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DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks
DQRA: عامل مسیریابی کوانتومی عمیق برای مسیریابی درهم تنیده در شبکه های کوانتومی-2022 Quantum routing plays a key role in the development of the next-generation network system. In
particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping
among particles (e.g., photons) associated with nodes in the network. From another side of computing,
machine learning has achieved numerous breakthrough successes in various application domains, including
networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum
networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model
for quantum networks that employs machine learning architectures to construct the routing path for the
maximum number of demands (source–destination pairs) within a time window. Specifically, we present a
deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA
utilizes an empirically designed deep neural network that observes the current network states to accommodate
the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training
process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated
requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a
rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in
extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the
model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum
networks.
INDEX TERMS: Deep learning | deep reinforcement learning (DRL) | machine learning | next-generation network | quantum network routing | quantum networks. |
مقاله انگلیسی |
2 |
High-accuracy in the classification of butchery cut marks and crocodile tooth marks using machine learning methods and computer vision algorithms
دقت بالا در طبقه بندی علائم برش قصابی و علائم دندان تمساح با استفاده از روش های یادگیری ماشین و الگوریتم های بینایی کامپیوتری-2022 Some researchers using traditional taphonomic criteria (groove shape and presence/absence of microstriations) have cast some doubts about the potential equifinality presented by crocodile tooth marks and
stone tool butchery cut marks. Other researchers have argued that multivariate methods can efficiently
separate both types of marks. Differentiating both taphonomic agents is crucial for determining the earliest evidence of carcass processing by hominins. Here, we use an updated machine learning approach
(discarding artificially bootstrapping the original imbalanced samples) to show that microscopic features
shaped as categorical variables, corresponding to intrinsic properties of mark structure, can accurately
discriminate both types of bone modifications. We also implement new deep-learning methods that
objectively achieve the highest accuracy in differentiating cut marks from crocodile tooth scores (99%
of testing sets). The present study shows that there are precise ways of differentiating both taphonomic
agents, and this invites taphonomists to apply them to controversial paleontological and archaeological
specimens.
keywords: تافونومی | علائم برش | علائم دندان | فراگیری ماشین | یادگیری عمیق | شبکه های عصبی کانولوشنال | قصابی | Taphonomy | Cut marks | Tooth marks | Machine learning | Deep learning | Convolutional neural networks | Butchery |
مقاله انگلیسی |
3 |
Pursuits in Collision Affiliation, Disaffiliation, and Multimodality in Persian Interaction
تعقیب وابستگی برخورد، عدم وابستگی و چندوجهی در تعامل فارسی-2022 This study is on pursuing an interactional outcome in the face of a co-interactant’s resistance. Despite
at least a forty-year history of research on pursuits in social interaction (Jefferson, 1981; Pomerantz,
1984b), there is still much to explore about this ubiquitous social phenomenon. This research employs
a multimodal conversation analytic methodology to address some less-explored questions on pursuits:
what practices does an interactant use to further their course of action against their co-interactant’s
resistance? Do the details of these practices have implications for the trajectory of the interaction
towards escalation or de-escalation? What do these practices tell us about the agentive stance adopted
by the pursuing party? And how can interactants heading towards an escalated pursuit manage
disaffiliation? Two different types of pursuit sequences are introduced: persisting in furthering one’s
course of action and gradually desisting from a course of action. The findings show a novel
phenomenon called multimodal gradation: a temporally coordinated up- or downgrading of a multitude
of resources that are simultaneously used in formatting a social action. Borrowing Mondada’s terms
(2014), a whole “multimodal Gestalt” by which a turn at talk is delivered is up- or downgraded.
Multimodal upgrading of a pursuit turn projects further expansions to the pursuit sequence and it can
escalate an initial clash. On the other hand, multimodal downgrading of a pursuit turn projects a
contingent sequence closure and de-escalation. Also, upgrading the multimodal Gestalt of a pursuit turn
displays the pursuing party’s stronger agentive stance compared to downgrading the turn. The project
introduces another multimodal phenomenon termed mock aggression. Used between intimate
interactants, mock aggression offers opportunities for affiliation despite its aggressive appearance. The
findings have implications for our understanding of sequence and preference organization in CA,
multimodality, agency, and conflict management. Data are in Persian and collected in Iran. |
مقاله انگلیسی |
4 |
Curriculum-Based Deep Reinforcement Learning for Quantum Control
یادگیری تقویتی عمیق مبتنی بر برنامه درسی برای کنترل کوانتومی-2022 Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for
different complex systems without prior knowledge of the control
landscape. To achieve a fast and precise control for quantum
systems, we propose a novel DRL approach by constructing a
curriculum consisting of a set of intermediate tasks defined by
fidelity thresholds, where the tasks among a curriculum can be
statically determined before the learning process or dynamically
generated during the learning process. By transferring knowledge
between two successive tasks and sequencing tasks according to
their difficulties, the proposed curriculum-based DRL (CDRL)
method enables the agent to focus on easy tasks in the early
stage, then move onto difficult tasks, and eventually approaches
the final task. Numerical comparison with the traditional methods
[gradient method (GD), genetic algorithm (GA), and several
other DRL methods] demonstrates that CDRL exhibits improved
control performance for quantum systems and also provides an
efficient way to identify optimal strategies with few control pulses.
