OpenRaiser/Intern-Atlas huggingface dataset:
Non-Parametric Inference Adaptive to Intrinsic Dimension
Venue: arXiv cs.LG 2019
Year: 2019

We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension $D$ of the conditioning variable is larger than the sample size $n$, estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic dimension $d$, as measured by locally low doubling measures. Our estimation is based on a sub-sampled ensemble of the $k$-nearest neighbors ($k$-NN) $Z$-estimator. We show that if the intrinsic dimension of the covariate distribution is equal to $d$, then the finite sample estimation error of our estimator is of order $n^{-1/(d+2)}$ and our estimate is $n^{1/(d+2)}$-asymptotically normal, irrespective of $D$. The sub-sampling size required for achieving these results depends on the unknown intrinsic dimension $d$. We propose an adaptive data-driven approach for choosing this parameter and prove that it achieves the desired rates. We discuss extensions and applications to heterogeneous treatment effect estimation.
Anticipation and next action forecasting in video: an end-to-end model with memory
Venue: arXiv cs.CV 2019
Year: 2019

Action anticipation and forecasting in videos do not require a hat-trick, as far as there are signs in the context to foresee how actions are going to be deployed. Capturing these signs is hard because the context includes the past. We propose an end-to-end network for action anticipation and forecasting with memory, to both anticipate the current action and foresee the next one. Experiments on action sequence datasets show excellent results indicating that training on histories with a dynamic memory can significantly improve forecasting performance.
DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
Venue: arXiv cs.CV 2019
Year: 2019

Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics. Optimal implementations of these applications have traditionally involved the use of spline-based representations at their core. Most such methods attempt to solve optimization problems that minimize an output-target mismatch. However, these optimization techniques require an initialization that is close enough, as they are local methods by nature. We propose a deep learning architecture that adapts to perform spline fitting tasks accordingly, providing complementary results to the aforementioned traditional methods. We showcase the performance of our approach, by reconstructing spline curves and surfaces based on input images or point clouds.
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
Venue: arXiv cs.CV 2019
Year: 2019

As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years. It is efficient and accurate for sparse scenes, but suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this drawback, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. Through neatly coupling with GreedyNMS without losing efficiency, consistent improvements have been achieved in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In addition, Pairwise-NMS can be integrated into any learning based detectors (Both of Faster-RCNN and DPM detectors are tested in this paper), thus building a bridge between GreedyNMS and end-to-end learning detectors.
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Compared with other semantic segmentation tasks, portrait segmentation requires both higher precision and faster inference speed. However, this problem has not been well studied in previous works. In this paper, we propose a lightweight network architecture, called Boundary-Aware Network (BANet) which selectively extracts detail information in boundary area to make high-quality segmentation output with real-time( >25FPS) speed. In addition, we design a new loss function called refine loss which supervises the network with image level gradient information. Our model is able to produce finer segmentation results which has richer details than annotations.
Deep-learning-based identification of odontogenic keratocysts in hematoxylin- and eosin-stained jaw cyst specimens
Venue: arXiv cs.CV 2019
Year: 2019

The aim of this study was to develop a digital histopathology system for identifying odontogenic keratocysts in hematoxylin- and eosin-stained tissue specimens of jaw cysts. Approximately 5000 microscopy images with 400$\times$ magnification were obtained from 199 odontogenic keratocysts, 208 dentigerous cysts, and 55 radicular cysts. A proportion of these images were used to make training patches, which were annotated as belonging to one of the following three classes: keratocysts, non-keratocysts, and stroma. The patches for the cysts contained the complete lining epithelium, with the cyst cavity being present on the upper side. The convolutional neural network (CNN) VGG16 was finetuned to this dataset. The trained CNN could recognize the basal cell palisading pattern, which is the definitive criterion for diagnosing keratocysts. Some of the remaining images were scanned and analyzed by the trained CNN, whose output was then used to train another CNN for binary classification (keratocyst or not). The area under the receiver operating characteristics curve for the entire algorithm was 0.997 for the test dataset. Thus, the proposed patch classification strategy is usable for automated keratocyst diagnosis. However, further optimization must be performed to make it suitable for practical use.
Nonparametric Inverse Dynamic Models for Multimodal Interactive Robots
Venue: arXiv cs.RO 2019
Year: 2019

Direct design of a robot's rendered dynamics, such as in impedance control, is now a well-established control mode in uncertain environments. When the physical interaction port variables are not measured directly, dynamic and kinematic models are required to relate the measured variables to the interaction port variables. A typical example is serial manipulators with joint torque sensors, where the interaction occurs at the end-effector. As interactive robots perform increasingly complex tasks, they will be intermittently coupled with additional dynamic elements such as tools, grippers, or workpieces, some of which should be compensated and brought to the robot side of the interaction port, making the inverse dynamics multimodal. Furthermore, there may also be unavoidable and unmeasured external input when the desired system cannot be totally isolated. Towards semi-autonomous robots, capable of handling such applications, a multimodal Gaussian process regression approach to manipulator dynamic modelling is developed. A sampling-based approach clusters different dynamic modes from unlabelled data, also allowing the seperation of perturbed data with significant, irregular external input. The passivity of the overall approach is shown analytically, and experiments examine the performance and safety of this approach on a test actuator.
Model checking: the interval way
Venue: arXiv cs.LO 2019
Year: 2019

[...] The most famous model checking (MC) techniques were developed from the late 80s, bearing in mind the well-known "point-based" temporal logics LTL and CTL. However, while the expressiveness of such logics is beyond doubt, there are some properties we may want to check over system models that are inherently "interval-based" and thus cannot be expressed by point-based temporal logics, e.g., "the proposition p has to hold in at least an average number of system states in a given computation sector". Here interval temporal logics (ITLs) come into play, providing an alternative setting for reasoning about time. Such logics deal with intervals, instead of points, as their primitive entities: this feature gives them the ability of expressing temporal properties, such as actions with duration, accomplishments, and temporal aggregations, which cannot be dealt with in standard point-based logics. The Halpern and Shoham's modal logic of time intervals (HS) is one of the most famous ITLs: it features one modality for each of the 13 possible ordering relations between pairs of intervals, apart from equality. In this thesis we focus our attention on MC based on HS, in the role of property specification language, for which a little work has been done if compared to MC for point-based temporal logics. We study the complexity of such problem considering different system models, and we also compare the expressive power of HS to that of LTL, CTL and CTL* in MC. [...]
Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods
Venue: arXiv cs.LG 2019
Year: 2019

In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient populations. Our models consistently perform better on single-ethnicity populations and provide different feature importance rankings when trained in different populations. Additionally, we show our models achieve higher Area Under Curve (AUC) score than the best reported in the literature. We also apply imbalanced classification techniques to improve classification performance when the number of patients who have survived from colorectal cancer is much larger than who have not. These results provide evidence in favor for increased consideration of patient ethnicity in cancer survivability prediction, and for more personalized medicine in general.
Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
Venue: arXiv cs.CV 2019
Year: 2019

Prevailing fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide fingerprints from a set of known spoof fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live fingerprints such that once the concept of "live" has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively.
Interference Regime Enforcing Rate Maximization for Non-Orthogonal Multiple Access (NOMA)
Venue: arXiv cs.IT 2019
Year: 2019

An interference regime enforcing rate maximization scheme is proposed to maximize the achievable ergodic sum-rate of the parallel Gaussian interference channels by enforcing very strong interference at receivers through power allocation whenever strong interference is observed. Applying successive interference cancellation (SIC) at the receivers, very strong interference can be completely eliminated in this case that enables non-orthogonal multiple access (NOMA). An optimization problem is formulated for ergodic rate maximization, where the conditions for creating very strong interference are included as additional constraints. Then, a generalized iterative waterfilling algorithm is derived to solve this optimization problem. The resulting power allocation scheme is compared in terms of the sum-rate with two other schemes when the interference is treated as noise with optimal or sub-optimal power allocation. The results show that the interference regime enforcing scheme provides major improvement to the sum-rate of parallel Gaussian interference channels and enables NOMA.
Thermal switch of oscillation frequency in Belousov-Zhabotinsky liquid marbles
Venue: arXiv cs.ET 2019
Year: 2019

External control of oscillation dynamics in the Belousov-Zhabotinsky reaction is important for many applications including encoding computing schemes. When considering the BZ reaction there are limited studies dealing with thermal cycling, particularly cooling, for external control. Recently, liquid marbles (LMs) have been demonstrated as a means of confining the Belousov-Zhabotinsky reaction in a system containing a solid-liquid interface. BZ LMs were prepared by rolling 50 μl droplets in polyethylene (PE) powder. Oscillations of electrical potential differences within the marble were recorded by inserting a pair of electrodes through the LM powder coating into the BZ solution core. Electrical potential differences of up to 100mV were observed with an average period of oscillation ca. 44 s. BZ LMs were subsequently frozen to -1oC to observe changes in the frequency of electrical potential oscillations. The frequency of oscillations reduced upon freezing to 11mHz cf. 23mHz at ambient temperature. The oscillation frequency of the frozen BZ LM returned to 23mHz upon warming to ambient temperature. Several cycles of frequency fluctuations were able to be achieved.
RNN-based Generative Model for Fine-Grained Sketching
Venue: arXiv cs.CV 2019
Year: 2019

