Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonstationary SBL for SAR Imagery Under Nonzero Mean Additive Interference

Conventional sparse Bayesian learning (SBL) for synthetic aperture radar (SAR) imagery predominantly focuses on the signal model which is affected by the zero mean white Gaussian noise. Therefore, it is difficult to solve the problem of nonzero mean additive interference. In this letter, a new and nonstationary SBL (NS-SBL) algorithm is proposed, which is capable of handling the additive interference with nonzero mean and in general distributions. Specifically, to accommodate the strong direct-current power of the interference, the Gaussian mixture model (GMM) is introduced to fit the expectation, which is flexible enough to capture the complicated variation of additive interference. However, the intended additive perturbation will result in complicated likelihood distribution, which is difficult to get a fully Bayesian inference. To this end, hierarchical Bayesian inference is employed to solve the problem that the likelihood and prior are not conjugated. Further, the variational Bayesian (VB) method is adopted to overcome the difficulties in accessing closed-form solutions of the intended posteriors. Experimental results of synthetic and practical data indicate that the proposed algorithm has superior performance of anti-interference and super-resolution over other reported ones, especially under limited data and complicated environments.
A PAIR SWEEPING METHOD FOR SOME SIMULTANEOUS MATRIX DIAGONALIZATION

Simultaneous diagonalization of a set of symmetric matrice s s an essential problem in signal processing and numerical multilinear algebra. More precisely, it is the core problem in various blind source separation and numerical tensor dec omposition methods. We first propose a Jacobi-type sweeping method for l ikelihood based joint diagonalization of positive definite matrices. In many problems the matrices are indefinite. For this proble m various heuristic approaches have been taken such as minim izing the off-diagonal elements of the symmetric indefinite matri ces. In the second part of this report we show that the propose d Jacobi-type sweeping method can also be used as a mean to mini mize the off-diagonal elements of the set of symmetric indefi nit matrices under consideration.
Effects of Lake Baikal on Summertime Precipitation Climatology Over the Lake Surface

This study investigates the impacts of Lake Baikal, the largest by volume and the deepest freshwater lake in the world, on its nearby precipitation climate. Satellite observations and a reanalysis data set reveal that summertime precipitation amount is smaller over Lake Baikal than around it. A 15‐year regional climate simulation at a cloud‐resolving scale supports the smaller precipitation, and another simulation in which the lake is replaced by forest shows that the lake reduces summertime precipitation over it by 15%. The lake decreases daytime near‐surface air temperature, resulting in more convectively stable atmosphere over the lake. Latent heat flux is reduced along with the weakened convection, and the lower‐level moisture convergence and upper‐level moisture divergence over the lake are weakened.
Real-time diffuse optical tomography using reduced-orderlight propagation models based on a priori anatomical andfunctional information

This paper proposes a new fast 3D image reconstruction algorithm for Diffuse Optical Tomography using reduced order polynomial mappings from the space of optical tissue parameters into the space of flux measurements at the detector locations. The polynomial mappings are constructed through an iterative estimation process involving structure detection, parameter estimation and cross-validation using data generated by simulating a diffusion approximation of the radiative transfer equation incorporating a priori anatomical and functional information provided by MR scans and prior psychological evidence. Numerical simulation studies demonstrate that reconstructed images are remarkably similar in quality as those obtained using the standard approach, but obtained at a fraction of the time.
Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.
Secure Authentication Scheme for the Internet of Things

The Internet of Things (IoT) is based on an extensive and wide range of interconnected heterogeneous units’ general applications, including healthcare systems, environmental monitoring, household automation, and business automation. This work presents an approach variant of the elliptic curve; The cryptography approach is implemented to provide more security with fewer key sizes and with protocol enhancements to perform an efficient authentication process. In the process of authenticating the device, we use the Electronic Product Code (EPC) as a key to authentication, where the overhead of giving input is removed. Mention the methods followed to meet all your performance metrics (minimum execution time; low energy consumption, and qualitative comparison). This proposed scheme (i.e., the energy consumption of 0.27 mJ, the reduction in end delay of 0.058 sec., the reduction in the computation cost, and being more resistant to attack) is compared with other recent authentication protocols. The proposed system creates a secure network to lessen the damage if there is an attack in the IoT environment. The performance evaluation results indicate that the proposed scheme has a lower energy consumption and a more resistant authentication scheme, and we observe a trade-off between security and the lightweight factor.
Anticipating the futures of the gender dimension in research: Anticipating the futures of the gender dimension in research: Storying entangled practices and bodies Storying entangled practices and bodies

