In this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is explore user preference, influence and geographical POI recommendations. addition deriving preference based on user-based collaborative filtering exploring from friends, put special emphasis due spatial clustering phenomenon exhibited in check-in activities of LBSNs. We argue that among POIs plays an...

Data gathering is a common but critical operation in many applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering an effective topology control approach networks, which can increase scalability and lifetime. In this paper, we propose novel clustering schema EECS for better suits periodical data applications. Our elects cluster heads with more residual through local radio communication while...

While being the de facto standard coordinate representation for human pose estimation, heatmap has not been investigated in-depth. This work fills this gap. For first time, we find that process of decoding predicted heatmaps into final joint coordinates in original image space is surprisingly significant performance. We further probe design limitations method, and propose a more principled distributionaware method. Also, improve encoding (i.e. transforming ground-truth to heatmaps) by...

Social friendship has been shown beneficial for item recommendation years. However, existing approaches mostly incorporate social into recommender systems by heuristics. In this paper, we argue that influence between friends can be captured quantitatively and propose a probabilistic generative model, called influenced selection(SIS), to model the decision making of selection (e.g., what book buy or where dine). Based on SIS, mine linked personal preferences users through statistical...

Existing human pose estimation approaches often only consider how to improve the model generalisation performance, but putting aside significant efficiency problem. This leads development of heavy models with poor scalability and cost-effectiveness in practical use. In this work, we investigate under-studied practically critical To end, present a new Fast Pose Distillation (FPD) learning strategy. Specifically, FPD trains lightweight neural network architecture capable executing rapidly low...

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the usually drops as possibilities confusion increase. Interestingly, class patterns follow hierarchical structure over classes. We present visual-analytics methods to reveal and analyze this hierarchy similar classes relation with CNN-internal data. found that not only dictates between it furthermore learning behavior CNNs. In particular, early layers...

In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation articulated objects, such as human beings animals. The key of our component is to embed the deformation model with exponential-maps-based parametrization into Gaussian Mixture Model. Benefiting from probabilistic measurement model, requires no explicit point correspondences opposed most existing methods. Consequently, approach less sensitive local minimum well handles fast complex motions....

We show that queue rationing under price controls is one driver of high-frequency trading. Uniform tick sizes constrain competition and create rents for liquidity provision, particularly securities with lower prices. The time priority rule allocates to traders (HFTs) because their speed advantage. An increase in relative size, defined as uniform divided by security prices, increases the fraction provided HFTs but harms liquidity. find message-to-trade ratio a poor cross-sectional proxy HFTs'...

In this paper we present a novel autonomous pipeline to build personalized parametric model (pose-driven avatar) using single depth sensor. Our method first captures few high-quality scans of the user rotating herself at multiple poses from different views. We fit each incomplete scan template fitting techniques with generic human template, and register all every pose global consistency constraints. After registration, these watertight models are used train in fashion similar SCAPE method....

Abstract Big data is revolutionizing the finance industry and has potential to significantly shape future research in finance. This special issue contains papers following 2019 NBER-RFS Conference on Data. In this introduction issue, we define “big data” phenomenon as a combination of three features: large size, high dimension, complex structure. Using discuss how new builds these features push frontier fundamental questions across areas finance—including corporate finance, market...

Stock exchange operators compete for order flow by setting "make" fees limit orders and "take" market orders. When traders can quote continuous prices, on total fee, because choose prices that perfectly neutralize any fee division. The 1-cent minimum tick size, however, prevents from neutralizing nonneutrality of division between make take (1) allows an operator to establish exchanges differ in structure engage second-degree price discrimination (2) destroys the Bertrand equilibrium, leads...

State-of-the-art NLP models can often be fooled by human-unaware transformations such as synonymous word substitution. For security reasons, it is of critical importance to develop with certified robustness that provably guarantee the prediction not altered any possible In this work, we propose a robust method based on new randomized smoothing technique, which constructs stochastic ensemble applying random substitutions input sentences, and leverage statistical properties certify robustness....

Source-free object detection (SFOD) needs to adapt a detector pre-trained on labeled source domain tar-get domain, with only unlabeled training data from the domain. Existing SFOD methods typically adopt pseudo labeling paradigm model adaption alternating between predicting labels and fine-tuning model. This approach suffers both unsatisfactory accuracy of due presence shift lim-ited use target data. In this work, we present novel Learning Overlook Domain Style (LODS) method such limitations...

Infrared dim-small target detection, as an important branch of object has been attracting research attention in recent decades. Its challenges mainly lie the small sizes and dim contrast to background images. Recent schemes on it focus improving feature representation spatio-temporal domains only single-slice temporal scope. More cross-slice motion, i.e., past future, is seldom considered enhance features. To use motion context, this article proposes a sliced network (SSTNet) with...

For small-object detection, vision patterns can only provide limited support to feature learning. Most prior schemes mainly depend on a single pattern learn object features, seldom considering more latent motion patterns. In the real world, humans often efficiently perceive small objects through multipattern signals. Inspired by this observation, article attempts address detection from new prospective of To fulfill purpose, it regards real-world moving as spatiotemporal sequences static...

Drift compensation is an important issue for electronic nose systems. Traditional methods are costly and laborious because they need to frequently recalibrate referred gases or continually provide data labeling. In this paper, a new drift method proposed. The inspiration of our originated from semi-supervised domain adaption that can effectively tackle the mismatches between source target domain. approach, weighted geodesic flow kernel initially constructed, then combination such kind...

Moving object detection is about foreground and background separation based on motion detection. Detecting moving objects from similarly colored (known as camouflage problem) has been a long-standing open question in this field. Discriminative modeling (DM), which focuses enhancing the performance to distinguish with discriminative features well-designed classifiers, widely used for However, DM may tend fail when encountering problem, class separability camouflaged areas generally poor. In...

Machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and effective pattern recognition algorithm for the detection, identification, or quantification of various odors. Data collected by are multivariate time series signals with complex structure, these become more difficult to analyze due drift. In this work, we focus on improving classification performance under drift using deep learning method, which popular nowadays. Compared other methods,...