Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Bio-Fortified Orange Fleshed Sweet Potato Adoption on Farming Household Welfare in Osun State Nigeria

The level of poverty in a household is a reflection of low welfare. There is need to develop ways of enhancing agricultural production to improve output and consequently, meet up with growing food demand and enhance farm income which further upgrade the welfare of the farming households. As a result, the study examined the effect of orange fleshed sweet potato (OFSP) adoption on farming household welfare. Primary data were collected with the aid of structured questionnaire using multistage sampling technique. Data collected were analysed using descriptive statistics, double-hurdle regression models and ordinary least square models (OLS). The results revealed that most of the OFSP farmers were male (94%), 98% of them were married with average age of 51 years. The mean years of education was 12 years, 46.42 % of them belong to farmers association and had an average farm size of 1.53 hectares. The study revealed a high level of awareness (83.77%) and adoption (74.34%) of the OFSP technology. The categories of household welfare used revealed a large difference in between them in terms of their mean per capita expenditure. Estimates of the first hurdle (Probit Model) revealed that adoption is influenced by age, household size, and education among others. The second hurdle estimates on the decision variables that influence the intensity of use of OFSP technology includes children below 5 years, farming experience, income number of extension visit etc. The OLS estimates showed that intensity of use of the OFSP significantly affect farming household welfare level. The study therefore conclude that adoption of OFSP technology directly influence household welfare. It was recommended that the youth should be encouraged to engage in farming activities.
Using dual-mappings to evade automated unpackers

Automated unpackers such as Renovo, Saﬀron, and Pandora’s Bochs attempt to dynamically unpack ex-ecutables by detecting the execution of code from regions of virtual memory that have been written to. While this is an elegant method of detecting dynamic code execution, it is possible to evade these unpack-ers by dual-mapping physical pages to two distinct virtual address regions where one region is used as an editable mapping and the second region is used as an executable mapping. In this way, the editable mapping is written to during the unpacking process and the executable mapping is used to execute the unpacked code dynamically. This eﬀectively evades automated unpackers which rely on detecting the execution of code from virtual addresses that have been written to.
SPINNING ELECTRIC FIELD–INDUCED CHANGES IN STATISTI CS OF BACKGROUND GAMMA-RADIATION. CUMULATIVE AFTEREFFECT IN THE DETECTOR

Публикация рассматривает влияние электрического вращения на фоновое гаммаизлучение. Исследование выявило как нарушение статистики нормального квантового распределения, так и присутствие накопительного эффекта последействия в детекторе. В главном, результаты соответствуют тенденции, наблюдавшейся российскими физиками для влияния вращающихся масс на гамма-радиацию. Полученные результаты говорят о том, что в наблюдаемом эффекте важен сам факт вращения, а не его конкретный носитель.
Calcium-Dependent Protein Kinase: A Tool for Plants to Crack the Calcium Code

Ca signals are involved in most aspects of growth and development of plant, including response to hormone signaling, various biotic and abiotic stresses, germination, cell division, cell expansion, pollen tube growth and fertilization. The calcium-dependent protein kinases (CDPKs) constitute one of the largest Ca sensing subfamilies of plant-specific protein kinases that decodes the transient changes of Ca concentration in the cytoplasm in response to extrinsic and intrinsic cues. The unique domain structure of CDPKs makes them not only “sensors” but also “responders” to these Ca signatures. A multigene family consisting of 34, 31 and 20 genes in Arabidopsis, rice and wheat, respectively, encodes CDPKs. The multigenic nature and diverse spatial and temporal differential expression have been reported in many plant species, which emphasizes on the precise role of isoforms in developmental (e.g. pollen tube) as well as stress responsive pathways (e.g. ROS). The regulation of CDPKs has been reported to be at transcriptional and post translational level. The signaling pathways mediated by CDPKs have also been found to overlap with MAP kinase pathways, suggesting of an intricate network, which regulate precise responses of plants. The proteins interacting with CDPKs are diverse in their function (e.g. transcription factor, channel protein, v-SNARE) which indicates that CDPKs play important role in regulating the Ca signaling cascade, leading to extremely precise response of plants during development and adaptation to environmental cues. This functional diversity and their crosstalks are being discussed in this review. _____________________________________________________________________________________________________________
The preservation of material flows in recycling processes for further usage

