Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Retinal Pigment Epithelium Alterations and Chorioretinal Vascular Network Analyses in Patients under Treatment with BRAF/MEK Inhibitor for Different Malignancies: A Pilot Study

In the last two decades, an increasing number of so-called molecular-targeted therapies have become available for the treatment of patients with advanced malignancies. These drugs have included inhibitors of proteins in the MAPK pathway, such as BRAF and MEK inhibitors, which are characterized by a distinct toxicity profile. The eye is particularly susceptible to adverse effects due to MEK inhibitors, and the term MEKAR (MEK-inhibitor-associated retinopathy) indicates the presence of subretinal fluid, mimicking central serous chorioretinopathy (CSC). The pathogenesis of the retinal alterations related to MAPK pathway inhibitors is still unclear, and questions are still open. The present study aims to assess the presence of retinal pigment epithelium alterations as predictive parameters for retinal toxicity, analyzing, at the same time, the chorioretinal vascular network in patients undergoing BRAF/MEK inhibitor treatment for different malignancies.
Physics-Informed Neural Networks and their Implementation in MATLAB

An analysis was made of physics-informed neural networks used to solve partial differential equations. The prospects for the implementation of physics-informed neural networks in the MATLAB system are shown. An algorithm for solving partial differential equations in MATLAB using physics-informed neural networks has been developed. On the example of a model problem described by the Poisson equation, physics-informed neural networks were implemented and studied, which showed that MATLAB can be successfully used to implement such networks. MATLAB made it possible to solve the Poisson equation up to the mean square value of the loss function equal to 0.01. The best results were obtained by networks with a small number of layers (3–4) and a sufficiently large number of neurons in each layer (50–100). Comparison with known results showed that MATLAB was inferior to TensorFlow in terms of learning speed. The application of the Optimization Toolbox MATLAB for the implementation of the L-BFGS quasi-Newtonian learning algorithm for physics-informed neural networks was studied. The quasi-Newtonian algorithm makes it possible to increase the accuracy of solving the problem, but requires a lot of training time. As further research, it is recommended to expand the capabilities of the Deep Learning Toolbox by including quasi-Newtonian learning algorithms, in particular, the Levenberg-Marquard algorithm, and new neural network architectures, for example, networks of radial basis functions.
Electromagnetic scattering by a multilayer elliptic cylinder under transverse-magnetic illumination: series solution in terms of Mathieu functions

An exact solution to the electromagnetic scattering by a dielectric multilayer infinite cylinder of elliptic cross section is proposed. The interfaces between different media, which are assumed to be lossless and nonmagnetic, are confocal elliptic cylinders. Starting from the series expansions in terms of Mathieu functions, an efficient recursive procedure for the computation of fields and radar cross sections per unit length under a transverse-magnetic illumination is developed. The mathematical formulation is detailed and some numerical results are provided.
The Effectiveness of Membership-Based Free Shipping: An Empirical Investigation of Consumers’ Purchase Behaviors and Revenue Contribution

Membership-based free shipping (MFS) has been increasingly adopted by online retailers. However, its effectiveness is understudied. This study leverages a consumer transaction data set provided by an online retailer and uses a stacked difference-in-differences approach to quantify the enrollment effect of MFS on consumers’ purchase behaviors and their net revenue contribution to the retailer. Interestingly, MFS enrollment does not lift average consumers’ spending at the beginning of the enrollment, as they break large orders into smaller ones. Retailers do not gain incremental net revenue due to the increased shipping costs during this period. However, the free shipping benefit may build a switching barrier that motivates consumers to purchase more often with larger order sizes over time. It eventually leads to increased spending and revenue contribution. MFS also increases members’ purchase variety and impulse purchases. In addition, the authors find that there is a greater increase in net revenue contribution after enrollment for light buyers, variety seekers, and those who are willing to pay shipping fees before enrollment. Surprisingly, MFS has a negative impact on the revenue contribution of heavy buyers, the conventional best-value consumers. Moreover, MFS can effectively strengthen consumer–retailer relationships by reducing customer churn.
Deformable Density Estimation via Adaptive Representation

