Miracle-dz/UniFAR-FTUs huggingface dataset:
Profitability Analysis Of Cassava Tuber Marketing In Oyo State, Nigeria: Implication For Sustainable Development Goals

Received: 18 April, 2016 Accepted: 19 June, 2016 Published: 10 September, 2016 The first of the eight millennium development goals (MDGs) focused on eradicating extreme poverty and hunger, with one of the specific targets being to halve, between 1990 and 2015, the proportion of people who suffer from hunger. As a food crop, cassava fits well into the poverty and hunger eradicating agenda because it is available allyear round, ensuring steady income and food security for households. Only a profitable and efficient marketing system can achieve this because market is the link between food producers and consumers. This study was carried out to investigate the profitability and efficiency of cassava tuber marketing in Oyo State, Nigeria. Multistage sampling technique was employed to sample 240 cassava tuber marketers in the study area. Data obtained were analysed using the descriptive, budgetary and OLS regression analysis. Data analysis revealed that mean age of cassava tuber marketers was 39.01years and most (70.8%) of them were male. and married (91.7%), with mean household size of 5.33 persons. Most (47.50%) of the respondents received secondary education with average of 10.42 years spent in school. Respondents claimed average marketing experience of 11.17years and personal savings was endorsed by 80.0% of them as the major source of finance. Budgetary analysis revealed that the business is profitable with gross margin of #38,989.59 per tonne transacted. The BCR was found to be 1.43. The challenges faced in cassava tuber marketing include injury to tubers during transportation, high transportation cost and illegal tax. The OLS regression analysis revealed that purchase cost of cassava tuber, labour cost and transport cost are variables influencing revenue generated by respondents. The study concluded that cassava tuber marketing is a profitable enterprise in the study area and associated marketing functions are efficiently performed by respondents. The study recommends that cassava marketers should form registered trade union through which the issue of illegal and excessive tax collection from members could be addressed. © 2016 PSCI Publisher All rights reserved.
Influence of Different Storage Methods on the Viability and Field Establishment of Seed Rhizome Ginger (Zingibere officinale R

Three to four months time gap between harvesting and next planting of ginger that could lead to significant loss of planting materials necessitated devising appropriate storage method (s) of seed rhizome ginger. This experiment was conducted for two cropping seasons (2009 and 2010) to assess and identify suitable storage method (s) using two promising ginger varieties (Yali and Tepi local) and five types of storage methods. The storage treatments included: keeping seed rhizomes under thatched roof shelter on the ground, thatched roof shelter on one meter raised bed or structure, in ground pits covered with thin grass mulch, buried in pits and under tree shade covered with mulch materials. Necessary data of ginger rhizomes were recorded at different stages (before planting, during harvest and after planting) and analyzed. Percent shriveled of rhizomes were significantly influenced among the varieties. Maximum shriveled (41%) rhizomes were obtained from Tepi local. All other parameters were not significantly affected by variety. However, the storage methods significantly influenced all the parameters considered. Maximum seed rhizome viability 85.7 and 85.4% were recorded from seed rhizomes kept under tree shade and from seed rhizomes kept in pits covered with mulch materials, respectively. Whereas maximum fresh rhizome yield 282.4 and 275.2 Q•ha -1 (quintal per hectare) were obtained from seed rhizomes kept under tree shade covered with mulch materials and from seed rhizomes kept under thatched roof shelter on the ground. The results disclosed that there are more safe options to keep seed rhizomes which could be designed and constructed easily at farmers' level.
Pretherapeutic Assessment by Multidetector Computed Tomography for Thyroid Cartilage Invasion in Laryngeal Cancer: A Double‑edged Sword

Abstract Introduction:Almost one-fourth of head and neck cancers in India are laryngeal cancers. Both conservative and surgical therapeutic approaches are available. According to present tumor-node-metastasis staging protocol, thyroid cartilage invasion is a crucial criterion for diagnosing advanced stages of the disease. A major cartilage invasion depicts T4A stage of disease for which surgical treatment is required. Aims: The present study aims to evaluate the accuracy of multidetector computed tomography (MDCT) in evaluation thyroid cartilage invasion in T3 and T4 stage of laryngeal cancers. Materials and Methods: It is a retrospective analysis done in the Department of Radiology, Pramukhswami Medical College, Anand, Gujarat, on 22 patients of T3 and T4 stage of laryngeal cancer who presented for pretherapeutic MDCT neck evaluation. The MDCT results were retrospectively reviewed and compared with postoperative histopathological results. Statistical analysis was done for each parameter as positive predictive value (PPV) (main statistical parameter), negative predictive value, sensitivity, and specificity. Results: MDCT showed a PPV of 60.00% in detecting any type of thyroid cartilage invasion, 66.66% for major and 33.33% for minor cartilage invasion. Extralaryngeal spread of disease was the most specific marker for cartilage involvement. In total, 31.8% of cases were downgraded in staging by pathology. Conclusion: Overestimation of thyroid cartilage invasion by MDCT is a reality which should be in mind before making final therapeutic decisions. Although crucial, it should not be the sole criteria preventing while making a surgical versus conservative therapeutic call.
Constructions for the Elekes-Szabó and Elekes-Rónyai Problems

We give a construction of a non-degenerate polynomial $F\in \mathbb R[x,y,z]$ and a set $A$ of cardinality $n$ such that $F$ vanishes on $\Omega(n^{3/2})$ points of $A \times A \times A$, thus providing a new lower bound construction for the Elekes–Szabó problem. We also give a related construction for the Elekes–Rónyai problem restricted to a subgraph. This consists of a polynomial $f\in \mathbb R[x,y]$ that is not additive or multiplicative, a set $A$ of size $n$, and a subset $P\subset A\times A$ of size $\Omega(n^{3/2})$ on which $f$ takes only $n$ distinct values.
Executive Compensation Packages and Return to Shareholders at Selected Deposit Money Banks in Lagos State, Nigeria

Background: The competitive and dynamic operating environment at deposit money banks in Lagos State requires focussed management of resources which attractive compensation management of the organisation executive directors could guarantee. The operating environment has brought about series of challenges including rendering poor services, culminating in steady decline of profitability which had impacted negatively on returns accruable to shareholders. Methods: The study employed cross sectional survey research design where four hundred and twenty-nine copies of the research instruments were administered to purposively selected respondents who were senior and executive staff of the selected banks in Lagos State. The response rate on the questionnaires distributed was 398, which represents 92.8% of the study population. The data were processed using (SPSS version 26). Results: The result of multiple regression analysis showed that executive bonus (β = 0.086, p = 0.009) and recognition (β = 0.133, p = 0.007) have positive and significant effect on return to shareholders and base salary (β = 0.054, p = 0.293) and supplemental benefits (B = 0.040, p = 0.179) also have positive, but insignificant effect on return to shareholders. Further analysis revealed a collective positive significant effect of executive compensation sub-variables on customer acquisition (F(4,392)) = 9.248, p<0.000, R 2 = 0.078) indicating that combination of executive compensation sub-variables was statistically significant in explaining changes in return to shareholders. Conclusion: The study concluded that executive compensation packages is important to shareholders value creation and management of deposit money banks should ensure that their senior and executive staff compensation management are attractive to motivate higher productivity. It is recommended that process driven compensation management should be instituted to promote competitive spirit and ensure objective evaluations to reduce conflicts arising from perceived double standard treatment.
Serum hepcidin level and iron status in a sample of obese Egyptian children

ObjectiveThe aim of this study was to assess serum hepcidin level and iron status and their correlation with BMI in a sample of obese Egyptian children. Participants and methodsA total of 50 obese children with mean age 7.71±2.55 years and 30 apparently normal controls with mean age 6.88±1.69 years were enrolled in this study. All children were subjected to full history taking, anthropometric assessment and measurement of hemoglobin, serum ferritin, total iron-binding capacity (TIBC), and hepcidin levels. ResultsHemoglobin and ferritin levels were lower, whereas TIBC and serum hepcidin levels were higher in obese children than controls. Serum hepcidin level correlated positively with Z-score BMI (r=0.781, P=0.00) and TIBC9 (r=0.523, P=0.00) but negatively with Hb (r=−0.715, P=0.00) and ferritin (r=−0.218, P=0.05). ConclusionThe present study reported lower Hb and ferritin levels but higher TIBC and hepcidin levels among obese children. Strong positive correlation was found between hepcidin and Z-score BMI. Therefore, it could be concluded that obesity in children is associated with increased serum hepcidin level with a state of iron depletion.
Deep Unfolding Network for Image Super-Resolution

Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels and noise levels under a unified MAP (maximum a posteriori) framework, learning-based methods generally lack such flexibility. To address this issue, this paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods. Specifically, by unfolding the MAP inference via a half-quadratic splitting algorithm, a fixed number of iterations consisting of alternately solving a data subproblem and a prior subproblem can be obtained. The two subproblems then can be solved with neural modules, resulting in an end-to-end trainable, iterative network. As a result, the proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods. Extensive experiments demonstrate the superiority of the proposed deep unfolding network in terms of flexibility, effectiveness and also generalizability.
Stellar wind effects on the atmospheres of close-in giants: a possible reduction in escape instead of increased erosion

The atmospheres of highly irradiated exoplanets are observed to undergo hydrodynamic escape. However, due to strong pressures, stellar winds can confine planetary atmospheres, reducing their escape. Here, we investigate under which conditions atmospheric escape of close-in giants could be confined by the large pressure of their host star’s winds. For that, we simulate escape in planets at a range of orbital distances ([0.04, 0.14] au), planetary gravities ([36, 87 per cent] of Jupiter’s gravity), and ages ([1, 6.9] Gyr). For each of these simulations, we calculate the ram pressure of these escaping atmospheres and compare them to the expected stellar wind external pressure to determine whether a given atmosphere is confined or not. We show that although younger close-in giants should experience higher levels of atmospheric escape, due to higher stellar irradiation, stellar winds are also stronger at young ages, potentially reducing escape of young exoplanets. Regardless of the age, we also find that there is always a region in our parameter space where atmospheric escape is confined, preferably occurring at higher planetary gravities and orbital distances. We investigate confinement of some known exoplanets and find that the atmosphere of several of them, including π Men c, should be confined by the winds of their host stars, thus potentially preventing escape in highly irradiated planets. Thus, the lack of hydrogen escape recently reported for π Men c could be caused by the stellar wind.
Multi-Task Learning Enhanced Single Image De-Raining

Rain removal in images is an important task in computer vision filed and attracting attentions of more and more people. In this paper, we address a non-trivial issue of removing visual effect of rain streak from a single image. Differing from existing work, our method combines various semantic constraint task in a proposed multi-task regression model for rain removal. These tasks reinforce the model's capabilities from the content, edge-aware, and local texture similarity respectively. To further improve the performance of multi-task learning, we also present two simple but powerful dynamic weighting algorithms. The proposed multi-task enhanced network (MENET) is a powerful convolutional neural network based on U-Net for rain removal research, with a specific focus on utilize multiple tasks constraints and exploit the synergy among them to facilitate the model's rain removal capacity. It is noteworthy that the adaptive weighting scheme has further resulted in improved network capability. We conduct several experiments on synthetic and real rain images, and achieve superior rain removal performance over several selected state-of-the-art (SOTA) approaches. The overall effect of our method is impressive, even in the decomposition of heavy rain and rain streak accumulation.The source code and some results can be found at:this https URL.
Cross-Modal Deep Face Normals With Deactivable Skip Connections

We present an approach for estimating surface normals from in-the-wild color images of faces. While data-driven strategies have been proposed for single face images, limited available ground truth data makes this problem difficult. To alleviate this issue, we propose a method that can leverage all available image and normal data, whether paired or not, thanks to a novel cross-modal learning architecture. In particular, we enable additional training with single modality data, either color or normal, by using two encoder-decoder networks with a shared latent space. The proposed architecture also enables face details to be transferred between the image and normal domains, given paired data, through skip connections between the image encoder and normal decoder. Core to our approach is a novel module that we call deactivable skip connections, which allows integrating both the auto-encoded and image-to-normal branches within the same architecture that can be trained end-to-end. This allows learning of a rich latent space that can accurately capture the normal information. We compare against state-of-the-art methods and show that our approach can achieve significant improvements, both quantitative and qualitative, with natural face images.
Adaptive Name Entity Recognition under Highly Unbalanced Data

For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc. In this report, we present our experiments on a neural architecture composed of a Conditional Random Field (CRF) layer stacked on top of a Bi-directional LSTM (BI-LSTM) layer for solving NER tasks. Besides, we also employ a fusion input of embedding vectors (Glove, BERT), which are pre-trained on the huge corpus to boost the generalization capacity of the model. Unfortunately, due to the heavy unbalanced distribution cross-training data, both approaches just attained a bad performance on less training samples classes. To overcome this challenge, we introduce an add-on classification model to split sentences into two different sets: Weak and Strong classes and then designing a couple of Bi-LSTM-CRF models properly to optimize performance on each set. We evaluated our models on the test set and discovered that our method can improve performance for Weak classes significantly by using a very small data set (approximately 0.45\%) compared to the rest classes.
Askaryan radiation from neutrino-induced showers in ice

