Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy dissipative characteristic schemes for the diffusive Oldroyd‐B viscoelastic fluid

In this paper, we propose new energy dissipative characteristic numerical methods for the approximation of diffusive Oldroyd‐B equations that are based either on the finite element or finite difference discretization. We prove energy stability of both schemes and illustrate their behavior on a series of numerical experiments. Using both the diffusive model and the logarithmic transformation of the elastic stress, we are able to obtain methods that converge as mesh parameter is refined. Copyright © 2015 John Wiley & Sons, Ltd.
Applied voltage dependence of nano-oxidation of ferromagnetic thin films using atomic force microscope

Nanodots of Ni, CoFe, and Cr oxide were fabricated by the nano-oxidation technique using atomic force microscope. The dot size was controlled from 40 to 200 nm by changing the pulse voltage applied to the cantilever from 2 to 10 V. In order to evaluate the size of the nanostructures quantitatively, the electric field emitted from the cantilever was calculated. The threshold electric field strength was defined as the minimum strength to promote the oxidation. The threshold field strength of the order of 107 V/m was derived by fitting the experimental results. The voltage dependence of the size of fabricated Cr-oxide dots was fitted well by the calculation. The dot size of the ferromagnet-based oxide was fluctuating and did not agree with the calculation. From the theoretical analysis, it was suggested that the size of the nanostructures did not depend on the distance between the cantilever and film surface, but significantly depended on the curvature radius of the cantilever.
Multidimensional Particle Filter for Long-Term Visual Teach and Repeat in Changing Environments

When a mobile robot is asked to navigate intelligently in an environment, it needs to estimate its own and the environment's state. One of the popular methods for robot state and position estimation is particle filtering (PF). Visual Teach and Repeat (VT&R) is a type of navigation that uses a camera to navigate the robot along the previously traversed path. Particle filters are usually used in VT&R to fuse data from odometry and camera to estimate the distance traveled along the path. However, in VT&R, there are other valuable states that the robot can benefit from, especially when moving through changing environments. We propose a multidimensional particle filter to estimate the robot state in VT&R navigation. Apart from the traveled distance, our particle filter estimates lateral and heading deviation from the taught path as well as the current appearance of the environment. This appearance is estimated using maps created across various environmental conditions recorded during the previous traversals. The joint state estimation is based on contrastive neural network architecture, allowing self-supervised learning. This architecture can process multiple images in parallel, alleviating the potential overhead caused by computing the particle filter over the maps simultaneously. We conducted experiments to show that the joint robot/environment state estimation improves navigation accuracy and robustness in a continual mapping setup. Unlike the other frameworks, which treat the robot position and environment appearance separately, our PF represents them as one multidimensional state, resulting in a more general uncertainty model for VT&R.
Time Dependent Hartree Fock Theory of Multiphoton Ionization

The theory of multiphoton ionization for single electron atoms (or pseudo-one electron atoms such as sodium and cesium) is relatively well established from the standpoint of the knowing the relevant equations to be solved and interpreting the results. There is still some debate about the above threshold effects, but ionization rates have been calculated for high order processes, and the results agree well with experiment. This is not the case for multi-electron systems. In the limit of laser fields which are very strong compared to the Coulombic interactions, collective motion should be expected. At lower laser intensities, linear response theory or perturbation theory has proven to be adequate to model the absorption processes. In this regime, the inter-electronic forces are stronger than the photon-electron interaction so that the perturbation series is convergent. For the intermediate regime, say >1014W/cm2, these two kinds of interactions should be treated on the same footing. A complete, exact, many body calculation for this problem is not possible so that several approximations have been considered. Independent particle models seem to be the most reasonable, with time dependent Hartree Fock being able to include most of the important physical effects, particularly as the number of electrons increases. The major approximation of this approach is that the state of the system is represented by a single, determinantal wave function for all time. This constraint limits the kinds of information which can be obtained from the calculations to averaged quantities. It has the merit, however, of including the effects of the time evolution of the electronic charge density on the absorption process. The redistribution of absorbed energy between all the system's electrons, and the rate at which this occurs is part and parcel of the calculations. This energy exchange is done self consistently so that an understanding of the total absorption dynamics is possible. This seems a minimum requirement in order to investigate any collective effects.
Dynamic Treatment Regimes using Reinforcement Learning: A Cautious Generalization Approach

