Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of the behavioural immune system on covid-19 lockdown attitudes: The relationship with authoritarianism and collectivism

Abstract Background and objectives The behavioural immune system (BIS) is a motivational system that facilitates the avoidance of pathogens and is thought to have evolved as a less costly mechanism to combat infectious diseases compared to the physiological immune system. The Parasite Stress Theory of Social Values predicts that authoritarianism and collectivist attitudes may impact the BIS by predisposing people to support disease control measures, including severe restrictions like lockdowns or stay-at-home orders. This study investigates the relationship between authoritarianism, collectivism and Covid-19 worry on support for lockdown measures during the Covid-19 pandemic. Methodology A total of 187 UK participants were recruited to complete an online survey, which was administered between 22 June and 7 July 2020. The survey included measures of authoritarianism, collectivism, Covid-19 worry and support for lockdown measures. The data were analysed using moderated parallel mediation analysis. Results Covid-19 worry had a direct effect on support for and enforcement of lockdown measures, but not on the idea that adherence to lockdown rules should be an individual choice. The relationship between Covid Worry and Support for and Enforcement of lockdown measures was not mediated by authoritarianism, nor was it moderated by Collectivism. Collectivism and Authoritarianism were related to increased levels of support for lockdown independently from Covid Worry but were not related to enforcement. Conclusions and implications Support for lockdown restrictions and enforcement was mainly associated with covid worry. Our findings do not support the parasite stress theory of social values and indicate that the BIS manifested in a more direct way, and not through social values.
Near-real-time evaluation of the evolution of a seismic excitation: Application to the January 8, 2013 Lemnos seismic sequence.

H παρακολούθηση και eκτίμηση της eξέλιξης σeισμικών eξάρσeων σe σχeδόν πραγματικό χρόνο έχeι μeγάλη πρακτική σημασία, καθώς μπορeί να παρέχeι χρήσιμeς πληροφορίeς αξιοποιήσιμeς για την αντισeισμική προστασία. Eπί πλέον παρέχeι eνδeίξeις για την πιθανή eξέλιξη της διέγeρσης κάτι eξαιρeτικά σημαντικό τόσο για τους πολίτeς όσο και για τις αρχές. Για την eξυπηρέτηση αυτού του σκοπού, υλοποιήθηκe ένα ημι-αυτοματοποιημένο λογισμικό το οποίο απαρτίζeται από ένα πλήθος eπιμέρους υποπρογραμμάτων. Κύριος στόχος του λογισμικού αυτού eίναι η σe σχeδόν πραγματικό χρόνο αυτοματοποιημένη παρακολούθηση μιας σeισμικής διέγeρσης που eίναι σe eξέλιξη. Έτσι, μέσω διαδοχικών υπολογισμών των παραμέτρων της διέγeρσης και κατασκeυής των αντίστοιχων διαγραμμάτων eπιτυγχάνeται η eξαγωγή συμπeρασμάτων σχeτικά μe την eξέλιξή της. Συγκeκριμένα, το λογισμικό παράγeι ανά τακτά χρονικά διαστήματα (οριζόμeνα από το χρήστη) γραφήματα που πeριγράφουν την κατά μέγeθος, χωρική, χρονική, και χωρο-χρονική κατανομή καθώς και τη μeταβολή του μέσου μeγέθους των σeισμών-μeλών της διέγeρσης, δίνοντας κατ’ αυτόν τον τρόπο τη δυνατότητα διαμόρφωσης τeκμηριωμένης eκτίμησης σχeτικά μe την ομαλή ή μη eξέλιξη της διέγeρσης. Eφαρμογή του λογισμικού eπιχeιρήθηκe κατά τη διάρκeια της σeισμικής ακολουθίας η οποία eκδηλώθηκe στις 8/1/2013 ΝΑ της Λήμνου μe κύριο σeισμό μeγέθους Μ=5.9 (14:16 GMT). Τα διαδοχικά γραφήματα που προέκυψαν από την eφαρμογή του παραπάνω λογισμικού κατά τις πρώτeς eπτά μέρeς, που αποτέλeσαν την κύρια φάση αυτής της διέγeρσης, έδeιξαν «ομαλή» eξέλιξη της διέγeρσης μe χαρακτηριστικά τυπικής μeτασeισμικής ακολουθίας.
Cross-sectional examination of correlates of sexual victimization disclosure via #MeToo

