52 research outputs found
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
Compositional and domain generalization present significant challenges in
semantic parsing, even for state-of-the-art semantic parsers based on
pre-trained language models (LMs). In this study, we empirically investigate
improving an LM's generalization in semantic parsing with two simple
techniques: at the token level, we introduce a token preprocessing method to
preserve the semantic boundaries of tokens produced by LM tokenizers; at the
sequence level, we propose to use special tokens to mark the boundaries of
components aligned between input and output. Our experimental results on two
text-to-SQL semantic parsing datasets show that our token preprocessing,
although simple, can substantially improve the LM performance on both types of
generalization, and our component boundary marking method is particularly
helpful for compositional generalization.Comment: 9 pages, to be published in ACL202
Pursuing Equilibrium of Medical Resources via Data Empowerment in Parallel Healthcare System
The imbalance between the supply and demand of healthcare resources is a
global challenge, which is particularly severe in developing countries.
Governments and academic communities have made various efforts to increase
healthcare supply and improve resource allocation. However, these efforts often
remain passive and inflexible. Alongside these issues, the emergence of the
parallel healthcare system has the potential to solve these problems by
unlocking the data value. The parallel healthcare system comprises
Medicine-Oriented Operating Systems (MOOS), Medicine-Oriented Scenario
Engineering (MOSE), and Medicine-Oriented Large Models (MOLMs), which could
collect, circulate, and empower data. In this paper, we propose that achieving
equilibrium in medical resource allocation is possible through parallel
healthcare systems via data empowerment. The supply-demand relationship can be
balanced in parallel healthcare systems by (1) increasing the supply provided
by digital and robotic doctors in MOOS, (2) identifying individual and
potential demands by proactive diagnosis and treatment in MOSE, and (3)
improving supply-demand matching using large models in MOLMs. To illustrate the
effectiveness of this approach, we present a case study optimizing resource
allocation from the perspective of facility accessibility. Results demonstrate
that the parallel healthcare system could result in up to 300% improvement in
accessibility
Evolutionary City: Towards a Flexible, Agile and Symbiotic System
Urban growth sometimes leads to rigid infrastructure that struggles to adapt
to changing demand. This paper introduces a novel approach, aiming to enable
cities to evolve and respond more effectively to such dynamic demand. It
identifies the limitations arising from the complexity and inflexibility of
existing urban systems. A framework is presented for enhancing the city's
adaptability perception through advanced sensing technologies, conducting
parallel simulation via graph-based techniques, and facilitating autonomous
decision-making across domains through decentralized and autonomous
organization and operation. Notably, a symbiotic mechanism is employed to
implement these technologies practically, thereby making urban management more
agile and responsive. In the case study, we explore how this approach can
optimize traffic flow by adjusting lane allocations. This case not only
enhances traffic efficiency but also reduces emissions. The proposed
evolutionary city offers a new perspective on sustainable urban development,
highliting the importance of integrated intelligence within urban systems.Comment: 11 pages, 11 figure
Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization
With a growing complexity of the intelligent traffic system (ITS), an
integrated control of ITS that is capable of considering plentiful
heterogeneous intelligent agents is desired. However, existing control methods
based on the centralized or the decentralized scheme have not presented their
competencies in considering the optimality and the scalability simultaneously.
To address this issue, we propose an integrated control method based on the
framework of Decentralized Autonomous Organization (DAO). The proposed method
achieves a global consensus on energy consumption efficiency (ECE), meanwhile
to optimize the local objectives of all involved intelligent agents, through a
consensus and incentive mechanism. Furthermore, an operation algorithm is
proposed regarding the issue of structural rigidity in DAO. Specifically, the
proposed operation approach identifies critical agents to execute the smart
contract in DAO, which ultimately extends the capability of DAO-based control.
In addition, a numerical experiment is designed to examine the performance of
the proposed method. The experiment results indicate that the controlled agents
can achieve a consensus faster on the global objective with improved local
objectives by the proposed method, compare to existing decentralized control
methods. In general, the proposed method shows a great potential in developing
an integrated control system in the ITSComment: 6 pages, 6 figures. To be published in 2023 IEEE 26th International
Conference on Intelligent Transportation Systems (ITSC
IR Design for Application-Specific Natural Language: A Case Study on Traffic Data
In the realm of software applications in the transportation industry,
Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their
ease of use and various other benefits. With the ceaseless progress in computer
performance and the rapid development of large-scale models, the possibility of
programming using natural language in specified applications - referred to as
Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits
greater flexibility and freedom, which, in turn, leads to an increase in
computational complexity for parsing and a decrease in processing performance.
To tackle this issue, our paper advances a design for an intermediate
representation (IR) that caters to ASNL and can uniformly process
transportation data into graph data format, improving data processing
performance. Experimental comparisons reveal that in standard data query
operations, our proposed IR design can achieve a speed improvement of over
forty times compared to direct usage of standard XML format data
Self-retracting motion of graphite micro-flakes: superlubricity in micrometer scale
Through experimental study, we reveal superlubricity as the mechanism of
self-retracting motion of micrometer sized graphite flakes on graphite
platforms by correlating respectively the lock-up or self-retraction states
with the commensurate or incommensurate contacts. We show that the
scale-dependent loss of self-retractability is caused by generation of contact
interfacial defects. A HOPG structure is also proposed to understand our
experimental observations, particularly in term of the polycrystal structure.
The realisation of the superlubricity in micrometer scale in our experiments
will have impact in the design and fabrication of micro/nanoelectromechanical
systems based on graphitic materials
Features of cardiovascular magnetic resonance native T1 mapping in maintenance hemodialysis patients and their related factors
AbstractPurpose Increased myocardial T1 values on cardiovascular MRI (CMRI) have been shown to be a surrogate marker for myocardial fibrosis. The use of CMRI in patients on hemodialysis (HD) remains limited. This research aimed to explore the characteristics of native T1 values in HD patients and identify factors related to T1 values.Methods A total of thirty-two patients on HD and fourteen healthy controls were included in this study. All participants underwent CMRI. Using modified Look-Locker inversion recovery (MOLLI) sequence, native T1 mapping was achieved. Native CMRI T1 values were compared between the two groups. In order to analyze the relationship between T1 values and clinical parameters, correlation analysis was performed in patients on HD.Results Patients on HD exhibited elevated global native T1 values compared to control subjects. In the HD group, the global native T1 value correlated positively with intact parathyroid hormone (iPTH) (r = 0.418, p = 0.017) and negatively with triglycerides (r= −0.366, p = 0.039). Moreover, the global native T1 value exhibited a positive correlation with the left ventricular end-diastolic volume indexed to body surface area (BSA; r = 0.528, p = 0.014), left ventricular end-systolic volume indexed to BSA (r = 0.506, p = 0.019), and left ventricular mass indexed to BSA (r = 0.600, p = 0.005). A negative correlation was observed between the global native T1 value and ejection fraction (r = 0.-0.551, p = 0.010).Conclusion The global native T1 value was prolonged in HD patients compared with controls. In the HD group, the global T1 value correlated strongly with iPTH, triglycerides, and cardiac structural and functional parameters
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