Miracle-dz/UniFAR-FTUs huggingface dataset:
Early-season mapping of winter wheat in China based on Landsat and Sentinel images

Abstract. Early-season crop identification is of great importance for monitoring crop growth and predicting yield for decision makers and private sectors. As one of the largest producers of winter wheat worldwide, China outputs more than 18 % of the global production of winter wheat. However, there are no distribution maps of winter wheat over a large spatial extent with high spatial resolution. In this study, we applied a phenology-based approach to distinguish winter wheat from other crops by comparing the similarity of the seasonal changes of satellite-based vegetation index over all croplands with a standard seasonal change derived from known winter wheat fields. Especially, this study examined the potential of early-season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for 3 years (2016–2018) over 11 provinces, which produce more than 98 % of the winter wheat in China. A comprehensive assessment based on survey samples revealed producer's and user's accuracies higher than 89.30 % and 90.59 %, respectively. The estimated winter wheat area exhibited good correlations with the agricultural statistical area data at the municipal and county levels. In addition, the earliest identifiable time of the geographical location of winter wheat was achieved by the end of March, giving a lead time of approximately 3 months before harvest, and the optimal identifiable time of winter wheat was at the end of April with an overall accuracy of 89.88 %. These results are expected to aid in the timely monitoring of crop growth. The 30 m winter wheat maps in China are available via an open-data repository (DOI: https://doi.org/10.6084/m9.figshare.12003990, Dong et al., 2020a).
Global Localization With a Single-Line LiDAR by Dense 2D Signature and 1D Registration

Global localization is a key problem that needs to be solved for single-line LiDAR based robot navigation since it will directly affect the estimation accuracy of the robot’s initial pose and the success rate of recovering the robot’s state when it loses its local pose. Existing studies to deal with this problem usually extract feature points from laser beams and then resort to fast retrieval and registration methods to further determine the robot’s pose. Although these methods have achieved good results in specific scenes, they often fail to perform well when the robot is far away from the map-building trajectory. It is therefore highly desirable to develop more robust techniques for this problem. In this work, we propose a novel solution which is based on <italic>“Dense 2D Signature and 1D Registration”</italic>, D2S1R for short. By establishing a dense signature database for 2D locations and combining with the fast retrieval technology, the 2D search space is extremely compressed. Furthermore, fast yaw angle determination is achieved by converting scan points to 1D space and measuring the difference of scan contours based on relative entropy. Experimental results on several complex indoor scenes show that D2S1R can complete global localization within <inline-formula> <tex-math notation="LaTeX">$0.03{s}$ </tex-math></inline-formula> on an ordinary CPU in an area of nearly <inline-formula> <tex-math notation="LaTeX">$4,000{m}^{2}$ </tex-math></inline-formula>. Besides, on the premise of achieving a location accuracy of <inline-formula> <tex-math notation="LaTeX">$0.08\pm 0.04{m}$ </tex-math></inline-formula> and an orientation accuracy of 0.72±0.60°, it can achieve an average success rate of 95% on all test datasets.
Biofuels Production Process: Microorganisms Utilizing Carbon to Produce Butanol and Ethanol

Microbial fermentation for bio-based products is quickly becoming an integral component of the world infrastructure, as the processes encompassing the synthesis of these natural products becomes more efficient, environmentally friendly and cost effective to compete with existing products. Rising energy costs and increased awareness of global warming have motivated production of biomass-derived fuels and polymers. Bio-butanol and bio-ethanol are currently the most desired fermentation products, as these compounds can be applied to multiple uses, including the foundation for green fuel sources. This review focuses on biofuel production; butanol and ethanol from yeast and bacteria, and how these products are efficient and can be suitable alternative for petrochemical products. It also focuses on utilizing waste to be used as the carbon source for microorganisms to produce bio-based products in an attempt to reduce the overall cost.
R-Score: A Novel Approach to Compare Monte Carlo Renderings

In this paper, we propose a new approach for the comparison and analysis of Monte Carlo (MC) rendering algorithms. It is based on a novel similarity measure called render score (RS) that is specifically designed for MC rendering, statistically motivated, and incorporates bias and variance. Additionally, we propose a comparison scheme that alleviates the need for practically converged reference images (RIs). Our approach can be used to compare and analyze different rendering methods by revealing detailed (per-pixel) differences and subsequently potential conceptual or implementation-related issues, thereby offering a more informative and meaningful alternative to commonly used metrics.
Journal of Development Innovations Vol. 4, No. 1, 2020

Are households in Nepal investing remittance income to improve quality of education of their children? This paper investigates the question using data from the third round of Nepal Living Standard Survey (NLSS). Quality education in this paper is measured as private school enrollment, hiring of private tutor, expenditure in private tutoring, and expenditure in school tuition. Measuring causal effect of remittance on these variables is challenging because of two separate self-selection issues namely: self-selection of household into migration and decision of migrants to remit. These challenges are addressed by restricting analysis to households with migrants and using instrumental variable (IV) regression. Controlling for hosts of observable household level characteristic, child level characteristic and using region fixed effect there is no evidence of causal effect of remittance on private school enrollment and hiring of private tutor. However, there is positive minimal impact of remittance on remittance on expenditure in private tuition and on expenditure in school tuition. Journal of Development Innovations
Sediment sampling, sample preparation and general analysis

2.1 Sampling design for sediment quality assessments Sampling design should be considered as a major component of the study design and broader framework applied for a sediment quality assessment program (see Chapter 1, Fig. 1.1) (ANZECC/ARMCANZ, 2000a; USEPA, 2001, 2006). First, the study type needs to be determined, and this will lead to a definition of the study scope (spatial boundaries, scale and duration), and the design of the required sampling program to achieve the data requirements. In many cases, sediment investigations are descriptive studies, simply designed to investigate the spatial and temporal distribution of contaminants, for ‘state of the environment’ reporting, for compliance monitoring, or to guide management actions such as dredging. In rarer instances, the objective may be to examine contaminant transport and depositional processes. In most instances, the assessment objectives are likely to be driven by regulatory requirements and evaluation of the potential impacts on ecosystem or human health. Quality assurance (QA) needs to be addressed in any assessment exercise. This should include a consideration of the desired sampling and data quality objectives. Standard operating procedures should exist for methods that may be replicated and require audit or review to check compliance against the QA plans. The assessment of data quality objectives will usually require statistical evaluation to ensure that the right type, quantity and quality of environmental data are collected. There are several documents that provide guidance on these matters (for example, ANZECC/ARMCANZ, 2000a; USEPA, 2001, 2006). Specific details on the design of a sampling program for an ecological assessment are provided in Chapter 7. Factors such as the depth of sediment collected, in-field processing and the degree of replication required may differ for different assessments and environments. The design of a sampling program for sediments needs to consider:
Hunting for Insider Threats Using LSTM-Based Anomaly Detection

Insider threats are one of the most difficult problems to solve, given the privileges and information available to insiders to launch different types of attacks. Current security systems can record and analyze sequences from a deluge of log data, potentially becoming a tool to detect insider threats. The issue is that insiders mix the sequence of attack steps with valid actions, reducing the capacity of security systems to programmatically detect the attacks. To address this shortcoming, we introduce LADOHD, an anomaly detection framework based on Long-Short Term Memory (LSTM) models, which learns the expected event patterns in a computer system to identify attack sequences even when attacks span for a long time. The applicability of the framework is demonstrated on a dataset of 38.9 million events collected from a commercial network of 30 computers over twenty days and where a 4-day long insider threat attack occurs. Results show that LADOHD outperforms the anomaly detection system used to protect the commercial network with a True Positive Rate of 97.29% and a False Positive Rate of 0.38%. Experiments also show that LSTMs have higher prediction precision in variable-length sequences than methods like Hidden Markov Models, a crucial requirement in sequence-analysis-based anomaly detection techniques.
A Review: Restoring the Images and Remove Blurry Effects from Scanned Document Images Using Various MATLAB Algorithm

Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this research work, i consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. In the proposed methods, which makes use of an a priori noise model, has been evaluated on various types of images. Inverse filtering and Wiener filter based algorithms and technique of image processing will be implementation for experiment. [1] [2] This article is aimed to provide a basic knowledge of image degradation and restoration process. Offline handwriting recognition approaches proceed by segmenting characters into smaller pieces which are recognized separately. The recognition result of a word is then the composition of the individually recognized parts. Inspired by results in cognitive psychology, researchers have begun to focus on holistic word recognition approaches. Here we present some techniques used in past for word recognition approach for degraded documents, which is motivated by the fact that for severely degraded documents a segmentation of words into characters will produce very poor results. [6] [11] The quality of the original documents does not allow us to recognize them with high accuracy - our goal here is to analyze the transcriptions that will allow successful retrieval of images, which has been shown to be feasible even in such noisy environments. We believe that this is the first systematic approach to recognizing words in historical manuscripts with extensive experiments.
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

Effective lifelong learning across diverse tasks requires the transfer of diverse knowledge, yet transferring irrelevant knowledge may lead to interference and catastrophic forgetting. In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks. We ﬁrst show that the lifelong learning performance of several current deep learning architectures can be signiﬁcantly improved by transfer at the appropriate layers. We then develop an expectation-maximization (EM) method to automatically select the appropriate transfer conﬁg-uration and optimize the task network weights. This EM-based selective transfer is highly effective, balancing transfer performance on all tasks with avoiding catastrophic forgetting, as demonstrated on three algorithms in several lifelong object classiﬁcation scenarios.
Truncated Cauchy Power–Inverse Exponential Distribution: Theory and Applications

In this article, a new distribution is introduced, which is generated from the truncated Cauchy power-G family of distribution named as truncated Cauchy powerinverse exponential distribution (TCP-IE). We have explored various statistical and mathematical properties, shapes and behavior of the proposed distribution through probability density function (PDF) plot, cumulative distribution function (CDF) plot, and hazard rate function. We illustrated the estimation of the parameters and their corresponding confidence interval using the maximum likelihood estimation (MLE) method for the (TCP-IE) distribution. Two real data sets are taken to assess the suitability and applicability of purposed distribution. It is observed that it can be used quite effectively to analyze lifetime data and performs betteras compared to theother three distributions namelyexponential power, Marshall-Olkin Extended Exponential (MOEE) distribution and generalized Rayleigh distributions.
An Improved Sampling Algorithm for Imbalanced Data Sets in the context of Ordinal Classification

Classification of data becomes problematic due to class imbalance nature and classes having ordering relationship. The main objective of this research is to improve the classification accuracy and maintain the class order for nonlinear patterns in the context of ordinal classification. This work proposes a novel collinear based modified sampling technique to strengthen the probable area of synthetic pattern. Experimental results with data sets shows that, our proposed collinear based modified sampling yields better ordinal classification performance in terms of accuracy and sensitivity compared with currently existing solutions. The performance evaluation is conducted in terms of the parameters called Maximum Mean Absolute Error, Geometric Mean. This technique provides an effective and efficient solution for imbalanced complex data sets.
Approaches Which Output Infinitely Many Graphs With Small Local Antimagic Chromatic Number

An edge labeling of a connected graph $G = (V, E)$ is said to be local antimagic if it is a bijection $f:E \to\{1,\ldots ,|E|\}$ such that for any pair of adjacent vertices $x$ and $y$, $f^+(x)\not= f^+(y)$, where the induced vertex label $f^+(x)= \sum f(e)$, with $e$ ranging over all the edges incident to $x$. The local antimagic chromatic number of $G$, denoted by $\chi_{la}(G)$, is the minimum number of distinct induced vertex labels over all local antimagic labelings of $G$. In this paper, we (i) give a sufficient condition for a graph with one pendant to have $\chi_{la}\ge 3$. A necessary and sufficient condition for a graph to have $\chi_{la}=2$ is then obtained; (ii) give a sufficient condition for every circulant graph of even order to have $\chi_{la} = 3$; (iii) construct infinitely many bipartite and tripartite graphs with $\chi_{la} = 3$ by transformation of cycles; (iv) apply transformation of cycles to obtain infinitely many one-point union of regular (possibly circulant) or bi-regular graphs with $\chi_{la} = 2,3$. The work of this paper suggests many open problems on the local antimagic chromatic number of bipartite and tripartite graphs.
Hawkes-modeled telecommunication patterns reveal relationship dynamics and personality traits

It is not news that our mobile phones contain a wealth of private information about us, and that is why we try to keep them secure. But even the traces of how we communicate can also tell quite a bit about us. In this work, we start from the calling and texting history of 200 students enrolled in the Netsense study, and we link it to the type of relationships that students have with their peers, and even with their personality profiles. First, we show that a Hawkes point process with a power-law decaying kernel can accurately model the calling activity between peers. Second, we show that the fitted parameters of the Hawkes model are predictive of the type of relationship and that the generalization error of the Hawkes process can be leveraged to detect changes in the relation types as they are happening. Last, we build descriptors for the students in the study by jointly modeling the communication series initiated by them. We find that Hawkes-modeled telecommunication patterns can predict the students' Big5 psychometric traits almost as accurate as the user-filled surveys pertaining to hobbies, activities, well-being, grades obtained, health condition and the number of books they read. These results are significant, as they indicate that information that usually resides outside the control of individuals (such as call and text logs) reveal information about the relationship they have, and even their personality traits.
Compression-aware Continual Learning using Singular Value Decomposition

We propose a compression based continual task learning method that can dynamically grow a neural network. Inspired from the recent model compression techniques, we employ compression-aware training and perform low-rank weight approximations using singular value decomposition (SVD) to achieve network compaction. By encouraging the network to learn low-rank weight filters, our method achieves compressed representations with minimal performance degradation without the need for costly fine-tuning. Specifically, we decompose the weight filters using SVD and train the network on incremental tasks in its factorized form. Such a factorization allows us to directly impose sparsity-inducing regularizers over the singular values and allows us to use fewer number of parameters for each task. We further introduce a novel shared representational space based learning between tasks. This promotes the incoming tasks to only learn residual task-specific information on top of the previously learnt weight filters and greatly helps in learning under fixed capacity constraints. Our method significantly outperforms prior continual learning approaches on three benchmark datasets, demonstrating accuracy improvements of 10.3%, 12.3%, 15.6% on 20-split CIFAR-100, miniImageNet and a 5-sequence dataset, respectively, over state-of-the-art. Further, our method yields compressed models that have ~3.64x, 2.88x, 5.91x fewer number of parameters respectively, on the above mentioned datasets in comparison to baseline individual task models. Our source code is available at this https URL.
Multivariate Time-series Anomaly Detection via Graph Attention Network

Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different time-series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue. Our framework considers each univariate time-series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of multivariate time-series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and a reconstruction-based model, obtaining better time-series representations through a combination of single-timestamp prediction and reconstruction of the entire time-series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.
A criterion for the inductive Alperin weight condition

We give a criterion that simplifies the checking of the inductive Alperin weight condition for the remaining open cases of simple groups of Lie type (see Theorem 3.3). It is strongly related in form to the criterion of the second author for the inductive McKay conditions (see Späth, Bull. Lond. Math. Soc. 44 (2012), no. 3, 426–438, 2.12) that has proved very useful. The proof follows from a Clifford theory for weights intrinsically present in the proof of reduction theorems of the Alperin weight conjecture given by Navarro–Tiep and the second author. We also give a related criterion for the inductive blockwise Alperin weight condition (Theorem 4.5).
Electrical Control for Extension of Ramsey Spin Coherence Time of Ion-Implanted Nitrogen Vacancy Centers in diamond

The extension of the spin coherence times is a crucial issue for quantum information and quantum sensing. In solid state systems, suppressing noises with various techniques have been demonstrated. On the other hand, an electrical control for suppression is important toward individual controls of on-chip quantum information devices. Here we show the electrical control for extension of the spin coherence times of 40 nm-deep ion-implanted single nitrogen vacancy center spins in diamond by suppressing magnetic noises. We applied 120 V DC across two contacts spaced by 10 micrometers. The spin coherence times, estimated from a free-induction-decay and a Hahn-echo decay, were increased up to about 10 times (reaching 10 microseconds) and 1.4 times (reaching 150 microseconds), respectively. From the quantitative analysis, the dominant decoherence source depending on the applied static electric field was elucidated. The electrical control for extension can deliver a sensitivity enhancement to the DC sensing of temperature, pressure and electric (but not magnetic) fields, opening a new technique in solid-state quantum information devices.
Identification of young nearby runaway stars based on
 Gaia
 data and the lithium test
 †

Young nearby runaway stars are suitable to search for their place of origin and possibly associated objects, for example neutron stars. Tetzlaff, Neuhauser & Hohle (2011) selected young ($\le 50$ Myr) runaway star candidates from Hipparcos, for which they had estimated the ages from the location in the Hertzsprung-Russell diagram and evolutionary models. Here, we redetermine or constrain their young ages more precisely not only by using the new Gaia DR2 data, but also by measuring lithium, which is a youth indicator. For 308 stars, we took spectra to search for the strong resonance doublet of the lithium-7 isotope at 6708 $\unicode{xC5}$. The spectra were taken with the Echelle spectrograph FLECHAS at the University Observatory Jena between February 2015 and June 2018 and with TRES between April 2011 and June 2017 at the Fred L. Whipple Observatory. We found 208 stars with significant occurrence of lithium in their spectra, and five possess a possible age younger or about 50 Myr. Three of these targets are even closer than GJ 182, the nearest known runaway star at about 24 pc. Theses stars are young runaway stars suitable for further investigation of their origin from either a dynamical or supernova ejection.