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

An Inverse Galois Deformation Problem

Suppose \bar{\rho}: \Gal({\bar{F}/F}) \rightarrow \GL_2(\mathbf{k}) is a residual Galois representation satisfying several mild conditions, where $F$ is a number field and $\mathbf{k}$ is a finite field with characteristics $p \geq 7$. In this work, we show that for any finite flat reduced complete intersection over $W(\mathbf{k})$, $\mathcal{R}$, we can construct a deformation problem defined by local conditions imposed on some finite set of places in $F$, such that the corresponding universal deformation ring of $\bar{\rho}$ is $\mathcal{R}$. It's a theorem of Wiles that if the local conditions are chosen to express restriction to deformations coming from modular forms, then the corresponding universal deformation ring is a finite flat reduced complete intersection, so our work can be regarded as a converse to Wiles' result

Does female labor scarcity encourage innovation? Evidence from China's gender imbalance

PRIFPRI3; CRP2; G Cross-cutting gender theme; DCA; ISIDSGD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

A Water-Stable Organic-Inorganic Hybrid Perovskite for Solar Cells by Inorganic Passivation

Organic-inorganic hybrid halide perovskite solar cells (PSCs) have been a trending topic in recent years. Significant progress has been made to increase their power conversion efficiency (PCE) to more than 20%. However, the poor stability of PSCs in both working and non-working conditions results in rapid degradation through multiple environmental erosions such as water, heat, and UV light. Attempts have been made to resolve the rapid-degradation problems, including formula changes, transport layer improvements, and encapsulations, but none of these have effectively resolved the dilemma. This paper reports our findings on adding inorganic films as surface-passivation layers on top of the hybrid perovskite materials, which not only enhance stability by eliminating weak sites but also prevent water penetration by using a water-stable layer. The surface-passivated hybrid perovskite layer indicates a slight increase of bandgap energy (Eg = 1.76 eV), compared to the original methylammonium lead iodide (MAPbI3, Eg = 1.61 eV) layer, allowing for more stable perovskite layer with a small sacrifice in the photoluminescence property, which represents a lower charge diffusion rate and higher bandgap energy. Our finding offers an alternative approach to resolving the low stability issue for PSC fabrication

Trade and Investment Among BRICS: Analysis of Impact of Tariff Reduction and Trade Facilitation Based on Dynamic Global CGE Model

So far, there are few researches based on GTAP model focusing on the inter-regional trade activities among BRICS countries. Specifically, few studies have paid special attention to tariff exemption or trade facilitation scenario analysis. On the other hand, these topics broadly exist in global, multilateral and bilateral trade agreements and dialogues. One of the most prominent issues calling for in-depth study is the dynamic changing characteristics of emerging economies’ trade activities. BRICS countries differ greatly with respect to their trade volume, structure, dependence and environment, which lead to diversified sensitivities to tariff and trade facilitation. As the largest export-oriented emerging economy, China is more sensitive to tariffs and trade facilitation due to its large trade volume of manufactured goods and primary goods. Brazil and Russia are traditional resources exporter and thus they are less sensitive to tariffs and trade facilities because of the monopoly power. India is more dependent on service trade and commodity trade market is usually protected. However, since all of the BRICS counties have joined WTO and the global trade context is transforming, we need to involve the political and economic dynamics into global trade model to simulate the economic impacts. In this paper, we established a dynamic global CGE model to analyze the effects of free trade and trade facilitation in BRICS countries. In the settings of our model, we use adaptive expectation other than pure rational expectation to reflect the situation that BRICS countries are in the midst of transformation. The results show that the dynamic trade changing paths of these countries are quite different from those of developed countries. When trade facilitation increases, the results of China show that China’s agricultural products will see a huge growth in the future. One reason is that agricultural products are very sensitive to trade facilitation, especially sensitive to factors like custom clearance time. Car trade will also see a huge growth under the scenario that car tariffs are reduced

The Impact of Study Setting on Clinical Characteristics in Older Chinese Adults with Subjective Cognitive Decline: Baseline Investigation of Convenience and Population-Based Samples

Background. Subjective cognitive decline (SCD) is the earliest symptom stage of Alzheimer’s disease (AD). Previous studies have shown that the study setting is an important influence factor of SCD. However, the effect of this factor among a Chinese population with SCD is not clear. Here, we aim to compare the clinical characteristics of SCD between a convenience and a population-based sample in China. Methods. We included a convenience sample of 212 SCD subjects and a population-based sample of 110 SCD subjects. We performed univariate analysis to evaluate the between-group differences in sociodemographic characteristics, neuropsychological performance, psychiatric conditions, different cognitive domains, and the SCD-plus criteria. Multiple linear regression model was established, adjusted for sex, age, and education, and compared the neuropsychological performance between the groups. Results. The convenience sample had more years of education, a higher family history of dementia, and higher neuropsychological and anxiety depression score than the population-based sample. Using sex, age, education, group as the independent variables, and neuropsychological score as the dependent variable, multiple linear regression model was established; a statistically significant neuropsychological score difference (MoCA-B, AVLT-H-N4, AVLT-H-N5, AVLT-H-N7, AFT, and STT-B) was found between the two samples. In the SCD cognitive domains, the population-based sample had more complaints about declines in their language and planning domains. For SCD-plus criteria in memory domain, the convenience sample had more complaints, worry, and cognitive decline within the last 5 years, along with medical help-seeking. Conclusion. There were some different characteristics among SCD individuals between convenience samples and population-based samples in China

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

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