DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation

Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous representation generation approaches tend to rarely address three challenges: 1) how to represent the intricate semantic intentions of mobility inexpensively; 2) complex and weak spatial-temporal dependencies due to the sparsity and heterogeneity of the trajectory data; 3) route selection preferences and their correlation to driving behavior. In this paper, we propose a novel multimodal fusion model, DouFu, for trajectory representation joint learning, which applies multimodal learning and attention fusion module to capture the internal characteristics of trajectories. We first design movement, route, and global features generated from the trajectory data and urban functional zones and then analyze them respectively with the attention encoder or feed forward network. The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding. With the global semantic feature, DouFu produces a comprehensive embedding for each trajectory. We evaluate representations generated by our method and other baseline models on classification and clustering tasks. Empirical results show that DouFu outperforms other models in most of the learning algorithms like the linear regression and the support vector machine by more than 10%.Comment: 11 pages, 7 figure

Unsupervised Path Representation Learning with Curriculum Negative Sampling

Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in navigation systems. Existing studies often learn task-specific path representations in a supervised manner, which require a large amount of labeled training data and generalize poorly to other tasks. We propose an unsupervised learning framework Path InfoMax (PIM) to learn generic path representations that work for different downstream tasks. We first propose a curriculum negative sampling method, for each input path, to generate a small amount of negative paths, by following the principles of curriculum learning. Next, \emph{PIM} employs mutual information maximization to learn path representations from both a global and a local view. In the global view, PIM distinguishes the representations of the input paths from those of the negative paths. In the local view, \emph{PIM} distinguishes the input path representations from the representations of the nodes that appear only in the negative paths. This enables the learned path representations to encode both global and local information at different scales. Extensive experiments on two downstream tasks, ranking score estimation and travel time estimation, using two road network datasets suggest that PIM significantly outperforms other unsupervised methods and is also able to be used as a pre-training method to enhance supervised path representation learning.Comment: This paper has been accepted by IJCAI-2

Uncovering Intratumoral And Intertumoral Heterogeneity Among Single-Cell Cancer Specimens

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. Developing such an approach is of great translational relevance and interest, as single-cell expression data are now often collected across numerous experimental conditions (e.g., representing different drug perturbation conditions, CRISPR knockdowns, or patients undergoing clinical trials) that need to be compared. In this work, “Phenotypic Earth Mover\u27s Distance” (PhEMD) is presented as a solution to this problem. PhEMD is a general method for embedding a “manifold of manifolds,” in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). PhEMD is applied to a newly-generated, 300-biospecimen mass cytometry drug screen experiment to map small-molecule inhibitors based on their differing effects on breast cancer cells undergoing epithelial–mesenchymal transition (EMT). These experiments highlight EGFR and MEK1/2 inhibitors as strongly halting EMT at an early stage and PI3K/mTOR/Akt inhibitors as enriching for a drug-resistant mesenchymal cell subtype characterized by high expression of phospho-S6. More generally, these experiments reveal that the final mapping of perturbation conditions has low intrinsic dimension and that the network of drugs demonstrates manifold structure, providing insight into how these single-cell experiments should be computational modeled and visualized. In the presented drug-screen experiment, the full spectrum of perturbation effects could be learned by profiling just a small fraction (11%) of drugs. Moreover, PhEMD could be integrated with complementary datasets to infer the phenotypes of biospecimens not directly profiled with single-cell profiling. Together, these findings have major implications for conducting future drug-screen experiments, as they suggest that large-scale drug screens can be conducted by measuring only a small fraction of the drugs using the most expensive high-throughput single-cell technologies—the effects of other drugs may be inferred by mapping and extending the perturbation space. PhEMD is also applied to patient tumor biopsies to assess intertumoral heterogeneity. Applied to a melanoma dataset and a clear-cell renal cell carcinoma dataset (ccRCC), PhEMD maps tumors similarly to how it maps perturbation conditions as above in order to learn key axes along which tumors vary with respect to their tumor-infiltrating immune cells. In both of these datasets, PhEMD highlights a subset of tumors demonstrating a marked enrichment of exhausted CD8+ T-cells. The wide variability in tumor-infiltrating immune cell abundance and particularly prominent exhausted CD8+ T-cell subpopulation highlights the importance of careful patient stratification when assessing clinical response to T cell-directed immunotherapies. Altogether, this work highlights PhEMD’s potential to facilitate drug discovery and patient stratification efforts by uncovering the network geometry of a large collection of single-cell biospecimens. Our varied experiments demonstrate that PhEMD is highly scalable, compatible with leading batch effect correction techniques, and generalizable to multiple experimental designs, with clear applicability to modern precision oncology efforts

Sensory neuron lineage mapping and manipulation in the Drosophila olfactory system.

Nervous systems exhibit myriad cell types, but understanding how this diversity arises is hampered by the difficulty to visualize and genetically-probe specific lineages, especially at early developmental stages prior to expression of unique molecular markers. Here, we use a genetic immortalization method to analyze the development of sensory neuron lineages in the Drosophila olfactory system, from their origin to terminal differentiation. We apply this approach to define a fate map of nearly all olfactory lineages and refine the model of temporal patterns of lineage divisions. Taking advantage of a selective marker for the lineage that gives rise to Or67d pheromone-sensing neurons and a genome-wide transcription factor RNAi screen, we identify the spatial and temporal requirements for Pointed, an ETS family member, in this developmental pathway. Transcriptomic analysis of wild-type and Pointed-depleted olfactory tissue reveals a universal requirement for this factor as a switch-like determinant of fates in these sensory lineages

Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods