Miracle-dz/UniFAR-FTUs huggingface dataset:
An Update on Radiative Transfer Model Development at Atmospheric and Environmental Research, Inc.

Over the last decade, a suite of radiative transfer models has been developed at Atmospheric and Environmental Research, Inc. (AER) with support from the Atmospheric and Radiation Measurement (ARM) Program. These models span the full spectral regime from the microwave to the ultraviolet, and range from monochromatic to band calculations. Each model combines the latest spectroscopic advancements with radiative transfer algorithms to efficiently compute radiances, fluxes, and cooling rates. These models have been extensively validated against high-resolution spectral measurements and broadband irradiance measurements. Several of these models are part of the broadband heating rate profile value-added product (BBHRP VAP), currently being established at the ARM Southern Great Plains (SGP) site.
ESO-Based Robust and High-Precision Tracking Control for Aerial Manipulation

—This paper studies the tracking control problem of an aerial manipulator that consists of a quadcopter ﬂying base and a Delta robotic arm. We propose a novel control approach that consists of extended state observers (ESOs) for dynamic coupling estimation, ESO-based ﬂight controllers, and a cooperative trajectory planner. Compared to the state-of-the-art approaches, the proposed one has some attractive features. First, it requires much less measurement information as opposed to the full-body control approaches and hence can be implemented conveniently and efﬁciently in practice. Second, while the existing approaches estimate the coupling effect based on precise models, the proposed ESOs can do that based on much less information about the system model. The proposed approach is veriﬁed by four experiments on a real aerial manipulation platform. The experimental results show that the average tracking error can reach 1 cm by the proposed approach as opposed to 10 cm by the PX4 baseline controller. Although force control is not considered speciﬁcally in the approach, the system can complete aerial weaving tasks thanks to the ESOs in the presence of drag forces applied to the end-effector during manipulation.
Fabrication and Characterization of High Aspect Ratio Perpendicular Patterned Information Storage Media in an Al2O3/GaAs Substrate

In a new approach, we have fabricated 6:1 aspect ratio magnetic nanocolumns, 60–250 nm in diameter, embedded in a hard aluminum-oxide/gallium-arsenide (Al2O3/GaAs) substrate. The fabrication technique uses the highly selective etching properties of GaAs and AlAs, and highly efficient masking properties of Al2O3 to create small diameter, high aspect ratio holes. Nickel (Ni) is subsequently electroplated into the holes, followed by polishing, which creates a smooth and hard surface appropriate for future reading and writing of the columns as individual bits for high density information storage. We have used magnetic force microscopy and scanning magneto-resistance microscopy to characterize the resulting magnets. We find the columns more magnetically stable than previously achieved with magnets embedded in a SiO2 substrate. Such stability is necessary before further writing of perpendicular patterned media can be demonstrated.
Bayesian Cognitive Science, Unification, and Explanation

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1. Introduction2. What a Great Many Phenomena Bayesian Decision Theory Can Model3. The Case of Information Integration4. How Do Bayesian Models Unify? 5. Bayesian Unification: What Constraints Are There on Mechanistic Explanation? 5.1. Unification constrains mechanism discovery5.2. Unification constrains the identification of relevant mechanistic factors5.3. Unification constrains confirmation of competitive mechanistic models6. Conclusion Appendix Introduction What a Great Many Phenomena Bayesian Decision Theory Can Model The Case of Information Integration How Do Bayesian Models Unify? Bayesian Unification: What Constraints Are There on Mechanistic Explanation? 5.1. Unification constrains mechanism discovery5.2. Unification constrains the identification of relevant mechanistic factors5.3. Unification constrains confirmation of competitive mechanistic models Unification constrains mechanism discovery Unification constrains the identification of relevant mechanistic factors Unification constrains confirmation of competitive mechanistic models Conclusion Appendix
Confined Natural Convection In The Low PrandtlNumber Limit

