Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies

Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction-in an approach called audio inpainting. Current state-of-The art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant

A Cluster-Based Method for Action Segmentation Using Spatio-Temporal and Positional Encoded Embeddings

A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-Temporal features from videos in samples of 0.5s using a pre-Trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks

A Guide to Noxious Plants as an Educational Resource of Veterinary Medicine Students

The School of Veterinary UNRN is located in the town of Choele Choel (39° 17\u27S, 65° 39\u27W), in Northern Patagonia, Argentina. The climate is semi-arid with a historical average annual continental rainfall of 303 mm, with marked daily and seasonal temperature ranges. However, the type of vegetation that can be found in the area ranges fromxerophytic shrubs to hydrophytic vegetation because the arid plateau is crossed by the broad valley of Río Negro. Due to the topographic distribution of agricultural and livestock farms, the toxic plants for livestock may be found in the irrigated valleys or arid shrubby camps

Microparticles from patients with metabolic syndrome induce in vivo endothelial dysfunction and vascular hypo-reactivity by increasing oxidative and nitrosative stresses in mouse aorta

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Estrogenic and Anti-Inflammatory Activities of a Steroidal Indoxyl

The estrogenic and anti-inflammatory activities of 3-methoxy-16, 17-seco-16-norestra-1,3,5-trien-15-(2'-indoxyliden)-17-oic acid is reported. After intraperitoneal administration, the dose of this compound required to reduce swelling of the rat paw by 50% (ED50) was 14.1 mg/kg using the carrageenan-induced rat paw oedema anti-inflammatory assay method. Indomethacin had an ED50 of 3.2 mg/kg in this assay while dexamethasone had an ED50 of 1.7 mg/kg. The estrogenic activity of the compound after intramuscular administration in rats was 0.72 relative to diethylstilbestrol, when the two compounds were assayed at three dose levels of 1.0, 0.3 and 0.1 mg/kg. Key Words: Steroidal indoxyl, synthesis, estrogenic, anti-inflammatory East and Central African Journal of Pharmaceutical Sciences Vol.5(3) 2002: 44-4

A Cluster-Matching-Based Method for Video Face Recognition

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.Comment: 13 page

Saturn Platform: Foundation Model Operations and Generative AI for Financial Services

Saturn is an innovative platform that assists Foundation Model (FM) building and its integration with IT operations (Ops). It is custom-made to meet the requirements of data scientists, enabling them to effectively create and implement FMs while enhancing collaboration within their technical domain. By offering a wide range of tools and features, Saturn streamlines and automates different stages of FM development, making it an invaluable asset for data science teams. This white paper introduces prospective applications of generative AI models derived from FMs in the financial sector