Miracle-dz/UniFAR-FTUs huggingface dataset:
Manufacturing Process of Optically Transparent Ultrasound Transducer: A Review

Ultrasound (US) transducer is a crucial component for several imaging modalities, where acoustic sensing is utilized as a part of hybrid or combination of pure optical and US imaging. However, conventional US transducers are opaque, and the optical components in the system require a different pathway to avoid any interference. The absence of coaxial integration limits the optical illumination area, adds complexity, and makes the system bulky. Moreover, the system performance is deteriorated due to the unoptimized illumination in hybrid imaging and the lack of correlation between the data acquired through the combined modalities. To overcome these drawbacks, different approaches to realize optically transparent ultrasound transducers (TUTs) have been explored that include piezoelectric-based and microelectromechanical system-based capacitive and piezoelectric ultrasonic transducers. Unlike conventional opaque transducers, each component of TUTs requires specific materials and a certain recipe which all together determine the overall performance of the transducer. In this article, we have performed a comparative analysis on the materials and on the fabrication process used to make different types of TUT.
A New Regularization Method for Fast and Accurate Propagation of Roto-Translationally Coupled Asteroids

There is a well-distinguished group of asteroids for which the roto-translational coupling is known to have a non-negligible effect in the long-term. The study of such asteroids suggests the use of specialized propagation techniques, where perturbation methods make their best. The techniques from which the special regularization method DROMO is derived, have now been extended to the attitude dynamics, with equally remarkable results in terms of speed and accuracy, thus making the combination of these algorithms specially well-suited to deal with the propagation of bodies with strong attitude coupling.
Inter- and intrafraction dose variations in robotic stereotactic body radiation therapy (SBRT) for perihilar cholangiocarcinoma in the prospective phase I STRONG trial

Using fiducial-marker-based robotic respiratory tumor tracking, we treated perihilar cholangiocarcinoma patients in the STRONG trial with 15 daily fractions of 4 Gy. For each of the included patients, in-room diagnostic-quality repeat CTs (rCT) were acquired pre- and post-dose delivery in 6 treatment fractions to analyze inter- and intrafraction dose variations. Planning CTs (pCTs) and rCTs were acquired in expiration breath-hold. Analogous to treatment, spine and fiducials were used to register rCTs with pCTs. In each rCT, all OARs were contoured, and the target was rigidly copied from the pCT based on grey values. The rCTs acquired were used to calculate the doses to be delivered through the treatment-unit settings. On average, target doses in rCTs and pCTs were similar. However, due to target displacements relative to the fiducials in rCTs, 10% of the rCTs showed PTV coverage losses of >10%. Although target coverages had been planned below desired values in order to protect OARs, many pre-rCTs contained OAR constraint violations: 44.4% for the 6 major constraints. Most OAR dose differences between pre- and post-rCTs were not statistically significant. The dose deviations observed in repeat CTs represent opportunities for more advanced adaptive approaches to enhancing SBRT treatment quality.
Evaluation of Advanced Platelet-Rich Fibrin Associated with Zucchelli Technique in the Treatment of Cairo Type 1 Gingival Recession: Randomized Clinical Trial

Aims: To evaluate the effectiveness of the Coronally Advanced Flap Modified by Zucchelli with A-PRF in root coverage in patients with Cairo type I gingival recession. Study Design: Randomized Clinical Trial. Place and Duration of Study: Sample: Department of Periodontics, between January and April 2022. Methodology: The Consolidated Standards of Reporting Trials 2010 protocol was followed for sample selection and distribution. It was a prospective clinical study, split-mouth parallel randomized, including 54 recessions within Group MCAF+SCTG: Zucchelli Technique + SCTG; and Group 2: Zucchelli Technique + A-PRF; 6 blood samples were collected; centrifugation (1300 rpm for 14 minutes, with G force of 210); and preparation of A-PRF membranes in the Stainless Steel Kit (Intra-Luck®). For the analysis of the results, the periodontal indices of height and width of the GR, probing depth, clinical insertion level, gingival level, keratinized tissue width, keratinized gingival tissue thickness, and sensitivity level, were used in the initial period, with 90 and 180 days of follow-up. GraphPad Prism 8.0 program® was used for statistical analysis. ANOVA and TUKEY tests were performed. Kruskall-WALLIS and DUNN tests were performed to analyze the pain parameter, with significance level of 5%. Results: Parameters probing depth and bleeding index remained without statistically significant changes in both groups. There was improvement in parameters height and width of the recession, height of the inserted gum, thickness of the gum and level of sensitivity in both groups. There was no statistically significant difference between groups. Conclusion: There was clinical improvement in all criteria evaluated for both groups. Thus, the Coronally Advanced Flap modified by Zucchelli associated with A-PRF is as effective as the Coronally Advanced Flap modified by Zucchelli associated with SCTG.
An improved particle swarm optimizer and its application in blind signals separation

