680 research outputs found
The Influence of Spatial Distribution of Transport Infrastructure on Transport Equity in China
Urban transport infrastructure plays a key role and creates the basic condition in the development of the urban space. Meanwhile, urban gathering and diffusion bring the different urban spatial structure directly can change the spatial distribution of infrastructure. The influence of transport equity made by spatial distribution of urban transport infrastructure has obvious stages. At every stage, transport equity and spatial distribution of transport infrastructure mutually influence and interact. The intensity of the influence of transport equity by spatial distribution of transport infrastructure is not always the same, but sees a different situation with the simultaneous development of evolution stages
Symmetric failures in symmetric control systems
AbstractThis paper discusses the fault-tolerance of symmetric systems with respect to controllability, which is a fundamental characteristic of control systems. In particular, we reveal the underlying mathematical mechanism of the loss of controllability for symmetric systems induced by failures. Based on the decomposition of the symmetric systems into subsystems under the symmetry, the controllability of the entire system can be discussed by checking that of each subsystem. The analysis of the fault-tolerance in this paper is an extension of this idea with the aid of the chain-adapted transformation matrix for the decomposition. The result is shown as a necessary condition for symmetric systems to retain the controllability despite some symmetric failures. We also discuss sufficient conditions
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?
Event co-occurrences have been proved effective for event extraction (EE) in
previous studies, but have not been considered for event argument extraction
(EAE) recently. In this paper, we try to fill this gap between EE research and
EAE research, by highlighting the question that ``Can EAE models learn better
when being aware of event co-occurrences?''. To answer this question, we
reformulate EAE as a problem of table generation and extend a SOTA prompt-based
EAE model into a non-autoregressive generation framework, called TabEAE, which
is able to extract the arguments of multiple events in parallel. Under this
framework, we experiment with 3 different training-inference schemes on 4
datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the
model to extract all events in parallel, it can better distinguish the semantic
boundary of each event and its ability to extract single event gets
substantially improved. Experimental results show that our method achieves new
state-of-the-art performance on the 4 datasets. Our code is avilable at
https://github.com/Stardust-hyx/TabEAE.Comment: Accepted to ACL 2023 main conferenc
DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks
Influence maximization aims to find a subset of seeds that maximize the
influence spread under a given budget. In this paper, we mainly address the
data-driven version of this problem, where the diffusion model is not given but
needs to be inferred from the history cascades. Several previous works have
addressed this topic in a statistical way and provided efficient algorithms
with theoretical guarantee. However, in their settings, though the diffusion
parameters are inferred, they still need users to preset the diffusion model,
which can be an intractable problem in real-world practices. In this paper, we
reformulate the problem on the attributed network and leverage the node
attributes to estimate the closeness between the connected nodes. Specifically,
we propose a machine learning-based framework, named DSCom, to address this
problem in a heuristic way. Under this framework, we first infer the users'
relationship from the diffusion dataset through attention mechanism and then
leverage spectral clustering to overcome the influence overlap problem in the
lack of exact diffusion formula. Compared to the previous theoretical works, we
carefully designed empirical experiments with parameterized diffusion models
based on real-world social networks, which prove the efficiency and
effectiveness of our algorithm
Content Creator versus Brand Advertiser? The Effect of Inserting Advertisements in Videos on Influencers Engagement
Influencer advertising has become an indispensable component of online marketing due to the exponential growth of social influencers and their influence. Whereas the effectiveness of using influencer endorsements is well studied from the brand or company perspective, how the commercial endorsements affect influencers themselves is an important yet unrevealed question. We empirically examine the instantaneous (measured using live comment sentiment) and longer-term (measured using video feedback and follower number change) influence of inserting advertisements in videos on influencers’ reputation. We further investigate how this effect can be moderated when influencers demonstrate stronger endorsement by showing their faces during advertisements. Our result suggests that inserting advertisements have a negative impact on both instantaneous and longer-term viewer engagement; advertisements with influencers’ face showing moderate the negative effect of advertisements on viewers’ instantaneous response, while the different impact between advertisements with/out influencers showing their faces is not significant in the longer term
The structure of kagome superconductors CsVSb in the charge density wave states
The structure of charge density wave states in AVSb (A = K, Rb, Cs)
kagome superconductors remains elusive, with three possible
candidates: tri-hexagonal, star-of-David, and their
mixture. In this study, we conducted a systematic first-principles
investigation of the nuclear quadrupole resonance (NQR) and nuclear magnetic
resonance (NMR) spectra for the CsVSb structures.
By comparing our simulations with experimental data, we have concluded that the
NQR spectrum indicates the tri-hexagonal structure as the proper structure for
CsVSb after its charge density wave phase transition. The NMR
calculation results obtained from the tri-hexagonal structure are also
consistent with the experimental data.Comment: 8 pages, 3 figures and appendi
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
Modern Entity Linking (EL) systems entrench a popularity bias, yet there is
no dataset focusing on tail and emerging entities in languages other than
English. We present Hansel, a new benchmark in Chinese that fills the vacancy
of non-English few-shot and zero-shot EL challenges. The test set of Hansel is
human annotated and reviewed, created with a novel method for collecting
zero-shot EL datasets. It covers 10K diverse documents in news, social media
posts and other web articles, with Wikidata as its target Knowledge Base. We
demonstrate that the existing state-of-the-art EL system performs poorly on
Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that
scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We
also show that our baseline achieves competitive results on TAC-KBP2015 Chinese
Entity Linking task.Comment: WSDM 202
- …