January 27, 2020

3261 words 16 mins read

Paper Group ANR 1104

Statistical significance in high-dimensional linear mixed models. High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning. How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing. Learning Domain Invariant Representations for Child-Adult Classification fro …

Statistical significance in high-dimensional linear mixed models


Title	Statistical significance in high-dimensional linear mixed models
Authors	Lina Lin, Mathias Drton, Ali Shojaie
Abstract	This paper concerns the development of an inferential framework for high-dimensional linear mixed effect models. These are suitable models, for instance, when we have $n$ repeated measurements for $M$ subjects. We consider a scenario where the number of fixed effects $p$ is large (and may be larger than $M$), but the number of random effects $q$ is small. Our framework is inspired by a recent line of work that proposes de-biasing penalized estimators to perform inference for high-dimensional linear models with fixed effects only. In particular, we demonstrate how to correct a `naive’ ridge estimator in extension of work by B"uhlmann (2013) to build asymptotically valid confidence intervals for mixed effect models. We validate our theoretical results with numerical experiments, in which we show our method outperforms those that fail to account for correlation induced by the random effects. For a practical demonstration we consider a riboflavin production dataset that exhibits group structure, and show that conclusions drawn using our method are consistent with those obtained on a similar dataset without group structure. \|
Tasks
Published	2019-12-16
URL	https://arxiv.org/abs/1912.07578v1
PDF	https://arxiv.org/pdf/1912.07578v1.pdf
PWC	https://paperswithcode.com/paper/statistical-significance-in-high-dimensional
Repo
Framework

High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning


Title	High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning
Authors	Andrea Gobbi, Marco Cristoforetti, Giuseppe Jurman, Cesare Furlanello
Abstract	Climate change impacts could cause progressive decrease of crop quality and yield, up to harvest failures. In particular, heat waves and other climate extremes can lead to localized food shortages and even threaten food security of communities worldwide. In this study, we apply a deep learning architecture for high resolution forecasting (300 m, 10 days) of the Leaf Area Index (LAI), whose dynamics has been widely used to model the growth phase of crops and impact of heat waves. LAI models can be computed at 0.1 degree spatial resolution with an auto regressive component adjusted with weather conditions, validated with remote sensing measurements. However model actionability is poor in regions of varying terrain morphology at this scale (about 8 km at the Alps latitude). Our deep learning model aims instead at forecasting LAI by training multiscale multitemporal (MSMT) data from the Copernicus Global Land Service (CGLS) project for all Europe at 300m resolution and medium-resolution historical weather data. Further, the deep learning model inputs integrate high-resolution land surface features, known to improve forecasts of agricultural productivity. The historical weather data are then replaced with forecast values to predict LAI values at 10 day horizon on Europe. We propose the MSMT model to develop a high resolution crop-specific warning system for mitigating damage due to heat waves and other extreme events.
Tasks
Published	2019-09-13
URL	https://arxiv.org/abs/1909.07786v1
PDF	https://arxiv.org/pdf/1909.07786v1.pdf
PWC	https://paperswithcode.com/paper/high-resolution-forecasting-of-heat-waves
Repo
Framework

How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing


Title	How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing
Authors	Qiong Tian, Yue Yang, Jiaqi Wen, Fan Ding, Jing He
Abstract	With the rapid development of information and communication technology (ICT), taxi business becomes a typical electronic commerce mode. However, one traditional problem still exists in taxi service, that greedy taxi drivers may deliberately take unnecessary detours to overcharge passengers. The detection of these fraudulent behaviors is essential to ensure high-quality taxi service. In this paper, we propose a novel framework for detecting and analyzing the detour behaviors both in off-line database and among on-line trips. Applying our framework to real-world taxi data-set, a remarkable performance (AUC surpasses 0.98) has been achieved in off-line classification. Meanwhile, we further extend the off-line methods to on-line detection, a warning mechanism is introduced to remind drivers and an excellent precision (AUC surpasses 0.90) also has arrived in this phases. After conducting extensive experiments to verify the relationships between pricing regulations and detour behaviors, some quantitative pricing suggestions, including rising base fare and reducing distance-based fare rate, are provided to eliminate detour behaviors from the long term.
Tasks
Published	2019-10-15
URL	https://arxiv.org/abs/1910.06949v3
PDF	https://arxiv.org/pdf/1910.06949v3.pdf
PWC	https://paperswithcode.com/paper/how-to-eliminate-detour-behaviors-in-e
Repo
Framework

Learning Domain Invariant Representations for Child-Adult Classification from Speech


Title	Learning Domain Invariant Representations for Child-Adult Classification from Speech
Authors	Rimita Lahiri, Manoj Kumar, Somer Bishop, Shrikanth Narayanan
Abstract	Diagnostic procedures for ASD (autism spectrum disorder) involve semi-naturalistic interactions between the child and a clinician. Computational methods to analyze these sessions require an end-to-end speech and language processing pipeline that go from raw audio to clinically-meaningful behavioral features. An important component of this pipeline is the ability to automatically detect who is speaking when i.e., perform child-adult speaker classification. This binary classification task is often confounded due to variability associated with the participants’ speech and background conditions. Further, scarcity of training data often restricts direct application of conventional deep learning methods. In this work, we address two major sources of variability - age of the child and data source collection location - using domain adversarial learning which does not require labeled target domain data. We use two methods, generative adversarial training with inverted label loss and gradient reversal layer to learn speaker embeddings invariant to the above sources of variability, and analyze different conditions under which the proposed techniques improve over conventional learning methods. Using a large corpus of ADOS-2 (autism diagnostic observation schedule, 2nd edition) sessions, we demonstrate upto 13.45% and 6.44% relative improvements over conventional learning methods.
Tasks
Published	2019-10-25
URL	https://arxiv.org/abs/1910.11472v1
PDF	https://arxiv.org/pdf/1910.11472v1.pdf
PWC	https://paperswithcode.com/paper/learning-domain-invariant-representations-for
Repo
Framework

Human Body Parts Tracking: Applications to Activity Recognition


Title	Human Body Parts Tracking: Applications to Activity Recognition
Authors	Aras R. Dargazany
Abstract	As cameras and computers became popular, the applications of computer vision techniques attracted attention enormously. One of the most important applications in the computer vision community is human activity recognition. In order to recognize human activities, we propose a human body parts tracking system that tracks human body parts such as head, torso, arms and legs in order to perform activity recognition tasks in real time. This thesis presents a real-time human body parts tracking system (i.e. HBPT) from video sequences. Our body parts model is mostly represented by body components such as legs, head, torso and arms. The body components are modeled using torso location and size which are obtained by a torso tracking method in each frame. In order to track the torso, we are using a blob tracking module to find the approximate location and size of the torso in each frame. By tracking the torso, we will be able to track other body parts based on their location with respect to the torso on the detected silhouette. In the proposed method for human body part tracking, we are also using a refining module to improve the detected silhouette by refining the foreground mask (i.e. obtained by background subtraction) in order to detect the body parts with respect to torso location and size. Having found the torso size and location, the region of each human body part on the silhouette will be modeled by a 2D-Gaussian blob in each frame in order to show its location, size and pose. The proposed approach described in this thesis tracks accurately the body parts in different illumination conditions and in the presence of partial occlusions. The proposed approach is applied to activity recognition tasks such as approaching an object, carrying an object and opening a box or suitcase.
Tasks	Activity Recognition, Human Activity Recognition
Published	2019-07-02
URL	https://arxiv.org/abs/1907.05281v1
PDF	https://arxiv.org/pdf/1907.05281v1.pdf
PWC	https://paperswithcode.com/paper/human-body-parts-tracking-applications-to
Repo
Framework

Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction


Title	Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Authors	Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang
Abstract	Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. Specifically, in the proposed framework, 1) we use an entity-aware word embedding method to integrate both relative position information and head/tail entity embeddings, aiming to highlight the essence of entities for this task; 2) we develop a self-attention mechanism to capture the rich contextual dependencies as a complement for local dependencies captured by piecewise CNN; and 3) instead of using selective attention, we design a pooling-equipped gate, which is based on rich contextual representations, as an aggregator to generate bag-level representation for final relation classification. Compared to selective attention, one major advantage of the proposed gating mechanism is that, it performs stably and promisingly even if only one sentence appears in a bag and thus keeps the consistency across all training examples. The experiments on NYT dataset demonstrate that our approach achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics.
Tasks	Entity Embeddings, Relation Classification, Relation Extraction
Published	2019-11-27
URL	https://arxiv.org/abs/1911.11899v1
PDF	https://arxiv.org/pdf/1911.11899v1.pdf
PWC	https://paperswithcode.com/paper/self-attention-enhanced-selective-gate-with
Repo
Framework

