Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable inter-blockchain communication framework for improving scalability

The scalability problem is a well-known problem in blockchain implementation. Traditional solutions such as sharding and side chain aim to solve this problem within one blockchain system. On the other hand, some companies seek to use interoperability for overall scalability. By learning from these works, this study proposes a novelty inter-blockchain communication framework that provides secure interoperability for each blockchain. In this framework, cross-chain tasks need to follow a workflow between the two systems. The result of these tasks will peg to all other blockchains through the blockchain's terminated branch transferred in a gossip network. The workload of anchoring is dynamically compressed and eventually achieved. For a given rate of cross-chain tasks, our framework can guarantee the number of message, which are directly related to the task, will not change according to the scale of the system.
A multi-chord stellar occultation by the large trans-Neptunian object (174567) Varda

We present results from the first recorded stellar occultation by the large trans-Neptunian object (174567) Varda that was observed on September 10$^{\rm th}$, 2018. Varda belongs to the high-inclination dynamically excited population, and has a satellite, Ilmare, which is half the size of Varda. We determine the size and albedo of Varda and constrain its 3D shape and density. Thirteen different sites in the USA monitored the event, five of which detected an occultation by the main body. A best-fitting ellipse to the occultation chords provides the instantaneous limb of the body, from which the geometric albedo is computed. The size and shape of Varda are evaluated, and its bulk density is constrained, using Varda's mass known from previous works. The best-fitting elliptical limb has semi-major (equatorial) axis of $(381 \pm 3)$km and an apparent oblateness $0.043\pm0.036$ corresponding to an apparent area-equivalent radius $R'_{\rm equiv}= (373\pm8)$km and geometric albedo $p_v=0.097\pm 0.004$ assuming a visual absolute magnitude $H_V=3.81\pm0.01$. Using three possible rotational periods for the body (4.76~h, 5.91~h, and 7.87~h), we derive corresponding MacLaurin solutions. Furthermore, given the low-amplitude ($0.06\pm0.01$) mag of the single-peaked rotational light-curve for the aforementioned periods, we consider the double periods. For the 5.91~h period (the most probable) and its double (11.82~h), we find bulk densities and true oblateness of $\rho=(1.52\pm0.05)$ g cm$^{-3}$, $\epsilon=0.232\pm0.036$ and $\rho=(1.25\pm0.04)$ g cm$^{-3}$, $\epsilon=0.079\pm0.044$. However, it must be noted that the other solutions cannot be excluded just yet.
Locally associated graphical models

The notion of multivariate total positivity has proved to be useful in finance and psychology but may be too restrictive in other applications. In this paper we propose a concept of local association, where highly connected components in a graphical model are positively associated and study its properties. Our main motivation comes from gene expression data, where graphical models have become a popular exploratory tool. We focus the exposition on Gaussian distributions but our methods readily extend to non-paranormal distributions. Motivated by a convex optimization problem that arises in this context, we develop a GOLAZO approach that generalizes a number of optimization procedures that arise in the context of graphical models (e.g. the GLASSO) and propose a simple block-coordinate descent optimization procedure for solving the dual problem. Our results on existence of the optimum for such problems are of separate interest.
Matrix methods for perfect signal recovery underlying range space of operators

The most important purpose of this article is to investigate perfect reconstruction underlying range space of operators in finite dimensional Hilbert spaces by a new matrix method. To this end, first, we obtain more structures of the canonical K$$ K $$ ‐dual. Then, we survey the problem of recovering and robustness of signals when the erasure set satisfies the minimal redundancy condition or the K$$ K $$ ‐frame is maximal robust. Furthermore, we show that the error rate is reduced under erasures if the K$$ K $$ ‐frame is of uniform excess. Toward the protection of encoding frame (K‐dual) against erasures, we introduce a new concept so called (r,k)$$ \left(r,k\right) $$ ‐matrix to recover lost data and solve the perfect recovery problem via matrix equations. Moreover, we discuss the existence of such matrices by using minimal redundancy condition on decoding frames for operators. We exhibit several examples that illustrate the advantage of using the new matrix method with respect to the previous approaches in existence construction. And finally, we provide the numerical results to confirm the main results in the case noise‐free and test sensitivity of the method with respect to noise.
Random Projections and Dimension Reduction

