Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Analysis of Count Data from Mixed Populations

In applications such as gene regulatory network analysis based on single-cell RNA sequencing data, samples often come from a mixture of different populations and each population has its own unique network. Available graphical models often assume that all samples are from the same population and share the same network. One has to first cluster the samples and use available methods to infer the network for every cluster separately. However, this two-step procedure ignores uncertainty in the clustering step and thus could lead to inaccurate network estimation. Motivated by these applications, we consider the mixture Poisson log-normal model for network inference of count data from mixed populations. The latent precision matrices of the mixture model correspond to the networks of different populations and can be jointly estimated by maximizing the lasso-penalized log-likelihood. Under rather mild conditions, we show that the mixture Poisson log-normal model is identifiable and has the positive definite Fisher information matrix. Consistency of the maximum lasso-penalized log-likelihood estimator is also established. To avoid the intractable optimization of the log-likelihood, we develop an algorithm called VMPLN based on the variational inference method. Comprehensive simulation and real single-cell RNA sequencing data analyses demonstrate the superior performance of VMPLN.
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy

Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, including the recently emerging deep learning-based methods, still show limited generality under the multi-modality environment. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was hosted at NeurIPS 2022 to tackle this problem. We propose MEDIAR, a holistic pipeline for cell instance segmentation under multi-modality in this challenge. MEDIAR harmonizes data-centric and model-centric approaches as the learning and inference strategies, achieving a 0.9067 F1-score at the validation phase while satisfying the time budget. To facilitate subsequent research, we provide the source code and trained model as open-source: https://github.com/Lee-Gihun/MEDIAR
Effect of Winglet Dihedral on a Tip Vortex

The effect of winglet dihedral (-87.5 ≤ δ ≤ 87.5 deg) on the near-field tip vortex flow structure behind a swept and tapered NACA 0015 wing was investigated at Re=1.81 × 10 5 . The winglet dihedral led to a significantly reduced (increased) peak tangential velocity and vorticity (core radius) compared to a baseline wing. The values of core circulation (core axial velocity) could be below or above the baseline-wing value, depending on the winglet angle, and exhibited a local maximum (minimum) at δ=0 deg. The inner region of the tip vortex flow exhibited a self-similar behavior for a wing with and with no winglet. The lift-induced drag was always reduced by the addition of a winglet. The negative dihedral was more effective in reducing the lift-induced drag compared to the corresponding winglet of positive dihedral. A large discrepancy in the lift-induced drag was, however, observed between the wake integral method and the classical lifting-line theory.
Neural Implicit Mapping via Nested Neighborhoods

We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (SDF) in real-time. We rely on nested neighborhoods of zero-level sets of neural SDFs, and mappings between them. This framework supports animations and achieves real-time performance without the use of spatial data-structures. It consists of three uncoupled algorithms representing the rendering steps. The multiscale sphere tracing focuses on minimizing iteration time by using coarse approximations on earlier iterations. The neural normal mapping transfers details from a fine neural SDF to a surface nested on a neighborhood of its zero-level set. It is smooth and it does not depend on surface parametrizations. As a result, it can be used to fetch smooth normals for discrete surfaces such as meshes and to skip later iterations when sphere tracing level sets. Finally, we propose an algorithm for analytic normal calculation for MLPs and describe ways to obtain sequences of neural SDFs to use with the algorithms.
Dimension Reduction for Fréchet Regression †

With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fr\'echet regression model (Peterson&M\"uller 2019) provides a promising framework for regression analysis with metric space-valued responses. In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fr\'echet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors and to provide a visual inspection tool for Fr\'echet regression. Our approach is flexible enough to turn any existing SDR method for Euclidean (X,Y) into one for Euclidean X and metric space-valued Y. The basic idea is to first map the metric-space valued random object $Y$ to a real-valued random variable $f(Y)$ using a class of functions, and then perform classical SDR to the transformed data. If the class of functions is sufficiently rich, then we are guaranteed to uncover the Fr\'echet SDR space. We showed that such a class, which we call an ensemble, can be generated by a universal kernel. We established the consistency and asymptotic convergence rate of the proposed methods. The finite-sample performance of the proposed methods is illustrated through simulation studies for several commonly encountered metric spaces that include Wasserstein space, the space of symmetric positive definite matrices, and the sphere. We illustrated the data visualization aspect of our method by exploring the human mortality distribution data across countries and by studying the distribution of hematoma density.
Study of $e^+e^-\rightarrow\Omega^{-}\bar\Omega^{+}$ at center-of-mass energies from 3.49 to 3.67 GeV

