Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of Regularization Strategies for Subsampled Single-Shell Diffusion MRI

Conventional single-shell diffusion MRI experiments acquire sampled values of the diffusion signal from the surface of a sphere in q-space. However, to reduce data acquisition time, there has been recent interest in using regularization to enable q-space undersampling. Although different regularization strategies have been proposed for this purpose (i.e., sparsity-promoting of the spherical ridgelet representation and Laplace-Beltrami Tikhonov regularization), there has not been a systematic evaluation of the strengths, weaknesses, and potential synergies of the different regularizers. In this work, we use real diffusion MRI data to systematically evaluate the performance characteristics of these different approaches and determine whether one approach is fundamentally more powerful than the other. Results from retrospective subsampling experiments suggest that both regularization strategies offer largely similar reconstruction performance (though with different levels of computational complexity) with some degree of synergy (albeit, relatively minor).
Reservoir Computing for Jurkat T-cell Segmentation in High Resolution Live Cell Ca2+ Fluorescence Microscopy

The reservoir computing (RC) paradigm is exploited to detect Jurkat T cells and antibody-coated beads in fluorescence microscopy data. Recent progress in imaging of subcellular calcium (Ca2+) signaling offers a high spatial and temporal resolution to characterize early signaling events in T cells. However, data acquisition with dual-wavelength Ca2+ indicators, the photo-bleaching at high acquisition rate, low signal-to-noise ratio, and temporal fluctuations of image intensity entail corporation of post-processing techniques into Ca2+ imaging systems. Besides, although continuous recording enables real-time Ca2+ signal tracking in T cells, reliable automated algorithms must be developed to characterize the cells, and to extract the relevant information for conducting further statistical analyses. Here, we present a robust two-channel segmentation algorithm to detect Jurkat T lymphocytes as well as antibody-coated beads that are utilized to mimic cell-cell interaction and to activate the T cells in microscopy data. Our algorithm uses the reservoir computing framework to learn and recognize the cells - taking the spatiotemporal correlations between pixels into account. A comparison of segmentation accuracy on testing data between our proposed method and the deep learning U-Net model confirms that the developed model provides accurate and computationally cheap solution to the cell segmentation problem.
Understanding systematic reviews and meta-analyses

In the last two decades, the number of systematic reviews and meta-analyses (SRMA), including individual participant/ patient data meta-analysis and network meta-analysis, has increased exponentially. SRMAs are intended to provide robust and reliable evidence to guide clinical practice. However, issues of overlapping, redundancy, duplication and incompleteness need to be considered when interpreting SRMA results. A Medline free text search for “meta-analysis” yielded 8,039 items published before 1999, 29,959 items between 2000 and 2009, and 111,663 items between 2010 and 2019, a 373% increase each decade. A free text search for “individual participant data meta-analysis OR individual patient data meta-analysis” yielded four items before 1999. There were 128 items between 2000 and 2009, and 700 items between 2010 and 2019, a 547% increase. Similarly, a free text search for “network meta-analysis” yielded no items before 1999. There were 21 items between 2000 and 2009, and 2,647 items between 2010 and 2019, a 12,605% increase in the last decade. Ioannidis has described the increasing trend in SRMA as an epidemic which can now be considered of ‘catastrophic’ proportions since clinicians, researchers and policy makers are inundated with so many reviews on the same topic with divergent results.
The World Federation of Societies of Biological Psychiatry (WFSBP) 2020 guidelines for the pharmacological treatment of paraphilic disorders

Abstract Objectives: The primary aim of these guidelines is to evaluate the role of pharmacological agents in the treatment and management of patients with paraphilic disorders, with a focus on the treatment of adult males. Because such treatments are not delivered in isolation, the role of specific psychotherapeutic interventions is also briefly covered. These guidelines are intended for use in clinical practice by clinicians who diagnose and treat patients, including sexual offenders, with paraphilic disorders. The aim of these guidelines is to bring together different views on the appropriate treatment of paraphilic disorders from experts representing different countries in order to aid physicians in clinical decisions and to improve the quality of care. Methods: An extensive literature search was conducted using the English-language-literature indexed on MEDLINE/PubMed (1990−2018 for SSRIs) (1969−2018 for hormonal treatments), supplemented by other sources, including published reviews. Results: Each treatment recommendation was evaluated and discussed with respect to the strength of evidence for its efficacy, safety, tolerability, and feasibility. The type of medication used depends on the severity of the paraphilic disorder and the respective risk of behaviour endangering others. GnRH analogue treatment constitutes the most relevant treatment for patients with severe paraphilic disorders. Conclusions: An algorithm is proposed with different levels of treatment for different categories of paraphilic disorders accompanied by different risk levels.
Disorder Originated Unusual Mobility in Crystalline InGaZnO4

