Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting

ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility

TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction

Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework. First, we formulate a sparsity optimization problem that extracts low-level temporal features based on traffic speed readings and then generalizes higher level features as phases of traffic incidents. Second, we propose novel constraints on feature similarity exploiting prior knowledge about the spatial connectivity of the road network to predict the incident duration. The proposed problem is challenging to solve due to the orthogonality constraints, non-convexity objective, and non-smoothness penalties. We develop an algorithm based on the alternating direction method of multipliers (ADMM) framework to solve the proposed formulation. Extensive experiments and comparisons to other models on real-world traffic data and traffic incident records justify the efficacy of our model

Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival time and the technology class of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions. The dataset and code have been made available online

XopZ and ORP1C cooperate to regulate the virulence of Xanthomonas oryzae pv. oryzae on Nipponbare

Bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae (Xoo) has always been considered to be one of the most severe worldwide diseases in rice. Xoo strains usually use the highly conserved type III secretion system (T3SS) to deliver virulence effectors into rice cells and further suppress the host’s immunity. Previous studies reported that different Xanthomonas outer protein (Xop) effectors include XopZ from one strain appear to share functional redundancies on suppressing rice PAMP-triggered immunity (PTI). But only xopZ, except other xop genes, could significantly impaire Xoo virulence when individually deleting in PXO99 strains. Thus, the XopZ effector should not only suppress rice PTI pathway, but also has other unknown indispensable pathological functions in PXO99–rice interactions. Here, we also found that ∆xopZ mutant strains displayed lower virulence on Nipponbare leaves compared with PXO99 strains. We identified an oxysterol-binding related protein, ORP1C, as a XopZ-interacting protein in rice. Further studies found that rice ORP1C preliminarily played a positive role in regulating the resistance to PXO99 strains, and XopZ–ORP1C interactions cooperated to regulate the compatible interactions of PXO99-Nipponbare rice. The reactive oxygen species (ROS) burst and PTI marker gene expression data indicated that ORP1C were not directly relevant to the PTI pathway in rice. The deeper mechanisms underlying XopZ–ORP1C interaction and how XopZ and ORP1C cooperate for regulating the PXO99–rice interactions require further exploration

Blocking Influence at Collective Level with Hard Constraints (Student Abstract)

Influence blocking maximization (IBM) is crucial in many critical real-world problems such as rumors prevention and epidemic containment. The existing work suffers from: (1) concentrating on uniform costs at the individual level, (2) mostly utilizing greedy approaches to approximate optimization, (3) lacking a proper graph representation for influence estimates. To address these issues, this research introduces a neural network model dubbed Neural Influence Blocking (\algo) for improved approximation and enhanced influence blocking effectiveness. The code is available at https://github.com/oates9895/NIB

Progress in blood biomarkers of subjective cognitive decline in preclinical Alzheimer's disease

Abstract. Alzheimer's disease (AD) is a neurodegenerative disease that gradually impairs cognitive functions. Recently, there has been a conceptual shift toward AD to view the disease as a continuum. Since AD is currently incurable, effective intervention to delay or prevent pathological cognitive decline may best target the early stages of symptomatic disease, such as subjective cognitive decline (SCD), in which cognitive function remains relatively intact. Diagnostic methods for identifying AD, such as cerebrospinal fluid biomarkers and positron emission tomography, are invasive and expensive. Therefore, it is imperative to develop blood biomarkers that are sensitive, less invasive, easier to access, and more cost effective for AD diagnosis. This review aimed to summarize the current data on whether individuals with SCD differ reliably and effectively in subjective and objective performances compared to cognitively normal elderly individuals, and to find one or more convenient and accessible blood biomarkers so that researchers can identify SCD patients with preclinical AD in the population as soon as possible. Owing to the heterogeneity and complicated pathogenesis of AD, it is difficult to make reliable diagnoses using only a single blood marker. This review provides an overview of the progress achieved to date with the use of SCD blood biomarkers in patients with preclinical AD, highlighting the key areas of application and current challenges