Index Terms: Curriculum learning | deep reinforcement learning (DRL) | quantum control. |
مقاله انگلیسی |
5 |
Management of Pediatric Atopic Dermatitis by Primary Care Providers: A Systematic Review
مدیریت درماتیت آتوپیک اطفال توسط ارائه دهندگان مراقبت های اولیه: مرور سیستماتیک-2021 BACKGROUND: Primary care providers (PCPs), including
pediatricians and general practitioners, are often the first to see
children with eczema/atopic dermatitis (AD). Little is known
about management of pediatric AD by PCPs and adherence to
national guidelines.
OBJECTIVE: To review existing literature examining management components of pediatric AD (topical corticosteroids
[TCS], topical calcineurin inhibitors [TCIs], antihistamines,
bathing, emollients, and diet) by PCPs.
DATA SOURCES: PubMed/Medline and Embase.
STUDY ELIGIBILITY CRITERIA: English-language articles dated
2015 to 2020 reporting outcomes addressing management of
pediatric AD by PCPs.
STUDY APPRAISAL AND SYNTHESIS METHODS: Two authors
independently screened titles/abstracts, reviewed full-text
articles, extracted relevant data, and evaluated study quality.
Disagreements were resolved by a third author.
RESULTS: Twenty articles were included. Surveys and
national database analyses were the most common methodologies (n = 7 each). PCPs commonly prescribed TCS but had a
preference for low-potency agents, overprescribed nonsedating
antihistamines, and avoided TCIs. PCPs commonly recommended emollients, although this was not universal. Data characterizing nonmedication management were limited.
LIMITATIONS: Most studies did not examine individual patient
encounters, but rather relied on providers reporting their general behaviors. Provider behavior may vary based on country
of practice.
CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Knowledge and management gaps exist among PCPs in treating pediatric AD in key areas including knowledge of TCS safety
profiles and prescribing of TCIs. The current literature is
largely limited to small studies that evaluate prescribing
behaviors with limited data characterizing nonmedication
management, highlighting the need for future research in this
area.
KEYWORDS: atopic dermatitis | eczema | health care delivery | primary care provider | pediatric |
مقاله انگلیسی |
6 |
Agility and system documentation in large-scale enterprise system projects: a knowledge management perspective
چابکی و اسناد سیستم در پروژه های سیستم سازمانی در مقیاس بزرگ: دیدگاه مدیریت دانش-2021 The growth of the agile approach usage comes with a deemphasis on formal documentation (explicit knowledge) and an increased
reliance on personal interactions (tacit knowledge) for knowledge transfer. However, the sharing of tacit knowledge poses
challenges. The agile approach is prone to knowledge hoarding, as well as knowledge loss from employee turnover and
reassignment during periods of significant organizational changes. This study proposes a model that frames documentation and
personal interactions as co-agents of system knowledge transfer. We report the preliminary confirmation of crucial antecedents
along the dimensions of codification and personalization strategies to support our model. We present a set of findings on current
practices, as well as a set of knowledge-sharing issues on system documentation based on three main categories. The first category
incorporates system development approaches applied in large-scale enterprise systems projects. The second and the third categories
comprise eight knowledge management themes, classified into the dimensions of personalization and codification for knowledge
sharing and document practices. Finally, we put forward five propositions based on our findings.
Keywords: Agile system implementation | system documentation | system knowledge | tacit knowledge | explicit knowledge | knowledge sharing |
مقاله انگلیسی |
7 |
Accounting-based downside risk and stock price crash risk: Evidence from China
ریسک نزولی مبتنی بر حسابداری و خطر سقوط قیمت سهام: شواهدی از چین-2021 In the past 15 years, an emerging literature has extensively studied individual stock price crash risk, which refers to the likelihood
of an abrupt and large-scale drop in stock prices (e.g., Chen et al., 2001; Hutton et al., 2009; Jin and Myers 2006; Kim et al., 2011a, Li
and Zhang 2011b; Kim and Zhang 2016). An important strand of this literature focuses on the Chinese emerging markets where,
arguably, the extent of “bad news hoarding” is severer compared to developed markets due to China’s less effective corporate
governance environment (Wang et al., 2020). In this paper, we examine the relationship between accounting-based downside risk and
stock price crash risk using a large sample of Chinese listed firms.
