Article propose a framework for automatic diagnosis of breast cancer using ultrasound images

The article propose a framework for automatic diagnosis of breast cancer using mammogram images

We propose a novel reconstruction scheme for reconstructing charged particles in digital tracking calorimeters using model-free reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures for tracking without the dependency on simulated or manually labeled data. Here we optimize by trial-and-error a behavior policy acting as a heuristic approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern data processing pipelines used in high energy physics experiments and related high energy physics driven applications tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the usual high multiplicity of charged particles as well as the occurring physical interactions, randomly deflecting the particles from their initial path, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing possible track hypotheses through edge connections between particles in the sensitive detector layers. We demonstrate in a comprehensive study on simulated data generated for a particle detector used for proton computed tomography, the overall high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth information. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking algorithms in high energy physics.

High spectral and energy efficiencies are vital for the implementation of smart cities. To this end, this paper investigates the performance of an amplify-and-forward (AF) relay-aided coordinated direct and relay transmission (CDRT) protocol in a downlink non-orthogonal multiple access (NOMA) based Internet-of-things (IoT) network in which source shares the direct as well as the relayed links to the IoT near-user (NU) and the far-user (FU). Different from existing literature, we exploit incremental relaying (IR) along with combining at the NU (and not just at the FU) to achieve a 20% increase in FU throughput while ensuring the desired performance at NU. Considering practical imperfect successive interference cancellation (SIC), we analyze the throughput performance. Both NU and FU accrue large throughput gains, and doubling of the energy efficiency (EE) is achieved over the non-incremental AF CDRT NOMA scheme and its relayed OMA counterpart. We accomplish this by intelligently exploiting feedback bits in the second phase of signalling to avoid unwanted relaying, which saves energy and improves EE, which is very much required from the perspective of IoT-based energy-efficient smart cities. It is seen that the proper choice of the power allocation coefficient and the target information rate is crucial for maximizing the sum throughput and EE of the considered IoT Network. Finally, we validate the correctness of the theoretical analysis and the superiority of the proposed scheme through Monte Carlo simulations.

Self-report on height and weight had huge result on the precision of a screen for overweight and corpulence. Real and reported body size each helps to find the underreporting body weight. Self-reports will stay significant wellbeing reconnaissance apparatus yet ought not to be depended on solely to identify weight issues. Exactness checks of self-report information might further develop reviews of weight issues in youngsters. This Classification helps to improve the understanding the underweight and the under height causes. We consider the dataset employed to isolate the underweight and under heightened individuals of both orientations as per individual levels and loads. To isolate individuals, this study utilized an assortment of regression approaches, including linear and logistic regression and classification approaches like decision tree and random Forest techniques with the help of these techniques we achieve the self-reported anthropometric measurements can be used to determine BMI for purposes of weight classification.

A document by Shouvik Datta . Click on the document to view its contents.

Despite the prevalence and success of prediction algorithms in several areas, most notably medicine, climate and geology, their successes in the field of football are still very modest, and this is completely logical. The outcome of the match depends on a large and varied human effort, as it is not limited to the level of the two competing teams, but politics and economics play a very important role, especially in major tournaments such as the World Cup held in Qatar. In this paper, we will propose mathematical methods to predict the results of football matches in general, according to a number of data and information. Financial institutions including Goldman Sachs, UBS and ING have not been able to predict the winner of the last two World Cups, although Liberium Capital made an exception after Joachim Clement’s algorithm figured out the winner of the previous two World Cups. But Joachim Clement told the Financial Market Watch news site that his model determines only 45 percent of a team’s chances of winning the championship, while the remaining 55 percent is sheer luck. We do not seek in this paper to leave an opportunity for luck, we will develop a number of different mathematical equations, each of which serves a form of football matches. Then we will programmatically formulate these mathematical equations to create applications capable of picking the winning team. We believe that mathematics is the language of the universe and we will speak this language simply to determine the winning team in any football match. Note: our test case will be all matches in QATAR 2022 world-Cup.

This is the era of internet and there are a lot of Social Media platforms out there that facilitate the sharing a vast variety of user generated content. LinkedIn is a business and employment oriented social platform whereas Twitter is used to share recent news trends and updates. Pinterest is about discovering new content and ideas while Facebook is more about catching up with friends and family. These networks and virtual communities cast a tremendous amount of influence on their users. Here we have a similar content-oriented platform called Social. It is designed and built for the users to connect and share digital content (like text, images and/or gifs) related to community, social, healthcare and welfare services. MERN stands for MongoDB, ExpressJS, ReactJS and NodeJS (the four key technologies that make the stack) and is used to built this fully responsive web application in conjugation with other APIs and tools.

Nothing is rock-steady in the stock market, which isa very volatile market. Nevertheless, there are a variety of ways and approaches one may utilise to learn about this dynamic movement and be prepared for it as technology develops. The focus of this essay is on different methods for quickly identifying market trends. The suggested strategy is comprehensive because it includes pre-processing the stock market dataset, a range of feature engineering techniques, and the integration of a customised deep learning-based system for forecasting stock market price patterns. The best and most suggested method for prediction is the model with the least amount of error. In order to conduct this study, we used three distinct models and ran sentiment analysis on news articles mentioning the firm or the stock. The results of this classification have given investors additional information to help them make decisions about where to stake their money as well as clear and incisive insight into the market's irregular ups and downs.

In this work, we consider a real-time IoT monitoring system in which an energy harvesting sensor with a finite-size battery measures a physical process and transmits the status updates to an aggregator. The aggregator, equipped with caching capabilities, can serve the external requests of a destination network with either a stored update or a fresh update from the sensor. We assume that the destination network act as a gossiping network in which the update packets are forwarded among the nodes in a randomized setting. We utilize the Markov Decision Process framework to model and optimize the network’s average Version Age of Information (AoI) and obtain the optimal policy at the aggregator. The structure of the optimal policy is analytically demonstrated and numerically verified. Numerical results highlight the effect of the system parameters on the average Version AoI. The simulations reveal the superior performance of the optimal policy compared to a set of baseline policies.

In this paper, a new method is used to solve the above problems: the current flight state of UAV is considered in UAV flight prediction, and the UAV track is predicted by establishing a model according to different flight states of UAV. This method improves the accuracy and foresight of prediction, and can be used in UAV safety monitoring technology.

Upper-limb exoskeletons (ULEs) have the potential to assist humans to accomplish tasks by distributing a heavy load. ULEs are designed to be comfortable and lightweight wearable robots which has resulted in complexity in their structures, actuators, and power transmissions. Additionally, different ULE vendor structures and biomechanical variations between humans resulted in dissimilar coordinated ULE systems. These complex multi-robot systems can be handled by adaptive virtual decomposition control (VDC) if unknown dynamic models are not considered. Hence, this paper proposes a new distributed framework for the adaptive impedance-based VDC method to the address abovementioned challenges and thereby enhance the system performance and robustness. To that end, the proposed control method has a prediction capability and an ancillary control law for coordinated dissimilar ULEs holding a common object. The system stability is analyzed using the input-to-state stability approach. For validation, the proposed control and a state-of-the-art adaptive VDC methods are implemented on a symmetric bimanual exoskeleton with seven degrees of freedom for each arm. Four bimanual task scenarios are performed with different internal arm forces and with/without an obstacle. The experimental results show the superiority of the proposed control method.

