Miracle-dz/UniFAR-FTUs huggingface dataset:
A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes

In many classification models, data is discretized to better estimate its distribution. Existing discretization methods often target at maximizing the discriminant power of discretized data, while overlooking the fact that the primary target of data discretization in classification is to improve the generalization performance. As a result, the data tend to be over-split into many small bins since the data without discretization retain the maximal discriminant information. Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data. More specifically, the Max-Dependency criterion maximizes the statistical dependency between the discretized data and the classification variable while the Min-Divergence criterion explicitly minimizes the JS-divergence between the training data and the validation data for a given discretization scheme. The proposed MDmD criterion is technically appealing, but it is difficult to reliably estimate the high-order joint distributions of attributes and the classification variable. We hence further propose a more practical solution, Max-Relevance-Min-Divergence (MRmD) discretization scheme, where each attribute is discretized separately, by simultaneously maximizing the discriminant information and the generalization ability of the discretized data. The proposed MRmD is compared with the state-of-the-art discretization algorithms under the naive Bayes classification framework on 45 machine-learning benchmark datasets. It significantly outperforms all the compared methods on most of the datasets.
Hole spin manipulation in inhomogeneous and nonseparable electric fields

The usual models for electrical spin manipulation in semiconductor quantum dots assume that the confinement potential is separable in the three spatial dimensions and that the AC drive field is homogeneous. However, the electric field induced by the gates in quantum dot devices is not fully separable and displays significant inhomogeneities. Here, we address the electrical manipulation of hole spins in semiconductor heterostructures subject to inhomogeneous vertical electric fields and/or in-plane AC electric fields. We consider Ge quantum dots electrically confined in a Ge/GeSi quantum well as an illustration. We show that the lack of separability between the vertical and in-plane motions gives rise to an additional spin-orbit coupling mechanism (beyond the usual linear and cubic in momentum Rashba terms) that modulates the principal axes of the hole gyromagnetic g-matrix. This non-separability mechanism can be of the same order of magnitude as Rashba-type interactions, and enables spin manipulation when the magnetic field is applied in the plane of the heterostructure even if the dot is symmetric (disk-shaped). More generally, we show that Rabi oscillations in strongly patterned electric fields harness a variety of g-factor modulations. We discuss the implications for the design, modeling and understanding of hole spin qubit devices.
On Reachable Assignments under Dichotomous Preferences

We consider the problem of determining whether a target item assignment can be reached from an initial item assignment by a sequence of pairwise exchanges of items between agents. In particular, we consider the situation where each agent has a dichotomous preference over the items, that is, each agent evaluates each item as acceptable or unacceptable. Furthermore, we assume that communication between agents is limited, and the relationship is represented by an undirected graph. Then, a pair of agents can exchange their items only if they are connected by an edge and the involved items are acceptable. We prove that this problem is PSPACE-complete even when the communication graph is complete (that is, every pair of agents can exchange their items), and this problem can be solved in polynomial time if an input graph is a tree.
Some strong limit theorems in averaging

The paper deals with the fast-slow motions setups in the discrete time X ε (( n + 1) ε ) = X ε ( nε ) + εB ( X ε ( nε ) ,ξ ( n )), n = 0 , 1 ,. , [ T/ε ] and the continuous time dX ε ( t ) dt = B ( X ε ( t ) , ξ ( t/ε )) . t ∈ [0 ,T ] where B is a smooth vector function and ξ is a suﬃciently fast mixing stationary stochastic process. It is known since [19] that if ¯ X is the averaged motion then G ε = ε − 1 / 2 ( X ε − ¯ X ) weakly converges to a Gaussian process G . We will show that for each ε the processes ξ and G can be redeﬁned on a suﬃciently rich probability space without changing their distributions so that E sup 0 ≤ t ≤ T | G ε − G | 2 M = O ( ε M/ 4 ) which gives also O ( ε 1 / 8 ) Prokhorov distance estimate between the distributions of G ε and G . We note that our mixing assumptions are adapted to fast motions generated by important classes of dynamical systems.
An E-PINN assisted practical uncertainty quantification for inverse problems

How to solve inverse problems is the challenge of many engineering and industrial applications. Recently, physics-informed neural networks (PINNs) have emerged as a powerful approach to solve inverse problems efficiently. However, it is difficult for PINNs to quantify the uncertainty of results. Therefore, this study proposed ensemble PINNs (E-PINNs) to handle this issue. The E-PINN uses ensemble statistics of several basic models to provide uncertainty quantifications for the inverse solution based on the PINN framework, and it is employed to solve the inverse problems in which the unknown quantity is propagated through partial differential equations (PDEs), especially the identification of the unknown field ( e.g. , space function) of a given physical system. Compared with other data-driven approaches, the suggested method is more than straightforward to implement, and also obtains high-quality uncertainty estimates of the quantity of interest (QoI) without significantly increasing the complexity of the algorithm. This work discusses the good properties of ensemble learning in field inversion and uncertainty quantification. The effectiveness of the proposed method is demonstrated through several numerical experiments. To enhance the robustness of models, adversarial training (AT) is applied. Furthermore, an adaptive active sampling (AS) strategy based on the uncertainty estimates from E-PINNs is also proposed to improve the accuracy of material field inversion problems.
The starred Dixmier's conjecture

Dixmier's famous question says the following: Is every algebra endomorphism of the first Weyl algebra, $A_1(F)$, where $F$ is a zero characteristic field, an automorphism? Let $\alpha$ be the exchange involution on $A_1(F)$: $\alpha(x)= y$, $\alpha(y)= x$. An $\alpha$-endomorphism of $A_1(F)$ is an endomorphism which preserves the involution $\alpha$. Then one may ask the following question, which may be called the "$\alpha$-Dixmier's problem $1$" or the "starred Dixmier's problem $1$": Is every $\alpha$-endomorphism of $A_1(F)$ an automorphism?
Hecke Algebras, SVD, and Other Computational Examples with {\sc CLIFFORD}

{\sc CLIFFORD} is a Maple package for computations in Clifford algebras $\cl (B)$ of an arbitrary symbolic or numeric bilinear form B. In particular, B may have a non-trivial antisymmetric part. It is well known that the symmetric part g of B determines a unique (up to an isomorphism) Clifford structure on $\cl(B)$ while the antisymmetric part of B changes the multilinear structure of $\cl(B). $ As an example, we verify Helmstetter's formula which relates Clifford product in $\cl(g)$ to the Clifford product in $\cl(B). $ Experimentation with Clifford algebras $\cl(B)$ of a general form~B is highly desirable for physical reasons and can be easily done with {\sc CLIFFORD}. One such application includes a derivation of a representation of Hecke algebras in ideals generated by q-Young operators. Any element (multivector) of $\cl(B)$ is represented in Maple as a multivariate Clifford polynomial in the Grassmann basis monomials although other bases, such as the Clifford basis, may also be used. Using the well-known isomorphism between simple Clifford algebras $\cl(Q)$ of a quadratic form Q and matrix algebras through a faithful spinor representation, one can translate standard matrix algebra problems into the Clifford algebra language. We show how the Singular Value Decomposition of a matrix can be performed in a Clifford algebra. Clifford algebras of a degenerate quadratic form provide a convenient tool with which to study groups of rigid motions in robotics. With the help from {\sc CLIFFORD} we can actually describe all elements of $\Pin(3)$ and $\Spin(3). $ Rotations in $\BR^3$ can then be generated by unit quaternions realized as even elements in $\cl^{+}_{0,3}. $ Throughout this work all symbolic computations are performed with {\sc CLIFFORD} and its extensions.
The Force of Impact on a Sphere Striking a Water Surface

Abstract : The entry of a solid body from air into water may take place in two ways: (i) if the speed is small and if the surface of the body is smooth, the entry occurs without the formation of a cavity (smooth entry); (ii) if the speed is great or if the surface of the body is rough or has an irregular shape, the flow detaches from the body when it has penetrated a short distance below the initial surface level (rough entry). In either case there is an initial period following contact with the surface during which the body experiences the greatest deceleration and which is of such short duration that cavitation has not yet developed. It is assumed that the nose of the projectile is spherical in shape and that the initial impact with the surface is vertical. The mass of the projectile is then visualized as concentrated into a sphere of the same radius as the nose. In a previous report (PB-19651) a first approximation was obtained to the force of impact on a sphere entering vertically by disregarding the rise of the surface; in the present report, a second approximation was obtained by estimating the effects of the surface motion and the theoretical estimates are compared with experimental results.
Machine Learning Models for SSD and HDD Reliability Prediction

This paper compares hard disk drives (HDDs) and solid-state drives (SSDs), the two most used storage devices in data centers, which frequently fail and are among the main causes of data center downtime. Using a six-year field data of 100,000 HDDs from the Backblaze dataset and a six-year field data of 30,000 SSDs from a Google data center, we characterize workload conditions that prompt drive failures. % and show that they differ from common expectation. We develop machine learning models that accurately predict the drive failure state several days in advance and provide highly interpretable results that are useful to identify the causes and symptoms of drive failures.
Modeling of THz Comb Emission in Difference-Frequency Quantum Cascade Lasers

The generation of terahertz (THz) frequency comb emission by intracavity difference frequency generation (DFG) in a mid-infrared (mid-IR) quantum cascade laser (QCL) is a promising alternative to direct THz QCL frequency comb generation. Concerning their room temperature operation capabilities, these devices are well suited for applications in rotational molecular spectroscopy and sensing. In order to better understand the second-order nonlinear mixing process required for the THz frequency comb generation, we present an extensive analysis of such THz DFG-QCL comb devices using a full-wave Maxwell-Bloch equation solver. All input parameters for the Maxwell-Bloch simulations, e.g. scattering rates or dephasing rates, are calculated self-consistently using an ensemble Monte Carlo simulation toolbox.
Efficiency Improvements in Compressing H2 Matrices for Translationally Invariant Kernels

H2 matrices provide efficient representations for compressible kernels such as those encountered in low-frequency electromagnetic modeling applications. It has recently been observed that H2 matrices obtained using matrix sampling techniques can be rendered translationally invariant using an iterative procedure. For some applications the computational costs associated with the previously reported procedure are high. This presentation will report strategies to reduce these costs for the kernels encountered in low-frequency electromagnetic modeling applications.
Computational modelling of the vortex state in high-temperature superconductors

The vortex state in high temperature superconductors is investigated using computer simulations. Vortices are represented as particles and we employ Langevin dynamics to study the statics and dynamics of the system. We show that the long-range nature of the vortex-vortex interaction can result in numerical artefacts, and provide two techniques to overcome these problems: (i) using a ‘smooth’ cut-off which reduces the interaction force near the cut-off smoothly to zero, and (ii) an infinite lattice summation technique applicable for a K0-Bessel function interaction potential. Using these methods, we investigate a two-dimensional vortex system driven over a weak random potential. We observe the moving Bragg glass regime, and study the recently predicted critical transverse force. Our results agree with and extend other theoretical and numerical works, and provide important confirmation for the moving glass theory. We investigate the critical transverse force as a function of system size, temperature, driving force and disorder strength. We provide numerical estimates to assist experimentalists in verifying its existence. We study vortex matter in three-dimensional layered superconductors in the limit of zero Josephson coupling. The long-range nature of the electromagnetic interaction between pancake vortices in the c-direction allows us to employ a meanfield method: all attractive inter-layer interactions are described by a substrate potential, which pancakes experience in addition to the in-layer pancake repulsion. Using an averaged pancake-density, we iteratively re-compute the substrate potential. The self-consistent method converges, depending on temperature, either to a pancake lattice or a pancake liquid. We investigate different methods to perform these simulation efficiently, and compute the instability line for the transition from solid to liquid, the melting line and the entropy jump across the transition.
Surface Energy Balance Measurements around Ocean Weather Station-T during OMLET/WCRP

Sea surface energy balance was analysed at the ocean weather station-T (OWS-T; 29°N, 135°E), south of Japan, based on direct measurements on board. The intensive observation periods were set up during OMLET (Ocean Mixed Layer Experiment) as two cruises of the R/V Hakuhomaru, University of Tokyo around OWS-T in Apr/May 1988 and Jan/Feb 1991. The first period corresponds to the decaying stage of the ocean mixed layer and the second one is the stage of the development. Radiation measurements and turbulent flux measurements by the eddy correlation method were carried out during the cruises as well as the upper air soundinss and the ocean measurements. A part of the results from OMLET'88 was already reported by Tsukamoto et at. (1990). The eddy correlation flux measurement is the most accurate one. However it is not always applicable for the sea-surface flux evaluations for a long term. The bulk aerodynamic formulas are usually accepted for practical purposes. The bulk transfer coefficient should be determined based on the eddy correlation flux measurements. However previous energy budget studies are based on universal bulk models and no confirmation was made of those results. In the present study, the bulk transfer coefficients were determined based on the eddy fluxes as a function of wind speed as in situ values. The bulk coefficients were applied to the continuous surface meteorological data for the evaluation of long-term fluxes. The net heat fluxes into the water were evaluated as +54 Wm −2 (downward) in Apr/May and −192 Wm −2 (upward) in Jan/Feb. The local time changes of the ocean mixed layer heat content were calculated with JMA buoy data as 356 Wm −2 and −297 Wm −2 , respectively. The difference of these values can be regarded as due to horizontal or vertical heat transfer
Optimization and Design of High-Speed Transmission Line Cables

In this paper, the layered structure of the high-speed cable is firstly analyzed, and then a 3-D model is established for the cable. After that, the accuracy of the model is verified by comparing the measured data with the simulation. Finally, due to the disturbances and deformations in the actual high-speed cable, the geometric parameters of the cable are varied. According to the standard, the high-speed cable is designed and optimized, where the optimal geometry size and position are obtained. It is found that by changing the parameters of the high-speed cable, their performance can be improved significantly.
Machine Learning Assisted Material and Device Parameter Extraction from Measurements of Thin Film Semiconductor Devices

