Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration method for sparse multi-view cameras by bridging with a mobile camera

Camera calibration that estimates the projective relationship between 3D and 2D image spaces is one of the most crucial processes for such 3D image processing as 3D reconstruction and 3D tracking. A strong calibration method, which needs to place landmarks with known 3D positions, is a common technique. However, as the target space becomes large, landmark placement becomes more complicated. Although a weak-calibration method does not need known landmarks to estimate a projective transformation matrix from the correspondence information among multi-view images, the estimation precision depends on the accuracy of the correspondence. When multiple cameras are arranged sparsely, detecting sufficient corresponding points is difficult. In this research, we propose a calibration method that bridges sparse multiple cameras with mobile camera images. The mobile camera captures video images while moving among sparse multi-view cameras. The captured video resembles dense multi-view images and includes sparse multi-view images so that weak-calibration is effective. We confirmed the appropriate spacing between the images through comparative experiments of camera calibration accuracy by changing the number of bridging images and applied our proposed method to multiple capturing experiments in a large-scale space and verified its robustness.
Magnetic Tunnel

Figure 1 Schematic illustration showing the mechanism of TMR. Top: for parallel aligned magnetization as sketched at left, electrons around the Fermi level with spin up (m) and spin down (k) are allowed to tunnel from majority to majority bands, and from minority to minority bands. Bottom: when the magnetization is antiparallel, tunneling takes place from majority to minority and minority to majority bands, leading to a reduction of total tunneling current. In terms of electrical resistance, this corresponds to a higher resistance when the magnetization of the two layers is oppositely aligned. Introduction
A real-time fine-grained visual monitoring system for retail store auditing

Automated visual analysis of the object is of prime importance to realize the real-time concept of the internet of things. In this paper, we proposed a real-time fine grained visual analytics system for tracing the visibility of products on retail store shelves. The proposed visual monitoring system (VMS) is aimed to achieve high rates of product recognition, regardless of several real-time challenges like occlusion, different lightening conditions, product orientation etc. To address all these issues, the VMS collects the local feature descriptors which are scale invariant, rotational invariant and illumination invariant from training template images. Once, the testing image uploaded from any camera enabled device, the VMS extracts same local features and matches with the target feature descriptors for finegrained object recognition. This paper also covers the performance of various state-of-the-art local feature descriptors for object detection in context of retail store monitoring/tracking. The performance of the VMS is tested on real time retail shelve images. The results after investigation, the proposed fine-grained VMS shows approximately 90% accuracy in brand level detection.
Towards multi-granularity multi-facet e-book retrieval

Generally speaking, digital libraries have multiple granularities of semantic units: book, chapter, page, paragraph and word. However, there are two limitations of current eBook retrieval systems: (1) the granularity of retrievable units is either too big or too small, scales such as chapters, paragraphs are ignored; (2) the retrieval results should be grouped by facets to facilitate user's browsing and exploration. To overcome these limitations, we propose a multi-granularity multi-facet eBook retrieval approach.
Deep Recurrent Neural Network for Multiple Time Slot Frequency Spectrum Predictions of Cognitive Radio

The main processes of a cognitive radio system include spectrum sensing, spectrum decision, spectrum sharing, and spectrum conversion. Experimental results show that these stages introduce a time delay that affects the spectrum sensing accuracy, reducing its efficiency. To reduce the time delay, the frequency spectrum prediction was proposed to alleviate the burden on the spectrum sensing. In this paper, the deep recurrent neural network (DRNN) was proposed to predict the spectrum of multiple time slots, since the existing methods only predict the spectrum of one time slot. The continuous state of a channel is divided into a many time slots, forming a time series of the channel state. Since there are more hidden layers in the DRNN than in the RNN, the DRNN has fading memory in its bottom layer as well as in the past input. In addition, the extended Kalman filter was used to train the DRNN, which overcomes the problem of slow convergence and the vanishing gradient of the gradient descent method. The spectrum prediction based on the DRNN was verified with a WiFi signal, and the error of the prediction was analyzed. The simulation results proved that the multiple slot spectrum prediction improved the spectrum efficiency and reduced the energy consumption of spectrum sensing.
Low complexity SIFT compression techniques

Visual search for mobile devices relies on transmitting wirelessly image descriptors to a remote server. Hence, descriptors needs to be compressed to reduce bandwidth occupancy and network latency. In this paper, we investigate on the compression ratio versus accuracy of two low complexity coding schemes, based on Arithmetic and Type coding, applied to SIFT local descriptors.
Microdistribution of Faunal Assemblages at Deep-Sea Hydrothermal Vents in the Southern Ocean

Chemosynthetic primary production by microbes supports abundant faunal assemblages at deep-sea hydrothermal vents, with zonation of invertebrate species typically occurring along physico-chemical gradients. Recently discovered vent fields on the East Scotia Ridge (ESR) in the Southern Ocean represent a new province of vent biogeography, but the spatial dynamics of their distinct fauna have yet to be elucidated. This study determines patterns of faunal zonation, species associations, and relationships between faunal microdistribution and hydrothermal activity in a vent field at a depth of 2,400 m on the ESR. Remotely operated vehicle (ROV) dives obtained high-definition imagery of three chimney structures with varying levels of hydrothermal activity, and a mosaic image of >250 m2 of seafloor co-registered with temperature measurements. Analysis of faunal microdistribution within the mosaiced seafloor reveals a consistent pattern of faunal zonation with increasing distance from vent sources and peak temperatures. Assemblages closest to vent sources are visibly dominated by a new species of anomuran crab, Kiwa n. sp. (abundance >700 individuals m−2), followed by a peltospiroid gastropod (>1,500 individuals m−2), eolepadid barnacle (>1,500 individuals m−2), and carnivorous actinostolid anemone (>30 individuals m−2). Peripheral fauna are not dominated by a single taxon, but include predatory and scavenger taxa such as stichasterid seastars, pycnogonids and octopus. Variation in faunal microdistribution on chimneys with differing levels of activity suggests a possible successional sequence for vent fauna in this new biogeographic province. An increase in δ34S values of primary consumers with distance from vent sources, and variation in their δ13C values also indicate possible zonation of nutritional modes of the vent fauna. By using ROV videography to obtain a high-resolution representation of a vent environment over a greater extent than previous studies, these results provide a baseline for determining temporal change and investigations of processes structuring faunal assemblages at Southern Ocean vents.
Selection and Characterization of Rupintrivir-Resistant Norwalk Virus Replicon Cells In Vitro

ABSTRACT Human norovirus (HuNoV) is a major cause of nonbacterial gastroenteritis worldwide, yet despite its impact on society, vaccines and antivirals are currently lacking. A HuNoV replicon system has been widely applied to the evaluation of antiviral compounds and has thus accelerated the process of drug discovery against HuNoV infection. Rupintrivir, an irreversible inhibitor of the human rhinovirus 3C protease, has been reported to inhibit the replication of the Norwalk virus replicon via the inhibition of the norovirus protease. Here we report, for the first time, the generation of rupintrivir-resistant human Norwalk virus replicon cells in vitro. Sequence analysis revealed that these replicon cells contained amino acid substitutions of alanine 105 to valine (A105V) and isoleucine 109 to valine (I109V) in the viral protease NS6. The application of a cell-based fluorescence resonance energy transfer (FRET) assay for protease activity demonstrated that these substitutions were involved in the enhanced resistance to rupintrivir. Furthermore, we validated the effect of these mutations using reverse genetics in murine norovirus (MNV), demonstrating that a recombinant MNV strain with a single I109V substitution in the protease also showed reduced susceptibility to rupintrivir. In summary, using a combination of different approaches, we have demonstrated that, under the correct conditions, mutations in the norovirus protease that lead to the generation of resistant mutants can rapidly occur.
Predicate Logic as a Modelling Language: The IDP System

With the technology of the time, Kowalski's seminal 1974 paper {\em Predicate Logic as a Programming Language} was a breakthrough for the use of logic in computer science. It introduced two fundamental ideas: on the declarative side, the use of the Horn clause logic fragment of classical logic, which was soon extended with negation as failure, on the procedural side the procedural interpretation which made it possible to write algorithms in the formalism. Since then, strong progress was made both on the declarative understanding of the logic programming formalism and in automated reasoning technologies, particularly in SAT solving, Constraint Programming and Answer Set Programming. This has paved the way for the development of an extension of logic programming that embodies a more pure view of logic as a modelling language and its role for problem solving. In this paper, we present the \idp language and system. The language is essentially classical logic extended with one of logic programmings most important contributions to knowledge representation: the representation of complex definitions as rule sets under well-founded semantics. The system is a knowledge base system: a system in which complex declarative information is stored in a knowledge base which can be used to solve different computational problems by applying multiple forms of inference. In this view, theories are declarative modellings, bags of information, descriptions of possible states of affairs. They are neither procedures nor descriptions of computational problems. As such, the \idp language and system preserve the fundamental idea of a declarative reading of logic programs, while they break with the fundamental idea of the procedural interpretation of logic programs.
Radiation Resistance of the U(Al, Si)3 Alloy: Ion-Induced Disordering

