Miracle-dz/UniFAR-FTUs huggingface dataset:
Constant-Cost Spatio-Angular Prefiltering of Glinty Appearance Using Tensor Decomposition

The detailed glinty appearance from complex surface microstructures enhances the level of realism but is both - and time-consuming to render, especially when viewed from far away (large spatial coverage) and/or illuminated by area lights (large angular coverage). In this article, we formulate the glinty appearance rendering process as a spatio-angular range query problem of the Normal Distribution Functions (NDFs), and introduce an efficient spatio-angular prefiltering solution to it. We start by exhaustively precomputing all possible NDFs with differently sized positional coverages. Then we compress the precomputed data using tensor rank decomposition, which enables accurate and fast angular range queries. With our spatio-angular prefiltering scheme, we are able to solve both the storage and performance issues at the same time, leading to efficient rendering of glinty appearance with both constant storage and constant performance, regardless of the range of spatio-angular queries. Finally, we demonstrate that our method easily applies to practical rendering applications that were traditionally considered difficult. For example, efficient bidirectional reflection distribution function evaluation accurate NDF importance sampling, fast global illumination between glinty objects, high-frequency preserving rendering with environment lighting, and tile-based synthesis of glinty appearance.
Multiple accelerated particle populations in the Cygnus Loop with Fermi-LAT

Context. The Cygnus Loop (G74.0-8.5) is a very well-known nearby supernova remnant (SNR) in our Galaxy. Thanks to its large size, brightness, and angular offset from the Galactic plane, it has been studied in detail from radio to γ -ray emission. The γ -rays probe the populations of energetic particles and their acceleration mechanisms at low shock speeds. Aims. We present an analysis of the γ -ray emission detected by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope over 11yr in the region of the Cygnus Loop. Methods. We performed detailed morphological and spectral studies of the γ -ray emission toward the remnant from 100MeV to 100GeV and compared it with X-ray, UV, optical, and radio images. The higher statistics with respect to the previous studies enabled us to decompose the emission from the remnant into two morphological components to model its nonthermal multiwavelength emission. Results. The extended γ -ray emission is well correlated with the thermal X-ray and UV emission of the SNR. Our morphological analysis reveals that a model considering two contributions from the X-ray and the UV emission regions is the best description of the γ -ray data. Both components show a curved spectrum, but the X-ray component is softer and more curved than the UV component, suggesting a different physical origin. The multiwavelength modeling of emission toward the SNR suggests that the nonthermal radio and γ -ray emission associated with the UV component is mostly due to the reacceleration of preexisting cosmic rays by radiative shocks in the adjacent clouds, while the nonthermal emission associated with the X-ray component arises from freshly accelerated cosmic rays.
A Review of Text Style Transfer Using Deep Learning

Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves; however, they adjust their speaking and writing style to a social context, an audience, an interlocutor, or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this article. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.
XPROAX-Local explanations for text classification with progressive neighborhood approximation

The importance of the neighborhood for training a local surrogate model to approximate the local decision boundary of a black box classifier has been already highlighted in the literature. Several attempts have been made to construct a better neighborhood for high dimensional data, like texts, by using generative autoencoders. However, existing approaches mainly generate neighbors by selecting purely at random from the latent space and struggle under the curse of dimensionality to learn a good local decision boundary. To overcome this problem, we propose a progressive approximation of the neighborhood using counterfactual instances as initial landmarks and a careful 2-stage sampling approach to refine counterfactuals and generate factuals in the neighborhood of the input instance to be explained. Our work focuses on textual data and our explanations consist of both word-level explanations from the original instance (intrinsic) and the neighborhood (extrinsic) and factual- and counterfactual-instances discovered during the neighborhood generation process that further reveal the effect of altering certain parts in the input text. Our experiments on real-world datasets demonstrate that our method outperforms the competitors in terms of usefulness and stability (for the qualitative part) and completeness, compactness and correctness (for the quantitative part).
Brand Attitude in Social Networks: The Role of eWoM

This study aims to examine the impact of electronic word-of-mouth (eWoM) marketing on branding attitudes in social networks. Specifically, we investigate the effects of eWoM activities on brand awareness, brand destruction, branding, brand image, and brand competition. To gather data, we conducted a survey among followers of the Vizland shoe page on the Instagram social network. Statistical analysis was performed using SPSS software, and hypotheses were tested using SmartPLS software. The results indicate that eWoM significantly and positively influences branding, brand image, and brand awareness, while it does not have an impact on brand destruction. Additionally, branding and brand destruction are found to play a crucial role in gaining competitive advantage. Therefore, eWoM activities contribute to enhancing the brand relationship.
On fast convergence rates for generalized conditional gradient methods with backtracking stepsize

