Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing the Relations Among Three Views Based on Artificial Neural Network

Multiple view geometry plays an important role in the traditional problem on the camera pose estimation from 2-D images. Given only 3 images of the same scene, one may apply the well-known fundamental matrix or trifocal tensor to make a robust computation of the poses among the three camera views. Practically, extracting the pose parameters from them is not straight forward. Multiple solutions arise and the final computed value may deviate from the actual one even if the closest solution is chosen since the process is highly nonlinear. In this paper, we are going to investigate the use of a machine learning approach to solve the problem. An artificial neural network is trained with noisy point features from three views to estimate the camera poses among them. It is found that the proposed method is able to compute accurate pose parameters under noisy environment. The effects of applying different amount of noises to the training and testing datasets have also been studied.
Pressure Sensitivity Coefficients of the BIPM Secondary Voltage Standards

At the 2016 CPEM, we presented the results of the re-evaluation of the temperature sensitivity coefficients of the BIPM secondary DC voltage standards based on Zener diodes. These standards are used as travelling references within the Key Comparison identified as BIPM.EM-K11 in the CIPM-MRA framework. As a follow-up of this work, we present here the reevaluation of the pressure sensitivity coefficients of the same standards. Since the standards are now fully characterized, their 10 V output was calibrated, for several consecutive days, using three completely independent measurement setups, based on three different Josephson Voltage Standards (JVSs). The results show that a repeatable significant systematic error of up to 1 part in 107 can appear for some specific artifacts depending on the operated measurement setup. We suspect the intrinsic properties (output impedance and leakage resistance to ground) of these standards to be responsible for the observed discrepancy.
The First Compact Model to Determine  $V_{T}$  Distribution for DG-FinFET Due to LER

We report the first compact model to estimate the <inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula> distribution of double gate-FinFET due to line edge roughness. We derive closed-form expressions, representing the compact model, for: 1) mean and standard deviation of fin width (<inline-formula> <tex-math notation="LaTeX">${W}_{\text {fin}}$ </tex-math></inline-formula>) in terms of geometrical and variability parameters of the FinFET; 2) <inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula> as a function of <inline-formula> <tex-math notation="LaTeX">${W}_{\mathrm {fin}}$ </tex-math></inline-formula> for uniform width fins; and 3) <inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula> in tapered fins using percolation. The <inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula> distribution produced from the compact model shows a good match with well-calibrated TCAD data and demonstrates an excellent accuracy (~3mV rms error) for a wide range of scaling and variability parameters. The model is simple, platform independent, and <inline-formula> <tex-math notation="LaTeX">$\sim {\text {10}}^{\text {4}}{\times }$ </tex-math></inline-formula> faster compared to TCAD. It enables intuitive understanding of design space for FinFET on the one hand, and accurate FinFET variability-aware circuits and system design on the other hand.
Weighing and Moving Mode Experiments in the KRISS Kibble Balance

KRISS Kibble balance was successfully set up and weighing and moving mode experiments were performed. The repeatability of weighing mode experiment, <tex>$(B\cdot l)_{stat}$</tex>, is about <tex>$3 \times 10^{-7}$</tex>. In moving mode, <tex>$(B\cdot l)_{dyn}$</tex>, was also evaluated with the region of <tex>$2.5\ \times 10^{-4}$</tex> over 10 mm range. Planck constant value will be measured through additional experiments.
Traceability to UTC Using the SIM Time Network Bilateral Comparisons

This paper describes how the Time and Frequency Laboratory of CENAMEP is preparing its process to use the SIM Time Network Bilateral Comparison as backup link to demonstrate the traceability to UTC for his calibration and dissemination services following the CCTF-WGMRA Guideline 9: CCTF criteria for obtaining traceability in Time and Frequency, and the procedures and uncertainty estimation explained in this document can be used as example for other Time and Frequency laboratories in SIM region that have similar characteristics as CENAMEP AIP, and are seeking to demonstrate traceability using the SIM Time Network.
A Novel Pedestrian Detection Method Based on Combination of LBP, HOG, and Haar-Like Features

