Miracle-dz/UniFAR-FTUs huggingface dataset:
Space Maneuvering Target Integration Detection and Parameter Estimation for a Spaceborne Radar System With Target Doppler Aliasing

Space maneuvering target detection and parameter estimation are the challenging problems in a spaceborne radar system. The complex motion of an observed target usually leads to the range migration (RM) and Doppler frequency migration, causing the difficulty in target detection. Furthermore, the relative high speed of space maneuvering target and limited pulse repetition frequency will result in Doppler ambiguity, further degrading the target detection performance. In this article, a novel and efficient coherent accumulation algorithm is proposed, which considers the velocity ambiguity and Doppler aliasing caused by the target radial velocity and acceleration, respectively. In the proposed method, the target radial velocity and acceleration are first separated by applying the time reversal transform technique. Then, the target radial velocity is estimated by using the Keystone transform. After compensating the velocity effects, the frequency reversal transform is proposed to remove the residual RM, and the acceleration estimation can be efficiently accomplished by the nonuniform fast Fourier transform. Compared with the conventional coherent integration methods, the proposed method can be suitable for the motion parameter estimation and coherent integration detection for a maneuvering target with velocity ambiguity and spectrum aliasing. Additionally, the proposed method is computationally efficient since only the complex multiplication and fast Fourier transform calculations are involved. Both simulation experiments and measured data results are provided to verify the effectiveness of the proposed algorithm.
Effect of Three Different Cryoprotectant Solutions in Solid Surface Vitrification (SSV) Techniques on the Development Rate of Vitrified Pronuclear-Stage Mouse Embryos

The objective of this study was to compare the effects of three different vitrification solutions on the development of pronuclear-stage (PN) Dinnyes mouse embryos into blastocyst stage after vitrification by solid surface vitrification (SSV) technique. For this aim, it was compared three experimental groups and a control group. Experimental groups were distinguished each other by the vitrification solution used in SSV. It was used 4 % Ethylene Glycol (EG) at 37 oC equilibration temperature in the first group (SSV-EG), 4% Dimethyl Sulfoxide (DMSO) at room temperature in the second group (SSV-DMSO) and 4% Propylene Glycol (PG) at room temperature in the third group (SSV-PG). After vitrification, the survived embryos were cultured to blastocyst stage in KSOM. It was determined a significant difference between the groups of SSV-EG and SSV-PG at developing rate to 2-cell stage (P < 0.05). Similarly, SSV-PG demonstrated significant differences with SSV-DMSO and the control group at developing rate to 3-8-cell stage (P < 0.05). When compared the rates of developing to morula stage among the groups, it was determined significant differences between SSV-PG and the control group at (P < 0.05); and between SSV-DMSO and SSV-PG (P < 0.01). Finally, it was compared the developing rates into blastocyst stage and found that SSV-EG demonstrated significant differences with SSV-PG and SSV-DMSO (P < 0.01). This study has shown that EG, DMSO and PG with trehalose can be used effectively as a cryoprotective agent in the quick freezing of pronuclear-stage mouse embryos. Finally, additional studies are needed to optimize the SSV method in further stage mouse embryos.
High-Intensity Interval Training Decreases Muscle Sympathetic Nerve Activity and Improves Peripheral Vascular Function in Patients With Heart Failure With Reduced Ejection Fraction.

Allan R.K. Sales, PhD; Luciene F. Azevedo, PhD; Thiago O.C. Silva, MS; Amanda G. Rodrigues, MD; Patricia A. Oliveira, MD; Camila P. Jordão, MS; Ana C.M. Andrade, MD, PhD; Ursula Urias, PhD; Guilherme V. Guimaraes, PhD; Edimar A. Bocchi, MD, PhD; Maria Janieire N.N. Alves, MD, PhD; Ludhmila A. Hajjar, MD, PhD; Roberto K. Filho, MD, PhD; Zachary I. Grunewald, MS, RDN; Luis A. Martinez-Lemus, DVM, PhD; Jaume Padilla, PhD; Carlos E. Negrão , PhD
Machine Learning-Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival.

