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Found 28 papers, 3 papers with code
“细粒度英汉机器翻译错误分析语料库”的构建与思考(Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation and Its Implications)
机器翻译错误分析旨在找出机器译文中存在的错误, 包括错误类型、错误分布等, 它在机器翻译研究和应用中起着重要作用。该文将人工译后编辑与错误分析结合起来, 对译后编辑操作进行错误标注, 采用自动标注和人工标注相结合的方法, 构建了一个细粒度英汉机器翻译错误分析语料库, 其中每一个标注样本包括源语言句子、机器译文、人工参考译文、译后编辑译文、词错误率和错误类型标注;标注的错误类型包括增词、漏词、错词、词序错误、未译和命名实体翻译错误等。标注的一致性检验表明了标注的有效性;对标注语料的统计分析结果能有效地指导机器翻译系统的开发和人工译员的后编辑。
MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis
Furthermore, we leverage two data augment strategies and auxiliary tasks to improve the performance on few-label data and zero-shot cross-lingual settings.
Multi-Interval Energy-Reserve Co-Optimization with SoC-Dependent Bids from Battery Storage
We consider the problem of co-optimized energy-reserve market clearing with state-of-charge (SoC) dependent bids from battery storage participants.
Enhancing Microgrid Resilience with Green Hydrogen Storage
We consider the problem of hydrogen storage integration in microgrids to improve the electricity supply resilience.
Convexifying Regulation Market Clearing of State-of-Charge Dependent Bid
We show that the complex nonconvex market clearing problem can be convexified by a simple restriction on the SoC-dependent bid, rendering the intractable market clearing computation to standard linear programs.
Wholesale Market Participation of DERA: DSO-DERA-ISO Coordination
Distributed energy resource aggregators (DERAs) must share the distribution network together with the distribution utility in order to participate in the wholesale electricity markets that are operated by independent system operators (ISOs).
Wholesale Market Participation of DERA: Competitive DER Aggregation
We consider the aggregation of distributed energy resources (DERs) by a profit-seeking DER aggregator (DERA), participating directly in the wholesale electricity market.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
LEMaRT: Label-Efficient Masked Region Transform for Image Harmonization
We present a simple yet effective self-supervised pre-training method for image harmonization which can leverage large-scale unannotated image datasets.
DSO-DERA Coordination for the Wholesale Market Participation of Distributed Energy Resources
We design a coordination mechanism between a distribution system operator (DSO) and distributed energy resource aggregators (DERAs) participating directly in the wholesale electricity market.
Wholesale Market Participation of Storage with State-of-Charge Dependent Bids
Wholesale market participation of storage with state-of-charge (SoC) dependent bids results in a non-convex cost in a multi-interval economic dispatch, which requires a mixed-integer linear program in the market clearing.
Convexifying Market Clearing of SoC-Dependent Bids from Merchant Storage Participants
State-of-charge (SoC) dependent bidding allows merchant storage participants to incorporate SoC-dependent operation and opportunity costs in a bid-based market clearing process.
Competitive DER Aggregation for Participation in Wholesale Markets
Also obtained are DERA's bid and offer curves for its participation in the wholesale energy market and the optimal schedule of behind-the-meter resources.
Pricing Real-time Stochastic Storage Operations
Pricing storage operation in the real-time market under demand and generation stochasticities is considered.
Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion
Existing low-rank tensor completion (LRTC) approaches aim at restoring a partially observed tensor by imposing a global low-rank constraint on the underlying completed tensor.
A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics
This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics.
Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading
Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients.
Pricing Energy Storage in Real-time Market
It is also shown that such settlements give rise to disincentives for generating firms and storage participants to bid truthfully, even when these market participants are rational price-takers in a competitive market.
Regioned Episodic Reinforcement Learning
Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.
The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification.
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
(1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may be very close to those of student, which limits the upper-bound of the student.
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy.
Kernelized Support Tensor Train Machines
In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for image classification.
Pricing Multi-Interval Dispatch under Uncertainty Part I: Dispatch-Following Incentives
Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts.
Matrix Product Operator Restricted Boltzmann Machines
This work presents the matrix product operator RBM (MPORBM) that utilizes a tensor network generalization of Mv/TvRBM, preserves input formats in both the visible and hidden layers, and results in higher expressive power.
Deep Compression of Sum-Product Networks on Tensor Networks
Sum-product networks (SPNs) represent an emerging class of neural networks with clear probabilistic semantics and superior inference speed over graphical models.
A Support Tensor Train Machine
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms.
Hybrid Dirac Semimetal in CaAgBi Materials Family
Based on their formation mechanisms, Dirac points in three-dimensional systems can be classified as accidental or essential.
Materials Science