The simulation of thin film semiconductor devices is challenging, partly due to the unknown material and device parameters. In this contribution, we present two different approaches to determine the missing material and device parameters from measurements. They both have in common that they are based on machine learning (ML) and numerical models. First, a numerical model describing the experiment is used to generate synthetic data to train a machine learning model the underlying material parameters. After successful training, a measurement is presented to the ML model to predict the parameters. In a more recent physics-informed ML approach, we integrate the model into the ML method and thus reduce the training data set.
LOW MASS RATIO CONTACT BINARY SYSTEMS HN UMa AND II UMa - III

We present newly observed BVRI CCD light curves for low mass ratio contact binaries, HN UMa and II UMa. The absolute dimensions of these objects were obtained by applying the Wilson-Devinney program to previously published spectroscopic analysis and to our observed photometric data. The evolutionary status of all 21 low mass ratio contact binary system including HN UMa and II UMa was then considered. The secondaries of all low mass ratio contact binaries are located below the zero age main sequence in HR diagram. This phenomenon could be explained by mass loss from the secondary component in the low mass contact binary system because even small mass loss affects luminosity decrease in the low mass stars.
Evaluating Empirical Relationships among Prediction, Measurement, and Scaling Invariance. Research Report. ETS RR-11-06.

The purpose of this study was to consider the relationships of prediction, measurement, and scaling invariance when these invariances were simultaneously evaluated in psychometric test data. An approach was developed to evaluate prediction, measurement, and scaling invariance based on linear and nonlinear prediction, measurement, and scaling functions. The approach was used to evaluate the relationships among 12 pairs of tests in 6 datasets for gender invariance. The prediction, measurement, and scaling invariance results were found to be similar for most of the test relationships evaluated, in that all 3 invariances were more likely to be established for highly correlated tests than for less highly correlated tests. The invariance results appeared to be well summarized by intercept differences in the linear prediction, measurement, and scaling functions. The implications of the results were discussed with respect to the relationships among prediction, measurement, and scaling invariances described in prior theoretical and empirical research. Suggestions for extending theoretical and empirical invariance research were provided.
Fractional Order Controller Design Using the Direct Synthesis Method

A method to design fractional order controllers for fractional order processes using the direct synthesis (DS) approach is proposed. The DS method is one of the most commonly used technique for tuning proportional-integral-derivative (PID) controllers. However, this approach has not been used for fractional order models. In this work, the DS method is adopted to take advantage of its simplicity; moreover, the procedure results in the structure of the controller as well as the controller parameters and the fractional orders of controller components. The resulting fractional order controllers are similar to the tilt-integral-derivative (TID) controller. Mathematical formulations are provided to obtain controllers for models with different degrees of the denominator polynomial; time-delay models are also considered. Simulation results are presented to demonstrate the efficacy of the designed controllers.
Enhanced Transmission Model for Predicting Coupling Between Electromagnetic Field and a Twisted-Wire Pair of Complex Dielectric Coatings

In this paper, an enhanced electromagnetic field coupling to twisted wire pair (TWP) model is developed and applied for predicting the terminal voltage/current responses. In contrast to the classic transmission line (TL) model, the proposed approach takes account the perturbation effect on external field due to the presence of coatings by assigning a weight (coating coefficient Cd) to the source terms contributed by transversal field component. The case study shows a significant improvement of the modeling accuracy with respect to classic TL model.
Quantified is simplified; Treating the spatial entropy as continuous for prognostics of early ovarian cancer

A substantial number of studies have proven that analysing the texture of DNA-specific stained cancer cell nuclei can provide robust and reliable prognostic information. Such information is important to make a qualified selection of the appropriate treatments for the patients. A recent texture approach based on adaptive features extracted from the class specific dual entropy matrix (CSDEM) has shown promising results. The approach used relatively coarse quantification of the entropy values to reduce overfitting. This quantification can easily reduce the performance of the approach, and will certainly require detailed domain knowledge in order to fully utilise its potential. We will in this study describe a method that uses the class specific entropy values in their continuous nature. The method uses an adaptive continuous discrimination function, based on density estimation, that is able to estimate the discriminative value of the entropies on a continuous scale. We have evaluated our method using statistical bootstrapping on a dataset containing about 38 000 cell nucleus images collected from 134 patients with early ovarian cancer. We achieve results that are consistently better than the quantified approach based on CSDEM, and our results are more easily obtained as domain knowledge requirements are reduced. Considering our method as a generalisation to the continuous domain, this is a good result that reinforces the promise of using the class specific entropies for prognostics of early ovarian cancer.