Disagreement-based Active Learning in Online Settings


Title	Disagreement-based Active Learning in Online Settings
Authors	Boshuang Huang, Sudeep Salgia, Qing Zhao
Abstract	We study online active learning for classifying streaming instances within the framework of statistical learning theory. At each time, the learner decides whether to query the label of the current instance. If the decision is to not query, the learner predicts the label and receives no feedback on the correctness of the prediction. The objective is to minimize the number of queries while constraining the number of prediction errors over a horizon of length $T$. We consider a general concept space with a finite VC dimension $d$ and adopt the agnostic setting. We develop a disagreement-based online learning algorithm and establish its $O(dT^{\frac{2-2\alpha}{2-\alpha}}\log^2 T)$ label complexity and bounded regret in terms of classification errors, where $\alpha$ is the Tsybakov noise parameter. The proposed algorithm is shown to outperform existing online active learning algorithms as well as extensions of representative offline algorithms developed under the PAC setting.
Tasks	Active Learning
Published	2019-04-19
URL	https://arxiv.org/abs/1904.09056v4
PDF	https://arxiv.org/pdf/1904.09056v4.pdf
PWC	https://paperswithcode.com/paper/online-active-learning-label-complexity-vs
Repo
Framework

End-to-end feature fusion siamese network for adaptive visual tracking


Title	End-to-end feature fusion siamese network for adaptive visual tracking
Authors	Dongyan Guo, Jun Wang, Weixuan Zhao, Ying Cui, Zhenhua Wang, Shengyong Chen
Abstract	According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long-term tracking task. Motivated by them, we proposed an end-to-end feature fusion framework based on Siamese network, named FF-Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted features are then put into a weight generation layer to obtain the channel weights, which indicate the importance of different feature channels. Both features and the channel weights are utilized in a template generation layer to generate a discriminative template. Finally, the corresponding response maps created by the convolution of the search region features and the template are applied with a fusion layer to obtain the final response map for locating the target. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular Temple-Color, OTB50 and UAV123 benchmarks.
Tasks	Visual Tracking
Published	2019-02-04
URL	http://arxiv.org/abs/1902.01057v1
PDF	http://arxiv.org/pdf/1902.01057v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-feature-fusion-siamese-network-for
Repo
Framework

An Empirical Study of Sections in Classifying Disease Outbreak Reports


Title	An Empirical Study of Sections in Classifying Disease Outbreak Reports
Authors	Son Doan, Mike Conway, Nigel Collier
Abstract	Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet. Unlike scientific articles which are available in a strongly structured form, news articles are usually loosely structured. In this chapter, we investigate the importance of each section and the effect of section weighting on performance of text classification. The experimental results show that (1) classification models using the headline and leading sentence achieve a high performance in terms of F-score compared to other parts of the article; (2) all section with bag-of-word representation (full text) achieves the highest recall; and (3) section weighting information can help to improve accuracy.
Tasks	Text Classification
Published	2019-11-21
URL	https://arxiv.org/abs/1911.09319v1
PDF	https://arxiv.org/pdf/1911.09319v1.pdf
PWC	https://paperswithcode.com/paper/an-empirical-study-of-sections-in-classifying
Repo
Framework

Divide-and-Conquer Large Scale Capacitated Arc Routing Problems with Route Cutting Off Decomposition


Title	Divide-and-Conquer Large Scale Capacitated Arc Routing Problems with Route Cutting Off Decomposition
Authors	Yuzhou Zhang, Yi Mei
Abstract	The capacitated arc routing problem is a very important problem with many practical applications. This paper focuses on the large scale capacitated arc routing problem. Traditional solution optimization approaches usually fail because of their poor scalability. The divide-and-conquer strategy has achieved great success in solving large scale optimization problems by decomposing the original large problem into smaller sub-problems and solving them separately. For arc routing, a commonly used divide-and-conquer strategy is to divide the tasks into subsets, and then solve the sub-problems induced by the task subsets separately. However, the success of a divide-and-conquer strategy relies on a proper task division, which is non-trivial due to the complex interactions between the tasks. This paper proposes a novel problem decomposition operator, named the route cutting off operator, which considers the interactions between the tasks in a sophisticated way. To examine the effectiveness of the route cutting off operator, we integrate it with two state-of-the-art divide-and-conquer algorithms, and compared with the original counterparts on a wide range of benchmark instances. The results show that the route cutting off operator can improve the effectiveness of the decomposition, and lead to significantly better results especially when the problem size is very large and the time budget is very tight.
Tasks	Problem Decomposition
Published	2019-12-29
URL	https://arxiv.org/abs/1912.12667v1
PDF	https://arxiv.org/pdf/1912.12667v1.pdf
PWC	https://paperswithcode.com/paper/divide-and-conquer-large-scale-capacitated
Repo
Framework

Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU


Title	Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU
Authors	Amir Farzad, T. Aaron Gulliver
Abstract	Log messages are now widely used in software systems. They are important for classification as millions of logs are generated each day. Most logs are unstructured which makes classification a challenge. In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detection and log classification. These models are used to convert unstructured log data to trained features which is suitable for classification algorithms. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification and is used here to show that the models can be generalized to other text classification tasks. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms.
Tasks	Anomaly Detection, Sentiment Analysis, Text Classification
Published	2019-11-20
URL	https://arxiv.org/abs/1911.08744v1
PDF	https://arxiv.org/pdf/1911.08744v1.pdf
PWC	https://paperswithcode.com/paper/log-message-anomaly-detection-and
Repo
Framework