We present a semianalytical method for the calculation of coherent Askaryan radiation in showers induced by neutrinos of any flavor in ice. We compare our results with those of a full Monte Carlo simulation based on the zhaires code. This approach is able to reproduce the vector potential and hence electric field at any experimentally relevant observer position in the time domain. This work extends published results only valid for electron-induced showers. We establish the validity of the semianalytical calculation of the radio signal produced by all types of neutrino-induced showers in ice. The method is computationally efficient and only requires as inputs the longitudinal charge excess profile of the showers and a parametrization of the vector potential in the far-field region of the shower at the Cherenkov angle that we also provide. Our methodology avoids tracking the contributions to the electric field from millions of particles every time the radio pulse has to be calculated at a given observer position. These results can be readily used in the interpretation of the data taken by experiments, and in the planning and design of future initiatives based on the radio technique in ice.
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting

Developing efficient and principled neural architecture optimization methods is a critical challenge of modern deep learning. Recently, Liu et al.[19] proposed a splitting steepest descent (S2D) method that jointly optimizes the neural parameters and architectures based on progressively growing network structures by splitting neurons into multiple copies in a steepest descent fashion. However, S2D suffers from a local optimality issue when all the neurons become"splitting stable", a concept akin to local stability in parametric optimization. In this work, we develop a significant and surprising extension of the splitting descent framework that addresses the local optimality issue. The idea is to observe that the original S2D is unnecessarily restricted to splitting neurons into positive weighted copies. By simply allowing both positive and negative weights during splitting, we can eliminate the appearance of splitting stability in S2D and hence escape the local optima to obtain better performance. By incorporating signed splittings, we significantly extend the optimization power of splitting steepest descent both theoretically and empirically. We verify our method on various challenging benchmarks such as CIFAR-100, ImageNet and ModelNet40, on which we outperform S2D and other advanced methods on learning accurate and energy-efficient neural networks.
Design and optimal control of a tiltrotor micro-aerial vehicle for efficient omnidirectional flight

Omnidirectional micro-aerial vehicles (MAVs) are a growing field of research, with demonstrated advantages for aerial interaction and uninhibited observation. While systems with complete pose omnidirectionality and high hover efficiency have been developed independently, a robust system that combines the two has not been demonstrated to date. This paper presents the design and optimal control of a novel omnidirectional vehicle that can exert a wrench in any orientation while maintaining efficient flight configurations. The system design is motivated by the result of a morphology design optimization. A six-degree-of-freedom optimal controller is derived, with an actuator allocation approach that implements task prioritization, and is robust to singularities. Flight experiments demonstrate and verify the system’s capabilities.
Contextual Bandit-Based Channel Selection for Wireless LANs with Interference-Driven Feature Extraction

This paper proposes a radio channel selection algorithm based on a contextual multi-armed bandit (CMAB) for a wireless local area network (WLAN) environment, in which the access probability of each access point (AP) and the throughput model are not given in advance. The problem to be considered inherently involves the exploration to obtain the knowledge of the throughput distribution, in which a realized value is observed only after attempting to select each channel. This can be formulated as a multi-armed bandit (MAB) problem; particularly, we focus on the usefulness of the surrounding channel allocation information as the side information and determine that CMAB is appropriate. However, directly applying common CMAB algorithms to the such problems can lead to the lack of learning efficiency when the number of contexts is large. To reduce the computational complexity of the CMAB algorithms, feature extraction is designed by focusing on interference with neighboring and same-channel APs after channel selection of a target AP, which also contributes to the learning efficiency. To learn the optimal channel efficiently, this study investigates the most efficient method among the typical CMAB algorithms, including epoch-greedy, LinUCB, and Thompson sampling. The simulation results reveal that the algorithm based on JointLinUCB learns most efficiently under the environment where the access probabilities of the APs are extremely different.
Deep Markov Spatio-Temporal Factorization

We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latents inferred using stochastic variational inference. The innovation in DMSTF is that we parameterize weights in terms of a deep Markovian prior extendable with a discrete latent, which is able to characterize nonlinear multimodal temporal dynamics, and perform multidimensional time series forecasting. DMSTF learns a low dimensional spatial latent to generatively parameterize spatial factors or their functional forms in order to accommodate high spatial dimensionality. We parameterize the corresponding variational distribution using a bidirectional recurrent network in the low-level latent representations. This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal. Our experiments, which include simulated and real-world data, demonstrate that DMSTF outperforms related methodologies in terms of predictive performance for unseen data, reveals meaningful clusters in the data, and performs forecasting in a variety of domains with potentially nonlinear temporal transitions.