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–Devising a dynamic pricing policy with always valid online statistical learning procedures is an important and as yet unresolved problem. Most existing dynamic pricing policies, which focus on the faithfulness of adopted customer choice models, exhibit a limited capability for adapting to the online uncertainty of learned statistical models during the pricing process. In this article, we propose a novel approach for designing a dynamic pricing policy based on regularized online statistical learning with theoretical guarantees. The new approach overcomes the challenge of continuous monitoring of the online Lasso procedure and possesses several appealing properties. In particular, we make the decisive observation that the always-validity of pricing decisions builds and thrives on the online regularization scheme. Our proposed online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (OORMLP) pricing policy with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity overall decision points; envelope prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design more sample-efficient and robust dynamic pricing algorithms in practice. In theory, the proposed OORMLP algorithm exploits the sparsity structure of high-dimensional models and secures a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online Lasso procedure that resolves dynamic pricing problems at the process level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate OORMLP in different synthetic and real pricing problem settings and demonstrate that OORMLP advances the state-of-the-art methods. for this article are available online. [ABSTRACT FROM AUTHOR]

Osmanlı Devleti’nden Türkiye Cumhuriyeti’ne kalan sorunlu miraslardan birisi eşkıyalık ve çetecilikti. On yıl boyunca devam savaş döneminde asayişi sağlamakla görevli jandarmanın cephelere savaşmak için gönderilmesiyle birlikte huzur ve güven ortamı bozuldu. Uzun savaş yıllarının ortaya çıkardığı firari askerler meselesi de ülke içerisinde faaliyet gösteren eşkıya sayısının artmasına neden oldu. Eşkıyalık, 1914’ten itibaren her zamankinden daha güçlü bir Anadolu gerçeğine dönüştü. On yıl boyunca devam eden savaş, asayişin bozulmasına neden olduğu gibi asayişi sağlaması beklenen kolluk kuvvetlerinin de yıpranmasına sebep olmuştu. Söz konusu dönemde devlet-toplum veya toplum-birey arasındaki kopuşun kanunsuz bir hüviyete bürünmüş hâli olarak ortaya çıkan eşkıyalığın bertaraf edilmesi yeni kurulan Türkiye Cumhuriyeti’ni uzun süre uğraştırdı. 1924’ten itibaren ise ülke genelinde büyük çaplı isyan ve ayaklanma faaliyetleri görüldü. Toplumun belirli bir grubunu temsil etme iddiasıyla faaliyet gösterenlerin yanı sıra azımsanamayacak miktarda da şahsi çıkarları için eşkıyalık yapanların varlıkları söz konusuydu. Ülkenin belirli bölgelerinde hüküm süren feodal yapı, yoksulluk, uzun süre silah altında kalma, vergi yükü, toprak sahibiyle olan ilişkiler eşkıyalığın artmasına neden olan en temel sebepti. Türkiye Cumhuriyeti Devleti asayişi bozan unsurlarla mücadele edebilmek için önce asayişi sağlamakla mükellef olan kurumlarla kamu idaresini yeniden düzenledi. 1931’den itibaren eşkıyalığa karşı sergilenen kararlı tutum yedi yıl gibi bir sürede bu durumu ülke gündeminden çıkarmayı başardı. Çalışmada Cumhuriyetin tesisinden itibaren Türkiye’de görülen eşkıyalık türleri arşiv belgelerine göre kategorize ve tasnif edildi.

Copyright of Turcology Research is the property of Ataturk University Coordinatorship of Scientific Journals and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mavi Atlas is the property of Mavi Atlas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ottoman Legacy Studies (OMAD) / Osmanlı Mirası Araştırmaları Dergisi is the property of Journal of Ottoman Legacy Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Social Sciences Studies is the property of Social Sciences Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Through repeated interactions, firms today refine their understanding of individual users' preferences adaptively for personalization. In this paper, we use a continuous-time bandit model to analyze firms that recommend content to multihoming consumers, a representative setting for strategic learning of consumer preferences to maximize lifetime value. In both monopoly and duopoly settings, we compare a forward-looking recommendation algorithm that balances exploration and exploitation to a myopic algorithm that only maximizes the quality of the next recommendation. Our analysis shows that, compared with a monopoly, firms competing for users' attention focus more on exploitation than exploration. When users are impatient, competition decreases the return from developing a forward-looking algorithm. In contrast, development of a forward-looking algorithm may hurt users under monopoly but always benefits users under competition. Competing firms' decisions to invest in a forward-looking algorithm can create a prisoner's dilemma. Our results have implications for artificial intelligence adoption and for policy makers on the effect of market power on innovation and consumer welfare. This paper was accepted by Dmitri Kuksov, marketing. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4722. [ABSTRACT FROM AUTHOR]

A Data-Driven Approach to Improve Care Unit Placements in Hospitals The choice of care unit upon hospital admission is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. These decisions require carefully weighing the benefits of improved health outcomes against the opportunity cost of reserving higher level care beds for potentially more complex patients arriving in the future. In "Data-Driven Hospital Admission Control: A Learning Approach," Zhalechian, Keyvanshokooh, Shi, and Van Oyen introduce a data-driven algorithm to address this challenging task. By focusing on reducing the readmission risk of patients, the algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) determine the best care unit placement for a patient based on the observed information and occupancy levels to minimize total readmission risk. The algorithm is supported by a performance guarantee, and its effectiveness is showcased using real-world hospital system data. The choice of care unit upon admission to the hospital is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. The care unit placement decisions involve capturing the trade-off between the benefit of better health outcomes versus the opportunity cost of reserving higher level of care beds for potentially more complex patients arriving in the future. By focusing on reducing the readmission risk of patients, we develop an online algorithm for care unit placement under the presence of limited reusable hospital beds. The algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) choose the best care unit placement for a patient based on the observed information and the occupancy level of the care units. We prove that our online algorithm admits a Bayesian regret bound. We also investigate and assess the effectiveness of our methodology using hospital system data. Our numerical experiments demonstrate that our methodology outperforms different benchmark policies. Funding: This work was supported by the National Science Foundation [Grant CMMI 1548201]. C. Shi was supported by Amazon Research Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2020.0481. [ABSTRACT FROM AUTHOR]

The stereotypes that characterize the figure of the outlaw seem to intensely blend folklore and legend, developing tales and narratives that definitely have the tone of a fairy tale. In reality, the image of the bandit-brigand, which nineteenth-century society and culture conveyed, constitutes the reflection of the new national political realities and the emergence of a bourgeois class which, since the second half of the eighteenth century, developed a concept of popular culture as a distinctive trait of the subordinate classes. In the background we can clearly glimpse the cultural and ideological detachment that has arisen from a world characterized by customs and orality, as well as the difficulties in grasping the social and political dimension of realities of the past with a different concept of order and of rationality. [ABSTRACT FROM AUTHOR]

Neural Architecture Search (NAS) is a highly challenging task that requires consideration of search space, search efficiency, and adversarial robustness of the network. In this paper, to accelerate the training speed, we reformulate NAS as a multi-armed bandit problem and present Anti-Bandit NAS (ABanditNAS) method, which exploits Upper Confidence Bounds (UCB) to abandon arms for search efficiency and Lower Confidence Bounds (LCB) for fair competition between arms. Based on the presented ABanditNAS, the adversarially robust optimization and architecture search can be solved in a unified framework. Specifically, our proposed framework defends against adversarial attacks based on a comprehensive search of denoising blocks, weight-free operations, Gabor filters, and convolutions. The theoretical analysis on the rationality of the two confidence bounds in ABanditNAS are provided and extensive experiments on three benchmarks are conducted. The results demonstrate that the presented ABanditNAS achieves competitive accuracy at a reduced search cost compared to prior methods. [ABSTRACT FROM AUTHOR]

