Miracle-dz/UniFAR-FTUs huggingface dataset:
Romani Girls Matter: Developing a Participatory Action Research Protocol for Reproductive Justice

Despite the last few decades’ advances towards social and gender justice, reproductive decisions are still a source of oppression for many European Romani women and girls. This protocol aims to propose a model to empower Romani women and girls’ reproductive decisions, inspired by Reproductive Justice—the recognition of women and girls’ ability to make safe and free decisions about their bodies and reproduction. Through Participatory Action Research, 15–20 Romani girls and their families, two Romani platforms, and key agents from a rural and an urban context in Spain will participate. They will (1) contextualize Romani women and girls’ inequities, (2) build partnerships, (3) implement Photovoice and advocate for their gender rights, and (4) assess the initiative’s related changes, using self-evaluation techniques. Qualitative and quantitative indicators will be collected to assess impacts among participants, while tailoring and assuring the quality of the actions. Expected outcomes include the creation and consolidation of new social networks, and the promotion of Romani women and girls’ leadership. For this, Romani organizations must be transformed into empowering settings for their communities, spaces where Romani women and girls assume responsibility of the initiatives, being these tailored to their real needs and interests, and guaranteeing transformative social changes.
Nonlinear optical processes at quadrupole polariton resonance in Cu2O as probed by a Z-scan technique

O when the input laser frequency is tunedto the two-photon quadrupole polariton resonance. The Z-scan response at allows us to accurately estimatethe absolute number of polaritons generated via two-photon absorption. A striking dip is observed near the 2Z-scan focus which basically arises fromAuger-type recombination of polaritons. Under high excitation levels,the 3 Z-scan shows strong third harmonic generation. Based on the nonlinear optical parameters determined,we estimate the experimental polariton density achievable and propose a direction for polariton-based Bose-Einstein condensation in Cu
A Comprehensive Survey on SAR ATR in Deep-Learning Era

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field.
Investigations into local piezoelectric properties by atomic force microscopy

We describe nanoscale characterization of sol-gel produced ferroelectric thin films of lead–zirconate–titanate. We have performed quantitative localized measurements of surface polarization charge density using atomic force microscopy techniques in conjunction with electric field calculations. We show that domains with radii of 40 nm may by written and subsequently characterized, and we analyze the dependence of domain size on write voltage and write time, and show that surface contaminants influence the formation of domains.
Occurrence of Winter Stratospheric Sudden Warming Events and the Seasonal Timing of Spring Stratospheric Final Warming

AbstractBased on the NCEP–NCAR reanalysis dataset covering 1958–2012, this paper demonstrates a statistically significant relationship between the occurrence of major stratospheric sudden warming events (SSWs) in midwinter and the seasonal timing of stratospheric final warming events (SFWs) in spring. Specifically, early spring SFWs that on average occur in early March tend to be preceded by non-SSW winters, while late spring SFWs that on average take place up until early May are mostly preceded by SSW events in midwinter. Though the occurrence (absence) of SSW events in midwinter may not always be followed by late (early) SFWs in spring, there is a much higher (lower) probability of late SFWs than early SFWs in spring after SSW (non-SSW) winters, particularly when the winter SSWs occur no earlier than early January or in the period from late January to early February. Diagnosis shows that, corresponding to an SSW (non-SSW) winter and the following late (early)-SFW spring, intensity of planetary wave acti.
Robot grasping system based on deep learning target detection

The traditional robot grasping system often uses fixed point grasping or demonstrative grasping, but with the increase in the diversity of grasping targets and the randomness of poses, the traditional grasping method is no longer sufficient. A robot grasping method based on deep learning target detection is proposed for a high error rate of target recognition and low success rate of grasping in the robot grasping process. The method investigates the robotic arm hand-eye calibration and the deep learning-based target detection and poses estimation algorithm. The Basler camera is used as the visual perception tool of the robot arm, the AUBO i10 robot arm is used as the main body of the experiment, and the PP-YOLO deep learning algorithm performs target detection and poses estimation on the object. Through the collection of experimental data, several grasping experiments were conducted on the diversity of targets randomly placed in the poses under real scenes. The results showed that the success rate of grasping target detection was 94.93% and the robot grasping success rate was 93.37%.
Acquisition of fuzzy rules using fuzzy neural networks with forgetting

