Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the Exact Local Convergence Rate of Sequential Convex Programming

Sequential Convex Programming (SCP) is an iterative algorithm for solving Nonlinear Programs (NLP) with "convex-over-nonlinear" substructure. At every iteration it solves a convex, but generally nonlinear, approximation to the original NLP, exploiting its outer convexities. It is already known that SCP has linear local convergence, though without a tight characterization of the rate. Sequential Convex Quadratic Programming (SCQP) is another "convex-over-nonlinear" substructure exploiting algorithm, for which a tight characterization of the convergence rate has already been obtained. In this paper we show under mild assumptions that in fact both methods have the same local linear convergence - or divergence - rate. We can therefore determine the convergence rate of SCP, which is tightly characterized by two simple matrix inequalities evaluated at the solution. We further reason why - as a heuristic - SCP should in general show more robust global convergence than SCQP. We illustrate this, as well as the local convergence rate, with a numerical example.
Personal Cloud Computing Adoption: Integrating IT Mindfulness with TAM

While extant literature is replete with studies on cloud computing adoption by organizations, there is a dearth of empirical literature on personal cloud computing adoption. This study aims at understanding how individuals adopt cloud computing within the Kenyan context. Specifically, the study integrates IT Mindfulness (ITM) with TAM in order to understand Intention to Adopt (ITA) personal cloud storage services. A priori model consisting of ITM, TAM and ITA variables was constructed. Structural Equation Modelling (SEM) was used with a convenient sample of 98 students drawn from two public universities to test and validate the model. The results indicate significant positive relationships amongst 1) ITM and ITA, 2) ITM and Perceived Usefulness (PU) and 3) PU and Perceived Ease of Use(PEU). The study, by introducing IT Mindfulness, enriches the theoretical repertoire of IT innovation adoption literature. The cloud services vendors will find the results useful in understanding how personal traits determine adoption.
Symbolic Abstractions for Periodic Event-triggered Linear Control Systems

This paper studies the construction of symbolic abstractions for periodic event-triggered systems. To construct symbolic abstractions, the original event-triggered mechanism is over- and under-approximated, and thus the abstract event-triggered mechanisms are different from the original one, which leads to the asynchronous phenomenon between the original system and the constructed symbolic abstractions. To deal with this issue, an interface is proposed to guarantee the synchronization between the original and abstract event-triggering mechanisms and the equivalence relations between the original system and the constructed symbolic model. Furthermore, we study the controller refinement based on these two constructed symbolic models. Finally, the obtained results are illustrated via a numerical example.
UWB Channel Characterization for Wireless Capsule Endoscopy Localization

Wireless capsule endoscopy (WCE) is a medical device for the inspection of some parts of the gastrointestinal (GI) tract. Currently, WCE transmits to the outside low resolution video, although high quality images would be necessary for improving the detection of diseases. Ultra wideband (UWB) frequency band is a good candidate to achieve such requirement due to its large available bandwidth. Besides, the knowledge of the exact location of WCE would also help physicians to locate the suspicious tissue so diagnosis time could be shortened. Received Signal Strength (RSS) based localization is one of the simplest localization techniques. However, RSS localization requires a good knowledge of the path loss model, what is not easy for in-body communications. The objective of this paper is to provide a review of the problem of modelling the path loss for WCE in UWB as well as give implementation guidelines for WCE localization.
Indicators of Malnutrition in Under 5 Pakistani Children: A DHS Data Secondary Analysis

Objective: To assess the rates of stunting, wasting and underweight in under 5 Pakistani children and identify their associated indicators. Methodology: Data was retrieved from DHS program website for sub-analysis. The outcome variables were constructed according to WHO standards for malnutrition indicators. The three study outcomes of stunting, wasting and underweight were defined on the basis of -2 SD levels of the standard corresponding Z-scores for age of children. STATA 10.0 software was used for measurements of final dataset of 3071 children. Results: High prevalence of stunting, wasting and underweight (45.1%, 10.4% and 26.7% respectively) was identified in this study. Maternal and fathers secondary or above education, health facility delivery, cesarean section and rich SES children have less odds of becoming stunted. Whereas children of older mothers, child age above 2 years, small birth size, 5 or above birth order, rural residence with no toilet facility had greater odds to develop stunting. Similar characteristics of children with underweight were identified; however, very few indicators for wasting were identified. Conclusion: A very high number of under 5 Pakistani children are malnourished. Health budget should be increased and new facilities for educating and counseling patients are required to prevent malnutrition.
Long term water stress inactivates Rubisco in subterranean clover

In long-term field experiments, during consecutive years, microswards of subterranean clover were irrigated to minimise water deficits or subjected to progressively increasing drought over 30 days. Both leaf water potential and relative water content steadily decreased during the experiments. Plants affected by drought grew more slowly and photosynthesis was decreased. Photosynthetic rate (A) and Rubisco were analysed in relation to midday water potentials and relative water contents. The difference in A between droughted and irrigated plants increased progressively, in part as a result of decreased stomatal conductance and CO2 concentration within leaf (Ci). However, A-Ci curves suggest that the photosynthetic capacity in plants experiencing long-term stress was reduced by 50% when compared with irrigated plants. Drought decreased both the initial and the total Rubisco activity per unit area in a similar way but did not reduce the amount of Rubisco protein per unit leaf area. Thus, the specific activity of Rubisco, rather than its activation state, decreased suggesting that under water stress the active sites were blocked by inhibitors.
Numerically Implementing the API Based Solution to Learn a Plant Dynamics for Stochastic Control Concurrently

This paper considers the case of filters acting in a control system subject to a priori parameter uncertainty and abrupt changes. The Original (theoretical) Performance Index (OPI), which is the mean-squared state prediction error, is originally inapplicable: it cannot be calculated in practice for filter optimization purposes. Meeting the challenges, we construct the Auxiliary Performance Index (API) with the following advantages: 1) it can be calculated from the available data; 2) it has the same minimizer as the original criterion. Having the same minimizer is called "equimodality" between OPI and API. Such a solution makes it possible to apply all the practical methods of optimization to find a minimum of the OPI by calculating the minimum of API. Essentially, that it does this job with no bias in parameter estimates. The API-based solution is critically distinct from the well-known Minimum Prediction Error identification approach because the latter defines the minimized prediction error as the difference between the system (noisy) output and predictor output, and it is not what the OPI means at all.
Individual and interactive role of Trichoderma and Mycorrhizae in controlling wilt disease and growth reduction in Cajanus cajan caused by Fusarium udum

The aim of present study was to investigate the effect of individual and co-inoculation of Trichoderma and arbuscular mycorrhizal fungi (AMF) in controlling wilt disease and growth reduction in Cajanus cajan (L.) Millsp. caused by Fusarium udum. In this regard, three different isolates of Trichoderma, i.e. Trichoderma harzianum, T. viride and Th0126K, and consortium of AMF (Myc, mixture of Glomus cerebriforme, G. intraradices and G. mosseae) individually and in combinations were tested in net house. Among all the treatments tested, co-inoculation of T. harzianum and Myc gave maximum growth, however inoculation of Myc alone was sufficient for growth promotion (p < 0.05). Fusarium considerably reduced all the parameters studied, except dry weight and enhanced disease severity. Results clearly showed that different isolates of Trichoderma produced varied results with Myc. Thus, it can be stated that soil must be pretreated with biocontrol agents to achieve enhanced growth and effective protection against the Fusarium.
Data Informativity for Observability: An Invariance-based Approach

Observability is a central concept within systems and control. In particular, many solutions to control problems have observability as a prerequisite. This paper is interested in characterizing observability in the case where the system is not fully known, but where state measurements are available only. As these measurements may not uniquely determine the system, we provide necessary and sufficient conditions under which all systems consistent with the measurements are observable.
A Single-Shot, Pixel Encoded 3D Measurement Technique for Structure Light

The Structure Light System (SLS) is a general concept and it is one of the cheapest methods for the non-contact-based 3D reconstruction. The existing single-shot SLS which is primarily based on the spatial encoding techniques are not optimal in terms of resolution and digitally encoded patterns. Those schemes are not flexible, controllable, and designed up to the level of the pixel. So, to increase the resolution and to implement a flexible controllable pattern we proposed a novel heuristic method based on the spatial neighborhood. In this paper, we propose a multi-resolution SLS which can be implemented with a set of 25 geometrical shaped distinct symbols or alphabets to use in the projection pattern as shape primitive. The size of each symbol is well defined in pixels which enabled us to have access and control up to the full resolution of the projector. The shape descriptive parameters for each symbol or alphabet are also defined and computed. To spread the alphabets in a controllable manner, a method is defined to generate a robust pseudo-random sequence of any required size with a certain number of alphanumeric bases, to be employed in the projection pattern concerning the measured resolution. This arrangement will enable us to design the projection patterns according to the required surface area and the resolution. A new technique is developed for the decoding of the captured image pattern. The decoding process depends upon the classification of symbols which is based on shape descriptive parameters. The searching in the neighborhood of a symbol is carried out through computing the location information, grid distance, and direction information to find the codewords which are used to establish the correspondence.
A Mobile Ad-hoc Strategy to Enhance ICT Based Education in Zimbabwean Rural Schools

The research was motivated by an existing digital disparity between a rural and an urban learner, hence the need to adopt a strategy to counteract problems inherent in the rural schooling environment. Hence, the objectives of the study were to find out the adoption rate of the ICTs in rural schools, as well as the importance of ICTs in rural schooling environments. In addition, the case study sought to ascertain challenges being experienced in such learning environment and in that regard identify solutions to mitigate the identified challenges. The research was based on a qualitative research methodology using a case study strategy. Among other findings, the case study results unearthed the lack of ICT infrastructure and/ or resources, financial incapacitation of rural schools, national economic downturn, government policy inconsistence and lack of in-service training. However, some schools were ready to adopt ICT as evidenced by presence of ICT teachers and somewhat very constrained resources. To circumvent the bottlenecks identified in the rural education system, the study recommended a mobile ad hoc computing strategy, which could provide a somewhat cost-effective mobile learning environment.
Spatially-Correlated Hardware-Impaired Massive MIMO FD Relaying with MIMO Users

A practical massive multi-input multi-output (mMIMO) relay, due to limited antenna spacing and the use of cost-effective equipment, usually experiences spatially correlated channels and hardware impairments, respectively. We consider a hardware-impaired two-way full-duplex (FD) spatially-correlated mMIMO relaying with multi-antenna FD user-pairs, and derive a closed-form spectral efficiency (SE) expression which is valid for a practical number of finite relay antennas. We next use this closed-form SE result to obtain asymptotic hardware and power scaling laws, which are not present in the existing literature. We also numerically validate the closed-form SE expression and study the impact of spatial correlation and hardware impairments on SE.
Can low sea urchin densities control macro-epiphytic biomass in a north-east Atlantic maerl bed ecosystem (Bay of Brest, Brittany, France)?

The distribution, abundance and feeding behaviour of sea urchins (Paracentrotus lividus, Psammechinus miliaris, Sphaerechinus granularis) and the macro-algal biomass were assessed in two maerl beds within a north-east Atlantic coastal ecosystem (Bay of Brest, Brittany, France). To study the possible control of herbivorous sea urchins on the macrophytic algae, one maerl bed under the influence of urban sewage (northern site) was compared to one less influenced by urban and industrial outlets (southern site). Macrophytic biomass in the northern site was estimated as 40-fold higher than in the southern site where sea urchins were, on average, 38-times more numerous. Preliminary results support the hypothesis that grazing of sea urchin, even in low densities, can be a factor regulating the macrophytic biomass on maerl beds except in too nutrient enriched environments. So in the northern basin of the Bay, data pointed out the role of anthropogenic impacts on macrophytic biomass increase which was concomitant with the progressive disappearance of sensitive herbivorous species like sea urchins, both processes result in change in the ecosystem.
Functional leaf anatomy of plants with crassulacean acid metabolism.

Crassulacean acid metabolism (CAM) has evolved independently on dozens of occasions and is now found in over 7% of plant species. In this study, the leaf structure of a phylogenetically diverse assemblage of 18 CAM plants was compared with six C3 plants and four C4 plants to assess whether consistent anatomical patterns that may reflect functional constraints are present. CAM plants exhibited increased cell size and increased leaf and mesophyll thickness relative to C3 and C4 species. CAM species also exhibited reduced intercellular air space (IAS) and reduced length of mesophyll surface exposed to IAS per unit area (Lmes / area). The low volume of IAS and low exposure of mesophyll surface to IAS likely increases internal resistance to CO2 in CAM tissues. While this diffusional barrier may limit uptake of CO2 during Phases II and IV, carbon economy could be enhanced through the reduced loss of internal CO2 during all four phases of CAM.
Susceptibility of Iberian trees to Phytophthora ramorum and P. cinnamomi

The capacity of Phytophthora ramorum to colonize the inner bark of 18 native and two exotic tree species from the Iberian Peninsula was tested. Living logs were wound-inoculated in a growth chamber with three isolates belonging to the EU1 and two to the NA1 clonal lineages of P. ramorum. Most of the Quercus species ranked as highly susceptible in experiments carried out in summer, with mean lesion areas over 100 cm2 in Q. pubescens, Q. pyrenaica, Q. faginea and Q. suber and as large as 273 cm2 in Q. canariensis, ca. 40 days after inoculation. Quercus ilex ranked as moderately susceptible to P. ramorum, forming lesions up to 133 cm2 (average 17·2 cm2). Pinus halepensis and P. pinea were highly susceptible, exhibiting long, narrow lesions; but three other pine species, P. pinaster, P. nigra and P. sylvestris, were resistant to slightly susceptible. No significant difference in aggressiveness was found between the isolates of P. ramorum. In addition, there was evidence of genetic variation in susceptibility within host populations, and of significant seasonal variation in host susceptibility in some Quercus species. The results suggest a high risk of some Iberian oaks to P. ramorum, especially in forest ecosystems in southwestern Spain, where relict populations of Q. canariensis grow amongst susceptible understory species such as Rhododendron ponticum and Viburnum tinus. One isolate of P. cinnamomi used as positive control in all the inoculations was also highly aggressive to Iberian oaks and Eucalyptus dalrympleana.
Sensor Fault Identification in Linear Dynamic Systems *

The paper studies the problem of sensor fault identification in linear control systems under the disturbances. To address the problem, sliding mode observers are used. The suggested approach for constructing sliding mode observer is based on the reduced order model of the initial system. This allows to reduce complexity of sliding mode observer and relax the conditions imposed on the initial system.
Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it’s curable at an initial stage to reduce the patient’s mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.
Labour markets and wages in Australia in 2012

During 2012, the labour market continued to show considerable diversity in outcomes for different labour market groups. Employment growth was slower and the number of employees searching for full-time work rose alongside falling participation rates compared with the previous year. Overall, the employment situation for men was not looking as strong as for women, although women continued to exhibit higher levels of labour underutilisation. Earnings indicators suggest increased wages in low-paid sectors, although this was coming off a low base and may be indicative of catch-up for slow growth in recent years. The relative value of the minimum wage is now at its lowest level in six years, suggesting some evidence of growing earnings inequality. Recent debates in the mass media about labour productivity and industrial relations regulation appear to have limited grounding in national accounting and labour market data.
Optimizing CNN's using Cumulative Learning with Auxiliary Networks for Traffic Light Recognition

The number of fatal victims due to traffic accidents is frightening. Neural networks are already used to try to minimize this problem, however there is still an open task training those networks with maximum effectiveness. This work proposes an extension of the concept of Multiple Self-coordinated Neural Networks applied to convolutional neural networks in order to recognize traffic lights. The idea is to present a method that performs self-improvements in the network and avoid local minimums in new training. The proposed method can contribute by reducing the time spent on training in choosing hyperparameters. The preliminary results were promising, reaching 95.33% of accuracy and opening the range of new research that can be carried out in this area.
Fish Shoals Behavior Detection Based on Convolutional Neural Network and Spatiotemporal Information

Behavior is the first visible change in an animal species after exposure to its own or environmental stressors and is a sensitive indicator. Fish are social animals, and the abnormality of group behavior is more indicative about a particular event than individual behavior, providing more effective informeqation about environmental or group social changes. The group behavior is not only reflected in the spatial distribution, but also reflected in the temporal behavior of the group and individual movement changes under the influence of pressure factors. This paper proposes a group behavior discrimination method based on convolutional neural network and spatiotemporal information fusion, which intends to make use of the prominent performance of convolutional neural network in image recognition and state classification, and imitating the attentional mechanism of ventral channel and dorsal channel when the human brain processes visual signals. Some pressure environments are made in laboratory, the behavior states of fish shoals are recorded, and the sample database of shoals’ behavior state is established by combining the spatial information of shoals’ spatial distribution with the time information reflected in the movement behavior. A simple convolutional neural network is constructed to quickly identify the behavior state of fish shoals. The effects of bath size and training epoch on network training speed and recognition accuracy are discussed, and the visualization of the intermediate data of the convolutional neural network is studied. Shown from the results of experiments of this paper, different behavior states of fish shoals can be recognized and classified effectively by using the simple convolutional neural network and spatiotemporal fusion images. What’s more, from the visualization of network intermediate data, it is found that the convolutional neural network has a higher discrimination power to the image edge feature than the image gray-value feature.