Miracle-dz/UniFAR-FTUs huggingface dataset:
A UAV-Based Aircraft Surface Defect Inspection System via External Constraints and Deep Learning

In the field of aircraft maintenance, regular inspection of fuselage surface during the aircraft life cycle is a vital task to ensure the aircraft quality and flight safety. Currently, the inspection task is generally carried out manually in an indoor hangar, which is with low efficiency and reliability. In this article, a novel system based on the unmanned aerial vehicle (UAV) is presented to achieve automated aircraft surface inspection efficiently. The hardware is established with a lightweight and low-cost flight platform, on which a sensor containing an inertial measurement unit (IMU) and a camera is equipped for UAV localization. A high-resolution camera is equipped to collect images of fuselage for defect detection. Our inspection framework is mainly composed of two modules: the UAV localization module and the defect detection module. The localization module is designed to estimate the relative pose between the UAV and the aircraft, providing the foundation for image positioning on the aircraft surface. The existing visual–inertial odometry (VIO) approach is adopted to implement the pose estimation. To reduce the large drifts caused by the VIO approach, a novel method is proposed to deploy precalibrated ArUco markers around the aircraft, which serve as external constraints for the VIO objective to realize joint optimization of the camera pose. In addition, an adaptive weighting method is proposed, which takes into consideration the recognition effect of markers to balance the external constraints. The defect detection module aims to detect defects on the fuselage surface from images captured by the high-resolution camera, which is implemented based on deep learning. To address the issue of detection on a few training samples, the transfer learning strategy is exploited to first pretrain the model on a public defect dataset and then fine-tune it on our collected aircraft defect dataset. After detecting the defects, the defective region is reflected on the fuselage surface through the UAV pose on the corresponding frame provided by the localization module, realizing the accurate defect localization. Experiments on both the simulation environment and real data demonstrate the superiority of our proposed external localization module and the effectiveness of the crack detection module.
Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections

Abstract In this work we present a multiagent deep reinforcement learning approach for autonomous driving vehicles that is able to operate in traffic networks with unsignalized intersections. The key aspects of the proposed study are the introduction of route-agents as the main building block of the system, as well as a collision term that allows the cooperation among vehicles and the construction of an efficient reward function. These have the advantage of establishing an enhanced collaborative multiagent deep reinforcement learning scheme that manages to control multiple vehicles and navigate them safely and efficiently-economically to their destination. In addition, it provides the beneficial flexibility to lay down a platform for transfer learning and reusing knowledge from the agents’ policies in handling unknown traffic scenarios. We provide several experimental results in simulated road traffic networks of variable complexity and diverse characteristics using the SUMO environment that empirically illustrate the efficiency of the proposed multiagent framework.
Computer-Aided Detection and Diagnosis of Neurological Disorder

Nowadays, neurological problems are more regular, representing a worry to pregnant ladies, guardians, healthy babies, and kids. Neurological problems emerge in a wide assortment of structures, each with its arrangement of beginnings, inconveniences, and results. The conclusion of neurological illnesses is an evolving concern and predominantly troublesome difficulty for current medication. Current diagnosis advancements (e.g., MRI and EEG) produce immense information (in proportion and aspect) as location, checking, and therapy of nervous system illnesses. As a common rule, investigation of that enormous clinical information is performed physically by specialists to distinguish and figure out the irregularities. It is a genuinely troublesome errand for an individual to collect, make due, investigate, and absorb enormous amounts of information through visible review. As an outcome, the specialist has been requesting electronic conclusion frameworks known as “computer-aided diagnosis” that can consequently identify the nervous system irregularities utilizing the essential clinical information. This framework further develops uniformity of findings, builds treatment outcomes, protects lives, and lessens price and time. As of late, few examinations have improved the computer-aided design frameworks for the executives of enormous clinical information for determination appraisal. This paper investigates the difficulties of tremendous clinical information giving. This article fundamentally evaluated and looked at the exhibition of existing AI and comprehensive learning approaches for identifying nervous system illnesses. A far-reaching piece of this concentration also shows different modalities and illness-determined datasets that identify and record pictures, signs, addresses, and so forth. Restricted related works are additionally summed up on nervous system illnesses, as this space has essentially less work zeroed in on illness and recognition rules. A portion of the standard assessment measurements is likewise introduced in this review for improved outcome examination.
Defensive emotions and evaluative judgements: Sensitivity to anger and fear predicts moral judgements, whereas sensitivity to disgust predicts aesthetic judgements

Abstract Aesthetic and moral evaluations engage appetitive and defensive emotions. While the role played by pleasure in positive aesthetic and moral judgements has been extensively researched, little is known about how defensive emotions influence negative aesthetic and moral judgements. Specifically, it is unknown which defensive emotions such judgements tap into, and whether both kinds of judgement share a common emotional root. Here, we investigated how participants' individual sensitivity to disgust, fear, anger and sadness predicted subjective judgements of aesthetic and moral stimuli. Bayesian modelling revealed that participants who were more sensitive to anger and fear found conventional and moral transgressions more wrong. In contrast, participants who were more sensitive to disgust disliked asymmetrical geometric patterns and untidy rooms more. These findings suggest that aesthetic and moral evaluations engage multiple defensive emotions, not just disgust, and that they may rely on different defensive emotions as part of their computational mechanism.
Detecting change points in neural population activity with contrastive metric learning

Finding points in time where the distribution of neural responses changes (change points) is an important step in many neural data analysis pipelines. However, in complex and free behaviors, where we see different types of shifts occurring at different rates, it can be difficult to use existing methods for change point (CP) detection because they can't necessarily handle different types of changes that may occur in the underlying neural distribution. Additionally, response changes are often sparse in high dimensional neural recordings, which can make existing methods detect spurious changes. In this work, we introduce a new approach for finding changes in neural population states across diverse activities and arousal states occurring in free behavior. Our model follows a contrastive learning approach: we learn a metric for CP detection based on maximizing the Sinkhorn divergences of neuron firing rates across two sides of a labeled CP. We apply this method to a 12-hour neural recording of a freely behaving mouse to detect changes in sleep stages and behavior. We show that when we learn a metric, we can better detect change points and also yield insights into which neurons and sub-groups are important for detecting certain types of switches that occur in the brain.
An Adaptive Low-Complexity Abnormality Detection Scheme for Wearable Ultrasonography

Doppler ultrasonography (DUS) is widely used in medical diagnosis due to its low-cost, non-invasive nature, and real-time operation. Its applications have further expanded with the emergence of point-of-care and wearable devices, the demand for which is rapidly increasing. However, current DUS abnormality detection methods are too computationally intensive for such resource-constrained platforms. This brief presents a low-complexity real-time abnormality detection scheme that enables development of wearable DUS devices. It uses an approximated Fourier transform and a novel greedy algorithm to detect spectrogram envelopes on-the-fly from the stream of samples, thus significantly reducing power and area requirements while achieving a detection accuracy of 96% on a mixture of 25 normal and abnormal test cases. A real-time ASIC implementation of the scheme in 180-nm CMOS consumes 16.8 ${\mu }\text{W}$ at a clock frequency of 80 kHz while occupying a layout area of 0.64 mm2.
COVID-19 course in granulomatosis with polyangiitis: single center experience with review of the literature

Background/aim Coronavirus disease 2019 (COVID-19) shares some clinical features with new-onset granulomatosis with polyangiitis (GPA) or GPA flare that may lead to a challenge in differential diagnosis. To date, little is known whether GPA can be induced by COVID-19. Herein, we aimed to seek the frequency and mortality rates of COVID-19 in our GPA cohort, and along with the literature cases, to evaluate clinical features and treatments of GPA patients with COVID-19. We also tried to identify clinical features of COVID-19 induced GPA. Materials and methods As of July 2021, we conducted a systematic literature review using different spelling combinations of “COVID-19 and GPA” in the PUBMED database. In total, 18 cases were found in the literature, 6 of them had COVID-19 induced GPA. The remaining 12 of literature cases and 6 cases in our GPA cohort (n = 81) had a COVID-19 infection while followed-up with GPA. We grouped these 18 patients as GPA+COVID-19. Results The frequency of COVID-19 was 7.4% in GPA cohort and mortality rate was 33% in GPA patients with COVID-19. The most common symptoms of GPA+COVID-19 patients were fever, cough, arthralgia/myalgia, and malaise. The most frequent treatments for GPA before the COVID-19 infection were steroids (72%) and rituximab (56%). Three patients who received rituximab also had COVID-19 reinfection. In the literature cases, mortality was observed in 4 (22%) of 18 patients with GPA+COVID-19. The most common symptoms of COVID-19 induced GPA were dyspnea, fever and cough. Conclusion In our GPA cohort, we observed a higher mortality rate compared to global WHO data. In patients followed up with GPA, rituximab treatment may be precarious for both COVID-19 disease and reinfection. Our study also provided some clues about the diagnostic challenge of GPA induced by COVID-19.
Succession of Microbial Community during the Co-Composting of Food Waste Digestate and Garden Waste

Microorganisms are of critical importance during the composting process. The aim of this study was to reveal the bacterial and fungal compositions of a composting pile of food waste digestate and garden waste, where the succession of the microbial communities was monitored using Illumina MiSeq sequencing. We explored the efficiency of composting of different microorganisms to judge whether the composting system was running successfully. The results showed that the composting process significantly changed the bacterial and fungal structure. Firmicutes, Proteobacteria, and Bacteroidota were the dominant phyla of the bacterial communities, while Ascomycota was the dominant phylum of the fungal communities. Moreover, the highest bacterial and fungal biodiversity occurred in the thermophilic stage. The physical and chemical properties of the final compost products conformed to the national standards of fertilizers. The efficient composting functional microbes, including Cladosporium, Bacillus and Saccharomonospora, emerged to be an important sign of a successfully operating composting system.
Progressive Vascular Abnormalities in the Aging 3xTg-AD Mouse Model of Alzheimer’s Disease

Vascular dysfunction and structural abnormalities in Alzheimer’s disease (AD) are known to contribute to the progression of the pathology, and studies have tended to ignore the role of the vasculature in AD progression. We utilized the 3xTg-AD mouse model of AD to examine individual cerebral vessels and the cortical vascular network across the lifespan. Our vessel painting approach was used to label the entire cortical vasculature, followed by epifluorescence microscopy. The middle cerebral artery (MCA) tree was assessed with confocal microscopy, and a new method was developed to assess branching patterns as a measure of aging-related changes. We found that vascular remodeling was profoundly altered at 4–6 months of age, when the 3xTg-AD mouse is known to transition to cognitive impairment and Aβ deposition in both sexes. Analysis of vascular features (density, junctions, length) of the MCA territory highlighted sex-dependent differences across the 3xTg-AD mouse lifespan, with no alterations in branching patterns. Our current cerebrovascular angioarchitectural analyses demonstrate progressive alterations in individual cortical vessels, as well as in the vascular network of the cortex. These new findings advance our understanding of brain anatomy and physiology in the 3xTg-AD mouse, while potentially identifying unique diagnostic signatures of AD progression.
What Do Long Data Tell Us About the Inflation Hike Post COVID-19 Pandemic?

To what extent is the recent spike in inflation driven by a change in its permanent component? We estimate a semi-structural model of output, inflation, and the nominal interest rate in the United States over the period 1900-2021. The model predicts that between 2019 and 2021 the permanent component of inflation rose by 51 basis points. If instead we estimate the model using postwar data (1955--2021), the permanent component of inflation is predicted to have increased by 238 basis points. A possible interpretation of this finding is that the model estimated on the shorter sample assigns a larger increase in the permanent component of inflation because the period 1955-2021 does not contain sudden sparks in inflation like the one observed in the aftermath of the COVID-19 pandemic but only gradual ones---the great inflation of the 70s took more than 10 years to build up. By contrast, the period 1900-1954 is plagued with sudden inflation hikes---including one around the 1918 Spanish flu pandemic---which the estimated model endogenously recalls and uses to interpret inflation around the COVID-19 episode. This result suggests that prewar data might be of use to understand recent inflation dynamics.
Cerebron: A Reconfigurable Architecture for Spatiotemporal Sparse Spiking Neural Networks

Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. Compared to SNNs with simple fully connected structures, those extensive structures (e.g., standard convolutions, depthwise convolutions, and pointwise convolutions) can deal with more complicated tasks but lead to difficulties in hardware mapping. In this work, we propose a novel reconfigurable architecture, Cerebron, which can fully exploit the spatiotemporal sparsity in SNNs with maximized data reuse and propose optimization techniques to improve the efficiency and flexibility of the hardware. To achieve flexibility, the reconfigurable compute engine is compatible with a variety of spiking layers and supports inter-computing-unit (CU) and intra-CU reconfiguration. The compute engine can exploit data reuse and guarantee parallel data access when processing different convolutions to achieve memory efficiency. A two-step data sparsity exploitation method is introduced to leverage the sparsity of discrete spikes and reduce the computation time. Besides, an online channelwise workload scheduling strategy is designed to reduce the latency further. Cerebron is verified on image segmentation and classification tasks using a variety of state-of-the-art spiking network structures. Experimental results show that Cerebron has achieved at least $17.5\times $ prediction energy reduction and $20\times $ speedup compared with state-of-the-art field-programmable gate array (FPGA)-based accelerators.
The Importance Of Quantifying Financial Returns On Information System (IS) Investment For Organizations: An Analysis

