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Decomposing transverse momentum balance contributions for quenched jets in PbPb collisions at √=2.76sNN=2.76 TeV
(Springer International Publishing, 2016)Interactions between jets and the quark-gluon plasma produced in heavy ion collisions are studied via the angular distributions of summed charged-particle transverse momenta (pT) with respect to both the leading and ... -
A decomposition based metaheuristic approach for solving rapid needs assessment routing problem
(Elsevier, 2021-09)This paper proposes a decomposition based tabu search algorithm for solving multi-cover routing problem in the case of rapid need assessment. Rapid needs assessment aims to evaluate impact of a disaster at different sites ... -
A decomposition-based heuristic for a waste cooking oil collection problem
(Springer, 2020-01-01)Every year, a tremendous amount of waste cooking oil (WCO) is produced by households and commercial organizations, which poses a serious threat to the environment if disposed improperly. While businesses such as hotels and ... -
A decomposition-based metaheuristic approach for solving the rapid needs assessment routing problem
(2021-01-18)This study proposes a decomposition-based tabu search algorithm for a multi-cover routing problem (MCRP), which aims to classify and evaluate the impacts of the disaster in different sites and the needs of different community ... -
Deep learning based event recognition in aerial imagery
(IEEE, 2023)In this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the ... -
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
(Elsevier, 2023-08)Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this ... -
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
(Springer, 2024-02-12)Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high ... -
Deep learning-based speaker-adaptive postfiltering with limited adaptation data for embedded text-to-speech synthesis systems
(Elsevier, 2023-06)End-to-end (e2e) speech synthesis systems have become popular with the recent introduction of text-to-spectrogram conversion systems, such as Tacotron, that use encoder–decoder-based neural architectures. Even though those ... -
Deep multi-object symbol learning with self-attention based predictors
(IEEE, 2023)This paper proposes an architecture that can learn symbolic representations from the continuous sensorimotor experience of a robot interacting with a varying number of objects. Unlike previous works, this work aims to ... -
Deep Q-learning based optimization of VLC systems with dynamic time-division multiplexing
(IEEE, 2020)The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network ... -
Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
(IEEE, 2020)NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as ... -
Deep reinforcement learning approach for trading automation in the stock market
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" ... -
Deep reinforcement learning approach for trading automation in the stock market
(IEEE, 2022)Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' ... -
Deep reinforcement learning for acceptance strategy in bilateral negotiations
(TÜBİTAK, 2020)This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance ... -
Deep transformer-based asset price and direction prediction
(IEEE, 2024)The field of algorithmic trading, driven by deep learning methodologies, has garnered substantial attention in recent times. Within this domain, transformers, convolutional neural networks, and patch embedding-based ... -
Deepsym: Deep symbol generation and rule learning for planning from unsupervised robot interaction
(AI Access Foundation, 2022)Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended ... -
Defeating populists: The case of 2019 Istanbul elections
(Taylor & Francis, 2021)How can populist competitive authoritarian regimes be defeated through elections? In this article, we focus on the 2019 municipal campaign strategy of the opposition Istanbul candidate Ekrem İmamoglu as a case study of a ... -
Defect-aware nanocrossbar logic mapping through matrix canonization using two-dimensional radix sort
(ACM, 2011-08)Nanocrossbars (i.e., nanowire crossbars) offer extreme logic densities but come with very high defect rates; stuck-open/closed, broken nanowires. Achieving reasonable yield and utilization requires logic mapping that is ... -
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Defining architectural viewpoints for quality concerns
(Springer Science+Business Media, 2011)A common practice in software architecture design is to apply architectural views to model the design decisions for the various stakeholder concerns. When dealing with quality concerns, however, it is more difficult to ...
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