Miracle-dz/UniFAR-FTUs huggingface dataset:
Rank Reduction with Convex Constraints

This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as finite dimensional optimization problems that are convex, apart from a low-rank constraint. Traditional model reduction techniques try to minimize the error between the original and the reduced system. Typically, the resulting reduced models, however, no longer fulfill physically meaningful constraints. This thesis considers the problem of model reduction with internal and external positivity constraints. Both problems are solved by means of balanced truncation. While internal positivity is shown to be preserved by a symmetry property; external positivity preservation is accomplished by deriving a modified balancing approach based on ellipsoidal cone invariance.In essence, positivity preserving model reduction attempts to find an infinite dimensional low-rank approximation that preserves nonnegativity, as well as Hankel structure. Due to the non-convexity of the low-rank constraint, this problem is even challenging in a finite dimensional setting. In addition to model reduction, the present work also considers such finite dimensional low-rank optimization problems with convex constraints. These problems frequently appear in applications such as image compression, multivariate linear regression, matrix completion and many more. The main idea of this thesis is to derive the largest convex minorizers of rank-constrained unitarily invariant norms. These minorizers can be used to construct optimal convex relaxations for the original non-convex problem. Unlike other methods such as nuclear norm regularization, this approach benefits from having verifiable a posterior conditions for which a solution to the convex relaxation and the corresponding non-convex problem coincide. It is shown that this applies to various numerical examples of well-known low-rank optimization problems. In particular, the proposed convex relaxation performs significantly better than nuclear norm regularization. Moreover, it can be observed that a careful choice among the proposed convex relaxations may have a tremendous positive impact on matrix completion. Computational tractability of the proposed approach is accomplished in two ways. First, the considered relaxations are shown to be representable by semi-definite programs. Second, it is shown how to compute the proximal mappings, for both, the convex relaxations, as well as the non-convex problem. This makes it possible to apply first order method such as so-called Douglas-Rachford splitting. In addition to the convex case, where global convergence of this algorithm is guaranteed, conditions for local convergence in the non-convex setting are presented. Finally, it is shown that the findings of this thesis also extend to the general class of so-called atomic norms that allow us to cover other non-convex constraints. (Less)
High-density linkage mapping and genetic dissection of resistance to broomrape (Orobanche crenata Forsk.) in pea (Pisum sativum L.)

Pea (Pisum sativum L.) is a widely cultivated legume of major importance for global food security and agricultural sustainability. Crenate broomrape (Orobanche crenata Forsk.) (Oc) is a parasitic weed severely affecting legumes, including pea, in the Mediterranean Basin and the Middle East. Previously, the identification of the pea line “ROR12”, displaying resistance to Oc, was reported. Two-year field trials on a segregant population of 148 F7 recombinant inbred lines (RILs), originating from a cross between “ROR12” and the susceptible cultivar “Sprinter”, revealed high heritability (0.84) of the “ROR12” resistance source. Genotyping-by-sequencing (GBS) on the same RIL population allowed the construction of a high-density pea linkage map, which was compared with the pea reference genome and used for quantitative trait locus (QTL) mapping. Three QTLs associated with the response to Oc infection, named PsOcr-1, PsOcr-2, and PsOcr-3, were identified, with PsOcr-1 explaining 69.3% of the genotypic variance. Evaluation of the effects of different genotypic combinations indicated additivity between PsOcr-1 and PsOcr-2, and between PsOcr-1 and PsOcr-3, and epistasis between PsOcr-2 and PsOcr-3. Finally, three Kompetitive Allele Specific PCR (KASP) marker assays were designed on the single-nucleotide polymorphisms (SNPs) associated with the QTL significance peaks. Besides contributing to the development of pea genomic resources, this work lays the foundation for the obtainment of pea cultivars resistant to Oc and the identification of genes involved in resistance to parasitic Orobanchaceae.
Distributed Cluster Storage Technology of Massive Remote Sensing Data

With the rapid development of remote sensing information science and technology and computer Internet technology, remote sensing applications in various fields continue to expand and deepen. Remote sensing data is gradually developing towards global coverage, multiple data sources, multiple spatial resolutions and multi temporal spatial characteristics, and the amount of data is growing explosively. In this case, how to realize the rapid distribution and sharing services of massive remote sensing data has become an extremely concerned issue in the field of Remote Sensing Science and technology and remote sensing application departments. This paper studies the distributed cluster storage technology of massive remote sensing data and its application in the field of remote sensing. The research results will be used to develop a new generation of distributed cluster storage system, which can support large amounts of data, high-speed access and low cost.
DIVERSITY AND ABUNDANCE OF AMPHIBIANS IN THE LONDON 50 HA ENVIRONMENTAL COMPENSATION PROJECT, PANAMA

Panama has 230 species of amphibians, of which 72 (31.4%) are threatened according to national legislation and 19 are endemic. The study area has gallery forests where the samplings were carried out, we registered 15 amphibian species that represent 6.5% of those registered in the country.
The pattern of healthy diets and zero hunger among households in South-West Nigeria: application of linear approximation to quadratic almost ideal demand system

Malnutrition and food insecurity remain a significant public health challenge globally, affecting millions of people, particularly in low- and middle-income countries. In Nigeria, the prevalence of malnutrition and food insecurity is high, with a significant burden in the South-West region. Despite several interventions aimed at promoting healthy diets and reducing food insecurity, little is known about the pattern of healthy diets and zero hunger among households, as well as the budget shares on the food items driving these outcomes. Therefore, this study analyzes the pattern of healthy diets and zero hunger among households in South-West Nigeria, using the primary data elicited from 600 rice-consuming households, drawn through a multistage random sampling technique. Descriptive statistics and Linear Approximation to Quadratic Almost Ideal Demand System (LA-QUAIDS) model were used to analyze the dataset. The estimated LA-QUAIDS model was helpful to decipher the relationships that may not be possible to discover using a single-equation model. The finding of this study revealed that rice, especially local (Ofada) rice, is the most consumed food item among the households in the study area with a monthly budget share of 0.195. This is an indication that households spend approximately 19.5% of their monthly food expenditure share on rice consumption. The results also indicated that yam flour (−0.10), cassava-gari (−2.12), and meat (−0.03) are net complements to rice, while the rest of the food items are regarded as net substitutes. The estimated compensated (−1.64) and uncompensated (−1.69) own-price elasticities of local rice, respectively, indicate that local rice variety is price inelastic, and the estimated expenditure elasticity (0.02) suggests that local rice is not a luxury food in the household food basket but a necessity. This study affirms the importance of locally produced foods, especially local rice to the households in the study area. In light of the findings, this study recommends adequate policy aimed at reducing the cost of local food production by boosting domestic production. Apparently, this will technically trigger market price adjustment, as shown in this study, where local rice was estimated to be own-price elastic in nature.
Hopf polyads, Hopf categories and Hopf group monoids viewed as Hopf monads

We associate, in a functorial way, a monoidal bicategory $\mathsf{Span}| \mathcal V$ to any monoidal bicategory $\mathcal V$. Two examples of this construction are of particular interest: Hopf polyads (due to Brugui\`eres) can be seen as Hopf monads in $\mathsf{Span}| \mathsf{Cat}$ while Hopf group monoids in a braided monoidal category $V$ (in the spirit of Turaev and Zunino), and Hopf categories over $V$ (by Batista, Caenepeel and Vercruysse) both turn out to be Hopf monads in $\mathsf{Span}| V$. Hopf group monoids and Hopf categories are Hopf monads on a distinguished type of monoidales fitting the framework studied recently by B\"ohm and Lack. These examples are related by a monoidal pseudofunctor $V\to \mathsf{Cat}$.
SEMC-Net: A Shared-Encoder Multi-Class Learner

