Recurrent Poisson Factorization for Temporal Recommendation

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes are available at https://github.com/AHosseini/RP

Geotechnical behaviour of the carbonate sand-granulated tire mixture

149-155Carbonate sand-tire mixture is used in this research as a soil improvement method to address the environmental problems regarding the accumulation of scrap tires in coastal areas. The stress-strain behaviour, internal friction angle, and the particle breakage of the carbonate sand-tire mixture are studied, and the results are compared to that of pure carbonate sand. The results revealed that the addition of the granulated tires to the carbonate sand changed its behaviour. The addition of granulated tires resulted in a decrease of both the friction angle and the quantity of particle breakage

Geotechnical behaviour of the carbonate sand-granulated tire mixture

Carbonate sand-tire mixture is used in this research as a soil improvement method to address the environmental problems regarding the accumulation of scrap tires in coastal areas. The stress-strain behaviour, internal friction angle, and the particle breakage of the carbonate sand-tire mixture are studied, and the results are compared to that of pure carbonate sand. The results revealed that the addition of the granulated tires to the carbonate sand changed its behaviour. The addition of granulated tires resulted in a decrease of both the friction angle and the quantity of particle breakage

Gemino: Practical and Robust Neural Compression for Video Conferencing

Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates. Recently, several neural alternatives have been proposed that reconstruct talking head videos at very low bitrates using sparse representations of each frame such as facial landmark information. However, these approaches produce poor reconstructions in scenarios with major movement or occlusions over the course of a call, and do not scale to higher resolutions. We design Gemino, a new neural compression system for video conferencing based on a novel high-frequency-conditional super-resolution pipeline. Gemino upsamples a very low-resolution version of each target frame while enhancing high-frequency details (e.g., skin texture, hair, etc.) based on information extracted from a single high-resolution reference image. We use a multi-scale architecture that runs different components of the model at different resolutions, allowing it to scale to resolutions comparable to 720p, and we personalize the model to learn specific details of each person, achieving much better fidelity at low bitrates. We implement Gemino atop aiortc, an open-source Python implementation of WebRTC, and show that it operates on 1024x1024 videos in real-time on a A100 GPU, and achieves 2.9x lower bitrate than traditional video codecs for the same perceptual quality.Comment: 12 pages, 6 appendi

An Observational Cohort of First Episode Psychosis in Iran:The Azeri Recent Onset Acute Phase Psychosis Survey (ARAS Cohort) Study Protocol

Background: Most of our knowledge about the etiology, course, treatment, and outcome of schizophrenia spectrum and other psychotic disorders stems from Western countries. Data from populations living in other geographical areas and low- and middle-income countries, with different genomes (ethnicity) and exposomes (e.g., culture and social support, drugs of abuse, religion), will add to our knowledge of this complex disorder. Methods: The Azeri Acute phase/Recent onset psychosis Survey (ARAS) has been initiated to study the course of the disorder in patients with recent-onset psychosis using validated diagnostic tools and a comprehensive outcome monitoring system, aiming to reveal indicators for understanding the risk and resilience factors and for choosing the best-personalized treatment strategy. All participants will be evaluated for clinical signs and symptoms as well as risk and resilience factors and will be followed up for 1, 3, and 5 years for outcomes in several domains. A hierarchical cluster method will be applied to identify the number of clusters for each outcome. Defined models will be applied to assess the predictive value of cognition on symptomatic and functional outcomes at follow-up. Discussion: The ARAS cohort will yield significant academic- (research and education) and care-related achievements. ARAS data and experience will have value both in being a useful model for other parts of this region and in an expansion of the currently available knowledge