The treatment of chronic-like illnesses such has HIV infection, cancer or chronic depression implies long-lasting treatments that can be associated with low quality outcome, painful side effects and expensive costs. To enhance these treatments, clinicians often adopt what we call Dynamic Treatment Regimes (DTRs). DTRs are sets of sequential decision rules defining what actions should be taken at a specific instant to treat a patient based on information observed up to that instant. Since a few years, a growing research community is working on the development of formal methods (mainly issued from mathematics, statistics and control theory) that allow to infer from clinical data high-quality DTRs. We propose in this framework a consistent algorithm of quadratic complexity [3] that infer from clinical data a sequence of treatment actions by maximizing a recently proposed lower bound on the return depending on the initial state [2]. The algorithm (called CGRL for Cautious Generalization for Reinforcement Learning) has cautious generalization properties, i.e. it avoids taking treatment actions for which the sample of clinical data is too sparse to make safe generalization.
Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks

Simple Summary Supervised deep learning techniques can now automatically process whole dermoscopic images and obtain a diagnostic accuracy for melanoma that exceeds that of specialists. These automatic diagnosis systems are now appearing in clinics. However, the computational techniques used cannot be easily interpreted by the experts using the systems, and they still fail to detect a minority of melanomas. We describe an approach that detects critical irregularities in pigment patterns, a clinical feature, and uses this knowledge to improve deep learning diagnostic accuracy. In this research, we trained a deep learning network to identify visible patterns associated with melanoma. We combine these patterns with a supervised whole-image deep learning method to improve diagnostic accuracy and provide a publicly available dataset with the clinical structures annotated. Abstract Deep learning has achieved significant success in malignant melanoma diagnosis. These diagnostic models are undergoing a transition into clinical use. However, with melanoma diagnostic accuracy in the range of ninety percent, a significant minority of melanomas are missed by deep learning. Many of the melanomas missed have irregular pigment networks visible using dermoscopy. This research presents an annotated irregular network database and develops a classification pipeline that fuses deep learning image-level results with conventional hand-crafted features from irregular pigment networks. We identified and annotated 487 unique dermoscopic melanoma lesions from images in the ISIC 2019 dermoscopic dataset to create a ground-truth irregular pigment network dataset. We trained multiple transfer learned segmentation models to detect irregular networks in this training set. A separate, mutually exclusive subset of the International Skin Imaging Collaboration (ISIC) 2019 dataset with 500 melanomas and 500 benign lesions was used for training and testing deep learning models for the binary classification of melanoma versus benign. The best segmentation model, U-Net++, generated irregular network masks on the 1000-image dataset. Other classical color, texture, and shape features were calculated for the irregular network areas. We achieved an increase in the recall of melanoma versus benign of 11% and in accuracy of 2% over DL-only models using conventional classifiers in a sequential pipeline based on the cascade generalization framework, with the highest increase in recall accompanying the use of the random forest algorithm. The proposed approach facilitates leveraging the strengths of both deep learning and conventional image processing techniques to improve the accuracy of melanoma diagnosis. Further research combining deep learning with conventional image processing on automatically detected dermoscopic features is warranted.
A geometric analysis of convex demixing

Demixing is the task of identifying multiple signals given only their sum and prior information about their structures. Examples of demixing problems include (i) separating a signal that is sparse with respect to one basis from a signal that is sparse with respect to a second basis; (ii) decomposing an observed matrix into low-rank and sparse components; and (iii) identifying a binary codeword with impulsive corruptions. This thesis describes and analyzes a convex optimization framework for solving an array of demixing problems. Our framework includes a random orientation model for the constituent signals that ensures the structures are incoherent. This work introduces a summary parameter, the statistical dimension, that reflects the intrinsic complexity of a signal. The main result indicates that the difficulty of demixing under this random model depends only on the total complexity of the constituent signals involved: demixing succeeds with high probability when the sum of the complexities is less than the ambient dimension; otherwise, it fails with high probability. The fact that a phase transition between success and failure occurs in demixing is a consequence of a new inequality in conic integral geometry. Roughly speaking, this inequality asserts that a convex cone behaves like a subspace whose dimension is equal to the statistical dimension of the cone. When combined with a geometric optimality condition for demixing, this inequality provides precise quantitative information about the phase transition, including the location and width of the transition region.
Transcriptomic Effects on the Mouse Heart Following 30 Days on the International Space Station

Efforts to understand the impact of spaceflight on the human body stem from growing interest in long-term space travel. Multiple organ systems are affected by microgravity and radiation, including the cardiovascular system. Previous transcriptomic studies have sought to reveal the changes in gene expression after spaceflight. However, little is known about the impact of long-term spaceflight on the mouse heart in vivo. This study focuses on the transcriptomic changes in the hearts of female C57BL/6J mice flown on the International Space Station (ISS) for 30 days. RNA was isolated from the hearts of three flight and three comparable ground control mice and RNA sequencing was performed. Our analyses showed that 1147 transcripts were significantly regulated after spaceflight. The MAPK, PI3K-Akt, and GPCR signaling pathways were predicted to be activated. Transcripts related to cytoskeleton breakdown and organization were upregulated, but no significant change in the expression of extracellular matrix (ECM) components or oxidative stress pathway-associated transcripts occurred. Our results indicate an absence of cellular senescence, and a significant upregulation of transcripts associated with the cell cycle. Transcripts related to cellular maintenance and survival were most affected by spaceflight, suggesting that cardiovascular transcriptome initiates an adaptive response to long-term spaceflight.
Therapies for obsessive-compulsive disorder: Current state of the art and perspectives for approaching treatment-resistant patients

Even though obsessive compulsive disorder (OCD) is one of the ten most disabling diseases according to the WHO, only 30–40% of patients suffering from OCD seek specialized treatment. The currently available psychotherapeutic and pharmacological approaches, when properly applied, prove ineffective in about 10% of cases. The use of neuromodulation techniques, especially Deep Brain Stimulation, is highly promising for these clinical pictures and knowledge in this domain is constantly evolving. The aim of this paper is to provide a summary of the current knowledge about OCD treatment, while also discussing the more recent proposals for defining resistance.
Robust Tensor Decomposition for Heterogeneous Beamforming Under Imperfect Channel State Information

We propose a new robust variation of the tensor decomposition known as the multi-linear generalized singular value decomposition (ML-GSVD), and demonstrate its effectiveness in the design of joint transmit (TX) and receive (RX) beamforming (BF) for both the downlink (DL) and the uplink (UL) of cell-free massive multiple-input multiple-output (CF-mMIMO) systems. The new technique, dubbed the robust orthogonality-enforcing ML-GSVD (ROEML-GSVD), mitigates the degradation in the DL and UL BF design due to the tensor decomposition error and the channel state information (CSI) imperfection by exploiting their independence. Numerical results demonstrate that the proposed method is highly effective, leading not only to a significant reduction on the tensor decomposition error compared to state-of-the-art (SotA) methods, but also to the improvement in the data rate achieved by the subsequent beamformers in heterogeneous scenarios where users have distinct numbers of antennas.
Capsule Endoscopic MIMO Antenna With Radiation Pattern and Polarization Diversity

This article presents a novel compact dual-element capsule endoscopic multiple-input-multiple-output (MIMO) antenna at a 915 MHz industrial, scientific, and medical (ISM) band. Both radiating elements of the MIMO antenna system are circularly polarized (CP) at the desired frequency band (915 MHz). In fact, Antenna- 1 operates at left-handed CP (LHCP) and Antenna- 2 operates at right-handed CP (RHCP), enabling polarization diversity at 915 MHz. Moreover, the radiating elements radiate in two opposite directions; thus, they enable pattern diversity. The performance of the proposed antenna system is analyzed in a realistic human phantom. Reactive slots in the patch antennas are incorporated to achieve compactness ( $\pi \times $ (4.7) $^{\mathrm{ 2}}\,\,\times0.13$ = 9.01 mm3) and CP characteristics. Furthermore, a rectangular slot on the ground plane is introduced to reduce the mutual coupling between the radiating elements. The proposed antenna has compact dimensions of 9.01 mm3, a peak realized gain of −24.6 dBiC, and a high isolation level of 29.7 dB within the whole band. The specific absorption rate (SAR), link margin (LM) and main MIMO channel parameters are investigated which show good results. The key features of this antenna are its compact size, high isolation level between the antennas, and polarization and pattern diversity.
Approaches to robust ℋ︁∞ static output feedback control of discrete‐time piecewise‐affine systems with norm‐bounded uncertainties

This paper investigates the problem of robust ℋ︁∞ static output feedback controller design for a class of discrete‐time piecewise‐affine systems with norm‐bounded time‐varying parametric uncertainties. The objective is to design a piecewise‐linear static output feedback controller guaranteeing the asymptotic stability of the resulting closed‐loop system with a prescribed ℋ︁∞ disturbance attenuation level. Based on a piecewise Lyapunov function combined with S‐procedure, Projection lemma, and some matrix inequality convexifying techniques, several novel approaches to the static output feedback controller analysis and synthesis are developed for the underlying piecewise‐affine systems. It is shown that the controller gains can be obtained by solving a set of strict linear matrix inequalities (LMIs) or a family of LMIs parameterized by one or two scalar variables, which are numerically efficient with commercially available software. Finally, three simulation examples are provided to illustrate the effectiveness of the proposed approaches. Copyright © 2010 John Wiley & Sons, Ltd.
Multidimensional proximities and interorganizational coinnovation performance: The roles of intraorganizational collaboration network inefficiency

