Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Journal of Guidance and Counseling in Schools: Current Perspectives

Museums are founded on the preservation, classification and exhibition of artifacts collected from rare cabinets to imperial treasures. The aim of this study was to examine how arrangements in museum differentiate parental scaffolding behaviour. This research was carried out in Ankara University Toy Museum, and 50 parent–child couples participated. It was investigated how the situation of playing ‘find the difference’ game differentiates parental scaffolding behaviour. Interactions between parents and children in the museum were videotaped and speeches were transcribed. Speeches were divided into scaffolding categories and frequencies were calculated. As a result of the analysis, 20 parents played the game with their children. According to the results of the research, museums can support parents’ scaffolding behaviour. In Turkey, there is limited information on how parents are using museums for their children. Studies carried out in this field can provide more information about how practices can be conducted in this field.
A Cartesian Grid Method for Solving the Streamfunction Vorticity Equations in Irregular Geometries

and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the nal examining committee have been made. Date: In presenting this dissertation in partial fullllment of the requirements for the Doc-torial degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this thesis is allowable only for scholary purposes, consistant with \fair use" as prescribed in the U.S. to whom the author has granted \the right to reproduce and sell (a) copies of the manuscript in microform and/or (b) printed copies of the manuscript made from microform. " Signature Date University of Washington Abstract I present a new algorithm for solving the streamfunction-vorticity equations in irregular , multiply connected regions. To avoid mesh generation diiculties associated with unstructured, body tted grids, I embed the irregular domain in a uniform Carte-sian mesh. The governing partial diierential equations are discretized using standard nite volume and nite diierence methods away from the irregular boundary. Near the irregular boundary, special discretizations are used to impose boundary conditions. I solve the vorticity transport equation using the high-resolution algorithms in the clawpack package and capacity form diierencing to handle the irregular geometry. By modifying the capacity function for small cells cut by the boundary, I can avoid the small cell instability problem associated with explicit Cartesian grid methods and achieve stability for Courant numbers close to 1. To solve for the streamfunction, I use the Immersed Interface Method, a second order nite diierence method developed by LeVeque and Li for solving PDEs in irregular regions. Velocities at cell edges, including those cut by the boundary, are obtained by diierencing the streamfunction. I compute the ow around a circular cylinder at Reynolds number 20, 40, 50 and 100 and show that I get very good agreement with both experimental and computational results. Flow in other geometries is also shown. 1.1 Sketch of typical domain in which streamfunction-vorticity equations are solved. The ow domain is the multiply-connected region exterior to all rigid bodies, shown here by shaded regions. At the boundaries of these embedded objects, we must impose no normal-ow and no-slip boundary conditions. The irregular domain is embedded in a uniform
Hit movie prediction scheme in social network environments

With the rapid spread of social network services, it has become very commonplace for people to share opinions on various issues using short comments. In many cases, we can discover public opinions or trends of user comments, which are very useful in many applications. In this paper, we propose a scheme for discovering public opinions on a movie trailer and predicting whether the movie will be a box office hit based on the public opinion and marketing properties. Through experiments, we show that our scheme can give satisfactory result
Limbus Vertebra and Chronic Low Back Pain

Limbus vertebra is defined as the presence of an ossicle or an adjacent bone affecting the margin angle of the vertebral bodies. The characteristic appearance on plain films is a detached fragment with triangular morphology and sclerotic margins. It represents a marginal herniation of the nucleus pulposus during childhood or adolescence through the vertebral end-plate and beneath the apophyseal ring. Usually it is considered an incidental finding and an asymptomatic entity. However, several studies have suggested a significant relationship between the limbus vertebra and the low back pain (LBP), possibly due to an underlying intervertebral disc degeneration (IDD). In order to contribute to the debate, we present the case of an adult male who has a double limbus vertebra and suffers from chronic LBP.
©MultiHIV DNA Vaccine Delivered by Transcutaneous and Intramuscular Injection With or Without Electroporation in HIV-1 Positive Subjects

Haidari, G; Day, Suzanne; Wood, M; Ridgers, H; Cope, Alethea V; Fleck, Sue; Yan, Celine; Reijonen, Kalevi; Hannaman, Drew; Spentzou, Aggeliki; +6 more. Hayes, Peter; Vogt, A; Combadiere, Behazine; Cook, Adrian; McCormack, Sheena; Shattock, Robin J; (2019) The Safety and Immunogenicity of GTU R ©MultiHIV DNA Vaccine Delivered by Transcutaneous and Intramuscular Injection With or Without Electroporation in HIV-1 Positive Subjects on Suppressive ART. Frontiers in Immunology, 10. DOI: https://doi.org/10.3389/fimmu.2019. 02911 Downloaded from: http://researchonline.lshtm. ac.uk/id/eprint/4656237/
Identification of Lin-Sca 1 + kit + CD 34 + Flt 3-short-term hematopoietic stem cells capable of rapidly reconstituting and rescuing myeloablated recipients Running head : Mouse CD 34 + flt 3-short-term stem cells

