In a digestibility experiment, six adult wethers of the Karagouniko breed were used to determine the nutritive value of dried citrus pulp. The rations consisted of 800 g of hay and 75, 150, 225, 300, 375, and 450 g of citrus pulp. The apparent digestibilities of the DM, OM, CP, ether extract, crude fiber, and N-free extract for dried citrus pulp were 78.6, 87.2, 52.7, 82.0, 93.2, and 83.1%, respectively. Energy content was estimated to be 1.66 Mcal of NE(L)/kg of DM. In a second experiment, 26 lactating ewes of the Karagouniko breed were used to study the nutrient utilization of dried citrus pulp for milk yield when citrus pulp was used as a replacement for cereal grains. The ewes were divided into two groups immediately postweaning and fed daily 700 g of alfalfa hay, 300 g of wheat straw, and 580 or 550 g of concentrates with or without 30% citrus pulp, respectively. The inclusion of citrus pulp in rations for ewes had no significant effect on milk yield and composition but decreased the C4 to C10 fatty acids. Citrus pulp is a valuable, high energy by-product that can partly replace cereal grains in sheep rations without adverse effect on milk yield or composition.
Plant identification from images has become a rapidly developing research field in computer vision and is particularly challenging due to the morphological complexity of plants. The availability of large databases of plant images, and the research advancements in image processing, pattern recognition and machine learning, have resulted in a number of remarkably accurate and reliable image-based plant identification techniques, overcoming the time and expertise required for conventional plant identification, which is feasible only for expert botanists. In this paper, we introduce the GReek vAScular Plants (GRASP) dataset, a set of images composed of 125 classes of different species, for the automatic identification of vascular plants of Greece. In this context, we describe the methodology of data acquisition and dataset organization, along with the statistical features of the dataset. Furthermore, we present results of the application of popular deep learning architectures to the classification of the images in the dataset. Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.
In line with cross-linguistic research aiming at identifying criterial features that discriminate the CEFR proficiency levels, the present study investigates language elements that are core characteristics of each proficiency level for Greek L2. It is based on a graded corpus of 150 written narratives produced by young L2 learners (aged 8-14) at levels A2 to B2. This corpus was annotated with respect to a set of features at both the sentence and discourse level, such as clause subordination, connectives, modifiers and grammatical accuracy. Statistical analysis identified certain aspects of these features that discriminate language proficiency levels in L2 Greek narratives and are put forward as criterial features. These include the frequency of dependent and centre-embedded clauses, the gradual decrease of additive and the emergence of contrastive and inferential connectives, the felicitous use of clitics, as well as the use of evaluative adverbs and adjectives.
Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. Detecting and assessing Aphasia in patients is a difficult, time-consuming process, and numerous attempts to automate it have been made, the most successful using machine learning models trained on aphasic speech data. Like in many medical applications, aphasic speech data is scarce and the problem is exacerbated in so-called "low resource" languages, which are, for this task, most languages excluding English. We attempt to leverage available data in English and achieve zero-shot aphasia detection in low-resource languages such as Greek and French, by using language-agnostic linguistic features. Current cross-lingual aphasia detection approaches rely on manually extracted transcripts. We propose an end-toend pipeline using pre-trained Automatic Speech Recognition (ASR) models that share cross-lingual speech representations and are fine-tuned for our desired low-resource languages. To further boost our ASR model's performance, we also combine it with a language model. We show that our ASR-based end-toend pipeline offers comparable results to previous setups using human-annotated transcripts.
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