Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance

In recent years, the advent of new hardware and software technologies for navigation and control has made Unmanned Aerial Vehicles (UAVs) ever more autonomous and efficient. As a consequence, it is now possible to have drones moving within complex environments, such as cities or indoor areas. One of the main requirements for intelligent mission planning in such environments is the ability to correctly and efficiently detect and avoid obstacles. For this reason, various libraries have been created for the simulation of UAV navigation in virtual environments, in order to test algorithms for automatic obstacle detection and collision avoidance before deploying the drones in the real world. Usually, the performance of these algorithms depends on various parameters as well as specific application settings. However, while different parameter configurations can be easily tested in simulation, their number can be too large to allow a complete exploration of the parameter space or a manual tuning. Furthermore, a full analytical model of the parameters’ influence on the algorithmic performance can be hard to obtain. Yet, it is extremely important to find their optimal values to allow collision-free navigation. In this direction, we propose here a thorough exploration, based on an Evolutionary Algorithm (EA), of the parameter space of the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. Our results show that the proposed EA is a viable solution for finding optimal parameter settings that can be generalizable to different scenarios characterized by different complexity levels.
Target Dysbiosis of Gut Microbes as a Future Therapeutic Manipulation in Alzheimer’s Disease

Alzheimer’s disease (AD) is commonly an age-associated dementia with neurodegeneration. The pathogenesis of AD is complex and still remains unclear. The inflammation, amyloid β (Aβ), and neurofibrillary tangles as well misfolded tau protein in the brain may contribute to the occurrence and development of AD. Compared with tau protein, Aβ is less toxic. So far, all efforts made in the treatments of AD with targeting these pathogenic factors were unsuccessful over the past decades. Recently, many studies demonstrated that changes of the intestinal environment and gut microbiota via gut–brain axis pathway can cause neurological disorders, such as AD, which may be involved in the pathogenesis of AD. Thus, remodeling the gut microbiota by various ways to maintain their balance might be a novel therapeutic strategy for AD. In the review article, we analyzed the characteristics of gut microbiota and its dysbiosis in AD and its animal models and investigated the possibility of targeting the gut microbiota in the treatment of the patients with AD in the future.
Optimal Selection of Customized Features for Implementing Seizure Detection in Wearable Electroencephalography Sensor

Wearable electroencephalography sensor integrated with seizure detection algorithm has become an emerging research topic in health management for patients with refractory epilepsy. To enhance seizure detection accuracy and reduce power consumption, this paper has proposed a selection method of optimal features customized for each patient. A feature pool consisting of several popular features is constructed. Then, the customized multi-domain features for each patient are selected from feature pool based on minimum redundancy maximum relevance. By removing redundant features, the computational complexity of on-line seizure detection can be greatly reduced, which is desired in wearable device where low-power and real-time operation are required. Four test datasets are used to evaluate the performance of proposed method, with detection accuracy calculated. Experiment results show that different datasets vary in optimal features. By using the proposed method, the detection accuracy and the value of area under receiver operating characteristic curve can be improved by 11.49% and 0.13 respectively, while feature dimension being reduced to 1.
A 4-GS/s Digitally Interpolated 8-Bit Concurrent Binary Search ADC

This letter describes a 4-GS/s four-way time-interleaved binary search analog-to-digital converter (ADC) in 65-nm CMOS. The ADC improves the conversion speed and resolution by concurrent binary searching on overlapping search space. After concurrent searching, the two successive approximation register ADC with reference offset generate 8-bit codes which are digitally interpolated to generate 9-bit code including 1-bit redundancy. Taking advantage of the higher conversion rate in the first two cycles and digital interpolation, ADC requires only six cycles to generate a 9-bit resolution at 1-GS/s rate. Consuming 28 mW, the implemented prototype achieves 39.56-dB SNDR and 48.21-dB SFDR at Nyquist frequency.
Special issue for the twentieth edition of the workshop 'From objects to agents'

The ﬁrst edition of the Workshop ‘From Objects to Agents’ ( WOA ) was held in Parma in May 2000. The workshop started as a joint initiative of the Agents and Multi-Agent Systems Working Group of the Italian Association for Artiﬁcial Intelligence ( AIxIA ) together with the Italian Association for Advanced Technologies based on Object-Oriented Concepts ( TABOO ). The workshop was intended to provide a forum for researchers and practitioners interested in understanding the possibilities that the intricate connection between agent technologies and object-oriented technologies can open. The ﬁrst edition of the workshop counted more than ﬁfty registered participants, from the academia and from the software industry, who attended the event for their interest in agent technologies and object-oriented technologies. When TABOO ceased to exist, the workshop started to be primarily focused on all topics related to agents and multi-agent systems, and it initiated to be advertised as a stand-alone initiative with an international perspective organized by an independent community of researchers and practitioners based in Italy. As such, the twenty editions of the work-shop have always been located in Italy, and the Steering Committee of the workshop have constantly
Subsampled Sum-Modified-Laplacian for Adaptive Loop Filter in Versatile Video Coding

