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نتیجه جستجو - Probability distribution

تعداد مقالات یافته شده: 29
ردیف عنوان نوع
1 Guesswork of a Quantum Ensemble
حدس و گمان یک گروه کوانتومی-2022
The guesswork of a quantum ensemble quantifies the minimum number of guesses needed in average to correctly guess the state of the ensemble, when only one state can be queried at a time. Here, we derive analytical solutions of the guesswork problem subject to a finite set of conditions, including the analytical solution for any qubit ensemble with uniform probability distribution. As explicit examples, we compute the guesswork for any qubit regular polygonal and polyhedral ensemble.
Index Terms: Guesswork | quantum states | quantum measurements | quantum state discrimination.
مقاله انگلیسی
2 Hash Function Based on Controlled Alternate Quantum Walks With Memory (September 2021)
عملکرد هش بر اساس راه رفتن کوانتومی جایگزین کنترل شده با حافظه (سپتامبر 2021)-2022
We propose a Quantum inspired Hash Function using controlled alternate quantum walks with Memory on cycles (QHFM), where the jth message bit decides whether to run quantum walk with one-step memory or to run quantum walk with two-step memory at the jth time step, and the hash value is calculated from the resulting probability distribution of the walker. Numerical simulation shows that the proposed hash function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions based on quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property; and theoretical analysis indicates that the time and space complexity of the new scheme are not greater than those of its peers. The good performance of QHFM suggests that quantum walks that differ not only in coin operators but also in memory lengths can be combined to build good hash functions, which, in turn, enriches the construction of controlled alternate quantum walks.
INDEX TERMS: Controlled alternate quantum walks (CAQW) | hash function | quantum walks with memory (QWM) | statistical properties | time and space complexity.
مقاله انگلیسی
3 Preparation of Quantum Superposition Using Partial Negation
تهیه برهم نهی کوانتومی با استفاده از نفی جزئی-2022
The preparation of a quantum superposition is the key to the success of many quantum algorithms and quantum machine learning techniques. The preparation of an incomplete or a non-uniform quantum superposition with certain properties is a non-trivial task. In this paper, an n-qubits variational quantum circuit using partial negation and controlled partial negation operators is proposed to prepare a quantum superposition from a given space of probability distributions. The speed of the preparation process and the accuracy of the prepared superposition has special importance to the success of any quantum algorithm. The proposed method can be used to prepare the required quantum superposition in O(n) steps and with high accuracy when compared with relevant methods in literature.
keywords: Quantum superposition | quantum state | partial negation | data encoding | prepared amplitudes | acquired amplitudes
مقاله انگلیسی
4 شدت و پتانسیل انتقال کرونا در کره جنوبی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 14
اهداف: ازآنجایی که اولین مورد کروناویروس جدید 2019(کوید-19) در 20 ژانویۀ 2020 در کرۀ شمالی شناسایی شد، تعداد موارد به سرعت افزایش یافت به طوری که تا 6 مارس 2020، منجربه ابتلای6284 مورد و فوت42 نفر شد. اولین تحقیق درمورد گزارش تعداد تکثیر کوید-19 در کرۀ جنوبی را برای بررسی سرعت شیوع بیماری، ارائه می دهیم.
روش کار: موارد روزانۀ تأیید شدۀ کوید-19 در کرۀ جنوبی از منابع عمومی موجود استخراج شد. با استفاده از توزیع تجربی گزارشات دارای تأخیر و شبیه سازی مدل رشد کلی، تعداد تکثیر مؤثر را برمبنای توزیع احتمال گسستۀ فاصلۀ زایشی ارزیابی کردیم.
نتایج: چهار گروه اصلی را شناسایی و تعداد تکثیر را 1.5(1.6-1.4 CI: 95%) برآورد کردیم. به علاوه، نرخ رشد طبیعی 0.6 (0.7، 0.6 CI: 95%) و مقیاس بندی پارامتر رشد 0.8 (0.8،0.7 CI: 95%) برآورد شدند، که نشان-دهندۀ پویایی رشد زیر نمایی کوید-19 می باشد. نرخ مرگ و میر موارد خام در بین مردان (1.1%) در مقایسه با زنان (0.4%) بیشتر است و با افزایش سن افزایش می یابد.
نتیجه گیری: نتایج ما انتقال پایدار اولیۀ کوید-19 در کرۀ جنوبی را نشان می دهد و از اجرای اقدامات فاصله گذاری اجتماعی برای کنترل سریع شیوع بیماری حمایت می کند.
کلمات کلیدی: کروناویروس | کوید-19 | کره | تعداد تکثیر
مقاله ترجمه شده
5 Combining gaze and AI planning for online human intention recognition
تلفیق برنامه ریزی نگاه و هوش مصنوعی برای تشخیص آنلاین نیت انسان-2020
Intention recognition is the process of using behavioural cues, such as deliberative actions, eye gaze, and gestures, to infer an agent’s goals or future behaviour. In artificial intelligence, one approach for intention recognition is to use a model of possible behaviour to rate intentions as more likely if they are a better ‘fit’ to actions observed so far. In this paper, we draw from literature linking gaze and visual attention, and we propose a novel model of online human intention recognition that combines gaze and model-based AI planning to build probability distributions over a set of possible intentions. In human-behavioural experiments (n =40) involving a multi-player board game, we demonstrate that adding gaze-based priors to model-based intention recognition improved the accuracy of intention recognition by 22% (p <0.05), determined those intentions ≈90 seconds earlier (p <0.05), and at no additional computational cost. We also demonstrate that, when evaluated in the presence of semi-rational or deceptive gaze behaviours, the proposed model is significantly more accurate (9% improvement) (p <0.05) compared to a model-based or gaze only approaches. Our results indicate that the proposed model could be used to design novel human-agent interactions in cases when we are unsure whether a person is honest, deceitful, or semi-rational.
Keywords: Intention recognition | Gaze | Planning
مقاله انگلیسی
6 Performance evaluation of web service response time probability distribution models for business process cycle time simulation
ارزیابی عملکرد مدلهای توزیع احتمال پاسخ زمان سرویس وب برای شبیه سازی چرخه فرآیند کسب و کار-2020
Context: The adoption of Business Process Management (BPM) is enabling companies to improve the pace of building new capabilities, enhancing existing ones, and measuring process performance to identify bottlenecks. It is essential to compute the cycle time of the process to assess the performance of a business process. The cycle time typically forms part of service level agreements (SLAs) and is a crucial contributor to the overall user experience and productivity. The simulation technique is versatile and has broad applicability for determining realistic cycle time using historical data of web service response time. BPM tools offer inadequate support for modeling input data used in simulation in the form of descriptive statistics or standard probability distributions like normal, lognormal, which results in inaccurate simulation results. Objective: We evaluate the effectiveness of different parametric and non-parametric probability distributions for modeling data of web service response time. We further assess how the choice of probability distribution impacts the accuracy of the simulated cycle time of a business process. The work is the first of such a study using real-world data for encouraging Business Process Simulation Specification (BPSim) standard setters and BPM tools to enhance their support for such distributions in their simulation engine. Method: We consider several parametric and non-parametric distributions and explore how well these distributions fit web service response time from extensive public and a real-world dataset. The cycle time of the business process of a real-world system is simulated using the identified distributions to model the underlying web service data. Results: Our results show that kernel distribution is the most suitable choice, followed by Burr. Kernel outperforms Burr by 86.63% for the public and 84.21% for the real-world dataset. The choice of distribution affects the percentile ranks like 90 and above than the median. The use of single-point values underestimates cycle time values at higher percentiles. Conclusion: Based on our empirical results, we recommend the addition of kernel and Burr to the current list of distributions supported by BPSim and BPM tools.
Keywords: Simulation input modeling | Parametric distributions | Non-parametric distributions | Performance evaluation | Web service response time | Cycle time
مقاله انگلیسی
7 Interpretable policies for reinforcement learning by empirical fuzzy sets
سیاست های قابل تفسیر برای یادگیری تقویتی توسط مجموعه های فازی تجربی-2020
This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning (IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous action space. The learning process resembles that of human beings by clustering the encountered states, developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement. It adopts the actor–critic architecture whereas being different from mainstream policy gradient methods. The value function is approximated through the fuzzy system AnYa. The state–action space is discretized into a static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value of the node being the consequent. Values of consequents are updated using the Sarsa(????) algorithm. Probability distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA), Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets (εFS). The fuzzy kernel of IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with four problems, namely Mountain Car, Continuous Gridworld, Pendulum Position, and Tank Level Control, are presented as a proof of the proposed concept.
Keywords: Interpretable fuzzy systems | Reinforcement learning | Probability distribution learning | Autonomous learning systems | AnYa type fuzzy systems | Empirical Fuzzy Sets
مقاله انگلیسی
8 Optimal surface estimation and thresholding of confocal microscope images of biofilms using Beers Law
تخمین بهینه سطح و آستانه گرفتن تصاویر میکروسکوپ کانفوکال بیوفیلم ها با استفاده از قانون Beers -2020
Beers Law explains how light attenuates into thick specimens, including thick biofilms. We use a Bayesian optimality criterion, the maximum of the posterior probability distribution, and computationally efficiently fit Beers Law to the 3D intensity data collected from thick living biofilms by a confocal scanning laser microscope. Using this approach the top surface of the biofilm and an optimal image threshold can be estimated. Biofilm characteristics, such as bio-volumes, can be calculated from this surface. Results from the Bayesian approach are compared to other approaches including the method of maximum likelihood or simply counting bright pixels. Uncertainty quantification (i.e., error bars) can be provided for the parameters of interest. This approach is applied to confocal images of stained biofilms of a common lab strain of Pseudomonas aeruginosa, stained biofilms of Janthinobacterium isolated from the Antarctic, and biofilms of Staphylococcus aureus that have been genetically modified to fluoresce green.
Keywords: Attenuation | Thresholding | Maximum likelihood | Beer-Lambert Law | Bayesian | Confocal microscope image analysis
مقاله انگلیسی
9 A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy
یک الگوریتم تکامل افتراقی چند هدفه ناهمواری چشم انداز تناسب اندام با یک استراتژی یادگیری تقویتی-2020
Optimization is the process of finding and comparing feasible solutions and adopting the best one until no better solution can be found. Because solving real-world problems often involves simulations and multiobjective optimization, the results and solutions of these problems are conceptually different from those of single-objective problems. In single-objective optimization problems, the global optimal solution is the solution that yields the optimal value of the objective function. However, for multiobjective optimization problems, the optimal solutions are Pareto-optimal solutions produced by balancing multiple objective functions. The strategic variables calculated in multiobjective problems produce different effects on the mapping imbalance and the search redundancy in the search space. Therefore, this paper proposes a fitness landscape ruggedness multiobjective differential evolution (LRMODE) algorithm with a reinforcement learning strategy. The proposed algorithm analyses the ruggedness of landscapes using information entropy to estimate whether the local landscape has a unimodal or multimodal topology and then combines the outcome with a reinforcement learning strategy to determine the optimal probability distribution of the algorithm’s search strategy set. The experimental results showthat this novel algorithm can ameliorate the problem of search redundancy and search-space mapping imbalances, effectively improving the convergence of the search algorithm during the optimization process.
Keywords: Multiobjective | Differential Evolution | Reinforcement Learning | Fitness Landscape | Search Strategy
مقاله انگلیسی
10 Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation
آنلاین RBM: در حال رشد محدودیت ماشین بولتزمن در پرواز برای نمایندگی بدون نظارت-2020
In this work, we endeavor to investigate and propose a novel unsupervised online learning algorithm, namely the Online Restricted Boltzmann Machine (O-RBM). The O-RBM is able to construct and adapt the architecture of a Restricted Boltzmann Machine (RBM) artificial neural network, according to the statistics of the streaming input data. Specifically, for a training data that is not fully available at the onset of training, the proposed O-RBM begins with a single neuron in the hidden layer of the RBM, progressively adds and suitably adapts the network to account for the variations in streaming data distributions. Such an unsupervised learning helps to effectively model the probability distribution of the entire data stream, and generates robust features. We will demonstrate that such unsupervised representations can be used for discriminative classifications on a set of multi-category and binary classification problems for unstructured image and structured signal data sets, having varying degrees of class-imbalance. We first demonstrate the O-RBM algorithm and characterize the network evolution using the simple and conventional multi-class MNIST image dataset, aimed at recognizing hand-written digit. We then benchmark O-RBM performance to other machine learning, neural network and Class RBM techniques using a number of public non-stationary datasets. Finally, we study the performance of the O-RBM on a real-world problem involving predictive maintenance of an aircraft component using time series data. In all these studies, it is observed that the O-RBM converges to a stable, concise network architecture, wherein individual neurons are inherently discriminative to the class labels despite unsupervised training. It can be observed from the performance results that on an average O-RBM improves accuracy by 2.5%–3% over conventional offline batch learning techniques while requiring at least 24%–70% fewer neurons.
Keywords: Restricted Boltzmann Machine | Online learning | Unsupervised representation
مقاله انگلیسی
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