In this paper,an improved particle swarm optimizer is first presented by replacing personal best particle with the weight average of personal best particles in swarm,and adjusting adaptively acceleration coefficients based on particle's density in swarm.The proposed algorithm applies more particles' information,and adjusts adaptively "cognition" component and "social" component,thus improves convergence performance.Then,based on second order correlation coefficient and high order correlation coefficient,a cost function of independence of signals is proposed,and an algorithm for linear blind signals separation is presented by applying the improved particle swarm optimizer to the cost function.The experiment results demonstrate that the improved particle swarm optimizer is superior to original particle swarm optimization,and the algorithm for linear blind signals separation is efficient.
Ultra Wideband (UWB) Localization Using Active CIR-Based Fingerprinting

Indoor positioning systems using Ultra Wideband (UWB) achieve high positioning accuracy (<inline-formula> <tex-math notation="LaTeX">${< }30$ </tex-math></inline-formula> cm). However, traditional localization approaches require many packet exchanges (e.g. two-way ranging) or challenging clock synchronization (e.g. time difference of arrival). To remedy this, we propose active fingerprinting using the channel impulse response (CIR) from a single UWB packet received at each UWB anchor. The proposed neural network anchor-subset selection method with Savitzky-Golay filter achieves a low mean absolute error (<inline-formula> <tex-math notation="LaTeX">$20.9 - 87.0$ </tex-math></inline-formula> cm), in contrast to signal strength based fingerprinting approaches that realize accuracies of <inline-formula> <tex-math notation="LaTeX">$2 - 3$ </tex-math></inline-formula> m. Finally, with CIR interpolation the data collection overhead is reduced.
Influence of Folate-Targeted Gold Nanoparticles on Subcellular Localization and Distribution into Lysosomes

The cell interaction, mechanism of cell entry and intracellular fate of surface decorated nanoparticles are known to be affected by the surface density of targeting agents. However, the correlation between nanoparticles multivalency and kinetics of the cell uptake process and disposition of intracellular compartments is complicated and dependent on a number of physicochemical and biological parameters, including the ligand, nanoparticle composition and colloidal properties, features of targeted cells, etc. Here, we have carried out an in-depth investigation on the impact of increasing folic acid density on the kinetic uptake process and endocytic route of folate (FA)-targeted fluorescently labelled gold nanoparticles (AuNPs). A set of AuNPs (15 nm mean size) produced by the Turkevich method was decorated with 0–100 FA-PEG3.5kDa-SH molecules/particle, and the surface was saturated with about 500 rhodamine-PEG2kDa-SH fluorescent probes. In vitro studies carried out using folate receptor overexpressing KB cells (KBFR-high) showed that the cell internalization progressively increased with the ligand surface density, reaching a plateau at 50:1 FA-PEG3.5kDa-SH/particle ratio. Pulse-chase experiments showed that higher FA density (50 FA-PEG3.5kDa-SH molecules/particle) induces more efficient particle internalization and trafficking to lysosomes, reaching the maximum concentration in lysosomes at 2 h, than the lower FA density of 10 FA-PEG3.5kDa-SH molecules/particle. Pharmacological inhibition of endocytic pathways and TEM analysis showed that particles with high folate density are internalized predominantly by a clathrin-independent process.
Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm

In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization.
Accurate Backscattering Studies for Double‐Layered Array of Infinitely Long Cylinders

