Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Delamination Detection System for Concrete Decks by Using Convolutional Neural Network

Bridges in Japan, especially those managed by municipalities, deteriorate over time. Due to lack of civil engineers in municipalities, appropriate and automated assistance for degradation judgement is thought to be important for the concerned authorities. Automated judgement systems for some types of damage (e.g., cracks) started to be developed by geometrical approaches. Yet, there is no comprehensive method to detect more complicated types of damage, such as delamination, for regular inspection. This research aims to develop a delamination-detection system which identifies the location of the damage. Images with delaminated parts were provided by Niigata Prefecture (in Japan), and annotation of the location of delamination and/or rebar exposure was conducted. Fully Convolutional Network (FCN), one of the deep learning networks for pixel-to-pixel segmentation, was used to detect the areas of the delamination and rebar exposure. The result of the training aided by FCN showed a good agreement with the result with the naked eye. The soundness, judged based on the FCN result according to the inspection code of Niigata Prefecture, was close to the soundness judgement at the site. These outcomes support the reliability of the system to detect delamination and rebar exposure in manual inspection. This technology is expected to be used in bridges’ inspection at municipalities, which have a lack of inspection engineers.
2D-3D CNN Based Architectures for Spectral Reconstruction from RGB Images

Hyperspectral cameras are used to preserve fine spectral details of scenes that are not captured by traditional RGB cameras that comprehensively quantizes radiance in RGB images. Spectral details provide additional information that improves the performance of numerous image based analytic applications, but due to high hyperspectral hardware cost and associated physical constraints, hyperspectral images are not easily available for further processing. Motivated by the performance of deep learning for various computer vision applications, we propose a 2D convolution neural network and a 3D convolution neural network based approaches for hyperspectral image reconstruction from RGB images. A 2D-CNN model primarily focuses on extracting spectral data by considering only spatial correlation of the channels in the image, while in 3D-CNN model the inter-channel co-relation is also exploited to refine the extraction of spectral data. Our 3D-CNN based architecture achieves very good performance in terms of MRAE and RMSE. In contrast to 3D-CNN, our 2D-CNN based architecture also achieves comparable performance with very less computational complexity.
Joint Pose and Expression Modeling for Facial Expression Recognition

Facial expression recognition (FER) is a challenging task due to different expressions under arbitrary poses. Most conventional approaches either perform face frontalization on a non-frontal facial image or learn separate classifiers for each pose. Different from existing methods, in this paper, we propose an end-to-end deep learning model by exploiting different poses and expressions jointly for simultaneous facial image synthesis and pose-invariant facial expression recognition. The proposed model is based on generative adversarial network (GAN) and enjoys several merits. First, the encoder-decoder structure of the generator can learn a generative and discriminative identity representation for face images. Second, the identity representation is explicitly disentangled from both expression and pose variations through the expression and pose codes. Third, our model can automatically generate face images with different expressions under arbitrary poses to enlarge and enrich the training set for FER. Quantitative and qualitative evaluations on both controlled and in-the-wild datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.
A State-ofthe-Art Review of Augmented Reality in Engineering Analysis and Simulation

Augmented reality (AR) has recently become a worldwide research topic. AR technology renders intuitive computer-generated contents on users’ physical surroundings. To improve process efficiency and productivity, researchers and developers have paid increasing attention to AR applications in engineering analysis and simulation. The integration of AR with numerical simulation, such as the finite element method, provides a cognitive and scientific way for users to analyze practical problems. By incorporating scientific visualization technologies, an AR-based system superimposes engineering analysis and simulation results directly on real-world objects. Engineering analysis and simulation involving diverse types of data are normally processed using specific computer software. Correct and effective visualization of these data using an AR platform can reduce the misinterpretation in spatial and logical aspects. Moreover, tracking performance of the AR platforms in engineering analysis and simulation is crucial as it influences the overall user experience. The operating environment of the AR platforms requires robust tracking performance to deliver stable and accurate information to the users. In addition, over the past several decades, AR has undergone a transition from desktop to mobile computing. The portability and propagation of mobile platforms has provided engineers with convenient access to relevant information in situ. However, on-site working environment imposes constraints on the development of mobile AR-based systems. This paper aims to provide a systematic overview of AR in engineering analysis and simulation. The visualization, tracking techniques as well as the implementation on mobile platforms are discussed. Each technique is analyzed with respect to its pros and cons, as well its suitability to particular types of applications.
Deep Adversarial Subspace Clustering

