Indirect Match Highlights Detection with Deep Convolutional Neural Networks

Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience. A big effort is spent in designing techniques which find automatically highlights, in order to automatize the otherwise manual editing process. Most of the state-of-the-art approaches try to solve the problem by training a classifier using the information extracted on the tv-like framing of players playing on the game pitch, learning to detect game actions which are labeled by human observers according to their perception of highlight. Obviously, this is a long and expensive work. In this paper, we reverse the paradigm: instead of looking at the gameplay, inferring what could be exciting for the audience, we directly analyze the audience behavior, which we assume is triggered by events happening during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to extract visual features from cropped video recordings of the supporters that are attending the event. Outputs of the crops belonging to the same frame are then accumulated to produce a value indicating the Highlight Likelihood (HL) which is then used to discriminate between positive (i.e. when a highlight occurs) and negative samples (i.e. standard play or time-outs). Experimental results on a public dataset of ice-hockey matches demonstrate the effectiveness of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with ICIAP 201

Removal of acetaldehyde from saliva by mucoadhesive formulations containing cysteine and chlorhexidine diacetate: a possible approach to the prevention of oral cavity alcohol-related cancer

The aim of our work has been to develop buccoadhesive formulations (tablets) containing both L-cysteine and chlorhexidine diacetate and to verify their ability to reduce oral acetaldehyde produced after alcoholic drinks consumption

Chromogranin A: From Laboratory to Clinical Aspects of Patients with Neuroendocrine Tumors

Background. Neuroendocrine tumors (NETs) are characterized by having behavior and prognosis that depend upon tumor histology, primary site, staging, and proliferative index. The symptoms associated with carcinoid syndrome and vasoactive intestinal peptide tumors are treated with octreotide acetate. The PROMID trial assesses the effect of octreotide LAR on the tumor growth in patients with well-differentiated metastatic midgut NETs. The CLARINET trial evaluates the effects of lanreotide in patients with nonfunctional, well-, or moderately differentiated metastatic enteropancreatic NETs. Everolimus has been approved for the treatment of advanced pancreatic NETs (pNETs) based on positive PFS effects, obtained in the treated group. Sunitinib is approved for the treatment of patients with progressive gastrointestinal stromal tumor or intolerance to imatinib, because a randomized study demonstrated that it improves PFS and overall survival in patients with advanced well-differentiated pNETs. In a phase II trial, pasireotide shows efficacy and tolerability in the treatment of patients with advanced NETs, whose symptoms of carcinoid syndrome were resistant to octreotide LAR. An open-label, phase II trial assesses the clinical activity of long-acting repeatable pasireotide in treatment-naive patients with metastatic grade 1 or 2 NETs. Even if the growth of the neoplasm was significantly inhibited, it is still unclear whether its antiproliferative action is greater than that of octreotide and lanreotide. Because new therapeutic options are needed to counter the natural behavior of neuroendocrine tumors, it would also be useful to have a biochemical marker that can be addressed better in the management of these patients. Chromogranin A is currently the most useful biomarker to establish diagnosis and has some utility in predicting disease recurrence, outcome, and efficacy of therapy

The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data. The previous approaches have attempted to address SFVUDA by leveraging self-supervision (e.g., enforcing temporal consistency) derived from the target data itself. In this work, we take an orthogonal approach by exploiting "web-supervision" from Large Language-Vision Models (LLVMs), driven by the rationale that LLVMs contain a rich world prior surprisingly robust to domain-shift. We showcase the unreasonable effectiveness of integrating LLVMs for SFVUDA by devising an intuitive and parameter-efficient method, which we name Domain Adaptation with Large Language-Vision models (DALL-V), that distills the world prior and complementary source model information into a student network tailored for the target. Despite the simplicity, DALL-V achieves significant improvement over state-of-the-art SFVUDA methods.Comment: Accepted at ICCV2023, 14 pages, 7 figures, code is available at https://github.com/giaczara/dall

Experimental and numerical study of an air lock purging system

High concentrations of H2S in offshore wells represent a major concern for personnel safety: if a significant external H2S contamination occurs, depending on wells and process conditions, it may prove impossible an effective evacuation, and thus Temporary Refuge (TR) provisions must be set-up to provide prompt availability of safe and reliable protection to personnel. Air Locks (ALs) to enter the TR may be necessary to ensure isolation of the safe internal environment when entering into the TR. ALs modelling is essential to verify that sufficient time for entering the TR is available to all personnel in case of accident. Nevertheless, due to the extreme conditions (high toxicity, short characteristic times, high purging air velocity etc.), experimental modeling of the AL can prove difficult and very expensive. Given the importance of ALs efficiency in a real emergency situation, simulation of its performances in realistic condition and optimization of the design of air purges to ensure the required efficiency is a factor of extreme importance for the overall safety of the installation. In this work, the purging efficiency of a typical AL has been analyzed through a combined approach of experimental tests and CFD simulations, to prove the capability of CFD modeling to analyse real AL conditions. A scaled model have been realized and analyzed using CO2 as tracing gas to determine the concentration field; even if the realization constraints above mentioned do not allow for a full scaling of all the involved variables, fluid-dynamic conditions have been set to reproduce real AL purging capabilities as close as possible. Experimental results have been used for a fine tuning and validation of the CFD tool in an operative range close to a real configuration, through the comparison of the obtained flow and concentration fields with those predicted by the CFD simulations of the experimental set-up. Subsequently, the tuned CFD approach has been used to simulate a typical AL and to check its performances

Vocabulary-free Image Classification

Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories. In this work, we first empirically verify that representing this semantic space by means of an external vision-language database is the most effective way to obtain semantically relevant content for classifying the image. We then propose Category Search from External Databases (CaSED), a method that exploits a pre-trained vision-language model and an external vision-language database to address VIC in a training-free manner. CaSED first extracts a set of candidate categories from captions retrieved from the database based on their semantic similarity to the image, and then assigns to the image the best matching candidate category according to the same vision-language model. Experiments on benchmark datasets validate that CaSED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction.Comment: Accepted at NeurIPS2023, 19 pages, 8 figures, code is available at https://github.com/altndrr/vi