During the exploitation of nuclear reactors, various U-Al based ternary intermetallides are formed in the fuel-cladding interaction layer. Structure and physical properties of these intermetallides determine the radiation resistance of cladding and, ultimately, the reliability and lifetime of the nuclear reactor. In current research, U(Al, Si)3 composition was studied as a potential constituent of an interaction layer. Phase content of the alloy of an interest was ordered U(Al, Si)3, structure of which was reported earlier, and pure Al (constituting less than 20 vol % of the alloy). This alloy was investigated prior and after the irradiation performed by Ar ions at 30 keV. The irradiation was performed on the transmission electron microscopy (TEM, JEOL, Japan) samples, characterized before and after the irradiation process. Irradiation induced disorder accompanied by stress relief. Furthermore, it was found that there is a dose threshold for disordering of the crystalline matter in the irradiated region. Irradiation at doses equal or higher than this threshold resulted in almost solely disordered phase. Using the program “Stopping and Range of Ions in Matter” (SRIM), the parameters of penetration of Ar ions into the irradiated samples were estimated. Based on these estimations, the dose threshold for ion-induced disordering of the studied material was assessed.
The Blast Behavior of Fiber Reinforced Thermoplastic Laminates

This article presents the results of a series of blast tests on a carbon fiber-reinforced poly-ether-imide (PEI) and a glass fiber-reinforced PEI composite. Initially, the fracture properties of the two composite systems were characterized through a series of flexural and interlaminar fracture tests. Blast testing was then undertaken on a ballistic pendulum facility, capable of measuring the impulse imparted by the plastic explosive. Delamination, localized fiber buckling, fiber fracture, and shear failure at the boundary of the clamped plates were identified as the primary failure mechanisms in the laminates, with their severity depending on the panel thickness and the applied impulse. Delamination was very localized along the center plane of the laminate, a reflection of the very high interlaminar fracture toughness of these composites. The critical impulse for rear surface fiber fracture has been found to increase rapidly with laminate thickness for the range of panels considered here. The impulses associated with the onset of rear-surface fiber fracture and complete failure of the target were similar, suggesting that rear surface fiber fracture is a pre-cursor to complete failure in these laminates. Limited tests on the glass fiber-reinforced PEI system showed that it offers a superior blast resistance to its carbon fiber counterpart.
Optimal Sensor Placement: A Robust Approach

We address the problem of optimally placing sensor networks for convection-diffusion processes where the convective part is perturbed. The problem is formulated as an optimal control problem where the integral Riccati equation is a constraint and the design variables are sensor locations. The objective functional involves a term associated to the trace of the solution to the Riccati equation and a term given by a constrained optimization problem for the directional derivative of the previous quantity over a set of admissible perturbations. This paper addresses the existence of the derivative with respect to the convective part of the solution to the Riccati equation, the well-posedness of the optimization problem, and finalizes with a range of numerical tests.
AFM-Based Fabrication of Nanofluidic Device for Medical Application

Recent developments in science and engineering have advanced the atomic manufacturing of nanoscale structures, allowing for improved high-performance technologies. Among them, AFM-based nanomachining is considered a potential manufacturing tool for operations including machining, patterning, and assembling with in situ metrology and visualization. In this work, atomic force microscopy (AFM) is employed in the fabrication of a nanofluidic device for DNA stretching application. Nanofluidic channels with various depths and widths are fabricated using AFM indentation and scratching techniques. To introduce the fluid inside the nanochannels, microchannels are made on both sides of the nanochannels. Photolithography technique is used to fabricate microfluidic channels on silicon wafers. A 3D Molecular Dynamics (MD) model is applied to guide the design and fabrication of nanodevices using nanoscratching. The correlation between the scratching conditions, including applied force, scratching depth, and distant between any two scratched grooves and the defect mechanism in the substrate/ workpiece is investigated. The MD model allows proper process parameter identification resulting in more accurate nanochannel sizes.
Link of a ubiquitous human coronavirus to dromedary camels

Significance Our results raise a scenario for the natural history of a ubiquitous respiratory coronavirus (CoV) that has established itself in humans after it was likely acquired from camels. This scenario reminds us of the pandemic potential of the Middle East respiratory syndrome CoV, an agent that is thought to be acquired from camels without presently causing sustained human-to-human transmission. The four human coronaviruses (HCoVs) are globally endemic respiratory pathogens. The Middle East respiratory syndrome (MERS) coronavirus (CoV) is an emerging CoV with a known zoonotic source in dromedary camels. Little is known about the origins of endemic HCoVs. Studying these viruses’ evolutionary history could provide important insight into CoV emergence. In tests of MERS-CoV–infected dromedaries, we found viruses related to an HCoV, known as HCoV-229E, in 5.6% of 1,033 animals. Human- and dromedary-derived viruses are each monophyletic, suggesting ecological isolation. One gene of dromedary viruses exists in two versions in camels, full length and deleted, whereas only the deleted version exists in humans. The deletion increased in size over a succession starting from camelid viruses via old human viruses to contemporary human viruses. Live isolates of dromedary 229E viruses were obtained and studied to assess human infection risks. The viruses used the human entry receptor aminopeptidase N and replicated in human hepatoma cells, suggesting a principal ability to cause human infections. However, inefficient replication in several mucosa-derived cell lines and airway epithelial cultures suggested lack of adaptation to the human host. Dromedary viruses were as sensitive to the human type I interferon response as HCoV-229E. Antibodies in human sera neutralized dromedary-derived viruses, suggesting population immunity against dromedary viruses. Although no current epidemic risk seems to emanate from these viruses, evolutionary inference suggests that the endemic human virus HCoV-229E may constitute a descendant of camelid-associated viruses. HCoV-229E evolution provides a scenario for MERS-CoV emergence.
Topological and Graph-Coloring Conditions on the Parameter-Independent Stability of Second-Order Networked Systems

In this paper, we study parameter-independent stability in qualitatively heterogeneous passive networked systems containing damped and undamped nodes. Given the graph topology and a set of damped nodes, we ask if output consensus is achieved for all system parameter values. For given parameter values, an eigenspace analysis is used to determine output consensus. The extension to parameter-independent stability is characterized by a coloring problem, named the richly balanced coloring (RBC) problem. The RBC problem asks if all nodes of the graph can be colored red, blue and black in such a way that (i) every damped node is black, (ii) every black node has blue neighbors if and only if it has red neighbors, and (iii) not all nodes in the graph are black. Such a colored graph is referred to as a richly balanced colored graph. Parameter-independent stability is guaranteed if there does not exist a richly balanced coloring. The RBC problem is shown to cover another well-known graph coloring scheme known as zero forcing sets. That is, if the damped nodes form a zero forcing set in the graph, then a richly balanced coloring does not exist and thus, parameter-independent stability is guaranteed. However, the full equivalence of zero forcing sets and parameter-independent stability holds only true for tree graphs. For more general graphs with few fundamental cycles an algorithm, named chord node coloring, is proposed that significantly outperforms a brute-force search for solving the NP-complete RBC problem.
Optical Coherence Tomography-Based Scattering Properties of Retinal Vessels in Glaucoma Patients

ABSTRACT Purpose: Clinical trials have demonstrated that retinal blood flow deficiencies are present in patients with open-angle glaucoma (OAG). We introduce a method for facilitating retinal vessel analysis: The intensity of the distal shadow of vessels in optical coherence tomography (OCT) caused by the scattered signal is analyzed, compared between healthy subjects and OAG patients and correlated with OCT angiography (OCT-A) flow density. Patients and Methods: We recruited 80 patients with diagnosed OAG (mean age 63.4 ± 13.2 years) and 80 healthy age-matched control subjects for comparison, and 20 patients for the correlation with OCT-A flow density. Patients received perimetry, peripapillary OCT measurements, and selected patients OCT-A of the papillary area. The vessel shadow intensity (VSI) is based on peripapillary OCT scans: the intensity of the distal vessel shadow was automatically compared to its surroundings, separated by arteries and veins. Flow density of the OCT-A scan was calculated by binarization and quantification of the pixel density. Results: The VSI for arteries was in OAG patients with 7.52 ± 2.62 [%] significantly lower compared to healthy subjects (9.03 ± 3.38 [%], p = 0.0029). In veins, the VSI was as well significantly lower for OAG patients (14.9 ± 3.59 [%]) compared to healthy subjects (17.46 ± 4.45 [%], p < 0.0001). Furthermore, in OAG patients there was a significant correlation of mean deviation of the visual field results with the veins’ VSI (p = 0.0006; r = −0,454). There was no significant correlation of scattering properties with OCT-A flow density (p > 0.05). Conclusions: We conclude that the OCT-based analysis of the scattering properties of retinal vessels differs significantly between patients with OAG and healthy subjects. Furthermore, changes in the scattering properties of veins correlated with the stage of the disease in terms of visual field deficits. These properties might complement existing measurements of ocular blood flow including OCT-A flow density.
Crosswalk Localization from Low Resolution Satellite Images to Assist Visually Impaired People

In this article, we propose a model for crosswalk detection and localization by using satellite images captured from Google Maps, for the purpose of assisting visually impaired people. The detection is performed by an SVM classifier, which is combined with Google Road Map to speed up computation time and to eliminate some possible false alarms. We assume that a visually impaired person holds a smartphone with an embedded GPS, which is used to initialize the extraction of images from Google Maps, as well as to assist its user by providing audio feedback of the nearest detected crosswalk. This issue brings forward significant interest and it is also very challenging, mainly due to illumination changes, occlusion, image noise and resolution, besides the quality of crosswalks that sometimes are badly painted in many developing countries. Experimental results indicate that the proposed model works well in low resolution images, effectively detecting and localizing crosswalks in simulated scenarios.