Miracle-dz/UniFAR-FTUs huggingface dataset:
Impurity band in lithium-diffused and annealed GaAs: Conductivity and Hall effect measurements

Originally n-type Si-doped GaAs turns p-type after Li indiffusion and consequent annealing at 200-600°C. Temperature-dependent conductivity and Hall effect measurements carried out in the temperature range 30-300 K revealconduction via impurity bands made up of shallow acceptors in addition to the valence-band conduction. Li diffusion into GaAs reduces the free-carrier concentration which leads to electrical resistivity as high as 10 7 Ω cm. Annealing highly resistive samples at temperatures above 200 °C significantly decreases the room-temperature resistivity to 0,1-1 Ω cm, depending on the annealing temperature. For samples annealed at 300-400°C we observe a metallic-type conduction which contributes to the conduction even at room temperature. In samples annealed at 500-600 °C the temperature dependence of the resistivity indicates that impurity conduction sets in at temperatures below 100 K. We discuss this conduction change in relation to enhanced gallium vacancy (V G a ) and gallium antisite (Ga A s ) concentration in Li indiffused and annealed samples.
A method to measure the complex permeability of thin films at ultra-high frequencies

A swept frequency method for measuring the magnetic permeability of thin films for frequencies up to 2 GHz with a resolution at 1 GHz of /spl sim/0.1 /spl mu/m in permeance is described. It is based on the measurement of impedance of a strip loop loaded with the sample under test. The method employs a commercial impedance or network analyzer, is simple, and suitable for permeability measurements of thin magnetic films which are used in recording heads and microwave components.
Electric modulus-based analysis of the dielectric relaxation in carbon black loaded polymer composites

A thorough investigation of the ac electrical properties of carbon black (CB) mixed into ethylene butylacrylate copolymer has been conducted using ac impedance spectroscopy in the frequency range 10 Hz–100 kHz and over the temperature range of 150 (Tg−48 K)–319 (Tg+121 K) K. For this investigation, a series of eight samples were prepared with various filler contents above the percolation threshold. Using the electric modulus formalism it has been found that the Cole–Cole equation of dielectric relaxation expressed in the electric modulus form is capable of quantitatively describing the experimental data from which we extract the relaxation time and a parameter α which gauges the broadening of the loss spectrum. The small values of α ranging from 0.06 to 0.10 suggest a behavior close to the state of a single relaxation time. Furthermore, the relaxation time as a function of temperature is characterized by an Arrhenius behavior. While the effective activation energy is on the order of 60 meV and is insensit.
Frequency analysis of low flows using the Akaike information criterion

It is the current practice in frequency analysis of low flows to consider only three-parameter distributions in which one of the parameters represents a nonzero lower bound. When applied to the small samples typically available, this practice results in incorrect low flow estimates. These errors are related to errors in the estimated lower bound. To preclude this possibility, it is proposed that the current practice be changed to include the selection of a two-parameter distribution in certain situations. To assess this proposal, the Akaike information criterion (AIC) is used to compare the suitability of the most commonly used three-parameter distribution (three-parameter Weibull) and three two-parameter distributions (two-parameter Weibull, Gumbel, and lognormal) to low flow data for 51 long-term hydrometric stations across Canada. For 75% of the stations, a two-parameter distribution is selected over the three-parameter distribution if the selection criterion is minimum AIC. In about one third of the r.
Information gain and a general measure of correlation