Using calculations from first principles, the site disorder between Ga and Zn of crystalline InGaZnO<sub>4</sub> is shown to play key roles in its unique transport properties. The analysis based on Density Functional Theory reveals that the various types of scattering centers stem from the charge imbalance between the ordered and disordered structures are the prime factor in explaining the carrier mobility of the crystalline InGaZnO<sub>4</sub>. The phonon dispersion of crystalline InGaZnO<sub>4</sub> is also examined to include the influence of scattering by polar optical phonons. The Hall mobility calculation based on Boltzmann transport equation under relaxation time approximation shows that the site disorders of <inline-formula> <tex-math notation="LaTeX">$ {2.5\times }{ {10}}^{ {21}} { {\mathrm{cm}}}^{ {-3}}{\sim } {5\times }{ {10}}^{ {21}}{ {\mathrm{cm}}}^{ {-3}}$ </tex-math></inline-formula> can dominate the transport character. The comparisons with experimental data demonstrate excellent agreement in the wide ranges of temperature (100~300 K) and doping densities (10<sup>16</sup>~10<sup>20</sup> cm<sup>−3</sup>). Our results provide a critical measure to estimate a device performance utilizing quasi-crystalline InGaZnO<sub>4</sub> along with a deeper understanding of its peculiar transport characters.
Hyperiid Amphipods (Crustacea: Peracarida) and Spring Mesoscale Features in the Gulf of Mexico

The distribution and abundance of the hyperiid amphipods collected during April–May 1986 was analysed in order to study their relationship to mesoscale features in the southern Gulf of Mexico. Hyperiids were represented by 79 species, Lestrigonus bengalensis (65% of total hyperiids), Primno abyssalis (4.3%), Anchylomera blossevillei (2.3%) and Lestrigonus schizogeneios (2.3%) being the most abundant. Several mesoscale features (two cyclonic and three anticyclonic eddies, two upwelling areas) were active in the surveyed area. Cluster analysis of density and composition produced five groups of stations that were found to be related to some of these features. Overall, the mesoscale features that most influenced the local hyperiid community were the upwelling areas; they were related both with the highest and the lowest hyperiid densities and with high and low diversity figures. Overall day/night variations were normal, with highest night-time densities; this pattern was not consistent in the different mesoscale features studied. Reverse migrations were possibly occurring, or migration patterns were lacking, at some of these systems. The hyperiid community structure related to the anticyclonic eddies differed somewhat among the eddies; however, the oceanographic exchange with the surrounding gulf waters probably obscured sharper faunistic differences between the communities.
Avian responses to extreme weather across functional traits and temporal scales

Extreme weather, including heat waves, droughts, and high rainfall, is becoming more common and affecting a diversity of species and taxa. However, researchers lack a framework that can anticipate how diverse species will respond to weather extremes spanning weeks to months. Here we used high‐resolution occurrence data from eBird, a global citizen science initiative, and dynamic species distribution models to examine how 109 North American bird species ranging in migration distance, diet, body size, habitat preference, and prevalence (commonness) respond to extreme heat, drought, and rainfall across a wide range of temporal scales. Across species, temperature influenced species’ distributions more than precipitation at weekly and monthly scales, while precipitation was more important at seasonal scales. Phylogenetically controlled multivariate models revealed that migration distance was the most important factor mediating responses to extremely hot or dry weeks; residents and short‐distance migrants occurred less often following extreme heat. At monthly or seasonal scales, less common birds experienced decreases in occurrence following drought‐like conditions, while widespread species were unaffected. Spatial predictions demonstrated variation in responses to extreme weather across species’ ranges, with predicted decreases in occurrence up to 40% in parts of ranges. Our results highlight that extreme weather has variable and potentially strong implications for birds at different time scales, but these responses are mediated by life‐history characteristics. As weather once considered extreme occurs more frequently, researchers and managers require a better understanding of how diverse species respond to extreme conditions.
Using simulation to improve wildlife surveys: wintering mallards in Mississippi, USA

Wildlife conservation plans generally require reliable data about population abundance and density. Aerial surveys often can provide these data; however, associated costs necessitate designing and conducting surveys efficiently. We developed methods to simulate population distributions of mallards (Anas platyrhynchos) wintering in western Mississippi, USA, by combining bird observations from three previous strip-transect surveys and habitat data from three sets of satellite images representing conditions when surveys were conducted. For each simulated population distribution, we compared 12 primary survey designs and two secondary design options by using coefficients of variation (CV) of population indices as the primary criterion for assessing survey performance. In all, 3 of the 12 primary designs provided the best precision (CV ≤ 11.7%) and performed equally well (diff ≤ 0.6%). Features of the designs that provided the largest gains in precision were optimal allocation of sample effort among strata and configuring the study area into five rather than four strata, to more precisely estimate mallard indices in areas of consistently high density. Of the two secondary design options, we found including a second observer to double the size of strip transects increased precision or decreased costs, whereas ratio estimation using auxiliary habitat data from satellite images did not increase precision appreciably. We recommend future surveys of mallard populations in our study area use the strata we developed, optimally allocate samples among strata, employ PPS or EPS sampling, and include two observers when qualified staff are available. More generally, the methods we developed to simulate population distributions from prior survey data provide a cost-effective method to assess performance of alternative wildlife surveys critical to informing management decisions, and could be extended to account for effects of detectability on estimates of true abundance.
The Classification of Enzymes by Deep Learning

Enzymes, as a group of crucial biocatalysts produced by living cells, enable the chemical reactions in organisms to be more efficient. According to the properties of the reactions catalyzed by enzymes, the Enzyme Commission (EC) number system divided enzymes into 6 primary main classes in 1961: oxidoreductases (EC1), transferases (EC2), hydrolases (EC3), lyases (EC4), isomerases (EC5), and ligases (EC6). These six categories did not change for many years until a new class, the translocases (EC7), was added in August 2018. Different enzymes have different properties of catalytic reaction, and the prediction of enzyme classes is a very important research topic, allowing us to further study the structure and function of enzyme molecules when we know the category of enzyme. Because the number of enzymes whose function remains unknown is enormous, it is time-consuming to use biological experiments to determine enzyme characteristics. Thus, devising various computational models to predict enzyme classes has become a feasible scheme. In hope of giving researchers more inspiration and ideas for predicting the EC number of enzymes by machine learning, we summarize a variety of research methods used in the prediction of enzyme families in this research.
A Physics-Based TCAD Simulator for Superconducting Electronics Based on Josephson Junctions

Superconducting electronics are promising candidates for complementing semiconductor electronics. However, a comprehensive suite of superconducting design and simulation tools that is necessary to streamline and automate the design process is either nonexistent or very limited. Here, we describe Synopsys’ advances in the technology computer-aided design (TCAD) front for physics-based simulation of superconducting electronics based on Josephson junctions (JJs). We employ Keldysh–Gor’kov–Nambu Green's function formalism within the context of the self-consistent mean-field theory of Bogoliubov and de-Gennes to simulate the equilibrium and transport properties of JJ-based devices in one dimension. Using these Green's functions, we calculate various electrical properties and effects, including order parameter and proximity effect, current-phase characteristics and current-density distribution for arbitrary temperatures, material properties and composition, and device dimensions. We further expand the TCAD simulator using the Floquet–Keldysh–Gor’kov–Nambu Green's function for calculating current-voltage characteristics of JJs in nonequilibrium and capture various quantum mechanical effects including multiple Andreev reflections (MAR) for constant applied voltage.
Attacks and Defenses in Short-Range Wireless Technologies for IoT

The Internet of Things, abbreviated as IoT, is a new networking paradigm composed of wireless and wired networks, geographically distributed and interconnected by a “secured” backbone, essentially, the Internet. It connects billions of heterogeneous devices, called Things, using different communication technologies and provides end-users, all over the world, with a variety of smart applications. IoT constitutes a new evolution for the Internet in terms of diversity, size, and applications. It also invites cybercriminals who exploit IoT infrastructures to conduct large scale, distributed, and devastating cyberattacks that may have serious consequences. The security of IoT infrastructures strongly depends on the security of its wired and wireless infrastructures. Still, the wireless infrastructures are thought to be the most outspread, important, and vulnerable part of IoT. To achieve the security goals in the wireless infrastructures of IoT, it is crucial to have a comprehensive understanding of IoT attacks, their classification, and security solutions in such infrastructures. In this paper, we provide a survey of attacks related to the wireless infrastructures of IoT in general, and to the most used short-range wireless communication technologies in the resource-constrained part of IoT in particular. Namely, we consider Wi-Fi, Bluetooth, ZigBee, and RFID wireless communication technologies. The paper also provides a taxonomy of these attacks based on a security service-based attack classification and discusses existing security defenses and mechanisms that mitigate certain attacks as well as the limitations of these security mechanisms.
Approximate GP Inference for Nonlinear Dynamical System Identification Using Data-Driven Basis Set

We address the problem of nonlinear dynamical system identification in state space formulation using an approximate Gaussian process (GP) regression framework where the basis for GP are learned from data. Approximate GP inference is used to address the high computational cost of exact GP frameworks, and is based on basis function expansion concept. We address the design of these basis functions using data and eigenvalue decomposition framework. The need for such design arises in practical applications where the collected data has outliers, discontinuities and other non-stationary characteristics which limit not only the applicability but also the performance of traditional GP framework with inherent stationary assumptions. Using the available data set, a kernel eigenvalue problem is formulated which is solved using Monte Carlo techniques to construct basis functions. Then, a low-rank approximation to the exact GP is obtained by an approach similar to kernel principle component analysis (k-PCA). The coefficients in the basis function expansion and other unknown parameters are learned from data using sequential Monte Carlo technique. The proposed GP framework for dynamical system identification is tested and validated against fixed basis framework using simulations and experimental data.
Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction

Image feature extraction based on deep disentangled representation learning of PET images is proposed for the prediction of lymphoma treatment response. Our method encodes PET images as spatial representations and modality representations by performing supervised tumor segmentation and image reconstruction. In this way, the whole image features (global features) as well as tumor region features (local features) can be extracted without the labor-intensive tumor segmentation and feature calculation procedure. The learned global and local image features are then joined with several prognostic factors evaluated by physicians based on clinical information, and used as input of a SVM classifier for predicting outcome results of lymphoma patients. In this study, 186 lymphoma patient data were included for training and testing the proposed model. The proposed method was compared with the traditional straightforward feature extraction method. The better prediction results of the proposed method also show its efficiency for prognostic prediction related feature extraction in PET images.
Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

This paper reviews studies on neural networks in aerodynamic data modeling. In this paper, we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs). Subsequently, the history and fundamental methodologies of neural networks are introduced. Furthermore, we classify the neural networks based studies in aerodynamic data modeling and illustrate comparisons among them. These studies demonstrate that neural networks are effective approaches to aerodynamic data modeling. Finally, we identify three important trends for future studies in aerodynamic data modeling: a) the transformation method and physics informed models will be combined to solve high-dimensional partial differential equations; b) in the research area of steady aerodynamic response predictions, model-oriented studies and data-integration-oriented studies will become the future research directions, while in unsteady aerodynamic response predictions, radial basis function neural networks (RBFNNs) are the best tools for capturing the nonlinear characteristics of flow data, and convolutional neural networks (CNNs) are expected to replace long short-term memories (LSTMs) to capture the temporal characteristics of flow data; and c) in the field of steady or unsteady flow field reconstructions, the CNN-based conditional generative adversarial networks (cGANs) will be the best frameworks in which to discover the spatiotemporal distribution of flow field data.
A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

In the aftermath of a large-scale emergency, Unmanned Aerial Vehicles (UAVs) can play a key role as mobile communication systems supporting rescue operations on the ground. At the same time, the deployment of autonomous UAV swarms still poses severe challenges in terms of distributed mobility, swarm connectivity and mesh networking. To this purpose, we propose <inline-formula> <tex-math notation="LaTeX">$\mathsf {ELAPSE}$ </tex-math></inline-formula> (aErial LocAl Positioning System for Emergency), a novel, distributed framework for aerial mesh deployment that supports discovery and multi-hop connectivity among rescue personnel and emergency requesters. <inline-formula> <tex-math notation="LaTeX">$\mathsf {ELAPSE}$ </tex-math></inline-formula> integrates components of swarm mobility, positioning and Quality-of-Service (QoS) support, while targeting UAV devices at different levels of hardware complexity. Three contributions are provided in this study. First, we present a novel, bio-inspired swarm mobility algorithm which natively addresses QoS-based aerial mesh connectivity, coverage of the ground nodes and UAV collision avoidance through the abstraction of virtual springs. Second, we investigates its implementation when geo-location capabilities are not available: to this aim, we propose local-based and cooperative-based techniques through which each UAV can estimate the position of its neighbours, and hence correctly adjust its direction and speed. Third, we analyze the feasibility of the <inline-formula> <tex-math notation="LaTeX">$\mathsf {ELAPSE}$ </tex-math></inline-formula> framework through a twofold evaluation: i.e. a large-scale OMNeT++ simulation showing the effectiveness of the distributed mesh formation and localization techniques, and a small-case ground robotic testbed demonstrating the impact of QoS mechanisms on the system operations.
Compact Model of Dzyaloshinskii Domain Wall Motion-Based MTJ for Spin Neural Networks

Recent progress has demonstrated that current-induced domain wall motion (CIDWM) is able to achieve efficient and ultrafast magnetic switching in the case of spin–orbit torque (SOT) and Dzyaloshinskii–Moriya interaction (DMI). CIDWM-based devices are taken as promising candidates for the next-generation nonvolatile artificial neurons and synapses due to its excellent programmability, fast operation speed, low write power, and so on. In this article, we present a physics-based model of CIDWM magnetic tunnel junction (MTJ), which exhibits high performance based on experimental results. The proposed model integrates the CIDWM dynamics and nanowire MTJ resistance, showing great agreement with extensive physical simulation. A learning circuit based on CIDWM-MTJ, as a hybrid MTJ/CMOS circuit example, has been designed and simulated to validate its functionality. The proposed SPICE-compatible compact model will be useful for high-performance circuit and system evaluation and is expected to promote the research and development of CIDWM-based spintronics devices.