Paper presents a new time-series segmentation technique for mobile phone user behavior.
problem Current segmentation techniques do not accurately capture individual user behavior over time.
method Behavior-Oriented Time Segmentation (BOTS) technique that considers temporal coverage and number of incidences.
result BOTS technique better captures user behavior at various times of day and week.
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the…
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
Captures user behaviors in evolving time series data.
problem Modeling time series data with evolving patterns.
method Defines evolution genes, learns classifiers, and estimates segment distributions.
result Achieves better prediction results and provides explanations.
Graphical Lasso algorithm segments occupants' energy usage behaviors in HC-CP systems.
problem Improving sustainability and energy efficiency in HC-CP systems.
method Introduced a gamification framework and applied Graphical Lasso for energy usage segmentation.
result Characterized different energy usage behaviors in HC-CP systems.
This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering of the source vertices, the destination vertices and the time, to the data under study, in a way that does not require any hyper-parameter t…
WS-II algorithm segments trajectories with high accuracy.
problem Trajectory segmentation for diverse data sources.
method Supervised learning with sliding window analysis.
result WS-II outperforms other methods in all datasets.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
problem Difficulty in computing utility functions for high-player energy game-theoretic frameworks.
method Graphical Lasso based approach to cluster features leading to energy usage behaviors.
result Characteristic clusters demonstrating different energy usage behaviors identified.
CompILE learns reusable segments from demonstrations for hierarchical task execution.
problem Learning reusable, variable-length segments of hierarchical behavior from demonstrations.
method Unsupervised, fully-differentiable sequence segmentation module for latent encoding and re-composition.
result Model generalizes to longer sequences and unseen environments, learns task boundaries and event encodings.
New method learns behavioral representations from mobility data.
problem Analyzing behavioral similarity of moving individuals from CDR trajectories.
method mob2vec framework combining segmentation, generalization, and unsupervised learning.
result Mob2vec generates low-dimensional vector representations preserving mobility behavior similarities.
We provide a proof and analyze the asymptotic behavior of a formula for the linking number of line segments.
problem The invariant formula for the linking number of line segments and its asymptotic behavior.
method Detailed proof and asymptotic analysis of the formula.
result We provide a proof and analyze the asymptotic behavior of the formula for the linking number of line segments.
We consider the maximum likelihood (Viterbi) alignment of a hidden Markov model (HMM). In an HMM, the underlying Markov chain is usually hidden and the Viterbi alignment is often used as the estimate of it. This approach will be referred to as the Viterbi segmentation. The goodness of the Viterbi segmentation can be me…
Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular te…
Paper presents a novel time series clustering algorithm for financial inclusion.
problem Difficulty in understanding consumer financial behavior without restrictive credit scoring.
method Developed a novel time series clustering algorithm.
result Allows institutions to offer unique financial products based on customer needs.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series data.
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a …
Preformer improves Transformer for long-term time series forecasting.
problem Transformer's quadratic complexity and lack of context-awareness for long-term forecasting.
method Introduces Multi-Scale Segment-Correlation mechanism for efficient time series segmentation and context-aware attention.
result Preformer outperforms other Transformer-based methods in long-term time series forecasting.
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
A novel time series clustering method that considers segment typologies.
problem Lack of consideration for the similarity of different subsequences in time series clustering.
method Two-stage clustering: polynomial segmentation followed by hierarchical clustering of segments, then final clustering of time series.
result The method outperforms state-of-the-art techniques on UCR Time Series Classification Archive datasets.
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard appro…
Hybrid CNN improves segmentation and registration of white matter tracts.
problem Accurate analysis of longitudinal brain imaging data.
method A hybrid CNN integrating segmentation and registration into a single procedure.
result Hybrid CNN outperforms multistage pipelines in segmentation accuracy, consistency, and speed.
DVNet efficiently segments large neurovascular datasets using skip connections.
problem Challenges in segmenting large neurovascular datasets from high-throughput microscopy data.
method A fully-convolutional, deep, and densely-connected encoder-decoder network with skip connections.
result DVNet achieves superior performance in semantic segmentation of neurovascular datasets.
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our approach is based on the discovery of a set of latent, shared dynamical behaviors. Using a beta process prior, the size of the set and the sharing pattern are both inferred from data. We develop efficient M…
Embed-KCPD segments text without labels, outperforming baselines.
problem Unsupervised text segmentation is challenging due to lack of boundary labels.
method Embed-KCPD represents sentences as embeddings and estimates boundaries via penalized KCPD.
result Embed-KCPD outperforms strong unsupervised baselines across benchmarks.
Improved model-based RL for 2-agent tasks reduces error accumulation.
problem Accumulating errors in model-based reinforcement learning for multi-agent systems.
method Disentangled variational auto-encoder for latent variable models of multi-step trajectory segments.
result Our approach achieves better sample efficiency and learns both cooperative and adversarial behavior.
