Subsequence ≈ Subsequence
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference Open
A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the …
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Gene2vec: gene subsequence embedding for prediction of mammalian <i>N</i><sup>6</sup>-methyladenosine sites from mRNA Open
N 6 -Methyladenosine (m 6 A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 -methyladenosine sites are limited by the small amount…
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Self-Supervised Video Representation Learning with Odd-One-Out Networks Open
We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called odd-one-out learning. In this task, the machine is asked to identify the unrelated or odd element from a set of otherwise related elements. …
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A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music Open
The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstra…
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Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data Open
Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of o…
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iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach Open
Motivation Identification of enhancers and their strength is important because they play a critical role in controlling gene expression. Although some bioinformatics tools were developed, they are limited in discriminating enhancers from n…
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FAMSA: Fast and accurate multiple sequence alignment of huge protein families Open
Rapid development of modern sequencing platforms has contributed to the unprecedented growth of protein families databases. The abundance of sets containing hundreds of thousands of sequences is a formidable challenge for multiple sequence…
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Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting Open
Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and…
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A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series Open
Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven m…
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Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN Open
Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been …
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Context-Aware Visual Policy Network for Sequence-Level Image Captioning Open
Many vision-language tasks can be reduced to the problem of sequence\nprediction for natural language output. In particular, recent advances in image\ncaptioning use deep reinforcement learning (RL) to alleviate the "exposure\nbias" during…
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Discovering Subsequence Patterns for Next POI Recommendation Open
Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often…
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FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products Open
We introduce the Frequent Rainfall Observations on GridS (FROGS) database (Roca et al., 2019). It is composed of gridded daily-precipitation products on a common 1∘×1∘ grid to ease intercomparison and assessment exercises. The database inc…
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Series2Graph Open
Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches that have been proposed so far in the literature have severe limitations: they either require pri…
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference Open
A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the …
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Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach Open
Anomaly detection of time series is an important topic that has been widely studied in many application areas. A number of computational methods were developed for this task in the past few years. However, the existing approaches still hav…
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Simulating branching programs with edit distance and friends: or: a polylog shaved is a lower bound made Open
A recent, active line of work achieves tight lower bounds for fundamental problems under the Strong Exponential Time Hypothesis (SETH). A celebrated result of Backurs and Indyk (STOC'15) proves that computing the Edit Distance of two seque…
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Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm Open
Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and i…
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Sparse exchangeable graphs and their limits via graphon processes Open
In a recent paper, Caron and Fox suggest a probabilistic model for sparse graphs which are exchangeable when associating each vertex with a time parameter in $\mathbb{R}_+$. Here we show that by generalizing the classical definition of gra…
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Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data Open
Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of o…
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Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences Open
With the rapid global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is cur…
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Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity Open
ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described in patients' medical records with free texts, such as terms, phrases and paraphras…
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Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR Open
Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation whil…
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Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data Open
With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the …
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Time Series Classification by Sequence Learning in All-Subsequence Space Open
2017 IEEE International Conference on Data Engineering, San Diego, California, USA, 19-22 April 2017
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Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression Open
In recent years, with the rapid development of wind power generation, some problems are gradually highlighted. At present, one of the essential methods to solve these problems is to predict wind speed. In this paper, a hybrid BRR-EEMD meth…
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Sparse exchangeable graphs and their limits via graphon processes Open
In a recent paper, Caron and Fox suggest a probabilistic model for sparse graphs which are exchangeable when associating each vertex with a time parameter in R+. Here we show that by generalizing the classical definition of graphons as fun…
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Hybrid Method for Short-Term Time Series Forecasting Based on EEMD Open
To improve the prediction effect of time series, we make a systematic study of various time series prediction methods based on statistics and machine learning in this paper. In the experiment, we compare the prediction results of several p…
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A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD Open
The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by resear…
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Multivariate Fine-Grained Complexity of Longest Common Subsequence Open
We revisit the classic combinatorial pattern matching problem of finding a\nlongest common subsequence (LCS). For strings $x$ and $y$ of length $n$, a\ntextbook algorithm solves LCS in time $O(n^2)$, but although much effort has\nbeen spen…