron Fermi levels (n-type) and effective bandgap (p-type). Quantitative doping assessment is achieved by systematic spectral analysis, and is demonstrated on planar GaAs layers, and on single GaAs nanowires. This method can be extended to other semiconductors and nanostructures.
Auditing of Ultra Dense RFID Straws in Cryogenic Container at -196°C

This paper presents a new auditing system scheme based on radio frequency identification technologies for cryopreservation applications using liquid nitrogen at temperature of -196°C. Conventional approaches such as hand written labels or printed barcodes suffer from the significant manual labor work and freeze-thaw-damaging risk of the sample. The proposed scheme integrates miniaturized RFID electronic devices and antennas in the thin cryogenic straws without significantly changing their dimensions. In addition, 900 MHz frequency band is chosen for long reading range, which eliminates the need to take the preserved biological samples out of liquid nitrogen and minimizes the damaging risk. Four most important technical aspects for the system have been identified and investigated: 1. The link budget is evaluated by FDTD numerical simulations to make sure the RF signal strength meets the requirement for the reading range. 2. The total efficiency of small and tightly coupled RFID dipole antennas is studied and obtained by simulations. 3. The current-voltage behaviors of two types of transistors in typical RFID electronics have been characterized by measurements to identify the suitable transistor type for reliable circuit designs at -196°C. 4. The easy-to-access and anti-collision reading system for fast data collection is analyzed. The obtained results indicate that the proposed scheme is feasible, which can be beneficial for the relevant scientific fields.
Microorganisms’ Footprint in Neurodegenerative Diseases

Advancement of science has gifted the human a longer life; however, as neuron cells do not regenerate, the number of people with neurodegeneration disorders rises with population aging. Neurodegeneration diseases occur as a result of neuronal cells loss caused by environmental factors, genetic mutations, proteopathies and other cellular dysfunctions. The negative direct or indirect contributions of various microorganisms in onset or severity of some neurodegeneration disorders and interaction between human immune system and pathogenic microorganisms has been portrayed in this review article. This association may explain the early onset of neurodegeneration disorders in some individuals, which can be traced through detailed study of health background of these individuals for infection with any microbial disease with neuropathogenic microorganisms (bacteria, fungi, viruses). A better understanding and recognition of the relation between microorganisms and neurodegeneration disorders may help researchers in development of novel remedies to avoid, postpone, or make neurodegeneration disorders less severe.
Supporting the Adoption Funnel with Differential Effects from Traditional Advertising, Online Displays, and a Micro-Influencer Campaign

Growing a digital platform requires bringing users to the platform and converting them into adopting the online service or community. Growth may be supported with advertising, but knowledge about what type of advertising affects what stage in the adoption funnel is incomplete. This research investigates how television and print advertising, digital banners, and firm-created word-of-mouth from a micro-influencer campaign differentially affect aggregate-level searches, platform visits, and platform sign-ups of a new lifestyle and home decoration online community platform. Results from the endogeneity-bias-controlled funnel model indicate, that traditional advertising helps in early stages, digital banner advertisements bring users directly to the platform at intermediate stages, and firm-created word-of-mouth helps converting users in the last stages of the adoption funnel. These findings complement existing adoption funnel studies with fewer or different advertising or funnel variables. For managers, the results imply that advertising can address bottlenecks in the platform adoption funnel. © International Conference on Information Systems 2018, ICIS 2018.All rights reserved.
Optical-to-SAR Image Registration Using Modified Distinctive Order Based Self-Similarity Operator

Availability of the sufficient number of matching pairs is the major challenge for optical to SAR (Synthetic Aperture Radar) image registration. Recently, a distinctive order based self-similarity operator (DOBSS) has been proposed to register the optical images. In this paper, a modified version of the distinctive order based self-similarity (M-DOBSS) is proposed to register the optical and SAR images. The proposed M-DOBSS provides more distinctiveness than the standard DOBSS operator. It can significantly increase the number of matching pairs between the optical and SAR images. Experiments on different sets of optical-SAR image pairs demonstrates the effectiveness of the proposed method.
Linear Convergence of Stochastic Block-Coordinate Fixed Point Algorithms

Recent random block-coordinate fixed point algorithms are particularly well suited to large-scale optimization in signal and image processing. These algorithms feature random sweeping rules to select arbitrarily the blocks of variables that are activated over the course of the iterations and they allow for stochastic errors in the evaluation of the operators. The present paper provides new linear convergence results. These convergence rates are compared to those of standard deterministic algorithms both theoretically and experimentally in an image recovery problem.
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image

In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusion, and combines estimates from the color image and surface normals with learned attention maps to improve the depth accuracy especially for distant areas. Extensive experiments demonstrate that our model improves upon the state-of-the-art performance on KITTI depth completion benchmark. Ablation study shows the positive impact of each model components to the final performance, and comprehensive analysis shows that our model generalizes well to the input with higher sparsity or from indoor scenes.
Automatic Eye Gaze Estimation using Geometric & Texture-based Networks

Eye gaze estimation is an important problem in automatic human behavior understanding. This paper proposes a deep learning based method for inferring the eye gaze direction. The method is based on the use of ensemble of networks, which capture both the geometric and texture information. Firstly, a Deep Neural Network (DNN) is trained using the geometric features that are extracted from the facial landmark locations. Secondly, for the texture based features, three Convolutional Neural Networks (CNN) are trained i.e. for the patch around the left eye, right eye, and the combined eyes, respectively. Finally, the information from the four channels is fused with concatenation and dense layers are trained to predict the final eye gaze. The experiments are performed on the two publicly available datasets: Columbia eye gaze and TabletGaze. The extensive evaluation shows the superior performance of the proposed framework. We also evaluate the performance of the recently proposed swish activation function as compared to Rectified Linear Unit (ReLU) for eye gaze estimation.
Automatic Detection of Image Morphing by Topology-based Analysis

Topological Data Analysis (TDA) is an emerging framework for the understanding of Bigdata. This paper investigates and develops a TDA approach to image forensics that exploits the sensitivity to image tampering of a variety of persistent homological invariants of simplicial complexes constructed for certain automatically computed image texture landmarks. For each image, we construct sequences of simplicial complexes, whose vertices are the selected set of landmarks, for a sequence of distance thresholds and use a variety of homological invariants (e.g. number of connected components) to distinguish natural face images from morphed ones. We shall demonstrate the richness of TDA in dealing with image tampering by testing the performance of this approach on a large benchmark image dataset of passport photos in detecting various known morphing attacks.
Faraday Filtering on the Cs-D1-Line for Quantum Hybrid Systems

Narrow-band filtering of light is widely used in optical spectroscopy. Atomic filters, which rely on the Faraday effect, allow for GHz-wide transmission spectra, which are intrinsically matched to an atomic transition. We present an experimental realization and a theoretical study of a Faraday filter based on cesium and its D<sub>1</sub>-line-transition (<inline-formula> <tex-math notation="LaTeX">$6^{2}S_{1/2}\rightarrow 6^{2}P_{1/2}$ </tex-math></inline-formula>) around 894 nm. We also present the prospects and visions for combining this filter with the single photon emission of a single quantum dot, which matches with the atomic transition. The option to lock the spectral position of a quantum dot is discussed at the end of this letter.
Using Fuzzy Sets for Detecting Cyber Terrorism and Extremism in the Text

The concept of Cyber Security (CS) has been started to be used with the development of Internet technology. Nowadays, CS has vital importance and Cyber Terror and Extremism (CTE) is one of the CS problems. Terror must be detected before terrorism comes true. In other words, people who commit the crime must be detected automatically before they move on. At this stage, what people say about some issues is very valuable because sayings can be turned into actions. The aim of this study is to use Antisocial Behavior dataset to try to detect CTE in the text contents. To detect CTE, text documents should be converted to numerical vectors which consist of numerical weights of the terms present in the text documents. Vectors are computed by using four different weighting methods in our study. These methods are the well-known binary weighting, term frequency based weighting, term frequency and inverse document frequency based weighting, and our proposed fuzzy set based weighting methods. Naive Bayes Multinomial (NBM) and Support Vector Machines (SVM) are used as classifiers to compare the performances of the weighting methods for CTE detection. Our experimental analysis shows that fuzzy set based weighting method with SVM classifier gives the best classification accuracy which reaches up to 99%.
A Novel Computational Method of Skewing Leakage Reactance for a Doubly Skewed Rotor Induction Motor

In this paper, a novel computational method that can accurately calculate the skewing leakage reactance of a doubly skewed rotor induction motor is proposed. It is based on the three-dimensional finite-element models to calculate the magnetic energy stored in every section of the machine and the rotor bar currents. In addition, the magnetic saturation and magnetic variation in the axial direction are taken into account in the models. Also, the proposed method is applied to calculate the stator leakage reactances and rotor leakage reactances. The comparisons between the testing results and the computational results show that the proposed method can obtain the more accurate results than the conventional computational algorithm.
Towards Unconstrained Pointing Problem of Visual Question Answering: A Retrieval-based Method

The pointing problem of visual question answering (VQA) is that given an image and a question which asks for the location of the interested object, find a region that answers the question. It is an important research problem in VQA tasks and has many potential applications in our daily life. Most of the existing work on this task can only solve it in the form of multiple choices, i.e., given candidate answers in advance, and then selecting a correct one. In this paper, we propose a retrieval model, which can not only deal with the multiple-choices task, but also provide a feasible solution for the no-candidate-answer task. The principle of our method is to pull the question and correct answer close, and push the question and incorrect answer away in a common feature space. To our best knowledge, we are the first to use retrieval method to solve the unconstrained (no-candidate-answer) pointing problem of VQA. Furthermore, our proposed method outperforms the state-of-the-art methods on the Visual7W [1] dataset in terms of the pointing problem of VQA.
Hereditary Gingival Fibromatosis: a Case Report with Seven-Year Follow-up

Introduction Hereditary gingival fibromatosis (HGF) is a rare disease characterized by gingival enlargement, normal color with benign and firm consistency. This growth may be exacerbated by use of drugs and plaque build-up. The treatment for this clinical condition is surgical excision of the enlarged gingival tissue or the extraction of all teeth. Case Report A 20-year-old Brazilian female handicapped patient with a chief complaint of exaggerated gingival enlargement who had been prescribed Carbamazepine and Gardenal was referred to our center. According to the clinical presentation and family history, the final diagnosis of gingival enlargement was HGF. Full dental treatment was performed, including basic periodontal treatment, restorations, sealants, and gingivoplasty with internal bevel. Special care was taken to ensure that there was no change in patient’s anticonvulsant medication. The patient has been monitored for seven years without signs of recurrence of gingival hyperplasia due to constant professional and home control of plaque.