In recent years, scholars have been increasingly urged to address a gender dimension i.e. sex and gender impact in research. In this study, we explore scholars’ explicit and implicit views about the future of implementing gender impact assessment (GIA) in research. We do so by analysing a series of co-design workshops in which participants anticipated possible futures regarding the use of a GIA checklist. We conduct a narrative inquiry of participants’ stories consisting of the personas and scenarios created at the workshops. Our analysis reveals silenced viewpoints and tensions for adopting GIA, while unveiling quite stereotypical bodies and practices in the academic world. Based on our findings, we claim that storytelling approaches help create a safe space in which participants can express discomfort and conflicts playfully and with humour. This study contributes to advance co-design futures-making by accommodating plurality of voices when discussing sensitive topics such as gender equality.
NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intrafamily component but require that observations from different families be independent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behavior of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.
Neuroeconomics and Confirmation Theory

Neuroeconomics is a research program founded on the thesis that cognitive and neurobiological data constitute evidence for answering economic questions. I employ confirmation theory in order to reject arguments both for and against neuroeconomics. I also emphasize that some arguments for neuroeconomics will not convince the skeptics because these arguments make a contentious assumption: economics aims for predictions and deep explanations of choices in general. I then argue for neuroeconomics by appealing to a much more restrictive (and thereby skeptic-friendly) characterization of the aims of economics.
A modified Bregman proximal scheme to minimize the difference of two convex functions.

We introduce a family of new transforms based on the imitation of the Breg- man proximal mapping to minimize the difference of two convex functions. It can be seen as a descent scheme which takes into consideration the convex proper- ties of the two convex functions separately. A direct application of the proposed scheme to variational inclusion is given. 1 Preliminaries In non-convex programming, the fundamental property of convex problems that lo- cal solutions are global ones is no longer true. Therefore, methods using only local information are insufficient to locate global minima. Thus, optimality conditions for non-convex optimization problems have to take into account the form and the structure of the model. Here in this work, we are interested in a certain class of models called d.c. problems. These problems deal with a minimization or maximization of the difference of two or more convex functions. It is well known that with two convex functions g and h the sum g +h is again a convex function, as is the maximum max{g,h} and the multiple λg for any positive λ. The difference g−h, however, is not a convex function. This is why d.c. problems are difficult as they are non-convex problems (see (11, 8, 4) and references therein). In this work, we are presenting a modified Bregman proximal scheme to find out the minimum of the difference of two convex functions, using the convexity of both functions involved in the d.c model. The presented scheme is a type of descent method. Let E be a finite-dimensional vector space and let k ·,·ldenote the inner product in E.L etΘ(E) denote the set of convex proper and lower semi-continuous functions on E.L etf be a d.c function on E, that means that there exist two functions g and h both in Θ(E )s uch thatf(x )= g(x) − h(x), ∀ x ∈ E. Moreover, suppose that h(x) 9 ∞ for all x ∈ E and that ridom(g) ∩ ridom(h) 9 ∅. In this paper we are concerned with minimizing a d.c. function f on E: min∈E f(x).
Higher order contributions to the effective action of N= 2 and 4 supersymmetric Yang-Mills theories from heat kernel techniques in superspace

The one-loop effective action for N = 2 and N = 4 supersymmetric Yang-Mills theories are computed to order F 5 and F 6 respectively by the use of heat kernel techniques in N = 1 superspace. The computations are carried out via the intro- duction of a new method for computing DeWitt-Seeley coefficients in the coincidence limit. To order F 5 , the bosonic components of both N = 2 and N = 4 supersymmetric Yang-Mills theories are extracted and compared with the existing literature. For N = 4 super Yang-Mills theories the F 5 terms are found to be consistent with the non-Abelian Born-Infeld action computed to this order by superstring methods and various other means of computing deformations of supersymmetric Yang-Mills theory. The result proved to be the final piece of a puzzle, leaving little doubt that there exists a unique deformation of maximally symmetric super Yang-Mills theories at this order. The F 6 terms will be of importance for comparison with superstring calculations, including direct tests of the AdS/CFT conjecture. The bosonic components of N = 2 supersymmetric Yang-Mills are also shown to be consistent with existing literature, and will be of importance for testing of generalizations of the AdS/CFT conjecture.
The Diagnostic Potential of RNA Aptamers against the NS1 Protein of Dengue Virus Serotype 2

Simple Summary Dengue infection, a global threat, necessitates prompt action. An early diagnosis based on the dengue antigenic target is preferable. The current method of dengue diagnosis is based on antibodies, which have many disadvantages such as expensive production process and non-uniform batch synthesis. Aptamers are able to address shortcomings of antibodies, and with this in mind, we set out to isolate RNA aptamers against the NS1 protein of dengue virus serotype 2. In this study, we isolated two aptamers known as DENV-3 and DENV-6. Upon truncation to TDENV-3 and TDENV-6a, their performance in the direct ELASA improved. Using these aptamers, a sandwich ELASA was successfully developed for the detection of dengue NS1. The sensitivity of the sandwich ELASA was further improved by stabilization of the truncated aptamers and our repeated incubation strategy, attaining a LOD of 2 nM using NS1 spiked into human serum prepared using a dilution factor of 1:2000. Abstract Dengue infection, caused by the dengue virus, is a global threat which requires immediate attention and appropriate disease management. The current diagnosis of dengue infection is largely based on viral isolation, RT-PCR and serology-based detection, which are time-consuming and expensive, and require trained personnel. For early diagnosis of dengue, the direct detection of a dengue antigenic target is efficacious, and one such target is NS1. NS1-based detection is primarily antibody-centric and is beset by drawbacks pertaining to antibodies such as the high cost of synthesis and large batch-to-batch variation. Aptamers are potential surrogates of antibodies and are much cheaper, without exhibiting batch-to-batch variation. Given these advantages, we sought to isolate RNA aptamers against the NS1 protein of dengue virus serotype 2. A total of 11 cycles of SELEX were carried out, resulting in two potent aptamers, DENV-3 and DENV-6, with dissociation constant values estimated at 37.57 ± 10.34 nM and 41.40 ± 9.29 nM, respectively. These aptamers can be further miniaturized to TDENV-3 and TDENV-6a with an increased LOD upon their usage in direct ELASA. Moreover, these truncated aptamers are highly specific against the dengue NS1 while showing no cross-reactivity against the NS1 of the Zika virus, the E2 protein of the Chikungunya virus or the LipL32 protein of Leptospira, with target selectivity retained even in human serum. The usage of TDENV-3 as the capturing probe and TDENV-6a as the detection probe underpinned the development of an aptamer-based sandwich ELASA for the detection of dengue NS1. The sensitivity of the sandwich ELASA was further improved with the stabilization of the truncated aptamers and the repeated incubation strategy, which enabled a LOD of 2 nM when used with the target NS1 spiked in human serum diluted at 1:2000.
Parameterization of Some Surfaces of Revolution Through Curvature-Varying Curves: A Computational Analysis

This paper describes a computational analysis of various parameterizations of a surface of revolution. The idea is to generate the parameterization of the surface by using different (not necessarily planar) curves with varying curvature. The approach to the practical case of obtaining the parameterization of a torus through different planar curves other than circles, such as planar lemniscates. The analysis is also extended to the interesting case of nonplanar curves. Our work relies on the intensive use of Mathematica, a powerful computational system very well suited for symbolic calculations while also providing valuable numerical and graphical options, a user-friendly graphical interface and a compact and efficient programming language. All calculations in this paper have been carried out with Mathematica v8.0.
Exotic whiteflies and Conservation Biological Control in Coconut System

Exotic pests upset biotic balance, threaten biodiversity and distort the livelihood security of the nation. In coconut, five exotic whiteflies viz., spiralling whitefly, Aleurodicus dispersus Russell, rugose spiralling whitefly, Aleurodicus rugioperculatus Martin, Bondar’s nesting whitefly, Paraleyrodes bondari Peracchi, non-native nesting whitefly, Paraleyrodes minei Iaccarino and palm whitefly, Aleurotrachelus atratus Hempel were reported from India. Morphological and molecular identification of these invasive whiteflies were established by puparium and (or) adult taxonomy and cytochrome c oxidase subunit 1 (COI) gene, respectively. Co-existence of nesting whiteflies with other exotic whiteflies regulated population explosion and warranted correct diagnosis of whiteflies in coconut system. Weather parameters viz., precipitation, temperature and humidity play an important role in the gradient outbreak of exotic whiteflies. The aphelinid parasitoids (Encarsia guadeloupae Viggiani, Encarsia dispersa Polaszek), predators (Apertochrysa sp., Cybocephalus sp. coccinellids viz., Jauravia pallidula Motschulsky, Serangium parcesetosum Sicard, Cheilomenes sexmaculata (Fabricius) and entomopathogenic fungus Aschersonia sp. reduced the incursion potential by exotic whiteflies. A sooty mould scavenger beetle, Leiochrinus nilgirianus Kaszab (Tenebrionidae: Coleoptera) that devours sooty mould encrusted on palm leaflets during monsoon phase was reported for the first time from Kerala, India. Pesticide holiday approach, conservation biological control using the aphelinid parasitoids, predators and entomopathogenic fungus as well as in situ habitat conservation of L. nilgirianus through crop-habitat diversification strategy subdued the invasive potential of exotic whiteflies in coconut system by 60%-80% gaining economic benefit to the tune of 17.59 billion rupees. Strict quarantine and systematic surveillance are the need of the hour to combat the biosecurity risks entering the country.
Numerical and experimental studies to increase the HVAC fan performance for electrical vehicles – Part 2

