From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.Comment: 10 pages, 11 figure

Motion Based Event Recognition Using HMM

Motion is an important cue for video understanding and is widely used in many semantic video analyses. We present a new motion representation scheme in which motion in a video is represented by the responses of frames to a set of motion filters. Each of these filters is designed to be most responsive to a type of dominant motion. Then we employ hidden Markov models (HMMs) to characterize the motion patterns based on these features and thus classify basketball video into 16 events. The evaluation by human satisfaction rate to classification result is 75%, demonstrating effectiveness of the proposed approach to recognizing semantic events in video

An HMM-Based Framework for Video Semantic Analysis

Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis

A HMM Based Semantic Analysis Framework for Sports Game Event Detection

Video events detection or recognition is one of important tasks in semantic understanding of video content. Sports game video should be considered as a rule-based sequential signal. Therefore, it is reasonable to model sports events using hidden Markov models. In this paper, we present a generic, scalable and multilayer framework based on HMMs, called SG-HMMs (sports game HMMs), for sports game event detection. At the bottom layer of this framework, event HMMs output basic hypotheses based on low-level features. The upper layers are composed of composition HMMs, which add constraints on those hypotheses of the lower layer. Instead of isolated event recognition, the hypotheses at different layers are optimized in a bottom-up manner and the optimal semantics are determined by top-down process. The experimental results on basketball and volleyball videos have demonstrated the effectiveness of the proposed framework for sports game analysis

Acute myocardial infarction with left main coronary artery ostial negative remodelling as the first manifestation of Takayasu arteritis: a case report

BACKGROUND: Takayasu arteritis is a chronic inflammatory disease involving the aorta and its major branches. Acute myocardial infarction rarely but not so much presents in patients with Takayasu arteritis, and the preferable revascularization strategy is still under debate. CASE PRESENTATION: A 22-year-old female with Takayasu arteritis presented with acute myocardial infarction. Coronary angiography and intravenous ultrasound (IVUS) showed that the right coronary artery (RCA) was occluded and that there was severe negative remodelling at the ostium of the left main coronary artery (LMCA). The patient was treated by primary percutaneous transluminal coronary angioplasty (PTCA) with a scoring balloon in the LMCA, without stent implantation. After 3 months of immunosuppressive medication, the patient received RCA revascularization by stenting. There was progressive external elastic membrane (EEM) enlargement of the LMCA ostium demonstrated by IVUS at 3 and 15 months post-initial PTCA. CONCLUSION: Here, we report a case of Takayasu arteritis with involvement of the coronary artery ostium. Through PTCA and long-term immunosuppressive medication, we found that coronary negative remodelling might be reversible in patients with Takayasu arteritis

Frontier knowledge and future directions of programmed cell death in clear cell renal cell carcinoma

Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common renal malignancies of the urinary system. Patient outcomes are relatively poor due to the lack of early diagnostic markers and resistance to existing treatment options. Programmed cell death, also known as apoptosis, is a highly regulated and orchestrated form of cell death that occurs ubiquitously throughout various physiological processes. It plays a crucial role in maintaining homeostasis and the balance of cellular activities. The combination of immune checkpoint inhibitors plus targeted therapies is the first-line therapy to advanced RCC. Immune checkpoint inhibitors(ICIs) targeted CTLA-4 and PD-1 have been demonstrated to prompt tumor cell death by immunogenic cell death. Literatures on the rationale of VEGFR inhibitors and mTOR inhibitors to suppress RCC also implicate autophagic, apoptosis and ferroptosis. Accordingly, investigations of cell death modes have important implications for the improvement of existing treatment modalities and the proposal of new therapies for RCC. At present, the novel modes of cell death in renal cancer include ferroptosis, immunogenic cell death, apoptosis, pyroptosis, necroptosis, parthanatos, netotic cell death, cuproptosis, lysosomal-dependent cell death, autophagy-dependent cell death and mpt-driven necrosis, all of which belong to programmed cell death. In this review, we briefly describe the classification of cell death, and discuss the interactions and development between ccRCC and these novel forms of cell death, with a focus on ferroptosis, immunogenic cell death, and apoptosis, in an effort to present the theoretical underpinnings and research possibilities for the diagnosis and targeted treatment of ccRCC

Item-Level Social Influence Prediction with Probabilistic Hybrid Factor Matrix Factorization

Social influence has become the essential factor which drives the dynamic evolution process of social network structure and user behaviors. Previous research often focus on social influence analysis in network-level or topic-level. In this paper, we concentrate on predicting item-level social influence to reveal the users' influences in a more fine-grained level. We formulate the social influence prediction problem as the estimation of a user-post matrix, where each entry in the matrix represents the social influence strength the corresponding user has given the corresponding web post. To deal with the sparsity and complex factor challenges in the research, we model the problem by extending the probabilistic matrix factorization method to incorporate rich prior knowledge on both user dimension and web post dimension, and propose the Probabilistic Hybrid Factor Matrix Factorization (PHF-MF) approach. Intensive experiments are conducted on a real world online social network to demonstrate the advantages and characteristics of the proposed method

Uncovering and predicting the dynamic process of information cascades with survival model

Cascades are ubiquitous in various network environments. Predicting these cascades is decidedly nontrivial in various important applications, such as viral marketing, epidemic prevention, and traffic management. Most previous works have focused on predicting the final cascade sizes. As cascades are dynamic processes, it is always interesting and important to predict the cascade size at any given time, or to predict the time when a cascade will reach a certain size (e.g., the threshold for an outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, can we predict its cumulative cascade size at any later time? For such a challenging problem, an understanding of the micromechanism that drives and generates the macrophenomena (i.e., the cascading process) is essential. Here, we introduce behavioral dynamics as the micromechanism to describe the dynamic process of an infected node’s neighbors getting infected by a cascade (i.e., one-hop sub-cascades). Through data-driven analysis, we find out the common principles and patterns lying in the behavioral dynamics and propose the novel NEtworked WEibull Regression model for modeling it. We also propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics and present a scalable solution to approximate the cascading process with a theoretical guarantee. We evaluate the proposed method extensively on a large-scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art methods in multiple tasks including cascade size prediction, outbreak time prediction, and cascading process prediction