scholarly journals Generic SDE and GA-based workload modeling for cloud systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Cédric St-Onge ◽  
Souhila Benmakrelouf ◽  
Nadjia Kara ◽  
Hanine Tout ◽  
Claes Edstrom ◽  
...  
Keyword(s):  
Kalman Filter ◽  
User Behavior ◽  
Support Vector ◽  
Application Behavior ◽  
Load Fluctuation ◽  
Wide Range ◽  
Cloud Environments ◽  
High Degree ◽  
Workload Models

AbstractWorkload models are typically built based on user and application behavior in a system, limiting them to specific domains. Undoubtedly, such a practice creates a dilemma in a cloud computing (cloud) environment, where a wide range of heterogeneous applications are running and many users have access to these resources. The workload model in such an infrastructure must adapt to the evolution of the system configuration parameters, such as job load fluctuation. The aim of this work is to propose an approach that generates generic workload models (1) which are independent of user behavior and the applications running in the system, and can fit any workload domain and type, (2) model sharp workload variations that are most likely to appear in cloud environments, and (3) with high degree of fidelity with respect to observed data, within a short execution time. We propose two approaches for workload estimation, the first being a Hull-White and Genetic Algorithm (GA) combination, while the second is a Support Vector Regression (SVR) and Kalman-filter combination. Thorough experiments are conducted on real CPU and throughput datasets from virtualized IP Multimedia Subsystem (IMS), Web and cloud environments to study the efficiency of both propositions. The results show a higher accuracy for the Hull-White-GA approach with marginal overhead over the SVR-Kalman-Filter combination.

Dynamic Design Using the Kalman Filter for Flexible Systems With Epistemic Uncertainty

2015 ◽  
Author(s):  
Elham Keshavarzi ◽  
Matthew McIntire ◽  
Christopher Hoyle
Keyword(s):  
Kalman Filter ◽  
Epistemic Uncertainty ◽  
Time Frame ◽  
Flexible Systems ◽  
Dynamic Markets ◽  
Wide Range ◽  
Engineered Systems ◽  
Filter Approach ◽  
Reliability And Robustness ◽  
High Degree

Most engineered systems have to exhibit a high degree of reliability and robustness. They are high in cost and complexity and often incorporate highly sophisticated materials, components, design and other technologies. Therefore, they face uncertainties in categories ranging from technical issues to market changes. This includes a wide range of epistemic uncertainties, such as demand or budget uncertainty; due to increasingly dynamic markets it has become important for systems to cope with these uncertainties. In this paper, a Kalman filter approach is applied to control the design as uncertainties are resolved in a discrete time frame. It is shown how the Kalman filter approach treats the design as a stochastic control problem, in which the design is controlled throughout its lifecycle to compensate for sources of epistemic uncertainty, as the uncertainties are resolved. The proposed method is applicable to flexible systems where changing the design is possible. A design framework is proposed encompassing a set of definitions, metrics, the methodology, and a case study of a spaceborne system.

Understanding Conformational Entropy in Small Molecules

2020 ◽  
Author(s):  
Lucian Chan ◽  
Garrett Morris ◽  
Geoffrey Hutchison
Keyword(s):  
Small Molecules ◽  
Degrees Of Freedom ◽  
Absolute Error ◽  
Low Energy ◽  
Standard Entropy ◽  
Free Energies ◽  
Conformational Entropy ◽  
Wide Range ◽  
High Degree ◽  
Empirical Corrections

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy.

Cerebellotrigeminal Dermal Dysplasia (Gómez-López-Hernández Syndrome)

2018 ◽  
Vol 16 (05) ◽  
pp. 362-368 ◽  
Author(s):  
Federica Sullo ◽  
Agata Polizzi ◽  
Stefano Catanzaro ◽  
Selene Mantegna ◽  
Francesco Lacarrubba ◽  
...  
Keyword(s):  
De Novo ◽  
Chromosomal Rearrangements ◽  
Number Of Patients ◽  
Wide Range ◽  
Visual Problems ◽  
Neurodevelopmental Abnormalities ◽  
Clinical Triad ◽  
High Degree ◽  
Motor Handicap

Cerebellotrigeminal dermal (CTD) dysplasia is a rare neurocutaneous disorder characterized by a triad of symptoms: bilateral parieto-occipital alopecia, facial anesthesia in the trigeminal area, and rhombencephalosynapsis (RES), confirmed by cranial magnetic resonance imaging. CTD dysplasia is also known as Gómez-López-Hernández syndrome. So far, only 35 cases have been described with varying symptomatology. The etiology remains unknown. Either spontaneous dominant mutations or de novo chromosomal rearrangements have been proposed as possible explanations. In addition to its clinical triad of RES, parietal alopecia, and trigeminal anesthesia, CTD dysplasia is associated with a wide range of phenotypic and neurodevelopmental abnormalities.Treatment is symptomatic and includes physical rehabilitation, special education, dental care, and ocular protection against self-induced corneal trauma that causes ulcers and, later, corneal opacification. The prognosis is correlated to the mental development, motor handicap, corneal–facial anesthesia, and visual problems. Follow-up on a large number of patients with CTD dysplasia has never been reported and experience is limited to few cases to date. High degree of suspicion in a child presenting with characteristic alopecia and RES has a great importance in diagnosis of this syndrome.

scholarly journals Euglycemic Diabetic Ketoacidosis after a Single Dose of Empagliflozin in a Patient with Pancreatitis