Liping Yang*, David Bryder*, Jörgen Adolfsson, Jens Nygren, Robert Månsson, Mikael Sigvardsson and Sten Eirik W. Jacobsen Hematopoietic Stem Cell Laboratory, Lund Strategic Research Center for Stem Cell Biology and Cell Therapy, Lund University, Lund, Sweden * These authors contributed equally to this work These studies were supported by grants from: ALF (Governmental Public Health Grant), Alfred Österlund Foundation, Funds of Lunds Sjukvårdsdistrikt, the Swedish Research Council, Swedish Foundation for Strategic Research, Swedish Cancer Society, the Swedish Society of Pediatric Cancer and the Tobias Foundation. The Lund Stem Cell Center is supported by a Center of Excellence grant from the Swedish Foundation for Strategic Research.
DEVELOPMENT OF HIGH PERFORMANCE FERROELECTRIC MATERIAL FOR CAPACITOR AND ACTUATOR APPLICATIONS

A ternary system of composition 0.7PMN-0.2PT-0.1PZN and 0.7PMN-0.1PT-0.2PZN were investigated employing columbite route. A detailed and systematic study of phase, dielectric properties and microstructure were carried out and compared. Ceramics of composition 0.7PMN-0.1PT-0.2PZN demonstrated larger grain size and higher density than ceramics of composition 0.7PMN-0.2PT-0.1PZN. The composition 0.7PMN-0.1PT-0.2PZN exhibited enhanced dielectric properties in the sintering temperature 1100-1170 o C while composition 0.7PMN-0.2PT-0.1PZN exhibited enhanced dielectric properties in the sintering temperature range 1170-1180 o C. Dielectric properties seem to be dependent on density and intrinsic dielectric constant of PMN, PZN for single phase perovskite ceramic. The ceramic of composition 0.7PMN-0.1PT-0.2PZN proved to be the best composition showing enhanced dielectric properties at lower sintering temperature as compared to 0.7PMN-0.2PT-0.1PZN.
The Mental Health Benefits of Acquiring a Home in Older Age: A Fixed-Effects Analysis of Older Americans

Author affiliations: King’s College London, Department of Global Health and Social Medicine (Emilie Courtin, Jennifer Dowd and Mauricio Avendano); London School of Economics and Political Science, LSE Health and Social Care, Department of Social Policy (Emilie Courtin); CUNY Graduate School of Public Health and Health Policy (Jennifer Dowd); Harvard T.H. Chan School of Public Health, Department of Social and Behavioral Sciences (Mauricio Avendano).
Improving Automated Driving Through POMDP Planning With Human Internal States

This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. Approximate POMDP solutions are obtained through the partially observable Monte Carlo planning with observation widening (POMCPOW) algorithm. This approach outperforms over-confident and conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP baselines, POMCPOW typically cuts the rate of unsafe situations in half or increases the success rate by 50%.
Improved Approximation for Fair Correlation Clustering

Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge. Motivated by this, we study Fair Correlation Clustering where the data points may belong to different protected groups and the goal is to ensure fair representation of all groups across clusters. Our paper significantly generalizes and improves on the quality guarantees of previous work of Ahmadi et al. and Ahmadian et al. as follows. - We allow the user to specify an arbitrary upper bound on the representation of each group in a cluster. - Our algorithm allows individuals to have multiple protected features and ensure fairness simultaneously across them all. - We prove guarantees for clustering quality and fairness in this general setting. Furthermore, this improves on the results for the special cases studied in previous work. Our experiments on real-world data demonstrate that our clustering quality compared to the optimal solution is much better than what our theoretical result suggests.
Meta-Reinforcement Learning with Self-Modifying Networks

Deep Reinforcement Learning has demonstrated the potential of neural networks tuned with gradient descent for solving complex tasks in well-delimited environments. However, these neural systems are slow learners producing specialized agents with no mechanism to continue learning beyond their training curriculum. On the contrary, biological synaptic plasticity is persistent and manifold, and has been hypothesized to play a key role in executive functions such as working memory and cognitive flexibility, potentially supporting more efficient and generic learning abilities. Inspired by this, we propose to build networks with dynamic weights, able to continually perform self-reflexive modification as a function of their current synaptic state and action-reward feedback, rather than a fixed network configuration. The resulting model, MetODS (for Meta-Optimized Dynamical Synapses) is a broadly applicable meta-reinforcement learning system able to learn efficient and powerful control rules in the agent policy space. A single layer with dynamic synapses can perform one-shot learning, generalizes navigation principles to unseen environments and manifests a strong ability to learn adaptive motor policies.
A Reddit Dataset for the Russo-Ukrainian Conflict in 2022