Empirical evaluation of full-reference image quality metrics on MDID database


Title	Empirical evaluation of full-reference image quality metrics on MDID database
Authors	Domonkos Varga
Abstract	In this study, our goal is to give a comprehensive evaluation of 32 state-of-the-art FR-IQA metrics using the recently published MDID. This database contains distorted images derived from a set of reference, pristine images using random types and levels of distortions. Specifically, Gaussian noise, Gaussian blur, contrast change, JPEG noise, and JPEG2000 noise were considered.
Tasks
Published	2019-10-02
URL	https://arxiv.org/abs/1910.01050v2
PDF	https://arxiv.org/pdf/1910.01050v2.pdf
PWC	https://paperswithcode.com/paper/empirical-evaluation-of-full-reference-image
Repo
Framework

A Subword Level Language Model for Bangla Language


Title	A Subword Level Language Model for Bangla Language
Authors	Aisha Khatun, Anisur Rahman, Hemayet Ahmed Chowdhury, Md. Saiful Islam, Ayesha Tasnim
Abstract	Language models are at the core of natural language processing. The ability to represent natural language gives rise to its applications in numerous NLP tasks including text classification, summarization, and translation. Research in this area is very limited in Bangla due to the scarcity of resources, except for some count-based models and very recent neural language models being proposed, which are all based on words and limited in practical tasks due to their high perplexity. This paper attempts to approach this issue of perplexity and proposes a subword level neural language model with the AWD-LSTM architecture and various other techniques suitable for training in Bangla language. The model is trained on a corpus of Bangla newspaper articles of an appreciable size consisting of more than 28.5 million word tokens. The performance comparison with various other models depicts the significant reduction in perplexity the proposed model provides, reaching as low as 39.84, in just 20 epochs.
Tasks	Language Modelling, Text Classification
Published	2019-11-15
URL	https://arxiv.org/abs/1911.07613v1
PDF	https://arxiv.org/pdf/1911.07613v1.pdf
PWC	https://paperswithcode.com/paper/a-subword-level-language-model-for-bangla
Repo
Framework

Structured Sparsification of Gated Recurrent Neural Networks


Title	Structured Sparsification of Gated Recurrent Neural Networks
Authors	Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry Vetrov
Abstract	Recently, a lot of techniques were developed to sparsify the weights of neural networks and to remove networks’ structure units, e.g. neurons. We adjust the existing sparsification approaches to the gated recurrent architectures. Specifically, in addition to the sparsification of weights and neurons, we propose sparsifying the preactivations of gates. This makes some gates constant and simplifies LSTM structure. We test our approach on the text classification and language modeling tasks. We observe that the resulting structure of gate sparsity depends on the task and connect the learned structure to the specifics of the particular tasks. Our method also improves neuron-wise compression of the model in most of the tasks.
Tasks	Language Modelling, Text Classification
Published	2019-11-13
URL	https://arxiv.org/abs/1911.05585v1
PDF	https://arxiv.org/pdf/1911.05585v1.pdf
PWC	https://paperswithcode.com/paper/structured-sparsification-of-gated-recurrent
Repo
Framework

Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application


Title	Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application
Authors	Soumyajit Mitra, P S Sastry
Abstract	In this paper we address the problem of discovering a small set of frequent serial episodes from sequential data so as to adequately characterize or summarize the data. We discuss an algorithm based on the Minimum Description Length (MDL) principle and the algorithm is a slight modification of an earlier method, called CSC-2. We present a novel generative model for sequence data containing prominent pairs of serial episodes and, using this, provide some statistical justification for the algorithm. We believe this is the first instance of such a statistical justification for an MDL based algorithm for summarizing event sequence data. We then present a novel application of this data mining algorithm in text classification. By considering text documents as temporal sequences of words, the data mining algorithm can find a set of characteristic episodes for all the training data as a whole. The words that are part of these characteristic episodes could then be considered the only relevant words for the dictionary thus resulting in a considerably reduced feature vector dimension. We show, through simulation experiments using benchmark data sets, that the discovered frequent episodes can be used to achieve more than four-fold reduction in dictionary size without losing any classification accuracy.
Tasks	Text Classification
Published	2019-04-01
URL	http://arxiv.org/abs/1904.00516v1
PDF	http://arxiv.org/pdf/1904.00516v1.pdf
PWC	https://paperswithcode.com/paper/summarizing-event-sequences-with-serial
Repo
Framework