Sampling and Subspace Methods for Learning Sparse Group Structures in Computer Vision

The unprecedented growth of data in volume and dimension has led to an increased number of computationally-demanding and data-driven decision-making methods in many disciplines, such as computer vision, genomics, finance, etc. Research on big data aims to understand and describe trends in massive volumes of high-dimensional data. High volume and dimension are the determining factors in both computational and time complexity of algorithms. The challenge grows when the data are formed of the union of group-structures of different dimensions embedded in a high-dimensional ambient space. To address the problem of high volume, we propose a sampling method referred to as the Sparse Withdrawal of Inliers in a First Trial (SWIFT), which determines the smallest sample size in one grab so that all group-structures are adequately represented and discovered with high probability. The key features of SWIFT are: (i) sparsity, which is independent of the population size; (ii) no prior knowledge of the distribution of data, or the number of underlying group-structures; and (iii) robustness in the presence of an overwhelming number of outliers. We report a comprehensive study of the proposed sampling method in terms of accuracy, functionality, and effectiveness in reducing the computational cost in various applications of computer vision. In the second part of this dissertation, we study dimensionality reduction for multi-structural data. We propose a probabilistic subspace clustering method that unifies soft- and hard-clustering in a single framework. This is achieved by introducing a delayed association of uncertain points to subspaces of lower dimensions based on a confidence measure. Delayed association yields higher accuracy in clustering subspaces that have ambiguities, i.e. due to intersections and high-level of outliers/noise, and hence leads to more accurate self-representation of underlying subspaces. Altogether, this dissertation addresses the key theoretical and practically issues of size and dimension in big data analysis

Probabilistic Sparse Subspace Clustering Using Delayed Association

Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Similar to some recent works, we integrate these two steps using a joint optimization approach. We make the following contributions: (i) we estimate the reliability of the cluster assignment for each point before assigning a point to a subspace. We group the data points into two groups of "certain" and "uncertain", with the assignment of latter group delayed until their subspace association certainty improves. (ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i.e. when subspaces intersect or data are contaminated with outliers/noise. (iii) We demonstrate experimentally that such delayed probabilistic association leads to a more accurate self-representation and final clusters. The proposed method has higher accuracy both for points that exclusively lie in one subspace, and those that are on the intersection of subspaces. (iv) We show that delayed association leads to huge reduction of computational cost, since it allows for incremental spectral clustering

Managing University of Sharjah Setting and Infrastructure Towards a Sustainable and Livable Campus

This paper describes the setting and infrastructure management at the University of Sharjah (UoS) as a continuous effort towards a livable and sustainable campus. The UoS has been participating in the UI GreenMetric World Universities Ranking (UIGWUR) since 2017 to measure its performance in the field of sustainability for continuous improvement. During the last three years, the UoS has succeeded in being among the best 150 universities in the SI category by achieving 70% of the score. However, the UoS managed to get 75% of the total score in this KPI and 100% in the open space per person KPI ratio. To become one of the leading universities, the Landscape and Building Management Sustainability Circle (LBMSC) at the Sustainability Office has analyzed the KPIs and suggested an action plan for continuous improvement. Two KPIs can be improved: sustainability efforts and the total area covered in plants. The UoS shall increase the sustainability efforts and budget and increase the internal and external planting in the coming years. For some KPIs, it cannot be applied to desert regions. It is recommended that the UIGWUR revisit its KPIs and make them more flexible and applicable worldwide. Furthermore, for the open space ratio to the total area KPI, it is recommended to revisit the distribution of the points to have fair comparison. Action plans to improve the sustainability and livability of the campus have also been addressed.Keyword: Setting and infrastructure, LBMSC, livable campus, open area, forest, water absorption, green area, sustainability efforts/budget, and GreenMetri

Common polymorphisms in genes related to vitamin D metabolism affect the response of cognitive abilities to vitamin D supplementation