We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade‐off between exploration and exploitation in this adaptation. Borrowing ideas from the online learning literature, we propose Daisee, a partition‐based AIS algorithm. We further introduce a notion of regret for AIS and show that Daisee has 풪(T(logT)34) cumulative pseudo‐regret, where T$$ T $$ is the number of iterations. We then extend Daisee to adaptively learn a hierarchical partitioning of the sample space for more efficient sampling and confirm the performance of both algorithms empirically. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Sciences Institute / Sosyal Bilimler Enstitüsü Dergisi is the property of Bingol University / Rectorate and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many studies on the exploration policies for stochastic multi-agent bandit (MAB) problems demonstrate that integrating the experience of other group members accelerates the learning of optimal actions. However, the basic assumption of the classical MAB problem that the expected rewards are agent-independent is invalid in many real-world problems. The group members have different expected rewards for the possible actions, perhaps due to the different initial states or local environments. To solve the MAB problem with agent-dependent expected rewards, we develop a decentralized exploration policy in which agents apply confidence-weighting to integrate the experience of other group members and to estimate the expected rewards. Theoretical analysis demonstrates that the acceleration of learning still works in the agent-dependent case, and numerical simulation results verify that the proposed exploration policy outperforms the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Copyright of Erciyes Akademi is the property of Erciyes Universitesi Sosyal Bilimler Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A static analysis method is one of the popular methods of software code analysis. Such method allows checking the code for compliance with the language specification as well as finding potential vulnerabilities. In this work, a static analysis of a corpus of listings of open-source Python applications is performed. Using the Bandit library, statistical values of various categories of potential vulnerabilities are found, and a rating table of vulnerabilities detected in the dataset involved is constructed. A qualitative analysis of threats is performed according to their severity based on the CWE data. [ABSTRACT FROM AUTHOR]

Security is one of the essential issues for every individual and society. States want to be protected from internal and external dangers and to make the people feel safe against possible dangers. Law enforcement agencies such as the gendarmerie and the police ensure the peace and security of states in the world. Places that felt insecure often became enceinte with problems. Due to the troubles in the last period of the Ottoman Empire, various outbursts arose. Especially after the First World War, when the central authority was shaken, an opportunity arose for many bandits. Therefore, gaps have emerged in the state at the point of public order and security. The country has made various attempts to fill the void. The study deals with the evolution of the zaptiye into the gendarmerie and then the interaction of the banditry activities of the Ottoman gendarme in Sivas. The prime source for unearthing all of this was the The Ottoman Archive of the Presidency of the State Archives of the Presidency. The bandit activities encountered in the archive scans are conveyed in chronological order and in all their aspects. The Gendarmerie took care to suppress the corruption committed by the bandits with all its means. The questions of exactly where, by whom and how the activities were carried out were answered. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) environment is developing as a recently discovered caliber for computing applications over the network. Fault tolerance is one of the foremost issues in CC environment. Since the negligence in resource have a profound effect on job execution, throughput, response time and performance of the entire network. In this work, in order to address the issue, Quadruple Osmotic Hybrid Classification and Whale Optimization (QOHC-WO) is introduced to fault-tolerance under the requirement of different user request tasks. Initially, Quadruple Fault Tolerance Level is applied to allocate the fault tolerance level. Followed by, Hybrid Vector Classifier is used to categorize the user request tasks (task) and cloud server nodes (node). Then, the Osmotic function is employed for performing the migration among virtual machines with lesser response time. This helps to solve the maximum level of fault issue. Finally, Improved Whale Optimization Algorithm is applied to find the optimal allocation of tasks with the corresponding node. In addition, the Bandit function and Whale optimization are used to address the trade-off between exploitation and exploration. Experimental setup of the proposed QOHC-WO method and existing methods are carried out with different factors such as task response time, the number of VM migrations, and percentage of fault detected rate with respect to a number of tasks. The analyzed results validate that the proposed QOHC-WO method achieves a higher fault detection rate with minimum response time as well as task migration than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The explore/exploit tradeoff is a fundamental property of choice selection during reward-guided decision making. In perceptual decision making, higher certainty decisions are more motorically precise, even when the decision does not require motor accuracy. However, while we can parametrically control uncertainty in perceptual tasks, we do not know what variables - if any - shape motor precision and reflect subjective certainty during reward-guided decision making. Touchscreens are increasingly used across species to measure choice, but provide no tactile feedback on whether an action is precise or not, and therefore provide a valuable opportunity to determine whether actions differ in precision due to explore/exploit state, reward, or individual variables. We find all three of these factors exert independent drives towards increased precision. During exploit states, successive touches to the same choice are closer together than those made in an explore state, consistent with exploit states reflecting higher certainty and/or motor stereotypy in responding. However, exploit decisions might be expected to be rewarded more frequently than explore decisions. We find that exploit choice precision is increased independently of a separate increase in precision due to immediate past reward, suggesting multiple mechanisms regulating choice precision. Finally, we see evidence that male mice in general are less precise in their interactions with the touchscreen than females, even when exploiting a choice. These results suggest that as exploit behavior emerges in reward-guided decision making, individuals become more motorically precise reflecting increased certainty, even when decision choice does not require additional motor accuracy, but this is influenced by individual differences and prior reward. These data uncover the hidden potential for touchscreen tasks in any species to uncover the latent neural states that unite cognition and movement.