SUMMARY Given a parametric model of dependence between two random quantities, X and Y, the notion of information gain can be used to define a measure of correlation. This definition of correlation generalizes both the usual product-moment correlation coeffi- cient for the bivariate normal model and the multiple correlation coefficient in the standard linear regression model. The use of this information-based correlation in a descriptive statistical analysis is examined and several examples are given. If the dependence between two random quantities, X and Y, is modelled parametri- cally, then the concept of information gain can be used to define a measure of correlation. This correlation coefficient can appear in two possible contexts, depending on whether one models the joint distribution of X and Y, or just the conditional distribution of Y given X. An important motivating feature for this information-based correlation coefficient is the fact that it generalizes both the usual product-moment correlation coefficient for the bivariate normal model and the usual multiple correlation coefficient for the standard multiple regression model with normal errors. Our intuition is well developed for these usual correlation coefficients, and hopefully our intuition will still be applicable for-the information-based correlation in more general modelling situations of parametric dependence. Further, since our correlation coefficient is based on information gain, we might hope to extend our intuition to interpret the information gain in any statistical modelling situation where we want to assess how much better a more complicated model is than a simpler model. The concept of information gain for general statistical models is described in ? 2. This concept is then used to define an information-based measure of correlation; the joint case is covered in ? 3 and the conditional case in ? 4. Estimation of the correlation coefficient is carried out by estimating the corresponding information gain; see ? 5-7. The use of information gain for the purpose of model choice in a descriptive statistical analysis is discussed in ? 8 and a comparison between our approach and Akaike's information criterion is given in ? 9. Some examples of the use of this information-based correlation are given in ? 10 and 11.
Prevalence, General and Periodontal Risk Factors of Gastroesophageal Reflux Disease in China

Purpose There is insufficient information about the prevalence and risk factors of gastroesophageal reflux disease (GERD) in the Chinese adult population. We aimed to assess the prevalence and identify the risk factors of GERD in China. Methods We collected data from a nationally representative sample (50,991 subjects) of Chinese adults from a large nation-wide cross-sectional survey. GERD was diagnosed by a standardized Chinese-language GERD questionnaire with a score of ≥ 8. The demographic characteristics, comorbidities and periodontal factors of all participants were collected. Results Fifty-thousands-one-hundred-eighty-three participants were finally included in this study. The overall prevalence of GERD was 5.6% (95% CI, 5.4–5.8%) among the general Chinese population aged 20 years or older. Women, smokers, and people with older age, BMI ≥ 25.0 kg/m2, urban residence, lower education level or comorbidities were more prevalent with GERD (p < 0.001). Symptoms of severe periodontitis (OR = 1.40, 95% CI 1.28–1.52, p < 0.001) and lower frequency of tooth brushing (OR = 2.01, 95% CI 1.76–2.29, p < 0.001) were significantly associated with risk of GERD. Conclusion Symptom-based GERD is highly prevalent in the Chinese population. Overweight and smoking are major preventable risk factors for GERD. Periodontal factors are novel potential risk factors for GERD and should be given more attention in GERD prevention.
Relative perturbation results for matrix eigenvalues and singular values

It used to be good enough to bound absolute of matrix eigenvalues and singular values. Not any more. Now it is fashionable to bound relative errors. We present a collection of relative perturbation results which have emerged during the past ten years. No need to throw away all those absolute error bound, though. Deep down, the derivation of many relative bounds can be based on absolute bounds. This means that relative bounds are not always better. They may just be better sometimes – and exactly when depends on the perturbation.
Calibration technique for acoustic scattering measurements

Tungsten carbide spheres are used as calibration targets in laboratory acoustic scattering measurements. Though the steady‐state response of any metal sphere in water greatly differs from a rigid body return, over almost the entire frequency spectrum, the rigid body and elastic returns can be separated in a short pulse, broadband experiment. This rigid body echo can then be used as a reference to normalize the scattering returns from targets of interest.
Chemiexcitation: Mammalian Photochemistry in the Dark†

Light is one way to excite an electron in biology. Another is chemiexcitation, birthing a reaction product in an electronically excited state rather than exciting from the ground state. Chemiexcited molecules, as in bioluminescence, can release more energy than ATP. Excited states also allow bond rearrangements forbidden in ground states. Molecules with low‐lying unoccupied orbitals, abundant in biology, are particularly susceptible. In mammals, chemiexcitation was discovered to transfer energy from excited melanin, neurotransmitters, or hormones to DNA, creating the lethal and carcinogenic cyclobutane pyrimidine dimer. That process was initiated by nitric oxide and superoxide, radicals triggered by ultraviolet light or inflammation. Several poorly understood chronic diseases share two properties: inflammation generates those radicals across the tissue, and cells that die are those containing melanin or neuromelanin. Chemiexcitation may therefore be a pathogenic event in noise‐ and drug‐induced deafness, Parkinson's disease, and Alzheimer's; it may prevent macular degeneration early in life but turn pathogenic later. Beneficial evolutionary selection for excitable biomolecules may thus have conferred an Achilles heel. This review of recent findings on chemiexcitation in mammalian cells also describes the underlying physics, biochemistry, and potential pathogenesis, with the goal of making this interdisciplinary phenomenon accessible to researchers within each field.
A detailed analysis of adhesion mechanics between a compliant elastic coating and a spherical probe