It is possible that vitamin D acts as a neurosteroid and that vitamin D deficiency may have an adverse impact on brain function and cognitive function. There are a few reports that have demonstrated an association between polymorphisms of genes involved in vitamin D metabolism and neurodegenerative disease. We aimed to evaluate the relationship between common, functional vitamin D–associated gene variants and cognitive abilities and to investigate the effect size of this polymorphism on cognitive capabilities associated with high-dose vitamin D supplementation. A total of 319 healthy adolescents received a high dose of vitamin D (50,000 IU)/week for 9 weeks. A questionnaire was used to assess cognitive abilities at baseline and after treatment. The genotypes of the CYP2R1-rs10766197 and GC-rs4588 variants were determined using TaqMan genotyping techniques. At baseline, total cognitive ability scores were higher in the AA group who were homozygous for the uncommon allele, compared with the other (AG and GG) genotypes of the CYP2R1-rs10766197 polymorphism (104.9 ± 27.8 vs. 79.1 ± 38.8 vs. 73.1 ± 25.6; p < 0.001, respectively). During the supplementation period, cognitive ability scores increased in individuals with the AG and GG genotypes, while individuals with a AA genotype did not show significant change in total score after intervention (p = 0.17). For GC SNP (rs4588), no major differences at baseline and trial-net change of cognitive tasks score were observed between the genotypes under three genetic models (pSNP = 0.67). Vitamin D supplements have trait-dependent effects on cognitive performance that suggests a causal role for vitamin D in cognitive performance. The rs10766197 variant, near the CYP2R1 gene locus, significantly modified the efficacy of high-dose vitamin D3 supplementation for its effects on improving cognitive abilities indicate that some subjects might require a higher dose to benefit from in terms of cognitive performance

The Hospitalization Rate of Cerebral Venous Sinus Thrombosis before and during COVID-19 Pandemic Era: A Single-Center Retrospective Cohort Study

Objectives: There are several reports of the association between SARS-CoV-2 infection (COVID-19) and cerebral venous sinus thrombosis (CVST). In this study, we aimed to compare the hospitalization rate of CVST before and during the COVID-19 pandemic (before vaccination program). Materials and methods: In this retrospective cohort study, the hospitalization rate of adult CVST patients in Namazi hospital, a tertiary referral center in the south of Iran, was compared in two periods of time. We defined March 2018 to March 2019 as the pre-COVID-19 period and March 2020 to March 2021 as the COVID-19 period. Results: 50 and 77 adult CVST patients were hospitalized in the pre-COVID-19 and COVID-19 periods, respectively. The crude CVST hospitalization rate increased from 14.33 in the pre-COVID-19 period to 21.7 per million in the COVID-19 era (P = 0.021). However, after age and sex adjustment, the incremental trend in hospitalization rate was not significant (95% CrI: -2.2, 5.14). Patients \u3e 50-year-old were more often hospitalized in the COVID-19 period (P = 0.042). SARS-CoV-2 PCR test was done in 49.3% out of all COVID-19 period patients, which were positive in 6.5%. Modified Rankin Scale (mRS) score ≥3 at three-month follow-up was associated with age (P = 0.015) and malignancy (P = 0.014) in pre-COVID period; and was associated with age (P = 0.025), altered mental status on admission time (P\u3c0.001), malignancy (P = 0.041) and COVID-19 infection (P = 0.008) in COVID-19 period. Conclusion: Since there was a more dismal outcome in COVID-19 associated CVST, a high index of suspicion for CVST among COVID-19 positive is recommended

Metformin inhibits polyphosphate-induced hyper-permeability and inflammation

Circulating inflammatory factor inorganic polyphosphate (polyP) released from activated platelets could enhance factor XII and bradykinin resulted in increased capillary leakage and vascular permeability. PolyP induce inflammatory responses through mTOR pathway in endothelial cells, which is being reported in several diseases including atherosclerosis, thrombosis, sepsis, and cancer. Systems and molecular biology approaches were used to explore the regulatory role of the AMPK activator, metformin, on polyP-induced hyper-permeability in different organs in three different models of polyP-induced hyper-permeability including local, systemic shortand systemic long-term approaches in murine models. Our results showed that polyP disrupts endothelial barrier integrity in skin, liver, kidney, brain, heart, and lung in all three study models and metformin abrogates the disruptive effect of polyP. We also showed that activation of AMPK signaling pathway, regulation of oxidant/ anti-oxidant balance, as well as decrease in inflammatory cell infiltration constitute a set of molecular mechanisms through which metformin elicits it's protective responses against polyP-induced hyper-permeability. These results support the clinical values of AMPK activators including the FDA-approved metformin in attenuating vascular damage in polyP-associated inflammatory diseases.Peer reviewe

Accurate and Robust Localization of Duplicated Region in Copy-Move Image Forgery

Many studies in image forgery detection have demonstrated the importance of reviewing different types of the forgeries and possible detection methods. Region duplication is an efficient operation to create image manipulation. In this type of tampering a region in an image is copied and moved onto a different area of the same image. Post processing approaches, such as possible geometrical and illumination adjustments can be applied in the duplication region to create a consistent image and to hide the forgery. The local feature extraction methods have powerful recognition and point matching applications. Since, there is a duplicated area in the copy move forged images; the local feature extraction methods can present a good outcome in detecting the forgeries. There are traditional methods in this area that used feature extraction advantages to detect the copy-move forgery in images. However, the methods mostly evaluate their accuracy in a small size dataset and without comparing with other existing methods. Moreover, locating the duplication area is the second necessarily step, after detecting and distinguishing the forged images and non-forged ones. This step is not investigated enough in the literatures. In this thesis, the use of feature extraction methods in both detection and localization stages is studied. The efficiency of presented method was verified in a large dataset including different combinations of affine transform operation. The final results were compared with the existing methods, thereby revealing more accuracy in the detection of such image forgery

Bond Strength of Composite Resin to Pulp Capping Biomaterials after Application of Three Different Bonding Systems

Background and aims. Bonding of composite resin filling materials to pulp protecting agents produces an adhesive joint which is important for the quality of filling as well as success of restoration. We aimed to assess the bond strength of composite resin to three pulp capping biomaterials: Pro Root mineral trioxide aggregate (PMTA), Root MTA (RMTA) and calcium enriched mixture (CEM) cement, using three bonding systems [a total-etch (Single Bond) and two self-etch systems (Protect bond and SE Bond)]. Materials and methods. Ninety acrylic molds, each containing a 6×2-mm hole, were divided into 3 groups and filled with PMTA, RMTA and CEM cements. The samples in each experimental group were then randomly divided into 3 subgroups; Single Bond, Protect Bond and SE Bond bonding systems were applied to the tested materials. Cylindrical forms of composite resin (Z100, 2×2 mm) were placed onto the samples and cured. Shear bond strength values were measured for 9 subgroups using a universal testing machine. Data were analyzed using two-way ANOVA. Results. The average shear bond strengths of Z100 composite resin after application of Single Bond, Protect Bond and SE Bond systems were as follows; PMTA: 5.1±2.42, 4.56±1.96 and 4.52±1.7; RMTA: 4.71±1.77, 4.31±0.56 and 4.79±1.88; and CEM cement: 4.75±1.1, 4.54±1.59 and 4.64±1.78 MPa, respectively. The type of pulp capping material, bonding system and their interacting effects did not have a significant effect on the bond strengths of composite resin to pulp capping biomaterials. Conclusion. Within the limitations of this in vitro study, bond strength of composite resin to two types of MTA as well as CEM cement were similar following application of the total-etch or self-etch bonding systems

Swift: Sparse Withdrawal Of Inliers In A First Trial

We study the simultaneous detection of multiple structures in the presence of overwhelming number of outliers in a large population of points. Our approach reduces the problem to sampling an extremely sparse subset of the original population of data in one grab, followed by an unsupervised clustering of the population based on a set of instantiated models from this sparse subset. We show that the problem can be modeled using a multivariate hypergeometric distribution, and derive accurate mathematical bounds to determine a tight approximation to the sample size, leading thus to a sparse sampling strategy. We evaluate the method thoroughly in terms of accuracy, its behavior against varying input parameters, and comparison against existing methods, including the state of the art. The key features of the proposed approach are: (i) sparseness of the sampled set, where the level of sparseness is independent of the population size and the distribution of data, (ii) robustness in the presence of overwhelming number of outliers, and (iii) unsupervised detection of all model instances, i.e. without requiring any prior knowledge of the number of embedded structures. To demonstrate the generic nature of the proposed method, we show experimental results on different computer vision problems, such as detection of physical structures e.g. lines, planes, etc., as well as more abstract structures such as fundamental matrices, and homographies in multi-body structure from motion