Using data samples of $e^+e^-$ collisions collected with the BESIII detector at eight center-of-mass energy points between 3.49 and 3.67 GeV, corresponding to an integrated luminosity of 670 pb$^{-1}$, we present the upper limits of Born cross sections and the effective form factor for the process $e^+e^-\rightarrow\Omega^{-}\bar\Omega^{+}$. A fit to the cross sections using a pQCD-derived energy dependent function shows no significant threshold effect. The upper limit on the measured effective form factor is consistent with a theoretical prediction within the uncertainty of 1$\sigma$. These results provide new experimental information on the production mechanism of $\Omega$.
Root and power transformations in optics

If the performance of an optical system A can be executed by a cascade of n identical optical systems B, we term the system B the nth root of A. At the same time A is the nth power of B. It is shown that, in principle, any optical system can be decomposed into its roots of any order. The procedure is facilitated by a merger of the ray matrix representation and the canonical operator representation of first-order optical systems. The results are demonstrated by several examples, including the fractional Fourier transform, which is just one special case in a complete group structure. Moreover, it is shown that the root and power transformations themselves represent special cases of a much more general family of transformations. Application in optical design, optical signal processing, and resonator theory can be envisaged.
Large‐eddy simulation of turbulent flow in a street canyon

The turbulent flow inside an idealized urban street canyon with an aspect ratio of one is studied by means of large‐eddy simulation. The Regional Atmospheric Modelling System is configured to simulate the turbulent flow in a neutrally stratified atmosphere with the initial wind perpendicular to the street canyon axis. The mean velocity components, resolved‐scale turbulent kinetic energy (RS‐TKE), the skewness and kurtosis of the resolved‐scale velocity components (u along the canyon and w vertically) are compared with wind‐tunnel measurements. The comparison indicates that a reasonable agreement is achieved. The simulation slightly underestimates the intensity of the primary eddy. It is found that distribution of the RS‐TKE is very asymmetric: high in the vicinity of the downstream wall, and uniformly low in the vicinity of the upstream wall. The analyses of skewness and kurtosis indicate that there is a layer just below the rooftop in the canyon where ejection events dominate. Quadrant analysis of resolved‐scale velocity fluctuations, u′ and w′, under the rooftop at the centre of the canyon reveals that the exchange of momentum across the canyon top is contributed unevenly by different events. Weak ejection events dominate the frequency of occurrences, but fewer strong sweep events contribute the majority of the total momentum transfer. The features of momentum transfer are further investigated by analysing the spatial–temporal variations of u′, w′,and u′w′ at the roof level. It is found that the variation of these variables is highly intermittent and is associated with multi‐scale turbulent events. The period of eddies containing high RS‐TKE is attributed to the Kelvin–Helmhotz instabilities. These results improve our understanding of the turbulent structure in street canyon flow. Copyright © 2004 Royal Meteorological Society
On Storing Data Objects of Business Processes on Blockchain Channels

Blockchain systems provide strong support for enforceability if all data relevant for transaction verification is stored on-chain. An example is the effective prevention of double spending in the bitcoin network. However, this approach has substantial drawbacks if privacy is a concern. One alternative in the enterprise context are architectures where access to the blockchain can be restricted to specific participants, and separate blockchains (channels) between subsets of participants can be established. While such architectures are promising for supporting privacy, there can still be trade-offs between the supported levels of enforcement and the degree of privacy that can be natively supported by those systems. In this paper, we follow a model-based approach, where privacy and enforceability requirements are explicitly modeled in business processes. We provide a detailed analysis of how different levels of data privacy affect the possible placement of data in channels on Hyperledger Fabric (HLF) and to what degree decisions over data with privacy requirements can be enforced by the built-in transaction verification mechanism. Finally, we present a prototype based on distributed oracles that allows us to enforce the correctness of decisions over data located on different channels.
Zariski pairs of line arrangements with twelve lines

Using the invariant developed in [6], we differentiate four arrangements with the same combinatorial information but in different deformation classes. From these arrangements, we construct four other arrangements such that there is no orientation-preserving homeomorphism between them. Furthermore, some couples of arrangements among this 4-tuplet form new arithmetic Zariski pairs, i.e. a couple of arrangements with the same combinatorial information but with different embedding in $\mathbb{CP}^2$.
Motion induced eddy current testing system based on rotating magnets

Non-destructive testing, especially of safety relevant components plays an increasing role in modern times. A portable version of a motion induced eddy current testing (MECT) system is introduced in this study. Measurements with different specimen and different defects are performed to prove the system’s ability for defect detection in electrically conducting materials. Additional studies on the alignment angle and the rotational velocity help to understand the functional principle of the portable MECT system.
Diffuse minor ions upstream of simulated quasi-parallel shocks

