A Simple and Effective Method of Cross-Lingual Plagiarism Detection

We present a simple cross-lingual plagiarism detection method applicable to a large number of languages. The presented approach leverages open multilingual thesauri for candidate retrieval task and pre-trained multilingual BERT-based language models for detailed analysis. The method does not rely on machine translation and word sense disambiguation when in use, and therefore is suitable for a large number of languages, including under-resourced languages. The effectiveness of the proposed approach is demonstrated for several existing and new benchmarks, achieving state-of-the-art results for French, Russian, and Armenian languages

The system of soil protection and general balance of main nutrient elements in perennial plantations of semi-desert natural soil zone of Armenia

Received: March 27th, 2022 ; Accepted: June 3rd, 2022 ; Published: July 5th, 2022 ; Correspondence: [email protected] aim of the research is to study the biological removal of the main nutrient elements from the most common technical grape varieties, as well as from apricot and peach plantations in the farms, situated on semi-desert natural soils of Armenia, to identify the extent of their input and losses due to natural factors and to calculate the balance associated with the soil conservation system in the absence of comprehensive fertilization. In the inter-row spaces of all fruit plantations and even vineyards of the republic, grass cover of productive significance has been established (4.5–6.5 t ha-1 yield of air-dry grass), through which the removal of nutrient elements is 2–3 times higher than the biological removal through trees and vines. The research was conducted in 2015–2020, in the grape, apricot and peach plantations of the semi-desert natural zone of Armenia (Armavir region), where the irrigation norm is 5,000 m3 ha-1 , and the atmospheric precipitation is 256 mm, through which 40 kg ha-1 N, 2 kg ha-1 P2O5, and 44 kg ha-1 K2O enter the soil. The losses due to erosion and washing away are N - 12 kg ha-1 , P2O5 - 7 kg ha-1 , K2O - 75 kg ha-1 . The balance of nutrient elements in all plantations is negative, nitrogen in plantations with industrial grass cover is 154, P2O5 - 52, K2O - 311, and in the system of black fallow - 15, 16 and 85 kg ha-1 , respectively. The negative balance of nitrogen in apricot and peach plantations is 121, P2O5 - 44, K2O - 296 kg ha-1

Production removal of the main nutrient elements from winter wheat and barley crops in the conditions of the Ararat Valley of Armenia

Received: February 1st, 2022 ; Accepted: February 19th, 2023 ; Published: February 20th, 2023 ; Correspondence: [email protected] aim of the research is to identify the extent of production removal of the main nutrient elements in the irrigated grain-growing lands of Armenia, to optimize the norms of organo-mineral fertilizers, to stabilize the yield of plants and prevent dehumification. The field experiments were carried out in the conditions of the Ararat Valley on winter wheat and barley in 2020–2022. The production removal of nitrogen by 5–7 t ha-1 grain of winter wheat and 9.6–13.3 t ha-1 straw varies between 155–247, P2O5: 60–88, K2O: 134–197 kg ha-1 , and the amounts removed by barley grain 4.5–6.5 t ha-1 and straw 6.9–9.7t ha-1 were 122–194, 49–77 and 106–159 kg ha-1 , respectively. The amounts of nitrogen and potassium production removal from plant crops are about 2 times higher than the doses of applied fertilizers, and the amount of phosphorus is almost balanced by these doses

Quantum-Classical Multiple Kernel Learning

As quantum computers become increasingly practical, so does the prospect of using quantum computation to improve upon traditional algorithms. Kernel methods in machine learning is one area where such improvements could be realized in the near future. Paired with kernel methods like support-vector machines, small and noisy quantum computers can evaluate classically-hard quantum kernels that capture unique notions of similarity in data. Taking inspiration from techniques in classical machine learning, this work investigates simulated quantum kernels in the context of multiple kernel learning (MKL). We consider pairwise combinations of several classical-classical, quantum-quantum, and quantum-classical kernels in an empirical investigation of their classification performance with support-vector machines. We also introduce a novel approach, which we call QCC-net (quantum-classical-convex neural network), for optimizing the weights of base kernels together with any kernel parameters. We show this approach to be effective for enhancing various performance metrics in an MKL setting. Looking at data with an increasing number of features (up to 13 dimensions), we find parameter training to be important for successfully weighting kernels in some combinations. Using the optimal kernel weights as indicators of relative utility, we find growing contributions from trainable quantum kernels in quantum-classical kernel combinations as the number of features increases. We observe the opposite trend for combinations containing simpler, non-parametric quantum kernels.Comment: 15 pages, Supplementary Information on page 15, 6 main figures, 1 supplementary figur

Imaging carious dental tissues with multiphoton fluorescence lifetime imaging microscopy

In this study, multiphoton excitation was utilized to image normal and carious dental tissues noninvasively. Unique structures in dental tissues were identified using the available multimodality (second harmonic, autofluorescence, and fluorescence lifetime analysis) without labeling. The collagen in dentin exhibits a strong second harmonic response. Both dentin and enamel emit strong autofluorescence that reveals in detail morphological features (such as dentinal tubules and enamel rods) and, despite their very similar spectral profiles, can be differentiated by lifetime analysis. Specifically, the carious dental tissue exhibits a greatly reduced autofluorescence lifetime, which result is consistent with the degree of demineralization, determined by micro-computed tomography. Our findings suggest that two-photon excited fluorescence lifetime imaging may be a promising tool for diagnosing and monitoring dental caries

Deep Lake: a Lakehouse for Deep Learning

Traditional data lakes provide critical data infrastructure for analytical workloads by enabling time travel, running SQL queries, ingesting data with ACID transactions, and visualizing petabyte-scale datasets on cloud storage. They allow organizations to break down data silos, unlock data-driven decision-making, improve operational efficiency, and reduce costs. However, as deep learning takes over common analytical workflows, traditional data lakes become less useful for applications such as natural language processing (NLP), audio processing, computer vision, and applications involving non-tabular datasets. This paper presents Deep Lake, an open-source lakehouse for deep learning applications developed at Activeloop. Deep Lake maintains the benefits of a vanilla data lake with one key difference: it stores complex data, such as images, videos, annotations, as well as tabular data, in the form of tensors and rapidly streams the data over the network to (a) Tensor Query Language, (b) in-browser visualization engine, or (c) deep learning frameworks without sacrificing GPU utilization. Datasets stored in Deep Lake can be accessed from PyTorch, TensorFlow, JAX, and integrate with numerous MLOps tools