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ردیف | عنوان | نوع |
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91 |
Measurement-Induced Boolean Dynamics for Open Quantum Networks
دینامیک بولی ناشی از اندازه گیری برای شبکه های کوانتومی باز-2022 In this paper, we study the recursion corresponding
to the measurement outcomes for open quantum networks
under sequential measurements. Open quantum networks are
networked quantum subsystems (e.g., qubits) with the state evolutions described by a continuous Lindblad master equation. When
measurements are performed sequentially along such continuous
dynamics, the quantum network states undergo probabilistic
jumps and the corresponding measurement outcomes can be
described by a vector of probabilistic Boolean variables. The
induced recursion of the Boolean vectors forms a probabilistic
Boolean network. First of all, we show that the state transition
of the induced Boolean network can be explicitly represented
through a real version of the master equation. Next, when the
open quantum dynamics are relaxing in the sense that they
possess a unique equilibrium as a global attractor, structural
properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences
of this relaxing property. Particularly, we show that generically,
relaxing quantum dynamics lead to irreducible and aperiodic
chains for the measurement outcomes. Finally, we show that for
quantum consensus networks which are a type of non-relaxing
open quantum network dynamics, the communication classes of
the measurement-induced Boolean networks are encoded in the
quantum Laplacian of the underlying interaction graph.
Index Terms: quantum networks | open quantum systems | quantum measurements | Boolean networks |
مقاله انگلیسی |
92 |
GAFL: Global adaptive filtering layer for computer vision
GAFL: لایه فیلتر تطبیقی جهانی برای بینایی کامپیوتر-2022 We devise a universal global adaptive filtering layer, GAFL, capable of ‘‘learning’’ optimal frequency filter for
each image in a dataset together with the weights of the base neural network that performs some computer
vision task. The proposed approach takes the source image in the spatial domain, selects the best frequencies
in the Fourier domain for the benefit of the global task, and prepends the inverse-transform image to the
main neural network for a joint training. Remarkably, such a simple add-on layer, capable of optimizing the
frequency content of an input for a specific task, dramatically improves the performance of the main network
regardless of its design. We observe that the light networks gain a noticeable boost in the performance metrics;
whereas, the training of the heavy ones converges faster when GAFL is prepended to the main architecture.
We showcase the performance of the layer in four classical computer vision tasks: classification, segmentation,
denoising, and erasing, considering popular natural and medical data benchmarks.
keywords: Adaptive neural layer | Efficient training | Fourier filtering |
مقاله انگلیسی |
93 |
Measurement Crosstalk Errors in Cloud-Based Quantum Computing
خطاهای متقابل اندازه گیری در محاسبات کوانتومی مبتنی بر ابر-2022 Quantum technologies available currently contain noise in general, often dubbed
noisy intermediate-scale quantum systems. We here present the verification of
noise in measurement readout errors in cloud-based quantum computing services,
IBMQ and Rigetti, by directly performing quantum detector tomography, and show
that there exist measurement crosstalk errors. We provide the characterization and
the quantification of noise in a quantum measurement of multiple qubits. We
remark that entanglement is found as a source of crosstalk errors in a
measurement of three qubits.
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مقاله انگلیسی |
94 |
Co-segmentation inspired attention module for video-based computer vision tasks
ماژول توجه الهام گرفته از تقسیم بندی مشترک برای وظایف بینایی کامپیوتری مبتنی بر ویدئو-2022 Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between
those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing
pre-trained models to perform object detection, object segmentation and/or object pose estimation. Although
using pre-trained models is a viable approach, it has several limitations in the need for an exhaustive annotation
of object categories, a possible domain gap between datasets and a bias that is typically present in pre-trained
models. In this work, we propose to utilize the common rationale that a sequence of video frames capture a
set of common objects and interactions between them, thus a notion of co-segmentation between the video
frame features may equip the model with the ability to automatically focus on task-specific salient regions
and improve the underlying task’s performance in an end-to-end manner. In this regard, we propose a generic
module called ‘‘Co-Segmentation inspired Attention Module’’ (COSAM) that can be plugged in to any CNN
model to promote the notion of co-segmentation based attention among a sequence of video frame features.
We show the application of COSAM in three video-based tasks namely: (1) Video-based person re-ID, (2) Video
captioning, & (3) Video action classification and demonstrate that COSAM is able to capture the task-specific
salient regions in video frames, thus leading to notable performance improvements along with interpretable
attention maps for a variety of video-based vision tasks, with possible application to other video-based vision
tasks as well.
keywords: توجه | تقسیم بندی مشترک | شناسه شخص | زیرنویس ویدیویی | طبقه بندی ویدیویی | Attention | Co-segmentation | Personre-ID | Video-captioning | Video classification |
مقاله انگلیسی |
95 |
Memristor Crossbar Arrays Performing Quantum Algorithms
آرایه های ضربدری ممریستور که الگوریتم های کوانتومی را انجام می دهند-2022 There is a growing interest in quantum computers
and quantum algorithm development. It has been proved that
ideal quantum computers, with zero error rates and large
decoherence times, can solve problems that are intractable
for today’s classical computers. Quantum computers use two
resources, superposition and entanglement, that have no classical
analog. Since quantum computer platforms that are currently
available comprise only a few dozen of qubits, the use of quantum
simulators is essential in developing and testing new quantum
algorithms. We present a novel quantum simulator based on
memristor crossbar circuits and use them to simulate well-known
quantum algorithms, namely the Deutsch and Grover quantum algorithms. In quantum computing the dominant algebraic
operations are matrix-vector multiplications. The execution time
grows exponentially with the simulated number of qubits, causing
an exponential slowdown in quantum algorithm execution using
classical computers. In this work, we show that the inherent
characteristics of memristor arrays can be used to overcome this
problem and that memristor arrays can be used not only as independent quantum simulators but also as a part of a quantum computer stack where classical computers accelerators are
connected. Our memristive crossbar circuits are re-configurable
and can be programmed to simulate any quantum algorithm.
Index Terms— Memristors | memristor crossbars | quantum algorithms | quantum simulators. |
مقاله انگلیسی |
96 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
97 |
Mobile Control Plane Design for Quantum Satellite Backbones
طراحی هواپیمای کنترل سیار برای ستون فقرات ماهواره ای کوانتومی-2022 The interconnection of quantum computers
through the so-called Quantum Internet is a very
promising approach.
The most critical issues concern the physical
layer, considering that the creation of entanglement over long distances is still problematic.
Given the difficulty that usually arises from fiber
optics due to exponential losses, the introduction of intermediate quantum repeaters (QRs)
allows mitigating the problem. A quantum satellite network based on QRs on satellites deployed
over low Earth orbit could make it possible to
overcome the constraints of terrestrial optical
networks. Hence, the recent technological developments in terms of quantum satellite communications motivated our investigation on an ad
hoc quantum satellite backbone design based on
the software defined networking paradigm with a
control plane directly integrated into the constellation itself. Our aim is to outline some guidelines
by comparing several options. Specifically, the
focus is to analyze different architectural solutions
making some considerations on their feasibility,
possible benefits, and costs. Finally, we performed
some simulations on the architectures we considered the most promising, concluding that the integration of the control plane in the constellation
itself is the most appropriate solution.
keywords: |
مقاله انگلیسی |
98 |
Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022 Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In
recent years, social media data have become a promising data source for the assessment of cultural ecosystem
services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the
CES and landscape features from social media photos by manual content analysis. In addition, relatively few
studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this
study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition,
image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud
Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and
landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that
the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’
photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes
(corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated
image analysis on social media photos. We also noticed that tourists paid more attention to the place identity
featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants,
flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and
CES assessment, which could provide insights for urban park planning and tourism management. keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision |
مقاله انگلیسی |
99 |
A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022 The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing |
مقاله انگلیسی |
100 |
Multilevel 2-D Quantum Wavelet Transforms
تبدیل موجک کوانتومی دو بعدی چندسطحی-2022 Wavelet transform is being widely used in classical
image processing. One-dimension quantum wavelet transforms
(QWTs) have been proposed. Generalizations of the 1-D QWT
into multilevel and multidimension have been investigated but
restricted to the quantum wavelet packet transform (QWPTs),
which is the direct product of 1-D QWPTs, and there is no transform between the packets in different dimensions. A 2-D QWT
is vital for image processing. We construct the multilevel 2-D
QWT’s general theory. Explicitly, we built multilevel 2-D Haar
QWT and the multilevel Daubechies D4 QWT, respectively. We
have given the complete quantum circuits for these wavelet transforms, using both noniterative and iterative methods. Compared
to the 1-D QWT and wavelet packet transform, the multilevel
2-D QWT involves the entanglement between components in different degrees. Complexity analysis reveals that the proposed
transforms offer exponential speedup over their classical counterparts. Also, the proposed wavelet transforms are used to realize
quantum image compression. Simulation results demonstrate that
the proposed wavelet transforms are significant and obtain the
same results as their classical counterparts with an exponential
speedup.
Index Terms: Multilevel 2-D-Daubechies quantum wavelet transform (QWT) | multilevel 2-D-Haar QWT | quantum image processing. |
مقاله انگلیسی |