2021 ◽  
Vol 11 (2) ◽  
pp. 216-218
Author(s):  
Marta Brandão Calçada ◽  
Luís Fernandes ◽  
Rita Soares Costa ◽  
Sara Montezinho ◽  
Filipa Martins Duarte ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Ketoacidosis ◽  
Drug Class ◽  
Clinical Suspicion ◽  
Laboratory Findings ◽  
Sodium Glucose Cotransporter ◽  
History Of ◽  
High Degree ◽  
Euglycemic Diabetic Ketoacidosis

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are the most recently approved drug class for the treatment of type 2 diabetes mellitus (T2D). Although they are largely well-tolerated, their intake has been associated with euglycemic diabetic ketoacidosis (DKA) in some rare cases. We report the case of a 70-year-old male with type 2 diabetes and no history of DKA, who started therapy with empagliflozin one day before presenting with acute pancreatitis and laboratory findings consistent with euglycemic DKA. SGLT2i can induce euglycemic DKA from the first dose. Given the atypical presentation, a high degree of clinical suspicion is required to recognize this complication.

scholarly journals Controlling Teleportation-Based Locomotion in Virtual Reality with Hand Gestures: A Comparative Evaluation of Two-Handed and One-Handed Techniques

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 715
Author(s):  
Alexander Schäfer ◽  
Gerd Reis ◽  
Didier Stricker
Keyword(s):  
Virtual Reality ◽  
Virtual Worlds ◽  
User Preferences ◽  
Interaction Techniques ◽  
Hand Gestures ◽  
Wide Range ◽  
Effectiveness And Efficiency ◽  
High Degree ◽  
Additional Hardware ◽  
Comprehensive Study

Virtual Reality (VR) technology offers users the possibility to immerse and freely navigate through virtual worlds. An important component for achieving a high degree of immersion in VR is locomotion. Often discussed in the literature, a natural and effective way of controlling locomotion is still a general problem which needs to be solved. Recently, VR headset manufacturers have been integrating more sensors, allowing hand or eye tracking without any additional required equipment. This enables a wide range of application scenarios with natural freehand interaction techniques where no additional hardware is required. This paper focuses on techniques to control teleportation-based locomotion with hand gestures, where users are able to move around in VR using their hands only. With the help of a comprehensive study involving 21 participants, four different techniques are evaluated. The effectiveness and efficiency as well as user preferences of the presented techniques are determined. Two two-handed and two one-handed techniques are evaluated, revealing that it is possible to move comfortable and effectively through virtual worlds with a single hand only.

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

AGN1 vs AGN2 dichotomy as seen from the point of view of ionized outflows

2019 ◽  
Vol 15 (S356) ◽  
pp. 96-96
Author(s):  
Eleonora Sani
Keyword(s):  
Accretion Rate ◽  
Point Of View ◽  
Emission Lines ◽  
Black Hole Mass ◽  
X Ray ◽  
Bolometric Luminosity ◽  
Wide Range ◽  
Strong Contrast

AbstractI present a detailed study of ionized outflows in a large sample of 650 hard X-ray detected AGN. Taking advantage of the legacy value of the BAT AGN Spectroscopic Survey (BASS, DR1), we are able to reveal the faintest wings of the [OIII] emission lines associated with outflows. The sample allows us to derive the incidence of outflows covering a wide range of AGN bolometric luminosity and test how the outflow parameters are related with various AGN power tracers, such as black hole mass, Eddington ratio, luminosity. I’ll show how ionized outflows are more frequently found in type 1.9 and type 1 AGN (50% and 40%) with respect to the low fraction in type 2 AGN (20%). Within such a framework, I’ll demonstrate how type 2 AGN outflows are almost evenly balanced between blue- and red-shifted winds. This, in strong contrast with type 1 and type 1.9 AGN outflows which are almost exclusively blue-shifted. Finally, I’ll prove how the outflow occurrence is driven by the accretion rate, whereas the dependence of outflow properties with respect to the other AGN power tracers happens to be quite mild.

scholarly journals Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 607
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang ◽  
Guimei Cui
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Support Vector ◽  
Multiple Model ◽  
Single Model ◽  
Adaptive Unscented Kalman Filter ◽  
Time Periods ◽  
Atmospheric Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

The PM2.5 concentration model is the key to predict PM2.5 concentration. During the prediction of atmospheric PM2.5 concentration based on prediction model, the prediction model of PM2.5 concentration cannot be usually accurately described. For the PM2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM2.5 concentration prediction is improved in whole time period.

Krypton 85 myocardial blood flow: precordial scintillation versus coronary sinus sampling

1965 ◽  
Vol 209 (4) ◽  
pp. 705-710 ◽  
Author(s):  
Michael D. Klein ◽  
Lawrence S. Cohen ◽  
Richard Gorlin
Keyword(s):  
Blood Flow ◽  
Myocardial Blood Flow ◽  
Coronary Sinus ◽  
Human Subjects ◽  
Radioactive Decay ◽  
Empirical Correction ◽  
Wide Range ◽  
Actual Flow ◽  
Flow Through ◽  
High Degree

Myocardial blood flow in human subjects was assessed by comparative simultaneous measurement of krypton 85 radioactive decay from coronary sinus and precordial scintillation. Empirical correction of postclearance background from precordial curves yielded a high degree of correlation between flows derived from the two sampling sites (r = .889, P < .001). Comparison of left and right coronary flows in nine subjects revealed similarity in flow through the two vessels over a wide range of actual flow values (r = .945, P < .001).

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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