Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of a Newly Developed Instrument Establishing Links Between Motivation and Academic Hardiness

The purpose of the study was to establish the reliability, the structural and the convergent validity of the “Dimensions of Academic Hardiness Questionnaire” for late elementary school children. A sample of children (N = 1264) aged 10-12 years completed the questionnaire and the “Athens Coping Scale”. Multiple fit indices provided support that the 9-factor model had a good fit to the data. Reliability coefficients ranged from . 68 to . 83. The study provided also preliminary evidence of convergent validity of the “Dimensions of Academic Hardiness” scores with one theoretically related measure, the “Athens Coping Scale”. The results enrich the notion of Academic Hardiness in late elementary school children as the role of awareness and the role of children’s previous experiences has been distinguished. The relation between the “Dimensions of Academic Hardiness” and achievement goal orientations in children learning is also noted. These findings are discussed in the context of the relevant literature.
A new approach for the interpretation of the dynamic and mechanical properties of polymer nanocomposites above and below the glass transition region

In this work, we present a new model for the interpretation of the local dynamic behavior and the mechanical reinforcement mechanism in polymer nanocomposites. The temperature dependence of the dynamics in the glassy region is described by a new equation which assumes an Arrhenius component in the cooperative diffusion. By doing so, a characteristic temperature which can be identified as the glass transition temperature (Tg) emerges, while an additional parameter for the extension of the super-Arrhenius region is incorporated. Based on thermodynamic arguments, the dynamical heterogeneities are then related to structural heterogeneities in a manner consistent with the idea of a sigmoidal shape in the cohesion energy. Incorporation of this temperature dependence of the cohesion energy to a Sanchez-Lacombe Equation of State, results in a sound description of the experimental temperature and pressure dependence of the density. Moreover, comparison with experimental data shows that the enhancement of mechanical properties in polymer nanocomposites can be associated with the extent of the glassy region.
Exact Hard Monotonic Attention for Character-Level Transduction

Many common character-level, string-to-string transduction tasks, e.g., grapheme-to-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, non-monotonic models, outperform popular monotonic models. In this work, we ask the following question: Is monotonicity really a helpful inductive bias in these tasks? We develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. With the help of dynamic programming, we are able to compute the exact marginalization over all alignments. Our models achieve state-of-the-art performance on morphological inflection. Furthermore, we find strong performance on two other character-level transduction tasks. Code is available at https://github.com/shijie-wu/neural-transducer.
Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

Recent technological advances have proliferated the available computing power, memory, and speed of modern Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs). Consequently, the performance and complexity of Artificial Neural Networks (ANNs) is burgeoning. While GPU-accelerated Deep Neural Networks (DNNs) currently offer state-of-the-art performance, they consume large amounts of power. Training such networks on CPUs is inefficient, as data throughput and parallel computation is limited. FPGAs are considered a suitable candidate for performance critical, low power systems, e.g. the Internet of Things (IOT) edge devices. Using the Xilinx SDAccel or Intel FPGA SDK for OpenCL development environment, networks described using the high-level OpenCL framework can be accelerated on heterogeneous platforms. Moreover, the resource utilization and power consumption of DNNs can be further enhanced by utilizing regularization techniques that binarize network weights. In this paper, we introduce, to the best of our knowledge, the first FPGA-accelerated stochastically binarized DNN implementations, and compare them to implementations accelerated on both GPUs and FPGAs. All our developed networks are trained and benchmarked using the popular MNIST and CIFAR-10 datasets. For our binarized and conventional FPGA-based networks, we achieve a >16-fold improvement in power consumption, compared to their GPU-accelerated counterparts. Also, our binarized FPGA-based networks require >25% shorter inference times, compared to their GPU-based counterparts.
Survival of the Fittest in PlayerUnknown BattleGround

