Zobrazeno 1 - 10
of 1 061
pro vyhledávání: '"P, Avramidis"'
Autor:
Kocmi, Tom, Avramidis, Eleftherios, Bawden, Rachel, Bojar, Ondrej, Dvorkovich, Anton, Federmann, Christian, Fishel, Mark, Freitag, Markus, Gowda, Thamme, Grundkiewicz, Roman, Haddow, Barry, Karpinska, Marzena, Koehn, Philipp, Marie, Benjamin, Murray, Kenton, Nagata, Masaaki, Popel, Martin, Popovic, Maja, Shmatova, Mariya, Steingrímsson, Steinþór, Zouhar, Vilém
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking and supersedes it. The purpose of this report is not to interpret any
Externí odkaz:
http://arxiv.org/abs/2407.19884
Coffee leaf rust, a foliar disease caused by the fungus Hemileia vastatrix, poses a major threat to coffee production, especially in Central America. Climate change further aggravates this issue, as it shortens the latency period between initial infe
Externí odkaz:
http://arxiv.org/abs/2407.14737
Autor:
Kocmi, Tom, Zouhar, Vilém, Avramidis, Eleftherios, Grundkiewicz, Roman, Karpinska, Marzena, Popović, Maja, Sachan, Mrinmaya, Shmatova, Mariya
High-quality Machine Translation (MT) evaluation relies heavily on human judgments. Comprehensive error classification methods, such as Multidimensional Quality Metrics (MQM), are expensive as they are time-consuming and can only be done by experts,
Externí odkaz:
http://arxiv.org/abs/2406.11580
Autor:
Lee, Jihwan, Kommineni, Aditya, Feng, Tiantian, Avramidis, Kleanthis, Shi, Xuan, Kadiri, Sudarsana, Narayanan, Shrikanth
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework for Fully-End-to-end Speech Decoding from EEG signals. Our approach a
Externí odkaz:
http://arxiv.org/abs/2406.08644
Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ab
Externí odkaz:
http://arxiv.org/abs/2403.03222
Autor:
Avramidis, Kleanthis, Chang, Melinda Y., Sharma, Rahul, Borchert, Mark S., Narayanan, Shrikanth
A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime example of such conditions, resulting from damage to visual pathways in the
Externí odkaz:
http://arxiv.org/abs/2402.09655
Autor:
Fabiana Vieira Duarte de Souza Reis, Carlos Izaias Sartorão Filho, Luis Sobrevia, Caroline Baldini Prudencio, Bruna Bologna, Luana Favaro Iamundo, Adriely Magyori, Luiz Takano, Raissa Escandiussi Avramidis, Rafael Guilen de Oliveira, Marilza Vieira Cunha Rudge, Angélica Mércia Pascon Barbosa, Diamater Study Group
Publikováno v:
Clinical Diabetes and Endocrinology, Vol 10, Iss 1, Pp 1-7 (2024)
Abstract Background The literature has been evolving to standardize gestational diabetes mellitus (GDM) diagnosis and terminology. The significance of timing in diagnosing hyperglycemia during pregnancy is underlined by evidence that women diagnosed
Externí odkaz:
https://doaj.org/article/cfbbe6ef7f6144d69c5f7743eab379f9
Autor:
Avramidis, Kleanthis, Kunc, Dominika, Perz, Bartosz, Adsul, Kranti, Feng, Tiantian, Kazienko, Przemysław, Saganowski, Stanisław, Narayanan, Shrikanth
Ubiquitous sensing from wearable devices in the wild holds promise for enhancing human well-being, from diagnosing clinical conditions and measuring stress to building adaptive health promoting scaffolds. But the large volumes of data therein across
Externí odkaz:
http://arxiv.org/abs/2309.15292
Traditional music search engines rely on retrieval methods that match natural language queries with music metadata. There have been increasing efforts to expand retrieval methods to consider the audio characteristics of music itself, using queries of
Externí odkaz:
http://arxiv.org/abs/2308.12610
This paper presents the approach and results of USC SAIL's submission to the Signal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic patients. Relapse prediction has proven to be challenging, primarily due t
Externí odkaz:
http://arxiv.org/abs/2304.08614