ABSTRACT Background: Sexual victimization is a serious public health problem, with a range of negative impacts on mental and physical health. Responses that individuals get to disclosure of sexual victimization play an important role in recovery. With the increased use of social media, more survivors are talking about their experiences of sexual victimization online. Research is needed to document the correlates of online disclosure of sexual victimization. Objective: The current study examined the role of demographic characteristics, assault severity, coping strategies, and social isolation as putative correlates of disclosing sexual victimization online via the hashtag #MeToo. Methods: A sample of 637 adults recruited via social media who reported a history of sexual victimization since the age of 14 completed self-report surveys using online survey software to assess disclosure of sexual victimization, assault severity, coping strategies, and social isolation. Results: Multivariate analyses suggest that levels of emotion-focused coping were positively associated with the disclosure of sexual victimization online via #MeToo. Further, individuals who had experienced completed rape by verbal coercion were less likely to disclose via #MeToo, compared to individuals who experienced other forms of assault. Conclusion: Coping strategies and assault severity play an important role in determining whether survivors disclose sexual victimization online via #MeToo. Findings suggest that individuals may disclose via #MeToo to seek support or express their emotions. Further, individuals whose sexual victimization experiences do not conform to ‘typical’ sexual victimization experiences are less likely to disclose via #MeToo.
Consideration of non-phase-matched nonlinear effects in the design of quasi-phase-matching crystals for optical parametric oscillators

Femtosecond optical parametric oscillators (OPOs) are widely used in ultrafast nonlinear frequency conversion and quantum information. However, conventional OPOs based on quasi-phase-matching (QPM) crystals have many parasitic non-phase-matched processes which decreases the conversion efficiency. Here, we propose nine-wave coupled equations (NWCEs) to simulate all phase-matched and non-phase-matched interactions in QPM crystals to improve conventional three-wave coupled equations (TWCEs), especially for the situation of high intensity ultrashort pulses and complexly structured crystals. We discuss how to design the poling period of QPM crystal to maximize the conversion efficiency of signal light for a given OPO system. The simulation reveals that the OPO based on chirped periodically poled lithium niobate (CPPLN) with certain chirp rate has higher signal wave conversion efficiency than that of a PPLN, and demonstrates that NWCEs illustrates more details of the pulse evolution in the OPO cavity. Starting from a CPPLN, an aperiodically poled lithium niobate (APPLN) design is available by modifying the domain lengths of the crystal and optimizing the OPO output power via dynamical optimization algorithm. The results show that by using a properly designed APPLN crystal, a 1600nm OPO, when pumped by a femtosecond laser with 1030nm central wavelength, 150 femtosecond pulse duration and 5 GW/cm2 power intensity at the focus, can achieve very efficient output with a signal light conversion efficiency of 50.6%, which is higher than that of PPLN (25.2%) and CPPLN (40.2%). The scheme in this paper will provide a reference for the design of nonlinear QPM crystals of OPOs and will help to understand the complex nonlinear dynamical behavior in OPO cavities.
CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers

Point cloud completion is an indispensable task for recovering complete point clouds due to incompleteness caused by occlusion, limited sensor resolution, etc. The family of coarse-to-fine generation architectures has recently exhibited great success in point cloud completion and gradually became mainstream. In this work, we unveil one of the key ingredients behind these methods: meticulously devised feature extraction operations with explicit cross-resolution aggregation. We present Cross-Resolution Transformer that efficiently performs cross-resolution aggregation with local attention mechanisms. With the help of our recursive designs, the proposed operation can capture more scales of features than common aggregation operations, which is beneficial for capturing fine geometric characteristics. While prior methodologies have ventured into various manifestations of inter-level cross-resolution aggregation, the effectiveness of intra-level one and their combination has not been analyzed. With unified designs, Cross-Resolution Transformer can perform intra- or inter-level cross-resolution aggregation by switching inputs. We integrate two forms of Cross-Resolution Transformers into one up-sampling block for point generation, and following the coarse-to-fine manner, we construct CRA-PCN to incrementally predict complete shapes with stacked up-sampling blocks. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margin on several widely used benchmarks. Codes are available at https://github.com/EasyRy/CRA-PCN.
From Pixel to Slide image: Polarization Modality-based Pathological Diagnosis Using Representation Learning

Thyroid cancer is the most common endocrine malignancy, and accurately distinguishing between benign and malignant thyroid tumors is crucial for developing effective treatment plans in clinical practice. Pathologically, thyroid tumors pose diagnostic challenges due to improper specimen sampling. In this study, we have designed a three-stage model using representation learning to integrate pixel-level and slice-level annotations for distinguishing thyroid tumors. This structure includes a pathology structure recognition method to predict structures related to thyroid tumors, an encoder-decoder network to extract pixel-level annotation information by learning the feature representations of image blocks, and an attention-based learning mechanism for the final classification task. This mechanism learns the importance of different image blocks in a pathological region, globally considering the information from each block. In the third stage, all information from the image blocks in a region is aggregated using attention mechanisms, followed by classification to determine the category of the region. Experimental results demonstrate that our proposed method can predict microscopic structures more accurately. After color-coding, the method achieves results on unstained pathology slides that approximate the quality of Hematoxylin and eosin staining, reducing the need for stained pathology slides. Furthermore, by leveraging the concept of indirect measurement and extracting polarized features from structures correlated with lesions, the proposed method can also classify samples where membrane structures cannot be obtained through sampling, providing a potential objective and highly accurate indirect diagnostic technique for thyroid tumors.
Motion Control of Interactive Robotic Arms Based on Mixed Reality Development

Mixed Reality (MR) is constantly evolving to inspire new patterns of robot manipulation for more advanced Human- Robot Interaction under the 4th Industrial Revolution Paradigm. Consider that Mixed Reality aims to connect physical and digital worlds to provide special immersive experiences, it is necessary to establish the information exchange platform and robot control systems within the developed MR scenarios. In this work, we mainly present multiple effective motion control methods applied on different interactive robotic arms (e.g., UR5, UR5e, myCobot) for the Unity-based development of MR applications, including GUI control panel, text input control panel, end-effector object dynamic tracking and ROS-Unity digital-twin connection.
High entropy ceramics for applications in extreme environments

Compositionally complex materials have demonstrated extraordinary promise for structural robustness in extreme environments. Of these, the most commonly thought of are high entropy alloys, where chemical complexity grants uncommon combinations of hardness, ductility, and thermal resilience. In contrast to these metal-metal bonded systems, the addition of ionic and covalent bonding has led to the discovery of high entropy ceramics. These materials also possess outstanding structural, thermal, and chemical robustness but with a far greater variety of functional properties which enable access to continuously controllable magnetic, electronic, and optical phenomena. In this experimentally focused perspective, we outline the potential for high entropy ceramics in functional applications under extreme environments, where intrinsic stability may provide a new path toward inherently hardened device design. Current works on high entropy carbides, actinide bearing ceramics, and high entropy oxides are reviewed in the areas of radiation, high temperature, and corrosion tolerance where the role of local disorder is shown to create pathways toward self-healing and structural robustness. In this context, new strategies for creating future electronic, magnetic, and optical devices to be operated in harsh environments are outlined.
DDPM based X-ray Image Synthesizer

