Miracle-dz/UniFAR-FTUs huggingface dataset:
A Path from Traditional Bullying to Cyberbullying in South Korea: Examining the Roles of Self-Control and Deviant Peer Association in the Different Forms of Bullying

Despite a recent growth in studies on cyberbullying, extant knowledge on the underlying mechanisms of cyberbullying remain limited. The objective of the present study is to explore the dynamics of cyberbullying via traditional bullying, self-control, and delinquent peer association. Specifically, the following hypotheses guide the present study: (1) traditional bullying, low self-control, and delinquent peer association are predictive of cyberbullying, respectively, (2) the interaction between traditional bullying and low self-control has a significant impact on cyberbullying, and (3) the interaction between traditional bullying and delinquent peer association has a significant impact on cyberbullying. The present study relies on five waves of the Korean Youth Panel Survey (KYPS), a representative sample of South Korean adolescents. Data collection occurred annually and respondents were 14 years old at the first wave in 2003. KYPS is an almost gender-equal and racially/ethnically homogenous sample. Results of cross-lagged dynamic panel models show (1) significant effects of traditional bullying on cyberbullying with and without low self-control and delinquent peer affiliation, (2) the respective roles of self-control and delinquent peer association in the prediction of cyberbullying, and (3) an interaction effect between low self-control and traditional bullying on cyberbullying. These findings demonstrate the theoretical validity of self-control theory and social learning theory in online delinquent behavior as well as confirm their cross-cultural generalizability in a non-Western sample. The findings also highlight the importance of investing in early life-course prevention/intervention programs and policies to prevent and/or reduce the occurrence of bullying, regardless of whether it is being perpetrated face-to-face or online, and these programs and policies should also target components to improve self-control and reduce delinquent peer associations.
Predicting UPDRS Motor Symptoms in Individuals With Parkinson's Disease From Force Plates Using Machine Learning

Parkinson's disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold. First, to evaluate quiet standing as an additional, out-of-clinic, objective feature to predict UPDRS-III subscores related to motor symptom severity; and second, to use quiet standing to detect the presence of motor symptoms. Force plate data were collected from 42 PD patients and 43 healthy controls during quiet standing (a task involving standing still with eyes open and closed) as a feasible task in clinics. Predicting each subscore of the UPDRS-III could aid in identifying progression of PD and provide specialists additional tools to make an informed diagnosis. Random Forest feature importance indicated that features correlated with range of center of pressure (i.e., the medial-lateral and anterior-posterior sway) were most useful in the prediction of the top PD prediction subscores of postural stability (r = 0.599; p = 0.014), hand tremor of the left hand (r = 0.650; p = 0.015), and tremor at rest of the left upper extremity (r = 0.703; p = 0.016). Quiet standing can detect body bradykinesia (AUC-ROC = 0.924) and postural stability (AUC-ROC = 0.967) with high predictability. Although there are limited data, these results should be used as a feasibility study that evaluates the predictability of individual UPDRS-III subscores using quiet standing data.
Unravelling the effects of ibuprofen-acetaminophen infused copper-bioglass towards the creation of root canal sealant

Impact towards the tuneable characteristics of bioactive glasses (BAGs) has been explored; as there is no root canal sealant till date with ideal characteristics competent enough to manoeuvre the perplexing root canal architecture. Combeite, calcite and traces of cuprorivaite crystalline phases were validated for material formation, in which Cu 2P (XPS) peak authenticating the presence of copper in bioglass network (Cu-BAG). Spherical and platelet-like morphologies were observed and the grain size of Cu-BAG (∼100 nm) was lesser as compared to BAG (∼1 µm). These particle distributions impacted the porosity, and dominant non-bridging oxygens in Cu-BAG influences ionic dissolution, which subsequently enhanced the mineralization. These bioactive materials were loaded with acetaminophen and ibuprofen, corresponding organic moieties was confirmed through Fourier transform infra-red. These drugs loaded bioactive materials exhibited tremendous anti-inflammatory and anti-microbial behaviour with better sealing ability. Drug loaded bioglass paste filled in biomechanically prepared root canal was estimated for sealing potential, mineralization, micro leakage, and fracture resistance properties. Hydroxyl apatite growth was noted on the sealants, flower like protuberance confirmed the sealing potential of the prepared material. Bioglass exhibited promising characteristics required in a root canal sealant. This investigation is a step further towards tailoring the properties of bioactive materials as promising candidates in root canal obturation and thereof.
Element-wise estimation error of generalized Fused Lasso

The main result of this article is that we obtain an elementwise error bound for the Fused Lasso estimator for any general convex loss function $\rho$. We then focus on the special cases when either $\rho$ is the square loss function (for mean regression) or is the quantile loss function (for quantile regression) for which we derive new pointwise error bounds. Even though error bounds for the usual Fused Lasso estimator and its quantile version have been studied before; our bound appears to be new. This is because all previous works bound a global loss function like the sum of squared error, or a sum of Huber losses in the case of quantile regression in Padilla and Chatterjee (2021). Clearly, element wise bounds are stronger than global loss error bounds as it reveals how the loss behaves locally at each point. Our element wise error bound also has a clean and explicit dependence on the tuning parameter $\lambda$ which informs the user of a good choice of $\lambda$. In addition, our bound is nonasymptotic with explicit constants and is able to recover almost all the known results for Fused Lasso (both mean and quantile regression) with additional improvements in some cases.
Fluorine Abundances in the Galactic Nuclear Star Cluster

Abundances of fluorine (19F), as well as isotopic ratios of 16O/17O, are derived in a sample of luminous young (∼107–108 yr) red giants in the Galactic center (with galactocentric distances ranging from 0.6–30 pc), using high-resolution infrared spectra and vibration-rotation lines of H19F near λ2. 3 μm. Five of the six red giants are members of the Nuclear star cluster that orbits the central supermassive black hole. Previous investigations of the chemical evolution of 19F in Galactic thin and thick-disk stars have revealed that the nucleosynthetic origins of 19F may be rather complex, resulting from two, or more, astrophysical sites; fluorine abundances behave as a primary element with respect to Fe abundances for thick-disk stars and as a secondary element in thin-disk stars. The Galactic center red giants analyzed fall within the thin-disk relation of F with Fe, having near-solar, to slightly larger, abundances of Fe (〈[Fe/H]〉 = +0.08 ± 0.04), with a slight enhancement of the F/Fe abundance ratio (〈[F/Fe]〉 = +0.28 ± 0.17). In terms of their F and Fe abundances, the Galactic center stars follow the thin-disk population, which requires an efficient source of 19F that could be the winds from core-He burning Wolf–Rayet stars, or thermally pulsing AGB stars, or a combination of both. The observed increase of [F/Fe] with increasing [Fe/H] found in thin-disk and Galactic center stars is not predicted by any published chemical evolution models that are discussed, thus a quantitative understanding of yields from the various possible sources of 19F remains unknown.
Neural Network Training on In-Memory-Computing Hardware With Radix-4 Gradients

Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has motivated hardware accelerators to enhance compute efficiency, but where data movement and accessing are proving to be key bottlenecks. In-Memory Computing (IMC) is an approach with the potential to overcome this, whereby computations are performed in-place within dense 2-D memory. However, IMC fundamentally trades efficiency and throughput gains for dynamic-range limitations, raising distinct challenges for training, where compute precision requirements are seen to be substantially higher than for inferencing. This paper explores training on IMC hardware by leveraging two recent developments: (1) a training algorithm enabling aggressive quantization through a radix-4 number representation; (2) IMC leveraging compute based on precision capacitors, whereby analog noise effects can be made well below quantization effects. Energy modeling calibrated to a measured silicon prototype implemented in 16 nm CMOS shows that energy savings of over $400\times $ can be achieved with full quantizer adaptability, where all training MVMs can be mapped to IMC, and $3\times $ can be achieved for two-level quantizer adaptability, where two of the three training MVMs can be mapped to IMC.
The management of scientific and technological infrastructures: The case of the Mexican National Laboratories

Abstract The effectiveness of research units is assessed on the basis of their performance in relation to scientific, technological, and innovation production, the quality of their results, and their contribution to the solution of scientific and social problems. We examine the management practices employed in some Mexican National Laboratories to identify those practices that could explain their effectiveness in meeting their objectives. The results of other research that propose common elements among laboratories with outstanding performance are used and verified directly in the field. Considering the inherent complexity of each field of knowledge and the sociospatial characteristics in which the laboratories operate, we report which management practices are relevant for their effectiveness, how they contribute to their consolidation as fundamental scientific and technological infrastructures, and how these can be translated into indicators that support the evaluation of their performance.
Decay of multiple dark matter particles to dark radiation in different epochs does not alleviate the Hubble tension