The incidence of armed banditry has exacerbated the security conundrum in Nigeria to create a multidimensional security quandary. Meanwhile, the extant studies on the matter offer insubstantial theoretical explanations. This study attempts an extensive approach that captures the necessitating factors for banditry from an opportunistic standpoint. This is heralded on the presupposition that crime occurs as a result of the opportunities presented to carry it out. Therefore, this study adopts the theories of crime opportunities, such as routine activity theory, rational choice and crime pattern to expound on the incidence of armed banditry in Nigeria. It was found out that opportunities play a role in banditry; the phenomenon is concentrated in time and space; the opportunities for the menace is highly specific and that the incidence produces opportunities for other crimes. Solutions rooted in the theoretical proposition of situational crime prevention and crime prevention through environmental design theories are thence proffered. [ABSTRACT FROM AUTHOR]

Türkiye’nin Doğu Anadolu bölgesinde yer alan Van, sahip olduğu 19.069 km2 yüz ölçümü ile coğrafi ve stratejik açıdan son derece büyük bir öneme sahiptir. Şehrin, İran ile sınır olması bu önemini bir kat daha arttırmaktadır. Tarih boyunca çeşitli medeniyetlere ev sahipliği yapan Van, içerisinde barındırdığı kültür mozaiği ile büyük devletlerin hâkimiyet mücadelesine de sahne olmuştur. İşte bu devletlerden bir tanesi Osmanlı diğeri ise dönemi ile mütenasip olmak üzere Safevî Devleti’dir. Yavuz Sultan Selim zamanında iki devlet arasında Çaldıran Savaşı (1514) meydana gelmiş ve bu savaş Osmanlı Devleti’nin galibiyeti ile neticelenmiştir. Çaldıran Savaşı’ndan sonra Safevîlerin Anadolu sahasında gerçekleştirmek istediği siyasî ve dinî emelleri kesintiye uğrasa da bilhassa sınır boylarında yaşanan olaylar Cumhuriyet dönemine kadar canlılığını korumuştur. Van ile birlikte İran’a komşu olan illerimizden bir diğeri de Ağrı’dır. Özellikle bu coğrafyada yaşanan sınır hadiseleri Cumhuriyet’in ilk dönemlerinde belirgin bir şekilde kendisini göstermiştir. Türkiye ile İran arasında her ne kadar bu hadiselerin önlenmesine yönelik adımlar atılsa da İran’ın eşkıya ve şekavet olaylarını desteklemesi meseleyi zaman zaman çözümü güç bir siyasî çıkmaza sürüklemiştir. Bu çalışmada 1925-1932 yılları arasında Van ve Ağrı özelinde meydana gelen sınır hadiseleri ve bu hadiseler karşısında devletlerin tutumu bilhassa dönemin basın organları ve arşiv belgeleri ışığında ele alınmıştır.

Copyright of Journal of Social Sciences Institute / Sosyal Bilimler Enstitüsü Dergisi is the property of Bingol University / Rectorate and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigate the problem of online convex optimization with unknown delays, in which the feedback of a decision arrives with an arbitrary delay. Previous studies have presented delayed online gradient descent (DOGD), and achieved the regret bound of O (D) by only utilizing the convexity condition, where D ≥ T is the sum of delays over T rounds. In this paper, we further exploit the strong convexity to improve the regret bound. Specifically, we first propose a variant of DOGD for strongly convex functions, and establish a better regret bound of O (d log T) , where d is the maximum delay. The essential idea is to let the learning rate decay with the total number of received feedback linearly. Furthermore, we extend the strongly convex variant of DOGD and its theoretical guarantee to the more challenging bandit setting by combining with the classical (n + 1) -point and two-point gradient estimators, where n is the dimensionality. To the best of our knowledge, this is the first work that solves online strongly convex optimization under the general delayed setting. [ABSTRACT FROM AUTHOR]