Recycling processes generate many material flows that are of equal quality as primary resources gained from mining operations. Often they save resources like material and energy. This paper discusses the information needed to evaluate the potential of materials flows in recycling processes to preserve primary resources. Main issue to look at are the uncertainties connected to recycling processes and the dissipative losses during the life cycle of products. Neural networks might be a solution to cope with vague data and high data ranges in recycling processes. In this paper we are presenting an approach how neural networks can support our attempt to handle the uncertainties connected to material flows.
Looting in Peru

5 Abstract Illegal excavations represent one of the main risk factors which affect the 6 archaeological heritage all over the world, in particular in those countries, from 7 Southern America to Middle East, where the surveillance on site is little effective 8 and time consuming and the aerial surveillance is non practicable due to military or 9 political restrictions. In such contexts satellite remote sensing offers a suitable 10 chance to monitor this phenomenon. The chapter deals with the results obtained 11 on some areas of Cahuachi (Peru) by using a time series of very high resolution 12 satellite images. The rate of success in detecting changes related to archaeological 13 looting has been fruitfully improved by adopting a semiautomatic approach based 14 on spatial autocorrelation.
Inheritance tax planning: Spousal bequests and under-reporting of inheritances in Sweden∗

People’s planning to evade the inheritance tax curtails its merits. However, the extent of planning remains a matter of argument. According to popular belief, it is widespread, but few estimates have been presented. This study estimates the extent of estate size under-reporting, a form of inheritance tax planning, using the repeal of the Swedish tax on spousal bequests, in 2004, and a regression discontinuity design. The results show that, on average, estate sizes were 17 percent lower, and that the share of estates that completely escaped tax payments was 26 percent larger due to under-reporting. As a consequence, government revenues from the tax were only half of what they would have been without under-reporting. Moreover, preferences and means for under-reporting were not only prevalent among the wealthy, but also among those receiving relatively small inheritances. The study contributes to a growing literature on tax avoidance and evasion by estimating the extent of estate size under-reporting, its effect on government revenues and by showing that it was widespread in the population.
A market-based algorithm for predicting soccer outcomes ∗

The main objective of this study is to develop a market-based forecasting algo-rithm for predicting soccer outcomes. A football model with defensive and oﬀensive parameters is estimated using information from prediction markets (Betfair Exchange Market). Prediction markets are an interesting testing ground to test the validity of new statistical techniques, since all participating agents observe the relevant information without asymmetries, the events have a clear completion date and the ﬁnal outcome is not inﬂuenced by market activity. A time-homogeneous independent Poisson model is estimated applying the following techniques: OLS, Weighted OLS and hierarchical bayesian methods. The prediction capability of the proposed methodology is tested using ”De Finetti” measure, which is deﬁned as the Euclidean distance between the prediction and the realisation. The results obtained indicate that the models have good predictive properties, being able to improve market predictions, except in the last weeks of the season. So, it can be concluded that the use of information from prediction markets can lead to more accurate estimates of the probability distribution of future events.
The Effect of Ethical Practices on The Relationship Between Strategic Leadership and Service Delivery of County Governments in Kenya

Public sector entities are striving towards enhanced service delivery and an understanding of mix of factors which can help achieve these enhanced services is a continuous endeavor. The study sought to understand whether ethical practices affect the relationship between strategic leadership and service delivery using a case of County Governments in Kenya. The relevant theories underpinning this study were the New Public Management (NPM) theory and the agency theory. This study used a positivist research philosophy and a cross sectional survey. The sample was282 respondents was drawn from drawn from the Education, Health services, Public works, Economic planning, Natural resources and Agriculture departments. The study used purely primary data which was collected using a structured questionnaire. Descriptive and inferential statistics were used in the data analysis. simple and multiple linear regressions were used. The study found that ethical practices and strategic leadership were significant in influencing service delivery among county governments in Kenya. The study recommends that the county leadership should concentrate on building and monitoring ethical behaviours among the county officers for quality service provisions. There is also need to audit, review and strengthen existing code of conducts and enforcement of sanctions on unethical practices by county officials.
RevealNet: Seeing Behind Objects in RGB-D Scans

During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geometry can be fundamentally limiting for many applications, e.g., a robot needs to know the unseen geometry to perform a precise grasp on an object. Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry. This will open up new possibilities for interactions with objects in a scene, for instance for virtual or robotic agents. We tackle this problem by introducing RevealNet, a new data-driven approach that jointly detects object instances and predicts their complete geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts. RevealNet is an end-to-end 3D neural network architecture that leverages joint color and geometry feature learning. The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass. We show that predicting complete object geometry improves both 3D detection and instance segmentation performance. We evaluate on both real and synthetic scan benchmark data for the new task, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.
Inertial-Only Optimization for Visual-Inertial Initialization

We formulate for the first time visual-inertial initialization as an optimal estimation problem, in the sense of maximum-a-posteriori (MAP) estimation. This allows us to properly take into account IMU measurement uncertainty, which was neglected in previous methods that either solved sets of algebraic equations, or minimized ad-hoc cost functions using least squares. Our exhaustive initialization tests on EuRoC dataset show that our proposal largely outperforms the best methods in the literature, being able to initialize in less than 4 seconds in almost any point of the trajectory, with a scale error of 5.3% on average. This initialization has been integrated into ORB-SLAM Visual-Inertial boosting its robustness and efficiency while maintaining its excellent accuracy.
Topological Effects on Attacks Against Vertex Classification

Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about robustness to adversarial manipulation can prevent potential users from adopting proposed methods when the consequence of action is very high. This paper considers two topological characteristics of graphs and explores the way these features affect the amount the adversary must perturb the graph in order to be successful. We show that, if certain vertices are included in the training set, it is possible to substantially an adversary's required perturbation budget. On four citation datasets, we demonstrate that if the training set includes high degree vertices or vertices that ensure all unlabeled nodes have neighbors in the training set, we show that the adversary's budget often increases by a substantial factor---often a factor of 2 or more---over random training for the Nettack poisoning attack. Even for especially easy targets (those that are misclassified after just one or two perturbations), the degradation of performance is much slower, assigning much lower probabilities to the incorrect classes. In addition, we demonstrate that this robustness either persists when recently proposed defenses are applied, or is competitive with the resulting performance improvement for the defender.
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection

Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage on matching of local or global geometrical-only descriptors which discard intensity reading. In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition. We propose a novel global descriptor, intensity scan context (ISC), that explores both geometry and intensity characteristics. To improve the efficiency for loop closure detection, an efficient two-stage hierarchical re-identification process is proposed, including binary-operation based fast geometric relation retrieval and intensity structure re-identification. Thorough experiments including both local experiment and public datasets test have been conducted to evaluate the performance of the proposed method. Our method achieves better recall rate and recall precision than existing geometric-only methods.
Optimal HDR and Depth from Dual Cameras

Dual camera systems have assisted in the proliferation of various applications, such as optical zoom, low-light imaging and High Dynamic Range (HDR) imaging. In this work, we explore an optimal method for capturing the scene HDR and disparity map using dual camera setups. Hasinoff et al. (2010) have developed a noise optimal framework for HDR capture from a single camera. We generalize this to the dual camera set-up for estimating both HDR and disparity map. It may seem that dual camera systems can capture HDR in a shorter time. However, disparity estimation is a necessary step, which requires overlap among the images captured by the two cameras. This may lead to an increase in the capture time. To address this conflicting requirement, we propose a novel framework to find the optimal exposure and ISO sequence by minimizing the capture time under the constraints of an upper bound on the disparity error and a lower bound on the per-exposure SNR. We show that the resulting optimization problem is non-convex in general and propose an appropriate initialization technique. To obtain the HDR and disparity map from the optimal capture sequence, we propose a pipeline which alternates between estimating the camera ICRFs and the scene disparity map. We demonstrate that our optimal capture sequence leads to better results than other possible capture sequences. Our results are also close to those obtained by capturing the full stereo stack spanning the entire dynamic range. Finally, we present for the first time a stereo HDR dataset consisting of dense ISO and exposure stack captured from a smartphone dual camera. The dataset consists of 6 scenes, with an average of 142 exposure-ISO image sequence per scene.
Reply to "Comment on 'An Alternative Approach for the Determination of Mean Free Paths of Electron Scattering in Liquid Water Based on Experimental Data'"

In a recent comment, Ruth Signorell raises a number of issues that she considers to question the validity of our approach to determine mean free paths for electron scattering in liquid water and our comparison with the results on amorphous ice by Michaud, Wen, and Sanche. Here, we show that these critiques are unjustified, being either unfounded or based on misconceptions by the author of the comment. We nevertheless welcome the opportunity to further clarify certain aspects of our work that we did not discuss in detail in our letter.
Damping of elementary excitations in one-dimensional dipolar Bose gases

In the presence of dipolar interactions the excitation spectrum of a Bose gas can acquire a local minimum. The corresponding quasiparticles are known as rotons. They are gaped and do not decay at zero temperature. Here we study the decay of rotons in one-dimensional Bose gases at low temperatures. It predominantly occurs due to the backscattering of thermal phonons on rotons. The resulting rate scales with the third power of temperature and is inversely proportional to the sixth power of the roton gap near the solidification phase transition. The hydrodynamic approach used here enables us to find the decay rate for quasiparticles at practically any momenta, with minimal assumptions on the exact form of the interparticle interactions. Our results are an essential prerequisite for the description of all the dissipative phenomena in dipolar gases and have direct experimental relevance.
Semantic Holism and Word Representations in Artificial Neural Networks

Artificial neural networks are a state-of-the-art solution for many problems in natural language processing. What can we learn about language and meaning from the way artificial neural networks represent it? Word representations obtained from the Skip-gram variant of the word2vec model exhibit interesting semantic properties. This is usually explained by referring to the general distributional hypothesis, which states that the meaning of the word is given by the contexts where it occurs. We propose a more specific approach based on Frege's holistic and functional approach to meaning. Taking Tugendhat's formal reinterpretation of Frege's work as a starting point, we demonstrate that it is analogical to the process of training the Skip-gram model and offers a possible explanation of its semantic properties.
DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves in a communication-constrained underwater environment is essential for many applications such as underwater exploration, mapping, multi-robot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image-to-image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP. Experimental results in real-world underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness and accuracy of the proposed technique in terms of translation error and orientation error over the state-of-the-art methods. The code is publicly available.
Exact fixation probabilities for the Birth-Death and Death-Birth frequency-dependent Moran processes on the star graph

Broom and Rychtař [Proc. R. Soc. A (2008) 464, 2609--2627] found an exact solution for the fixation probabilities of the Moran process for a structured population, in which the interaction structure among individuals is given by the so-called star graph, i.e. one central vertex and $n$ leaves, the leaves connecting only to the center. We generalize on their solution by allowing individuals' fitnesses to depend on the population frequency, and also by allowing a possible change in the order of reproduction and death draws. In their cited paper, Broom and Rychtař considered the birth-death (BD) process, in which at each time step an individual is first drawn for reproduction and then an individual is selected for death. In the death-birth (DB) process, the order of the draws is reversed. It may be seen that the order of the draws makes a big difference in the fixation probabilities. Our solution method applies to both the BD and the DB cases. As expected, the exact formulae for the fixation probabilities are complicated. We will also illustrate them with some examples and provide results on the asymptotic behavior of the fixation probabilities when the number $n$ of leaves in the graph tends to infinity.
Vulnerability Assessment of Load Frequency Control Considering Cyber Security

Security is one of the biggest concern in power system operation. Recently, the emerging cyber security threats to operational functions of power systems arouse high public attention, and cybersecurity vulnerability thus become an emerging topic to evaluate compromised operational performance under cyber attack. In this paper, vulnerability of cyber security of load frequency control (LFC) system, which is the key component in energy manage system (EMS), is assessed by exploiting the system response to attacks on LFC variables/parameters. Two types of attacks: 1) injection attack and 2) scale attack are considered for evaluation. Two evaluation criteria reflecting the damage on system stability and power generation are used to quantify system loss under cyber attacks. Through a sensitivity-based method and attack tree models, the vulnerability of different LFC components is ranked. In addition, a post-intrusion cyber attack detection scheme is proposed. Classification-based schemes using typical classification algorithms are studied and compared to identify different attack scenarios.