Many organizations and businesses have found it difficult to invest in new projects in recent years, Aside from the fact that it is difficult to measure information systems costs and investment returns, how to determine costs and returns on investment before starting a business is an important practice, and there are numerous issues that have caused complications and cost increases for some businesses. The importance of information system investments was assessed using a descriptive analysis methodology. In order for the company to make an informed decision on the return on investment, it must first engage in lengthy debate and analysis to gain a thorough grasp of the business processes and the environment in which it is embedded. The paper also compares with relevant examples the several approaches and methodologies for quantifying the financial Return on Investment (ROI) in order to ease decision making by most investors while investing in Information System in organizations.
Tunneling spectroscopy of the Andreev Bound States in a Carbon Nanotube

Superconductivity is a fascinating electronic order in which electrons pair up due to an attractive interaction and condense in a macroscopic quantum state that can carry dissipationless currents, i.e. supercurrents. In hybrid structures where superconductors (S) are put in contact with non-superconducting material (X), electronic pairs propagating from the superconductor "contaminate" the nonsuperconducting material conferring it superconducting-like properties close to the interface, among which the ability to carry supercurrent. This "contamination", known as the superconducting proximity effect is a truly generic phenomenon. The transmission of a supercurrent through any S-X-S structure is explained by the constructive interference of pairs of electrons traversing X. Indeed, much as in an optical Fabry-Perot resonator, such constructive interference of electronic pairs occurs only for special resonant electronic states in X, known as the Andreev Bound States (ABS). In the recent years it has been possible to fabricate a variety of nanostructures in which X could be for instance nanowires, carbon nanotubes or even molecules. Such devices have in common that their X contains only few conduction electrons which implies that ABS are also in small number. In this case, if one wants to quantitatively understand proximity effect in these systems, it is necessary to understand in detail how individual ABS form. This can be seen as a central question in the development of nanoscale superconducting electronics. In this thesis, we observed individual ABS by tunneling spectroscopy in a carbon nanotube.
Composition and Conservation Value of Epiphytic Lichen Communities on Common Ash in North-Western Alps: A First Assessment

In the western sector of the Alps, and particularly in the Aosta Valley, lichenological communities on broad-leaved trees have received very little attention, and information about lichen species associated with common ash (Fraxinus excelsior L.) are still scanty. Therefore, in this study, we analyzed epiphytic lichen communities of ash trees to estimate their composition, their conservation value, and their association with some key environmental variables. Our results show that lichen communities appear to be different in terms of occurrence and frequencies in different sampling sites. The tested environmental variables contribute to shape the lichen communities, which are significantly different (p < 0.05) in sites characterized by different elevation, solar radiation, and source vicinity. The conservation value of lichen species was assessed by comparing distributional data at the national and local level. Our findings show that, in the Italian Alps, ash trees represent an important substrate for locally, or even nationally, rare lichens: 14 recorded species were not previously known in the study area, and some (Lecanora impudens and Rinodina polyspora) were included in the red list of Italian epiphytic lichens.
Numerical methods for open-loop and closed-loop optimization of linear control systems

The canonical optimal control problem is considered for a linear time-dependent system in the class of discrete-time feasible controls. Based on linear programming, two primal and two dual methods for constructing optimal open-loop controls are described. A method for synthesizing optimal feedback controls is presented. The results are illustrated by analyzing a fifth-order problem. The efficiency of the methods is estimated.
Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches

Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is why special attention must be paid to its monitoring. This paper presents a methodology for diagnosing an aircraft air compressor bearing using neural networks that have been optimized by genetic algorithms. We used in our study a database of vibratory signals that were recorded on a test bench from bearings with different defects. The faults features are extracted from these noisy signals using the estimate of the spectral density. The diagnostic capacity of obtained model has been demonstrated by a comparative study with two other automatic classifiers, which are discriminant analysis and neural networks whose training has been done with the Back-Propagation algorithm. This approach has the advantage of simultaneously ensuring the optimal structure of the neural network and accomplishing its learning. The importance of this study is the construction of a diagnostic tool that is characterized by efficiency, speed of decision making and ease of implementation not only on the computers on the ground, but also on the mounted calculators on aircraft.