Deep Metric Learning with Soft Orthogonal Proxies

Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies that are assigned to different classes may end up being closely located in the embedding space and hence having a hard time to distinguish between positive and negative items. Alternatively, they may become highly correlated and hence provide redundant information with the model. To address these issues, we propose a novel approach that introduces Soft Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to be as orthogonal as possible and hence control their positions in the embedding space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an encoder to extract contextual features from images along with a DML objective. The objective is made of the Proxy Anchor loss along with the SO regularization. We evaluate our method on four public benchmarks for category-level image retrieval and demonstrate its effectiveness with comprehensive experimental results and ablation studies. Our evaluations demonstrate the superiority of our proposed approach over state-of-the-art methods by a significant margin

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Estimation of scour propagation rates around pipelines while considering simultaneous effects of waves and currents conditions

Seabed offshore pipelines are widely applied to carry fluid over long distances of the seafloor. The design of offshore pipelines is conducted to bear quite a few environmental loading circumstances in order to provide a well-guarded and reliable fluid transition. Fluid leakage and pipeline vibration due to a failure of the pipeline are the prime causes of accidental catastrophes. Scour phenomena occur around offshore pipelines due to currents and/or wave conditions, consequently causing the susceptibility to pipeline failure. Then, scouring propagation rates require to be studied in three dimensions, namely beneath and normal to the offshore pipeline and the longitudinal direction of itself. In this research, Artificial Intelligent (AI) models are used to derive new regression equations based on the laboratory data for the estimation of 3D scour propagation patterns while seafloor offshore pipelines are exposed to simultaneous impacts of currents and waves. In this way, chiefly based on the experimental investigations conducted by Cheng and colleagues, seven sets of dimensional parameters were given in terms of the Shields’ parameter due to currents and waves, the Keulegan–Carpenter number, the ratio of embedment depth to pipeline diameter, the ratio of orbital velocity to current velocity, and the wave/current angle of attack. Dimensionless parameters were used to provide regression-based equations to evaluate scour propagation rates in three dimensions. The performance of AI models was evaluated by various statistical measures. The model based on our proposed equations performed better than the reported models in the literature. Even more importantly, we indicated that our model inherently has a reliable physical consistency for variations of dimensionless parameters against the scour propagation patterns

Estimation of Scour Propagation Rates around Pipelines While Considering Simultaneous Effects of Waves and Currents Conditions

Seabed offshore pipelines are widely applied to carry fluid over long distances of the seafloor. The design of offshore pipelines is conducted to bear quite a few environmental loading circumstances in order to provide a well-guarded and reliable fluid transition. Fluid leakage and pipeline vibration due to a failure of the pipeline are the prime causes of accidental catastrophes. Scour phenomena occur around offshore pipelines due to currents and/or wave conditions, consequently causing the susceptibility to pipeline failure. Then, scouring propagation rates require to be studied in three dimensions, namely beneath and normal to the offshore pipeline and the longitudinal direction of itself. In this research, Artificial Intelligent (AI) models are used to derive new regression equations based on the laboratory data for the estimation of 3D scour propagation patterns while seafloor offshore pipelines are exposed to simultaneous impacts of currents and waves. In this way, chiefly based on the experimental investigations conducted by Cheng and colleagues, seven sets of dimensional parameters were given in terms of the Shields’ parameter due to currents and waves, the Keulegan–Carpenter number, the ratio of embedment depth to pipeline diameter, the ratio of orbital velocity to current velocity, and the wave/current angle of attack. Dimensionless parameters were used to provide regression-based equations to evaluate scour propagation rates in three dimensions. The performance of AI models was evaluated by various statistical measures. The model based on our proposed equations performed better than the reported models in the literature. Even more importantly, we indicated that our model inherently has a reliable physical consistency for variations of dimensionless parameters against the scour propagation patterns

High dimensionality reduction by matrix factorization for systems pharmacology

The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.</p

Role of Si3N4 on Microstructure and Hardness of Hot-pressed ZrB2−SiC Composites

The impact of Si3N4 content on the hardness and microstructural developments of ZrB2-SiC material has been investigated thoroughly in the present investigation. Having prepared the raw materials in a jar mill, the ZrB2-SiC samples containing various amounts of Si3N4 were hot-pressed at 1850 °C. Furthermore, XRD, FESEM, and HRTEM were utilized to evaluate the microstructure of samples. The formation of in-situ h-BN was proved by the mentioned methods. Also, it was shown that the Vickers hardness of ZrB2-SiC increases up to 20 GPa in presence of 4.5 wt% Si3N4 which is 3 GPa more than the sample without Si3N4. Results show that the positive effect of increased relative density on hardness is more than the negative effect of h-BN soft phase formation

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.</p

Decoding clinical biomarker space of COVID-19:exploring matrix factorization-based feature selection methods

Abstract One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases