Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting Deep Architectures for Head Motion Prediction in 360° Videos

Head motion prediction is an important problem with 360\degree\ videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article we first show the startling result that all such existing methods, which attempt to benefit both from the history of past positions and knowledge of the video content, perform worse than a simple no-motion baseline. We then propose an LSTM-based architecture which processes the positional information only. It is able to establish state-of-the-art performance and we consider it our position-only baseline. Through a thorough root cause analysis, we first show that the content can indeed inform the head position prediction for horizons longer than 2 to 3s, the trajectory inertia being predominant earlier. We also identify that a sequence-to-sequence auto-regressive framework is crucial to improve the prediction accuracy over longer prediction windows, and that a dedicated recurrent network handling the time series of positions is necessary to reach the performance of the position-only baseline in the early prediction steps. This allows to make the most of the positional information and ground-truth saliency. Finally we show how the level of noise in the estimated saliency impacts the architecture's performance, and we propose a new architecture establishing state-of-the-art performance with estimated saliency, supporting its assets with an ablation study.
Structural Basis of Stereospecificity in the Bacterial Enzymatic Cleavage of (cid:2) -Aryl Ether Bonds in Lignin * □ S

from lignin’s monoaromatic units. Degrada-tionofligninhasbeenrelativelywellcharacterizedinfungi,butitis less well understood in bacteria. A catabolic pathway for the enzymatic breakdown of aromatic oligomers linked via (cid:2) -aryl ether bonds typically found in lignin has been reported in the bacterium Sphingobium sp. SYK-6. Here, we present x-ray crystal structures and biochemical characterization of the glutathione-dependent (cid:2) -etherases, LigE and LigF, from this pathway. The crystal struc-turesshowthatbothenzymesbelongtothecanonicaltwo-domain fold and glutathione binding site architecture of the glutathione S -transferase family. Mutagenesis of the conserved active site serine in both LigE and LigF shows that, whereas the enzymatic activity is reduced, this amino acid side chain is not absolutely essential for catalysis. The results include descriptions of cofactor binding sites, substrate binding sites, and catalytic mechanisms. Because (cid:2) -aryl ether bonds account for 50–70% of all interunit linkages in lignin, understanding the mechanism of enzymatic (cid:2) -aryl ether cleavagehassignificantpotentialforinformingongoingstudieson the valorization of lignin.
Turbulent Particle Transport In H-Mode Plasmas On Diii-D

Particle transport is an important topic in plasma physics. It determines the density profile of a burning plasma within a tokamak – a magnetic confinement device. Microscopic turbulent particle transport is two orders of magnitude larger than other transport mechanisms for electrons and small ions. In order to confine a plasma in a tokamak with a core density that exceeds the fusion criteria [1], it is essential to study turbulent particle transport. This thesis investigates how different plasma parameters such as the toroidal rotation and microscopic instabilities affect turbulent particle transport in the DIII-D tokamak. First, we show how toroidal rotation can indirectly affect particle transport, through its contribution to the radial electric field and thus the E × B shearing rate. The plasma discharge which has best confinement is the one whose E × B shearing rate is larger than or at least similar to the growth rates that drive turbulent transport at the plasma edge. Second, for the first time on DIII-D, we observe a correlation between electron density gradient and instability mode frequency in the plasma core. We find that, when the turbulence is driven by the ion temperature gradient (ITG), the local density gradient increases as the the absolute frequency of the dominant unstable mode decreases. Once the dominant unstable mode switches over to the trapped electron mode (TEM) regime, the local density gradient decreases again. As a result the density gradient reaches a maximum when the mode has zero frequency, which is corresponds to the cross over from ITG to TEM. This correlation opens a new opportunity for future large burning plasma devices such as ITER to increase the core density by controlling the turbulence regime. Finally, we show that, in low density regime, a reduction in core density is observed when electron cyclotron heating (ECH) is applied. This reduction is not the result of a change in turbulence regime nor the result of a change in the density gradient in the core. Through detailed time-dependent experimental analysis, linear gyro-kinetic simulations, and comparison to turbulence measurements we show that this reduction in core density is the result of an increase in turbulence drive at the plasma edge.
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards

In the classic multi-armed bandits problem, the goal is to have a policy for dynamically operating arms that each yield stochastic rewards with unknown means. The key metric of interest is regret, defined as the gap between the expected total reward accumulated by an omniscient player that knows the reward means for each arm, and the expected total reward accumulated by the given policy. The policies presented in prior work have storage, computation and regret all growing linearly with the number of arms, which is not scalable when the number of arms is large. We consider in this work a broad class of multi-armed bandits with dependent arms that yield rewards as a linear combination of a set of unknown parameters. For this general framework, we present efficient policies that are shown to achieve regret that grows logarithmically with time, and polynomially in the number of unknown parameters (even though the number of dependent arms may grow exponentially). Furthermore, these policies only require storage that grows linearly in the number of unknown parameters. We show that this generalization is broadly applicable and useful for many interesting tasks in networks that can be formulated as tractable combinatorial optimization problems with linear objective functions, such as maximum weight matching, shortest path, and minimum spanning tree computations.
A Case of Vaccine-Induced Thrombocytopenic Thrombosis Manifesting as Cerebral Venous Thrombosis and Intracerebral Bleed

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but detrimental syndrome that has been most commonly reported after the administration of vaccination for the prevention of viral infections. VITT often presents with thrombosis at unusual sites such as cerebral venous sinuses, portal, splanchnic or hepatic veins, in association with thrombocytopenia and elevated anti-platelet factor 4 (aPF-4) antibodies. We describe the case of a young male patient who developed thrombocytopenia, cerebral sinus venous thrombosis, and intracerebral bleed 12 days after receiving the Ad26.COV2. S (Janssen/Johnson&Johnson) COVID-19 vaccine.
PROPERTIES OF WEAK LENSING CLUSTERS DETECTED ON HYPER SUPRIME-CAM's 2.3 deg2 FIELD

We present properties of moderately massive clusters of galaxies detected by the newly developed Hyper Suprime-Cam on the Subaru telescope using weak gravitational lensing. Eight peaks exceeding a signal-to-noise ratio (S/N) of 4.5 are identified on the convergence S/N map of a 2.3 deg2 field observed during the early commissioning phase of the camera. Multi-color photometric data are used to generate optically selected clusters using the Cluster finding algorithm based on the Multiband Identification of Red-sequence galaxies algorithm. The optical cluster positions were correlated with the peak positions from the convergence map. All eight significant peaks have optical counterparts. The velocity dispersion of clusters is evaluated by adopting the Singular Isothemal Sphere fit to the tangential shear profiles, yielding virial mass estimates, M 500 c ? > , of the clusters which range from 2.7 × 1013 to 4.4 × 10 14 M ⊙ ? > . The number of peaks is considerably larger than the average number expected from ΛCDM cosmology but this is not extremely unlikely if one takes the large sample variance in the small field into account. We could, however, safely argue that the peak count strongly favors the recent Planck result suggesting a high &sgr; 8 ? > value of 0.83. The ratio of stellar mass to the dark matter halo mass shows a clear decline as the halo mass increases. If the gas mass fraction, fg, in halos is universal, as has been suggested in the literature, the observed baryon mass in stars and gas shows a possible deficit compared with the total baryon density estimated from the baryon oscillation peaks in anisotropy of the cosmic microwave background.
High energy resummation and electroweak corrections in dijet production at hadronic colliders

Coloured final states are ubiquitous at hadron colliders such as the Large Hadron Collider (LHC). Therefore understanding high energy perturbative quantum chromodynamics (QCD) at these experiments is essential not only as a test of the Standard Model, but also because these processes form the dominant background to many searches for new physics. One such ‘standard candle’ is the production of a dilepton pair in association with dijets. Here we present a new description of this final state (through the production of a Z0 boson and γ∗). This calculation adds to the fixed-order accuracy the dominant logarithms in the limit of large partonic centre-of-mass energy to all orders in the strong coupling αs. This is achieved within the framework of High Energy Jets. This calculation is made possible by extending the high energy treatment to take into account the multiple t-channel exchanges arising from Z0 and gamma* -emissions off several quark lines. The correct description of the interference effects from the various t-channel exchanges requires an extension of the subtraction terms in the all-order calculation. We describe this construction and compare the resulting predictions to a number of recent analyses of LHC data. The description of a wide range of observables is good, and, as expected, stands out from other approaches in particular in the regions of large dijet invariant mass and large dijet rapidity spans. In addition we also present the application of the High Energy Jets framework to two new experimental scenarios. Firstly, we show a comparison of High Energy Jets matched to the ARIADNE parton shower to an ATLAS study of gap activity in dijet events. We see that our description agrees well with the data throughout and in many distributions gives the best theoretical description. This shows the extra logarithmic corrections are essential to describe data already in LHC Run I. Secondly, we present a study of Z0/γ∗ plus dijets at 100 TeV. We compare the behaviour of the high energy logarithmic enhancements to the QCD perturbative series at 7 TeV and 100 Tev and see that at any high energy hadronic Future Circular Collider (FCC) the effects described by our resummation become significantly more important.
health behaviors and quality of life in chinese survivors of cervical cancer: a retrospective study