This paper, broadly speaking, covers the use of randomness in two main areas: low-rank approximation and kernel methods. Low-rank approximation is very important in numerical linear algebra. Many applications depend on matrix decomposition algorithms that provide accurate low-rank representations of data. In modern problems, however, various factors make this hard to accomplish. One solution to these problems is the use of random projections. Instead of directly computing the matrix factorization, we randomly project the matrix onto a lower-dimensional subspace and then compute the factorization. Often, we are able to do this without significant loss of accuracy. We describe how randomization can be used to create more efficient algorithms to perform low-rank matrix approximation, as well as introducing a novel randomized algorithm for matrix decomposition. Compared to standard approaches, random algorithms are often faster and more robust. With these randomized algorithms, analyzing massive data sets becomes tractable. Kernel methods are almost diametrically opposite from low-rank approximation. The idea is to project low-dimensional data into a higher-dimensional 'feature space,' such that it is linear separable in the feature space. This enables the model to learn a nonlinear separation of the data. As before, with large data matrices, computing the kernel matrix can be expensive, so we use randomized methods to approximate the matrix. In addition, we propose an extension of the random Fourier features kernel in which hyperparameter values are randomly sampled from an interval or Borel set. The experiments discussed in this paper can be found on our website at this https URL.
Revisiting Adversarially Learned Injection Attacks Against Recommender Systems

Recommender systems play an important role in modern information and e-commerce applications. While increasing research is dedicated to improving the relevance and diversity of the recommendations, the potential risks of state-of-the-art recommendation models are under-explored, that is, these models could be subject to attacks from malicious third parties, through injecting fake user interactions to achieve their purposes. This paper revisits the adversarially-learned injection attack problem, where the injected fake user ‘behaviors’ are learned locally by the attackers with their own model – one that is potentially different from the model under attack, but shares similar properties to allow attack transfer. We found that most existing works in literature suffer from two major limitations: (1) they do not solve the optimization problem precisely, making the attack less harmful than it could be, (2) they assume perfect knowledge for the attack, causing the lack of understanding for realistic attack capabilities. We demonstrate that the exact solution for generating fake users as an optimization problem could lead to a much larger impact. Our experiments on a real-world dataset reveal important properties of the attack, including attack transferability and its limitations. These findings can inspire useful defensive methods against this possible existing attack.
Deep Detail Enhancement for Any Garment

Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low‐resolution physically‐based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data‐driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch‐based formulation, that produces high‐resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light‐weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion.
Security checklists for Ethereum smart contract development: patterns and best practices

In recent years Smart Contracts and DApps are becoming increasingly important and widespread thanks to the properties of blockchain technology. In most cases DApps are business critical, and very strict security requirements should be assured. Developing safe and reliable Smart Contracts, however, is not a trivial task. Several researchers have studied the security issues, however none of these provide a simple and intuitive tool to overcome these problems. In this paper we collected a list of security patterns for DApps. Moreover, based on these patterns, we provide the reader with security assessment checklists that can be easily used for the development of SCs. We cover the phases of design, coding, and testing and deployment of the software lifecycle. In this way, we allow developers to easily verify if they applied all the relevant security patterns to their smart contracts. We focus all the analysis on the most popular Ethereum blockchain, and on the Solidity language.
A polynomial invariant for veering triangulations

We introduce a polynomial invariant $V_\tau \in \mathbb{Z}[H_1(M)/\text{torsion}]$ associated to a veering triangulation $\tau$ of a $3$-manifold $M$. In the special case where the triangulation is layered, i.e. comes from a fibration, $V_\tau$ recovers the Teichmuller polynomial of the fibered faces canonically associated to $\tau$. Via Dehn filling, this gives a combinatorial description of the Teichmuller polynomial for any hyperbolic fibered $3$-manifold. For a general veering triangulation $\tau$, we show that the surfaces carried by $\tau$ determine a cone in homology that is dual to its cone of positive closed transversals. Moreover, we prove that this is $\textit{equal}$ to the cone over a (generally non-fibered) face of the Thurston norm ball, and that $\tau$ computes the norm on this cone in a precise sense. We also give a combinatorial description of $V_\tau$ in terms of the $\textit{flow graph}$ for $\tau$ and its Perron polynomial. This perspective allows us to characterize when a veering triangulation comes from a fibration, and more generally to compute the face of the Thurston norm determined by $\tau$.
Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving

Semantic segmentation is important for many real-world systems, e.g., autonomous vehicles, which predict the class of each pixel. Recently, deep networks achieved significant progress w.r. t. the mean Intersection-over Union (mIoU) with the cross-entropy loss. However, the cross-entropy loss can essentially ignore the difference of severity for an autonomous car with different wrong prediction mistakes. For example, predicting the car to the road is much more servery than recognize it as the bus. Targeting for this difficulty, we develop a Wasserstein training framework to explore the inter-class correlation by defining its ground metric as misclassification severity. The ground metric of Wasserstein distance can be pre-defined following the experience on a specific task. From the optimization perspective, we further propose to set the ground metric as an increasing function of the pre-defined ground metric. Furthermore, an adaptively learning scheme of the ground matrix is proposed to utilize the high-fidelity CARLA simulator. Specifically, we follow a reinforcement alternative learning scheme. The experiments on both CamVid and Cityscapes datasets evidenced the effectiveness of our Wasserstein loss. The SegNet, ENet, FCN and Deeplab networks can be adapted following a plug-in manner. We achieve significant improvements on the predefined important classes, and much longer continuous playtime in our simulator.
Uma sequencia didatica para ensinar as leis de Newton

The technological and digital reality has broadly expanded, reaching the school. Therefore, this research aims to develop a didactic sequence, using as strategy a comic book, Figures, Gifs and videos, programs for activities applied to comics using Newton's Laws. The research aimed to analyze how this pedagogical tool contributed to student learning. It is a qualitative research in education, whose data were collected in the course of the pedagogical action, through questionnaires, analyzed comparatively. The strategies were well accepted among adolescents, which is why their application favored advances in the learning of concepts and in the application of knowledge of classical physics in everyday events
Hematoma Mimicking Breast Cancer on CT Scan and Breast Ultrasound

There are many benign breast lesions that mimic breast cancer on breast imaging. Postlumpectomy scar, hematoma, fat necrosis, diabetic mastopathy, and granulomatous mastitis are examples of benign breast lesions that have suspicious breast imaging findings. Mammogram and breast ultrasound are the imaging studies to evaluate breast findings. CT scan is not used to evaluate breast findings because it delivers high radiation dose to the breast, and breast tissue is often confused as breast masses on CT scan. The following case demonstrates an incidentally detected breast mass on CT scan performed to assess for pulmonary embolism. The CT scan and subsequent breast ultrasound both demonstrated suspicious breast imaging findings. Final pathology from ultrasound-guided biopsy revealed hematoma. This benign finding was concordant with the patient’s medical history of cirrhosis with low platelet count and medication history of warfarin.
Prevention and control of Aedes transmitted infections in the post-pandemic scenario of COVID-19: challenges and opportunities for the region of the Americas

The coronavirus disease of 2019 (COVID-19) pandemic challenges public health systems around the world. Tropical countries will face complex epidemiological scenarios involving the simultaneous transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with viruses transmitted by Aedes aegypti. The occurrence of arboviral diseases with COVID-19 in the Latin America and the Caribbean (LAC) region presents challenges and opportunities for strengthening health services, surveillance and control programs. Financing of training, equipment and reconversion of hospital spaces will have a negative effect on already the limited resource directed to the health sector. The strengthening of the diagnostic infrastructure reappears as an opportunity for the national reference laboratories. Sharing of epidemiological information for the modeling of epidemiological scenarios allows collaboration between health, academic and scientific institutions. The fear of contagion by COVID-19 is constraining people with arboviral diseases to search for care which can lead to an increase in serious cases and could disrupt the operation of vector-control programs due to the reluctance of residents to open their doors to health personnel. Promoting intense community participation along with the incorporation of long lasting innovations in vector control offers new opportunities for control. The COVID-19 pandemic offers challenges and opportunities that must provoke positive behavioral changes and encourage more permanent self-care actions.
Impact of fatty acid desaturase (FADS) genotypes on the relationship between serum lipids and dietary fat intake in young Mexican college students

Introduction: Recent genome-wide association studies (GWA) of the Mexican population has identified several genetic loci associated with blood lipid levels in adults. The fatty acid desaturase (FADS) gene cluster in young Mexican college students has not been studied before. Objective: Our aim was to report on the SNP in the FADS gene cluster associated with lipid profiles in young Mexicans from the college-age UP AMIGOS cohort. Methods: Anthropometrics, cardiovascular disease history, and serum lipid profile were determined (n=445, aged 18-25 yrs.). Genotyping was conducted using the fluorescent polarization method. Genotype-phenotype associations were assessed using multiple linear regression models adjusted for sex, age and BMI. Results: Significant associations were found between carriers of the minor allele of FADS1/FADS2 genotypes and TC, LDL-C and non-HDL-C concentrations. In contrast, the association between the FADS1/FADS2 genotype and HDL-C, TG and VLDL were not significant. Conclusion: Our results suggest that FADS1/FADS2 genotypes were associated with TC, LDL-C and non-HDL-C concentrations starting at a young age. Moreover, these results replicate previous findings concerning European and Asian adult populations. Other factors such as diet should also be taken into consideration in order to better understand the onset of dyslipidemia among young Mexican college students.
Disseminated Coccidioidomycosis in Africa

Background Coccidioidomycosis is an endemic disease in the Americas. No cases have been reported in Africa. Patient A 23-year-old HIV seronegative Ugandan man was referred to Mulago National Referral Hospital in Kampala, Uganda with a 10-month history of haemoptysis and difficulty breathing, and a 6-month history of localized swellings on the extremities. He had associated weight loss and drenching sweats, but no fevers. He had taken anti-tuberculosis medicine for 2 months with no improvement. He had never travelled out of Uganda. On physical examination, he had cystic swellings and ulcerated lesions on the extremities. He had tachypnoea, crackles in the chest and mild hepatomegaly. Bronchoscopic examination showed two masses occluding the right main bronchus. Bronchoscopic biopsy showed findings consistent with coccidioidomycosis. The patient improved with antifungal treatment and was discharged. Conclusion We report the first case of disseminated coccidioidomycosis with pulmonary and cutaneous manifestations in Africa. LEARNING POINTS Coccidioidomycosis is an endemic disease in the Americas and may now be present in Africa. The patient had taken anti-tuberculosis medicine for 2 months with no improvement. Coccidioidomycosis should be considered in the differential diagnosis of tuberculosis.
Podocytes—The Most Vulnerable Renal Cells in Preeclampsia

Preeclampsia (PE) is a disorder that affects 3–5% of normal pregnancies. It was believed for a long time that the kidney, similarly to all vessels in the whole system, only sustained endothelial damage. The current knowledge gives rise to a presumption that the main role in the development of proteinuria is played by damage to the podocytes and their slit diaphragm. The podocyte damage mechanism in preeclampsia is connected to free VEGF and nitric oxide (NO) deficiency, and an increased concentration of endothelin-1 and oxidative stress. From national cohort studies, we know that women who had preeclampsia in at least one pregnancy carried five times the risk of developing end-stage renal disease (ESRD) when compared to women with physiological pregnancies. The focal segmental glomerulosclerosis (FSGS) is the dominant histopathological lesion in women with a history of PE. The kidney’s podocytes are not subject to replacement or proliferation. Podocyte depletion exceeding 20% resulted in FSGS, which is a reason for the later development of ESRD. In this review, we present the mechanism of kidney (especially podocytes) injury in preeclampsia. We try to explain how this damage affects further changes in the morphology and function of the kidneys after pregnancy.
Importance of microbial extracellular vesicle in the pathogenesis of asthma and chronic obstructive pulmonary disease and its diagnostic potential

There are rising evidences of the human microbiome as a potentially influential player that is actively engaged in shaping the pathogenetic processes and other unresolved issues both in asthma and other chronic respiratory diseases, particularly of the airways. The biological components such as microbiome in inhaled air can induce immune dysfunction and inflammation, leading to inflammatory pulmonary disorders such as asthma and chronic obstructive pulmonary disease (COPD). Microbe-derived extracellular vesicles (EVs) with biologically active information or functions can reprogram their respective target cells and EV may have a role for the development of asthma and COPD. To evaluate the role of microbe-derived EV in the pathogenesis of asthma and COPD and its role in diagnosis, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement method was used for the study. An electronic search was performed using PubMed, PubMed Central, and Embase up to 2020. EVs serve as an intercellular transporter of miRNAs for cell-to-cell communication in the lungs. Bacteria-derived EVs have distinctive characteristics in the lungs of patients with asthma and COPD compared to healthy controls. Furthermore, bacterial EV IgG antibody titers in serum were significantly higher in patients with asthma and COPD than in healthy controls, suggesting that antibacterial EV antibodies titers can be used as a diagnostic tool for lung disease. Taken together, microbial EVs and miRNAs have important roles in the pathogenesis of asthma and COPD and they can provide novel diagnostic biomarkers for asthma and COPD.
Inhibiting Caspase-12 Mediated Inflammasome Activation protects against Oxygen-Glucose Deprivation Injury in Primary Astrocytes

Stroke is one of the leading causes of death worldwide. Accumulating evidence suggests that NLRP3 inflammasome activation plays an important role in ischemic stroke injury. However, the existence of the NLRP3 inflammasome in astrocytes remains controversial. In this study, we demonstrated the presence of the NLRP3 inflammasome in primary mouse astrocytes and investigated the role of caspase-12 in NLRP3 inflammasome activation and cell injury in an in vitro astrocyte oxygen-glucose deprivation (OGD) model. Astrocytes exposed to 2, 3, and 4 h of OGD exhibited increased cell injury and apoptosis, and the protein levels of caspase-12, cleaved caspase-3, NLRP3 inflammasome components, and IL-1β were also significantly elevated. Interestingly, pretreatment with the caspase-12-specific inhibitor Z-ATAD-FMK attenuated cell injury and apoptosis and decreased the levels of NLRP3, caspase-1, IL-1β, and cleaved caspase-3 in the OGD group. In conclusion, Z-ATAD-FMK protected astrocytes against OGD-induced cell death and inhibited NLPR3-inflammasome activation. Our results indicate that caspase-12 and its potential regulation of NLRP3 inflammasome activation might be a promising target for treatment of ischemic stroke.