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ردیف | عنوان | نوع |
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51 |
Equivalence Checking of Quantum Circuits With the ZX-Calculus
بررسی هم ارزی مدارهای کوانتومی با ZX-calculus-2022 As state-of-the-art quantum computers are capable of running increasingly complex algorithms, the need for
automated methods to design and test potential applications
rises. Equivalence checking of quantum circuits is an important,
yet hardly automated, task in the development of the quantum
software stack. Recently, new methods have been proposed that
tackle this problem from widely different perspectives. One of
them is based on the ZX-calculus, a graphical rewriting system
for quantum computing. However, the power and capability of
this equivalence checking method has barely been explored.
The aim of this work is to evaluate the ZX-calculus as a
tool for equivalence checking of quantum circuits. To this end,
it is demonstrated how the ZX-calculus based approach for
equivalence checking can be expanded in order to verify the
results of compilation flows and optimizations on quantum
circuits. It is also shown that the ZX-calculus based method
is not complete—especially for quantum circuits with ancillary
qubits. In order to properly evaluate the proposed method,
we conduct a detailed case study by comparing it to two other
state-of-the-art methods for equivalence checking: one based
on path-sums and another based on decision diagrams. The
proposed methods have been integrated into the publicly available
QCEC tool (https://github.com/cda-tum/qcec) which is
part of the Munich Quantum Toolkit (MQT).
Index Terms: Quantum computing | formal verification | quantum circuit. |
مقاله انگلیسی |
52 |
A novel method of fish tail fin removal for mass estimation using computer vision
یک روش جدید حذف باله دم ماهی برای تخمین جرم با استفاده از بینایی کامپیوتر-2022 Fish mass estimation is extremely important for farmers to get fish biomass information, which could be useful to
optimize daily feeding and control stocking densities and ultimately determine optimal harvest time. However,
fish tail fin mass does not contribute much to total body mass. Additionally, the tail fin of free-swimming fish is
deformed or bent for most of the time, resulting in feature measurement errors and further affecting mass
prediction accuracy by computer vision. To solve this problem, a novel non-supervised method for fish tail fin
removal was proposed to further develop mass prediction models based on ventral geometrical features without
tail fin. Firstly, fish tail fin was fully automatically removed using the Cartesian coordinate system and image
processing. Secondly, the different features were respectively extracted from fish image with and without tail fin.
Finally, the correlational relationship between fish mass and features was estimated by the Partial Least Square
(PLS). In this paper, tail fins were completely automatically removed and mass estimation model based on area
and area square has been the best tested on the test dataset with a high coefficient of determination (R2) of 0.991,
the root mean square error (RMSE) of 7.10 g, the mean absolute error (MAE) of 5.36 g and the maximum relative
error (MaxRE) of 8.46%. These findings indicated that mass prediction model without fish tail fin can more
accurately estimate fish mass than the model with tail fin, which might be extended to estimate biomass of free-
swimming fish underwater in aquaculture. keywords: برداشتن باله دم | اتوماسیون | ماهی | تخمین انبوه | بینایی کامپیوتر | Tail fin removal | Automation | Fish | Mass estimation | Computer vision |
مقاله انگلیسی |
53 |
Equivalence Checking of Sequential Quantum Circuits
بررسی هم ارزی مدارهای کوانتومی متوالی-2022 We define a formal framework for equivalence
checking of sequential quantum circuits. The model we adopt
is a quantum state machine, which is a natural quantum generalization of Mealy machines. A major difficulty in checking
quantum circuits (but not present in checking classical circuits)
is that the state spaces of quantum circuits are continuums. This
difficulty is resolved by our main theorem showing that equivalence checking of two quantum Mealy machines can be done with
input sequences that are taken from some chosen basis (which are
finite) and have a length quadratic in the dimensions of the state
Hilbert spaces of the machines. Based on this theoretical result,
we develop an (and to the best of our knowledge, the first) algorithm for checking equivalence of sequential quantum circuits
with running time O(23m+5l(23m + 23l)), where m and l denote
the numbers of input and internal qubits, respectively. The complexity of our algorithm is comparable with that of the known
algorithms for checking classical sequential circuits in the sense
that both are exponential in the number of (qu)bits. Several case
studies and experiments are presented.