Background: The aim of this study was to evaluate health behaviors and quality of life (QoL) in cervical cancer survivors, and to identify factors that may compromise or enhance their health-related QoL. Methods: Data were collected retrospectively from the records of 102 consecutive patients with cervical cancer treated from May 2007 to January 2009 at the People’s Hospital of Xintai City in Shandong Province. The study methodology was guided by the contextual model of health-related QoL. Results: The results showed the significant mediating effects of general health status and psychological well-being between life burden and health-related QoL, between sexual impact of the disease and health-related QoL, and between the patient–doctor relationship and health-related QoL. In addition, there were a significant association between health-related QoL, education level, tumor stage, marital status, and age. Life burden and the patient-doctor relationship was also related to the sexual impact of the disease. However, no significant difference in health-related QoL and sexual impact was observed according to type of treatment received. Conclusion: These results advance our understanding of the predictors of health-related QoL and the relationship between them. Health-related QoL in cervical cancer survivors may be improved by mediating life burden, sexual functioning, and the patient–doctor relationship.
A privacy-preserving distributed computational approach for distributed locational marginal prices

—An important issue in today’s electricity markets is the management of ﬂexibilities offered by new practices, such as smart home appliances or electric vehicles. By inducing changes in the behavior of residential electric utilities, demand response (DR) seeks to adjust the demand of power to the supply for increased grid stability and better integration of renewable energies. A key role in DR is played by emergent independent entities called load aggregators (LAs). We develop a new decentralized algorithm to solve a convex relaxation of the classical Alternative Current Optimal Power Flow (ACOPF) problem, which relies on local information only. Each computational step can be performed in an entirely privacy-preserving manner, and system-wide coordination is achieved via node-speciﬁc distribution locational marginal prices (DLMPs). We demonstrate the efﬁciency of our approach on a 15-bus radial distribution network.
Learning Smooth Representation for Unsupervised Domain Adaptation

In unsupervised domain adaptation, existing methods have achieved remarkable performance, but few pay attention to the Lipschitz constraint. It has been studied that not just reducing the divergence between distributions, but the satisfaction of Lipschitz continuity guarantees an error bound for the target distribution. In this paper, we adopt this principle and extend it to a deep end-to-end model. We define a formula named local smooth discrepancy to measure the Lipschitzness for target distribution in a pointwise way. Further, several critical factors affecting the error bound are taken into account in our proposed optimization strategy to ensure the effectiveness and stability. Empirical evidence shows that the proposed method is comparable or superior to the state-of-the-art methods and our modifications are important for the validity.
General model for stage–discharge prediction in multi-stage compound channels

ABSTRACT Simple and accurate estimates of stage–discharge characteristics are extremely important for supporting appropriate management of two- or multi-stage compound channels, which commonly form in natural rivers or are constructed in regulation projects for urban rivers. Although various methods are available for conveyance estimation of two-stage compound channels, very few are concerned with the multi-stage case. After a critical review of predictive methods for two-stage channels, this paper presents a general model for predicting the stage–discharge relationship in multi-stage compound channels. A multi-stage compound section was divided laterally into several homogeneous subsections, which were further classified into seven subdivision elements. In each element, we employed a general expression to analyse momentum balance, which accounts for momentum exchange between adjacent subsections. Then, a linear system with a tridiagonal coefficient matrix was used to estimate discharge in multi-stage compound channels. Experimental data from the literature were used to calibrate model parameters and examine the performance of the proposed model. The comparative results indicate that the proposed model produces satisfactory predictions. Finally, the main model parameters were discussed.
Research report A structural equation modeling analysis of attentional control: an event-related fMRI study

We report here the first investigation of the effective connectivity between neural structures supporting attentional control using structural equation modeling and functional magnetic resonance imaging (fMRI). Attentional control was examined by employing a modified version of the flanker task. We found that the inconsistent condition elicited a significantly greater number of path coefficients than the consistent condition. In addition, we report that the strength of the prefrontal paths common to both conditions were not different, but that the remaining six paths were different between conditions. Importantly, these results suggest that the relationship between regions supporting attentional control differ between task conditions but the strength of the relationship between some prefrontal regions is invariant between task conditions. Additionally, we found that the paths were significantly lateralized to the right hemisphere. These results are discussed in relation to theories of the function of each region in attentional control. D 2004 Elsevier B.V. All rights reserved. Theme: Neural basis of behavior Topic: Cognition
Sampling distributions of optimal portfolio weights and characteristics in small and large dimensions