As length scales decrease, adhesive forces become increasingly important. These adhesive forces contribute to the normal load in experiments conducted on thin layered systems using micro-probe instruments such as the surface force apparatus (SFA) and the atomic force microscope (AFM). Adhesion between these thin-layer systems was analysed by Sridhar et al (1997 J. Phys. D: Appl. Phys. 30 1710) for the SFA geometry and Johnson and Sridhar (2001 J. Phys. D: Appl. Phys. 34 683) for AFM using a numerical SJF (Sridhar–Johnson–Fleck) version of the JKR (Johnson–Kendal–Roberts) theory. In this paper, adhesion mechanics between a compliant elastic coating and a spherical probe is investigated using the SJF model in detail. When the substrate is rigid, the non-dimensional pull-off force may differ from the JKR value of −0.5 by as much as 90%. Computations of the contact size at zero load and pull-off force are presented for a range of values of adhesion energy. Finally, empirical relations for the contact load and contact compliance as a function of contact radius were obtained from the numerical data for practical layer-substrate material systems.
Multivariate statistical methods in hydrology - a comparison using data of known functional relationship

Conventionally, hydrologists have used regression analysis for solving their multivariate problems. Recently other multivariate statistical methods have been advocated. This paper discusses and compares the effectiveness of 6 methods of analysis: regression, principal component, varimax, oblimax, key cluster, and object. Strengths and weaknesses of each method are discussed, and the combination of principal component regression with varimax rotation of the factor weight matrix is recommended for an initial analysis of multifactor hydrologic problems. (31 refs.)
Rapid Data‐Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well‐known machine‐learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer‐thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2–6‐monolayer‐thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less‐explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions’ quality.
Don’t Leave the Heart Behind

2022. Applying the ethical framework for donation after circulatory death to thoracic normothermic regional perfusion procedures. American Journal of Transplantation 22 (5):1311–5. doi:10.1111/ajt.16959. Wall, A. , G. Testa, S. Wall, and S. Karp. 2022. Medical standards are aligned with normothermic regional perfusion practices and US legal standards for determining death. American Journal of Transplantation 22 (11):2709. doi:10.1111/ajt.17114.
MERLIN and VLA observations of compact steep-spectrum radio sources

Les observations VLA et MERLIN des spectres a pente raide compacts de radiosources extragalactiques sont presentees, a 18, 6 et 2 cm. Les cartes radio sont analysees et la nature des sources, quasar ou galaxie, est etudiee
Bounding Sets in Function Spaces with Applications to Nonlinear Operator Equations

We can compute with bounding sets of numbers, vectors, or functions using the techniques of interval mathematics. The techniques can be used to computationally verify sufficient conditions for existence, uniqueness, and convergence and to construct upper and lower bounds on solutions of nonlinear operator equations. Rounding errors are taken into account. We can compute with set-valued functions and operators on them. The techniques are also useful in search procedures for finding safe starting regions for iterative methods and for constructing natural stopping criteria.
Multivariate Interpolation and Standard Bases for Macaulay Modules

The K-linear space of all n-linearly recursive functions (1.1) (=evaluated differential forms) for which a zero-dimensional ideal I⊂ K[x1, …, xn] is the largest ideal which is contained in the kernel of all of them turns out to be the orthogonal K-space I⊥⊂(K[x1, …, xn])* of I and is known as Macaulay's inverse system of I. Making use of the antiderivative operatord, the whole space of all differential forms can be endowed with a structure of K[x1, …, xn]-module; with respect to finitely generated submodules of it (which we call Macaulay modules), we describe a dual analog of the Grobner bases theory. The motivation for studying Macaulay modules has to be found mainly in multivariate interpolation problems and in the theory of polynomial bialgebras, though some application to algebraic geometry is not excluded.
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach

Cooperative multi-agent problems often require coordination between agents, which can be achieved through a centralized policy that considers the global state. Multi-agent policy gradient (MAPG) methods are commonly used to learn such policies, but they are often limited to problems with low-level action spaces. In complex problems with large state and action spaces, it is advantageous to extend MAPG methods to use higher-level actions, also known as options, to improve the policy search efficiency. However, multi-robot option executions are often asynchronous, that is, agents may select and complete their options at different time steps. This makes it difficult for MAPG methods to derive a centralized policy and evaluate its gradient, as centralized policy always select new options at the same time. In this work, we propose a novel, conditional reasoning approach to address this problem and demonstrate its effectiveness on representative option-based multi-agent cooperative tasks through empirical validation. Find code and videos at: https://sites.google. com/view/mahrlsupp/
Optimization of body configuration and joint-driven attitude stabilization for transformable spacecrafts under solar radiation pressure

A solar sail is one of the most promising space exploration system because of its theoretically infinite specific impulse using solar radiation pressure (SRP). Recently, some researchers proposed"transformable spacecrafts"that can actively reconfigure their body configurations with actuatable joints. The transformable spacecrafts are expected to greatly enhance orbit and attitude control capability due to its high redundancy in control degree of freedom if they are used like solar sails. However, its large number of input poses difficulties in control, and therefore, previous researchers imposed strong constraints to limit its potential control capabilities. This paper addresses novel attitude control techniques for the transformable spacecrafts under SRP. The authors have constructed two proposed methods; one of those is a joint angle optimization to acquire arbitrary SRP force and torque, and the other is a momentum damping control driven by joint angle actuation. Our proposed methods are formulated in general forms and applicable to any transformable spacecraft that has front faces that can dominantly receive SRP on each body. Validity of the proposed methods are confirmed by numerical simulations. This paper contributes to making most of the high control redundancy of transformable spacecrafts without consuming any expendable propellants, which is expected to greatly enhance orbit and attitude control capability.
Generalized complexity of surfaces

In this article, we introduce the generalized complexity of a generalized Calabi–Yau pair p X,B, M q . This invariant compares the dimension of X and Picard rank of X with the sum of the coeﬃcients of B and M . It generalizes the complexity introduced by Shokurov. We show that a generalized log Calabi–Yau pair p X,B, M q of dimension 2 with generalized complexity 0 satisﬁes that X is toric. This generalizes a result due to Brown, McKernan, Svaldi, and Zhong in the case of surfaces. Furthermore, we show that a generalized klt log Calabi–Yau surface p X, M q with generalized complexity 0 satisﬁes that X » P 2 or X » P 1 ˆ P 1 . Thus, this invariant interpolates between the characterization of toric varieties and the Kobayashi-Ochiai Theorem. As an application, we show that 3-fold singularities with generalized complexity 0 are toric. Furthermore, we show a local version of Kobayashi-Ochiai Theorem in dimension 3.
Adaptive Resource Allocation for Workflow Containerization on Kubernetes

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named ARAS for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod's lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in CPU and memory resource usage rate.
Source-free Subject Adaptation for EEG-based Visual Recognition

This paper focuses on subject adaptation for EEG-based visual recognition. It aims at building a visual stimuli recognition system customized for the target subject whose EEG samples are limited, by transferring knowledge from abundant data of source subjects. Existing approaches consider the scenario that samples of source subjects are accessible during training. However, it is often infeasible and problematic to access personal biological data like EEG signals due to privacy issues. In this paper, we introduce a novel and practical problem setup, namely source-free subject adaptation, where the source subject data are unavailable and only the pre-trained model parameters are provided for subject adaptation. To tackle this challenging problem, we propose classifier-based data generation to simulate EEG samples from source subjects using classifier responses. Using the generated samples and target subject data, we perform subject-independent feature learning to exploit the common knowledge shared across different subjects. Notably, our framework is generalizable and can adopt any subject-independent learning method. In the experiments on the EEG-ImageNet40 benchmark, our model brings consistent improvements regardless of the choice of subject-independent learning. Also, our method shows promising performance, recording top-l test accuracy of 74.6% under the 5-shot setting even without relying on source data. Our code can be found at https://github.com/DeepBCI/Deep-BCI.