Brain tumour segmentation is a crucial task in medical imaging that involves identifying and delineating the boundaries of tumour tissues in the brain from MRI scans. Accurate segmentation plays an indispensable role in the diagnosis, treatment planning, and monitoring of patients with brain tumours. This study presents a novel approach to address the class imbalance prevalent in brain tumour segmentation using a shared-encoder multi-class segmentation framework. The proposed method involves training a single encoder class learner and multiple decoder class learners, which are designed to learn feature representation of a certain class subset, in addition to a shared encoder between them that extracts common features across all classes. The outputs of the complement-class learners are combined and propagated to a meta-learner to obtain the final segmentation map. The authors evaluate their method on a publicly available brain tumour segmentation dataset (BraTS20) and assess performance against the 2D U-Net model trained on all classes using standard evaluation metrics for multi-class semantic segmentation. The IoU and DSC scores for the proposed architecture stands at 0.644 and 0.731, respectively, as compared to 0.604 and 0.690 obtained by the base models. Furthermore, our model exhibits significant performance boosts in individual classes, as evidenced by the DSC scores of 0.588, 0.734, and 0.684 for the necrotic tumour core, peritumoral edema, and the GD-enhancing tumour classes, respectively. In contrast, the 2D-Unet model yields DSC scores of 0.554, 0.699, and 0.641 for the same classes, respectively. The approach exhibits notable performance gains in segmenting the T1-Gd class, which not only poses a formidable challenge in terms of segmentation but also holds paramount clinical significance for radiation therapy.
Automated Vertebrae Diagnosis in Spinal X-ray Images using Artificial Intelligence

Spinal diseases affect millions of people worldwide, and early diagnosis is crucial for better treatment outcomes. However, the traditional methods of diagnosis can be time-consuming and inaccurate. Therefore, it is crucial to have a fast and accurate diagnosis of the spine to prevent potential problems that can arise if the condition is not treated on time. This study focuses on using a deep neural network that has been trained on an X-ray dataset to create an efficient and cost-effective framework for classifying spinal vertebrae X-ray scans. The proposed framework extracts feature from multiple layers using a lightweight deep neural network to diagnose the spine health, followed by three fully connected layers of classification for the retrieved information. The proposed framework achieved an accuracy of 91.33% on the validation set, and 92.22% on the test set, indicating the robustness of the proposed architecture. A comparison of the proposed model with other deep neural networks is proved its efficiency based on the results of the experiments. This will be beneficial in developing a diagnostic framework that can be easily adapted to low-cost computers, resulting in faster processing of image frames containing various spine vertebrae x-ray images, with low-time complexity. Further research is needed to validate our approach and integrate it into clinical practice.
Association between Phase Angle and Subjective Perceptions of Health Variables in Cancer Patients

The phase angle, an increasingly studied healthcare tool, was studied to explore its relationship with psychological factors in cancer patients. The aim of this study was to investigate the relationship between the phase angle (PhA), obtained by the bioimpedance analysis of body composition, and psychological factors measured by questionnaire in cancer patients. The study included 311 patients who underwent bioimpedance testing to determine their PhA value; their psychological profiles were assessed using SF-36, FACIT, QLQ-C30, and GHQ-12 questionnaires. Mixed linear regression models were used to analyze the relationship between PhA and the psychological tests. The results showed a statistical correlation between PhA and the GHQ-12, FACIT, and SF-36 questionnaires, with higher PhA values associated with better results on the questionnaires. In the QLQ-C30 questionnaire, a correlation was observed between PhA and the functioning scales (p < 0.001), except for emotional and cognitive functioning (p = 0.148 and p = 0.544, respectively), but not in most of the symptom scales. The PhA is a useful tool for assessing the subjective health perception of cancer patients, especially with regard to psychological factors. While there is a statistically significant correlation, further research is required before confidently applying it in clinical practice. The current predictive value of this predictor for certain psychological aspects is limited, underscoring the need for additional research.
Films of Poly(Hydroxybutyrate) (PHB) and Copper with Antibacterial Activity