Deep generative models have shown great promise when it comes to synthesising novel images. While they can generate images that look convincing on a higher-level, generating fine-grained details is still a challenge. In order to foster research on more powerful generative approaches, this paper proposes a novel task: generative modelling of 2D tree skeletons. Trees are an interesting shape class because they exhibit complexity and variations that are well-suited to measure the ability of a generative model to generated detailed structures. We propose a new dataset for this task and demonstrate that state-of-the-art generative models fail to synthesise realistic images on our benchmark, even though they perform well on current datasets like MNIST digits. Motivated by these results, we propose a novel network architecture based on combining a variational autoencoder using Recurrent Neural Networks and a convolutional discriminator. The network, error metrics and training procedure are adapted to the task of fine-grained sketching. Through quantitative and perceptual experiments, we show that our model outperforms previous work and that our dataset is a valuable benchmark for generative models. We will make our dataset publicly available.
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
Venue: arXiv cs.SE 2019
Year: 2019

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.
Gradient Boosted Feature Selection
Venue: arXiv cs.LG 2019
Year: 2019

A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which satisfies all four of these requirements. The algorithm is flexible, scalable, and surprisingly straight-forward to implement as it is based on a modification of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and show that it matches or out-performs other state of the art feature selection algorithms. Yet it scales to larger data set sizes and naturally allows for domain-specific side information.
Vehicles Detection Based on Background Modeling
Venue: arXiv cs.CV 2019
Year: 2019

Background image subtraction algorithm is a common approach which detects moving objects in a video sequence by finding the significant difference between the video frames and the static background model. This paper presents a developed system which achieves vehicle detection by using background image subtraction algorithm based on blocks followed by deep learning data validation algorithm. The main idea is to segment the image into equal size blocks, to model the static reference background image (SRBI), by calculating the variance between each block pixels and each counterpart block pixels in the adjacent frame, the system implemented into four different methods: Absolute Difference, Image Entropy, Exclusive OR (XOR) and Discrete Cosine Transform (DCT). The experimental results showed that the DCT method has the highest vehicle detection accuracy.
Kelly Cache Networks
Venue: arXiv cs.NI 2019
Year: 2019

We study networks of M/M/1 queues in which nodes act as caches that store objects. Exogenous requests for objects are routed towards nodes that store them; as a result, object traffic in the network is determined not only by demand but, crucially, by where objects are cached. We determine how to place objects in caches to attain a certain design objective, such as, e.g., minimizing network congestion or retrieval delays. We show that for a broad class of objectives, including minimizing both the expected network delay and the sum of network queue lengths, this optimization problem can be cast as an NP- hard submodular maximization problem. We show that so-called continuous greedy algorithm attains a ratio arbitrarily close to $1 - 1/e \approx 0.63$ using a deterministic estimation via a power series; this drastically reduces execution time over prior art, which resorts to sampling. Finally, we show that our results generalize, beyond M/M/1 queues, to networks of M/M/k and symmetric M/D/1 queues.
Attributed Network Embedding via Subspace Discovery
Venue: arXiv cs.SI 2019
Year: 2019

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec.
Joint Stabilization and Direction of 360°Videos
Venue: arXiv cs.GR 2019
Year: 2019

360° video provides an immersive experience for viewers, allowing them to freely explore the world by turning their head. However, creating high-quality 360° video content can be challenging, as viewers may miss important events by looking in the wrong direction, or they may see things that ruin the immersion, such as stitching artifacts and the film crew. We take advantage of the fact that not all directions are equally likely to be observed; most viewers are more likely to see content located at ``true north'', i.e. in front of them, due to ergonomic constraints. We therefore propose 360° video direction, where the video is jointly optimized to orient important events to the front of the viewer and visual clutter behind them, while producing smooth camera motion. Unlike traditional video, viewers can still explore the space as desired, but with the knowledge that the most important content is likely to be in front of them. Constraints can be user guided, either added directly on the equirectangular projection or by recording ``guidance'' viewing directions while watching the video in a VR headset, or automatically computed, such as via visual saliency or forward motion direction. To accomplish this, we propose a new motion estimation technique specifically designed for 360° video which outperforms the commonly used 5-point algorithm on wide angle video. We additionally formulate the direction problem as an optimization where a novel parametrization of spherical warping allows us to correct for some degree of parallax effects. We compare our approach to recent methods that address stabilization-only and converting 360° video to narrow field-of-view video.