Most existing subspace clustering methods hinge on self-expression of handcrafted representations and are unaware of potential clustering errors. Thus they perform unsatisfactorily on real data with complex underlying subspaces. To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering. Specifically, DASC consists of a subspace clustering generator and a quality-verifying discriminator, which learn against each other. The generator produces subspace estimation and sample clustering. The discriminator evaluates current clustering performance by inspecting whether the re-sampled data from estimated subspaces have consistent subspace properties, and supervises the generator to progressively improve subspace clustering. Experimental results on the handwritten recognition, face and object clustering tasks demonstrate the advantages of DASC over shallow and few deep subspace clustering models. Moreover, to our best knowledge, this is the first successful application of GAN-alike model for unsupervised subspace clustering, which also paves the way for deep learning to solve other unsupervised learning problems.
Manifold Learning in Quotient Spaces : Supplementary Material

In this section, we provide the reader with a short mathematical background in relation with our work, which falls within geometry and topology applied to machine learning. In order to forget the unneeded information about 3D, we represent an intrinsic geometry as an element of X/G, the quotient of the original input space X by the action of the rotations group. The quotient space X/G is the set of all the orbits of the group action: X/G = {x | x ∈ X}, where x = {h.x | h ∈ G}, and (h, x) 7→ h.x is the group action. If (X,T ) is a topological space, then X/G is naturally equipped with the so-called quotient topology TX/G = { U ⊂ X/G | q−1(U) ∈ T } , where q is the canonical surjection which maps x onto its orbit. When X is a metric space, proposition 1 makes explicit the quotient topology in the case where the action is an isometry. Especially, under mild assumptions the quotient loss (Equation (2) in the paper) also defines a distance in the quotient space X/G. A Lie group G is a group that is also a differentiable manifold. More formally it is group with a differentiable manifold structure such that both group multiplication and inverse map are differentiable. Since the multiplication by an element h ∈ G defines a diffeomorphism from G to G that sends the identity element to h, the local behavior near the identity reflects the local behavior near any element h ∈ G. Therefore the tangent space at the identity together with its linear structure, i.e. the first order behavior of elements infinitesimally close to the identity, called Lie Algebra, is a central object in the study of Lie groups. Among the most common and intuitive Lie groups are rotation groups in ndimensional Euclidean space SO(n). In our paper, we use the Lie structure to parameterize an infinite group with a finite-dimensional vector h representing the transformation.
Globally Optimal Inlier Set Maximization for Atlanta Frame Estimation

In this work, we describe man-made structures via an appropriate structure assumption, called Atlanta world, which contains a vertical direction (typically the gravity direction) and a set of horizontal directions orthogonal to the vertical direction. Contrary to the commonly used Manhattan world assumption, the horizontal directions in Atlanta world are not necessarily orthogonal to each other. While Atlanta world permits to encompass a wider range of scenes, this makes the solution space larger and the problem more challenging. Given a set of inputs, such as lines in a calibrated image or surface normals, we propose the first globally optimal method of inlier set maximization for Atlanta direction estimation. We define a novel search space for Atlanta world, as well as its parameterization, and solve this challenging problem by a branch-and-bound framework. Experimental results with synthetic and real-world datasets have successfully confirmed the validity of our approach.
Journal of Experimental Social Psychology A sweeter win: When others help us outperform them ☆