Poly(3-hydroxybutyrate), PHB, is a hydrophobic biopolymer with good mechanical and barrier properties. However, neat PHB is a semicrystalline polymer with a relative high degree of crystallinity and poor film properties. In this work, this biopolymer was plasticized with glycerol tributyrate and functionalized with copper (II) sulfate, allowing us to obtain biodegradable antimicrobial flexible films. Films with the minimum inhibitory concentration (MIC) of copper (II) sulfate presented a higher roughness than neat PHB films. The presence of plasticizer significantly improved the copper sulfate diffusion process, which was evidenced by a greater inhibition halo for plasticized materials compared to unplasticized ones, at the same salt concentration. Plasticized PHB with 2.5% copper (II) sulfate inhibited both Gram-positive (Staphylococcus aureus) and Gram-negative (Pseudomona aeruginosa) bacteria, as determined by the bacterial inhibition halo. In addition, neat PHB films and PHB containing copper (II) sulfate did not show in vitro cytotoxicity in the L-929 cell line. Thus, plasticized PHB functionalized with copper (II) sulfate can be used as biodegradable antimicrobial flexible films for different applications.
Proximal ADMM with larger step size for two-block separable convex programming and its application to the correlation matrices calibrating problems

The alternating direction method of multipliers (ADMM) is a benchmark for solving two-block separable convex programming. However, as other first-order iteration methods, the ADMM also suffers from low convergence. In this paper, to accelerate the convergence of the ADMM, the restriction region of the Fortin and Glowinski’s constant γ in the ADMM is relaxed from ( 0, 1+ √ 5 2 ) to (0,+∞), thus we get a proximal ADMM with larger step size. By proving some properties of the method, we show its global convergence under mild conditions. Finally, some numerical experiments on the correlation matrices calibrating problems are given to demonstrate the efficiency and the performance of the new method. c ©2017 All rights reserved.
Selection of Powdery Mildew Resistant Brassica Genotypes Based on Disease Indexing and Microsatellite Markers

Powdery mildew disease of oilseed mustard caused by Erysiphe cruciferarum is a primary reason of yield reduction not only in India but also throughout the world. Identification and cultivation of resistant mustard genotypes against powdery mildew is the only way to overcome this challenge. In the present investigation, we targeted to screen 75 Brassica genotypes against powdery mildew based on disease indexing under field conditions and gene-specific molecular markers. Disease reaction on both the cotyledonary and true leaves was screened with using a modified 0-9 scale score as well as with nineteen disease linked microsatellite markers. In disease indexing under field conditions, genotypes viz., L-4 and PC-5 were identified as immune, China and RP-9 were considered as highly resistant and GSC-7 and PC-6 were recognized as resistant whilst genotypes i.e., RB-50, Pusa Bold, WRR-10 and GSL-1 were accredited as moderately resistant. Molecular markers based UPGMA dendrogram classified Rohini, WRR-22, PC-6, PusaBold, China, WRR-8, GSL-1, WRR-7, RH-749, L-4 and RB-50 as highly resistant mustard genotypes. In addition, disease linked marker cnu_m616 had the highest polymorphic information content (0.75) with greatest ability to differentiate resistant genotypes from susceptible genotypes, may be employed directly in mustard breeding programmes in future.
Bone Morphogenic protein-2 in the osteogenic/odontogenic differentiation of stem cells from apical papilla: A systematic review

The interaction between stem cells and growth factors plays a major role in the regeneration of the pulp-dentin complex. Studies have reported the potential role of Bone Morphogenic Proteins (BMPs) in the growth and differentiation of cells. The purpose of the present systematic review was to analyze the role of Bone Morphogenic Protein-2 (BMP-2) in the osteogenic/odontogenic differentiation of Stem cells from apical papilla (SCAP). The search was conducted across PubMed, Science Direct, Cochrane Library, and Google Scholar. In-vivo and in-vitro studies published between 2000 and 2022 were evaluated for the outcome. The quality assessment of the studies was done using the QUIN assessment tool. After applying the keywords, and filters along with inclusion & exclusion criteria, a total of six articles were selected for the present systematic review, all of which were in-vitro studies. The studies reported that BMP-2 significantly enhanced the osteogenic/odontogenic potential of SCAP cells. Moreover, BMP-2 could act synergistically with various other factors to bring about the desired effect. Being a potential growth factor in the regeneration of the pulp-dentin complex, the application of BMP-2 can be extended to the clinical scenario for various cell-free and cell-based regenerative approaches.
Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets

Drone fleets with onboard cameras coupled with DNN inferencing models can support diverse applications, from infrastructure monitoring to package deliveries. Here, we propose to use one or more “buddy” drones to help Visually Impaired People (VIPs) lead an active lifestyle. Video inferencing tasks from such drones are used to navigate the drone and alert the VIP to threats, and hence have strict execution deadlines. They have a choice to execute either on an accelerated edge like Nvidia Jetson linked to the drone, or on a cloud INFerencing-as-a-Service (INFaaS). However, making this decision is a challenge given the latency and cost trade-offs, and network variability in outdoor environments. We propose a deadline-driven heuristic to schedule a stream of diverse DNN inferencing tasks executing over video segments generated by multiple drones linked to an edge, with the option to execute on the cloud. We use strategies like task dropping, work stealing and migration, and dynamic adaptation to cloud variability, to fully utilize the captive edge with intelligent offloading to the cloud, to maximize the utility and the number of tasks completed. We evaluate our strategies using a setup that emulates a fleet of > 50 drones within city conditions supporting> 25 VIPs, with real DNN models executing on drone video streams, using Jetson Nano edges and AWS Lambda cloud functions. Our detailed comparison of our strategy exhibits a task completion rate of up to 91 %, up to 2.5× higher utility compared to the baselines and 68% higher utility with network variability.
Switched-Reluctance Motor Drive for More Electric Aircraft With Energy Storage Buffer

This article develops an switched-reluctance motor (SRM) drive for more electric aircraft (MEA) with energy storage buffer. The SRM drive is powered from the MEA electric power architecture (EPA) dc-bus by the aircraft synchronous generator (SG) via a boost switch-mode rectifier (SMR). The battery energy storage system (BESS) is connected to the dc-bus through a bidirectional boost–buck converter for providing energy support and smoothing the required driving and regenerative braking energies of motor loads. The BESS possesses satisfactory discharging/charging performances from the EPA and can absorb the SRM regenerative braking energy. The SRM drive energy can be supplied from the SG, the battery, or both with good power sharing characteristics. Satisfactory SRM driving performance is achieved via the properly designed commutation scheme, and current and speed controllers. The commutation shift approaches for enhancing winding current response and reducing the SRM vibrations under healthy and faulted cases proposed. Both current and speed responses are improved by applying the simple robust controls, and the adequate affairs are arranged to achieve the effective regenerative braking operation. All the power stages and whole system are evaluated experimentally. The efficiencies of the SRM drive are assessed in detail to verify its potential. The developed SRM drive can be used as various actuators in an MEA.
Circuit-Level Optimizations for Cryptography; True Random Number Generators: Design, Evaluation and Testing

Embedded electronics is now an essential companion in our daily lives. In today’s time, it is hard to find a person who doesn’t use cell phones, smart-cards and RFID tags on a daily basis. As more and more aspects of our private and professional lives continue to rely on embedded devices, security becomes a major concern. In this work, we look into several aspects of hardware security. The major part of this thesis is dedicated to true random number generators (TRNGs). Our contributions to this field include designing a novel all-digital entropy source and developing a methodology for efficient online testing. Part of the work was dedicated to experimental evaluation of physically unclonable functions (PUFs) – cryptographic primitives used for device authentication and countermeasures against device counterfeiting. Side channel attacks such as power-analysis are another significant threat to embedded security. Circuit-level countermeasures against the power analysis were applied in designing an embedded processor for elliptic curve cryptography (ECC) and in designing a side-channel secure SRAM-cell.