Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION OF POTENTIALLY TOXIC METALS (PTMS) ACCUMULATION AND TRANSLOCATION BY ALBIZIA LEBBECK FROM INDUSTRIAL SOIL

Field samples of Albizia lebbeck, deciduous tree with drought tolerant properties, growing on industrial soil (Challawa Industrial Estate) were collected and separated into leaves, stems, roots and associated ground soil to assess the accumulation and translocation of Six Potentially Toxic Metals (PTMs) (Zn, Cu, Cd, Cr, Pb and Ni ) from the soil. Atomic Absorption Spectroscopy (AAS) was used to assess their levels. The bioaccumulation/ transfer of metals from roots to shoots and from soil to roots were evaluated in terms of translocation and bioconcentration factor. TF values of 1.22 , 1.41, 3.57 and 1.86 for Cu , Cd , Pb and Ni respectively indicate that A. lebbeck was efficient in translocation of PTMs from roots to shoots and follows the trend Cu > Zn > Ni > Cr respectively. This depicts the plant as a likely candidate for phytoextraction of Zn, Cu, Cr and Ni. BCF values of Zn (0.94), Cu (0.85), Cd (1.37), Cr (1.25), Pb (2.3) and Ni (1.66) were noted for the PTMs. This reveals that A. lebbeck may be suitable for phytostabilization of Zn and Cr in the contaminated soils as it retains high concentration of these PTMs in its roots in the study area.
Bitcoin y la Compañía de los Mares del Sur : un análisis comparativo

This paper examines historical Bitcoin price data together with the price data of a well-known and generally accepted historical asset price bubble (the 1720 South Sea Bubble) with the aim of identifying possible similarities. In order to find empirical evidence of speculative bubble tendencies, the article analyses distribution moments and autoregressive models of time series of both assets. Results show that historical daily prices of both assets—taking into account one year before and one year after the maximum price level—clearly show the two phases of bubble expansion and subsequent crash. Furthermore, various similarities between the South Sea Bubble and Bitcoin can be found in descriptive statistics, such as mean of return, standard deviation, and skewness. Statistical tests also show several explosive moments in the time series of the South Sea Company and Bitcoin returns, which implies that both assets exhibit more than one financial bubble.
Small and Medium-sized Enterprises that Borrow from 'Alternative' Lenders in the United Kingdom: Who Are They?

Access to credit for small and medium-sized enterprises (SMEs) is an important condition of successful economic growth. Lending to SMEs is no longer restricted to banks: many new players (or alternative lenders) are entering the credit market. However, the research has not identified what kind of SMEs choose these alternative lenders. Are they just a random sample from the overall population of SMEs or are they different in some specific respects? Is their credit quality better or worse? This study provides a general overview of the external financing landscape for the UK SMEs and an exploratory analysis of the SME portfolio of one of the alternative lenders in the United Kingdom. The results indicate that clients of the alternative peer-to-peer lender are younger and have more debt, but they also have higher returns than a generic sample of UK SMEs. Their probability of default, as estimated by the SME Z-score, is lower. We conclude that the alternative markets for SME lending may be heterogeneous in terms of risk. At least some alternative lenders have a sound risk level and are attractive to high-quality borrowers. Therefore, they act as a substitute for traditional lending.
Data Donations for Digital Contact-Tracing: Short- and Long-term Effects of Monetary Incentives

Data donations promise to unlock the social benefits of personal data. Most recently, digital contact-tracing apps have been developed to collect individuals' donated contact and health data to fight the COVID-19 pandemic. As the effectiveness of contact-tracing apps hinges critically on widespread app installation and continuous usage, we investigate whether monetary incentives can stimulate adoption and data donations: In a randomized controlled online experiment, the effects of different monetary incentive mechanisms on participants' verified installations as well as short- and long-term usage of the German Corona-Warn-App (CWA) are evaluated. We find monetary incentives to be effective in the short run as they significantly increase app installations and app usage 14 days after installation, tripling the number of users compared to no compensation. An additional choice of non-monetary compensation in the form of an optional charity donation is found to be ineffective in promoting installations or usage beyond outcomes of monetary incentives, suggesting that there is no substantial crowding-out of intrinsic motives in the short run. However, the positive adoption stimulus of monetary incentives vanishes in the long run: After eight months, adopters in treatments with extrinsic monetary incentives are significantly more likely to have stopped using the app than intrinsically motivated adopters who do not receive monetary incentives. This suggests that one-time payments are not sufficient to promote app usage and continuous data donations in the long run. Finally, we present experimental evidence that empirical analyses based on hypothetical scenarios without verified actions are prone to over-estimating individuals' pro-social behavior in the context of data donations.
Multi-UAV Assisted Data Gathering in WSN: A MILP Approach For Optimizing Network Lifetime

