Miracle-dz/UniFAR-FTUs huggingface dataset:
3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction

Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. However, reconstructing a 3D caricature from a 2D caricature image remains a challenging task, mostly due to the lack of data. We propose to fill this gap by introducing 3DCaricShop, the first large-scale 3D caricature dataset that contains 2000 high-quality diversified 3D caricatures manually crafted by professional artists. 3DCaricShop also provides rich annotations including a paired 2D caricature image, camera parameters and 3D facial landmarks. To demonstrate the advantage of 3DCaricShop, we present a novel baseline approach for single-view 3D caricature reconstruction. To ensure a faithful reconstruction with plausible face deformations, we propose to connect the good ends of the detail-rich implicit functions and the parametric mesh representations. In particular, we first register a template mesh to the output of the implicit generator and iteratively project the registration result onto a pre-trained PCA space to resolve artifacts and self-intersections. To deal with the large deformation during non-rigid registration, we propose a novel view-collaborative graph convolution network (VC-GCN) to extract key points from the implicit mesh for accurate alignment. Our method is able to generate high-fidelity 3D caricature in a pre-defined mesh topology that is animation-ready. Extensive experiments have been conducted on 3DCaricShop to verify the significance of the database and the effectiveness of the proposed method. We will release 3DCaricShop upon publication.
Mutual Information State Intrinsic Control

Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts to remove this constraint by defining an intrinsic reward function. Motivated by the self-consciousness concept in psychology, we make a natural assumption that the agent knows what constitutes itself, and propose a new intrinsic objective that encourages the agent to have maximum control on the environment. We mathematically formalize this reward as the mutual information between the agent state and the surrounding state under the current agent policy. With this new intrinsic motivation, we are able to outperform previous methods, including being able to complete the pick-and-place task for the first time without using any task reward. A video showing experimental results is available at https://youtu.be/AUCwc9RThpk.
The Core Mass Function in the Orion Nebula Cluster Region: What Determines the Final Stellar Masses?

Applying dendrogram analysis to the CARMA-NRO C18O (J = 1–0) data having an angular resolution of ∼8″, we identified 692 dense cores in the Orion Nebula Cluster region. Using this core sample, we compare the core and initial stellar mass functions in the same area to quantify the step from cores to stars. About 22% of the identified cores are gravitationally bound. The derived core mass function (CMF) for starless cores has a slope similar to Salpeter’s stellar initial mass function (IMF) for the mass range above 1 M ⊙, consistent with previous studies. Our CMF has a peak at a subsolar mass of ∼0.1 M ⊙, which is comparable to the peak mass of the IMF derived in the same area. We also find that the current star formation rate is consistent with the picture in which stars are born only from self-gravitating starless cores. However, the cores must gain additional gas from the surroundings to reproduce the current IMF (e.g., its slope and peak mass), because the core mass cannot be accreted onto the star with 100% efficiency. Thus, the mass accretion from the surroundings may play a crucial role in determining the final stellar masses of stars.
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness—improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.
uTHCD: A New Benchmarking for Tamil Handwritten OCR

The robustness of a typical Handwritten character recognition system relies on the availability of comprehensive supervised data samples. There has been considerable work reported in the literature about creating the database for several Indic scripts, but the Tamil script has only one standardized database up to date. This paper presents the work done to create an exhaustive and extensive unconstrained Tamil Handwritten Character Database (uTHCD). The samples were generated from around 850 native Tamil volunteers including school-going kids, homemakers, university students, and faculty. The database consists of about 91000 samples with nearly 600 samples in each of 156 classes. This isolated character database is made publicly available as raw images and Hierarchical Data File (HDF) compressed file. The paper also presents several possible use cases of the proposed uTHCD database using Convolutional Neural Networks (CNN) to classify handwritten Tamil characters. Several experiments demonstrate that training on the proposed database helps traditional and contemporary classifiers perform on par or better than the existing dataset when tested with unseen data. With this database, we expect to set a new benchmark in Tamil handwritten character recognition and serve as a launchpad for developing robust language technologies for the Tamil script.
FES: A Fast Efficient Scalable QoS Prediction Framework

Quality-of-Service prediction of web service is an integral part of services computing due to its diverse applications in the various facets of a service life cycle, such as service composition, service selection, service recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm has to be faster in terms of prediction time so that it can be integrated into a real-time recommendation or composition system. The other important factor to consider while designing the prediction algorithm is scalability to ensure that the prediction algorithm can tackle large-scale datasets. The existing algorithms on QoS prediction often compromise on one goal while ensuring the others. In this paper, we propose a semi-offline QoS prediction model to achieve three important goals simultaneously: higher accuracy, faster prediction time, scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity of the given QoS invocation log matrix. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on four publicly available WS-DREAM datasets show the efficiency in terms of accuracy, scalability, fast responsiveness of our framework as compared to the state-of-the-art methods.
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy

This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models, with minimal loss of standard accuracy. We suggest creating a neighborhood around each training example, such that the label is kept constant for all inputs within that neighborhood. Unlike previous work that follows a similar principle, we apply this idea by extending the training set with multiple perturbations for each training example, drawn from within the neighborhood. These perturbations are model independent, and remain constant throughout the entire training process. We analyzed our method empirically on MNIST, SVHN, and CIFAR-10, under different attacks and conditions. Results suggest that the proposed approach improves standard accuracy over other defenses while having increased robustness compared to vanilla adversarial training.
Study of Proton, Deuteron, and Triton at 54.4 GeV

Transverse momentum spectra of proton, deuteron, and triton in gold-gold (Au-Au) collisions at 54.4 GeV are analyzed in different centrality bins by the blast wave model with Tsallis statistics. The model results are approximately in agreement with the experimental data measured by STAR Collaboration in special transverse momentum ranges. We extracted the kinetic freeze-out temperature, transverse flow velocity, and freeze-out volume from the transverse momentum spectra of the particles. It is observed that the kinetic freeze-out temperature is increasing from the central to peripheral collisions. However, the transverse flow velocity and freeze-out volume decrease from the central to peripheral collisions. The present work reveals the mass dependent kinetic freeze-out scenario and volume differential freeze-out scenario in collisions at STAR Collaboration. In addition, parameter characterizes the degree of nonequilibrium of the produced system, and it increases from the central to peripheral collisions and increases with mass .
MBAPose: Mask and Bounding-Box Aware Pose Estimation of Surgical Instruments with Photorealistic Domain Randomization

Surgical robots are usually controlled using a priori models based on the robots’ geometric parameters, which are calibrated before the surgical procedure. One of the challenges in using robots in real surgical settings is that those parameters can change over time, consequently deteriorating control accuracy. In this context, our group has been investigating online calibration strategies without added sensors. In one step toward that goal, we have developed an algorithm to estimate the pose of the instruments’ shafts in endoscopic images. In this study, we build upon that earlier work and propose a new framework to more precisely estimate the pose of a rigid surgical instrument. Our strategy is based on a novel pose estimation model called MBAPose and the use of synthetic training data. Our experiments demonstrated an improvement of 21 % for translation error and 26 % for orientation error on synthetic test data with respect to our previous work. Results with real test data provide a baseline for further research.
Characterization of Alumina, Silica, and Titania Supported Cobalt Catalysts

A multiple technique approach was used to characterize the structural, chemical, and electronic properties of Co catalysts supported on Al2O3, SiO2, and TiO2 as well as Co/Mn catalysts on TiO2. The morphology of the catalysts was studied by transmission electron microscopy followed by X-ray diffraction to determine the phase composition and the distribution of the crystallite size. The electronic properties of the calcined catalysts were investigated by X-ray photoelectron spectroscopy. Comparison with reference data allows identification of the cobalt-containing species in the surface. In agreement with adsorption experiments, the signal intensities yield the dispersion of the applied catalysts in the sequence Co/Al2O3>Co/TiO2>Co/SiO2. Temperature-programmed reduction and oxidation reveal the formation of various oxides in dependence on temperature as well as, in case of the alumina- and titania-supported cobalt catalysts, the formation of high-temperature compounds CoAl2O4 and CoTiO3, respectively. Dynamic and static adsorption studies and BET measurements complete the characterization of the supported catalysts.
Optimizing transmission conditions for multiple subdomains in the Magnetotelluric Approximation of Maxwell's equations