Optimal portfolio selection problems are determined by the (unknown) parameters of the data generating process. If an investor wants to realize the position suggested by the optimal portfolios, he/she needs to estimate the unknown parameters and to account for the parameter uncertainty in the decision process. Most often, the parameters of interest are the population mean vector and the population covariance matrix of the asset return distribution. In this paper, we characterize the exact sampling distribution of the estimated optimal portfolio weights and their characteristics. This is done by deriving their sampling distribution by its stochastic representation. This approach possesses several advantages, e.g. (i) it determines the sampling distribution of the estimated optimal portfolio weights by expressions, which could be used to draw samples from this distribution efficiently; (ii) the application of the derived stochastic representation provides an easy way to obtain the asymptotic approximation of the sampling distribution. The later property is used to show that the high-dimensional asymptotic distribution of optimal portfolio weights is a multivariate normal and to determine its parameters. Moreover, a consistent estimator of optimal portfolio weights and their characteristics is derived under the high-dimensional settings. Via an extensive simulation study, we investigate the finite-sample performance of the derived asymptotic approximation and study its robustness to the violation of the model assumptions used in the derivation of the theoretical results.
Blood Cadmium Is Associated with Osteoporosis in Obese Males but Not in Non-Obese Males: The Korea National Health and Nutrition Examination Survey 2008–2011

Osteoporosis in males is becoming an important health concern in an aging society. The aim of this study was to investigate the associations between cadmium exposure and osteoporosis by considering the effect of obesity in aged males using a representative sample of the Korean population. Using the fourth and fifth Korea National Health and Nutrition Examination Survey data, 1089 males over 50 years of age were analyzed. The blood cadmium concentration was measured. The bone mineral density in the total hip, femur neck, and lumbar spine was measured using dual-energy X-ray absorptiometry. T-scores to determine the presence of osteoporosis were calculated using a Korean reference. Subjects were stratified into two groups according to obesity status (body mass index <25 kg/m2 and ≥25 kg/m2). In comparison with obese subjects with blood cadmium <1.00 μg/L, those with blood cadmium >1.50 μg/L had odds ratios of 4.57 (95% confidence interval [CI] 1.49–14.01) and 5.71 (95% CI 1.99–16.38) at the femur neck and any site, respectively, after adjusting for potential confounders such as age, serum creatinine, vitamin D deficiency, smoking, alcohol drinking, and physical activity level. However, this association was not significant in non-obese males. In conclusion, the effect of cadmium on osteoporosis was different by obesity status in aged males.
The near field boundary of dewatering systems : estimating individual yields for wells operating under gravity flow

The subject of this thesis is the estimation of individual well yields, which is a fundamental part of a multi-well abstraction system design. A literature review of the subject shows that the current best practice for estimating individual well yields has several shortcomings and that further research on the topic is required for individual wells operating under gravity flow. The proposal by Sichardt (1927) for estimating the hydraulic gradient at entry into wells is reviewed and his suggestions are compared to the findings in the field. Pumping test data from eight individual abstraction wells, operating under gravity flow in aquifers having a range of permeability values, are presented. The permeability of the aquifer and the implied hydraulic entry gradient into the well were calculated from the data. The findings also show that Sichardt’s (1927) formula provides reasonable results for permeabilities in the range 1 x 10-5 up to 2.15 x 10-3m/s. For permeability values below 1 x 10-5 m/s Sichardt (1927) a reasonable estimation, but the results need to be used with caution.
Koopman based data-driven predictive control

—A class of data-driven control methods based on Willems’ fundamental lemma has been developed in recent years whereas most of them restrict the scope in linear time-invariant (LTI) system. In this paper, we extend a data-driven predictive control (DeePC) based on fundamental lemma into nonlinear systems with the aid of Koop-man operator theory. Numerical simulations are provided to show this new data-driven approach is competitive to model-based method. We also proposed a purely data-driven Koopman operator approximation framework and considered the uncertainty of approximation. Accordingly, a novel data-driven simulation framework based on Wasser-stein distance is designed, which outperforms the current formulation in dealing with uncertainty.
Gait recognition under different clothing conditions via deterministic learning

In real-world scenarios, the clothing difference constitutes one of the most common covariate factors that can affect the performance of gait recognition systems. This paper proposes a gait recognition method which is invariant to maximum number of clothing conditions. First, four kinds of timevarying silhouette features are selected to capture the spatiotemporal characteristics of gait motion. Second, frame-to-frame gait dynamics underlying different individuals ʼ gait features is effectively modeled by radial basis function ( RBF ) neural networks through deterministic learning. Gait patterns are represented as the gait dynamics underlying time-varying gait features. This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions. In order to eliminate the effect of clothing differences, the training patterns under different clothing conditions further constitute a uniform training dataset, containing all kinds of gait dynamics under different clothing conditions. A rapid recognition scheme is presented on published gait databases. Extensive experiments demonstrate the efficacy of the proposed method.