Recurrent Neural Networks for Online Video Popularity Prediction

In this paper, we address the problem of popularity prediction of online videos shared in social media. We prove that this challenging task can be approached using recently proposed deep neural network architectures. We cast the popularity prediction problem as a classification task and we aim to solve it using only visual cues extracted from videos. To that end, we propose a new method based on a Long-term Recurrent Convolutional Network (LRCN) that incorporates the sequentiality of the information in the model. Results obtained on a dataset of over 37'000 videos published on Facebook show that using our method leads to over 30% improvement in prediction performance over the traditional shallow approaches and can provide valuable insights for content creators

Shallow reading with Deep Learning: Predicting popularity of online content using only its title

With the ever decreasing attention span of contemporary Internet users, the title of online content (such as a news article or video) can be a major factor in determining its popularity. To take advantage of this phenomenon, we propose a new method based on a bidirectional Long Short-Term Memory (LSTM) neural network designed to predict the popularity of online content using only its title. We evaluate the proposed architecture on two distinct datasets of news articles and news videos distributed in social media that contain over 40,000 samples in total. On those datasets, our approach improves the performance over traditional shallow approaches by a margin of 15%. Additionally, we show that using pre-trained word vectors in the embedding layer improves the results of LSTM models, especially when the training set is small. To our knowledge, this is the first attempt of applying popularity prediction using only textual information from the title

Thermographic assessment of skin prick tests in comparison with the routine evaluation methods

Introduction: The skin prick test is still the first and basic procedure in the diagnosis of allergic diseases. The possibility of using a sensitive thermographic method supported by the mathematical model for the assessment of skin test results will be highlighted in the studies. Aim: To compare the proposed approach with routine planimetric and thermographic methods. Material and methods: A mathematical model of allergic reaction was developed. Simplifying assumptions of the IgE-mediated skin reaction is the essence of the model. Investigations were performed in a group of 40 patients. Results: Using the spatio-temporal evolution of temperature distributions, the ratios of the histamine released from mast cells to the control histamine were determined. The obtained values very well correlate with the standard evaluation of skin prick tests (correlation coefficient = 0.98). Conclusions: The proposed method of skin test evaluation presents several advantages. The continuous acquisition of data provides the monitoring of time course of the allergic response. The transport of mediator and its concentration were distinctly discriminated, which may be diagnostically useful, especially for abnormal cases. The high sensitivity of the method enables studying patients regardless of age and skin sensitivity

Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN

Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. In particular, there exists a demand for a fast simulation of the neutron Zero Degree Calorimeter, where existing Monte Carlo-based methods impose a significant computational burden. We propose an alternative approach to the problem that leverages machine learning. Our solution utilises neural network classifiers and generative models to directly simulate the response of the calorimeter. In particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step. Our approach increases the simulation speed by 2 orders of magnitude while maintaining the high fidelity of the simulation

Principal component analysis and internal reliability of the Polish version of MESA and UDI-6 questionnaires

Objectives: Urinary incontinence (UI) can affect up to 50% of the population of women over the age of 50. In order to objectively assess discomfort in women with UI prior to initiating treatment and monitoring the outcomes of the treatment, validated questionnaires need to be used to examine the impact of UI on health-related quality of life (HR-QoL). The Urogenital Distress Inventory — Short Form (UDI-6) and the Medical Epidemiologic and Social Aspects of Ageing (MESA) questionnaires are used typically. Assessment of the Polish translation of the MESA and UDI-6 questionnaires. Material and methods: 155 patients with symptoms of UI were enrolled. Each of the patients completed the MESA and UDI questionnaires prior to being examined. The final diagnosis was made after diagnostic tests were carried out in the patients. Results: Principle component analysis showed division of the Polish versions of the questionnaires into domains identical to the original version. Analyses of internal consistency reliability revealed high internal consistency for the MESA questionnaire (0.90) and a low reliability of the UDI-6 questionnaire (0.44). Conclusions: The Polish version of the MESA questionnaire was demonstrated to be a clinically useful diagnostic tool in the studied population, UDI-6 did not reached a sufficiently high reliability in the study group to be recommended as a diagnostic tool

Towards More Realistic Membership Inference Attacks on Large Diffusion Models

Generative diffusion models, including Stable Diffusion and Midjourney, can generate visually appealing, diverse, and high-resolution images for various applications. These models are trained on billions of internet-sourced images, raising significant concerns about the potential unauthorized use of copyright-protected images. In this paper, we examine whether it is possible to determine if a specific image was used in the training set, a problem known in the cybersecurity community and referred to as a membership inference attack. Our focus is on Stable Diffusion, and we address the challenge of designing a fair evaluation framework to answer this membership question. We propose a methodology to establish a fair evaluation setup and apply it to Stable Diffusion, enabling potential extensions to other generative models. Utilizing this evaluation setup, we execute membership attacks (both known and newly introduced). Our research reveals that previously proposed evaluation setups do not provide a full understanding of the effectiveness of membership inference attacks. We conclude that the membership inference attack remains a significant challenge for large diffusion models (often deployed as black-box systems), indicating that related privacy and copyright issues will persist in the foreseeable future

The influence of electromagnetic wave originating from WiFi router on water viscosity

Celem prezentowanej pracy było sprawdzenie, czy mikrofale generowane przez typowy router WiFi wpływają na lepkość wody, jak sugerują niektórzy autorzy. Pomysł bazuje na obserwacji ruchu magnetycznych nanocząstek pod mikroskopem w obecności mikrofal i bez nich. Ruch nanocząstek w polu widzenia mikroskopu był wymuszony przez zastosowanie stałego pola magnetycznego. Porównywano prędkości nanocząstek w obecności pola elektromagnetycznego i bez niego.The aim of the study was to investigate if microwaves generated by a typical WiFi router influence the viscosity of water, as suggested by some authors. The idea relies on the microscopic observation of magnetic nano-beads motion with and without microwave irradiation. A static magnetic field was applied to force the nano-beds movements in the microscope field of view (FOV). The nano-beds velocities under microwave irradiation were compared to their velocities without the electromagnetic stimulation (Wpływ fali elektromagnetycznej generowanej przez router WiFi na lepkość wody)