Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Crabgrass Detection in Aerial Imagery with Context

In this paper, we demonstrate the ability to discriminate between cultivated maize plant and crabgrass weed image segments using the context surrounding the image segments. While convolutional neural networks have brought state of the art accuracies within object detection, errors arise when objects in different classes share similar features. This scenario often occurs when objects in images are viewed at too small of a scale to discern distinct differences in features, causing images to be incorrectly classified or localized. To solve this problem, we will explore using context when classifying image segments. This technique involves feeding a convolutional neural network a central square image along with a border of its direct surroundings at train and test times. This means that although images are labelled at a smaller scale to preserve accurate localization, the network classifies the images and learns features that include the wider context. We demonstrate the benefits of this context technique in the object detection task through a case study of crabgrass detection in maize fields. In this standard situation, adding context alone nearly halved the error of the neural network from 7.1% to 4.3%. After only one epoch with context, the network also achieved a higher accuracy than the network without context did after 50 epochs. The benefits of using the context technique are likely to particularly evident in agricultural contexts in which parts (such as leaves) of several plants may appear similar when not taking into account the context in which those parts appear.
Error Estimates of Reiterated Stokes Systems via Fourier Transform Methods.

In this paper, we are interested in the error estimates of the reiterated Stokes systems in a bounded $C^{1,1}$ domain with Dirichlet boundary conditions. And we have obtained the $O(\varepsilon)$ error estimates for the velocity term and $O(\varepsilon^{1/2})$ error estimates for the pressure term. Compared to the general homogenization of Stokes systems problems, the difficulty in the reiterated homogenization is that we need to handle the different scales of $x$. To overcome this difficulty, we use the Fourier transform methods which was firstly introduced by the author in [10] to separate these different scales. We also note that this method may be adapted to a more general multi-scale homogenization problem.
The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. I. Identifying and Characterizing the Protostellar Content of the OMC-2 FIR4 and OMC-2 FIR3 Regions

We present Atacama Large Millimeter/submillimeter Array (0.87 mm) and Very Large Array (9 mm) observations toward OMC-2 FIR4 and OMC-2 FIR3 within the Orion integral-shaped filament, thought to be two of the nearest regions of intermediate-mass star formation. We characterize the continuum sources within these regions on ∼40 au (0.″1) scales and associated molecular line emission at a factor of ∼30 better resolution than previous observations at similar wavelengths. We identify six compact continuum sources within OMC-2 FIR4, four in OMC-2 FIR3, and one additional source just outside OMC-2 FIR4. This continuum emission is tracing the inner envelope and/or disk emission on less than 100 au scales. HOPS-108 is the only protostar in OMC-2 FIR4 that exhibits emission from high-excitation transitions of complex organic molecules (e.g., methanol and other lines) coincident with the continuum emission. HOPS-370 in OMC-2 FIR3, with L ∼ 360 L⊙, also exhibits emission from high-excitation methanol and other lines. The methanol emission toward these two protostars is indicative of temperatures high enough to thermally evaporate it from icy dust grains; overall, these protostars have characteristics similar to hot corinos. We do not identify a clear outflow from HOPS-108 in 12CO, but we find evidence of interaction between the outflow/jet from HOPS-370 and the OMC-2 FIR4 region. A multitude of observational constraints indicate that HOPS-108 is likely a low- to intermediate-mass protostar in its main mass accretion phase and is the most luminous protostar in OMC-2 FIR4. The high-resolution data presented here are essential for disentangling the embedded protostars from their surrounding dusty environments and characterizing them.
Computing the largest bond of a graph

A bond of a graph $G$ is an inclusion-wise minimal disconnecting set of $G$, i.e., bonds are cut-sets that determine cuts $[S,V\setminus S]$ of $G$ such that $G[S]$ and $G[V\setminus S]$ are both connected. Given $s,t\in V(G)$, an $st$-bond of $G$ is a bond whose removal disconnects $s$ and $t$. Contrasting with the large number of studies related to maximum cuts, there are very few results regarding the largest bond of general graphs. In this paper, we aim to reduce this gap on the complexity of computing the largest bond and the largest $st$-bond of a graph. Although cuts and bonds are similar, we remark that computing the largest bond of a graph tends to be harder than computing its maximum cut. We show that {\sc Largest Bond} remains NP-hard even for planar bipartite graphs, and it does not admit a constant-factor approximation algorithm, unless $P = NP$. We also show that {\sc Largest Bond} and {\sc Largest $st$-Bond} on graphs of clique-width $w$ cannot be solved in time $f(w)\times n^{o(w)}$ unless the Exponential Time Hypothesis fails, but they can be solved in time $f(w)\times n^{O(w)}$. In addition, we show that both problems are fixed-parameter tractable when parameterized by the size of the solution, but they do not admit polynomial kernels unless NP $\subseteq$ coNP/poly.
Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. MRI plays an essential role in the diagnosis and treatment assessment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigated the creation of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (Flair) MR images. These synthetic MR (synMR) images were assessed quantitatively with four metrics. The synMR images were also assessed qualitatively by an authoring physician with notions that synMR possessed realism in its portrayal of structural boundaries but struggled to accurately depict tumor heterogeneity. Additionally, this study investigated the synMR images created by generative adversarial network (GAN) to overcome the lack of annotated medical image data in training U-Nets to segment enhancing tumor, whole tumor, and tumor core regions on gliomas. Multiple two-dimensional (2D) U-Nets were trained with original BraTS data and differing subsets of the synMR images. Dice similarity coefficient (DSC) was used as the loss function during training as well a quantitative metric. Additionally, Hausdorff Distance 95% CI (HD) was used to judge the quality of the contours created by these U-Nets. The model performance was improved in both DSC and HD when incorporating synMR in the training set. In summary, this study showed the ability to generate high quality Flair, T2, T1, and T1CE synMR images using GAN. Using synMR images showed encouraging results to improve the U-Net segmentation performance and shows potential to address the scarcity of annotated medical images.
Testing Stellar Evolution with Asteroseismic Inversions of a Main-sequence Star Harboring a Small Convective Core

The goal of stellar evolution theory is to predict the structure of stars throughout their lifetimes. Usually, these predictions can be assessed only indirectly, for example by comparing predicted and observed effective temperatures and luminosities. Thanks now to asteroseismology, which can reveal the internal structure of stars, it becomes possible to compare the predictions from stellar evolution theory to actual stellar structures. In this work, we present an inverse analysis of the oscillation data from the solar-type star KIC 6225718, which was observed by the Kepler space observatory during its nominal mission. As its mass is about 20% greater than solar, this star is predicted to transport energy by convection in its nuclear-burning core. We find significant differences between the predicted and actual structure of the star in the radiative interior near to the convective core. In particular, the predicted sound speed is higher than observed in the deep interior of the star, and too low at a fractional radius of 0.25 and beyond. The cause of these discrepancies is unknown, and is not remedied by known physics in the form of convective overshooting or elemental diffusion.
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications

In recent work (Soltani, Kilmer, Hansen, BIT 2016), an algorithm for non-negative tensor patch dictionary learning in the context of X-ray CT imaging and based on a tensor-tensor product called the $t$-product (Kilmer and Martin, 2011) was presented. Building on that work, in this paper, we use of non-negative tensor patch-based dictionaries trained on other data, such as facial image data, for the purposes of either compression or image deblurring. We begin with an analysis in which we address issues such as suitability of the tensor-based approach relative to a matrix-based approach, dictionary size and patch size to balance computational efficiency and qualitative representations. Next, we develop an algorithm that is capable of recovering non-negative tensor coefficients given a non-negative tensor dictionary. The algorithm is based on a variant of the Modified Residual Norm Steepest Descent method. We show how to augment the algorithm to enforce sparsity in the tensor coefficients, and note that the approach has broader applicability since it can be applied to the matrix case as well. We illustrate the surprising result that dictionaries trained on image data from one class can be successfully used to represent and compress image data from different classes and across different resolutions. Finally, we address the use of non-negative tensor dictionaries in image deblurring. We show that tensor treatment of the deblurring problem coupled with non-negative tensor patch dictionaries can give superior restorations as compared to standard treatment of the non-negativity constrained deblurring problem.
Covariance Matrix Estimation With Non Uniform and Data Dependent Missing Observations

In this paper we study covariance estimation with missing data. We consider missing data mechanisms that can be independent of the data, or have a time varying dependency. Additionally, observed variables may have arbitrary (non uniform) and dependent observation probabilities. For each mechanism, we construct an unbiased estimator and obtain bounds for the expected value of their estimation error in operator norm. Our bounds are equivalent, up to constant and logarithmic factors, to state of the art bounds for complete and uniform missing observations. Furthermore, for the more general non uniform and dependent cases, the proposed bounds are new or improve upon previous results. Our error estimates depend on quantities we call scaled effective rank, which generalize the effective rank to account for missing observations. All the estimators studied in this work have the same asymptotic convergence rate (up to logarithmic factors).
GP4P4: Enabling Self-Programming Networks