The existing pedestrian detection methods are still challenging under abrupt illumination, different human shape, and cluttered backgrounds. In this contribution, we suggest a novel method to handle the above detection failures. On account of the fact that the potential of features are different and a single feature cannot extract the comprehensive information and human appearance can be better acquired by combinations of efficacious features, we combine HOG, LBP, and Haar-like features. Thus, the proposed method contains the edge, texture information, and local shape information. It should be mentioned that there has not been a method based on combination of these three features yet. After feature combination, linear SVM classifier is used to detect pedestrian images from nonpedestrian. In experiments, INRIA dataset, Daimler dataset, and ETH dataset are adopted as the training and testing sets. Each dataset was recorded in various environments, resolution, and background occlusion. As a result, employing three various datasets can help not only further enrich our data but also scrutinize the robustness and precision of the proposed method in more depth. The substantial experimental result indicated that the proposed scheme outperformed the state of the art methods in terms of the accuracy with comparable computational time.
Evaluating the Performances of an Operational Absolute Quantum Gravimeter

The Absolute Quantum Gravimeter (AQG) is an industry-grade gravimeter measuring $g$ with laser-cooled atoms, that has been developed by Muquans in close collaboration with RESIF (the French Seismologic and Geodetic Network, [2]) and the LNE-SYRTE laboratory. This paper reports on the evaluation of the first unit of the AQG and in particular on a statistical uncertainty on the measurement of $g$ better than 1.1 $\pmb{0}$−8 m/s? that has been reached in various types of environment and for month-long continuous measurements. The relevance of the AQG for a use in metrological laboratories is also discussed.
Volume Measurement of a 28Si-Enriched Sphere to Determine the Avogadro Constant at NMIJ

To determine the Avogadro constant by the International Avogadro Coordination using the x-ray crystal density method, the volume of a28Si-enriched sphere, named Si28kgOla, was measured at NMIJ. The volume was determined from the diameter measurement in 2175 directions by an optical interferometer. Taking into account the results of the surface characterization of the sphere by x-ray photoelectron spectroscopy and spectroscopic ellipsometry, the volume of the Si core excluding the surface layers was determined with a relative uncertainty of $\pmb{2\ \times\ 10^{-8}}$. Details of the interferometer and the uncertainty analysis of the volume measurement are presented.
Combined N‐terminal androgen receptor and autophagy inhibition increases the antitumor effect in enzalutamide sensitive and enzalutamide resistant prostate cancer cells

Multiple androgen receptor (AR)‐dependent and ‐independent resistance mechanisms limit the efficacy of current castration‐resistant prostate cancer (CRPC) treatment. Novel N‐terminal domain (NTD) binding AR‐targeting compounds, including EPI‐001 (EPI), have the promising ability to block constitutively active splice variants, which represent a major resistance mechanism in CRPC. Autophagy is a conserved lysosomal degradation pathway that acts as survival mechanism in cells exposed to anticancer treatments. We hypothesized, that promising NTD‐AR treatment may upregulate autophagy and that a combination of NTD‐AR and autophagy inhibition might therefore increase antitumor effects.
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks

The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.
Social network types, health, and well-being of older Asian Americans

Abstract Objectives: The purposes of this study were to: (1) develop an empirical typology of the social networks in diverse groups of older Asian Americans using both structural and subjective criterion variables; and (2) examine the relationship of the social network types to the measures of the health and well-being (self-rating of health, mental distress, and life satisfaction). Method: The participants included 533 older Asian Americans (Chinese, Asian Indian, Korean, Vietnamese, and other Asians) who participated in the 2015 Asian American Quality of Life Survey in Central Texas. Latent profile analysis (LPA) was conducted using seven social network-related variables. The identified typologies were then regressed on the indicators of health and well-being (poor rating of health, probable mental distress, and dissatisfaction with life). Results: The LPA identified the model with four network types as being most optimal. The groups were named “diverse-integrated” (37.5%), “moderately diverse-integrated” (21.2%), “marginally restricted-noncongregant” (22.1%), and “restricted-congregant” (19.1%). The results suggested that compared to the “diverse-integrated” group, the “marginally restricted-noncongregant type” fared worse than “the restricted-congregant group” in that the former had greater odds of both mental distress and dissatisfaction with life while the latter had higher odds only for dissatisfaction with life. Conclusion: Unlike previous network studies, network types were shaped in the continuum of different social ties and subjective evaluation on strength of social ties. Certain ethnic groups were over-represented in specific network types. Such findings suggest the importance of understanding ethnic group variations in network vulnerabilities when considering interventions.
Saliency-Based Spatiotemporal Attention for Video Captioning

Most existing video captioning methods ignore the visual saliency information in videos. We suppose that using saliency information can be helpful to generate more accurate video captions. Therefore, we propose a saliency-based spatiotemporal attention mechanism, and integrate it with the encoder-decoder framework of the classical video captioning model. Especially, we design a residual block which can use the saliency information to properly extract the visual feature of video frames. We evaluate our method on MSVD dataset and the results show that exploiting the visual saliency information can improve the performance of video captioning. Specifically, when compared with the traditional temporal attention method, our saliency-based temporal attention model can improve the METEOR and CIDEr metrics by 3.4% and 22.5% respectively. While by using the full saliency-based spatiotemporal attention mechanism, we can further improve the METEOR and CIDEr by 4.5% and 23.1 % respectively.
The effects of postpartum extended contact on mother-infant behaviors at two days and three months infant age /

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Subjective Age and Emotion Covariation: Findings from Two Daily Experience Studies.