In a gradually more interlinked world, the formation of collaborations with partners is increasingly regarded as an important driver for generating innovation. Although multidimensional proximities are important factors influencing interorganizational coinnovation performance, relevant empirical studies have not reached consistent conclusions. By focusing on organizational dyad and including intraorganizational collaboration network inefficiency as a moderating variable, we explore the effects of multidimensional proximities on interorganizational coinnovation performance. By reference to 5G patent data collected in China between 2011 and 2020, the research results based on the quadratic assignment procedure (QAP) model show that geographical proximity, cognitive proximity, and institutional proximity all improve interorganizational coinnovation performance. In addition, the inefficiency of intraorganizational collaboration networks decreases the positive effect of geographical proximity but increases the positive effects of cognitive and institutional proximity in this context. These findings have both theoretical and practical implications for organizational partner selection.
An alternative proof of Diestel's theorem

We show that Diestel's theorem on weak compactness of subsets of L1,(μ, X) can be derived as a simple corollary of James's theorem. It is a pleasure to acknowledge several stimulating conserversations with Dave Emmons and the remarks of an anonymous referee. Errors are, of course, solely mine.
Determinants of Diabetes Mellitus Prevalence in Indonesia

The number of people with diabetes mellitus (DM) worldwide continues to increase. In 2019, Indonesia was the seventh country with the largest number of people with DM worldwide. The people with DM in Indonesia were dominated by the productive age population. This study aims to determine the variables affecting the prevalence of DM in Indonesia in 2018. The analysis unit used is 34 provinces in Indonesia, where the data comes from the Health Ministry of the Republic of Indonesia and Statistics Indonesia. Graph analysis and multiple linear regression are the methods used in this study. DKI Jakarta has the highest DM prevalence in Indonesia, reaching 3.4 percent. The prevalences of obesity and hypertension have a positive effect on the prevalence of DM. The result shows that every one percent increase in the prevalence of obesity will increase the prevalence of DM by 0.049 percent. While, every one percent increase will increase the prevalence of DM by 0.168 percent. The percentage of the population smoking, not exercising, the unemployment rate, and the average length of schooling does not affect the prevalence of DM in Indonesia.
Presliding friction mode: Modelling and experimental study with a ball-screw-driven set-up

To cope with the increasing high precision requirement in many applications of ball-screw-driven stages, system dynamic behaviour under friction force should be carefully investigated, especially in the presliding friction regime. Presliding friction has many special characteristics such as elasticity, hysteresis with memory and plasticity. In this paper, dynamic modelling of a ball-screw-driven stage under presliding friction is experimentally investigated. The model is then validated by the consistency of results in experiments and simulations. Despite the complex structure of the system and the complicated nature of friction, the system can be described by a set of nonlinear state equations.
Attenuation correction in SPECT using active surfaces

To compensate for the photon attenuation in SPECT, we propose to determine the boundary of the uniform attenuation map by detecting the surface of the patient body from the emission data. We use an active surface model which is a three dimensional generalisation of the active contour model known as snakes. The model is an elastic surface that is deformed under the action of internal and external forces. The internal forces model the smoothness constraints while the external forces model the image constraints. In our case, the image constraints are three-dimensional detected edges obtained using the Zucker and Hummel operator applied on the emission volume. The finite difference method is used to solve the energy-minimisation problem for a surface. The segmentation of the patient body from the emission tomography reconstruction is compared to the segmentation from the transmission tomography reconstruction. Moreover, tomography reconstruction corrected for the non-uniform attenuation is compared to the tomography reconstruction corrected for the uniform attenuation. Uniform attenuation correction is performed using the boundary detected from the emission data.
Measurement and modeling of millimeter-wave scattering from tree foliage

Because the constituent elements of a tree canopy, namely the leaves, needles, branches, and trunks, have complex geometries with curvatures and surface roughness scales that are comparable to or larger than the wavelength at millimeter wavelengths, the traditional approach used to compute the phase function of the vegetation volume is totally impractical. In this paper we propose a relatively simple model for characterizing the phase function on the basis of direct experimental data. The model is used in conjunction with a solution of the radiative transfer equation to predict the backscattering behavior of tree canopies. The model is found to provide very good agreement with radar observations made at 35, 94, and 140 GHz.