Cosine-based explainable matrix factorization for collaborative filtering recommendation.

Recent years saw an explosive growth in the amount of digital information and the number of users who interact with this information through various platforms, ranging from web services to mobile applications and smart devices. This increase in information and users has naturally led to information overload which inherently limits the capacity of users to discover and find their needs among the staggering array of options available at any given time, the majority of which they may never become aware of. Online services have handled this information overload by using algorithmic filtering tools that can suggest relevant and personalized information to users. These filtering methods, known as Recommender Systems (RS), have become essential to recommend a range of relevant options in diverse domains ranging from friends, courses, music, and restaurants, to movies, books, and travel recommendations. Most research on recommender systems has focused on developing and evaluating models that can make predictions efficiently and accurately, without taking into account other desiderata such as fairness and transparency which are becoming increasingly important to establish trust with human users. For this reason, researchers have been recently pressed to develop recommendation systems that are endowed with the increased ability to explain why a recommendation is given, and hence help users make more informed decisions. Nowadays, state of the art Machine Learning (ML) techniques are being used to achieve unprecedented levels of accuracy in recommender systems. Unfortunately, most models are notorious for being black box models that cannot explain their output predictions. One such example is Matrix Factorization, a technique that is widely used in Collaborative Filtering algorithms. Unfortunately, like all black box machine learning models, MF is unable to explain its outputs. This dissertation proposes a new Cosine-based explainable Matrix Factorization model (CEMF) that incorporates a user-neighborhood explanation matrix (NSE) and incorporates a cosine based penalty in the objective function to encourage predictions that are explainable. Our evaluation experiments demonstrate that CEMF can recommend items that are more explainable and diverse compared to its competitive baselines, and that it further achieves this superior performance without sacrificing the accuracy of its predictions

An Explainable Autoencoder For Collaborative Filtering Recommendation

Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoderbased recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work can be considered to be the first step towards an explainable deep learning architecture based on Autoencoders

Outcomes of cardiopulmonary resuscitation in the emergency department

Objective: Cardiopulmonary resuscitation (CPR) is a lifesaving technique useful in the prevention of death or delaying it in a person with cardiac arrest. In this regard, demographic information about patients who need CPR is vital. Methods: In this cross-sectional study patients with cardiopulmonary arrest or arrhythmias admitted to Imam Reza and Sina educational hospitals of Tabriz University of Medical Sciences from 22 December 2013 to 21 December 2014 entered the study. Demographic information such as age, sex, cardiopulmonary resuscitation time, the place of cardiopulmonary arrest (outside or inside the hospital), the duration of resuscitation process, success or failure of the resuscitation process and the mechanism of cardiopulmonary arrest were obtained. Results: From a total of 354 cases of cardiopulmonary resuscitation, 281 cases (79%) were unsuccessful and 73 cases (21%) were successful. The average age of patients was 59 ± 22 years. The average time of the resuscitation process was 31 ± 12 minutes. There was a significant difference between the mean of age and resuscitation time in patients who had experienced successful or unsuccessful resuscitation (P = 0.0001). There was a significant relationship between sex and the success rate of resuscitation (P = 0.0001). In addition, a significant relationship between the success of the resuscitation operation and the ward of resuscitation was observed (P = 0.0001). Conclusion: The most common mechanism leading to cardiopulmonary arrest among patients was asystole. In this regard, no significant difference was observed between successful and unsuccessful resuscitation processes. It was also observed that the success of resuscitation from 8 am to 4 pm was more than any other time period

In Vitro Pharmacological Modulation of PIEZO1 Channels in Frontal Cortex Neuronal Networks

PIEZO1 is a mechanosensitive ion channel expressed in various organs, including but not limited to the brain, heart, lungs, kidneys, bone, and skin. PIEZO1 has been implicated in astrocyte, microglia, capillary, and oligodendrocyte signaling in the mammalian cortex. Using murine embryonic frontal cortex tissue, we examined the protein expression and functionality of PIEZO1 channels in cultured networks leveraging substrate-integrated microelectrode arrays (MEAs) with additional quantitative results from calcium imaging and whole-cell patch-clamp electrophysiology. MEA data show that the PIEZO1 agonist Yoda1 transiently enhances the mean firing rate (MFR) of single units, while the PIEZO1 antagonist GsMTx4 inhibits both spontaneous activity and Yoda1-induced increase in MFR in cortical networks. Furthermore, calcium imaging experiments revealed that Yoda1 significantly increased the frequency of calcium transients in cortical cells. Additionally, in voltage clamp experiments, Yoda1 exposure shifted the cellular reversal potential towards depolarized potentials consistent with the behavior of PIEZO1 as a non-specific cation-permeable channel. Our work demonstrates that murine frontal cortical neurons express functional PIEZO1 channels and quantifies the electrophysiological effects of channel activation in vitro. By quantifying the electrophysiological effects of PIEZO1 activation in vitro, our study establishes a foundation for future investigations into the role of PIEZO1 in neurological processes and potential therapeutic applications targeting mechanosensitive channels in various physiological contexts