We acquire fuzzy rules from data using a fuzzy neural network. First, we generate an initial fuzzy neural network of the specified number of fuzzy rules that have fewer good membership functions than generated using a self-organization algorithm by Kohonen. Then, we tune and prune fuzzy rules based on a structural learning algorithm with forgetting by Ishikawa (1996), where the numerals in the consequent part and the center values and widths of membership functions in the antecedent part are tuned and forgotten a little, and thus redundant rules and variables are pruned to acquire simpler, general rules. We apply the method to the iris classification problem, of Fisher (1936) and have a very good result.
Adaptive Adjustment of Factor’s Weight for a Multi-Sensor SLAM

A multi-sensor fusion simultaneous localization and mapping(SLAM) method based on factor graph optimization that can adaptively modify the weight of the graph factor is proposed in this study, to enhance the localization and mapping capability of autonomous robots in tough situations. Firstly, the algorithm fuses multi-lines lidar, monocular camera, and inertial measurement unit(IMU) in the odometry. Second, the factor graph is constructed using lidar and visual odometry as the unary edge and binary edge constraints, respectively, with the motion determined by IMU odometry serving as the primary odometry in the system. Finally, different increments of IMU odometry, lidar odometry and visual odometry are computed as favor factors to realize the adaptive adjustment of the factor’s weight. The suggested method has greater location accuracy and a better mapping effect in complex situations when compared to previous algorithms.
Robust adaptive motion/force control of uncertain nonholonomic mechanical systems

The position/force tracking control of Lagrangian mechanical systems with classical nonholonomic constraints is addressed. The main feature of the paper is that: 1) the control strategy is developed at the dynamic level and can deal with model uncertainties in the mechanical systems; 2) the proposed control law ensures the desired trajectory tracking of the configuration state of the closed-loop system; 3) the tracking error of the constraint force is uniformly ultimately bounded with a controllable bound; and 4) a global asymptotic stability result is obtained in the Lyapunov sense. A detailed numerical example is presented to illustrate the developed method.
Selection of Working Correlation Structure in Generalized Estimating Equations via Empirical Likelihood

Generalized estimating equations (GEE) are a popular class of models for analyzing discrete longitudinal data, and do not require the specification of a full likelihood. The GEE estimator for the regression parameter will be the most efficient if the working correlation matrix is correctly specified. Hence it is desirable to choose a working correlation matrix that is the closest to the underlying structure among a set of working structures. In the absence of a parametric likelihood, traditional likelihood-based model selection methods cannot be used for comparing GEE models. Combining the reliability of nonparametric methods with the flexibility and effectiveness of likelihood approaches, empirical likelihood (EL) has the potential to become a model selection tool for GEE. We propose an EL approach to select the working correlation structure in GEE. Our approach is compared to existing methods based on quasi-likelihood or resampling procedures. The effectiveness of the proposed method is demonstrated by simulations. Supplemental materials for this article are available online.
Simulation of Multiple Acoustic Diffraction Based on An Iterative Ray Tracing Method

The classical geometrical theory of diffraction is not available for simulating multiple diffractions in wave propagation. To improve the simulating accuracy for multiple diffractions, a multiple acoustic diffraction simulating method based on ray iteration tracing is proposed in this paper. In this method, the ray occurring at the edge of the wall is split into multiple sub-rays. Then, these sub-rays are continually traced based on the classical ray-tracing theory. The numerical verification demonstrates that this method is valid for simulating multiple diffractions.
Tapered optical fibers using CO2 laser and their sensing performances

In this paper, we proposed a simple tapering process of optical fibers using controlled CO2 laser. This is a response to the call for the rapid development of affordable, efficient, and reliable optical sensors. A laser with power of 36 W was focused on a small section of three optical fibers having core/cladding diameters in micrometer of 10/125 (sensor A), 62.5/125 (sensor B), and 200/225 (sensor C). The sensors were tested on solutions having refractive indices of 1.3325 to 1.4266. Our investigation revealed that sensor C offered highest sensitivity. Therefore, further characterizations on its sensing characteristics were conducted. Over 6 times repetitive measurement, sensor C showed excellent repeatability with average sensitivity and detection limit of 4.5941(78) a.u. /RIU and 3.97 × 10−4 RIU, respectively. The tapered large core fiber also had good reversibility. Furthermore, the stability test by applying sensor C to solutions with low, medium, and high refractive indices also showed that the sensor was relatively stable. Within 60 minutes measurement, we noticed increasing trends of normalized intensities. However, the intensity increment percentages were relatively small, i.e., 0.27%, 1.17%, and 1.75% respectively for refractive indices of 1.3325, 1.3921, and 1.4266. Thus, excellent tapered optical fiber sensor could be produced using CO2 laser.
Random partition models and complementary clustering of Anglo-Saxon place-names