Access to high-quality datasets in the medical industry limits machine learning model performance. To address this issue, we propose a Denoising Diffusion Probabilistic Model (DDPM) combined with a UNet architecture for X-ray image synthesis. Focused on pneumonia medical condition, our methodology employs over 3000 pneumonia X-ray images obtained from Kaggle for training. Results demonstrate the effectiveness of our approach, as the model successfully generated realistic images with low Mean Squared Error (MSE). The synthesized images showed distinct differences from non-pneumonia images, highlighting the model's ability to capture key features of positive cases. Beyond pneumonia, the applications of this synthesizer extend to various medical conditions, provided an ample dataset is available. The capability to produce high-quality images can potentially enhance machine learning models' performance, aiding in more accurate and efficient medical diagnoses. This innovative DDPM-based X-ray photo synthesizer presents a promising avenue for addressing the scarcity of positive medical image datasets, paving the way for improved medical image analysis and diagnosis in the healthcare industry.
Distributed Pose-graph Optimization with Multi-level Partitioning for Collaborative SLAM

The back-end module of Distributed Collaborative Simultaneous Localization and Mapping (DCSLAM) requires solving a nonlinear Pose Graph Optimization (PGO) under a distributed setting, also known as SE(d)-synchronization. Most existing distributed graph optimization algorithms employ a simple sequential partitioning scheme, which may result in unbalanced subgraph dimensions due to the different geographic locations of each robot, and hence imposes extra communication load. Moreover, the performance of current Riemannian optimization algorithms can be further accelerated. In this letter, we propose a novel distributed pose graph optimization algorithm combining multi-level partitioning with an accelerated Riemannian optimization method. Firstly, we employ the multi-level graph partitioning algorithm to preprocess the naive pose graph to formulate a balanced optimization problem. In addition, inspired by the accelerated coordinate descent method, we devise an Improved Riemannian Block Coordinate Descent (IRBCD) algorithm and the critical point obtained is globally optimal. Finally, we evaluate the effects of four common graph partitioning approaches on the correlation of the inter-subgraphs, and discover that the Highest scheme has the best partitioning performance. Also, we implement simulations to quantitatively demonstrate that our proposed algorithm outperforms the state-of-the-art distributed pose graph optimization protocols.
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review

According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low, middle, and high-income countries. In the past few years, the research community has explored IoT-enabled machine learning applications for outdoor air pollution prediction. The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used. Two research questions were formulated for this review. 1086 publications were collected in the initial PRISMA stage. After the screening and eligibility phases, 37 papers were selected for inclusion. A cost-based analysis was conducted on the findings to highlight high-cost monitoring, low-cost IoT and hybrid enabled prediction. Three methods of prediction were identified: time series, feature-based and spatio-temporal. This review's findings identify major limitations in applications found in the literature, namely lack of coverage, lack of diversity of data and lack of inclusion of context-specific features. This review proposes directions for future research and underlines practical implications in healthcare, urban planning, global synergy and smart cities.
Measuring the Stellar and Planetary Properties of the 51 Eridani System

In order to study exoplanets, a comprehensive characterization of the fundamental properties of the host stars, such as angular diameter, temperature, luminosity, and age, is essential, as the formation and evolution of exoplanets are directly influenced by the host stars at various points in time. In this paper, we present interferometric observations taken of directly imaged planet host 51 Eridani at the CHARA Array. We measure the limb-darkened angular diameter of HD 29391 to be $\theta_{\rm LD} = 0.450\pm 0.004 \rm ~mas$ and combining with the Gaia zero-point corrected parallax, we get a stellar radius of $1.45 \pm 0.01 \rm~R_{\odot}$. We use the PARSEC isochrones to estimate an age of $23.2^{+1.7}_{-1.6} \rm ~Myr$ and a mass of $1.550 \pm 0.005 \rm ~M_{\odot}$. The age and mass agree well with values in the literature, determined through a variety of methods ranging from dynamical age trace-backs to lithium depletion boundary methods. We derive a mass of $4.1^{+0.5}_{-0.4} \rm ~M_{Jup}$ for 51 Eri b using the Sonora Bobcat models, which further supports the possibility of 51 Eri b forming under either the hot-start formation model or the warm-start formation model.
Turnkey locking of quantum-dot lasers directly grown on Si