A special limiting case of enclosed natural convection is examined via combinations of analytical and computational methods. Under appropriate restrictions, the unsteady two-dimensional motion of a highly thermally conductive fluid may be described by the solution of a single equation involving the stream function, a characteristic Reynolds number, and the buoyant forcing term normalized to unity. The appearance of the biharmonic operator, along with homogeneous boundary conditions, makes this limiting case suitable for computation by an iterative Boundary Element Method. An alternative semi-analytical approach is also presented, in which a Green's function technique is employed to produce a particular solution for the non-linear interactions, and a superposition of flow modes is used to satisfy the altered boundary conditions. The use of biharmonic modes involves identification of various even and odd symmetries which develop as the Reynolds number is increased parametrically. The accuracies associated with this numerical technique are evaluated via direct comparisons to exact solutions that are specifically generated for this purpose.
Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering

Background: Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster’s key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.
A spectral mapping theorem for some representations of compact abelian groups

We show that if G is a compact abelian group and U is a weakly continuous representation of G by means of isometries on a Banach space X, then holds for each measure µ in reg(M(G)), where π(µ) denotes the generalized convolution operator in B(X) defined by , σ the usual spectrum in B(X), sp(U) the Arveson spectrum of U, the Fourier-Stieltjes transform of µ and reg(M(G)) the largest closed regular subalgebra of the convolution measure algebra M(G) of G. reg(M(G)) contains all the absolutely continuous measures and discrete measures.
Recent Advances in Field Effect Transistor Biosensors: Designing Strategies and Applications for Sensitive Assay

In comparison with traditional clinical diagnosis methods, field–effect transistor (FET)–based biosensors have the advantages of fast response, easy miniaturization and integration for high–throughput screening, which demonstrates their great technical potential in the biomarker detection platform. This mini review mainly summarizes recent advances in FET biosensors. Firstly, the review gives an overview of the design strategies of biosensors for sensitive assay, including the structures of devices, functionalization methods and semiconductor materials used. Having established this background, the review then focuses on the following aspects: immunoassay based on a single biosensor for disease diagnosis; the efficient integration of FET biosensors into a large–area array, where multiplexing provides valuable insights for high–throughput testing options; and the integration of FET biosensors into microfluidics, which contributes to the rapid development of lab–on–chip (LOC) sensing platforms and the integration of biosensors with other types of sensors for multifunctional applications. Finally, we summarize the long–term prospects for the commercialization of FET sensing systems.
“I Am Not Who You Think I Am”: Multiple, Hybrid and Racialized Identities of Canadian Muslim Youth in the Negotiation of Belonging and Citizenship

Abstract While other studies explore the challenges faced by Western Muslim youth, this study focuses on the challenges and development of a meaningful stable Canadian-Muslim identity in an era of global conflicts, collective surveillance, and suspicion. Identity-formation is complex, involving the configuration of many influences—direct and indirect, local and global, personal and impersonal. Sometimes aspects of fluid, and multiple identities conflict and individuals are faced with navigating competing and not necessarily commensurable influences. Such is the case with the Canadian Muslim Youth—CMY. Thematic analysis of 30 interviews with CMY, identified five major themes: (a) Navigation of multiple, complex, and hybrid identities; (b) Religious identity and spirituality; (c) “I am not what you think I am”—Media portrayals of Muslims; (d) Claiming inclusion and belonging in the face of anti-Muslim racism; and (e) Recommendations. Listening to CMY voices will help policymakers, practitioners, Muslim communities and organizations to develop strategies for positive youth development.
A Robust-Compact Model to Emulate Neuro-Mimetic Dynamics With Doped-HfO2 Ferroelectric-FET Based Neurons

A leap towards deriving a robust-compact model for ferroelectric-FET (FeFET) oscillator based spiking neurons has been developed. The compact model can capture and emulate neurons that exhibit both excitatory and inhibitory coupled dynamical behaviour, native to cortical neurons. The proposed model can reliably mimic neuronal dynamics for a broad-spectrum of FeFETs when, factored with appropriate validation of numerical simulations and physics-based models. The emulation of neuronal attributes based on FDSOI FeFET having qualitative agreement with previously reported data. The robustness of this analytical model has been investigated by tuning with ferro-dielectric (Fe) materials i.e., zirconium-doped HfO2 (HZO) and silicon-doped HfO2 (HSO) for mimicking the neurons. In continuance, the model is examined for emulating the cortical neurons by altering the inhibition (high-VGF) and excitation (low-VGF) inputs with a suitable frequency of operation for spiking neurons.
The Forward Method as a Solution Refinement in Rational Expectations Models