—Reddit consists of sub-communities that cover a focused topic. This paper provides a list of relevant subreddits for the ongoing Russo-Ukrainian crisis. We perform an exhaustive subreddit exploration using keyword search and shortlist 12 subreddits as potential candidates that contain nominal discourse related to the crisis. These subreddits contain over 300,000 posts and 8 million comments collectively. We provide an additional categorization of content into two categories, “R-U Conﬂict”, and “Military Related”, based on their primary focus. We further perform content characterization of those subreddits. The results show a surge of posts and comments soon after Russia launched the invasion. “Military Related” posts are more likely to receive more replies than “R-U Conﬂict” posts. Our textual analysis shows an apparent preference for the Pro-Ukraine stance in “R-U Conﬂict”, while “Military Related” retain a neutral stance.
Distributionally robust end-to-end portfolio construction

We propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parameter and the degree of robustness directly from data. End-to-end systems have an advantage in that information can be communicated between the prediction and decision layers during training, allowing the parameters to be trained for the final task rather than solely for predictive performance. However, existing end-to-end systems are not able to quantify and correct for the impact of model risk on the decision layer. Our proposed distributionally robust end-to-end portfolio selection system explicitly accounts for the impact of model risk. The decision layer chooses portfolios by solving a minimax problem where the distribution of the asset returns is assumed to belong to an ambiguity set centered around a nominal distribution. Using convex duality, we recast the minimax problem in a form that allows for efficient training of the end-to-end system.
A Fine Line: Total Least-Squares Line Fitting as QCQP Optimization

This note uses the Total Least-Squares (TLS) line-fitting problem as a canvas to explore some modern optimization tools. The contribution is meant to be tutorial in nature. The TLS problem has a lot of mathematical similarities to important problems in robotics and computer vision but is easier to visualize and understand. We demonstrate how to turn this problem into a Quadratically Constrained Quadratic Program (QCQP) so that it can be cast either as an eigenproblem or a Semi-Definite Program (SDP). We then turn to the more challenging situation where a Geman-McClure cost function and M-estimation are used to reject outlier datapoints. Using Black-Rangarajan duality, we show this can also be cast as a QCQP and solved as an SDP; however, with a lot of data the SDP can be slow and as such we show how we can construct a certificate of optimality for a faster method such as Iteratively Reweighted Least-Squares (IRLS).
Mildly Conservative Q-Learning for Offline Reinforcement Learning

Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between the learned policy and the behavior policy makes it necessary for the value function to stay conservative such that out-of-distribution (OOD) actions will not be severely overestimated. However, existing approaches, penalizing the unseen actions or regularizing with the behavior policy, are too pessimistic, which suppresses the generalization of the value function and hinders the performance improvement. This paper explores mild but enough conservatism for offline learning while not harming generalization. We propose Mildly Conservative Q-learning (MCQ), where OOD actions are actively trained by assigning them proper pseudo Q values. We theoretically show that MCQ induces a policy that behaves at least as well as the behavior policy and no erroneous overestimation will occur for OOD actions. Experimental results on the D4RL benchmarks demonstrate that MCQ achieves remarkable performance compared with prior work. Furthermore, MCQ shows superior generalization ability when transferring from offline to online, and significantly outperforms baselines. Our code is publicly available at https://github.com/dmksjfl/MCQ.
MEAT: Maneuver Extraction from Agent Trajectories

Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction models is limited to metrics averaged over all samples in the dataset. We propose an automated methodology that allows to extract maneuvers (e.g., left turn, lane change) from agent trajectories in such datasets. The methodology considers information about the agent dynamics and information about the lane segments the agent traveled along. Although it is possible to use the resulting maneuvers for training classification networks, we exemplary use them for extensive trajectory dataset analysis and maneuver-specific evaluation of multiple state-of-the-art trajectory prediction models. Additionally, an analysis of the datasets and an evaluation of the prediction models based on the agent dynamics is provided.
Anomalies, CPT and Leptogenesis

