230 research outputs found
Financial Competitiveness of Macau in Comparison with Other Gaming Destinations
This paper analyzes the financial competitiveness of the Macau gaming industry visa- vis its counterparts in North America and Europe. The analysis covers casino product structure, revenue composition, assets productivity and financial returns of Macau versus those of gaming destinations in North America and Europe. The findings reveal that while Macau is advantageously positioned in terms of assets productivity and financial returns, its casino product structure and revenue composition seem at odds with today\u27s gaming trend. Macau is facing challenges from emerging competitors in Asia. To maintain a stable gaming revenue growth and retain its competitiveness, Macau must modify its casino product structure and revenue composition. Pursuing a more diversified market is a critical step towards the goal
Generating Synthetic Data for Neural Keyword-to-Question Models
Search typically relies on keyword queries, but these are often semantically
ambiguous. We propose to overcome this by offering users natural language
questions, based on their keyword queries, to disambiguate their intent. This
keyword-to-question task may be addressed using neural machine translation
techniques. Neural translation models, however, require massive amounts of
training data (keyword-question pairs), which is unavailable for this task. The
main idea of this paper is to generate large amounts of synthetic training data
from a small seed set of hand-labeled keyword-question pairs. Since natural
language questions are available in large quantities, we develop models to
automatically generate the corresponding keyword queries. Further, we introduce
various filtering mechanisms to ensure that synthetic training data is of high
quality. We demonstrate the feasibility of our approach using both automatic
and manual evaluation. This is an extended version of the article published
with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page
p38MAPK plays a pivotal role in the development of acute respiratory distress syndrome
Acute respiratory distress syndrome (ARDS) is a life-threatening illness characterized by a complex pathophysiology, involving not only the respiratory system but also nonpulmonary distal organs. Although advances in the management of ARDS have led to a distinct improvement in ARDS-related mortality, ARDS is still a lifethreatening respiratory condition with long-term consequences. A better understanding of the pathophysiology of this condition will allow us to create a personalized treatment strategy for improving clinical outcomes. In this article, we present a general overview p38 mitogen-activated protein kinase (p38MAPK) and recent advances in understanding its functions. We consider the potential of the pharmacological targeting of p38MAPK pathways to treat ARDS
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty
Entity Linking (EL) is the task of automatically identifying entity mentions
in a piece of text and resolving them to a corresponding entity in a reference
knowledge base like Wikipedia. There is a large number of EL tools available
for different types of documents and domains, yet EL remains a challenging task
where the lack of precision on particularly ambiguous mentions often spoils the
usefulness of automated disambiguation results in real applications. A priori
approximations of the difficulty to link a particular entity mention can
facilitate flagging of critical cases as part of semi-automated EL systems,
while detecting latent factors that affect the EL performance, like
corpus-specific features, can provide insights on how to improve a system based
on the special characteristics of the underlying corpus. In this paper, we
first introduce a consensus-based method to generate difficulty labels for
entity mentions on arbitrary corpora. The difficulty labels are then exploited
as training data for a supervised classification task able to predict the EL
difficulty of entity mentions using a variety of features. Experiments over a
corpus of news articles show that EL difficulty can be estimated with high
accuracy, revealing also latent features that affect EL performance. Finally,
evaluation results demonstrate the effectiveness of the proposed method to
inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP
Symposium On Applied Computing (SAC 2019
Retrieve Anything To Augment Large Language Models
Large language models (LLMs) face significant challenges stemming from their
inherent limitations in knowledge, memory, alignment, and action. These
challenges cannot be addressed by LLMs alone, but should rely on assistance
from the external world, such as knowledge base, memory store, demonstration
examples, and tools. Retrieval augmentation stands as a vital mechanism for
bridging the gap between LLMs and the external assistance. However,
conventional methods encounter two pressing issues. On the one hand, the
general-purpose retrievers are not properly optimized for the retrieval
augmentation of LLMs. On the other hand, the task-specific retrievers lack the
required versatility, hindering their performance across the diverse retrieval
augmentation scenarios.
In this work, we present a novel approach, the LLM-Embedder, which
comprehensively supports the diverse retrieval augmentation needs of LLMs with
one unified embedding model. Training such a unified model is non-trivial, as
various retrieval tasks aim to capture distinct semantic relationships, often
subject to mutual interference. To address this challenge, we systematically
optimize our training methodology. This includes reward formulation based on
LLMs' feedback, the stabilization of knowledge distillation, multi-task
fine-tuning with explicit instructions, and homogeneous in-batch negative
sampling. These optimization strategies contribute to the outstanding empirical
performance of the LLM-Embedder. Notably, it yields remarkable enhancements in
retrieval augmentation for LLMs, surpassing both general-purpose and
task-specific retrievers in various evaluation scenarios. Our checkpoint and
source code are publicly available at
https://github.com/FlagOpen/FlagEmbedding
Deep Clustering Survival Machines with Interpretable Expert Distributions
Conventional survival analysis methods are typically ineffective to
characterize heterogeneity in the population while such information can be used
to assist predictive modeling. In this study, we propose a hybrid survival
analysis method, referred to as deep clustering survival machines, that
combines the discriminative and generative mechanisms. Similar to the mixture
models, we assume that the timing information of survival data is generatively
described by a mixture of certain numbers of parametric distributions, i.e.,
expert distributions. We learn weights of the expert distributions for
individual instances according to their features discriminatively such that
each instance's survival information can be characterized by a weighted
combination of the learned constant expert distributions. This method also
facilitates interpretable subgrouping/clustering of all instances according to
their associated expert distributions. Extensive experiments on both real and
synthetic datasets have demonstrated that the method is capable of obtaining
promising clustering results and competitive time-to-event predicting
performance
Deep Charge: A Deep Learning Model of Electron Density from One-Shot Density Functional Theory Calculation
Electron charge density is a fundamental physical quantity, determining
various properties of matter. In this study, we have proposed a deep-learning
model for accurate charge density prediction. Our model naturally preserves
physical symmetries and can be effectively trained from one-shot density
functional theory calculation toward high accuracy. It captures detailed atomic
environment information, ensuring accurate predictions of charge density across
bulk, surface, molecules, and amorphous structures. This implementation
exhibits excellent scalability and provides efficient analyses of material
properties in large-scale condensed matter systems
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