Recent advances in programmable switches have enabled network operators to build high-speed customized network functions. Although this is an important step towards self-* networks, operators are now faced with the burden of learning a new language and maintaining a repository of network function code. Inspired by the Intent-Based Networking paradigm, we propose a new framework, GP4P4: a genetic programming approach able to autonomously generate programs for P4-programmable switches directly from network intents. We demonstrate that GP4P4 is able to generate various small network functions in up to a few minutes; an important first step towards realizing the vision of `Self-Driving' networks.
Cosmic Microwave Background Anisotropy numerical solution (CMBAns). Part I. An introduction to Cl calculation

Cosmological Boltzmann codes are often used by researchers for calculating the CMB angular power spectra from different theoretical models, for cosmological parameter estimation, etc. Therefore, the accuracy of a Boltzmann code is of utmost importance. Different Markov Chain Monte Carlo based parameter estimation algorithms typically require 103–104 iterations of Boltzmann code. This makes the time complexity of such codes another critical factor. In the last two decades, several Boltzmann packages, such as CMBFAST, CAMB, CMBEasy, CLASS etc., have been developed. In this paper, we present a new cosmological Boltzmann code, CMBAns, that can be used for accurate calculation of the CMB power spectrum and BipoSH coefficients. At present, CMBAns is developed for a flat background matrix. It is mostly written in the C language. However, we borrowed the concept of class from C++. This gives researchers the flexibility to develop their own independent package based on CMBAns, without an in-depth understanding of the source code. We also develop multiple stand-alone facilities which can be directly compiled and run on a given parameter set. In this paper, we discuss all the mathematical formulation, approximation schemes, integration methods etc., that are used in CMBAns. The package will be made available through github for public use in the near future.
Granular Aluminum Meandered Superinductors for Quantum Circuits

We have designed superinductors made of strongly disordered superconductors for implementation in "hybrid" superconducting quantum circuits. The superinductors have been fabricated as meandered nanowires made of granular Aluminum films. Optimization of the device geometry enabled realization of superinductors with the inductance $\sim 1 {\mu}H$ and the self-resonance frequency over 3 GHz. These compact superinductors are attractive for a wide range of applications, from superconducting circuits for quantum computing to microwave elements of cryogenic parametric amplifiers and kinetic-inductance photon detectors.
Service orientation and innovation in the strategies of manufacturing SMEs

Manufacturing SMEs have an important role in the supply chains of larger firms. When large firms servitize their business, also SMEs may need to consider their strategies concerning services. Limited research attention has been directed at servitization as a strategic choice of SMEs and its different manifestations in service orientation and innovation. The aim of the paper is to increase understanding on the role of services in the strategies of manufacturing SMEs. The focus is on service orientation and innovation, and identification of differences between component manufacturers and equipment manufacturers. A qualitative, exploratory research strategy is employed in the context of nineteen technology-intensive manufacturing SMEs. The findings from SME managers’ interviews show that equipment manufacturers have a stronger service orientation than component manufacturers in terms of share of services, orientation to process-centric services, and service variety. The majority of respondents have experiences with in-house service development primarily, and customer-centric service development took place only in a few companies. Furthermore, companies featured services in their strategies either minimally, reactively or proactively. Equipment manufacturers were more likely to take the proactive approach whereas component manufacturers were more reactive or fully avoided services. This paper offers valuable knowledge about the ways in which technology-intensive SMEs feature service orientation and innovation into their strategies. Besides changes in offerings, capabilities and value networks, servitization can drive changes in the business scope and innovation processes of the manufacturing SMEs.
A flying start ? Long term consequences of time investments in infants in their first year of life

We study the impact of increasing the time the mother potentially spends with her child in the first year of life. In particular, we examine a reform that increased paid and unpaid maternity leave entitlements in Norway. The response to this reform was such that maternal leave increased by 4 months (from an average of 8 months) and family income was largely unaffected. We find that this increase in time with the child led to a 2.7% decline in high school dropout rates, going up to 5.2% for those whose mothers have less than 10 years of education. There is no impact of the reform on college attendance, suggesting that much of the impact is at the low end of the education distribution. *We thank Gerard van den Berg, Richard Blundell, Per-Anders Edin, Peter Fredriksson, Kjell-Erik Lommerud, and seminar participants at Yale, University of Bergen, University of Stavanger, University of Stockholm, Uppsala University, Tinbergen Institute, Norwegian School of Economics, University College London, ESSLE 2008, EEA 2008, SOLE2009 for useful comments on the paper. Løken and Salvanes are thankful to the Research Council of Norway for financial support. Carneiro gratefully acknowledges the financial support from the Economic and Social Research Council for the ESRC Centre for Microdata Methods and Practice (grant reference RES-589-28-0001), and the hospitality of Georgetown University, and the Poverty Unit of the World Bank Research Group.
Automatic Extraction of 3 d Body Measurements from 2 d Images of a Female Form