This paper generalizes the standard forward method of recursive substitution to a general class of linear rational expectations models with potentially multiple fundamental solutions. It is shown that the existence and uniqueness of the well-known forward solution are preserved in a general context. We also propose a key property embedded in the forward solution - the no-bubble condition - as an economically sensible solution refinement in the class of fundamental solutions. In the literature, the no-bubble condition has been assumed to rule out non-fundamental bubble solutions. We show that the forward solution is the only rational expectations equilibrium satisfying the no-bubble condition and consequently, it is the most relevant fundamental solution within the class of fundamental equilibria. Several economic examples are provided where the fundamental solutions obtained by other solution methods and refined by other solution selection criteria violate the no-bubble condition.
ULF magnetic signatures at the earth surface due to ground water flow - A possible precursor to earthquakes

Magnetic field fluctuations at the earth's surface of {Delta}{Beta}/{Beta} {approximately} 10{sup {minus}5} at < 1 Hz are shown to result from motion with a peak velocity of {approximately}4 cm/s of ground water ({approximately}4 S/m) at {approximately}5 km depth. Surface field changes can occur due to either divergence free fluid motion with transverse spatial wavelengths of a few tens of km, and/or homogeneous flow which displaces local inhomogeneities in the earth magnetic field.
An Adaptive Continuous Approach to Consensus Tracking of Nonlinear Multiagent Systems With a Nonautonomous Leader

It is challenging and critical to achieve zero error consensus tracking in multiagent systems (MASs) with nonautonomous leaders (i.e., leaders with nonzero inputs). The traditional approach is to use discontinuous controllers which may cause a chattering phenomenon. How to achieve zero error consensus tracking via a chattering-free controller is still open. We propose a class of adaptive continuous controllers to achieve zero error consensus tracking for Lipschitz nonlinear MASs with a nonautonomous leader and directed communication topology. Unlike existing works that use discontinuous functions to eliminate the impacts of leaders’ inputs, we use a continuous function by introducing an exponential decay function into the denominator. First, we design a continuous controller with fixed coupling strengths and prove that zero error consensus tracking can be achieved if the coupling strengths are greater than some positive constants. Second, we design a continuous controller with dynamic coupling strengths under which fully distributed zero error consensus tracking can be achieved. Moreover, the case with undirected communication topology is studied. Finally, three examples are given to verify the theoretical results. Specifically, convergence results between the continuous controller here and that is developed via the boundary layer technique are compared. Compared with existing works, the designed adaptive continuous controllers here can not only achieve zero error consensus tracking but also is chattering free.
Learning From Imbalanced Data Using Triplet Adversarial Samples

The imbalance of classes in real-world datasets poses a major challenge in machine learning and classification, and traditional synthetic data generation methods often fail to address this problem effectively. A major limitation of these methods is that they tend to separate the process of generating synthetic samples from the training process, resulting in synthetic data that lack the necessary informative characteristics for proper model training. We present a new synthetic data generation method that addresses this issue by combining adversarial sample generation with a triplet loss method. This approach focuses on increasing the diversity in the minority class while preserving the integrity of the decision boundary. Furthermore, we show that reducing triplet loss is equivalent to maximizing the area under the receiver operating characteristic curve under specific conditions, providing a theoretical basis for the effectiveness of our method. In addition, we present a model training approach to further improve the generalization of the model to small classes by providing a diverse set of synthetic samples optimized using our proposed loss function. We evaluated our method on several imbalanced benchmark tasks and compared it to state-of-the-art techniques, demonstrating that our method can deliver even better performance, making it an effective solution to the class imbalance problem.
Steepest descent methods for critical points in vector optimization problems

In this article, we present steepest descent methods for finding stationary (critical) points of vector optimization problems for maps from an Euclidean space to a Banach space with respect to the partial order induced by a closed, convex and pointed cone with a nonempty interior. Convergence of the generated sequence to a weakly efficient solution of our problem is established under some reasonable additional hypotheses.
Deciphering the Mechanism of Tolerance to Apple Replant Disease Using a Genetic Mapping Approach in a Malling 9 × M. × robusta 5 Population Identifies SNP Markers Linked to Candidate Genes

Apple replant disease (ARD) is a worldwide economic risk in apple production. Although several studies have shown that the wild apple accession Malus × robusta 5 (Mr5) is ARD-tolerant, the genetics of this tolerance have not yet been elucidated. A genetic mapping approach with a biparental population derived from contrasting parents involving molecular markers provides a means for marker-assisted selection of genetically complex traits and for determining candidate genes. In this study, we crossed the ARD-tolerant wild apple accession Mr5 and the ARD-susceptible rootstock ‘M9’ and analyzed the resultant progeny for ARD tolerance. Hence, a high-density genetic map using a tunable genotyping-by-sequencing (tGBS) approach was established. A total of 4804 SNPs together with 77 SSR markers were included in the parental maps comprising 17 linkage groups. The phenotypic responses to ARD were evaluated for 106 offspring and classified by an ARD-susceptibility index (ASI). A Kruskal–Wallis test identified SNP markers and one SSR marker on linkage groups (LG) 6 and 2 that correlated with ARD tolerance. We found nine candidate genes linked with these markers, which may be associated with plant response to ARD. These candidate genes provide some insight into the defense mechanisms against ARD and should be studied in more detail.
Geometrical State-Plane Analysis of Resonant Switched-Capacitor Converters: Demonstration on the Cascaded Multiresonant Converter

To realize high efficiency and high power density for resonant switched-capacitor (ReSC) converters, it is critical to have a thorough understanding of the soft-switching mechanism and design the converter appropriately. However, this can be challenging as the soft-switching operation depends on multiple variables and its design difficulty increases quickly with respect to the topology complexity. This article demonstrates a framework for using geometrical state-plane analysis to facilitate ReSC converter design. The methodology is first applied to a basic 2-to-1 ReSC converter and then extended to a new 4-to-1 ReSC converter topology we proposed, herein named a cascaded multiresonant converter. This topology has a reduced component count thanks to a new “switching bus” architecture. However, it comes at a cost, as the converter has multiple resonances within each switching cycle, complicating the soft-switching design. Despite the high circuit complexity, the state-plane analysis neatly provides analytical equations for all state parameters, which are verified by simulations and experiments. Our hardware prototype achieves 6000 W/in3 power density at 48-to-12 V conversion and 80 A continuous output. Its peak efficiency reaches 99.02%, and full-load efficiency reaches 97.67%, including gate drive loss. Both the power density and efficiency are superior to the state-of-the-art 48-to-12 V solutions.
Descriptive geometries as multigroups

fined by pairs of functions of two complex variables, the pseudo-conf ormal group G. This is characterized by the preservation of Kasner's pseudo-angle between a curve and a hypersurface at their common point. Any system of oo curves which ispseudoconformally equivalent to a parallel set of lines is said to be bi-isothermal. Any such system consists of «> isothermal families, each family living on a conformai surface. A characteristic property of bi-isothermal systems is that the pseudo-angle between this system and any parallel pencil of hyperplanes is a biharmonic function. Finally G is characterized within the group of point transformations by the preservation of the totality of all bi-isothermal systems. (Received September 29, 1941.)
Critique of the generalized cumulant expansion method

The method of ordered cumulants is presented for the solution of multiplicative stochastic processes. The relationship between this method and the irreducible cluster integral method of Mayer, which is used in the theory of the imperfect gas, is elucidated. The cluster property of ordered cumulants is proved. A critique of the literature in this area is presented which exposes some errors in the formulas. Several examples are indicated for the application of the ordered cumulant method.
ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instr.
Spin torque switching of perpendicular Ta∣CoFeB∣MgO-based magnetic tunnel junctions

Spin torque switching is investigated in perpendicular magnetic tunnel junctions using Ta∣CoFeB∣MgO free layers and a synthetic antiferromagnet reference layer. We show that the Ta∣CoFeB interface makes a key contribution to the perpendicular anisotropy. The quasistatic phase diagram for switching under applied field and voltage is reported. Low switching voltages, Vc 50 ns=290 mV are obtained, in the range required for spin torque magnetic random access memory. Switching down to 1 ns is reported, with a rise in switching speed from increased overdrive that is eight times greater than for comparable in-plane devices, consistent with expectations from a single-domain model.