To succeed in today's workplaces, people often need to outperform the persons who helped them succeed. In three studies we assessed how doing so a ﬀ ects well-being, prosocial behavior and social perceptions. In the ﬁ rst two studies participants took part in a competitive version of a virtual ball-toss game, with di ﬀ erent ﬁ nancial incentives in each study. Depending on condition participants either obtained the majority of the ball tosses or almost no ball tosses. Importantly, participants either “ earned ” this outcome as a result of their own performance or were “ granted ” this outcome as a result of the performance of the other players. Study 3 featured the same conditions and a combination of the incentives. However, participants now observed one of the games and rated the anticipated reaction of a focal player. The results revealed that (1) winning was better than losing, (2) especially when people's win was granted to them and less so when they earned it for themselves, (3) which resulted in higher well-being and prosocial behavior, and also maintained meta-perceptions and other-percep-tions of competence and enhanced meta-perceptions and other-perceptions of warmth. These results advance theories on interpersonal competition, social comparison, and in/exclusion.
Miniproposal Image Reconstruction for Multi-camera System

The multi-camera system uses multiple small aperture, portable and low-price cameras to capture multiple images from different viewpoints. Using the complementary informations exhibit among the captured images, a well-designed image processing algorithm can reconstruct a high-definition image by fusing information from different cameras. Comparing with traditional imaging devices which typically use a single, large aperture camera, the multi-camera system can achieves comparable/better image quality with sacrificing good traits such as portable, easyto-install and economical. Because of that, multi-camera system becomes increasingly popular for many industrial-level product, including AR/VR devices, smart phone, and surveillance system. Designing better reconstruction algorithm for the multi-camera system can, therefore, impact the imaging industry by further improving imaging performance or by further reducing the space/hardware/budget needed for such systems.
Information System Quality: Managers Perspective

To evaluate Information System Quality (ISQ) quantitatively, a model was constructed based on sub-models related to the five Information System (IS) components, namely, Human Resources, Hardware, Software and application, Procedure and Data, and all IS players perspectives are considered who are: Managers, Technical Staff, Functional Staff and Users. This paper focuses on the survey designed for managers in order to form the variable indicators from variable questions, via appropriate formulas in the first place, and to analyze data collected from IS managers of the Moroccan universities in the second one. This approach will allow diagnosing precisely the malfunctioning areas on ISQ by emphasizing on the components with less quality level. It will also enable making comparison of ISQ on different organizations with the mean of standardized values.
Providing gene-to-variant and variant-to-gene database identifier mappings to use with BridgeDb mapping services

Database identifier mapping services are important to make database information interoperable. BridgeDb offers such a service. Available mapping for BridgeDb link 1. genes and gene products identifiers, 2. metabolite identifiers and InChI structure description, and 3. identifiers for biochemical reactions and interactions between multiple resources that use such IDs while the mappings are obtained from multiple sources. In this study we created BridgeDb mapping databases for selections of genes-to-variants (and variants-to-genes) based on the variants described in Ensembl. Moreover, we demonstrated the use of these mappings in different software tools like R, PathVisio, Cytoscape and a local installation using Docker. The variant mapping databases are now described on the BridgeDb website and are available from the BridgeDb mapping database repository and updated according to the regular BridgeDb mapping update schedule.
Analytical high performance liquid chromatography method for estimating the combination of aspirin and omeprazole in bulk and tablet dosage form

A HPLC method was developed for the estimation of aspirin and omeprazole simultaneously in bulk drug and tablet dosage forms. 0.1 M potassium dihydrogen phosphate and acetonitrile in the ratio of 55:45 v/v at pH of 4.5 was used successfully as the mobile phase for the analysis of the selected drugs. Analysis of the selected drugs was carried out using Zodiac C18 (150 × 3.0 mm, 5 μm) column. The calibration range for omeprazole was 0.08-24 μg/ml, whereas, for aspirin was 0.65-195 μg/ml. The LOD and LOQ values were; 0.199 μg/ml and 0.65 μg/ml for aspirin, 0.024 μg/ml and 0.080 μg/ml for omeprazole, respectively. The percentage recovery of aspirin and omeprazole was found to be 99.75%-99.92% and 99.46%-99.57%, respectively. Percentage relative standard deviation (RSD%) was found to be in the range of 0.083%-0.085% for aspirin and 0.045%-0.047% for omeprazole. Forced degradation studies (such as alkaline, acidic, oxidative, thermal and photolytic degradation conditions) were performed on tablet sample as per the ICH guidelines. The peaks of degradants are well separated from the main peaks of aspirin and omeprazole. The proposed HPLC method was successfully applied for the quantification of aspirin and omeprazole in its pharmaceutical dosage forms without interference from common excipients.
Investigation of the Effects of Edaravone on Valproic Acid Induced Tissue Damage in Pancreas

Antiepileptik ve antikonvulzan etkili bir ilac olan valproik asit (VPA) oksidan ureminde artisa neden oldugundan toksik yan etkilere sahiptir. Bu calismanin amaci guclu bir serbest radikal yakalayicisi olan edaravonun VPA’nin yol actigi toksisite ve doku hasari uzerine etkilerini biyokimyasal ve histolojik olarak arastirmaktir. Disi Spraque Dawley turu sicanlar kontrol, edaravon (30 mg kg-1 gun-1), VPA (0.5 mg kg-1 gun- 1), VPA+edaravon (ayni dozlarda) olmak uzere dort gruba ayrildi. VPA ve edaravon intraperitonal olarak 7 gun boyunca verildi. Total protein, lipit peroksidasyon (LPO), siyalik asit (SA) ve glutatyon (GSH) duzeyleri ve alkalen fosfataz (ALP), doku faktoru (DF), superoksit dismutaz (SOD), glutatyon- S-transferaz (GST), katalaz (KAT), glutatyon peroksidaz (GPx) ve miyeloperoksidaz (MPO) aktiviteleri pankreas homojenatlarinda olculdu. LPO ve SA duzeyleri ve ALP, DF ve MPO aktiviteleri kontrol grubuna gore kiyaslandiginda VPA grubunda anlamli olarak artti. GST, KAT ve GPx aktiviteleri kontrol grubuna gore kiyaslandiginda VPA grubunda anlamli olarak azaldi. VPA grubunda morfolojik olarak belirgin bir hasar tespit edildi. Edaravonun olumlu etkisi SA, DF, KAT ve GPx parametrelerinde ve histolojik incelemede gozlemlendi. Bu nedenle, edaravon VPA’nin pankreastaki zararli etkilerinin giderilmesinde ve/veya azaltilmasinda yararli olabilir.
Development of conditioned taste aversion to Mascagnia rigida in goats 1

Barbosa R.R. , Pacífico da Silva I. & Benito Soto-Blanco B. 2008. Development of conditioned taste aversion to Mascagnia rigida in goats. Pesquisa Veterinária Brasileira 28(12):571-574. Programa de Pós-Graduação em Ciência Animal, Universidade Federal Rural do Semi-Árido, BR 110 Km 47, Mossoró, RN 59625-900, Brazil. E-mail: benito.blanco@pq.cnpq. br The aim of this study was to determine whether goats could be averted from consuming Mascagnia rigida, a toxic plant found in the semiarid region of northeastern Brazil. Fourteen male goats not previously familiarized to M. rigida were randomly allocated to two treatment groups: control (treated with 5.5mL water orally by a drenching gun) and lithium group (treated with 100mg LiCl/kg body weight orally by a drenching gun). For conditioning, goats were allowed to feed on M. rigida leaves for 15 min, followed by LiCl or water administration. The time spent on eating M. rigida leaves was measured. The conditioning was repeated daily until the LiCl-treated goats stopped eating M. rigida. On the 10th, 17th, and 24th day after conditioning, extinction trials of the M. rigida aversion were performed in goats by using single-choice tests. There was no difference between the two treatment groups with respect to the consumption of M. rigida on the first day of aversion conditioning, however, controls ingested increasing amounts of the plant on consecutive conditioning days. On the second day, five out of the seven goats in the lithium group did not eat the leaves, but on the third day, all the goats in the lithium group did not ingest M. rigida. This aversion persisted throughout all evaluated days. This indicates that goats can be easily conditioned by using lithium chloride to avoid eating M. rigida temporarily.
The Design of efficient initialization and crash recovery for log-based file systems over flash memory