The revolution of mass production and mass sizing in the 21 century has turned the perfect fit of a garment into a satisfactory one for an individual. A system for extracting body measurements for well-fitted garments at an affordable price is required. Even though 3D body scanning systems can give accurate results about the basic body dimensions, they have a limited use in the industry, especially in the small business setups. 2D images of a person can be used to determine their basic body dimensions. This paper proposes an inexpensive and automated system that will help the small business setups of garment manufacturers by giving a perfect fit to their customers. The experiments were performed on front and side 2D images of 40 young females (subjects) to extract thirteen of the basic body dimensions including the seven girth measurements [1]. All of them were first measured manually, and then 20 subjects were used for training to obtain the conversion formulas between the extracted and final obtained dimensions. The remaining 20 subjects were used for testing, i.e. for comparison between manual measurements and measurements from 2D imaging system. Keywords2D Images, Feature Points, MATLAB, Measurements -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 02-07-2018 Date of acceptance: 18-07-2018 -------------------------------------------------------------------------------------------------------------------------------------
MODELING AND SIMULATION OF DVR WITH ULTRACAPACITOR FOR FAST VOLTAGE SAG MITIGATION IN DISTRIBUTION SYSTEM

This paper presents the analysis of Ultra capacitor (UC) energy storage device interfaced with Dynamic Voltage Restorer (DVR) for fast mitigation of deeper and extended voltage sags using MATLAB/Simulink® software package. [1] The model is designed with a user-friendly icon and a dialog box like Simulink block libraries. Power quality issues are analysed in detail and ―Voltage Sag in distribution systems is taken for a case study. A new model of Ultra capacitor is proposed and is verified experimentally. Simulation results for Ultra capacitor model are also derived and are found in exact accordance to the experimental modelling. [2] A complete study of Dynamic Voltage Restorer and its simulation using two control strategies viz. d-q-o algorithm and PI controller algorithm is done. To interface Ultra capacitor with DVR, a buck-boost converter controlled with hybrid control algorithm is proposed. Numbers of simulations are carried out on distribution ―Test System and mitigation of voltage sag is observed in each case. The simulation results prove the advantages of the proposed system over the conventional system in terms of fastness sag depth and sag extension. The final results are going in the favour of DVR + UC system. The proposed method, if implemented practically will help the distribution system to improve power quality and can serve the customers in a better way. Index Terms – Ultracapacitor, Dynamic Voltage Restorer (DVR), Modelling, Voltage sag, Voltage swell
Effect of tartrazine on gastric mucosa and the possible role of recovery with or without riboflavin in adult male albino rat

Background: Tartrazine is one of the azo dyes that are the most common artificial food colors widely used in many food products. Tartrazine is used in many developing countries without strict regulations.Aim of the study: to investigate the effect of tartrazine on the gastric mucosa and to evaluate the possible role of recovery after its withdrawal with or without riboflavin in rat.Materials and Methods: Twenty-four adult male albino rats were equally divided into 4 groups; Control, riboflavin, tartrazine-treated group (orally administered 200 mg/kg/day tartrazine for 60 days), tartrazine-recovery group (orally administered 200 mg/kg/day tartrazine for 60 days then left without treatment for another 60 days) and tartrazine-recovery and riboflavin group (that were orally administered tartrazine for 60 days then stopped and followed with riboflavin for another 60 days). Glandular stomach specimens were processed for histological and immunohistochemical techniques.Results: Tartrazine-treated group depicted variable degrees of mucosal lesions with significant decrease in its thickness. Parietal cells with vacuolated cytoplasm and irregular nuclei, and vacuolated chief cells with pyknotic nuclei were detected. Dilated congested blood vessels and aggregated mononuclear cells were observed. Ultrastructural examination showed parietal and chief cells with condensed nuclei and dilated rough endoplasmic reticulum. Tartrazine-recovery group showed almost intact gastric mucosa. Tartrazine-recovery with riboflavin group showed a near normal gastric mucosa. Both Ki67 and iNOS-immunohistochemical expression showed a statistically significant increase upon tartrazine administration coupling to a significant decrease in Periodic-Acid-Schiff expression. Tartrazine-recovery group still revealed significant differences in these parameters compared to the control, while tartrazine-recovery with riboflavin showed non-significant differences from the control.Conclusion: Tartrazine affected the stomach and was alleviated by stopping it. Combined recovery along with riboflavin was more efficient on the recovery of gastric mucosa.
A hierarchical method for finding interactions: Jointly using linear correlation and rank correlation analysis

The Network Biology (ISSN 2220-8879; CODEN NBEICS) is an open access (BOAI definition), peer/open reviewed online journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology.It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: