با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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131 |
Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs
رمزگشاهای شبکه عصبی برای تصحیح خطای کوانتومی با استفاده از کدهای سطحی: کاوش فضایی مبادلات هزینه و عملکرد سخت افزار-2022 Quantum error correction (QEC) is required in quantum computers to mitigate the effect of
errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most
computationally expensive task in the classical electronic back-end. Decoders employing neural networks
(NN) are well-suited for this task but their hardware implementation has not been presented yet. This work
presents a space exploration of fully connected feed-forward NN decoders for small distance surface codes.
The goal is to optimize the NN for the high-decoding performance, while keeping a minimalistic hardware
implementation. This is needed to meet the tight delay constraints of real-time surface code decoding. We
demonstrate that hardware-based NN-decoders can achieve the high-decoding performance comparable to
other state-of-the-art decoding algorithms whilst being well below the tight delay requirements (≈ 440 ns)
of current solid-state qubit technologies for both application-specific integrated circuit designs (<30 ns) and
field-programmable gate array implementations (<90 ns). These results indicate that NN-decoders are viable
candidates for further exploration of an integrated hardware implementation in future large-scale quantum
computers.
INDEX TERMS: Application-specific integrated circuit (ASIC) | complementary metal-oxide semiconductor (CMOS) | CMOS integrated circuits | combinational circuits | cryo-CMOS decoding | cryogenic electronics | digital integrated circuits, error correction codes | feedforward neural networks (NNs) | field programmable gate array (FPGA) | fixed-point arithmetic | machine learning NNs | pareto analysis | quantum computing | quantum-error-correction (QEC) codes | supervised learning, surface codes (SCs). |
مقاله انگلیسی |
132 |
An overview of Human Action Recognition in sports based on Computer Vision
مروری بر تشخیص کنش انسانی در ورزش بر اساس بینایی کامپیوتری-2022 Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and
tennis to detect players and recognize their actions and teams activities during training, matches, warm-ups, or
competitions. HAR aims to detect the person performing the action on an unknown video sequence, determine the
actions duration, and identify the action type. The main idea of HAR in sports is to monitor a players performance, that is, to detect the player, track their movements, recognize the performed action, compare various
actions, compare different kinds and skills of acting performances, or make automatic statistical analysis.
As an action that can occur in the sports field refers to a set of physical movements performed by a player in
order to complete a task using their body or interacting with objects or other persons, actions can be of different
complexity. Because of that, a novel systematization of actions based on complexity and level of performance and
interactions is proposed.
The overview of HAR research focuses on various methods performed on publicly available datasets, including actions of everyday activities. That is just a good starting point; however, HAR is increasingly represented in sports and is becoming more directed towards recognizing similar actions of a particular sports domain. Therefore, this paper presents an overview of HAR applications in sports primarily based on Computer Vision as the main contribution, along with popular publicly available datasets for this purpose. keywords: یادگیری ماشین | تشخیص عمل انسانی | سیستم سازی اقدام | مجموعه داده های ورزشی | شناخت کنش انسان در ورزش | ورزش | Machine learning | Human Action Recognition | Action systematization | Sports dataset | Human action recognition in sports | Sport |
مقاله انگلیسی |
133 |
New Advanced Computing Architecture for Cryptography Design and Analysis by D-Wave Quantum Annealer
معماری محاسباتی پیشرفته جدید برای طراحی و تحلیل رمزنگاری توسط D-Wave Quantum Annealer-2022 Universal quantum computers are far from achieving practical applications. The D-Wave quantum computer
is initially designed for combinatorial optimizations. Therefore, exploring the potential applications of the D-Wave
device in the field of cryptography is of great importance. First, although we optimize the general quantum Hamiltonian
on the basis of the structure of the multiplication table (factor up to 1 005 973), this study attempts to explore the
simplification of Hamiltonian derived from the binary structure of the integers to be factored. A simple factorization
on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the
D-Wave device. Second, by using the quantum computing cryptography based on the D-Wave 2000Q system, this
research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired
Simulated Annealing (QISA) framework. Good functions and a high-performance platform are introduced, and
additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found. Further
comparison between QISA and Quantum Annealing (QA) on six-variable bent functions not only shows the potential
speedup of QA, but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a
practical cryptography design.