The goal of this paper was to predict the placement in the multiplayer game PUBG (playerunknown battleground). In the game, up to one hundred players parachutes onto an island and scavenge for weapons and equipment to kill others, while avoiding getting killed themselves. The available safe area of the game map decreases in size over time, directing surviving players into tighter areas to force encounters. The last player or team standing wins the round. In this paper specifically, we have tried to predict the placement of the player in the ultimate survival test. The data set has been taken from Kaggle. Entire dataset has 29 attributes which are categories to 1 label(winPlacePerc), training set has 4.5 million instances and testing set has 1.9 million. winPlacePerc is continuous category, which makes it harder to predict the survival of the fittest. To overcome this problem, we have applied multiple machine learning models to find the optimum prediction. Model consists of LightGBM Regression (Light Gradient Boosting Machine Regression), MultiLayer Perceptron, M5P (improvement on C4.5) and Random Forest. To measure the error rate, Mean Absolute Error has been used. With the final prediction we have achieved MAE of 0.02047, 0.065, 0.0592 and 0634 respectively.
Robust Neural Network Training using Periodic Sampling over Model Weights

Deep neural networks provide best-in-class performance for a number of computer vision problems. However, training these networks is computationally intensive and requires fine-tuning various hyperparameters. In addition, performance swings widely as the network converges making it hard to decide when to stop training. In this paper, we introduce a trio of techniques (PSWA, PWALKS, and PSWM) centered around periodic sampling of model weights that provide consistent and more robust convergence on a variety of vision problems (classification, detection, segmentation) and gradient update methods (vanilla SGD, Momentum, Adam) with marginal additional computation time. Our techniques use existing optimal training policies but converge in a less volatile fashion with performance improvements that are approximately monotonic. Our analysis of the loss surface shows that these techniques also produce minima that are deeper and wider than those found by SGD.
Fast and robust model selection based on ranks.

We consider the problem of identifying important predictors in large data bases, where the relationship between the response variable and the explanatory variables is specified by the general single index model, with unknown link function and unknown distribution of the error term. We utilize the natural robust and efficient approach, which relies on replacing values of the response variable with their ranks and then identifying important predictors by using the well known LASSO. The resulting RankLasso coincides with the previously proposed distribution-based LASSO, where the relationship with the rank approach was not realized. We refine the consistency results for RankLasso provided in the earlier papers and extend the scope of applications of this method by proposing its thresholded and adaptive versions. We present theoretical results which show that similarly as in the context of regular LASSO, the proposed modifications are model selection consistent under much weaker assumptions than RankLasso. These theoretical results are illustrated by extensive simulation study, which shows that the proposed procedures are indeed much more efficient than the vanilla version of RankLasso and that they can properly identify relevant predictors, even if the error terms come from the Cauchy distribution. The simulation study shows also that concerning model selection RankLasso performs substantially better than LADLasso, which is a well established methodology for robust model selection.
Stronger Constraints on the Evolution of the MBH− Relation up to z ∼ 0.6

We revisit the possibility of redshift evolution in the MBH−σ* relation with a sample of 22 Seyfert 1 galaxies with black holes (BHs) in the mass range and redshift range 0.03 < z < 0.57 with spectra obtained from spatially resolved Keck/Low-resolution Imaging Spectrometer observations. Stellar velocity dispersions were measured directly from the Mg ib region, taking into consideration the effect of Fe ii contamination, active galactic nucleus (AGN) dilution, and host-galaxy morphology on our measurements. BH masses are estimated using the Hβ line width, and the luminosity at 5100 Å is estimated from surface brightness decomposition of the AGN from the host galaxy using high-resolution imaging from the Hubble Space Telescope. Additionally, we investigate the use of the [O iii]λ5007 emission line width as a surrogate for stellar velocity dispersion, finding better correlation once corrected for Fe ii contamination and any possible blueshifted wing components. Our selection criteria allowed us to probe lower-luminosity AGNs and lower-mass BHs in the non-local universe than those measured in previous single-epoch studies. We find that any offset in the MBH−σ* relation up to z ∼ 0.6 is consistent with the scatter of local BH masses, and address the sources of biases and uncertainties that contribute to this scatter.
Vanishing viscosity of one-dimensional isentropic Navier-Stokes equations with density dependent viscous coefficient

In this paper, we study the vanishing viscosity of the isentropic compressible Navier-Stokes equations with density dependent viscous coefficient in the presence of the shock wave. Given a shock wave to the corresponding Euler equations, we can construct a sequence of solutions to one-dimensional compressible isentropic Navier-Stokes equations which converge to the shock wave as the viscosity tends to zero. The proof is given by an elementary energy method.
Orthogonal Deep Neural Networks

