Raman spectroscopy (RS) is a label-free molecular vibrational spectroscopy technique that is able to identify the molecular fingerprint of various samples making use of the inelastic scattering of monochromatic light. Because of its advantages of non-destructive and accurate detection, RS is...
01a Articolo in rivista
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In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent. This search for an appropriate description, both analytical and topological, has led to numerous...
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Despite the remarkable progress made in the field of Machine Translation (MT), current systems still struggle when translating ambiguous words, especially when these express infrequent meanings. In order to investigate and analyze the impact of lexical ambiguity on automatic translations, several...
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An important issue in coopetitive supply chains is ensuring business confidentiality when sharing sensitive information among partner actors. This challenge becomes even more complex in blockchain-based supply chains due to inherent transparency, conflicting with businesses’ need to safeguard...
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Objective: Limited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis. Anti-citrullinated peptide antibodies (ACPA) have been widely investigated and identified in up to 50% of X-ray detected erosive arthritis;...
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We present an optimization model for assigning orders to couriers developed for an Italian meal delivery firm focusing on Rome. The firm focuses on top-end restaurants and customers and pursues high Quality of Service through careful management of delays. Our model reflects that in the firm's...
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In this paper, we propose a branch-and-bound algorithm for solving non convex quadratic programming problems with box constraints (BoxQP). Our approach combines existing tools, such as semidefinite programming (SDP) bounds strengthened through valid inequalities, with a new class of optimality-...
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Support vector machines (SVMs) are well-studied supervised learning models for binary classification. Large amounts of samples can be cheaply and easily obtained in many applications. What is often a costly and error-prone process is to label these data points manually. Semi-supervised support...
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Binary analysis has become essential for software inspection and security assessment. As the number of software-driven devices grows, research is shifting towards autonomous solutions using deep learning models. In this context, a hot topic is the binary similarity problem, which involves...
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Long-Power Wide Area Networks (LPWAN) is a low-cost solution to deploy very-large scale Internet of Things (IoT) infrastructures with minimal requirements following a classic producer/consumer model. Inevitably such deployments will require a shift towards low-latency, distributed and collaborative...