195 research outputs found
A Gray-Box Dynamic Modeling Method for Variable Speed Direct Expansion Systems
In this paper, a gray-box dynamic modeling approach for variable-speed direct-expansion systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and reduced uncertainties. The modeling method was applied to a 3-ton variable-speed heat pump and proved to be capable of accurately capturing the system transient behaviors over a wide range of operating conditions
Development and Validation of an Accumulator Liquid-Level Estimator to Enable Zero-Superheat and Active Charge Control in Vapor-Compression Systems
Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
The extended structural context has made scientific paper summarization a
challenging task. This paper proposes CHANGES, a contrastive hierarchical graph
neural network for extractive scientific paper summarization. CHANGES
represents a scientific paper with a hierarchical discourse graph and learns
effective sentence representations with dedicated designed hierarchical graph
information aggregation. We also propose a graph contrastive learning module to
learn global theme-aware sentence representations. Extensive experiments on the
PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the
importance of capturing hierarchical structure information in modeling
scientific papers.Comment: CODI at ACL 202
HEGEL: Hypergraph Transformer for Long Document Summarization
Extractive summarization for long documents is challenging due to the
extended structured input context. The long-distance sentence dependency
hinders cross-sentence relations modeling, the critical step of extractive
summarization. This paper proposes HEGEL, a hypergraph neural network for long
document summarization by capturing high-order cross-sentence relations. HEGEL
updates and learns effective sentence representations with hypergraph
transformer layers and fuses different types of sentence dependencies,
including latent topics, keywords coreference, and section structure. We
validate HEGEL by conducting extensive experiments on two benchmark datasets,
and experimental results demonstrate the effectiveness and efficiency of HEGEL.Comment: EMNLP 202
SummIt: Iterative Text Summarization via ChatGPT
Existing text summarization systems have made significant progress in recent
years but typically generates summaries in a single step. The one-shot
summarization setting is sometimes inadequate, however, as the generated
summary may contain hallucinations or overlook important details related to the
reader's interests. In this paper, we address this limitation by proposing
SummIt, an iterative text summarization framework based on large language
models like ChatGPT. Our framework enables the model to refine the generated
summary iteratively through self-evaluation and feedback, closely resembling
the iterative process humans undertake when drafting and revising summaries. We
also explore using in-context learning to guide the rationale generation and
summary refinement. Furthermore, we explore the potential benefits of
integrating knowledge and topic extractors into the framework to enhance
summary faithfulness and controllability. We evaluate the performance of our
framework on three benchmark summarization datasets through empirical and
qualitative analyses. We also conduct a human evaluation to validate the
effectiveness of the model's refinements and find a potential issue of
over-correction. Our code is available at
\url{https://github.com/hpzhang94/summ_it}.Comment: work in progres
Extractive Summarization via ChatGPT for Faithful Summary Generation
Extractive summarization is a crucial task in natural language processing
that aims to condense long documents into shorter versions by directly
extracting sentences. The recent introduction of ChatGPT has attracted
significant interest in the NLP community due to its remarkable performance on
a wide range of downstream tasks. However, concerns regarding factuality and
faithfulness have hindered its practical applications for summarization
systems. This paper first presents a thorough evaluation of ChatGPT's
performance on extractive summarization and compares it with traditional
fine-tuning methods on various benchmark datasets. Our experimental analysis
reveals that ChatGPT's extractive summarization performance is still inferior
to existing supervised systems in terms of ROUGE scores. In addition, we
explore the effectiveness of in-context learning and chain-of-thought reasoning
for enhancing its performance. Furthermore, we find that applying an
extract-then-generate pipeline with ChatGPT yields significant performance
improvements over abstractive baselines in terms of summary faithfulness. These
observations highlight potential directions for enhancing ChatGPT's
capabilities for faithful text summarization tasks using two-stage approaches.Comment: Work in progres
Gray box dynamic modeling of vapor compression systems for control optimization
Buildings account for 75% of electricity use in the U.S. and more than 24% of building electrical energy is consumed by vapor compression equipment, including air-conditioners, refrigerators/freezers and heat pumps. Dynamic modeling of vapor compression systems (VCS) is particularly important for developing and validating optimal control strategies to maximize the system efficiency and reliability. However, existing modeling techniques are rarely used in control practices because of the significant model development effort and requirement of high computational resources.
This dissertation presents an efficient and robust gray-box dynamic modeling approach for VCS to support control optimization. The presented methodology allows automated construction of data-driven VCS models with minimum training data and human
inputs. The overall approach incorporates a multi-stage training procedure with separate estimation of the steady-state and dynamic model parameters along with a finite control volume scheme to achieve good model identifiability while ensuring adequate prediction accuracy. To improve model reliability, the modeling approach incorporates sensitivity analysis and de-correlating steps in a pre-conditioning procedure to avoid over-parameterization. The system-level training identifies
the refrigerant charge that minimizes the steady-state simulation errors while the dynamic modeling stage transforms the established steady-state system model into a dynamic counterpart, in which the optimal thermal capacitances of the heat exchanger walls are identified to best reproduce system transient responses
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
The computer-aided diagnosis (CAD) system can provide a reference basis for
the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs)
can not only extract visual elements such as colors and shapes but also
semantic features. As such they have made great improvements in many tasks of
dermoscopy images. The imaging of dermoscopy has no principal orientation,
indicating that there are a large number of skin lesion rotations in the
datasets. However, CNNs lack rotation invariance, which is bound to affect the
robustness of CNNs against rotations. To tackle this issue, we propose a
rotation meanout (RM) network to extract rotation-invariant features from
dermoscopy images. In RM, each set of rotated feature maps corresponds to a set
of outputs of the weight-sharing convolutions and they are fused using meanout
strategy to obtain the final feature maps. Through theoretical derivation, the
proposed RM network is rotation-equivariant and can extract rotation-invariant
features when followed by the global average pooling (GAP) operation. The
extracted rotation-invariant features can better represent the original data in
classification and retrieval tasks for dermoscopy images. The RM is a general
operation, which does not change the network structure or increase any
parameter, and can be flexibly embedded in any part of CNNs. Extensive
experiments are conducted on a dermoscopy image dataset. The results show our
method outperforms other anti-rotation methods and achieves great improvements
in dermoscopy image classification and retrieval tasks, indicating the
potential of rotation invariance in the field of dermoscopy images
The effect of childhood sexual abuse on depressive symptoms in female college students: a serial mediation model
ObjectiveChildhood sexual abuse (CSA) can have a negative impact on women’s psychological, emotional and social functioning. The purpose of this study was to explore the relationship between CSA and depressive symptoms in female college students, as well as the mediating roles of negative core schema and experiential avoidance.Methods515 female college students responded to the Sexual Abuse subscale of the Childhood Trauma Questionnaire, the Depression subscale of the Depression Anxiety Stress Scale, the Brief Core Schema Scales, and the Acceptance and Action Questionnaire – II. The structural equation modeling was used for the mediation analysis.ResultsThere was a significant positive correlation between CSA and depressive symptoms in female college students. The theoretical model was well fitted, χ2/df = 3.422, RMSEA = 0.069, CFI = 0.929, TLI = 0.919. The negative core schema played a mediating role between CSA and depressive symptoms. Experiential avoidance played a mediating role between CSA and depressive symptoms. The negative core schema and experiential avoidance played a serial mediating role between CSA and depressive symptoms.ConclusionThese results deepen our understanding of the relationship between CSA and depressive symptoms in female college students, and provide theoretical guidance for the prevention of depression in female college students. Attention should be paid to female college students who have experienced CSA, to eliminate the adverse influence of negative core schema on these students. Meanwhile, we should teach female college students to accept themselves as they are, and thereby reduce their use of experiential avoidance strategies
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