In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization. To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks’ weight matrices. We prove that the optimal bound w.r. t. the degree of isometry is attained when each weight matrix has a spectrum of equal singular values, among which orthogonal weight matrix or a non-square one with orthonormal rows or columns is the most straightforward choice, suggesting the algorithms of OrthDNNs. We present both algorithms of strict and approximate OrthDNNs, and for the later ones we propose a simple yet effective algorithm called Singular Value Bounding (SVB), which performs as well as strict OrthDNNs, but at a much lower computational cost. We also propose Bounded Batch Normalization (BBN) to make compatible use of batch normalization with OrthDNNs. We conduct extensive comparative studies by using modern architectures on benchmark image classification. Experiments show the efficacy of OrthDNNs.
HIV/AIDS Knowledge and Health-Related Attitudes and Behaviors Among Deaf and Hearing Adolescents in Southern Brazil

HIV/AIDS knowledge and health-related attitudes and behaviors among deaf and hearing adolescents in southern Brazil are described. Forty-two deaf students attending a special nonresidential public school for the deaf and 50 hearing students attending a regular public school, ages 15–21 years, answered a computer-assisted questionnaire. (There was simultaneous video translation of questions to Brazilian Sign Language.) A branched decision-tree structure was used to determine level of sexual experience and hearing status. Deaf participants scored lower on HIV/AIDS knowledge, demonstrating a need to improve school-based instruction and develop campaigns tailored to this group’s requirements. Though the hearing students reported more sexual activity than the deaf students, no other significant differences were found in health-related attitudes and behaviors. Two findings of concern are the high rate of sexual abuse reported by deaf participants and the large number of deaf adolescents reporting having a friend with AIDS.
Time-Varying Downlink Channel Tracking for Quantized Massive MIMO Networks

This paper proposes a Bayesian downlink channel estimation framework for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to estimate the model parameters of the sparse virtual channel. The expectation maximization (EM) algorithm is employed to reduce complexity. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of the virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP-based scheme is designed to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, the efficacy of the proposed framework is demonstrated through simulations.
Perspectives in superfluidity in resonantly driven polariton fluids

In this paper we discuss, within the Gross--Pitaevskii framework, superfluidity, soliton nucleation, and instabilities in a non-equilibrium polariton fluid injected by a spatially localized and continuous-wave coherent pump and flowing against a defect located outside the pump spot. In contrast to equilibrium condensates, the steady-state solutions of the driven-dissipative equations in this specific geometry hardly show a clean superfluid flow around the defect and rather feature a crossover from shallow to deep soliton-like perturbation. This is explained in terms of the properties of one-dimensional flows, in particular their weak dependence on the pump parameters and their rapid transition to a super-sonic regime under the effect of the quantum pressure; such a highly nonlinear behaviour calls for quantitative experimental tests of the underlying Gross--Pitaevskii equation. The role of disorder and of a incoherent reservoir in inducing non-stationary behaviours with moving vortices is also highlighted.
Minimax rates of estimation for smooth optimal transport maps

Brenier's theorem is a cornerstone of optimal transport that guarantees the existence of an optimal transport map $T$ between two probability distributions $P$ and $Q$ over $\mathbb{R}^d$ under certain regularity conditions. The main goal of this work is to establish the minimax rates estimation rates for such a transport map from data sampled from $P$ and $Q$ under additional smoothness assumptions on $T$. To achieve this goal, we develop an estimator based on the minimization of an empirical version of the semi-dual optimal transport problem, restricted to truncated wavelet expansions. This estimator is shown to achieve near minimax optimality using new stability arguments for the semi-dual and a complementary minimax lower bound. These are the first minimax estimation rates for transport maps in general dimension.
Effects of ketoprofen alone or in combination with local anesthesia during the castration of bull calves on plasma cortisol, immunological, and inflammatory responses.