Cloud providers offer virtual machines (VM) located in physical machines (PM) using the “pay-as-you-go” model to satisfy emerging demand for online computational services. If the instantaneous utilized capacity requested by VMs exceeds a certain threshold of the total capacity a PM can offer, a hotspot happens and may cause unacceptable VM performance degradation. Hotspots can be resolved by relocating some VMs to other PMs using live migration. However, the problem of selecting which VM(s) to release is challenging because the utilized capacity demanded by VMs changes continuously over time. In this work, we propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS), which predicts the utilized capacity range of each VM and applies the Γ-robustness theory to ensure that PM is hotspot-safe with desired probability. The latter allows us to formulate the hotspot resolution as a Γ-robust knapsack problem (Γ-RKP) that can be solved by a novel MILP model. Extensive experiments based on real-trace data and large-scale synthetic data demonstrate the effectiveness of the PMRS. More encouragingly, the application of the PMRS in the real-production environment benefits Huawei Cloud by resolving all existing and 94%+ potential future hotspots with minimal migration overhead.Cloud providers offer virtual machines (VM) located in physical machines (PM) using the “pay-as-you-go” model to satisfy emerging demand for online computational services. If the instantaneous utilized capacity requested by VMs exceeds a certain threshold of the total capacity a PM can offer, a hotspot happens and may cause unacceptable VM performance degradation. Hotspots can be resolved by relocating some VMs to other PMs using live migration. However, the problem of selecting which VM(s) to release is challenging because the utilized capacity demanded by VMs changes continuously over time. In this work, we propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS), which predicts the utilized capacity range of each VM and applies the Γ-robustness theory to ensure that PM is hotspot-safe with desired probability. The latter allows us to formulate the hotspot resolution as a Γ- robust knapsack problem (Γ-RKP) that can be solved by a novel MILP model. Extensive experiments based on real-trace data and large-scale synthetic data demonstrate the effectiveness of the PMRS. More encouragingly, the application of the PMRS in the real-production environment benefits Huawei Cloud by resolving all existing and 94%+ potential future hotspots with minimal migration overhead.

Fast frequency response services, designed to quickly balance the electrical grid within seconds, have a critical importance for managing sudden anomalies in low-inertia power systems. Battery systems often serve as versatile prosumers on the demand side to facilitate fast frequency response services. However, the nature of the fast frequency response services leads to a highly fluctuating power profile for batteries, which can shorten their lifetime. In contrast, distributed air-source heat pumps in residential areas have a substantial untapped potential to support fast frequency response services. This paper seeks to integrate them into the existing services through a controller upgrade. We analyze the influence of air-source heat pumps' inherent complex thermal dynamics on fast frequency response services, revealing control challenges posed by unpredictable operating condition changes. Such a challenge is tackled with a decentralized control strategy based on H∞ and inverse-droop control, guaranteeing practical and stable operations within the permitted operating condition range. Finally, the proposed fast frequency response service scheme is tested through multiphysics simulations on a small-size low-inertia residential microgrid. The obtained results strongly supported the proposed new service.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

Evolutionary sequential transfer optimization (ESTO), which attempts to enhance the evolutionary search of a target task using the knowledge captured from several previously-solved source tasks, has been receiving increasing research attention in recent years. Despite the tremendous approaches developed, it is worth noting that existing benchmark problems for ESTO are not well designed, as they are often simply extended from other benchmarks in which the relationships between the source and target tasks are not well analyzed. Consequently, the comparisons conducted on these problems are not systematic and can only provide numerical results without a deeper analysis of how an ESTO algorithm performs on problems with different properties. Taking this clue, this two-part paper revisits a large body of solution-based ESTO algorithms on a group of newly developed test problems, to help researchers and practitioners gain a deeper understanding of how to better exploit optimization experience towards enhanced optimization performance. Part A of the series designs a problem generator based on several newly defined concepts to generate benchmark problems with diverse properties, which are competitive in resembling real-world problems. Part B of the series empirically revisits various algorithms by answering five key research questions related to knowledge transfer. The results demonstrated that the performance of many ESTO algorithms is highly problem-dependent, which suggest the necessity of more research efforts on transferability measurement and enhancement in ESTO algorithm design. The source code of the benchmark suite developed in part A is available at https://github.com/XmingHsueh/Revisiting-S-ESTOs-PartA.

Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of nodes for DR implementing. Current studies resort to intuitive yet naive approaches for DR targeting, as price is implicitly associated with demand, though optimality cannot be ensured. In this paper, we derive such a relationship in the security-constrained economic dispatch via the multi-parametric programming theory, based on which the DR targeting problem is rigorously formulated as a mixed-integer quadratic programming problem aiming at reducing the averaged price to a reference level by efficiently reducing targeted nodes’ demand. A solution strategy is proposed to accelerate the computation. Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction. Besides, we empirically show that the proposed approach is immune to inaccurate system parameters, and can be generalized to variants of DR targeting tasks.

Kuramoto model based analysis of Josephson junction array in series and comparison with small numbers of junctions as compared to higher number as proposed in the Kuramoto model.