A generalized conditional gradient method for minimizing the sum of two convex functions, one of them differentiable, is presented. This iterative method relies on two main ingredients: First, the minimization of a partially linearized objective functional to compute a descent direction and, second, a stepsize choice based on an Armijo-like condition to ensure sufficient descent in every iteration. We provide several convergence results. Under mild assumptions, the method generates sequences of iterates which converge, on subsequences, towards minimizers. Moreover, a sublinear rate of convergence for the objective functional values is derived. Second, we show that the method enjoys improved rates of convergence if the partially linearized problem fulfills certain growth estimates. Most notably these results do not require strong convexity of the objective functional. Numerical tests on a variety of challenging PDE-constrained optimization problems confirm the practical efficiency of the proposed algorithm.
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning

We study a new two-time-scale stochastic gradient method for solving optimization problems, where the gradients are computed with the aid of an auxiliary variable under samples generated by time-varying Markov random processes parameterized by the underlying optimization variable. These time-varying samples make gradient directions in our update biased and dependent, which can potentially lead to the divergence of the iterates. In our two-time-scale approach, one scale is to estimate the true gradient from these samples, which is then used to update the estimate of the optimal solution. While these two iterates are implemented simultaneously, the former is updated"faster"(using bigger step sizes) than the latter (using smaller step sizes). Our first contribution is to characterize the finite-time complexity of the proposed two-time-scale stochastic gradient method. In particular, we provide explicit formulas for the convergence rates of this method under different structural assumptions, namely, strong convexity, convexity, the Polyak-Lojasiewicz condition, and general non-convexity. We apply our framework to two problems in control and reinforcement learning. First, we look at the standard online actor-critic algorithm over finite state and action spaces and derive a convergence rate of O(k^(-2/5)), which recovers the best known rate derived specifically for this problem. Second, we study an online actor-critic algorithm for the linear-quadratic regulator and show that a convergence rate of O(k^(-2/3)) is achieved. This is the first time such a result is known in the literature. Finally, we support our theoretical analysis with numerical simulations where the convergence rates are visualized.
Semantic Dense Reconstruction with Consistent Scene Segments

In this paper, a method for dense semantic 3D scene reconstruction from an RGB-D sequence is proposed to solve high-level scene understanding tasks. First, each RGB-D pair is consistently segmented into 2D semantic maps based on a camera tracking backbone that propagates objects' labels with high probabilities from full scans to corresponding ones of partial views. Then a dense 3D mesh model of an unknown environment is incrementally generated from the input RGB-D sequence. Benefiting from 2D consistent semantic segments and the 3D model, a novel semantic projection block (SP-Block) is proposed to extract deep feature volumes from 2D segments of different views. Moreover, the semantic volumes are fused into deep volumes from a point cloud encoder to make the final semantic segmentation. Extensive experimental evaluations on public datasets show that our system achieves accurate 3D dense reconstruction and state-of-the-art semantic prediction performances simultaneously.
Helfrich-Hurault elastic instabilities driven by geometrical frustration

The Helfrich-Hurault (HH) elastic instability is a well-known mechanism behind patterns that form as a result of strain upon liquid crystal systems with periodic ground states. In the HH model, layered structures undulate and buckle in response to local, geometric incompatibilities, in order to maintain the preferred layer spacing. Classic HH systems include cholesteric liquid crystals under electromagnetic field distortions and smectic liquid crystals under mechanical strains, where both materials are confined between rigid substrates. However, richer phenomena are observed when undulation instabilities occur in the presence of deformable interfaces and variable boundary conditions. Understanding how the HH instability is affected by deformable surfaces is imperative for applying the instability to a broader range of materials. In this review, we re-examine the HH instability and give special focus to how the boundary conditions influence the mechanical response of lamellar systems to geometrical frustration. We use lamellar liquid crystals confined within a spherical shell geometry as our model system. Made possible by the relatively recent advances in microfluidics within the past 15 years, liquid crystal shells are composed entirely of fluid interfaces and have boundary conditions that can be dynamically controlled at will. We examine past and recent work that exemplifies how topological constraints, molecular anchoring conditions, and boundary curvature can trigger the HH instability in liquid crystals with periodic ground states. We then end by identifying similar phenomena across a wide variety of materials, both biological and synthetic. With this review, we aim to highlight that the HH instability is a generic and often overlooked response of periodic materials to geometrical frustration.
Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications

In this study, a new method is proposed to assess heart and lung signal quality objectively and automatically on a 5-level scale in real-time, and to assess the effect of signal quality on vital sign estimation. A total of 207 10 s long chest sounds were taken from 119 preterm and full-term babies. Thirty of the recordings from ten subjects were obtained with synchronous vital signs from the Neonatal Intensive Care Unit (NICU). As a reference, seven annotators independently assessed the signal quality. For automatic quality classification, 400 features were extracted from the chest sounds. After feature ranking and selection, class balancing, and hyperparameter optimization, a variety of multi-class and ordinal classification and regression algorithms were trained. Then, heart rate and breathing rate were automatically estimated from the chest sounds. For the deep learning model, YAMNet, a deep convolutional neural network pre-trained on the AudioSet-Youtube corpus for sound classification was used. After modification of the final output layers of the neural network and class balancing, transfer learning was applied to YAMNet for heart and lung signal quality classification. The results of subject-wise leave-one-out cross-validation show that the best-performing models had a balanced accuracy of 56.8% and 51.2% for heart and lung qualities, respectively. The best-performing models for real-time analysis (<200 ms) had a balanced accuracy of 56.7% and 46.3%, respectively. Our experimental results underscore that increasing the signal quality leads to a reduction in vital sign error.
3D Pose Transfer with Correspondence Learning and Mesh Refinement

3D pose transfer is one of the most challenging 3D generation tasks. It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e.g., body shape) of the target mesh. Some previous works require key point annotations to build reliable correspondence between the source and target meshes, while other methods do not consider any shape correspondence between sources and targets, which leads to limited generation quality. In this work, we propose a correspondence-refinement network to achieve the 3D pose transfer for both human and animal meshes. The correspondence between source and target meshes is first established by solving an optimal transport problem. Then, we warp the source mesh according to the dense correspondence and obtain a coarse warped mesh. The warped mesh will be better refined with our proposed Elastic Instance Normalization, which is a conditional normalization layer and can help to generate high-quality meshes. Extensive experimental results show that the proposed architecture can effectively transfer the poses from source to target meshes and produce better results with satisfied visual performance than state-of-the-art methods.
Nonreciprocal optical response in parity-breaking superconductors

Superconductivity, an emergence of a macroscopic quantum state, gives rise to unique electromagnetic responses leading to perfect shielding of magnetic field and zero electrical resistance. In this paper, we propose that superconductors with the space-inversion symmetry breaking host giant nonreciprocal optical phenomena, such as photocurrent generation and second harmonic generation. The nonreciprocal optical responses show divergent behaviors in the low-frequency regime and originate from two-fold indicators unique to parity-breaking superconductors, namely, the nonreciprocal superfluid density and the Berry curvature derivative. Furthermore, the mechanism and frequency dependence are closely tied to the preserved temporal symmetry in the superconductor. The relation is useful for probing the space-time structure of the superconducting symmetry. The indicators characterize the low-frequency property of nonreciprocal optical responses and hence quantify the performance of superconductors in nonreciprocal optics.
Real Robot Challenge using Deep Reinforcement Learning

This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge, a challenge in which a three ﬁngered robot must carry a cube along speciﬁed goal trajectories. To solve Phase 1, we use a pure reinforcement learning approach which requires minimal expert knowledge of the robotic system or of robotic grasping in general. A sparse goal-based reward is employed in conjunction with Hindsight Experience Replay to teach the control policy to move the cube to the desired x and y coordinates. Simultaneously, a dense distance-based reward is employed to teach the policy to lift the cube to the desired z coordinate. The policy is trained in simulation with domain randomisation before being transferred to the real robot for evaluation. Although performance tends to worsen after this transfer, our best trained policy can successfully lift the real cube along goal trajectories via the use of an eﬀective pinching grasp 1 . Our approach outperforms all other submissions, including those leveraging more traditional robotic control techniques, and is the ﬁrst learning-based approach to solve this challenge.
Real-time detection of Rydberg excitation dynamics of cold atoms in an optical cavity

We report on the real-time detection of internal-state dynamics of cold Rubidium atoms being excited from the ground state to the 30D5/2 Rydberg state via two-photon excitation in a laddertype system. The atoms are first trapped and then released from a magneto-optic trap (MOT) positioned inside the mode of a low-finesse cavity. Then, Rydberg excitation is performed by a simultaneous pulse from two counter-propagating laser beams transverse to the cavity axis. This excitation changes the number of atoms able to absorb or emit cavity photons; thus, changing the collective atom-cavity coupling. We detect this by weakly driving the cavity and collecting the light transmitted through it with an avalanche photodiode (APD). We are able to observe not only the 2-photon excitation dynamics, but also the transfer of population out of the Rydberg state due to black-body radiation (BBR) and the decay back to the ground state.
Sensitivity and Specificity of Pulmonary Artery Pressure Measurement in Echocardiography and Correlation with Right Heart Catheterization

