15 research outputs found
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
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
Predicting stock market is vital for investors and policymakers, acting as a
barometer of the economic health. We leverage social media data, a potent
source of public sentiment, in tandem with macroeconomic indicators as
government-compiled statistics, to refine stock market predictions. However,
prior research using tweet data for stock market prediction faces three
challenges. First, the quality of tweets varies widely. While many are filled
with noise and irrelevant details, only a few genuinely mirror the actual
market scenario. Second, solely focusing on the historical data of a particular
stock without considering its sector can lead to oversight. Stocks within the
same industry often exhibit correlated price behaviors. Lastly, simply
forecasting the direction of price movement without assessing its magnitude is
of limited value, as the extent of the rise or fall truly determines
profitability. In this paper, diverging from the conventional methods, we
pioneer an ECON. The framework has following advantages: First, ECON has an
adept tweets filter that efficiently extracts and decodes the vast array of
tweet data. Second, ECON discerns multi-level relationships among stocks,
sectors, and macroeconomic factors through a self-aware mechanism in semantic
space. Third, ECON offers enhanced accuracy in predicting substantial stock
price fluctuations by capitalizing on stock price movement. 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
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
UR4NNV: Neural Network Verification, Under-approximation Reachability Works!
Recently, formal verification of deep neural networks (DNNs) has garnered
considerable attention, and over-approximation based methods have become
popular due to their effectiveness and efficiency. However, these strategies
face challenges in addressing the "unknown dilemma" concerning whether the
exact output region or the introduced approximation error violates the property
in question. To address this, this paper introduces the UR4NNV verification
framework, which utilizes under-approximation reachability analysis for DNN
verification for the first time. UR4NNV focuses on DNNs with Rectified Linear
Unit (ReLU) activations and employs a binary tree branch-based
under-approximation algorithm. In each epoch, UR4NNV under-approximates a
sub-polytope of the reachable set and verifies this polytope against the given
property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN
properties while providing confidence levels when reaching verification epoch
bounds and failing falsifying properties. Experimental comparisons with
existing verification methods demonstrate the effectiveness and efficiency of
UR4NNV, significantly reducing the impact of the "unknown dilemma".Comment: 11 pages, 4 figure
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