Miracle-dz/UniFAR-FTUs huggingface dataset:
RGB-D Local Implicit Function for Depth Completion of Transparent Objects

Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RGB-D image. Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed. Based on this representation, we present a novel frame-work that can complete missing depth given noisy RGB-D input. We further improve the depth estimation iteratively using a self-correcting refinement model. To train the whole pipeline, we build a large scale synthetic dataset with transparent objects. Experiments demonstrate that our method performs significantly better than the current state-of-the-art methods on both synthetic and real world data. In addition, our approach improves the inference speed by a factor of 20 compared to the previous best method, ClearGrasp [43]. Code will be released at https://research.nvidia. com/publication/2021-03_RGB-D-Local-Implicit.
Unsupervised Foreground-Background Segmentation with Equivariant Layered GANs

We propose an unsupervised foreground-background segmentation method via training a segmentation network on the synthetic pseudo segmentation dataset generated from GANs, which are trained from a collection of images without annotations to explicitly disentangle foreground and background. To efficiently generate foreground and background layers and overlay them to compose novel images, the construction of such GANs is fulfilled by our proposed Equivariant Layered GAN, whose improvement, compared to the precedented layered GAN, is embodied in the following two aspects. (1) The disentanglement of foreground and background is improved by extending the previous perturbation strategy and introducing private code recovery that reconstructs the private code of foreground from the composite image. (2) The latent space of the layered GANs is regularized by minimizing our proposed equivariance loss, resulting in interpretable latent codes and better disentanglement of foreground and background. Our methods are evaluated on unsupervised object segmentation datasets including Caltech-UCSD Birds and LSUN Car, achieving state-of-the-art performance.
The Effect of Thermal Pressure on Collisionless Magnetic Reconnection Rate

Modeling collisionless magnetic reconnection rate is an outstanding challenge in basic plasma physics research. While the seemingly universal rate of an order is often reported in the low-β regime, it is not clear how reconnection rate scales with a higher plasma β. Due to the complexity of the pressure tensor, the available reconnection rate model is limited to the low plasma-β regime, where the thermal pressure is arguably negligible. However, the thermal pressure effect becomes important when . Using first-principle kinetic simulations, we show that both the reconnection rate and outflow speed drop as β gets larger. A simple analytical framework is derived to take account of the self-generated pressure anisotropy and pressure gradient in the force balance around the diffusion region, explaining the varying trend of key quantities and reconnection rates in these simulations with different β. The predicted scaling of the normalized reconnection rate is in the high-β limit, where β i0 is the ion β of the inflow plasma.
Generalized Conflict-directed Search for Optimal Ordering Problems

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively decouple the problem is to judiciously order the events such that decisions can be made over sequences of tasks. As many problems encountered in practice are over-constrained, we must instead find relaxed solutions in which certain requirements are dropped. This motivates a formulation of optimality with respect to the costs of relaxing constraints and the problem of finding an optimal ordering under which this relaxing cost is minimum. In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning. Due to its ability to reason over generalized conflicts, GCDO is much more efficient in finding high-quality total orders than the previous conflict-directed approach CDITO. We demonstrate this by benchmarking on temporal network configuration problems, which involves managing networks over time and makes necessary tradeoffs between network flows against CDITO and Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two orders of magnitude more benchmark problems to optimality and twice the problems compared to CDITO and MILP within a runtime limit, respectively.
Application of A/O‐MBR for treatment of digestate from anaerobic digestion of cow manure

BACKGROUND: Anaerobic digestion (AD) is widely used as an animal manure bioconversion technology. However, the effluent obtained from the digester during the wet-AD process requires to be treated carefully before discharge if AD technology is to be developed as an effective and environmentally advantageous animal manure treatment. RESULTS: An anoxic/aerobic-membrane bioreactor(A/O-MBR) system has been developed for digestate treatment in an AD system treating cow manure. The performance of the A/O-MBR system in terms of removal of nitrogen, COD and phosphorus were investigated. Results indicated that the average removal efficiencies of NH4-N+, COD and PO43−-P were 98.1%, 96.3% and 76.6%, respectively. The fouled membrane from the A/O-MBR system was cleaned effectively using NaOH and a 30h soak time. CONCLUSION: This study suggests that it is technically feasible to use the A/O-MBR for the treatment of digestate from a cow manure AD system, and can provide an environmentally acceptable way for the application of wet-AD in animal manure treatment. Copyright © 2010 Society of Chemical Industry
Intuitive Tasks Planning Using Visuo-Tactile Perception for Human Robot Cooperation