In this paper, we study the problem of gathering data from large-scale wireless sensor networks using multiple unmanned air vehicles (UAVs) to gather data at designated rendezvouses, where the goal is to maximize the network lifetime. Previous proposals often consider a practical approach where the problem of determining a data gathering scheme is decomposed into 2 sub-problems: i) partitioning the networks into clusters for determining the rendezvouses as these obtained cluster heads; and ii) determining the paths for a set of a given number of UAVs to come gathering data at these rendezvouses which have been harvesting data within each local clusters, respectively. We try to deal with this as a whole optimization problem, expecting a significant increase in computation complexity which would bring new challenge in creating practical solutions for largescale WSNs. We introduce two alternatives mixed-integer linear programming (MILP) formulations and we show that our best model could solve the problem instances optimally with up to 50 sensor nodes in less than 30 minutes. Next, we propose a heuristic idea to reduce the number of variables in implementing the 3-index model to effectively handle larger-scale networks with size in hundreds. The experiment results show that our heuristic approach significantly prolongs the network lifetime compared to existing most efficient proposals.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked. To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking. Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking.
Occlusion-Robust Visual Markerless Bone Tracking for Computer-Assisted Orthopedic Surgery

Conventional computer-assisted orthopedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. To address this limitation, some previous studies have successfully adapted the existing deep learning framework to automatically segment and register the exposed femur surface for knee surgery, but these fail under real-world occlusion, which often happens during a real surgical procedure. Furthermore, such methods are hardware-specific and not accurate enough for clinic acceptance. In this article, we propose a learning-based RGB-D markerless tracking method that is robust against occlusion. To avoid expensive surgical data collection, which is a well-known challenge for surgical task training, we generate synthetic RGB-D data with various occlusion scenarios. A new segmentation network that features dynamic region-of-interest prediction and 3-D geometric segmentation is designed, to learn the occlusion-related knowledge from simulated instances. Intensive experiments show that our proposed method achieves new state-of-the-art results in markerless bone tracking. Furthermore, our method generalizes well to new cameras and new target models, including a cadaver, without the need for network retraining. By using a high-quality RGB-D camera, our proposed visual tracking method can achieve an accuracy of 1°–2° and 2–4 mm on a phantom knee, which meets the clinical standard.
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks. We host the results and videos at \url{https://sites.google. com/view/isaacgym-nvidia} and isaac gym can be downloaded at \url{https://developer.nvidia. com/isaac-gym}.
Propagation dynamics of solutions to spatially periodic reaction-diffusion systems with hybrid nonlinearity

In this paper we investigate the dynamical properties of a spatially periodic reaction-diffusion system whose reaction terms are of hybrid nature in the sense that they are partly competitive and partly cooperative depending on the value of the solution. This class of problems includes various biologically relevant models and in particular many models focusing on the Darwinian evolution of species. We start by studying the principal eigenvalue of the associated differential operator and establishing a minimal speed formula for linear monotone systems. In particular, we show that the generalized Dirichlet principal eigenvalue and the periodic principal eigenvalue may not coincide when the reaction matrix is not symmetric, in sharp contrast with the case of scalar equations. We establish a sufficient condition under which equality holds for the two notions. We also show that the propagation speed may be different depending on the direction of propagation, even in the absence of a first-order advection term, again in a sharp contrast with scalar equations. Next we reveal the relation between the hair-trigger property of front propagation and the sign of the periodic principal eigenvalue. Finally, we discuss the linear determinacy of the propagation speed and also establish the existence of travelling waves travelling whose speeds greater than the minimal speed is also proved. We apply our results to an important class of epidemiological models with genetic mutations.
Characterization of E. coli Phylogroups Causing Catheter-Associated Urinary Tract Infection

Purpose Characterization of different uropathogenic E. coli (UPEC) phylogroups is crucial to understand pathogenesis of urinary tract infection (UTI). The objective of our study was to evaluate the antibiotic resistance pattern, biofilm formation and pathogenicity islands (PAIs) of UPEC phylogroups isolated from catheter-associated UTI (CAUTI) compared to community UTI (Com-UTI). Patients and Methods This study included 90 UPEC strains recovered from CAUTI and Com-UTI. Antimicrobial susceptibility was tested by the Kirby–Bauer method and extended spectrum beta-lactamase (ESBL) production was confirmed using the combined disk. The biofilm formation was tested using the microtiter plate assay. Main E. coli phylogroups (A, B1, B2 and D) were detected by multiplex PCR and 2 multiplex PCR detected the 8 PAIs. Results Antibiotic resistance of UPEC strains showed a similar high resistance in CAUTI and Com-UTI. Isolates from CAUTI significantly produced biofilm higher than Com-UTI strains (68.9% vs 44.4%). In CAUTI and Com-UTI isolates, phylogroup A was the commonest (53.3% vs 48.9%, respectively). PAI IV536 was the most common in the strains from CAUTI (71.1%) and Com-UTI (73.3%). No significant relationship was detected between the studied characters and different phylogroups except the significant resistance to cefotaxime, ceftazidime and aztreonam among phylogroups from CAUTI isolates. Conclusion Increased antibiotic resistance and ESBLs were detected in UPEC strains from CAUTI and Com-UTI. The strains from CAUTI significantly produced biofilm higher than Com-UTI strains. Phylogroup A was the predominate phylogroup and PAI IV536 was the most prevalent marker in all phylogroups from both types of UTI.
REFINE: Random RangE FInder for Network Embedding

Network embedding approaches have recently attracted considerable interest as they learn low-dimensional vector representations of nodes. Embeddings based on the matrix factorization are effective but they are usually computationally expensive due to the eigen-decomposition step. In this paper, we propose a Random RangE FInder based Network Embedding (REFINE) algorithm, which can perform embedding on one million of nodes (YouTube) within 30 seconds in a single thread. REFINE is 10x faster than ProNE, which is 10-400x faster than other methods such as LINE, DeepWalk, Node2Vec, GraRep, and Hope. Firstly, we formulate our network embedding approach as a skip-gram model, but with an orthogonal constraint, and we reformulate it into the matrix factorization problem. Instead of using randomized tSVD (truncated SVD) as other methods, we employ the Randomized Blocked QR decomposition to obtain the node representation fast. Moreover, we design a simple but efficient spectral filter for network enhancement to obtain higher-order information for node representation. Experimental results prove that REFINE is very efficient on datasets of different sizes (from thousand to million of nodes and edges) for node classification, while enjoying a good performance.
The Effect of Liquid Kinematic Viscosity and Drain Time on the Volume Delivered from a Test Measure

Volumetric test measures are used for calibration and traceability of petroleum measurements and their low uncertainty is important for the equitable trade of valuable liquids. The volume delivered from a test measure depends in part on the volume of liquid retained on the test measure walls after the draining procedure is complete. Mixtures of propylene glycol and water with kinematic viscosity ranging from 1 mm/s to 55 mm/s were used to measure the delivered volumes of three test measures (19 L, 20 L, and 38 L) for 30 s drip times. The retained volume in a 60 L test measure was determined using water at 7 °C to 35 °C to change the liquid kinematic viscosity. Our experiments demonstrate that van Rossum’s [1] drainage theory applies to test measures: the retained volume is proportional to the square root of the ratio of kinematic viscosity to total drain time ( ∝ ⁄ ⁄ ). We use van Rossum’s theory and retained volume data from more than 300 test measures to estimate changes in the delivered volume due to drainage effects for various liquids and test measures up to 400 L. For example, neglecting other effects (e.g. evaporation, and surface tension), we estimate that a 20 L test measure calibrated in water will deliver 0.01 % more volume when used in gasoline and 0.05 % less volume when used in diesel fuel.
Spatio-Temporal Self-Attention Network for Video Saliency Prediction