Crowd counting is the basic task of crowd analysis and it is of great significance in the field of public safety. Therefore, it receives more and more attention recently. The common idea is to combine the crowd counting task with convolutional neural networks to predict the corresponding density map, which is generated by filtering the dot labels with specific Gaussian kernels. Although the counting performance is promoted by the newly proposed networks, they all suffer one conjunct problem, which is due to the perspective effect, there is significant scale contrast among targets in different positions within one scene, but the existing density maps can not represent this scale change well. To address the prediction difficulties caused by target scale variation, we propose a scale-sensitive crowd density map estimation framework, which focuses on dealing with target scale change from density map generation, network design, and model training stage. It consists of the Adaptive Density Map (ADM), Deformable Density Map Decoder (DDMD), and Auxiliary Branch. To be specific, the Gaussian kernel size variates adaptively based on target size to generate ADM that contains scale information for each specific target. DDMD introduces the deformable convolution to fit the Gaussian kernel variation and boosts the model’s scale sensitivity. The Auxiliary Branch guides the learning of deformable convolution offsets during the training phase. Finally, we construct experiments on different large-scale datasets. The results show the effectiveness of the proposed ADM and DDMD. Furthermore, the visualization demonstrates that deformable convolution learns the target scale variation.
Representation issues: Using mathematics in upper-division physics

Upper-division students must learn to apply sophisticated mathematics from algebra, limits, calculus, multivariable and vector calculus, linear algebra, complex variables, and ordinary and partial differential equations. The presenters in this session will discuss how the representations that we choose may affect whether students are able to use this mathematics spontaneously and correctly, whether they can move smoothly between representations, and the extent to which their understanding of the mathematics enhances their understanding of the physics. The discussant will incorporate the perspective of research in undergraduate mathematics education as it applies to the representations that have been presented.
Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States

Abstract. Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.
Controlling viscous fingering in tapered Hele-Shaw cells

We present a theoretical study of a variant of the classical viscous fingering instability, which occurs when a high viscosity fluid is displaced by a low viscosity fluid in a Hele-Shaw cell. In our system, the Hele-Shaw cell is tapered in the direction of fluid displacement. We consider two tapered Hele-Shaw geometries (rectilinear and radial), which have a constant depth gradient in the flow direction. We find that the presence of a depth gradient can alter the stability of the interface offering opportunities to control and tune fingering instabilities. In particular, the stability of the interface is now determined by both the viscosity contrast of the fluids and the ratio of the depth gradient to the capillary number of the system. We also demonstrate several applications of our analysis, including the inhibition of viscous fingering by controlling the injection flow rate in a radially tapered Hele-Shaw cell.
SAS Macros to Compute the Descriptive Test Index PDTS

The SAS macros %PDTS (for classical test theory) and %PDPP (for item response models) compute the probability of obtaining statistically different test scores PDTS (Müller, in press) from a psychological assessment. The descriptive test index PDTS is defined as the ratio of the number of statistically different test score pairs (SDTS) to the total number of all possible pair combinations from a finite sample. %PDTS considers the raw score metric and computes the least significant difference of two test scores by the formula of Gulliksen (1950) on the basis of test reliability, whereas %PDPP considers the theta metric (person parameter) and computes the least significant difference with a theta-related standard error, where the standard error of several item response theory (IRT) software packages can be used. Supplementary macros convert the output of the following software packages into an SAS file to apply %PDPP: %ReadBigPP reads BIGSTEPS (Linacre & Wright, 2003), %ReadWinPP reads WINMIRA (von Davier, 1998), %ReadBilPP reads BILOG-MG (Zimowski, Muraki, Mislevy, & Bock, 1996), and %ReadParPP reads PARSCALE (Muraki, & Bock, 1997). PDTS depends on the shape of the score distribution and the measurement error and provides an easy-to-understand summary for a test user who may not be familiar with psychometric theory.
A Lagrangian formulation for mechanically, thermally coupled electromagnetic diffusive processes with moving conductors

In a moving physical system, field variables can be described by functions of time and initial (reference) positions of particles (Lagrangian description) or functions of time and current positions of particles (Eulerian description). Lagrangian formulation exhibits several advantages in modeling electromagnetic (EM) diffusion problems with moving conductors. It results in a symmetric coefficient matrix which reduces computational cost and central memory requirement. It also eliminates numerical instability at hypervelocity. In the simulation of EM launchers, Lagrangian formulation facilitates EM analyses including end (breech and muzzle) effects, and the effects of imperfect rails and structural deformation. A detailed Lagrangian formulation of mechanically, and thermally coupled electromagnetic diffusive processes with moving conductors is presented in this paper. It is based on the quasistatic Maxwell's equations in terms of dual potentials: magnetic vector potential and electric scalar potential. With the Coulomb gauge condition, magnetic vector potential is uniquely determined. The formulation has been implemented in the finite element program 'EMAP3D' (Electro-Mechanical Analysis Program in 3 Dimension). The thermally coupled electromagnetic simulation of a railgun is illustrated. The distributions of current density, magnetic field, and temperature are presented. Also the profiles of velocity and current are shown. >
Systematic Literature Review on Solving Competitive Programming Problem with Artificial Intelligence (AI)

Computer programming has emerged in research, industry, and everyday life as a general-purpose problem-solving tool. From this expansion, there has been a steady rise in demand for tools that can help programmers be more productive or make programming easier using auto-completion and code-generation tools. Hence, the development of Artificial Intelligence (AI) systems capable of successfully understanding and generating code has the potential to change these tools and the way humans interact with them. Systems that produce code are not only beneficial but also serve as stepping-stones toward a better understanding of AI and how it relates to programming. One benchmark that is necessary for completing this development, is solving competitive programming (CP) problems which require a mastery of algorithms and complicated natural language, proves to be difficult for any AI model because it requires thinking like a human brain. In this study, the authors conducted a Systematic Literature Review (SLR) to better understand the evolution of program synthesis and also how different models such as AlphaCode and Codex perform when solving CP problems. In general, the authors find that even with the models that are specifically trained for solving CP problems, they still cannot think like a human when solving those problems. Thus, AI still has a long way to go before competing at the highest level of CP. From this literature review, it can be concluded that the code auto-completion and code-generation tools that are available now still do not meet the necessary benchmark which is solving CP tasks.
Structured Joint Sparse Orthogonal Nonnegative Matrix Factorization for Fault Detection

As modern industrial processes become complicated, and some faults are difficult to be detected due to noises and nonlinearity of data, data-driven fault detection (FD) has been extensively used to detect abnormal events in functional units. To obtain better FD performance of nonnegative matrix factorization (NMF), this article first proposes an FD method using the structured joint sparse orthogonal NMF (SJSONMF). The core idea is to incorporate the graph regularization, sparsity, and orthogonality constraints into the classical NMF, which enjoys stronger discriminative ability, removes redundancy of different basis vectors, and improves fault interpretability. More importantly, an optimization algorithm based on the proximal alternating nonnegative least squares (PANLS) is developed, which can guarantee and speed up the convergence. Finally, the effectiveness of the proposed method is demonstrated by the experiments on the benchmark Tennessee Eastman Process (TEP) and two practical bearing datasets. Particularly, compared with the classical NMF, the $T^{2}$ statistic has a gain of 33.13% for the fault IDV(16) on the TEP. The results show that the proposed model and algorithms are promising for FD.
Direct adaptive control of parabolic systems: algorithm synthesis and convergence and stability analysis

This paper presents a model reference adaptive control of a class of distributed parameter systems described by linear, n-dimensional, parabolic partial differential equations. Unknown parameters appearing in the system equation are either constant or spatially-varying. Distributed sensing and actuation are assumed to be available. Adaptation laws are obtained by the Lyapunov redesign method. It Is shown that the concept of persistency of excitation, which guarantees the parameter error convergence to zero in finite-dimensional adaptive systems, in infinite-dimensional adaptive systems should be investigated in relation to time variable, spatial variable, and also boundary conditions. Unlike the finite-dimensional case, in infinite-dimensional adaptive systems even a constant input is shown to be persistently exciting in the sense that it guarantees the convergence of parameter errors to zero. Averaging theorems for two-time scale systems which involve a finite dimensional slow system and an infinite dimensional fast system are developed. The exponential stability of the adaptive system, which is critical in finite dimensional adaptive control in terms of tolerating disturbances and unmodeled dynamics, is shown by applying averaging. >
Finite-Horizon Shield for Path Planning Ensuring Safety/Co-Safety Specifications and Security Policies

With the development of network technology, security in path planning problems has attracted widespread attention. We consider a path planning problem in which a planner computes a finite path that satisfies a specification. We assume that the specification includes mandatory safety/co-safety specifications. Moreover, we consider a security policy for this path. However, we assume that the information leaked to an intruder is not known beforehand. Then, we propose an enforcement mechanism referred to as a finite-horizon shield. This mechanism modifies the path computed by the planner as small as possible to satisfy the safety/co-safety specifications and security policy under the leaked information. We assume that the safety/co-safety specifications are described by LTL ${}_{f}$ formulas and the security policy by a hyperLTL ${}_{f}$ formula. Subsequently, we convert the formulas into quantified formulas and compute the modified path using a satisfiability modulo theories solver. As an example, we consider an opacity problem where there is another path whose leaked information is the same as that of the modified path. By simulations, it confirms that the output of shield depends on the leaked information and the modified path may have additional movements to ensure opacity. We also compare the computation time of the shield with that of a security-aware planning by simulation.
Existence, uniqueness and asymptotic behavior of solutions for a nonsmooth producer-grazer system with stoichiometric constraints

A reaction-diffusion-ODE system of stoichiometric producer-grazer type is considered in this paper. Since the system has nonsmooth nonlinearity, it is shown that the system has new dynamics different from the smooth case. We construct various types of discontinuous steady states and investigate their asymptotic behavior. In addition, the steady-state bifurcation near a constant solution is studied by treating the diffusion coefficient as a bifurcation parameter and the existence of Hopf bifurcation is derived. Our results cover the case where the number of positive equilibria of the kinetic system (i.e. without diffusion) changes from one to three in the spatial interval. Finally, some numerical simulations are given to illustrate the theoretical results.
Effects of ice and water clouds on rainfall: a partitioning analysis based on surface rainfall budget

The effects of ice and water clouds on rainfall are investigated through a partitioning analysis based on surface rainfall budget. The data come from five two‐dimensional idealized cloud‐resolving model simulations that are imposed by zero large‐scale vertical velocity and time‐invariant sea surface temperature. The grid‐scale rainfall simulation data from the last 10‐day equilibrium period are categorized by different rainfall processes to form eight rainfall types. The results show that the rainfall contribution of the rainfall with water vapor convergence is decreased when the radiative effects of ice and water clouds are excluded, whereas it is increased when the microphysical effects of ice clouds are excluded. The decreased rainfall contribution caused by the removal of radiative effects of ice clouds is much stronger than that caused by the elimination of radiative effects of water clouds, whereas it is weaker than the increased rainfall contribution caused by the exclusion of microphysical effects of ice clouds, which leads to the increased rainfall contribution caused by the total exclusion of ice microphysics. Copyright © 2011 Royal Meteorological Society
Magnitude of JNDs for diotic and dichotic perception of spectrally colored noise

As part of a series of studies concerned with the binaural perception of reverberant sound, just noticeable differences (JNDs) were determined for spectrally colored noise presented both diotically and dichotically. Test stimuli were prepared by passing white noise samples through simple all‐zero filters consisting of a delay, an attenuator, and an adder. By adjusting the delay and attenuation, various amounts of spectral distortion could be obtained. The sensitivity of the listener to changes in the amount of distortion or coloration was measured under two listening modes. For the diotic case, the same spectrally distorted noise was presented to both ears; for the dichotic case, pairs of complimentary filters were made simply by changing the sign of the attenuator in one of the filters. The listeners were less sensitive to the presence of coloration for the dichotic both in terms of absolute threshold and for the number of JNDs. The results will be discussed in terms of existing binaural processing models.
Real-time imaging of tissue vibration using a two-dimensional speckle tracking system

Tumors in soft tissues are often detected because of their higher stiffness relative to surrounding tissues. A number of groups have constructed systems which attempt to estimate stiffness by imaging the propagation of low frequency (l1 KHz) mechanical waves using modified one-dimensional flow measurement techniques. We have constructed a system which utilizes a two-dimensional speckle tracking technique to measure vibrations in soft tissues in real-time. The vibration source and speckle tracking system are completely synchronous, so phase information is preserved. We describe the system and present the results of initial calibration studies