Designing robotic tasks for co-manipulation necessitates to exploit not only proprioceptive but also exteroceptive information for improved safety and autonomy. Following such instinct, this research proposes to formulate intuitive robotic tasks following human viewpoint by incorporating visuo-tactile perception. The visual data using depth cameras surveils and determines the object dimensions and human intentions while the tactile sensing ensures to maintain the desired contact to avoid slippage. Experiment performed on robot platform with human assistance under industrial settings validates the performance and applicability of proposed intuitive task formulation.
Using Python for Model Inference in Deep Learning

Python has become the de-facto language for training deep neural networks, coupling a large suite of scientific computing libraries with efficient libraries for tensor computation such as PyTorch or TensorFlow. However, when models are used for inference they are typically extracted from Python as TensorFlow graphs or TorchScript programs in order to meet performance and packaging constraints. The extraction process can be time consuming, impeding fast prototyping. We show how it is possible to meet these performance and packaging constraints while performing inference in Python. In particular, we present a way of using multiple Python interpreters within a single process to achieve scalable inference and describe a new container format for models that contains both native Python code and data. This approach simplifies the model deployment story by eliminating the model extraction step, and makes it easier to integrate existing performance-enhancing Python libraries. We evaluate our design on a suite of popular PyTorch models on Github, showing how they can be packaged in our inference format, and comparing their performance to TorchScript. For larger models, our packaged Python models perform the same as TorchScript, and for smaller models where there is some Python overhead, our multi-interpreter approach ensures inference is still scalable.
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution

Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.
Mixed precision recursive block diagonalization for bivariate functions of matrices

Various numerical linear algebra problems can be formulated as evaluating bivariate function of matrices. The most notable examples are the Fr\'echet derivative along a direction, the evaluation of (univariate) functions of Kronecker-sum-structured matrices and the solution of Sylvester matrix equations. In this work, we propose a recursive block diagonalization algorithm for computing bivariate functions of matrices of small to medium size, for which dense liner algebra is appropriate. The algorithm combines a blocking strategy, as in the Schur-Parlett scheme, and an evaluation procedure for the diagonal blocks. We discuss two implementations of the latter. The first is a natural choice based on Taylor expansions, whereas the second is derivative-free and relies on a multiprecision perturb-and-diagonalize approach. In particular, the appropriate use of multiprecision guarantees backward stability without affecting the efficiency in the generic case. This makes the second approach more robust. The whole method has cubic complexity and it is closely related to the well-known Bartels-Stewart algorithm for Sylvester matrix equations when applied to $f(x,y)=\frac{1}{x+y}$. We validate the performances of the proposed numerical method on several problems with different conditioning properties.
Stability of sedimenting flexible loops

Abstract We study the behaviour of circular flexible loops sedimenting in a viscous fluid by numerical simulations and linear stability analysis. The numerical model involves a local slender-body theory approximation for the flow coupled to the Euler–Bernoulli elastic forces for an inextensible fibre. Starting from an inclined circle, we simulate the dynamics using truncated Fourier modes to observe three distinct regimes of motion: absolute stability, two- and three-dimensional dynamics, depending on the relative importance of the elastic and gravitational forces. We identify the governing parameter and develop a simple semi-analytic stability criterion, which we verify numerically. In all cases, sedimenting loops converge to stable, planar shape equilibria with one free parameter related to the initial conditions and material properties of the fibre.
Immutability and Design Patterns in Ruby

Functional Programming has seen a resurgence in interest in the last few years and is often mentioned in opposition to Object-Orientated Programming. However, Object-Orientated Programming can actually absorb some of the lessons of Functional Programming to improve performance and reliability. This paper examines three ways in which Immutability, a common Functional Programming technique, can be used to develop immutable objects in Ruby.
IN-VIVO ANTICANCER ACTIVITY OF AQUEOUS EXTRACT OF HOLOPTELEA INTEGRIFOLIA LEAVES AGAINST EHRLICH ASCITES CARCINOMA IN SWISS ALBINO MICE.

The main aim of the present study is to evaluate an Antitumor activity of Aqueous extract of leaves of Holoptelea integrifolia (HIAQ) in EAC induced Ascites tumor model: An In-vivo study. The Aqueous extract of leaves of Holoptelea integrifolia was prepared by cold maceration technique. Acute toxicity test was performed using Swiss albino rats before starting the in-vivo anticancer activity, were the MTD was more than 5000mg/kg. The in-vivo Antitumor activity was assessed by administering 250mg and 500mg/kg of Aqueous extract of HI leaves, orally for 9 days and Cisplatin (3.5mg/kg, i.p., Single dose). Various parameters like Change in body weight, Mean Survival Time, Percentage Increase in Life Span, Hematological & Biochemical parameters were assessed. All the parameters were considerably restored towards the normal values. The in-vivo activity of HI 500mg/kg was shown more significant results than 250mg/kg. Hence 500mg/kg was taken for combination study with standard drug Cisplatin.
New approach to electroweak symmetry nonrestoration

Marcela Carena, 2, ∗ Claudius Krause, 3, † Zhen Liu, ‡ and Yikun Wang 2, § Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA Enrico Fermi Institute and Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA NHETC, Dept. of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA (Dated: April 2021)
A review on synthesis, characterization, and applications of nano zero valent iron (nZVI) for environmental remediation

ABSTRACT The recent spark in the interest for the usage of nano zero valent iron (nZVI) as a remediation tool for contaminated land and groundwater is mainly due to its higher reactivity in comparison to micro ZVI, cost effectiveness, and potential to treat a broad range of contaminants. The authors review the recent developments and approaches made on synthesis on nZVI, strucuture and characterization of nZVI, the challenges faced in the transport of nZVI in the subsurface environment, and the augmentation of the motility of nZVI. The effective use of nZVI in remediating organic pollutants (halogenated organic compounds, pharmaceutical waste, and azo dyes) and inorganic pollutants (Ni2+, PO43−, Co2+, Cu2+) carried out in recent studies is discussed. The potential risks and limitations of this emerging nanotechnology are also addressed.
Crystal growth and anisotropic magnetic properties of quasi-two-dimensional 
(Fe1−xNix)2P2S6

We report the crystal growth by chemical vapor transport together with a thorough structural and magnetic characterization of the quasi-2D magnets (Fe$_{1-x}$Ni$_x$)$_2$P$_2$S$_6$ with $x_\textrm{Ni} = \textrm{0, 0.3, 0.5, 0.7, 0.9&1}$. As-grown crystals exhibit a layered morphology with weak van der Waals interactions between layers parallel to the crystallographic $ab$ plane of the monoclinic structure in the space group $C2/m$ (No. 12). Magnetization measurements reveal an antiferromagnetic ground state for all grown crystals. In the ordered state, the magnetization along different crystallographic directions is in agreement with an Ising anisotropy for $0 \leq x \leq 0.9$ and only for Ni$_2$P$_2$S$_6$ a different behavior is observed which is line with an anisotropic Heisenberg or XXZ model description. This striking abrupt change of anisotropy at a critical Ni concentration is coupled to a change in the width of the maximum in the magnetization around $T_\textrm{max}$. Furthermore, all intermediate compounds $0<x \leq 0.9$ exhibit an anisotropic magnetization already in the paramagnetic state similar to the parent compound Fe$_2$P$_2$S$_6$.
A Semidefinite Programming Approach to Discrete-time Infinite Horizon Persistent Monitoring

We investigate the problem of persistent monitoring, where a mobile agent has to survey multiple targets in an environment in order to estimate their internal states. These internal states evolve with linear stochastic dynamics and the agent can observe them with a linear observation model. However, the signal to noise ratio is a monotonically decreasing function of the distance between the agent and the target. The goal is to minimize the uncertainty in the state estimates over the infinite horizon. We show that, for a periodic trajectory with fixed cycle length, the problem can be formulated as a set of semidefinite programs. We design a scheme that leverages the spatial configuration of the targets to guide the search over this set of optimization problems in order to provide efficient trajectories. Results are compared to a state of the art approach and we obtain improvements of up to 91% in terms of cost in a simple scenario, with much lower computational time.
Ultra-compact dwarfs beyond the centre of the Fornax galaxy cluster: hints of UCD formation in low-density environments

Ultra-compact dwarf galaxies (UCDs) were serendipitously discovered by spectroscopic surveys in the Fornax cluster twenty years ago. Nowadays, it is commonly accepted that many bright UCDs are the nuclei of galaxies that have been stripped. However, this conclusion might be driven by biased samples of UCDs in high-density environments, on which most searches are based. With the deep optical images of the Fornax Deep Survey, combined with public near-infrared data, we revisit the UCD population of the Fornax cluster and search for UCD candidates, for the first time, systematically out to the virial radius of the galaxy cluster. Our search is complete down to magnitude mg = 21 mag or Mg ∼ -10.5 mag at the distance of the Fornax cluster. The UCD candidates are identified and separated from foreground stars and background galaxies by their optical and near-infrared colours. This primarily utilizes the u − i/i − Ks diagram and a machine learning technique is employed to incorporate other colour combinations to reduce the number of contaminants. The newly identified candidates (44) in addition to the spectroscopically confirmed UCDs (61), increases the number of known Fornax UCD considerably (105). Almost all of the new UCD candidates are located outside the Fornax cluster core (360 kpc), where all of the known UCDs were found. The distribution of UCDs within the Fornax cluster shows that a population of UCDs may form in low-density environments. This most likely challenges the current models of UCD formation.
Nanostructured molybdenum carbides supported on carbon nanotubes as efficient catalysts for one-step hydrodeoxygenation and isomerization of vegetable oils

Nanostructured molybdenum carbides supported on multi-walled carbon nanotubes (Mo2C/CNTs) with different loadings were prepared by carbothermal hydrogen reduction method and characterized with SEM, Raman, HAADF-STEM and XRD. Raman spectra showed that the specific G-band structure of carbon nanotubes promoted the formation of molybdenum carbide at lower temperatures. Compared with noble metals, molybdenum carbide exhibited better catalytic activity and resistance to leaching. The Mo2C/CNTs catalyst also showed high activity and selectivity for one-step conversion of vegetable oils into branched diesel-like hydrocarbons, which provided a promising approach to prepare high-grade diesel fuels from renewable resources.
Cytokines that Modulate the Differentiation of Th17 Cells in Autoimmune Uveitis

Increasing evidence has suggested that T helper 17 (Th17) cells play a central role in the pathogenesis of ocular immune disease. The association between pathogenic Th17 cells and the development of uveitis has been confirmed in experimental and clinical studies. Several cytokines affect the initiation and stabilization of the differentiation of Th17 cells. Therefore, understanding the mechanism of related cytokines in the differentiation of Th17 cells is important for exploring the pathogenesis and the potential therapeutic targets of uveitis. This article briefly describes the structures, mechanisms, and targeted drugs of cytokines—including interleukin (IL)-6, transforming growth factor-β1 (TGF-β1), IL-1β, IL-23, IL-27, IL-35, IL-2, IL-4, IL-21, and interferon (IFN)-γ—which have an important influence on the differentiation of Th17 cells and discusses their potential as therapeutic targets for treating autoimmune uveitis.
Leveraging Organizational Health Literacy to Enhance Health Promotion and Risk Prevention: A Narrative and Interpretive Literature Review

Organizational health literacy involves the health care organizations’ ability to establish an empowering and co-creating relationship with patients, engaging them in the design and delivery of health services in collaboration with health professionals. Although scholars agree that organizational health literacy contributes to health promotion and risk prevention via patient empowerment, literature is not consistent in depicting the interplay between organizational health literacy and preventive medicine. The article intends to shed light into this issue, summarizing current knowledge about this topic and advancing avenues for further development. A narrative literature review was performed through a systematic search on PubMed®, Scopus®, and Web of Science™. The review focused on 50 relevant contributions. Organizational health literacy triggers the transition towards a patient-centered approach to care. It complements individual health literacy, enabling patients to actively participate in health promotion and risk prevention as co-producers of health services and co-creators of value. However, many obstacles – including lack of time and limited resources available – prevent the transition towards health literate health care organizations. Two initiatives are required to overcome extant barriers. On the one hand, a health literate workforce should be prepared to increase the institutional ability of health care organizations to empower and engage patients in health co-creation. On the other hand, increased efforts should be made to assess organizational health literacy and to make its contribution to preventive medicine explicit.
The complete chloroplast genome of Agrimonia pilosa var. nepalensis (D. Don) Nakai

Abstract Agrimonia pilosa var. nepalensis (D. Don) Nakai is an herbaceous species of Rosaceae distributed in China. It has ornamental and ecological values. Lack of genetic background seriously hinders its further research and utilization. To provide genetic information for further study of it, complete chloroplast (cp) genome was characterized in this study. The genome is a circular molecule of 155,147 bp in length with overall GC content of 36.9%, which contains 85 protein-coding genes, eight ribosomal RNA genes, and 37 transfer RNA genes. It contains a typical tetrad structure, including a large single copy, a small single copy, and two inverted repeat regions. Phylogenetic analysis revealed that A. pilosa var. nepalensis and A. pilosa are closely related. Result of this study could provide genetic information for further research of A. pilosa var. nepalensis.