Ultra-low-noise laser sources are crucial for a variety of applications, including microwave synthesizers, optical gyroscopes, and the manipulation of quantum systems. Silicon photonics has emerged as a promising solution for high-coherence applications due to its ability to reduce system size, weight, power consumption, and cost (SWaP-C). Semiconductor lasers based on self-injection locking (SIL) have reached fiber laser coherence, but typically require a high-Q external cavity to suppress coherence collapse through frequency-selective feedback. Lasers based on external-cavity locking (ECL) are a low-cost and turnkey operation option, but their coherence is generally inferior to SIL lasers. In this work, we demonstrate quantum-dot (QD) lasers grown directly on Si that achieve SIL laser coherence under turnkey ECL. The high-performance QD laser offers a scalable and low-cost heteroepitaxial integration platform. Moreover, the QD laser's chaos-free nature enables a 16 Hz Lorentzian linewidth under ECL using a low-Q external cavity, and improves the frequency noise by an additional order of magnitude compared to conventional quantum-well lasers.
Linear-Quadratic Problems in Systems and Controls via Covariance Representations and Linear-Conic Duality: Finite-Horizon Case

Linear-Quadratic (LQ) problems that arise in systems and controls include the classical optimal control problems of the Linear Quadratic Regulator (LQR) in both its deterministic and stochastic forms, as well as $H^\infty$-analysis (the Bounded Real Lemma), the Positive Real Lemma, and general Integral Quadratic Constraints (IQCs) tests. We present a unified treatment of all of these problems using an approach which converts linear-quadratic problems to matrix-valued linear-linear problems with a positivity constraint. This is done through a system representation where the joint state/input covariance (the outer product in the deterministic case) matrix is the fundamental object. LQ problems then become infinite-dimensional semidefinite programs, and the key tool used is that of linear-conic duality. Linear Matrix Inequalities (LMIs) emerge naturally as conal constraints on dual problems. Riccati equations characterize extrema of these special LMIs, and therefore provide solutions to the dual problems. The state-feedback structure of all optimal signals in these problems emerge out of alignment (complementary slackness) conditions between primal and dual problems. Perhaps the new insight gained from this approach is that first LMIs, and then second, Riccati equations arise naturally in dual, rather than primal problems. Furthermore, while traditional LQ problems are set up in $L^2$ spaces of signals, their equivalent covariance-representation problems are most naturally set up in $L^1$ spaces of matrix-valued signals.
Efficient Indexing of Meta-Data (Extracted from Educational Videos)

Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to students online. A large number of colleges across the country are now using NPTEL videos in their classrooms. So more video lectures are being recorded, maintained, and uploaded. These videos generally contain information about that video before the lecture begins. We generally observe that these educational videos have metadata containing five to six attributes: Institute Name, Publisher Name, Department Name, Professor Name, Subject Name, and Topic Name. It would be easy to maintain these videos if we could organize them according to their categories. The indexing of these videos based on this information is beneficial for students all around the world to efficiently utilise these videos. In this project, we are trying to get the metadata information mentioned above from the video lectures.
A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook

Autonomous driving has rapidly developed and shown promising performance with recent advances in hardware and deep learning methods. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset surveys tried to review the datasets but either focused on a limited number or lacked detailed investigation of the characters of datasets. To this end, we present an exhaustive study of over 200 autonomous driving datasets from multiple perspectives, including sensor modalities, data size, tasks, and contextual conditions. We introduce a novel metric to evaluate the impact of each dataset, which can also be a guide for establishing new datasets. We further analyze the annotation process and quality of datasets. Additionally, we conduct an in-depth analysis of the data distribution of several vital datasets. Finally, we discuss the development trend of the future autonomous driving datasets.