Common cluster models for multi-type point processes model the aggregation of points of the same type. In complete contrast, in the study of Anglo-Saxon settlements it is hypothesized that administrative clusters involving complementary names tend to appear. We investigate the evidence for such a hypothesis by developing a Bayesian Random Partition Model based on clusters formed by points of different types (complementary clustering). As a result, we obtain an intractable posterior distribution on the space of matchings contained in a kk-partite hypergraph. We apply the Metropolis–Hastings (MH) algorithm to sample from this posterior. We consider the problem of choosing an efficient MH proposal distribution and we obtain consistent mixing improvements compared to the choices found in the literature. Simulated Tempering techniques can be used to overcome multimodality and a multiple proposal scheme is developed to allow for parallel programming. Finally, we discuss results arising from the careful use of convergence diagnostic techniques. This allows us to study a data set including locations and place-names of 1316 Anglo-Saxon settlements dated approximately around 750–850 AD. Without strong prior knowledge, the model allows for explicit estimation of the number of clusters, the average intra-cluster dispersion and the level of interaction among place-names. The results support the hypothesis of organization of settlements into administrative clusters based on complementary names.
Suspended sediment concentration prediction using long short-term memory (LSTM) networks at the Vu Gia Thu Bon catchment, Viet Nam

Understanding changes in sediment dynamics can significantly impact the sustainable development of the world. Modelling suspended sediment plays a crucial role because it allows decision makers and managers to formulate solutions that can avert the blockage of the sediments in a watershed. Estimates of suspended sediment is useful for global ecosystem service assessments and identify vulnerable aspects under climate change. In this study, it is suggested that the suspended sediment concentration at the Vu Gia-Thu Bon catchment in Vietnam be predicted using Long Short-Term Memory (LSTM) networks. Both the monthly suspended sediment concentration at the Thanh My station and the monthly runoff data are inputs. The findings compare the actual data to the predicted concentrations of suspended sediment. The training and testing sets use monthly data from 1978 to 2005 and from 2006 to 2019, respectively. To determine the best LSTM model for the catchment, the research analyzes different scenarios with various hyperparameters. The contrast demonstrates that the LSTM model can forecast suspended sediment concentration time series at the catchment.
Infrared small target detection based on YOLO v4

Infrared imaging is highly stealthy, reconnaissance and resistant to interference and currently has numerous practical applications in many fields, particularly in the military and civilian sectors. Infrared images are prone to drowning small targets in the background due to the lack of textural detail and the presence of a large amount of background noise. Small target detection on infrared images is therefore very challenging, and traditional model-driven algorithms are no longer very applicable in the face of complex and variable infrared images, so more and more people are turning to deep learning methods. We have designed a target detection network based on YOLO v4 with the following improvements, using GhostNet as the backbone network used to extract features and achieving a lighter weighting of the backbone network. We added a coordinate attention mechanism to the design of the feature aggregation process of the network, so that the network senses the spatial location relationship while the channel is attended to, enhancing the network’s ability to localise targets. To further reduce the number of network parameters, by using depthwise separable convolution in the feature aggregation part and detection part instead of normal convolution. Our experimental comparisons on the dataset NUAA-SISRT show that Ghost-CA-YOLO v4 is the leader in terms of detection accuracy and parameter efficiency. The model has an mAP metric of 73.31% and an F1 value of 0.78. The number of parameters is 44.8M, which is 1/5 of that of YOLO v4.
Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder

Objective: Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed mental and physical health problems, prior pharmacological treatments, and aggregate genetic factors has potential to inform risk stratification and mitigation strategies. Methods: In this study of 3,942 SCZ and 5,414 BPI patients receiving VA care, self-reported SB and ideation were assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). These cross-sectional data were integrated with electronic health records (EHR), and compared by lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. Polygenic scores (PGS) for traits related to psychiatric disorders, substance use, and cognition were constructed using available genomic data, and exploratory genome-wide association studies were performed to identify and prioritize specific loci. Results: Only 20% of veterans who self-reported SB had a corroborating ICD-9/10 code in their EHR; and among those who denied prior behaviors, more than 20% reported new-onset SB at follow-up. SB were associated with a range of psychiatric and non-psychiatric diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking, suicide attempt, and major depressive disorder were also associated with attempt and ideation. Conclusions: Among individuals with a diagnosed mental illness, a GWAS for SB did not yield any significant loci. Self-reported SB were strongly associated with clinical variables across several EHR domains. Overall, clinical and polygenic analyses point to sequelae of substance-use related behaviors and other psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in clinical settings, underscoring the value of regular screening based on direct, in-person assessments, especially among high-risk individuals.
Sustained improvements in brain health and metabolic markers 24 months following bariatric surgery