From an historical, sociological and cultural approach, in this paper I analyze the incorporation of the historical character of the bandit to Spanish Literature, especially, to Golden Age theater. Its distinctive features will be analyzed, such as the positive consideration that it generated among the audience, and how the trails through which it constantly transits were turned into a sort of epitome of its own idiosyncrasy. To illustrate the arguments presented, examples drawn from El cordobés valeroso Pedro Carbonero, a comedia by Lope de Vega representative of this subject, is brought up.

The September 11, 2001, attacks were not the first time that a private, foreign group attacked the United States mainland. Although not referred to as an act of "terrorism" at the time, the March 1916 raid of Francisco "Pancho" Villa and his men on Columbus, New Mexico, was understood by Americans of the early-twentieth century in much the same way. The discourse of the "bandit," as Villa was widely described at the time, connoted many of the same meanings that we ascribe to terrorists in the twenty-first century ⁠— criminality, incivility, and illegitimacy. This rhetoric served to dehumanize Villa and justified U.S. incursions on Mexican sovereignty in its fruitless pursuit of him and his militia. Moreover, Villa's political motivations for the attack reflect a modern understanding of terrorism. He sought revenge against the Woodrow Wilson administration for withdrawing its support of him during the Mexican Revolution and tried to goad the United States and Mexico into a wider war. His brief invasion nearly succeeded in bringing about his desired result. American understandings and approaches to Villa mirror many of the same strategies that have been used in the modern war on terror. [ABSTRACT FROM AUTHOR]

Conflict is inevitable in any organization, however, the dimension at which violent conflicts are being perpetrated in Nigeria is unprecedented. It is to this extent that this study examines the causes, the perpetrators, and the effects of violent conflict on the people and national development in Nigeria. This is with a view to establishing development challenges in Nigeria and proffer pragmatic solutions. The data for the study were gathered from primary and secondary sources. The study revealed that violent conflict in Nigeria could not be dissociated from bad governance manifesting in inequality, injustice, unemployment, and poverty among others. Furthermore, the study revealed that violent conflict has become economic activities, and people provoke it, in other to have food on their tables and keep their bodies and souls together. The perpetrator of violent conflict as revealed by the study included – the pastoralists, insurgents, bandits, and extremists, the majority of whom are youth. The effects of violent conflict in Nigeria included – dispossession of people off the necessities of life; displacement of people; losses of vital parts of their bodies; while many lost their lives. Furthermore, violent conflict has led to the destruction of few infrastructural facilities in Nigeria; ruined many economic businesses; and chased away foreign investors. The study further revealed that violent conflict brought about an increase in crime rates, and consequently an increase in security spending. The study concluded that violent conflict is a clog to national development, beyond this, it is capable of jeopardising the corporate existence of the nation. The study recommends that the government and other stakeholders in Nigeria’s governance and administration should first of all address the issue of poverty and inequality.

Copyright of Turkish Studies - Language & Literature is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of History Studies (13094688) is the property of History Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of History Studies (13094688) is the property of History Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. Multi-armed bandit (MAB) learning algorithms are used here to improve both the IoT network capability to support the expected massive number of objects and the energetic autonomy of the IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept, based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals are diminished for IoT devices that use MAB learning. Then we describe the first implementation of such algorithms on LoRa devices operating in a real LoRaWAN network at 868 MHz. We named this solution IoTligent. IoTligent does not add neither processing overhead, so it can be run into the IoT devices, nor network overhead, so that it requires no change to LoRaWAN protocol. Real-life experiments done in a real LoRa network show that IoTligent devices' battery life can be extended by a factor of 2, in the scenarios we faced during our experiment. Finally we submit IoTligent devices to very constrained conditions that are expected in the future with the growing number of IoT devices, by generating an artificial IoT massive radio traffic in anechoic chamber. We show that IoTligent devices can cope with spectrum scarcity that will occur at that time in unlicensed bands. [ABSTRACT FROM AUTHOR]

The war that took place between 1683 and 1718 and the following developments led to a social and economic collapse in Anatolian cities as well as in the entire empire. Since the majority of the administrators in the rural went to the battle during the Second Siege of Vienna and following wars, bandits who belong to different people and groups attempted banditry events that disturbed the social order in many regions of Anatolia. In fact, this irregular environment that occurred in the rural areas of Ottoman Empire, caused population mobility in cities and rural areas. In the early modern period, the characteristics of the cities formed the central-rural relations. In this context, it is important to evaluate the events of 18th century which was expressed as the period of breaking and change for the Ottoman Empire and the events between 1683 and 1718 which affect this century, in terms of Anatolian cities. In this study, bandit movements caused by the events in administrative, military, economic and social fields between 1683 and 1718 were therefore examined in the example of Manisa District. [ABSTRACT FROM AUTHOR]

Backscattering assisted wireless-powered communication combines ultralow-power backscatter transmitters with energy harvesting devices. This paper investigates the transmission mode selection problem of a hybrid relay that forwards data by switching between the active wireless-powered transmission and the passive ambient backscattering. It first presents a hybrid relay system model and derives its end-to-end success probability under theoretically optimal, but practically unrealistic, conditions. The transmission mode selection is then formulated as a stochastic two-armed bandit problem in a varying environment where the distributions of rewards are nonstationary. The proposed model selection scheme does not assume to have access to any channel states or network conditions, but merely relies on learning from past transmission records. Numerical analyses are performed to validate the proposed bandit-based mode selection approach. [ABSTRACT FROM AUTHOR]

The Fear of trial was a tried and tested tool of the legal systems of the old regime: a fearful response to the fear that pervaded society. In this context, Beccaria found the courage to go against the common thought of its time denying the goodness of the death penalty as an element of criminal justice. The philosophers of the time such as Voltaire and Kant, and even jurists such as Facchinei, Vergani and Giudici from Milan or as the Neapolitan Gaetano Majo (1757-1817) were all in favor of the death penalty. Starting from Majo, I followed the Neapolitan path to illustrate how fear was considered a necessary element in a literal war on brigandage and how, from the sixteenth century onwards, "increasingly terrible" measures were taken against highwaymen, until was decided to adopt against them the modules of the trial ad modum belli. It was then born a centuries-long spiral of a Law that might be seen as a fear's son, which facing the increase of crimes made the punishments even more terrible, but thus increasing the very crime that was wanted to fight: in a vicious circle to which Beccaria found the courage in opposing. [ABSTRACT FROM AUTHOR]

The long and tired wars which continued in the XVI. century from the end of the XVII. century had profound repercussions on the Ottoman Empire in the administrative, financial, military and social fields. Thuggery movements began to occur frequently in the cities, towns and villages of Anatolia then Rumelia. In addition to the masses of helpless unemployed in economic distress, military and administrative tasks were also included in this banditry movement. In the years following the defeat of Vienna II, the bandits who put great exercise influence over on the people under the pretext of the war environment undertook many activities such as latrocination, killings and robberies. During this period, the road between Istanbul and Belgrade, known as the central branch of Rumeli, became more intense than ever due to the conditions of war. Groups of bandits, who knew of this opportunity, hid in the appropriate places and frequently attacked the passengers, soldiers and officials passing by. Road safety has been seriously imperiled, particularly at level crossings between Sofia and Belgrade, which are more suited to attack and concealment. In order to prevent this, the Ottoman administration tried to increase the defensive forces of the fortresses and redoubts on the road and sent reinforcements from various places of Rumelia to neutralize the bandit. However, due to the security gap caused by the war environment and the support of the local Christian inhabitants , the bandit was able to continue its activities during the war years. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Social Research is the property of Journal of International Social Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Amme Idaresi Dergisi is the property of Public Administration Institute for Turkey & the Middle East (TODAIE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show that rank aggregation performs well.

Copyright of Erciyes Universitesi Sosyal Bilimler Dergisi is the property of Erciyes Universitesi Sosyal Bilimler Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)