We bring together diﬀerent puzzles in physics beyond the Standard Model of particle physics. Within our model baryogenesis, neutrino mass, strong CP and dark matter puzzles are related. The common ingredient in connecting these puzzles is the Kalb-Ramond ﬁeld, a two form present in the gravitational multiplet in the theory of closed strings. Leptons are fermions which we need to couple to gravitational degrees of freedom using a vierbein formalism. The presence of torsion provided by a Kalb-Ramond background leads us to ﬁrstly an eﬀective model involving the Einstein-Cartan formulation of gravity, a gauge theory, in which coupling to fermions is via a covariant derivative and secondly to a mechanism for CPT violation. This picture emerges from a low energy string eﬀective action obtained from a closed bosonic string theory after compactiﬁcation to four dimensions. The Kalb-Ramond ﬁeld in four dimensions can be identiﬁed with a pseudoscalar gravitational axion. Because of the presence of an axial anomaly this axion can couple with the gluon ﬁeld, and in this way allows a connection with the strong CP problem and axionic dark matter. gravitational interactions, non-gravitational gauge interactions and interactions of
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review

In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting. We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.
Probing electron-hole weights of an Andreev bound state by transient currents

Andreev bound states (ABSs) are localized quantum states that contain both electron and hole components. They ubiquitously reside in inhomogeneous superconducting systems. Following theoretical analysis, we propose to probe the electron-hole weights of an ABS via a local tunneling measurement that detects the transient current under a steplike pulse bias. With our protocol, the ABS energy level can also be obtained from peaks of the Fourier spectrum of the transient current. Our protocol can be applied to detect robust zero-energy Majorana bound states (MBSs), which have equal electron-hole weights, in candidate platforms where local tunneling spectroscopy measurement is possible. In the 1D Majorana nanowire model, we numerically calculate the electron-hole weights for diﬀerent types of low-energy bound states, including ABSs, quasi-MBSs, and MBSs. Equations (B4) and (B5) reveal three transport processes contributing to the transient current. (i) The coherent electron transitions between the probe and the n th eigen state of the dressed superconductor, i.e., H rS , result in an oscillating decaying current component with frequencies | ω | = | V ± Re E rn | and a damping rate Im E rn . (ii) The coherent electron transitions between the m th and n th eigen states of the dressed superconductor result in an oscillating decaying current component with frequencies | ω | = | Re E rn − Re E am | and a damping rate | Im E rn − Im E am | . (iii) After the sudden switch-on of the bias, the time evolution of the occupation probability of the n th eigen state of the dressed superconductor results in an exponentially decaying current component with a damping rate 2 | Im E rn | , which is just captured by our RE analysis in the main text.For the eﬀective Hamiltonian H (1)–(4) studied in the main text, m, n = { 1 , 2 } and process (ii) is absent due to that Γ are ;12 = Γ arh ;12 and Γ are ;21 = Γ arh ;21 render the associated coeﬃcients in Eq. (B5) being zeros.
Effect of localization on photoluminescence and zero-field splitting of silicon color centers

The study of defect centers in silicon has been recently reinvigorated by their potential applications in optical quantum information processing. A number of silicon defect centers emit single photons in the telecommunication $O$-band, making them promising building blocks for quantum networks between computing nodes. The two-carbon G-center, self-interstitial W-center, and spin-$1/2$ T-center are the most intensively studied silicon defect centers, yet despite this, there is no consensus on the precise configurations of defect atoms in these centers, and their electronic structures remain ambiguous. Here we employ \textit{ab initio} density functional theory to characterize these defect centers, providing insight into the relaxed structures, bandstructures, and photoluminescence spectra, which are compared to experimental results. Motivation is provided for how these properties are intimately related to the localization of electronic states in the defect centers. In particular, we present the calculation of the zero-field splitting for the excited triplet state of the G-center defect as the structure is transformed from the A-configuration to the B-configuration, showing a sudden increase in the magnitude of the $D_{zz}$ component of the zero-field splitting tensor. By performing projections onto the local orbital states of the defect, we analyze this transition in terms of the symmetry and bonding character of the G-center defect which sheds light on its potential application as a spin-photon interface.
Neural Bregman Divergences for Distance Learning

Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space. The study of non-Euclidean geometries is often not explored, which we believe is due to a lack of tools for learning non-Euclidean measures of distance. Recent work has shown that Bregman divergences can be learned from data, opening a promising approach to learning asymmetric distances. We propose a new approach to learning arbitrary Bergman divergences in a differentiable manner via input convex neural networks and show that it overcomes significant limitations of previous works. We also demonstrate that our method more faithfully learns divergences over a set of both new and previously studied tasks, including asymmetric regression, ranking, and clustering. Our tests further extend to known asymmetric, but non-Bregman tasks, where our method still performs competitively despite misspecification, showing the general utility of our approach for asymmetric learning.