Decaying cold dark matter (CDM) has been considered as a mechanism to tackle the tensions in the Hubble expansion rate and the clustering of matter. However, polarization measurements of the cosmic microwave background (CMB) severely constrain the fraction of dark matter decaying before recombination, and lensing of the CMB anisotropies by large-scale structure set strong constraints on dark matter decaying after recombination. Together, these constraints make an explanation of the Hubble tension in terms of decaying dark matter unlikely. In response to this situation, we investigate whether a dark matter ensemble with CDM particles decaying into free streaming dark radiation in different epochs can alleviate the problem. We find that it does not.
Sensitivity Analysis under the f -Sensitivity Models: Definition, Estimation and Inference

The digitization of the economy has witnessed an explosive growth of available observational data across a variety of domains, which has provided an exciting opportunity for causal inference that aims to discern the effectiveness of different treatment options. However, unlike in experimental data – where the designer has the full control to randomize treatment choices based on observable covariates – causal inference using observational data faces the key challenge of confoundedness: there might exist unobservable covariates that affect both the treatment choice and the outcome. In this paper, we aim to add to the emerging line of tools for sensitivity analysis that assesses the robustness of causal conclusions against potential unmeasured confounding. We propose a new sensitivity model where, in contrast to uniform bounds on the selection bias in the literature, we assume the selection bias is bounded “on average”. It generalizes the widely used Γ-marginal selection model of Tan (2006) and is particularly suitable for situations where the selection bias grows unbounded locally but, due to the impact of such region being small overall, is controlled at the population level. We study the partial identification of treatment effects under the new sensitivity model. From a distributional-shift perspective, we represent the bounds on counterfactual means via certain distributionally robust optimization programs. We then design procedures to estimate these bounds, and show that our procedure is doubly robust to the estimation error of nuisance components and remains valid even when the optimization step is off. In addition, we establish the Wald-type inference guarantee of our procedure that is again robust to the optimization step. Finally, we demonstrate our method and verify its validity with numerical experiments.
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems

Generative Adversarial Networks (GANs) have been shown to be powerful and flexible priors when solving inverse problems. One challenge of using them is overcoming representation error, the fundamental limitation of the network in representing any particular signal. Recently, multiple proposed inversion algorithms reduce representation error by optimizing over intermediate layer representations. These methods are typically applied to generative models that were trained agnostic of the downstream inversion algorithm. In our work, we introduce a principle that if a generative model is intended for inversion using an algorithm based on optimization of intermediate layers, it should be trained in a way that regularizes those intermediate layers. We instantiate this principle for two notable recent inversion algorithms: Intermediate Layer Optimization and the Multi-Code GAN prior. For both of these inversion algorithms, we introduce a new regularized GAN training algorithm and demonstrate that the learned generative model results in lower reconstruction errors across a wide range of under sampling ratios when solving compressed sensing, inpainting, and super-resolution problems.
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks

Neural networks tend to achieve better accuracy with training if they are larger -- even if the resulting models are overparameterized. Nevertheless, carefully removing such excess parameters before, during, or after training may also produce models with similar or even improved accuracy. In many cases, that can be curiously achieved by heuristics as simple as removing a percentage of the weights with the smallest absolute value -- even though magnitude is not a perfect proxy for weight relevance. With the premise that obtaining significantly better performance from pruning depends on accounting for the combined effect of removing multiple weights, we revisit one of the classic approaches for impact-based pruning: the Optimal Brain Surgeon(OBS). We propose a tractable heuristic for solving the combinatorial extension of OBS, in which we select weights for simultaneous removal, as well as a systematic update of the remaining weights. Our selection method outperforms other methods under high sparsity, and the weight update is advantageous even when combined with the other methods.
Effect of intrinsic quantum fluctuations on the phase diagram of anisotropic dipolar magnets

The rare-earth material LiHoF 4 is believed to be an experimental realization of the celebrated (dipolar) Ising model, and upon the inclusion of a transverse ﬁeld B x , an archetypal quantum Ising model. Moreover, by substituting the magnetic Ho ions by non-magnetic Y ions, disorder can be introduced into the system giving rise to a dipolar disordered magnet and at high disorders to a spin-glass. Indeed, this material has been scrutinized experimentally, numerically and theoretically over many decades with the aim of understanding various collective magnetic phenomena. One of the to-date open questions is the discrepancy between the experimental and theoretical B x − T phase diagram at low-ﬁelds and high temperatures. Here we propose a mechanism, backed by numerical results, that highlights the importance of quantum ﬂuctuations induced by the oﬀ-diagonal dipolar terms, in determining the critical temperature of anisotropic dipolar magnets in the presence and in the absence of a transverse ﬁeld. We thus show that the description as a simple Ising system is insuﬃcient to quantitatively describe the full phase diagram of LiHoF 4 , for the pure as well as for the dilute system. longitudinal dipolar interactions of the respective pairs. Similar results are obtained for an average over a random distribution of the spins.
Score matching enables causal discovery of nonlinear additive noise models