The two‐dimensional Helmholtz equation with Dirichlet's boundary conditions is rigorously solved for multiple arbitrary profiled cylinders. This describes the wave diffraction of E‐polarized electromagnetic field. The developed algorithm is used for calculation of the radar cross‐section of an infinitely long double‐layered array. In this article, we focus on the resonance response of the structure caused by excitation of the high Q‐factor oscillations. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2127–2131, 2013
Berberine Inhibits Endothelial Cell Proliferation via Repressing ERK1/2 Pathway

Abnormal angiogenesis plays a key role in cancer progression. In recent years, anti-angiogenic therapy has attracted increasing attention. Berberine (BBR), the main component extracted from Coptis (Ranunculaceae) rhizome, has an anti-angiogenic effect. However, the underlying mechanisms remain to be elucidated. Endothelial cell proliferation is a pivotal process in angiogenesis. In our research, we observed that BBR specifically downregulated the expression of the extracellular signal-regulated kinase 1/2 (ERK1/2) protein in human umbilical vein endothelial cells (HUVECs). The role of BBR in HUVEC proliferation was then assessed using methylthiazolyldiphenyl-tetrazolium bromide and cell counting Kit-8 (CCK-8) assays. The effect of BBR on the ERK1/2 signaling pathway was evaluated using Western blotting. BBR decreased HUVEC proliferation in a dose-dependent manner and inhibited the expression of phospho-ERK1/2 in HUVECs. PD98059, a specific inhibitor of ERK1/2 signaling, attenuated the BBR-induced decrease in the proliferation of HUVECs. Phorbol 12-myristate 13-acetate, a natural activator of ERK1/2 signaling, did not alter BBR-induced proliferation. In conclusion, BBR inhibited endothelial cell proliferation by suppressing ERK1/2 signaling. These findings may provide a potential therapeutic strategy for suppressing tumor growth.
AN APPROXIMATE MATRIX INVERSION PROCEDURE BY PARALLELIZATION OF THE SHERMAN–MORRISON FORMULA

Abstract The Sherman–Morrison formula is one scheme for computing the approximate inverse preconditioner of a large linear system of equations. However, parallelizing a preconditioning approach is not straightforward as it is necessary to include a sequential process in the matrix factorization. In this paper, we propose a formula that improves the performance of the Sherman–Morrison preconditioner by partially parallelizing the matrix factorization. This study shows that our parallel technique implemented on a PC cluster system of eight processing elements significantly reduces the computational time for the matrix factorization compared with the time taken by a single processor. Our study has also verified that the Sherman–Morrison preconditioner performs better than ILU or MR preconditioners.
Social Networks and Cardiovascular Disease Events in the Jackson Heart Study

Background Cardiovascular disease (CVD) disproportionately affects Black adults. Greater social networks (SNs), or social connectedness, may lower the risk of CVD events. This study determined the association of SNs and incident CVD and tested mediation by depressive symptoms, hypertension control, and diabetes control. Methods and Results We used the Social Network Index at exam 1 (2000–2004) to develop a continuous standardized SN score and binary categories (high versus low) among participants in the Jackson Heart Study (n=4686; mean age, 54.8 years). Surveillance of coronary heart disease, stroke, and heart failure events occurred after exam 1 (2005 for HF) until 2016. Using Cox proportional hazards regression, we estimated the association of SNs and CVD events by sex and tested the mediation of depressive symptoms, hypertension control, and diabetes control. Models adjusted for age, education, health behaviors, CVD comorbidities, and depressive symptoms. Among women, the SN score was associated with a lower hazard of stroke, coronary heart disease, and heart failure after full adjustment (hazard ratio [HR], 0.78 [95% CI, 0.64–0.95]; HR, 0.79 [95% CI, 0.71–0.88]; and HR, 0.78 [95% CI, 0.66–0.92], respectively). SN scores were also associated with a lower hazard of coronary heart disease in men (HR, 0.84 [95% CI, 0.75–0.94]) after full adjustment. High versus low SNs were associated with a lower hazard of coronary heart disease and heart failure among women after full adjustment. There was no evidence of mediation by depressive symptoms, diabetes control, and hypertension control. Conclusions Higher SNs may lower the risk of CVD events, especially in women.
Tropical raindrop size distribution for the prediction of rain attenuation of microwaves in the 10-40 GHz band

This paper describes the measurement of microwave rain attenuation over a ten-year period (1988-1998) in Singapore. The rain attenuation of (vertically and horizontally polarized) microwaves at 15, 21, and 38 GHz propagating over a path length of approximately 1.1 km were recorded. A negative-exponential raindrop size distribution model is then developed by fitting of the experimental data with the total extinction cross sections of oblate spheroidal raindrops computed from the T-matrix approach. Graphs for attenuation versus rain rate (at selected frequencies) and for attenuation versus frequency (at selected rain rates) are also given.
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms

In this paper we propose a general methodology to derive regret bounds for randomized multi-armed bandit algorithms. It consists in checking a set of sufficient conditions on the sampling probability of each arm and on the family of distributions to prove a logarithmic regret. As a direct application we revisit two famous bandit algorithms, Minimum Empirical Divergence (MED) and Thompson Sampling (TS), under various models for the distributions including single parameter exponential families, Gaussian distributions, bounded distributions, or distributions satisfying some conditions on their moments. In particular, we prove that MED is asymptotically optimal for all these models, but also provide a simple regret analysis of some TS algorithms for which the optimality is already known. We then further illustrate the interest of our approach, by analyzing a new Non-Parametric TS algorithm (h-NPTS), adapted to some families of unbounded reward distributions with a bounded h-moment. This model can for instance capture some non-parametric families of distributions whose variance is upper bounded by a known constant.
Long-Moody construction of braid representations and Katz middle convolution

The Long-Moody construction is a method to obtain representations of braid groups introduced by Long and Moody. Also the Katz middle convolution is known to be a method to construct local systems on $\mathbb{C}\backslash\{n\text{-points}\}$ introduced by Katz. In this paper, we explain that these two methods are naturally unified and define a new functor which we call the Katz-Long-Moody functor. This functor extends the framework of Katz algorithm to categories of local systems on various topological spaces, for example, $B_{n}$-bundles associated with simple Weierstrass polynomials, complements of hyperplane arrangements of fiber-type, link complements in the solid torus, and so on.
A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state-of-the-art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.
Research on CPI Prediction Based on Natural Language Processing

In the past, the seed keywords for CPI prediction were often selected based on empirical summaries of research and literature studies, which were prone to select omitted and invalid variables. In this paper, we design a keyword expansion technique for CPI prediction based on the cutting-edge NLP model, PANGU. We improve the CPI prediction ability using the corresponding web search index. Compared with the unsupervised pre-training and supervised downstream fine-tuning natural language processing models such as BERT and NEZHA, the PANGU model can be expanded to obtain more reliable CPI-generated keywords by its excellent zero-sample learning capability without the limitation of the downstream fine-tuning data set. Finally, this paper empirically tests the keyword prediction ability obtained by this keyword expansion method with historical CPI data.
Hierarchical Neural Program Synthesis

Program synthesis aims to automatically construct human-readable programs that satisfy given task specifications, such as input/output pairs or demonstrations. Recent works have demonstrated encouraging results in a variety of domains, such as string transformation, tensor manipulation, and describing behaviors of embodied agents. Most existing program synthesis methods are designed to synthesize programs from scratch, generating a program token by token, line by line. This fundamentally prevents these methods from scaling up to synthesize programs that are longer or more complex. In this work, we present a scalable program synthesis framework that instead synthesizes a program by hierarchically composing programs. Specifically, we first learn a task embedding space and a program decoder that can decode a task embedding into a program. Then, we train a high-level module to comprehend the task specification (e.g., input/output pairs or demonstrations) from long programs and produce a sequence of task embeddings, which are then decoded by the program decoder and composed to yield the synthesized program. We extensively evaluate our proposed framework in a string transformation domain with input/output pairs. The experimental results demonstrate that the proposed framework can synthesize programs that are significantly longer and more complex than the programs considered in prior program synthesis works. Website at https://thoughtp0lice.github. io/hnps_web/
Tradeoff of generalization error in unsupervised learning

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error (ME) and the data error (DE)—using a more complex model reduces the ME at the cost of the DE, with the DE playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.