Background: Obesity and its metabolic complications are associated with lower gray matter (GM) and white matter (WM) density, whereas weight loss after bariatric surgery leads to an increase in both measures. These increases of GM and WM density are significantly associated with post-operative weight loss and improvement of the metabolic/inflammatory profiles. While our recent studies demonstrated widespread increases in WM density 4 and 12 months after bariatric surgery, it is not clear if theses changes persist over time. The underlying mechanisms also remain unknown. In this regard, numerous studies demonstrate that the enlargement or hypertrophy of mature adipocytes, particularly in the visceral fat compartment, is an important marker of adipose tissue dysfunction and obesity-related cardiometabolic abnormalities. Objective: To assess whether the previously observed increases in WM and GM densities are maintained 24 months post-surgery, and to examine if pre-operative abdominal omental (OM) and subcutaneous (SC) adipocyte diameters are associated with WM and GM changes after bariatric surgery. Methods: 32 participants undergoing bariatric surgery were recruited. WM and GM densities were assessed from T1-weighted MRIs acquired prior to and 4-, 12-, and 24-months post-surgery using voxel-based morphometry. OM and SC adipose tissue samples were collected during the surgical procedure. OM and SC adipocyte diameters were measured by microscopy of fixed adipose tissue samples. Linear mixed-effects models were performed controlling for age, sex, surgery type, initial BMI, and diabetic status. Results: The average weight loss at 24 months was 33.5{+/-}7.2%. A widespread increase in WM density was observed 24 months post-surgery mainly in the cerebellum, brainstem, and corpus callosum (p<0.05, FDR) as well as some regions in GM density. Greater baseline OM adipocyte diameter was associated with greater changes in total WM density at 24 months (p=0.008). A positive trend was observed between SC adipocyte diameter and changes in total WM density at 24 months (p=0.05). Conclusion: Our results show prolonged increases of WM and GM densities up to 24 months post-bariatric surgery. Greater preoperative OM adipocyte diameter is associated with greater increases in WM density at 24 months, suggesting that individuals with excess visceral adiposity might benefit the most from surgery.
Incoherent elastic-neutron-scattering study of the vibrational dynamics and spin-related symmetry of protons in the KHCO 3 crystal

The potassium hydrogen carbonate crystal (KHCO3) and its deuterated analog (KDCO3) contain centrosymmetric dimer entities (HCO3 ) 2 , or (DCO3 ) 2 , linked by moderately strong hydrogen or deuterium bonds. The OH/OD bonds are virtually parallel to each other throughout the crystal. The incoherent elastic-neutronscattering functions ~IENSF! S(Qx ,Qy ,Qz50, v50) and S(Qx ,Qy50, Qz , v50) were measured over the momentum transfer range from ; 0t o ;30 A 21 , with resolution of DuQu; 1A 2 1 . The components of the momentum transfer Qx , Qy , and Qz were parallel to the mean directions of the stretching, in-plane, and out-of-plane bending modes of protons or deuterons, respectively. For KHCO3, the broad incoherent scattering peaks, centered at Q50, are not amenable to the IENSF calculated with various models: the single harmonic oscillator, the double minimum potential, and the pair of coupled harmonic oscillators. Then, the degenerate ground state of a pair of coupled protons is regarded as composed of indistinguishable fermions. According to the Pauli principle, the symmetric and antisymmetric coordinates are singlet and triplet states, respectively. The IENSF gives quantum interferences that compare favorably to the observations. The estimated mean-square amplitudes in the ground state are amenable to isolated harmonic proton oscillators. Incoherent scattering for KDCO3 is amenable to Gaussian profiles for bosons. There is no evidence for quantum interference. The profile narrowing reveals an increase of the mean-square amplitudes attributed to a rather large coupling of the deuteron dynamics to the crystal lattice. @S0163-1829~99! 03305-6#