Objectives This paper focuses on an aspect of emotional complexity as seen in the covariation between positive affect (PA) and negative affect (NA). Lifespan theories predict distinctive patterns of change in emotion covariation with chronological age. Nevertheless, research shows mixed evidence with most studies failing to find a significant connection between chronological age and emotion covariation. We propose to look beyond chronological age and assess the relationship between subjective age and emotion covariation. Subjective age refers to how old one perceives oneself to be, and therefore may be more pertinent to one's emotional experience than chronological age. We further explored whether the relationship between subjective age and emotion covariation is modified by chronological age. Method We used data from two daily diary study samples (N=188, mean age=57.84, range=29-100, and N=334, mean age=58.15, range=30-90). Results Multilevel models showed that individuals who perceived themselves as older showed stronger inverse PA-NA relationship, reflecting lower emotional complexity. Chronological age (net of subjective age) and emotion covariation were unrelated in both samples. Moreover, in Study 2 there was a three-way interaction between PA, subjective age and chronological age, suggesting that subjective age is more strongly related to emotion covariation among older adults than among younger adults. Discussion The relationship between subjective age and emotion covariation is discussed in light of lifespan theories.
Complement C3 Produced by Macrophages Promotes Renal Fibrosis via IL-17A Secretion

Complement synthesis in cells of origin is strongly linked to the pathogenesis and progression of renal disease. Multiple studies have examined local C3 synthesis in renal disease and elucidated the contribution of local cellular sources, but the contribution of infiltrating inflammatory cells remains unclear. We investigate the relationships among C3, macrophages and Th17 cells, which are involved in interstitial fibrosis. Here, we report that increased local C3 expression, mainly by monocyte/macrophages, was detected in renal biopsy specimens and was correlated with the severity of renal fibrosis (RF) and indexes of renal function. In mouse models of UUO (unilateral ureteral obstruction), we found that local C3 was constitutively expressed throughout the kidney in the interstitium, from which it was released by F4/80+macrophages. After the depletion of macrophages using clodronate, mice lacking macrophages exhibited reductions in C3 expression and renal tubulointerstitial fibrosis. Blocking C3 expression with a C3 and C3aR inhibitor provided similar protection against renal tubulointerstitial fibrosis. These protective effects were associated with reduced pro-inflammatory cytokines, renal recruitment of inflammatory cells, and the Th17 response. in vitro, recombinant C3a significantly enhanced T cell proliferation and IL-17A expression, which was mediated through phosphorylation of ERK, STAT3, and STAT5 and activation of NF-kB in T cells. More importantly, blockade of C3a by a C3aR inhibitor drastically suppressed IL-17A expression in C3a-stimulated T cells. We propose that local C3 secretion by macrophages leads to IL-17A-mediated inflammatory cell infiltration into the kidney, which further drives fibrogenic responses. Our findings suggest that inhibition of the C3a/C3aR pathway is a novel therapeutic approach for obstructive nephropathy.
Disturbed FLIERs in Planetary Nebulae

We examine the recent claim by Balick et al. that their observations of fast low-ionization emission regions in four planetary nebulae pose serious problems to the so-called fast-bullet model for the FLIERs. Using timescale estimates and the results of existing numerical studies, and from the phenomenology of 11 objects, we conclude that, contrary to the claim made in the above paper, the fast-bullet model remains consistent with observations.
GA-STIP: Action Recognition in Multi-Channel Videos With Geometric Algebra Based Spatio-Temporal Interest Points

Spatiotemporal interest points (STIPs) are often extracted for action recognition in videos. But most of the current interest point detection methods are not suitable for multi-channel videos. In this paper, a new geometric algebra (GA)-based STIP detection method is proposed for action recognition in multi-channel videos. Specifically, we take the advantage of GA to represent multi-channel videos and develop a new method to obtain multi-scale space, and then GA-based difference of Gaussian is computed to detect STIPs. Experimental results show that GA-STIP method can achieve better performance for multi-channel video action recognition.
Investigation of Optimal Train Movement Modes by Means of Simulation Modeling

The paper considers the problem of optimization of train traffic regimes, which continues to be relevant. An approach to its solution based on the use of simulation technology on the basis of modern tools is proposed. The structural diagram of the dynamic system “man-train”, characterizing the control activity of the drivers of modern locomotives, has been developed and a functional diagram of the automatic train control system has been proposed. The paper presents a mathematical model of train movement, which is based on the equation of motion and calculated relationships for determining the forces acting on the train. In the environment of Matlab / Simulink, an imitation model of train motion is developed, the feature of which, unlike known ones, is hybrid: mechanical and electrical processes are described by continuous models, and control processes are discrete-event model. The problem of train traffic regimes optimization and its reduction to a special problem of mathematical programming is considered. An algorithm for calculating the energy-optimal train trajectory based on the ideas of random search is proposed and the results of simulation are presented.
An Efficient Data Retrieval Parallel Reeb Graph Algorithm

The Reeb graph of a scalar function that is defined on a domain gives a topologically meaningful summary of that domain. Reeb graphs have been shown in the past decade to be of great importance in geometric processing, image processing, computer graphics, and computational topology. The demand for analyzing large data sets has increased in the last decade. Hence, the parallelization of topological computations needs to be more fully considered. We propose a parallel augmented Reeb graph algorithm on triangulated meshes with and without a boundary. That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure. We demonstrate the running time of our algorithm on standard datasets. As an application, we show how our algorithm can be utilized in mesh segmentation algorithms.
The JILA (Parks-Faller) 2010 Determination of the Newtonian Constant

We present here an update of the JILA (Parks-Faller) 2010 determination of the Newtonian Constant of gravitation (big $\pmb{G}$). Due to an Abbe error that was unaccounted for in the initial analysis, we adjust our value of the gravitational constant $\pmb{G}$ from $\pmb{(6.67234\pm\ 0.00014) \times 10^{-11}\text{m}^{3}\text{kg}^{-1}\text{s}^{-2}}$ to $\pmb{(6.67296\pm 0.00022)\times 10^{-11}\text{m}^{3}\text{kg}^{-1}\text{s}^{-2}}$. We discuss the source of the error and how it can be avoided in future iterations of this experiment.
Development of Multimethod Approach to Rubrication of Unstructed Electronic Text Documents in Various Conditions

At present, the tools of information and communication interaction of the municipal and federal authorities with the citizens and organizations are actively developing. The increasing volume of electronic information leads to the need to classify multiple incoming messages. However, the specific features of such documents (small volume, lack of structuring, presence of grammatical and syntactic errors, thesaurus non-stationarity, etc.) make their statistical analysis more difficult. Also, a significant difference in the conditions of their processing does not allow using a universal method of the text document classification. This raises the urgent task of developing a multimethod approach to the rubrication of unstructured electronic documents based on the application of probabilistic and intelligent methods of analyzing text data. The computational experiments carried out in the context of interrelated and non-interrelated rubrics showed the prospects of their practical application.
Energy-Aware Dynamic Resource Allocation in UAV Assisted Mobile Edge Computing Over Social Internet of Vehicles

Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles by integrating vehicle-to-everything communications and social networking properties into the vehicular environment. Through the provision of diverse socially-inspired applications and services, the emergence of SIoV helps to improve the road experience, traffic efficiency, road safety, travel comfort, and entertainment along the roads. However, the computation performance for those applications have been seriously affected by resource-limited on-board units as well as deployment costs and workloads of roadside units. Under such context, an unmanned aerial vehicle (UAV)-assisted mobile edge computing environment over SIoV with a three-layer integrated architecture is adopted in this paper. Within this architecture, we explore the energy-aware dynamic resource allocation problem by taking into account partial computation offloading, social content caching, and radio resource scheduling. Particularly, we develop an optimization framework for total utility maximization by jointly optimizing the transmit power of vehicle and the UAV trajectory. To resolve this problem, an energy-aware dynamic power optimization problem is formulated under the constraint of the evolution law of energy consumption state for each vehicle. By considering two cases, i.e., cooperation and noncooperation among vehicles, we obtain the optimal dynamic power allocation of the vehicle with a fixed UAV trajectory via dynamic programming method. In addition, under the condition of fixed power, a search algorithm is introduced to derive the optimized UAV trajectory based on acceptable ground-UAV distance metric and the optimal offloaded data size of the vehicle. Simulation results are presented to demonstrate the effectiveness of the proposed framework over alternative benchmark schemes.