PURPOSE Shapley additive explanation (SHAP) values represent a unified approach to interpreting predictions made by complex machine learning (ML) models, with superior consistency and accuracy compared with prior methods. We describe a novel application of SHAP values to the prediction of mortality risk in prostate cancer. METHODS Patients with nonmetastatic, node-negative prostate cancer, diagnosed between 2004 and 2015, were identified using the National Cancer Database. Model features were specified a priori: age, prostate-specific antigen (PSA), Gleason score, percent positive cores (PPC), comorbidity score, and clinical T stage. We trained a gradient-boosted tree model and applied SHAP values to model predictions. Open-source libraries in Python 3.7 were used for all analyses. RESULTS We identified 372,808 patients meeting the inclusion criteria. When analyzing the interaction between PSA and Gleason score, we demonstrated consistency with the literature using the example of low-PSA, high-Gleason prostate cancer, recently identified as a unique entity with a poor prognosis. When analyzing the PPC-Gleason score interaction, we identified a novel finding of stronger interaction effects in patients with Gleason ≥ 8 disease compared with Gleason 6-7 disease, particularly with PPC ≥ 50%. Subsequent confirmatory linear analyses supported this finding: 5-year overall survival in Gleason ≥ 8 patients was 87.7% with PPC < 50% versus 77.2% with PPC ≥ 50% (P < .001), compared with 89.1% versus 86.0% in Gleason 7 patients (P < .001), with a significant interaction term between PPC ≥ 50% and Gleason ≥ 8 (P < .001). CONCLUSION We describe a novel application of SHAP values for modeling and visualizing nonlinear interaction effects in prostate cancer. This ML-based approach is a promising technique with the potential to meaningfully improve risk stratification and staging systems.
Quantifying Success in Science: An Overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions.
Localization of neural activity from neuromagnetic data using varying-support sources

Magnetoencephalography (MEG) and Electroencephalography (EEG) measure in a non-invasive way the magnetic field and the electrical potential, respectively, induced by cerebral activity. These instruments have an outstanding temporal resolution and the recorded data could provide interesting insights of the dynamics of neural currents. In order to get reliable information on the unknown neural currents from the data, we need to solve a ill-posed inverse problem: the operator involved in the formulation of the model linking the neural activity and the measured data is such that the solution of the inversion problem is not unique and does not depend continuously on the data. Moreover, the choice of the model for the source deeply affects the representation of the solution: a distributed model for neural currents can encompass complex, spread brain activity but could be not accurate in the representation of focal brain activity, while the representation with dipolar sources could not represent properly the activity generated by patches of cortex. In this work we propose a modified version of the Particle Filter we employed so far for MEG data analysis: in this implementation the support of the sources is not more fixed to be a dipole but can change back and forth to be distributed, adapting itself among the time samples to the best configuration. We test the method with ad hoc synthetic data.
A Service Mesh-Based Load Balancing and Task Scheduling System for Deep Learning Applications

In recent years, there are increasing technologies that benefit from cloud computing and edge computing, especially the application in the Deep Learning and Internet of Things topics. Functionalities such as load balancing, traffic routing, and task scheduling, which used to be part of the software, are now enabled and provided as API or microservices on the underlying infrastructure. Therefore, when we want to deliver a container application to the users either on the cloud cluster or edge cluster, developers do not need to worry about these issues and only focus on the development of the functionality. Nevertheless, for the deployment stage, the system architect should design the fundamental architecture to build an ecosystem for the application. In this paper, a practical approach is proposed to develop a flexible and on-demand system for deep learning applications based on container and service mesh technologies. Our experiment chooses Kubernetes as the container orchestration platform and introduces Istio to implement the service mesh. We packaged one Flask based Deep learning model into a Docker container, and successfully deploy and provision this image classifier application to the public users with Functionalities like load balancing and task scheduling. Furthermore, we empower the system with an evaluation of the resource utilization services, and traffic flows inside the Kubernetes cluster in a visualized manner.
Trading Data Size and CNN Confidence Score for Energy Efficient CPS Node Communications

In a context of Cyber-Physical Systems (CPS), energy-efficiency is a critical factor to achieve long operational life-time. The constraint of using battery-powered devices adds degrees of complexity, especially in a hard to reach environment with scarce network and energy resources. The reporting of data consumes large amount of energy, reducing the life-time of both individual nodes and the CPS as a whole. One way to reduce the energy cost of communication is to reduce the number of Bits to transmit. However, this is a viable approach only if the transmitted data remain suitable for further analysis.In this paper, we report on the effect of reducing the image dimensions on the confidence score computed by a convolutional neural network (CNN) determining the species of animals present in images. We also report on the energy consumption of transmitting full vs. reduced dimensions of images. CPS devices and CNNs developed by the Distributed Arctic Observatory (DAO) project are used as experimental platforms.The results show that the energy needed to report the images can be reduced by up to 98% while only reducing the average confidence of determining the species correctly by 0.10%.
Deep Down the Rabbit Hole: On References in Networks of Decoy Elements

Deception technology has proven to be a sound approach against threats to information systems. Aside from well-established honeypots, decoy elements, also known as honeytokens, are an excellent method to address various types of threats. Decoy elements are causing distraction and uncertainty to an attacker and help detecting malicious activity. Deception is meant to be complementing firewalls and intrusion detection systems. Particularly insider threats may be mitigated with deception methods. While current approaches consider the use of multiple decoy elements as well as context-sensitivity, they do not sufficiently describe a relationship between individual elements. In this work, inter-referencing decoy elements are introduced as a plausible extension to existing deception frameworks, leading attackers along a path of decoy elements. A theoretical foundation is introduced, as well as a stochastic model and a reference implementation. It was found that the proposed system is suitable to enhance current decoy frameworks by adding a further dimension of inter-connectivity and therefore improve intrusion detection and prevention.
Generalizing the Statistical Analysis of Objects’ Spatial Coupling in Bioimaging

We introduce a novel paradigm for statistical analysis of spatial signals, applied to colocalization studies in bioimaging. Quantitative assessment of the spatial colocalization of different molecules in microscopy provides important functional information on cellular processes. By reducing objects (molecules or cluster of molecules) to their position (points), existing methods are predominantly restricted to point-based analyses, and scale poorly to applications involving large, asymmetric and complex-in-shape objects. We address this issue, and propose a statistical model for shape-based colocalization analysis. Our solution is a generalization of the Ripley's K-function for arbitrary shapes, and provides a method to statistically interpret the coupling of molecules to biological objects which are implicitly represented via level set embedding. Compared to the state-of-the-art, our solution is efficient and generic, and establishes the theoretical basis for an one-fits-all approach to tackle the heterogeneous challenges in robust interpretation of molecular association in biology. The efficacy of our method is established via synthetic simulations, and a practical application is also described to measure the statistical accumulation of synaptic molecules’ spots near the cell's boundary of cultured neurons.
Adaptive Mobility Models for Cluster-Based Wireless Sensor Network with Mobile Sink

Wireless Sensor Network is considered as an important technology in recent years, especially with introducing mobility to this kind of network. Mobility Control and management represent a challenge for Cluster-Based Wireless Sensor Network. For the aim of handling this challenge, many mobility models are proposed in the literature. In this paper, we propose two hybrid mobility models for the Mobile Sink with adaptive pause time in order to improve the data collection process in Wireless Sensor Network organized in clusters using LEACH protocol. The proposed mobility models are divided into two phases, which are the discovery phase of cluster heads in the network using Grid Mobility Model and data collection phase using mobility models based on metaheuristics algorithms in order to find the optimal trajectory of the Mobile Sink passing by all cluster heads discovered. The mobility models based on metaheuristics used in this paper are Tabu Search and Simulated Annealing algorithms, where we have adapted to Cluster-Based Wireless Sensor Network with adaptive pause time. To evaluate the efficiency of our proposal on data collection, we compare the proposed hybrid mobility models to Grid Mobility Model and Random Way Point Mobility Model. The simulation results show that our proposed hybrid mobility models perform better than other mobility models in data collection.
Novel Combined Variable Selection Approach Using Memetic Algorithm With Complex Harmonic Regularization

Variable selection has been highly successful in big data analyses, and regularization approaches are commonly used methods, which can automatically select important variables while constructing machine learning models. Due to the real datasets have complex relationships between relevant variable groups, many group-sparsity regularization approaches are proposed recently. However, these approaches usually are non-convex and sensitive to hyper-parameters. Therefore, optimization of the regularizations is a challenging task. In this paper, we present a novel memetic algorithm with the complex harmonic regularization (MA-CHR), which combines EC algorithm for hyper-parameters (global search) and complex harmonic regularization with path seeking strategy for the group-sparsity variable selection (local search). We further introduced a novel genetic individual representation (intron+exon) to efficient obtain the global optimal solution of this group-sparsity regularization. Simulation and five real data experiments demonstrate that the proposed MA-CHR method performs better than the state-of-the-art regularization methods in selecting groups of relevant variables and classification.
EFFECTS OF SELECTED SOIL PROPERTIES ON GROWTH OF HALOXYLON SP .IN SEGZI PLAIN (IRAN )

This study evaluated the effects of soil properties on growth of Haloxylon sp. in Segzi plain in the Eastern of I sfahan province(center of Iran ). One way to prevent the spread of blowing sand in a desert area is the biological fixation such asHaloxylon sp.Forthis research, a total of12 profiles werestudied.Depthof hardpan from thesoilsurface was determined.Then thesoilsamplings of 0-30, 30-60, 60-90, 90-120 cm depth were done.Plantphysiologicalparameters, including plant height andtopwere measured.In the study area, Haloxylon sp.plants were all of similar age . Soil physical and chemical characteris tics such as soil texture, total nitrogen (TN), pH, electrical conductivity (EC) , chloride ions(Cl)and sodium absorption ratio (SAR) were measured.For analysis of the SPSS software was used to compare the statistics and averages . These results showedthattheEC,SAR, Cl, pHandthepercentage ofclayhadnegative effects on plantphysiologicparameters.The percentage ofsand anddepth of hardpan from the soil surface ha dpositive effects on plant parameters.
Servicescapes: The Impact of Physical Surroundings on Customers and Employees

A typology of service organizations is presented and a conceptual framework is advanced for exploring the impact of physical surroundings on the behaviors of both customers and employees. The ability of the physical surroundings to facilitate achievement of organizational as well as marketing goals is explored. Literature from diverse disciplines provides theoretical grounding for the framework, which serves as a base for focused propositions. By examining the multiple strategic roles that physical surroundings can exert in service organizations, the author highlights key managerial and research implications.
Open-ocean orientation and return migration routes of chum salmon based on temperature data from data storage tags

Temperature data storage tags were applied to maturing chum salmon Oncorhynchus keta in the Bering Sea during summer 1998, 5 of which were recovered in the coastal waters around Japan. These tags recorded water temperatures experienced by the fish for periods of 62 to 118 d at resolutions of 15 to 30 min. We considered fine-scale aspects of the data by analyzing diel tempera- ture patterns in regard to the migration behavior and orientation of the fish. During the night, tem- peratures experienced by the fish changed little, and were among the highest temperatures experi- enced whereas daytime temperatures often showed dramatic changes as the fish changed depth. We analyzed a time series of temperature differences between sunrise and sunset as an indication of progress made by the fish against horizontal thermal gradients, which was assumed to represent changes in latitudinal position. Daytime differences were positive when the fish were migrating to warmer waters, whereas night-time differences were zero or slightly negative. This suggests that the fish made more progress migrating during the day than at night. We also considered large-scale aspects by examining oceanographic data in relation to the temperatures recorded by the tags. Analysis of possible migration routes based on large-scale patterns of sea surface temperature distri- butions suggests that zonal (east-west) routes are preferred. These data support the hypothesis that chum salmon utilize orientation cues associated with the sun during open ocean migration. However, whether salmon also utilize additional orientation or navigation mechanisms remains to be deter- mined.
Prognostic Significance of Serum Albumin in Patients With Acute Coronary Syndrome

In patients with unstable angina pectoris (UAP) or non-ST elevation myocardial infarction (NSTEMI), long-term mortality remains high despite improvements in the diagnosis and treatment. In this study, we investigated whether serum albumin level is a useful predictor of long-term mortality in patients with UAP/NSTEMI. Consecutive patients (n = 403) who were hospitalized with a diagnosis of UAP/NSTEMI were included in the study. Patients were divided into 2 groups based on the presence of hypoalbuminemia and the relationship between hypoalbuminemia and mortality was analyzed. Hypoalbuminemia was detected in 34% of the patients. The median follow-up period was 35 months (up to 45 months). Long-term mortality rate was 32% in the hypoalbuminemia group and 8.6% in the group with normal serum albumin levels (P < .001). On multivariate analysis, hypoalbuminemia, decreased left ventricular ejection fraction, and increased age were found to be independent predictors of mortality (P < .05). The cutoff value of 3.10 g/dL for serum albumin predicted mortality with a sensitivity of 74% and specificity of 67% (receiver-operating characteristic area under curve: 0.753, 95% CI: 0.685-0.822). All-cause long-term mortality rates were significantly increased in patients with hypoalbuminemia. On-admission albumin level was an independent predictor of mortality in patients with UAP/NSTEMI.
The Relationship between the Initial Anti‐factor Xa Measurement and the Duration of Direct Oral Anticoagulant Influence in Patients Transitioning to Heparin

Anticoagulation monitoring during transition from direct oral anticoagulants (DOAC) to heparin infusions is a significant challenge. Factor Xa inhibitors influence the heparin calibrated antifactor Xa assay. The University of Virginia (UVA) Medical Center utilized a corrected antifactor Xa assay (c‐AXA) during this transition period, which removes DOAC‐mediated antifactor Xa activity (d‐AXA) and reflects heparin‐specific activity. Currently, the duration of this influence is not well described.
Postictal brainstem hypoperfusion and risk factors for sudden unexpected death in epilepsy

Objective Since the strongest risk factor for sudden unexpected death in epilepsy (SUDEP) is frequent bilateral tonic-clonic seizures (BTCS), our aim was to determine whether postictal hypoperfusion in brainstem respiratory centers (BRCs) is more common following tonic-clonic seizures. Methods We studied 21 patients with focal epilepsies who underwent perfusion imaging with arterial spin labeling MRI. Subtraction maps of cerebral blood flow were obtained from the postictal and baseline scans. We identified 6 regions of interest in the brainstem that contain key BRCs. Patients were considered to have postictal BRC hypoperfusion if any of the 6 regions of interest were significantly hypoperfused. Results All 6 patients who experienced BTCS during the study had significant clusters of postictal hypoperfusion in BRCs compared to 7 who had focal impaired awareness seizures (7/15). The association between seizure type studied and the presence of BRC hypoperfusion was significant. Duration of epilepsy and frequency of BTCS were not associated with postictal brainstem hypoperfusion despite also being associated with risk for SUDEP. Conclusion Postictal hypoperfusion in brainstem respiratory centers occurs more often following BTCS than other seizure types, providing a possible explanation for the increased risk of SUDEP in patients who regularly experience BTCS.