The contribution of this study lies in a recently developed indicator of earnings fundamentals that is, arguably, more consistent with
“bad news hoarding”: accounting-based downside risk, hereafter denoted as ABDR. Studies have shown that investors care more about
downside losses than upside gain potentials and are therefore more sensitive to losses than to gains (e.g., Gul 1991; Kahneman and
Tversky 1979). Accordingly, Koonce et al. (2005) show that economic agents judge negative and positive expectations differently in
risk management, placing more emphasis on potential loss outcomes. However, earnings volatility and other existing accounting-based
downside risk measures consist of both downside and upside variabilities with equal weights and little research has examined the
downside risk of accounting-based measures. Konchitchki et al. (2016) are the first to construct measures of accounting-based
downside risk and examine its pricing implications in U.S. markets. In particular, this study uses the relative root lower partial
moment as a mathematical foundation to capture exposure to downside risk rather than the overall volatility. Accounting-based
downside risk measures focus on the below-expectation variability in firm performance measures, particularly return-on-assets (ROA).
We extend Konchitchki et al. (2016) by performing an investigation in the Chinese markets. Furthermore, we examine the variation keywords: Accounting-based downside risk | Stock price crash risk | Bad-news hoarding, China | ریسک نزولی مبتنی بر حسابداری | ریسک سقوط قیمت سهام | احتکار اخبار بد، چین |
مقاله انگلیسی |
8 |
Optimization of extended business processes in digital supply chains using mathematical programming
بهینه سازی فرآیندهای تجاری گسترده در زنجیره های تأمین دیجیتال با استفاده از برنامه ریزی ریاضی-2021 We propose a mathematical programming approach to optimize the business process transactions in digital supply chains. Five scheduling models from the Process Systems Engineering (PSE) area are applied
to schedule the processing of orders in a simplified Order-To-Cash (OTC) business process, which is modeled as a multistage network with parallel units (agents). Two case studies are presented to compare the
performance of the scheduling models on various sizes of a flexible jobshop representation of the OTC
process. The models are compared and scaled to select those that are more suitable to this application.
The continuous-time general precedence model provides an accurate representation of the real system
and performs well for small instances. The discrete-time State-Task Network (STN), however, proves most
efficient in terms of tractability, despite the well-known limitations resulting from discretizing time. The
tightness of the linear programming (LP) relaxations in the discrete-time STN framework, as well as the
ability of commercial solvers to perform preprocessing and apply heuristics to the STN formulation, enables finding near optimal solutions quickly even for larger instances. Keywords: Business process optimization | digital supply chain | order-to-cash | scheduling | mathematical programming |
مقاله انگلیسی |
9 |
Hybrid simulation models for spare parts supply chain considering 3D printing capabilities
مدل های شبیه سازی ترکیبی برای زنجیره تامین قطعات یدکی با توجه به قابلیت های چاپ سه بعدی-2021 In the era of Industry 4.0, 3D printing unlocks a wide array of solutions to rapidly-produce spare parts for maintenance operations. In this research, we propose a hybrid simulation approach, combining agent-based and discrete event simulation methods, to investigate how the adoption of 3D printing technologies to manufacture spare parts for maintenance operations will improve operational efficiency and effectiveness. Specifically, our framework is applied to the United States Navy’s fighter jet maintenance operations to study various network configurations, where 3D printing facilities may be centralized, decentralized, or hub configured. System performance in terms of the total cost, timeliness of delivery, and vulnerability under disruptions such as cyber- attacks and emergencies are evaluated. Lastly, the impact of 3D printing technological advancements on operational performance is investigated to obtain managerial insights. Keywords: 3D printing | Hybrid simulation | Maintenance operations | Supply chain network configuration |
مقاله انگلیسی |
10 |
مدلسازی شیفتهای کاری در پروژههای ساخت با استفاده از یک رویکرد مبتنی بر عامل برای به حداقل رساندن انتشار COVID19
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 25 گسترش COVID19 نحوه ارتباط ما با افراد دیگر و محیط ساختهمان را تغییر دادهاست، تغییر از حالت شخصی به فعالیتهای عمدتا مجازی. با این حال، برخی از فعالیتها عملا غیر ممکن هستند، مانند فعالیتهای ساختوساز. در نتیجه، صنعت ساختوساز به شدت تحتتاثیر پاندمی ناشی از COVID19 قرار گرفتهاست. در پاسخ به اثرات پاندمی، بخش ساختوساز باید جایگزینهایی را شناسایی کند که میتواند انتشار COVID19 را در میان کارگران در پروژههای ساختوساز به حداقل برساند. یعنی، با اختصاص دادن تقریبا نیمی از کارگران درگیر در یک پروژه به شیفت شب، می توان متوسط تعداد کارگران سالم را در یک پروژه تا ۲۰ نفر افزایش داد. تعیین تاثیر جایگزینهایی که ممکن است گسترش COVID19 را در میان کارگران ساختوساز کاهش دهد میتواند اجرای چنین جایگزینهایی را توسط مدیران ساختوساز تشویق کند.
کلمات کلیدی: ساخت | COVID19 | شیفتهای کاری | مدلسازی مبتنی بر عامل |
مقاله ترجمه شده |