Keywords: Quantum Annealing (QA) | factorization | Boolean functions | brain-inspired cognition |
مقاله انگلیسی |
134 |
Design of robot automatic navigation under computer intelligent algorithm and machine vision
طراحی ربات ناوبری خودکار تحت الگوریتم هوشمند کامپیوتر و بینایی ماشین-2022 This work aims to explore the robot automatic navigation model under computer intelligent algorithms and
machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high
cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image
processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low ef-
ficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-
based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox
Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The perfor-
mance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant
colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further
analysis of the proposed SSD neural network through comparison with other neural networks suggests that its
feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network
algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the
constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise
of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later
promotion and adoption of mobile robots in various fields. keywords: الگوریتم هوش کامپیوتری | بینایی ماشین | ربات | ناوبری خودکار | الگوریتم کلونی مورچه ها | Computer intelligence algorithm | Machine vision | Robot | Automatic navigation | Ant colony algorithm |
مقاله انگلیسی |
135 |
On the Capacity of Quantum Private Information Retrieval From MDS-Coded and Colluding Servers
در مورد ظرفیت بازیابی اطلاعات خصوصی کوانتومی از سرورهای کدگذاری شده و تبانی MDS-2022 In quantum private information retrieval (QPIR),
a user retrieves a classical file from multiple servers by downloading quantum systems without revealing the identity of the file. The
QPIR capacity is the maximal achievable ratio of the retrieved file
size to the total download size. In this paper, the capacity of QPIR
from MDS-coded and colluding servers is studied for the first
time. Two general classes of QPIR, called stabilizer QPIR and
dimension-squared QPIR induced from classical strongly linear
PIR are defined, and the related QPIR capacities are derived.
For the non-colluding case, the general QPIR capacity is derived
when the number of files goes to infinity. A general statement on
the converse bound for QPIR with coded and colluding servers
is derived showing that the capacities of stabilizer QPIR and
dimension-squared QPIR induced from any class of PIR are
upper bounded by twice the classical capacity of the respective
PIR class. The proposed capacity-achieving scheme combines the
star-product scheme by Freij-Hollanti et al. and the stabilizer
QPIR scheme by Song et al. by employing (weakly) self-dual
Reed–Solomon codes.
Index Terms: Private information retrieval (PIR) | information theoretic privacy | quantum information theory | capacity. |
مقاله انگلیسی |
136 |
VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data
تجسم ها به عنوان بازنمایی های میانی (VLAIR): رویکردی برای به کارگیری بینایی کامپیوتری مبتنی بر یادگیری عمیق برای داده های غیر مبتنی بر تصویر-2022 Deep learning algorithms increasingly support automated systems in areas such as human activity
recognition and purchase recommendation. We identify a current trend in which data is transformed
first into abstract visualizations and then processed by a computer vision deep learning pipeline. We
call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental
to support accurate recognition in a number of fields while also enhancing humans’ ability to
interpret deep learning models for debugging purposes or for personal use. In this paper we describe
the potential advantages of this approach and explore various visualization mappings and deep
learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity
recognition in an apartment) and show that VLAIR attains classification accuracy above classical
machine learning algorithms and several other non-image-based deep learning algorithms with several
data representations.
keywords: تجسم اطلاعات | شبکه های عصبی کانولوشنال | تشخیص فعالیت های انسانی | خانه های هوشمند | بازنمایی داده ها | نمایندگی های میانی | تفسیر پذیری | یادگیری ماشین | یادگیری عمیق | Information visualization | Convolutional neural networks | Human activity recognition | Smart homes | Data representation | Intermediate representations | Interpretability | Machine learning | Deep learning |
مقاله انگلیسی |
137 |
On the Logical Error Rate of Sparse Quantum Codes
در مورد میزان خطای منطقی کدهای کوانتومی پراکنده-2022 The quantum paradigm presents a phenomenon known as degeneracy that can potentially
improve the performance of quantum error correcting codes. However, the effects of this mechanism are
sometimes ignored when evaluating the performance of sparse quantum codes and the logical error rate is
not always correctly reported. In this article, we discuss previously existing methods to compute the logical
error rate and we present an efficient coset-based method inspired by classical coding strategies to estimate
degenerate errors and distinguish them from logical errors. Additionally, we show that the proposed method
presents a computational advantage for the family of Calderbank–Shor–Steane codes. We use this method
to prove that degenerate errors are frequent in a specific family of sparse quantum codes, which stresses
the importance of accurately reporting their performance. Our results also reveal that the modified decoding
strategies proposed in the literature are an important tool to improve the performance of sparse quantum
codes.
INDEX TERMS: Iterative decoding | quantum error correction (QEC) | quantum low density generator matrix codes | quantum low-density parity check (QLDPC) codes. |
مقاله انگلیسی |
138 |
Computer vision for assessing species color pattern variation from web-based community science images
بینایی کامپیوتری برای ارزیابی تنوع الگوی رنگ گونه ها از تصاویر علم جامعه مبتنی بر وب-2022 Openly available community science digital vouchers provide a wealth of data to study phenotypic change across space and time. However, extracting phenotypic data from these resources requires significant human effort. Here, we demonstrate a workflow and computer vision model for automatically categorizing species color pattern from community science images. Our work is focused on documenting the striped/unstriped color polymorphism in the Eastern Red-backed Salamander (Plethodon cinereus). We used an ensemble convolutional neural network model to analyze this polymorphism in 20,318 iNaturalist images. Our model was highly accurate (∼98%) despite image heterogeneity. We used the resulting annotations to document extensive niche overlap between morphs, but wider niche breadth for striped morphs at the range-wide scale. Our work showcases key design principles for using machine learning with heterogeneous community science image data to address questions at an unprecedented scale.
keywords: Computer science | Ecology | Evolutionary biology |
مقاله انگلیسی |
139 |
On the Realistic Worst-Case Analysis of Quantum Arithmetic Circuits
در مورد تحلیل واقعی بدترین حالت مدارهای محاسباتی کوانتومی-2022 We provide evidence that commonly held intuitions when designing quantum circuits can be
misleading. In particular, we show that 1) reducing the T-count can increase the total depth; 2) it may be
beneficial to trade controlled NOTs for measurements in noisy intermediate-scale quantum (NISQ) circuits;
2) measurement-based uncomputation of relative phase Toffoli ancillae can make up to 30% of a circuit’s
depth; and 4) area and volume cost metrics can misreport the resource analysis. Our findings assume that
qubits are and will remain a very scarce resource. The results are applicable for both NISQ and quantum errorcorrected protected circuits. Our method uses multiple ways of decomposing Toffoli gates into Clifford+T
gates. We illustrate our method on addition and multiplication circuits using ripple-carry. As a byproduct
result, we show systematically that for a practically significant range of circuit widths, ripple-carry addition
circuits are more resource-efficient than the carry-lookahead addition ones. The methods and circuits were
implemented in the open-source QUANTIFY software.
|
مقاله انگلیسی |
140 |
Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics
هوش مصنوعی در مقابل انتخاب طبیعی: استفاده از تکنیکهای بینایی کامپیوتری برای طبقهبندی زنبورها و تقلیدهای زنبور عسل-2022 Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of and , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.
keywords: Artificial intelligence | Bioinformatics | Computing methodology | Entomology | Zoology |
مقاله انگلیسی |