This paper proposes a Subsampled Sum-Modified-Laplacian (SSML) operator for the block classification of the Adaptive Loop Filter (ALF) in Versatile Video Coding (VVC). The VVC Test Model (VTM)-2.0 includes Geometry transformation-based ALF (GALF) with <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> block classification, a single <inline-formula> <tex-math notation="LaTeX">$7\times 7$ </tex-math></inline-formula> Luma diamond-shaped filter, and spatial adaptation at the Coding Tree Block (CTB) level to improve the coding efficiency of VVC. However, in the <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> block classification, 1-D (1-Dimensional) Modified-Laplacian (ML) values for various directions are calculated at all sample positions within an <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> window, and these are summed to derive the gradients for the corresponding directions. For a CTB where the Wiener filter is applied, every <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> Luma block within the CTB must calculate Sum-Modified-Laplacian (SML) operations, which results in increased computational complexity of the decoder. Therefore, four different subsampling patterns based on <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> block classification for the SSML operator that calculate the 1-D ML values at subsampled sample positions within the <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> window are proposed as attempts to reduce the computational complexity of the block classification process in the ALF of VTM-2.0. As all of the proposed subsampling patterns reduce the number of sample positions needed to calculate the 1-D ML operations by half, the experimental results demonstrate that the proposed method achieves total decoding time reductions in the range of 2% to 3%, while the coding gain of the ALF is maintained.
Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images

Glaucoma is one of the leading causes of irreversible vision loss, it progresses gradually without easily noticeable symptoms. The detection of glaucoma in the early stage is crucial as it may help to decelerate the progress. The traditional instrument methods are manual, time-consuming and less accurate. Hence, the automated diagnosis of glaucoma is needed for detection of glaucoma in the early stage with high accuracy. The flexible analytic wavelet transform (FAWT) based novel method has been proposed for the classification of glaucoma stages. In the proposed method, FAWT has been used to decompose the preprocessed images into various sub-band images. Then, ReliefF and sequential box-counting (SBC) algorithms are applied to extract the various entropies and fractal dimension (FD) based features, respectively. Further, the extracted feature values are ranked using Fisher’s linear discriminant analysis (LDA) dimensionally reduction. Finally, the higher rank features have been used for the classification of glaucoma stages using least squares-support vector machine (LS-SVM) classifier. The proposed method has been evaluated on publicly available large and diverse glaucoma database. The classification accuracy of the proposed method is 93.40% using tenfold cross-validation. The proposed method has demonstrated better performance for glaucoma classification as compared to the existing methods. The proposed method is ready to help the ophthalmologist in their daily screening for glaucoma detection.
A Review of Closed-Form Cramér-Rao Bounds for DOA Estimation in the Presence of Gaussian Noise Under a Unified Framework

The Cramér-Rao Bound (CRB) for direction of arrival (DOA) estimation has been extensively studied over the past four decades, with a plethora of CRB expressions reported for various parametric models. In the literature, there are different methods to derive a closed-form CRB expression, but many derivations tend to involve intricate matrix manipulations which appear difficult to understand. Starting from the Slepian-Bangs formula and following the simplest derivation approach, this paper reviews a number of closed-form Gaussian CRB expressions for the DOA parameter under a unified framework, based on which all the specific CRB presentations can be derived concisely. The results cover three scenarios: narrowband complex circular signals, narrowband complex noncircular signals, and wideband signals. Three signal models are considered: the deterministic model, the stochastic Gaussian model, and the stochastic Gaussian model with the a priori knowledge that the sources are spatially uncorrelated. Moreover, three Gaussian noise models distinguished by the structure of the noise covariance matrix are concerned: spatially uncorrelated noise with unknown either identical or distinct variances at different sensors, and arbitrary unknown noise. In each scenario, a unified framework for the DOA-related block of the deterministic/stochastic CRB is developed, which encompasses one class of closed-form deterministic CRB expressions and two classes of stochastic ones under the three noise models. Comparisons among different CRBs across classes and scenarios are presented, yielding a series of equalities and inequalities which reflect the benchmark for the estimation efficiency under various situations. Furthermore, validity of all CRB expressions are examined, with some specific results for linear arrays provided, leading to several upper bounds on the number of resolvable Gaussian sources in the underdetermined case.
Multi-Head CNN-LSTM with Prediction Error Analysis for Remaining Useful Life Prediction

Predicting accurate remaining useful life (RUL) of components plays a crucial role in making optimal decision for maintenance management. As sensor technology develops, multiple sensors are used to collect information for monitoring the condition of components. Deep learning architectures, such as convolutional neural network (CNN) and long short term memory (LSTM), can be considered as a successful end-to-end framework to predict RUL from the multivariate time series collected by those sensors. For that, we employ an architecture combining the parallel branch of CNN in series with LSTM which is referred to as multi-head CNN-LSTM. Furthermore, we propose a combination of the network with time series prediction error analysis (PEA). The prediction errors on the entire time series are estimated by recursive least squares (RLS) and single exponential smoothing (SES) respectively. We analyze each of the two sequences of prediction errors with the exponentially weighted moving average (EWMA) and combine them with the Fisher’s method. Finally, the output of the PEA is fed into the multi-head CNN-LSTM network as the additional input. We evaluate the performance of our method on the widely used C-MAPSS dataset. The experimental results suggest that using the PEA improves the performance of the deep learning-based RUL prediction model. Compared to other methods in recent literature, the proposed method achieves the state-of-the-art result on one sub-dataset and very competitive results on the others. In addition, it also shows promising results in the consecutive RUL prediction following the degradation process of components.
Digital Endocasting in Comparative Canine Brain Morphology

Computed tomography (CT) is one of the most useful techniques for digitizing bone structures and making endocranial models from the neurocranium. The resulting digital endocasts reflect the morphology of the brain and the associated structures. Our first aim was to document the methodology behind creating detailed digital endocasts of canine skulls. We created digital endocasts of the skulls of 24 different dog breeds and 4 wild canids for visualization and teaching purposes. We used CT scanning with 0.323 mm × 0.322 mm × 0.6 mm resolution. The imaging data were segmented with 3D Slicer software and refined with Autodesk Meshmixer. Images were visualized in 3D Slicer and surface models were converted to 3D PDFs to provide easier interactive access, and 3D prints were also generated for visualization purposes. Our second aim was to analyze how skull length and width relate to the surface areas of the prepiriform rhinencephalic, prefrontal, and non-prefrontal cerebral convexity areas of the endocasts. The rhinencephalic area ratio decreased with a larger skull index. Our results open the possibility to analyze the relationship between the skull and brain morphology, and to link certain features to behavior, and cognition in dogs.
Application of Images Segmentation for Evaluation Structure of White Brined Cheese

This paper presents an application of images segmentation in HSI (Hue, Saturation, Intensity) color space for evaluation of one sensory characteristic of white brined cheese quality- cheese structure. It is developed a Java application for multi-level images segmentation and the results are compared with experts’ evaluation of cheese structure. A correlation analysis shows that a combination of two colors in segmented image with four colors, could be used for structure evaluation of white brined cheese.
An 800-ps Origami True-Time-Delay-Based CMOS Receiver Front End for 6.5–9-GHz Phased Arrays

A true-time-delay-based (TTD-based) receiver front end with 800-ps time delay for 6.5–9-GHz wideband phased arrays has been designed for satellite communication in a 65-nm CMOS technology. The proposed receiver front end consists of a balanced low-noise amplifier (LNA), a 6-bit attenuator with 15.75-dB tuning range, a programmable TTD element featuring 800-ps delay with 25-ps delay step, and an output buffer amplifier. A differential origami inductor is proposed to boost the delay unit’s quality factor and mitigate the insertion loss introduced by the artificial transmission line (ATL). A current-mode radio-frequency (RF) multiplexer is designed to ensure a rapid delay switching. The receiver front end demonstrates a 3.6-dB noise figure (NF) with an 18-dB typical gain and an input 1-dB compression point (IP1dB) of −17 dBm at 6.5–9 GHz. This TTD element allows for a low insertion loss and a small chip area per unit time delay of ATL-based RF TTDs.
Semantic Segmentation Using a GAN and a Weakly Supervised Method Based on Deep Transfer Learning

Semantic image segmentation is of crucial importance to many applications, such as autonomous driving, robot vision, and scene understanding. However, the border of a segmented image tends to be rough, and the labeling process is tedious and labor-intensive. Therefore, this study is the first proposing to use a deep generative adversarial network (GAN) with double-layered upsampling based on max-pooling indexed deconvolution. Our proposed upsampling method replaces the bilinear interpolation upsampling method; i.e., we fuse the deep deconvolution method by saving the indices of relative locations of the max weights computed during pooling. Combined with the deep GAN, our upsampling method can improve the extraction of low-resolution features, and compensate for the loss of the image size. To further reduce the whole network’s dependence on labeled datasets, a weakly supervised feedback method is proposed. The unlabeled data can improve the generalization ability of the model. Considering the generalization to unseen image domains, we introduce transfer learning based on a deep GAN and a weakly supervised method. The segmentation model using the trained data in the source domain can obtain good segmentation in the target domain using transfer learning. Extensive experiments in various domains demonstrate the advantages of the proposed method compared to the generalization ability of semantic segmentation. This method also significantly decreases the dependence on labeled data and ensures the network accuracy.
Molecular studies on the radiation-resistant bacteria Deinococcus radiodurans and Deinobacter grandis