3D convolutional neural networks have achieved promising results for video tasks in computer vision, including video saliency prediction that is explored in this paper. However, 3D convolution encodes visual representation merely on fixed local spacetime according to its kernel size, while human attention is always attracted by relational visual features at different time. To overcome this limitation, we propose a novel Spatio-Temporal Self-Attention 3D Network (STSANet) for video saliency prediction, in which multiple Spatio-Temporal Self-Attention (STSA) modules are employed at different levels of 3D convolutional backbone to directly capture long-range relations between spatio-temporal features of different time steps. Besides, we propose an Attentional Multi-Scale Fusion (AMSF) module to integrate multi-level features with the perception of context in semantic and spatio-temporal subspaces. Extensive experiments demonstrate the contributions of key components of our method, and the results on DHF1K, Hollywood-2, UCF, and DIEM benchmark datasets clearly prove the superiority of the proposed model compared with all state-of-the-art models.
Out-of-plane magnetic anisotropy in bulk ilmenite $\text{CoTiO}_3$

M. Arruabarrena, A. Leonardo, 3 M. Rodriguez-Vega, Gregory A. Fiete, 6 and A. Ayuela 3 Materials Physics Center (CFM), 20018 Donostia, Spain Department of Applied Physics II, University of the Basque Country UPV/ EHU, Bilbao, Spain. Donostia International Physics Center (DIPC), 20018 Donostia, Spain Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Department of Physics, Northeastern University, Boston, MA 02115, USA Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA (Dated: August 25, 2021)
Jointly Learnable Data Augmentations for Self-Supervised GNNs

Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs. A Siamese network, which relies on data augmentation, is the popular architecture used in these methods. However, these methods rely on heuristically crafted data augmentation techniques. Furthermore, they use either contrastive terms or other tricks (e.g., asymmetry) to avoid trivial solutions that can occur in Siamese networks. In this study, we propose, GraphSurgeon, a novel SSL method for GNNs with the following features. First, instead of heuristics we propose a learnable data augmentation method that is jointly learned with the embeddings by leveraging the inherent signal encoded in the graph. In addition, we take advantage of the flexibility of the learnable data augmentation and introduce a new strategy that augments in the embedding space, called post augmentation. This strategy has a significantly lower memory overhead and run-time cost. Second, as it is difficult to sample truly contrastive terms, we avoid explicit negative sampling. Third, instead of relying on engineering tricks, we use a scalable constrained optimization objective motivated by Laplacian Eigenmaps to avoid trivial solutions. To validate the practical use of GraphSurgeon, we perform empirical evaluation using 14 public datasets across a number of domains and ranging from small to large scale graphs with hundreds of millions of edges. Our finding shows that GraphSurgeon is comparable to six SOTA semi-supervised and on par with five SOTA self-supervised baselines in node classification tasks. The source code is available at https://github.com/zekarias-tilahun/graph-surgeon.
Non-almost regular quaternary $m$-gonal forms

The maximal rank n which admits a non-almost regular m-gonal form is 4. In this article, for any given (a1, a2, a3, a4) ∈ N and sufficiently large m, we completely determine whether the m-gonal form 〈a1, a2, a3, a4〉 m is almost regular or not. And we show that for any m ≥ 3, there are infinitely many non-almost regular m-gonal forms.
DU-GAN: Generative Adversarial Networks With Dual-Domain U-Net-Based Discriminators for Low-Dose CT Denoising

Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Over the past few years, various deep learning techniques, especially generative adversarial networks (GANs), have been introduced to improve the image quality of LDCT images through denoising, achieving impressive results over traditional approaches. GAN-based denoising methods usually leverage an additional classification network, i.e., discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this article proposes a novel method, termed DU-GAN, which leverages U-Net-based discriminators in the GAN framework to learn both global and local differences between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net-based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net-based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net-based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior performance over recently published methods both qualitatively and quantitatively. Our source code is made available at https://github.com/Hzzone/DU-GAN.
Bi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing

Radio spectrum is a scarce resource. To meet demands, new wireless technologies must operate in shared spectrum over unlicensed bands (coexist). We consider coexistence of Long-Term Evolution (LTE) License-Assisted Access (LAA) with incumbent Wi-Fi systems. Our scenario consists of multiple LAA and Wi-Fi links sharing an unlicensed band; we aim to simultaneously optimize performance of both coexistence systems. To do this, we present a technique to continuously estimate the Pareto frontier of parameter sets (traces) which approximately maximize all convex combinations of network throughputs over network parameters. We use a dimensionality reduction approach known as active subspaces to determine that this near-optimal parameter set is primarily composed of two physically relevant parameters. A choice of two-dimensional subspace enables visualizations augmenting explainability and the reduced-dimension convex problem results in approximations which dominate random grid search.