We have performed a number of one-dimensional hybrid (particle ions, massless fluid electrons) simulations of quasi-parallel collisionless shocks in order to elucidate the origin of diffuse upstream minor ions. Minor ions are treated self-consistently by the hybrid code; however, a number of runs have also been performed wherein the minor ions are treated as test particles. We have investigated the dependence of the density ratio bias factor fnof; (the alpha to proton ratio of the diffuse ions normalized to the solar wind ratio) on shock Mach number, solar wind beta (thermal to magnetic pressure) and on angle ΘBno between shock normal and magnetic field and found strong dependencies on all parameters. An addition of 5% alpha particles changes the shock structure, resulting in a significant modification of the density ratio bias factor as compared to the test particle cases. In the case of a low beta solar wind (β ≃ 0.1) the solar wind alpha particles penetrate the shock ramp rather unaffected and gyrate in the downstream magnetic field as a whole. The alpha particle beam returns occasionally to the shock ramp and spatially localized clouds of alpha particles can be found in the upstream region. With increasing Mach number the gyroradius of the alpha particles increases and it becomes easier for the alpha particle beam to return from downstream to the shock; a high density of clouds is found upstream. In the case of a higher beta solar wind (β≳0.5) and moderate Mach numbers, no localized alpha particle clouds leave from downstream into the upstream region. This can be attributed to the smoother shock transition in the case of higher beta shocks. The effect of beta and Mach number is counteracting: high Mach number shocks can lead even at higher beta to minor ion clouds in the upstream region. Higher beta shock simulations result in an increase of fnof; with beta and ΘBno. The increase with beta is due to the fact that diffuse ions originate mainly from the high-energy tail of the distribution in the upstream rest frame. It is suggested that large ΘBno implies a more tangential downstream field and the alpha particles reach the shock during their downstream gyration easier.
A method of distortion correction with parallel lines

A method of distortion correction based on parallel lines is presented, not only for radial distortion coefficient but also for tangential distortion coefficients. Firstly, an approximate line passing through the center is approached by anamorphic curves. The coefficient of distortion is combined with the vision process to correct an anamorphic image for the final calibration of the liner model. Secondly, the point with only tangential distortion is gained by the actual point minus radial distortion. The distances from the point with tangential distortion and the ideal point to the principal point are equivalent. The Least-squares method is used to fit lines in theory, and the coefficients of tangential distortion are gotten.
A Central Limit Theorem for Additive Random Functions

In this paper the additive random functions ${\text{H}}(\Delta )$ of the semi-interval $\Delta = [s,t)$, satisfying the strong mixing condition (1), are considered.Let in formula (1) the variable $\alpha (\tau ) = O[\tau ^{ - 1 - \varepsilon } ]$ and ${\bf M}|{\text{H}}(\Delta _0 ) - {\bf M}{\text{H}}(\Delta _0 )|^{2 + \delta } \leqq M_0 ,\delta >2/\varepsilon $ for all $\Delta _0 = [t_0 ,t + t_0 )$, then, assuming condition (4), \[ {\bf P}\left\{ {\frac{{{\text{H}}(\Delta ) - {\bf M}{\text{H}}(\Delta )}}{{\sqrt {{\bf D}{\text{H}}(\Delta )} }} < x} \right\} \to \frac{1}{{\sqrt {2\pi } }}\int_{ - \infty }^x {e^{ - u^2/2}} du \] when $|\Delta | = t - s \to \infty $.
Comparison analysis of methods for deriving priorities in the analytic hierarchy process

Presents a comparison study of some methods for prioritisation in the analytic hierarchy process. The main objective of this analysis is to compare and evaluate a fuzzy preference programming method with the most popular prioritisation techniques: the eigenvector method, the weighted least squares method, the logarithmic least squared method and the goal programming method. The analysis is based on three evaluation criteria: the total deviation, measuring the Euclidean distance between the ratios obtained by the derived weights and the decision-maker ratios; the minimum violations criterion, measuring the rank reversal properties of the methods; and the conformity, indicating the outliers with respect to the other methods. The evaluation procedure uses randomly generated inconsistent pairwise comparison matrices of different dimensions.
CLAUSTROPHOBIA VISUAL STUDIES AMONG UNIVERSITI SAINS MALAYSIA (USM) STUDENTS: MEDIA MAPPING ART ASSISTS IN THE ACCEPTANCE OF SMALL SPACES

A study on visuals that can provide comfort to students of Universiti Sains Malaysia (USM) suffering from claustrophobia. According to previous studies, the visuals on display have helped claustrophobes in coping with their phobias. For example, Rizki Arif Darmawan and also Dody Pernady have made a study on claustrophobia patients using visuals from Virtual Reality (VR). The researcher uses VR to address this problem. The study needs to use virtual reality video game applications. The results of this project show that by using visuals, able to give claustrophobia patients to go through the narrow conditions that they must go through every day.
Exercise as an adjuvant therapy for breast cancer: a review of current exercise oncology evidence and perspectives

Objective: To describe the effects and moderators of exercise on fatigue and cardiorespiratory fitness in women with breast cancer as well as the relationship between exercise and survival in this group of patients. Methods: We undertook a narrative review describing and discussing studies examining the effects of exercise on fatigue and cardiorespiratory fitness in breast cancer patients. Also, relevant information regarding the relationship between exercise and survival in cancer patients was examined. Results: Exercise resulted in significant effects on cancer-related fatigue, with greater reductions observed in patients undertaking supervised exercise sessions or with higher fatigue levels. For cardiorespiratory fitness, exercise provides significant increases following aerobic-based exercise programs. Effects derived from exercise on cardiorespiratory fitness were more pronounced in younger patients, patients undertaking supervised aerobic-based exercise programs, or undertaking non-linear aerobic exercise prescriptions. Although epidemiological studies indicate associations between higher physical activity levels and overall survival, randomised controlled trials are necessary to confirm such a relationship for exercise in cancer patients. Conclusion: Sufficient evidence indicates that exercise promotes significant effects on fatigue and cardiorespiratory fitness in women with breast cancer. In addition, specific subgroups of patients based on age and baseline levels appear to respond more favourably than others.
Error-Reduced Elliptic Positioning via Joint Estimation of Location and a Balancing Parameter

Elliptic positioning (EP) has been a topic of lively interest to localization practitioners owing to widespread adoption of the bistatic configuration in many location-enabling technologies nowadays. This letter addresses the problem of non-Gaussian error mitigation in EP, by formulating it as joint estimation of target position coordinates and a balancing parameter (BP) for the bias errors. An alternating minimization algorithm is put forward to break the original formulation down into a conventional weighted nonlinear least squares (WNLS) location estimator and a closed-form BP-update step. With its objective being properly decomposed, the WNLS subproblem is converted into the difference-of-convex programming framework, to which an efficient iterative solution based on the concave-convex procedure is applicable. Simulation results demonstrate that the proposed error-reduced EP approach can outperform a number of existing methods in terms of localization accuracy.
Transmission Line Pulse Induced Breakdown of FinFETs

As a result of the shrinking size of semiconductor circuits, fin field-effect transistors (FinFETs) with better gate control ability become a promising candidate in advanced nanodevices. Enhanced reliability is required for complex three-dimensional conductive channels. Understanding and controlling dielectric breakdown is indispensable to increasing the quality and reliability of advanced devices. In this study, physical failures in FinFET devices induced by electrical overstress (EOS) and electrostatic discharge (ESD) were analyzed by using transmission electron microscopy (TEM) with nanoscale resolution. Two electric technology are direct current (DC) and transmission line pulse (TLP). Interaction between current-temperature positive feedback and dielectric breakdown-induced epitaxy (DBIE) was studied. Under the DC stressing, the Si grew along with three sides of the fin and the dielectric layer was destroyed by the stress. Contrarily, the TLP stress only wore and tore the dielectric layer and the Si grew downwards. The technology computer-aided design (TCAD) simulations show that the breakdown depends on the balance between current-temperature positive feedback and the DBIE mechanism. This work provides guidance for enhancing the reliability of FinFETs.
Model Predictive Control for Four-Switch Buck–Boost Converter Based on Tuning-Free Cost Function With Smooth Mode Transition

Four-switch buck-boost (FSBB) converter has the capability of bidirectional voltage step-down and step-up, which has attracted attentions in the fields of energy storage and solar energy. However, it faces the challenges in multimode operation and smooth mode transition, which rely on the complex mode detection scheme and cumbersome parameter tuning. In this article, the inherent limitation is analyzed through inconsistent frequency response and noncontinuous duty cycle operation region aspects. Then, a model predictive control (MPC) method characterized by tuning-free cost function is presented to achieve multimode control and smooth transition simultaneously. With the goal of ensuring the reference current tracking under inconsistent frequency response, a duty cycle and current ripples prediction are presented for all switching combinations. Then, a tuning-free cost function is utilized to overcome the gaps in noncontinuous duty cycle operation region and a smooth mode transition is achieved. Further, the robustness of the current tracking capability is analyzed mathematically considering the inductance variation and stray resistance variation. Finally, a comparative experiment proves the effectiveness. And it shows that the method can improve the efficiency by 0.4% compared with the traditional four-mode control.