Members of the genus Deinococcus are characterised by their extreme resistance to both the lethal and mutagenic effects of ionizing and ultraviolet (U.V.) radiations. It is known that the Deinococci have unusual excision repair for eliminating radiationinduced lesions in their DNA, but very little is known about their recombination repair, which, in many other bacteria, involves a protein analogous to the RecA protein of &herichia coli. Cloning the equivalent of the RecA gene from Deinococcus radiodurans was attempted by the construction of gene libraries and the use of their ability to complement recA mutations or their analogue in E.coli and Bacillus subtilis. Several vectors were employed; three E.coli vectors, viz, the high copy number plasmid pAT153, the expression vector pDR540 and the cosmid pJBFI-I; and a B.subtilis expression vector pPL608. The libraries were screened for recombination proficient phenotypes by selecting recipient bacteria for resistance to DNA-damaging chemicals, (either methyl methanesuiphonate, 4-nitroquinoline-1-oxide or mitomycin C), using either the recA hosts E.coli HBIO1, DH5a or JC10289 or the recE host B.subtilis 1A422. Some libraries were also screened in E.coli JC14604, where Lac colonies are formed by introduction of a functional recombination gene which can be identified by the formation of blue colonies on media containing 5-bromo-4-chloro-3indolyl-B-D-galactoside (X-gal). Restoration of the recombinational ability of D.radiodurans mutant rec30 by complementation was also attempted. A D.radiodurans recA gene analogue was not isolated using these techniques so direct selection methods were tried. Southern blotting
Update on diagnosis and management of neoplastic pericardial disease

ABSTRACT Introduction Pericardial neoplasms are uncommon, mostly due to secondary involvement of the pericardium by extracardiac tumors. Clinical presentation is nonspecific, frequently leading to a delayed diagnosis. Moreover, both benign and malignant pericardial tumors may be associated with myocardial infiltration and mechanical compression of cardiac chambers, possibly precipitating clinical conditions. Pericardial tumors are indeed a diagnostic and therapeutic clinical challenge. Areas covered This review aims to provide an overview of the main clinical characteristics of pericardial tumors, along with their management in clinical practice. Expert commentary Multimodality imaging (echocardiography, chest X-ray, CT, CMR, and PET) enable full characterization of pericardial neoplasms. An individualized strategy should be developed by a multidisciplinary team including cardiologists, oncologists, radiologists, and cardiac surgeons.
The association of the metabolic syndrome with target organ damage: focus on the heart, brain, and central arteries

ABSTRACT Introduction The metabolic syndrome (MetS) is an adverse metabolic state composed of obesity, hyperglycemia/pre-diabetes, hypertension, and dyslipidemia. It substantially increases the risk of type 2 diabetes, cardiovascular disease (CVD) and mortality, and has a huge impact on public health. Area covered The present review gives an update on the definition and prevalence of MetS, and its impact on cardiac structure and function as well as on the brain and central arteries. The association with CVD and mortality risk is discussed. Focus is mainly directed toward the subclinical target organ damage related to MetS. Data is also critically reviewed to provide evidence on the incremental prognostic value of overall MetS over its individual components. Expert commentary MetS is a clinical risk condition associated with subclinical and clinical CVD and mortality. Roughly, 30% of the world population suffer from MetS. As all components of the MetS are modifiable, optimal preventive and therapeutic measures should be initiated to improve CV risk control, particularly aggressively treating hypertension and hyperglycemia, and encouraging people to adopt healthy lifestyle as early as possible is of great importance.
Anti-Inflammatory Protection of Guinea Pigs by Egg Yolk

Summary The anti-inflammatory activity of egg yolk has been studied in weanling guinea pigs. 1. The active material resided in the alcohol soluble fraction and was absent from the protein and acetone soluble fractions. 2. The anti-inflammatory effect did not appear to be mediated by the adrenals. 3. Feeding egg yolk and yolk alcohol solubles resulted in an elevation of serum total lipids with no increase in cholesterol to phospholipid ratio. Inactive yolk fractions caused elevations of serum total lipids, phospholipids, cholesterol, and cholesterol to phospholipid ratio. 4. Serum levels of total N and non-protein N were unaffected by yolk alcohol solubles.