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Contrast-enhanced ultrasonography of the carotids

Vasileios Rafailidis, Georgios Pitoulias, Konstantinos Kouskouras, Dimitrios Rafailidis
2015 Ultrasonography  
ORCID: Vasileios Rafailidis:; Georgios Pitoulias:; Konstantinos Kouskouras: http://; Dimitrios Rafailidis  ... 
doi:10.14366/usg.15005 pmid:25868732 pmcid:PMC4603203 fatcat:ejvgblteyjhz7hchjczipsox4u

Ultrasonography of the healing process during a 3-month follow-up after a splenic injury

Vasileios Rafailidis, Dimitrios Apostolou, Christodoulos Kaitartzis, Dimitrios Rafailidis
2014 Ultrasonography  
ORCID: Vasileios Rafailidis:; Dimitrios Apostolou:; Chritsodoulos Kaitartzis:; Dimitrios Rafailidis  ... 
doi:10.14366/usg.14057 pmid:25623053 pmcid:PMC4484285 fatcat:qrgwnwelcbavzp74syx6dlzq74

Cross-Domain Collaborative Filtering via Translation-based Learning [article]

Dimitrios Rafailidis
2019 arXiv   pre-print
With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains. In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT. In our model, we learn the embedding
more » ... earn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains. In doing so, we efficiently compute the transitivity between feature latent embeddings, that is if feature pairs have high interaction weights in the latent space, then feature embeddings with no observed interactions across the domains will be closely related as well. We formulate our objective function as a ranking problem in factorization machines and learn the model's parameters via gradient descent. In addition, to better capture the non-linearity in user preferences across domains we extend the proposed CDT model by using a deep learning strategy, namely DeepCDT. Our experiments on six publicly available cross-domain tasks demonstrate the effectiveness of the proposed models, outperforming other state-of-the-art cross-domain strategies.
arXiv:1908.06169v1 fatcat:xvblxtn4cjalzafcuk6e67jo2a

Fountain's Sign as a Diagnostic Key in Acute Idiopathic Scrotal Edema: Case Report and Review of the Literature

Dimitrios Patoulias, Vasileios Rafailidis, Thomas Feidantsis, Maria Kalogirou, Dimitrios Rafailidis, Ioannis Patoulias
2018 Acta Medica  
The acute idiopathic scrotal edema (AISE) is a self-limited disease of unknown etiology, characterized by edema and erythema of the scrotum and the dartos, without expansion to the underlying layers of scrotum's wall or to the endoscrotal structures. Boys younger than 10 years old are usually involved in 60–90% of all cases. Diagnosis is made after exclusion of other causes of acute scrotum. We present a case of a 7-year old boy, who was admitted to the Emergency Department due to development
more » ... ue to development of scrotal edema and erythema over the last 48 hours, which extended to the base of the penis. The patient mentioned that he first noticed the erythema on the anterior surface of the right hemiscrotum, which gradually extended. Physical examination did not reveal presence of pathology involving the endoscrotal structures, indicative of need for urgent surgical intervention. Transillumination was negative for blue dot sign. Ultrasonographic examination of the scrotum documented the homogeneity of the testicular parenchyma, while color Doppler revealed the presence of fountain's sign (equal arterial blood supply to both testicles). Conservative strategy was followed and the patient gradually improved within the next three days. In conclusion, meticulous physical examination along with ultrasonographic examination of the suffering scrotum, especially with the highlighting of fountain's sign with color Doppler, document the diagnosis of AISE. Thus, need for urgent surgical investigation of the suffering scrotum due to diagnostic doubt is limited.
doi:10.14712/18059694.2018.22 pmid:30012249 fatcat:gi7jrl2p2nc7hioxu5fxxtdhj4

Two Congenital Anomalies in One: An Ectopic Gallbladder with Phrygian Cap Deformity

Vasileios Rafailidis, Sotirios Varelas, Naoum Kotsidis, Dimitrios Rafailidis
2014 Case Reports in Radiology  
Acknowledgment Vasileios Rafailidis MD has received a scholarship for his postgraduate studies by Onassis Foundation.  ... 
doi:10.1155/2014/246476 pmid:24716073 pmcid:PMC3971497 fatcat:d4dbjbyk3vgjhakm2flsjwy5nm

Sequence Adaptation via Reinforcement Learning in Recommender Systems [article]

Stefanos Antaris, Dimitrios Rafailidis
2021 arXiv   pre-print
Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item interactions is required as input to train the models. This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations. Hence, baseline strategies might ignore important sequential interactions or add noise to the models with redundant interactions,
more » ... eractions, depending on the variety of users' sequential behaviours. To overcome this problem, in this study we propose the SAR model, which not only learns the sequential patterns but also adjusts the sequence length of user-item interactions in a personalized manner. We first design an actor-critic framework, where the RL agent tries to compute the optimal sequence length as an action, given the user's state representation at a certain time step. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network, while at the same time we adapt the sequence length with the actor network in a personalized manner. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches. Finally, we make our implementation publicly available at
arXiv:2108.01442v1 fatcat:m6f7wm5pzrgkvoumwjpv2b6ls4

On Estimating the Training Cost of Conversational Recommendation Systems [article]

