Modelling the Accessibility Classification of Railway Lines: A case study of Northeast China railway network

A major problem addressed in railway network planning relates to distinguishing the role of the railway line in the network, and making a reasonable classification of the lines based on their role. Accessibility has been widely used to measure the role of transportation infrastructure in various studies, but few quantitative models for the classification of the role have been presented yet. In this paper, the line accessibility classification model is proposed, which aims to distinguish the role of railway lines in the network and to classify the lines into different grades. The practicability of the model is demonstrated through the case study of Northeast China railway network where the railway lines in Northeast China can be classified into three grades. The line accessibility classification model is supposed to be a strategic decision support tool for planners and policy makers to determine the classification of railway lines.</p

A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplet of an aspect term, an opinion term, and their corresponding sentiment polarity from the review texts. Due to the complexity of language and the existence of multiple aspect terms and opinion terms in a single sentence, current models often confuse the connections between an aspect term and the opinion term describing it. To address this issue, we propose a pairing enhancement approach for ASTE, which incorporates contrastive learning during the training stage to inject aspect-opinion pairing knowledge into the triplet extraction model. Experimental results demonstrate that our approach performs well on four ASTE datasets (i.e., 14lap, 14res, 15res and 16res) compared to several related classical and state-of-the-art triplet extraction methods. Moreover, ablation studies conduct an analysis and verify the advantage of contrastive learning over other pairing enhancement approaches.Comment: 12 pages, 4 figure

Classification of integers based on residue classes via modern deep learning algorithms

Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches on classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated Automated Machine Learning (AutoML) platforms from Amazon, Google and Microsoft, and found that they failed on this task without appropriately engineered features. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors, and demonstrated its effectiveness. Finally, we evaluated Large Language Models (LLMs) such as GPT-4, GPT-J, LLaMA and Falcon, and demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine-learning models, even in the era of AutoML and LLMs.Comment: Accepted at Pattern

HIV-1 TAT-mediated protein transduction and subcellular localization using novel expression vectors11The nucleotide sequences of vectors pETAT-1/2/11/12, pNB-3/13, pHis-TAT-GFP, pHis-TAT-m-GFP and pHis-GFP have been deposited in GenBank under accession numbers AF525441–525449.

AbstractSeveral novel prokaryotic and eukaryotic expression vectors were constructed for protein transduction and subcellular localization. These vectors employed an N-terminal stretch of 11 basic amino acid residues (47–57) from the human immunodeficiency virus type 1 (HIV-1) TAT protein transduction domain (PTD) for protein translocation and cellular localization. The vectors also contained a six-histidine (His6) tag at the N- or C-terminus for convenient purification and detection, and a multiple cloning site for easy insertion of foreign genes. Some heterologous genes including HSV-TK, Bcl-rambo, Smac/DIABLO and GFP were fused in-frame to TAT PTD and successfully overexpressed in Escherichia coli. The purified TAT-GFP fusion protein was able to transduce into the mammalian cells and was found to locate mainly in the cytosol when exogenously added to the cell culture medium. However, using a transfection system, mammalian-expressed TAT-GFP predominantly displayed a nuclear localization and nucleolar accumulation in mammalian cell lines. This discrepancy implies that the exact subcellular localization of transduced protein may depend on cell type, the nature of imported proteins and delivery approach. Taken together, our results demonstrate that a TAT PTD length of 11 amino acids was sufficient to confer protein internalization and its subsequent cellular localization. These novel properties allow these vectors to be useful for studying protein transduction and nuclear import

OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams

How to get insights from relational data streams in a timely manner is a hot research topic. This type of data stream can present unique challenges, such as distribution drifts, outliers, emerging classes, and changing features, which have recently been described as open environment challenges for machine learning. While existing studies have been done on incremental learning for data streams, their evaluations are mostly conducted with manually partitioned datasets. Thus, a natural question is how those open environment challenges look like in real-world relational data streams and how existing incremental learning algorithms perform on real datasets. To fill this gap, we develop an Open Environment Benchmark named OEBench to evaluate open environment challenges in relational data streams. Specifically, we investigate 55 real-world relational data streams and establish that open environment scenarios are indeed widespread in real-world datasets, which presents significant challenges for stream learning algorithms. Through benchmarks with existing incremental learning algorithms, we find that increased data quantity may not consistently enhance the model accuracy when applied in open environment scenarios, where machine learning models can be significantly compromised by missing values, distribution shifts, or anomalies in real-world data streams. The current techniques are insufficient in effectively mitigating these challenges posed by open environments. More researches are needed to address real-world open environment challenges. All datasets and code are open-sourced in https://github.com/sjtudyq/OEBench

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

There are many approaches that use weak-supervision to train networks to segment 2D images. By contrast, existing 3D approaches rely on full-supervision of a subset of 2D slices of the 3D image volume. In this paper, we propose an approach that is truly weakly-supervised in the sense that we only need to provide a sparse set of 3D point on the surface of target objects, an easy task that can be quickly done. We use the 3D points to deform a 3D template so that it roughly matches the target object outlines and we introduce an architecture that exploits the supervision provided by coarse template to train a network to find accurate boundaries. We evaluate the performance of our approach on Computed Tomography (CT), Magnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets. We will show that it outperforms a more traditional approach to weak-supervision in 3D at a reduced supervision cost.Comment: 13 Page

Extensional collapse of the Tibetan plateau: results of threedimensional finite element modeling

[1] Following their initial collision 50-70 Myr ago, the Indian and Eurasian plates have been continuously converging toward each other. Whereas the regional stress field is predominately compressive, the Late Cenozoic tectonics within the Tibetan Plateau features widespread crustal extension. Numerous causes of the extension have been proposed, but their relative roles remain in debate. We have investigated the major factors contributing to the Tibetan extension in a three-dimensional viscoelastic model that includes both lateral and vertical variations of lithospheric rheology and relevant boundary conditions. Constrained by the present topography and GPS velocity field, the model predicted predominately extensional stress states within the plateau crust, resulting from mechanical balance between the gravitational buoyancy force of the plateau and the tectonic compressive stresses. The predicted stress pattern is consistent with the earthquake data that indicate roughly E-W extension in most of Tibet and nearly N-S extension near the eastern margin of the Tibetan Plateau. We explored the parameter space and boundary conditions to examine the stress evolution during the uplift of the Tibetan Plateau. When the plateau was lower than 50% of its present elevation, strike-slip and reverse faults were predominate over the entire plateau, and no E-W crustal extension was predicted. Significant crustal extension occurs only when the plateau has reached $75% of its present elevation. Basal shear associated with underthrusting of the Indian plate beneath Tibet may have enhanced crustal extension in southern Tibet and the Himalayas, and a stronger basal shear during the Miocene may help to explain the development of the South Tibetan Detachment System

Effect of leading-edge erosion on the performance of offshore horizontal axis wind turbine using BEM method

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