Objective Echocardiography is helpful in assessment of pulmonary hemodynamic, however its correlation with Right heart catheterization (RHC) is conflicting. We conducted a study to evaluate sensitivity and specificity of pulmonary hemodynamic parameters measured in echocardiography. Furthermore its correlation with the values measured in RHC was assessed. Method Retrospective, cross-sectional study conducted at King Fahad medical City, Riyadh, Saudi Arabia. 95 adult patients referred for right heart catheterization were enrolled in the study. All the patients had echocardiography and RHC within one week of each other. Result Diabetes mellitus, hypertension and dyslipidemia were present among 55%, 66% and 41% of patients respectively. 85% of the study participants were diagnosed to have pulmonary hypertension and 79% of the study participants had postcapillary pulmonary hypertension. Sensitivity of pulmonary artery systolic pressure (PAPs), mean pulmonary artery pressure (PAPm) using PAPs and pulmonary artery acceleration velocity (PAcT) were 86%, 93% and 89% respectively. Correlation of PAPs, PAPm using PAPs and PAcT on echo with invasive hemodynamic in RHC were 0.56, 0.43 and 0.24 respectively. Among patients with moderate to severe Tricuspid Regurgitation (TR) and tricuspid annular plane systolic excursion (TAPSE) <1.5cm correlation of PAPs, PAPm using PAPs and PAcT on echocardiography with right heart catheterization were 0.31, 0.24 and 0.42 respectively. Conclusion Echocardiographic assessment of PAPs and PAPm has high sensitivity and weak to moderate correlation with hemodynamic data in RHC. PAPs measurement on echocardiogram has best correlation with invasive measurement followed by PAPm measurement using PAPs. Among patients with moderate to severe TR and TAPSE <1.5cm PAPm measurement using PAcT has better correlation than using PAPs.
Cognitive ocular motor deficits and white matter damage chronically after sports-related concussion

Abstract A history of concussion has been linked to long-term cognitive deficits; however, the neural underpinnings of these abnormalities are poorly understood. This study recruited 26 asymptomatic male Australian footballers with a remote history of concussion (i.e. at least six months since last concussion), and 23 non-collision sport athlete controls with no history of concussion. Participants completed three ocular motor tasks (prosaccade, antisaccade and a cognitively complex switch task) to assess processing speed, inhibitory control and cognitive flexibility, respectively. Diffusion tensor imaging data were acquired using a 3 T MRI scanner, and analysed using tract-based spatial statistics, to investigate white matter abnormalities and how they relate to ocular motor performance. Australian footballers had significantly slower adjusted antisaccade latencies compared to controls (P = 0.035). A significant switch cost (i.e. switch trial error > repeat trial error) was also found on the switch task, with Australian footballers performing increased magnitude of errors on prosaccade switch trials relative to prosaccade repeat trials (P = 0.023). Diffusion tensor imaging analysis found decreased fractional anisotropy, a marker of white matter damage, in major white matter tracts (i.e. corpus callosum, corticospinal tract) in Australian footballers relative to controls. Notably, a larger prosaccade switch cost was significantly related to reduced fractional anisotropy in anterior white matter regions found to connect to the prefrontal cortex (i.e. a key cortical ocular motor centre involved in executive functioning and task switching). Taken together, Australian footballers with a history of concussion have ocular motor deficits indicative of poorer cognitive processing speed and cognitive flexibility, which are related to reduce white matter integrity in regions projecting to important cognitive ocular motor structures. These findings provide novel insights into the neural mechanisms that may underly chronic cognitive impairments in individuals with a history of concussion.
Total Body Sodium Balance in Chronic Kidney Disease

Excess sodium intake is a leading but modifiable risk factor for mortality, with implications on hypertension, inflammation, cardiovascular disease, and chronic kidney disease (CKD). This review will focus mainly on the limitations of current measurement methods of sodium balance particularly in patients with CKD who have complex sodium physiology. The suboptimal accuracy of sodium intake and excretion measurement is seemingly more marked with the evolving understanding of tissue (skin and muscle) sodium. Tissue sodium represents an extrarenal influence on sodium homeostasis with demonstrated clinical associations of hypertension and inflammation. Measurement of tissue sodium has been largely unexplored in patients with CKD. Development and adoption of more comprehensive and dynamic assessment of body sodium balance is needed to better understand sodium physiology in the human body and explore therapeutic strategies to improve the clinical outcomes in the CKD population.