Classically transmission conditions between subdomains are optimized for a simplified two subdomain decomposition to obtain optimized Schwarz methods for many subdomains. We investigate here if such a simplified optimization suffices for the magnetotelluric approximation of Maxwell's equation which leads to a complex diffusion problem. We start with a direct analysis for 2 and 3 subdomains, and present asymptotically optimized transmission conditions in each case. We then optimize transmission conditions numerically for 4, 5 and 6 subdomains and observe the same asymptotic behavior of optimized transmission conditions. We finally use the technique of limiting spectra to optimize for a very large number of subdomains in a strip decomposition. Our analysis shows that the asymptotically best choice of transmission conditions is the same in all these situations, only the constants differ slightly. It is therefore enough for such diffusive type approximations of Maxwell's equations, which include the special case of the Laplace and screened Laplace equation, to optimize transmission parameters in the simplified two subdomain decomposition setting to obtain good transmission conditions for optimized Schwarz methods for more general decompositions.
Revisiting ResNets: Improved Training and Scaling Strategies

Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al., 2015) and studies these three aspects in an effort to disentangle them. Perhaps surprisingly, we find that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan&Le, 2019). Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1.7x - 2.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. In a large-scale semi-supervised learning setup, ResNet-RS achieves 86.2% top-1 ImageNet accuracy, while being 4.7x faster than EfficientNet NoisyStudent. The training techniques improve transfer performance on a suite of downstream tasks (rivaling state-of-the-art self-supervised algorithms) and extend to video classification on Kinetics-400. We recommend practitioners use these simple revised ResNets as baselines for future research.
Deep Consensus Learning

Both generative learning and discriminative learning have recently witnessed remarkable progress using Deep Neural Networks (DNNs). For structured input synthesis and structured output prediction problems (e.g., layout-to-image synthesis and image semantic segmentation respectively), they often are studied separately. This paper proposes deep consensus learning (DCL) for joint layout-to-image synthesis and weakly-supervised image semantic segmentation. The former is realized by a recently proposed LostGAN approach, and the latter by introducing an inference network as the third player joining the two-player game of LostGAN. Two deep consensus mappings are exploited to facilitate training the three networks end-to-end: Given an input layout (a list of object bounding boxes), the generator generates a mask (label map) and then use it to help synthesize an image. The inference network infers the mask for the synthesized image. Then, the latent consensus is measured between the mask generated by the generator and the one inferred by the inference network. For the real image corresponding to the input layout, its mask also is computed by the inference network, and then used by the generator to reconstruct the real image. Then, the data consensus is measured between the real image and its reconstructed image. The discriminator still plays the role of an adversary by computing the realness scores for a real image, its reconstructed image and a synthesized image. In experiments, our DCL is tested in the COCO-Stuff dataset. It obtains compelling layout-to-image synthesis results and weakly-supervised image semantic segmentation results.
Hamiltonian Monte Carlo in Inverse Problems; Ill-Conditioning and Multi-Modality.

The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Modern auto-diﬀerentiating software should allow more widespread usage in Bayesian inverse problems. This paper analyzes two major diﬃculties encoun-tered using HMC for inverse problems: poor conditioning and multi-modality. Novel results on preconditioning and replica exchange Monte Carlo parameter selection are presented in the context of spectroscopy. Recommendations are given for the number of integration steps as well as step size, preconditioner type and ﬁtting, annealing form and schedule. These recommendations are analyzed rigorously in the Gaussian case, and shown to generalize in a fusion plasma reconstruction.
Cosmological solutions and growth index of matter perturbations in 
f(Q)
 gravity

The present work studies one of Einstein’s alternative formulations based on the non-metricity scalar Q generalized as f ( Q ) theory. More speciﬁcally, we consider the power-law form of f ( Q ) gravity i.e. f ( Q ) = Q + α Q n . Here, we analyze the behavior of the cosmological model at the background and perturbation level. Using the dynamical system analysis, at the background level, the eﬀective evolution of the model is the same as that of the ΛCDM for | n | < 1. Interestingly, the geometric component of the theory solely determined the late-time acceleration of the Universe. We also examine the integrability of the model by employing the method of singularity analysis. In particular, we ﬁnd the conditions under which ﬁeld equations pass the Painlev´e test and hence possess the Painlev´e property. While the equations pass the Painlev´e test in the presence of dust for any value of n , the test is valid after the addition of radiation ﬂuid only for n < 1. Finally, at the perturbation level, the behavior of matter growth index signiﬁes a deviation of the model from the ΛCDM even for | n | < 1. to the matter Lagrangian. As a cosmological application they show that it can represent an alternative approach to dark energy (DE) [13]. Observational constraints on the background behavior of several f ( Q ) models have been performed by testing against various current background data such as Type Ia Supernovae, Pantheon data, Hubble data, etc. [14, 15]. These studies conclude that viable f ( Q ) models correspond to model parameters’ values which resemble the GR-based model viz. the Lambda Cold Dark Matter (ΛCDM) model. Mandal et. al. analyzed the energy conditions to restrict the parameters of the power-law and logarithmic f ( Q ) models
Generalized NS-algebras

We generalize to arbitrary categories of algebras the notion of an NS-algebra. We do this by using a bimodule property, as we did in our earlier work for defining the general notions of a dendriform and tridendriform algebra. We show that several types of operators lead to NS-algebras: Nijenhuis operators, twisted Rota-Baxter operators and relative RotaBaxter operators of arbitrary weight.
Reducing power and radical scavenging activities of phenolic extracts from Juglans regia hulls and shells

Iranian walnut shows genetic diversity and has a large distribution. Different species of these walnuts have many variations in genes resistant to dryness, diseases, pests and other factors. In order to compare the reducing power and radical scavenging activities of Iranian walnut hull and shell phenolic extracts, seven different cultivars (Duclouxiana, Fallax, Orientis, Mayette, Fernor, Mellanaise and Elit) were selected from Barandoz, Khaletabad, Jamalabad, Kahriz, Gaznagh, Ziva and Nazlo, West Azerbaijan province, Iran, in 2010. The fruits of these walnuts were collected, their hulls and shells were dried and ground, and then methanolic extracts were prepared from these hulls and shells. Total phenolic content was determined using the Folin-Ciocalteu (F–C) method. The extracts’ reducing power and scavenging capacity for radical nitrite, hydrogen peroxide and superoxide were evaluated. The results show that the antioxidant and antiradical activities of the walnut hull are higher than those of its shell phenolic extract among different cultivars of walnut. In addition, the reducing power of walnut hull and shell phenolic extract was positively correlated with the phenolic content and radical scavenging capacities of walnut hull and shell extracts were positively correlated with phenolic content and reducing power in different cultivars. Key words: Juglans regia, hull, shell, phenolic content, reducing power, radical scavenging.
Ion‐beam deposition of thin films of ferroelectric lead zirconate titanate (PZT)

Lead zirconate titanate is a solid solution with nominal composition Pb(ZrxTi1−x)O3. Ferroelectric thin films of this material have been successfully deposited by a focused ion‐beam sputtering technique in an O2 atmosphere. These films were characterized according to composition, crystal structure, and dielectric and ferroelectric properties. The effects of deposition temperature, heat treatment after deposition, and substrate and target material were investigated. The composition of the films was within 1 at. % of the composition of the multicomponent PZT target for substrate temperatures up to 200 °C. The Pb concentration decreased for substrate temperatures above 200 °C. Films deposited at substrate temperatures below 250 °C were microcrystalline. At 300 °C, the pyrochlore phase was obtained. Films with the ferroelectric perovskite structure were deposited above 400 °C, which is the lowest deposition temperature ever reported for this phase. O2 losses during postdeposition annealing resulted in a collap.