A more efficient impeller was developed to replace the HVAC fan from an electrified vehicle in order to lower his energy consumption. This will lead ultimately to an increase of the electrified vehicle range. Three different impellers were designed having the following modifications than the original HVAC fan: a different angle of attack for the blades, changed blade radius, modified blade length, different positioning of the blades, different number of blades. First study consisted in the testing of all three HVAC impellers using the numerical simulation method. In the second part of this study, the most promising new impeller, given the numerical results, was 3D printed and installed in a new casing, specially built for this study. Then the performance of the impeller it was experimentally tested, and the results were compared with the original fan experimental result from a previous study. The main conclusion is that the old impeller is around 240% less efficient than the new impeller and only by changing one simple component in the electrified vehicle design (HVAC fan in this case), the range of the car can grow with approximately 3.84km for each fully charged battery.
Prominent role of PM10 but not of circulating inflammation in the link between air pollution and the risk of neurodegenerative disorders

Background Several studies revealed an implication of air pollution in neurodegenerative disorders, although this link and the potential underlying mechanisms remain unclear. Objectives To analyze the impact of air pollution on neurodegenerative risk by testing multiple pollutants simultaneously, along with other potential risk/protective factors, and the role of circulating inflammation. Methods In the Moli-sani cohort (N=24,325; [≥]35 years; 51.9% women, baseline 2005-2010), we estimated yearly levels of exposure to nitrogen oxides, ozone, particulate matter (PM10), sulfur dioxide and BTX hydrocarbons in 2006-2018, applying residence geo-localization of participants and Kriging interpolation algorithm to land measurements of air pollutants. We performed a principal component (PC) analysis of pollutant levels and tested associations of the resulting PC scores with the incident risk of dementia (AD) and Parkinson's disease/parkinsonism (PD), through multivariable Cox PH regressions adjusted for age, sex, education level, and several professional and lifestyle exposures. Moreover, we tested whether a composite biomarker of circulating inflammation (INFLA-score) may explain part of these associations. Results Over 24,308 subjects with pollution data available (51.9% women, mean age 55.8(12.0) years), we extracted three PCs explaining [≥]5% of pollution exposure variance: PC1 (38.2%, tagging PM10), PC2 (19.5%, O3/CO/SO2), PC3 (8.5%, NOx/BTX hydrocarbons). Over a median (IQR) follow-up of 11.2(2.0) years, we observed statistically significant associations of PC1 with an increased risk of both AD (HR[CI] = 1.06[1.04-1.08]; 218 cases) and PD (1.05[1.03-1.06]; 405 incident cases), independent on other covariates. These associations were confirmed testing average PM10 levels during follow-up time (25[19-31]% and 19[15-24]% increase of AD and PD risk, per 1 g/m3 of PM10). INFLA-score explained a negligible (<1%) proportion of these associations. Discussion Air pollution - especially PM10 - is associated with increased neurodegenerative risk in the Italian population, independent on concurring risk factors, suggesting its reduction as a potential public health target.
Quijote-PNG: The Information Content of the Halo Mass Function

We study signatures of primordial non-Gaussianity (PNG) in the redshift-space halo field on nonlinear scales using a combination of three summary statistics, namely, the halo mass function (HMF), power spectrum, and bispectrum. The choice of adding the HMF to our previous joint analysis of the power spectrum and bispectrum is driven by a preliminary field-level analysis, in which we train graph neural networks on halo catalogs to infer the PNG f NL parameter. The covariance matrix and the responses of our summaries to changes in model parameters are extracted from a suite of halo catalogs constructed from the Quijote-png N-body simulations. We consider the three main types of PNG: local, equilateral, and orthogonal. Adding the HMF to our previous joint analysis of the power spectrum and bispectrum produces two main effects. First, it reduces the equilateral f NL predicted errors by roughly a factor of 2 while also producing notable, although smaller, improvements for orthogonal PNG. Second, it helps break the degeneracy between the local PNG amplitude, fNLlocal , and assembly bias, b ϕ , without relying on any external prior assumption. Our final forecasts for the PNG parameters are ΔfNLlocal=40 , ΔfNLequil=200 , ΔfNLortho=85 , on a cubic volume of 1Gpc/h3 , with a halo number density of n¯∼5.1×10−5h3Mpc−3 , at z = 1, and considering scales up to kmax=0.5hMpc−1 .