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal discovery methods. To showcase our approach, we also propose a new efficient method for approximating the score's Jacobian, enabling to recover the causal graph. Empirically, we find that the new algorithm, called SCORE, is competitive with state-of-the-art causal discovery methods while being significantly faster.
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition

Due to the development of machine learning and speech processing, speech emotion recognition has been a popular research topic in recent years. However, the speech data cannot be protected when it is uploaded and processed on servers in the internet-of-things applications of speech emotion recognition. Furthermore, deep neural networks have proven to be vulnerable to human-indistinguishable adversarial perturbations. The adversarial attacks generated from the perturbations may result in deep neural networks wrongly predicting the emotional states. We propose a novel federated adversarial learning framework for protecting both data and deep neural networks. The proposed framework consists of i) federated learning for data privacy, and ii) adversarial training at the training stage and randomisation at the testing stage for model robustness. The experiments show that our proposed framework can effectively protect the speech data locally and improve the model robustness against a series of adversarial attacks.
A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling

As a counterpoint to classical stochastic particle methods for linear diffusion equations, such as Langevin dynamics for the Fokker-Planck equation, we develop a deterministic particle method for the weighted porous medium equation and prove its convergence on bounded time intervals. This generalizes related work on blob methods for unweighted porous medium equations. From a numerical analysis perspective, our method has several advantages: it is meshfree, preserves the gradient flow structure of the underlying PDE, converges in arbitrary dimension, and captures the correct asymptotic behavior in simulations. The fact that our method succeeds in capturing the long time behavior of the weighted porous medium equation is significant from the perspective of related problems in quantization. Just as the Fokker-Planck equation provides a way to quantize a probability measure ρ ¯ \bar {\rho } by evolving an empirical measure ρ N ( t ) = 1 N ∑ i = 1 N δ X i ( t ) \rho ^N(t) = \frac {1}{N} \sum _{i=1}^N \delta _{X^i(t)} according to stochastic Langevin dynamics so that ρ N ( t ) \rho ^N(t) flows toward ρ ¯ \bar {\rho } , our particle method provides a way to quantize ρ ¯ \bar {\rho } according to deterministic particle dynamics approximating the weighted porous medium equation. In this way, our method has natural applications to multi-agent coverage algorithms and sampling probability measures. A specific case of our method corresponds to confined mean-field dynamics of training a two-layer neural network for a radial basis activation function. From this perspective, our convergence result shows that, in the overparametrized regime and as the variance of the radial basis functions goes to zero, the continuum limit is given by the weighted porous medium equation. This generalizes previous results, which considered the case of a uniform data distribution, to the more general inhomogeneous setting. As a consequence of our convergence result, we identify conditions on the target function and data distribution for which convexity of the energy landscape emerges in the continuum limit.
UKPGAN: A General Self-Supervised Keypoint Detector

Keypoint detection is an essential component for the object registration and alignment. In this work, we reckon keypoint detection as information compression, and force the model to distill out important points of an object. Based on this, we propose UKPGAN, a general self-supervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints align well with human annotated keypoint labels, and can be applied to SMPL human bodies under various non-rigid deformations. Furthermore, our keypoint detector trained on clean object collections generalizes well to real-world scenarios, thus further improves geometric registration when combined with off-the-shelf point descriptors. Repeatability experiments show that our model is stable under both rigid and non-rigid transformations, with local reference frame estimation. Our code is available on https://github.com/qq456cvb/UKPGAN.
Normal and Visibility Estimation of Human Face from a Single Image

Recent work on the intrinsic image of humans starts to consider the visibility of incident illumination and encodes the light transfer function by spherical harmonics. In this paper, we show that such a light transfer function can be further decomposed into visibility and cosine terms related to surface normal. Such decomposition allows us to recover the surface normal in addition to visibility. We propose a deep learning-based approach with a reconstruction loss for training on real-world images. Results show that compared with previous works, the reconstruction of human face from our method better reveals the surface normal and shading details especially around regions where visibility effect is strong.