Index Terms: Equivalence checking | mealy machines | quantum circuits | quantum computing | sequential circuits. |
مقاله انگلیسی |
54 |
ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022 Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to
detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive,
manual task. This study proposes a novel approach for automated plumage condition assessment using com-
puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects
hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of
input image characteristics, the method was evaluated using images with and without depth information in
resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of
subjective human annotations, plumage condition predictions were compared to manual assessments of one
observer and to matching annotations of two observers. Among all tested settings, performance metrics based on
matching manual annotations of two observers were equal or better than the ones based on annotations of a
single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of
98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it
was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not
generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a
resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using
lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of
plumage conditions in commercial laying hen farms. keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation |
مقاله انگلیسی |
55 |
Evolution of Quantum Computing: Theoretical and Innovation Management Implications for Emerging Quantum Industry
تکامل محاسبات کوانتومی: مفاهیم مدیریت نظری و نوآوری برای صنعت کوانتومی در حال ظهور-2022 Quantum computing is a vital research field in science
and technology. One of the fundamental questions hardly known
is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies
for economic, industrial, and social change. This study confronts
the question here by applying methods of computational scientometrics for publication analyses to explain the structure and
evolution of quantum computing research and technologies over
a 30-year period. Results reveal that the evolution of quantum
computing from 1990 to 2020 has a considerable average increase of
connectivity in the network (growth of degree centrality measure),
a moderate increase of the average influence of nodes on the flow
between nodes (little growth of betweenness centrality measure),
and a little reduction of the easiest access of each node to all other
nodes (closeness centrality measure). This evolutionary dynamics
is due to the increase in size and complexity of the network in
quantum computing research over time. This study also suggests
that the network of quantum computing has a transition from
hardware to software research that supports accelerated evolution
of technological pathways in quantum image processing, quantum
machine learning, and quantum sensors. Theoretical implications
of this study show the morphological evolution of the network in
quantum computing from a symmetric to an asymmetric shape
driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation
management based on R&D investments in new technological directions of quantum computing having a high potential for growth
and impact in science and markets.
Index Terms: Innovation management | quantum algorithms | quantum computing (QC) | quantum network | technological change | technological paradigm | technological trajectories. |
مقاله انگلیسی |
56 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
57 |
Exploring Potential Applications of Quantum Computing in Transportation Modelling
بررسی کاربردهای بالقوه محاسبات کوانتومی در مدل سازی حمل و نقل-2022 The idea that quantum effects could be harnessed
to allow faster computation was first proposed by Feynman.
As of 2020 we appear to have achieved ‘quantum supremacy’,
that is, a quantum computer that performs a given task faster
than its classical counterpart. This paper examines some possibilities opened up by potential future application of quantum
computing to transportation simulation and planning. To date,
no such research was found to exist, therefore we begin with
an introduction to quantum computing for the programmers
of transport models. We discuss existing quantum computing
research relevant to transportation, finding developments in
network analysis, shortest path computation, multi-objective
routing, optimization and calibration – of which the latter three
appear to offer the greater promise in future research. Two
examples are developed in greater detail, (1) an application of
Grover’s quantum algorithm for extracting the mean, which has
general applicability towards summarizing distributions which
are expensive to compute classically, is applied to an assignment
or betweenness model - quantum speedup is elusive in the
general case but achievable when trading speed for accuracy
for limited outputs; (2) quantum optimization is applied to an
activity-based model, giving a theoretically quadratic speedup.
Recent developments notwithstanding, implementation of quantum transportation algorithms will for the foreseeable future
remain a challenge due to space overheads imposed by the
requirement for reversible computation.
Index Terms: Quantum computing | assignment | betweenness | flows, activity models | tour models. |
مقاله انگلیسی |
58 |
Prediction of total volatile basic nitrogen (TVB-N) and 2-thiobarbituric acid (TBA) of smoked chicken thighs using computer vision during storage at 4 °C
پیشبینی کل نیتروژن بازی فرار (TVB-N) و اسید ۲-تیوباربیتوریک (TBA) ران مرغ دودی با استفاده از بینایی رایانه در طول نگهداری در دمای ۴ درجه سانتیگراد-2022 As the traditional indicators of freshness measurement of meat products, TVB-N and TBA have the disadvantage
of time-consuming, labor-intensive and destructive to the sample. The objective of this study was to investigate
the possibility of computer vision techniques to visualize the variation of TVB-N and TBA during the storage of
smoked chicken thighs. In this study, freshness indicators (TVB-N and TBA) and images of smoked chicken thighs
were obtained simultaneously every 3 days during storage at 4 ◦C. Then, the RGB color space was converted to
HSI and L*a*b* color spaces by color conversion algorithm, and the color parameters (RGB, HSI and L*a*b*)
were correlated with TVB-N and TBA, respectively, for establishing multiple regression models. Finally, visu-
alization maps of the spoilage were established by applying the multiple regression model to each pixel in the
image. The results showed that the multiple linear regression models of TBA and TVB-N based on the color
parameters L*, a*, I, S and R were well correlated (R 2 = 0.993 for TBA and R 2 = 0.970 for TVB-N). Distribution
maps of TBA and TVB-N changed color gradually from blue to red during storage, respectively. In conclusion, this
study demonstrated that distribution maps can be employed as a rapid, objective, and non-destructive method to
predict the TBA and TVB-N values of smoked chicken thighs during storage. keywords: ران مرغ دودی | بینایی کامپیوتر | خنکی | TVB-N | TBA | Smoked chicken thigh | Computer vision | Freshness |
مقاله انگلیسی |
59 |
Fault-Tolerant Coherent H∞ Control for Linear Quantum Systems
کنترل منسجم H∞ مقاوم در برابر خطا برای سیستم های کوانتومی خطی-2022 Robustness and reliability are two key requirements for developing practical quantum control systems.
The purpose of this article is to design a coherent feedback
controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop
quantum system has both fault tolerance and H∞ disturbance attenuation performance. This article first extends
the physical realization conditions from the time-invariant
case to the time-varying case for linear stochastic quantum
systems. By relating the fault-tolerant H∞ control problem
to the dissipation properties and the solutions of Riccati
differential equations, an H∞ controller for the quantum
system is then designed by solving a set of linear matrix inequalities. In particular, an algorithm is employed to introduce additional quantum inputs and to construct the corresponding input matrices to ensure the physical realizability
of the quantum controller. Also, we propose a real application of the developed fault-tolerant control strategy to
quantum optical systems. A linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values due to
the fault processes, can be modeled as a linear Markovian
jumping system. It is demonstrated that a quantum H∞
controller can be designed and implemented using some
basic optical components to achieve the desired control
goal for this class of systems.
Index Terms: Coherent feedback control | fault-tolerant quantum control | H∞ control | linear quantum systems | quantum controller. |
مقاله انگلیسی |
60 |
Barriers to computer vision applications in pig production facilities
موانع برنامه های بینایی کامپیوتری در تاسیسات تولید خوک-2022 Surveillance and analysis of behavior can be used to detect and characterize health disruption and welfare status
in animals. The accurate identification of changes in behavior is a time-consuming task for caretakers in large,
commercial pig production systems and requires strong observational skills and a working knowledge of animal
husbandry and livestock systems operations. In recent years, many studies have explored the use of various
technologies and sensors to assist animal caretakers in monitoring animal activity and behavior. Of these
technologies, computer vision offers the most consistent promise as an effective aid in animal care, and yet, a
systematic review of the state of application of this technology indicates that there are many significant barriers
to its widespread adoption and successful utilization in commercial production system settings. One of the most
important of these barriers is the recognition of the sources of errors from objective behavior labeling that are not
measurable by current algorithm performance evaluations. Additionally, there is a significant disconnect between the remarkable advances in computer vision research interests and the integration of advances and
practical needs being instituted by scientific experts working in commercial animal production partnerships. This
lack of synergy between experts in the computer vision and animal health and production sectors means that
existing and emerging datasets tend to have a very particular focus that cannot be easily pivoted or extended for
use in other contexts, resulting in a generality versus particularity conundrum.
This goal of this paper is to help catalogue and consider the major obstacles and impediments to the effective
use of computer vision associated technologies in the swine industry by offering a systematic analysis of computer vision applications specific to commercial pig management by reviewing and summarizing the following:
(i) the purpose and associated challenges of computer vision applications in pig behavior analysis; (ii) the use of
computer vision algorithms and datasets for pig husbandry and management tasks; (iii) the process of dataset
construction for computer vision algorithm development. In this appraisal, we outline common difficulties and
challenges associated with each of these themes and suggest possible solutions. Finally, we highlight the opportunities for future research in computer vision applications that can build upon existing knowledge of pig
management by extending our capability to interpret pig behaviors and thereby overcome the current barriers to
applying computer vision technologies to pig production systems. In conclusion, we believe productive collaboration between animal-based scientists and computer-based scientists may accelerate animal behavior studies
and lead the computer vision technologies to commercial applications in pig production facilities.
keywords: بینایی کامپیوتر | دامپروری دقیق | رفتار - اخلاق | یادگیری عمیق | مجموعه داده | گراز | Computer vision | Precision livestock farming | Behavior | Deep learning | Dataset | Swine |
مقاله انگلیسی |