V-Net speeds up brain tumor segmentation in MRI scans.
problem Manual tumor segmentation is time-consuming and inaccurate.
method Applied a volumetric, fully convolutional neural network (V-Net) to MRI scans.
result Achieved a whole tumor dice score of 0.89.
ESPRESSO segments time-series data for better human activity recognition.
problem Segmenting high-dimensional time-series data for applications like HAR.
method ESPRESSO combines entropy and shape analysis for multi-dimensional time-series segmentation.
result ESPRESSO outperforms four state-of-the-art methods across seven datasets.
Proposes VILMAP for finding motifs and segmenting words in time series.
problem Finding motifs and segmenting words in time series data.
method Self-Organizing Maps with variable input length.
result VILMAP identifies motifs of varying lengths and avoids catastrophic forgetting.
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the varia…
The paper examines how the Hurst exponent can reveal market regimes.
problem Determining market efficiency and behavior using time series data.
method Kurtosis analysis and Hurst exponent to identify market regimes.
result The Hurst exponent can distinguish between market regimes.
Efficient decoder design improves semantic segmentation for real-time automated driving.
problem Efficient design of decoders for real-time semantic segmentation in automated driving.
method Design of a novel non-bottleneck layer and family of decoders using VGG10 as an efficient encoder.
result Improvement of 10% in performance compared to a baseline.
This paper distills a complex travel mode choice model into simpler, interpretable models.
problem Lack of interpretability in complex machine learning models for travel behavior.
method Model distillation combined with market segmentation.
result Generated interpretable models that closely match the predictions of the original complex model.
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method…
EM-GAN uses GANs for fast stress analysis of multi-segment interconnects.
problem Fast and accurate stress analysis for EM failure assessment in multi-segment interconnects.
method Conditional GAN model trained on images of multi-segment wires and current densities.
result EM-GAN provides accurate stress distribution with 6.6% error and 8.3X speedup.
In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target var…
Deep ReLU networks generalize via piecewise linear interpolation.
problem Understanding how deep neural networks generalize well on real datasets despite memorizing training samples.
method Demonstrated that deep ReLU networks generalize via piecewise linear interpolation, providing a quantified analysis of generalization intervals.
result Generalization intervals of ReLU networks behave similarly along pairwise directions between samples of the same label in both real and random cases, suggesting the same interpolation mechanism.
Adapts CNN for robust medical image segmentation across different scanners and protocols.
problem Performance degradation of CNNs in medical image segmentation due to mismatch between training and test images.
method Designs a segmentation CNN as a concatenation of a shallow normalization CNN and a deep CNN. At test time, adapts the normalization sub-network for each test image using a denoising autoencoder.
result Consistently improves performance on multi-center MRI datasets of brain, heart, and prostate.
Global Planar Convolution boosts brain tumor segmentation by enhancing context perception.
problem Improving context perception in brain tumor segmentation networks.
method Introduced Global Planar Convolution module to enhance context aggregation in brain tumor segmentation.
result Global Planar Convolution eliminates the need for multiple representation levels in segmentation networks.
Inspired by trading, this method segments time series efficiently.
problem Time series segmentation for multivariate data.
method Normalize time series, treat each channel as a stock, use a posteriori trading signals for segmentation.
result Proposed method is faster and produces more intuitive results than existing models.
Joint network for real-time object detection and semantic segmentation.
problem Real-time object detection and semantic segmentation for automated driving.
method Shared encoder for object detection and semantic segmentation, using YOLO v2 and FCN8 decoders.
result Joint network achieves the same accuracy as separate networks and 30 fps for 1280x384 resolution.
Flow-SSN improves segmentation efficiency and accuracy.
problem Challenges in medical imaging segmentation, especially high-rank pixel-wise covariances.
method Generative segmentation model using discrete-time autoregressive and continuous-time flow variants.
result Flow-SSNs can estimate high-rank pixel-wise covariances efficiently without assuming rank or storing parameters.
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters…
Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. Thi…
Framework scores DeFi users based on liquidity and trading behavior.
problem Distinguishing between liquidity provision and active trading in DeFi.
method Rule-based decomposition, deep residual neural network, pool-level context.
result Deep residual neural network improves user scoring and risk assessment.
This survey analyzes deep learning methods for real-time semantic image segmentation.
problem Efficient and accurate semantic image segmentation for real-time applications.
method Comprehensive analysis of deep learning architectures and techniques for fast inference.
result Best-performing architectures and techniques for real-time semantic image segmentation.
The paper proposes a mixture model with segmentation for heterogeneous functional data.
problem Heterogeneity in time and population for functional data.
method Mixture model with segmentation of time, maximum likelihood estimator, EM algorithm with dynamic programming.
result The method is consistent and identifiable, and illustrated on simulated and real datasets.
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effective…
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of re…