To determine the effects of the anti-inflammatory ketoprofen, alone or with local anesthesia (LA) during castration on cortisol, immune, and acute phase responses, 40 Friesian calves (215 +/- 3.5 kg) were assigned as follows: 1) control, 2) surgical castration (SURG), 3) SURG following ketoprofen (3 mg/kg BW i.v.; SURG + K), 4) SURG following LA (9 mL of 2% lidocaine hydrochloride to each testis; SURG + LA), or 5) SURG following LA and K (SURG + LA + K). Total cortisol response was greater (P < 0.05) in SURG, SURG + LA, and SURG + K + LA calves than in control calves and was not different between control and SURG + K calves. The interval to peak cortisol was longer (P < 0.05) for SURG + K + LA calves than for either SURG or SURG + K calves. On d 3, KLH-induced interferon-gamma production was lower (P < 0.05) in SURG calves than in control calves, whereas concanavalin A-induced interferon-gamma production was lower (P < 0.05) in all castration groups than in control. On d 1 after surgery, fibrinogen was higher (P < 0.05) in SURG and SURG + LA calves than in control calves, whereas SURG + LA + K calves had lower (P < 0.05) fibrinogen than did SURG calves. Haptoglobin was higher (P < 0.05) in SURG calves on d 1, 3, and 7 than in control calves. On d 1 after surgery, SURG + K and SURG + LA + K calves had lower (P < 0.05) haptoglobin concentrations than SURG calves, whereas SURG + K calves had lower (P < 0.05) levels than SURG calves on d 3. In conclusion, surgical castration induced a significant elevation in cortisol secretion; the rise in cortisol was reduced to control levels by the administration of ketoprofen but not local anaesthetic. Thus, systemic analgesia using ketoprofen is more effective than local anesthesia during castration to alleviate the associated stress response.
A model for the radio/X-ray correlation in three neutron star low-mass X-ray binaries 4U 1728-34, Aql X-1ad, and EXO 1745-248

Observationally, for neutron star low-mass X-ray binaries, so far, the correlation between the radio luminosity $L_{\rm R}$ and the X-ray luminosity $L_{\rm X}$, i.e., $L_{\rm R}\propto L_{\rm X}^{\beta}$, has been reasonably well-established only in three sources 4U 1728-34, Aql X-1 and EXO 1745-248 in their hard state. The slope $\beta$ of the radio/X-ray correlation of the three sources is different, i.e., $\beta \sim 1.4$ for 4U 1728-34, $\beta \sim 0.4$ for Aql X-1, and $\beta \sim 1.6$ for EXO 1745-248. In this paper, for the first time we explain the different radio/X-ray correlation of 4U 1728-34, Aql X-1 and EXO 1745-248 with the coupled advection-dominated accretion (ADAF)-jet model respectively. We calculate the emergent spectrum of the ADAF-jet model for $L_{\rm X}$ and $L_{\rm R}$ at different $\dot m$ ($\dot m=\dot M/\dot M_{\rm Edd}$), adjusting $\eta$ ($\eta \equiv \dot M_{\rm jet}/\dot M$, describing the fraction of the accreted matter in the ADAF transfered vertically forming the jet) to fit the observed radio/X-ray correlations. Then we derive a fitting formula of $\eta$ as a function of $\dot m$ for 4U 1728-34, Aql X-1 and EXO 1745-248 respectively. If the relation between $\eta$ and $\dot m$ can be extrapolated down to a lower value of $\dot m$, we find that in a wide range of $\dot m$, the value of $\eta$ in Aql X-1 is greater than that of in 4U 1728-34 and EXO 1745-248, implying that Aql X-1 may have a relatively stronger large-scale magnetic field, which is supported by the discovery of the coherent millisecond X-ray pulsation in Aql X-1.
Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia

In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
Nuclear Transport of Yeast Proteasomes

Proteasomes are key proteases in regulating protein homeostasis. Their holo-enzymes are composed of 40 different subunits which are arranged in a proteolytic core (CP) flanked by one to two regulatory particles (RP). Proteasomal proteolysis is essential for the degradation of proteins which control time-sensitive processes like cell cycle progression and stress response. In dividing yeast and human cells, proteasomes are primarily nuclear suggesting that proteasomal proteolysis is mainly required in the nucleus during cell proliferation. In yeast, which have a closed mitosis, proteasomes are imported into the nucleus as immature precursors via the classical import pathway. During quiescence, the reversible absence of proliferation induced by nutrient depletion or growth factor deprivation, proteasomes move from the nucleus into the cytoplasm. In the cytoplasm of quiescent yeast, proteasomes are dissociated into CP and RP and stored in membrane-less cytoplasmic foci, named proteasome storage granules (PSGs). With the resumption of growth, PSGs clear and mature proteasomes are transported into the nucleus by Blm10, a conserved 240 kDa protein and proteasome-intrinsic import receptor. How proteasomes are exported from the nucleus into the cytoplasm is unknown.