While flash memory has been widely adopted for storage systems for various embedded systems, issues of performance and reliability have started receiving growing attention in recent years. How to provide efficient roll back and quick mounting for flash-memory file systems has become an important research topic in recent years, in addition to the work on effective garbage collection and superb runtime performance. Such an observation motivates our work on the investigation of efficient initialization and crash recovery of flash-memory file systems based on log structures. A methodology is proposed for the acceleration of mounting and crash recovery for log-based file systems. A system prototype based on a well-known flash-memory file system, YAFFS, was implemented with performance evaluation. Experimental results show that the proposed methodology can reduce mounting time significantly, regardless of whether the file system is properly unmounted.
Iterative parameter estimation and model prediction of a rotary unmanned aerial vehicle

This paper presents a method for identifying and predicting attitude dynamics in a rotary unmanned aerial vehicle UAV. A base model which uses a first principles model is simplified for use in real-time prediction. An integral-based parameter identification method is presented to identify the unknown intrinsic helicopter parameters outdoors with significant wind disturbance and on a test bench. The yaw axis is used as a proof-of-concept on both the tests. For the outdoor flight, the system identification is performed using open-loop commands from a test pilot. The test-bench experiments use proportional integral derivative PID control and thus provide a further validation of the methods in closed-loop as well as open-loop. The results show that all the major yaw dynamics were captured and good future state predictions of 0.1 to 0.3 seconds were obtained using slow time varying parameters and disturbance modelling.
Global Patterns of Gene Expression in the Aleurone of Wild-Type and dwarf1 Mutant Rice1[W]

The cereal aleurone layer is a model system for studying the regulation of transcription by gibberellin (GA) and abscisic acid (ABA). GA stimulates and ABA prevents the transcription of genes for α-amylases and other secreted hydrolytic enzymes, but how GA and ABA affect the transcription of other genes is largely unknown. We characterized gene expression in rice (Oryza sativa) aleurone using a half-genome rice microarray. Of the 23,000 probe sets on the chip, approximately 11,000 hybridized with RNA from rice aleurone treated with ABA, GA, or no hormone. As expected, GA regulated the expression of many genes, and 3 times as many genes were up-regulated by GA at 8 h than were down-regulated. Changes in gene expression resulting from ABA treatment were not consistent with the hypothesis that the role of ABA in this tissue is primarily to repress gene expression, and 10 times more genes were up-regulated by ABA at 8 h than were down-regulated by ABA. We also measured transcript abundance in aleurone of dwarf1 (d1) mutant rice. The d1 protein is the sole α-subunit of heterotrimeric G-proteins in rice. Genes up-regulated by GA or ABA had higher expression in wild type than in d1 aleurone, and genes down-regulated by GA had lower expression in wild type relative to d1 aleurone. The d1 mutation did not result in a decrease in sensitivity to GA at the level of transcription. Rather, changes in transcript abundance were smaller in the d1 mutant than in wild type.
Actions as Space-Time Shapes

Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach [14] for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure, and orientation. We show that these features are useful for action recognition, detection, and clustering. The method is fast, does not require video alignment, and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, nonrigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action, and low-quality video.