Stefanos Antaris, Dimitrios Rafailidis, Mohammad Aliannejadi
2020 arXiv   pre-print
Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns. However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high. To shed light on the high computational training time of state-of-the art conversational models, we examine five representative strategies and demonstrate this issue. Furthermore, we discuss
more » ... we discuss possible ways to cope with the high training cost following knowledge distillation strategies, where we detail the key challenges to reduce the online inference time of the high number of model parameters in conversational recommendation systems
arXiv:2011.05302v1 fatcat:kr7ua6ipn5fhrgkqdydwe426oe

Imaging of the malignant peripheral nerve sheath tumour with emphasis οn ultrasonography: correlation with MRI

Vasileios Rafailidis, Theodora Kaziani, Costas Theocharides, Athanasios Papanikolaou, Dimitrios Rafailidis
2014 Journal of Ultrasound  
Conflict of interest Vasileios Rafailidis, Theodora Kaziani, Costas Theocharides, Athanasios Papanikolaou, Dimitrios Rafailidis, declare that they have no conflict of interest.  ... 
doi:10.1007/s40477-014-0097-2 pmid:25177396 pmcid:PMC4142122 fatcat:2gij6w3bijgalk6qv4anxtafde

Scalable Spectral Clustering with Weighted PageRank [chapter]

Dimitrios Rafailidis, Eleni Constantinou, Yannis Manolopoulos
2014 Lecture Notes in Computer Science  
In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral clustering technique to achieve scalable and accurate clustering. In our experiments with two benchmark face and shape image data sets, we examine several landmark selection strategies for scalable spectral
more » ... calable spectral clustering that either ignore or consider the topological properties of the data in the affinity graph. Finally, we show that the proposed method outperforms baseline and accelerated spectral clustering methods, in terms of computational cost and clustering accuracy, respectively.
doi:10.1007/978-3-319-11587-0_27 fatcat:pgeusqowpzcbtaohaom6acphke

Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events [article]

Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
2021 arXiv   pre-print
In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast
more » ... nsure fast adaptation to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at
arXiv:2111.09412v1 fatcat:3mml2xxlrrconkowa4kkvncoee

Music search engines: Specifications and challenges

Alexandros Nanopoulos, Dimitrios Rafailidis, Maria M. Ruxanda, Yannis Manolopoulos
2009 Information Processing & Management  
Music Search Engines: Specifications and Challenges Alexandros Nanopoulos, Dimitrios Rafailidis, Maria M.  ...  Rafailidis and Y.  ... 
doi:10.1016/j.ipm.2009.02.002 fatcat:akwqkzoktrafvin465frgd5fke

VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions [article]

Stefanos Antaris, Dimitrios Rafailidis
2020 arXiv   pre-print
Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to
more » ... ween viewers has to be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at
arXiv:2011.05671v1 fatcat:a2azk3pgzng25b5xczkszikda4

Worldwide Prevalence of Head Lice

Matthew E. Falagas, Dimitrios K. Matthaiou, Petros I. Rafailidis, George Panos, Georgios Pappas
2008 Emerging Infectious Diseases  
5. Miller RW, Rabkin CS. Merkel cell carcinoma and melanoma: etiological similarities and differences. Cancer Epidemiol Biomarkers Prev. 1999;8:153-8. 6. Engels EA, Frisch M, Goedert JJ, Biggar RJ, Miller RW. Merkel cell carcinoma and HIV infection. . Analysis by polymerase chain reaction of the physical state of human papillomavirus type 16 DNA in cervical preneoplastic and neoplastic lesions.
doi:10.3201/eid1409.080368 pmid:18760032 pmcid:PMC2603110 fatcat:rp6svfu6nngxddwgwhxwm6tuhy

The Technological Gap Between Virtual Assistants and Recommendation Systems [article]

Dimitrios Rafailidis, Yannis Manolopoulos
2019 arXiv   pre-print
Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot
more » ... ted to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.
arXiv:1901.00431v2 fatcat:jpws7l3kwjgqfouwajwp2lmc4q

Clustering Attributed Multi-graphs with Information Ranking [chapter]

Andreas Papadopoulos, Dimitrios Rafailidis, George Pallis, Marios D. Dikaiakos
2015 Lecture Notes in Computer Science  
Attributed multi-graphs are data structures to model realworld networks of objects which have rich properties/attributes and they are connected by multiple types of edges. Clustering attributed multigraphs has several real-world applications, such as recommendation systems and targeted advertisement. In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Information Ranking, namely CAMIR. We introduce an iterative algorithm that ranks the different vertex
more » ... fferent vertex attributes and edge-types according to how well they can separate vertices into clusters. The key idea is to consider the 'agreement' among the attribute-and edge-types, assuming that two vertex properties 'agree' if they produced the same clustering result when used individually. Furthermore, according to the calculated ranks we construct a unified similarity measure, by down-weighting noisy vertex attributes or edge-types that may reduce the clustering accuracy. Finally, to generate the final clusters, we follow a spectral clustering approach, suitable for graph partitioning and detecting arbitrary shaped clusters. In our experiments with synthetic and real-world datasets, we show the superiority of CAMIR over several state-of-the-art clustering methods.
doi:10.1007/978-3-319-22849-5_29 fatcat:idq3pbksezdxbhaudl74pltrca
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