OpenRaiser/Intern-Atlas huggingface dataset:
Predictive Image Regression for Longitudinal Studies with Missing Data
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a predictive regression model for longitudinal images with missing data based on large deformation diffeomorphic metric mapping (LDDMM) and deep neural networks. Instead of directly predicting image scans, our model predicts a vector momentum sequence associated with a baseline image. This momentum sequence parameterizes the original image sequence in the LDDMM framework and lies in the tangent space of the baseline image, which is Euclidean. A recurrent network with long term-short memory (LSTM) units encodes the time-varying changes in the vector-momentum sequence, and a convolutional neural network (CNN) encodes the baseline image of the vector momenta. Features extracted by the LSTM and CNN are fed into a decoder network to reconstruct the vector momentum sequence, which is used for the image sequence prediction by deforming the baseline image with LDDMM shooting. To handle the missing images at some time points, we adopt a binary mask to ignore their reconstructions in the loss calculation. We evaluate our model on synthetically generated images and the brain MRIs from the OASIS dataset. Experimental results demonstrate the promising predictions of the spatiotemporal changes in both datasets, irrespective of large or subtle changes in longitudinal image sequences.
Towards A Deep Insight into Landmark-based Visual Place Recognition: Methodology and Practice
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we address the problem of landmark-based visual place recognition. In the state-of-the-art method, accurate object proposal algorithms are first leveraged for generating a set of local regions containing particular landmarks with high confidence. Then, these candidate regions are represented by deep features and pairwise matching is performed in an exhaustive manner for the similarity measure. Despite its success, conventional object proposal methods usually produce massive landmark-dependent image patches exhibiting significant distribution variance in scale and overlap. As a result, the inconsistency in landmark distributions tends to produce biased similarity between pairwise images yielding the suboptimal performance. In order to gain an insight into the landmark-based place recognition scheme, we conduct a comprehensive study in which the influence of landmark scales and the proportion of overlap on the recognition performance is explored. More specifically, we thoroughly study the exhaustive search based landmark matching mechanism, and thus derive three-fold important observations in terms of the beneficial effect of specific landmark generation strategies. Inspired by the above observations, a simple yet effective dense sampling based scheme is presented for accurate place recognition in this paper. Different from the conventional object proposal strategy, we generate local landmarks of multiple scales with uniform distribution from entire image by dense sampling, and subsequently perform multi-scale fusion on the densely sampled landmarks for similarity measure. The experimental results on three challenging datasets demonstrate that the recognition performance can be significantly improved by our efficient method in which the landmarks are appropriately produced for accurate pairwise matching.
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
Venue: arXiv cs.CL 2018
Year: 2018

Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images that resemble real images. Recently, GANs have been extended from generating images to generating sequences (e.g., poems, music and codes). Existing GANs on sequence generation mainly focus on general sequences, which are grammar-free. In many real-world applications, however, we need to generate sequences in a formal language with the constraint of its corresponding grammar. For example, to test the performance of a database, one may want to generate a collection of SQL queries, which are not only similar to the queries of real users, but also follow the SQL syntax of the target database. Generating such sequences is highly challenging because both the generator and discriminator of GANs need to consider the structure of the sequences and the given grammar in the formal language. To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar. The discriminator uses a tree-structured RNN to distinguish the generated trees from real trees. We show that TreeGAN can generate sequences for any CFG and its generation fully conforms with the given syntax. Experiments on synthetic and real data sets demonstrated that TreeGAN significantly improves the quality of the sequence generation in context-free languages.
Stakes are higher, risk is lower: Citation distributions are more equal in high quality journals
Venue: arXiv cs.DL 2018
Year: 2018

Psychology is a discipline standing at the crossroads of hard and social sciences. Therefore it is especially interesting to study bibliometric characteristics of psychology journals. We also take two adjacent disciplines, neurosciences and sociology. One is closer to hard sciences, another is a social science. We study not the journal citedness itself (impact factor etc.) but the citation distribution across papers within journals. This is, so to say, "indicators of the second order" which measure the digression from the journal's average of the citations received by individual papers. As is shown, such information about journals may also help authors to correct their publication strategies.
Latent Dirichlet Allocation for Internet Price War
Venue: arXiv cs.AI 2018
Year: 2018

Internet market makers are always facing intense competitive environment, where personalized price reductions or discounted coupons are provided for attracting more customers. Participants in such a price war scenario have to invest a lot to catch up with other competitors. However, such a huge cost of money may not always lead to an improvement of market share. This is mainly due to a lack of information about others' strategies or customers' willingness when participants develop their strategies. In order to obtain this hidden information through observable data, we study the relationship between companies and customers in the Internet price war. Theoretically, we provide a formalization of the problem as a stochastic game with imperfect and incomplete information. Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors. To our best knowledge, it is the first time that LDA is applied to game scenario. We conduct simulated experiments where our LDA model exhibits a significant improvement on finding strategies in the Internet price war by including all available market information of the market maker's competitors. And the model is applied to an open dataset for real business. Through comparisons on the likelihood of prediction for users' behavior and distribution distance between inferred opponent's strategy and the real one, our model is shown to be able to provide a better understanding for the market environment. Our work marks a successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.
Principles, Paradigms and the Future of UAV Drone Teams in Use for Engineering and Operation of Landscape-Scale Deployable Structures
Venue: arXiv cs.CY 2018
Year: 2018

Drone fleets, with counts on the order of O(100) to O(1000), will play important and significant roles in the automation of the deployment, operation, maintenance and repair of ubiquitous and pervasive landscape scale elongated structures, with the longest linear spatial dimensions of O(1 mile) to O(10 mile). The organization of the drone team to support the task is considered as a digital platform, (specifically, a digital multi-sided platform). A computational thinking approach is used to engineer the architecture of the platform.
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks
Venue: arXiv cs.AI 2018
Year: 2018

Designing shared neural architecture plays an important role in multi-task learning. The challenge is that finding an optimal sharing scheme heavily relies on the expert knowledge and is not scalable to a large number of diverse tasks. Inspired by the promising work of neural architecture search (NAS), we apply reinforcement learning to automatically find possible shared architecture for multi-task learning. Specifically, we use a controller to select from a set of shareable modules and assemble a task-specific architecture, and repeat the same procedure for other tasks. The controller is trained with reinforcement learning to maximize the expected accuracies for all tasks. We conduct extensive experiments on two types of tasks, text classification and sequence labeling, which demonstrate the benefits of our approach.
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
Venue: arXiv cs.CV 2018
Year: 2018

3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance.
A Directionally Selective Neural Network with Separated ON and OFF Pathways for Translational Motion Perception in a Visually Cluttered Environment
Venue: arXiv cs.NE 2018
Year: 2018

With respect to biological findings underlying fly's physiology in the past decade, we present a directionally selective neural network, with a feed-forward structure and entirely low-level visual processing, so as to implement direction selective neurons in the fly's visual system, which are mainly sensitive to wide-field translational movements in four cardinal directions. In this research, we highlight the functionality of ON and OFF pathways, separating motion information for parallel computation corresponding to light-on and light-off selectivity. Through this modeling study, we demonstrate several achievements compared with former bio-plausible translational motion detectors, like the elementary motion detectors. First, we thoroughly mimic the fly's preliminary motion-detecting pathways with newly revealed fly's physiology. Second, we improve the speed response to moving dark/light features via the design of ensembles of same polarity cells in the dual-pathways. Moreover, we alleviate the impact of irrelevant motion in a visually cluttered environment like the shifting of background and windblown vegetation, via the modeling of spatiotemporal dynamics. We systematically tested the DSNN against stimuli ranging from synthetic and real-world scenes, to notably a visual modality of a ground micro robot. The results demonstrated that the DSNN outperforms former bio-plausible translational motion detectors. Importantly, we verified its computational simplicity and effectiveness benefiting the building of neuromorphic vision sensor for robots.
Discriminative out-of-distribution detection for semantic segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes. However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input. These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive. We propose to address this issue by discriminative detection of OOD pixels in input data. Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task. Instead, we train a dedicated OOD model which discriminates the primary training set from a much larger "background" dataset which approximates the variety of the visual world. We perform our experiments on high resolution natural images in a dense prediction setup. We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset. We evaluate our approach on WildDash test, which is currently the only public test dataset that includes out-of-distribution images. The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.
Predicting Action Tubes
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we present a method to predict an entire `action tube' (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it. Predicting where an action is going to take place in the near future is essential to many computer vision based applications such as autonomous driving or surgical robotics. Importantly, it has to be done in real-time and in an online fashion. We propose a Tube Prediction network (TPnet) which jointly predicts the past, present and future bounding boxes along with their action classification scores. At test time TPnet is used in a (temporal) sliding window setting, and its predictions are put into a tube estimation framework to construct/predict the video long action tubes not only for the observed part of the video but also for the unobserved part. Additionally, the proposed action tube predictor helps in completing action tubes for unobserved segments of the video. We quantitatively demonstrate the latter ability, and the fact that TPnet improves state-of-the-art detection performance, on one of the standard action detection benchmarks - J-HMDB-21 dataset.
Measuring network resilience through connection patterns
Venue: arXiv cs.SI 2018
Year: 2018

Networks are at the core of modeling many engineering contexts, mainly in the case of infrastructures and communication systems. The resilience of a network, which is the property of the system capable of absorbing external shocks, is then of paramount relevance in the applications. This paper deals with this topic by advancing a theoretical proposal for measuring the resilience of a network. The proposal is based on the study of the shocks propagation along the patterns of connections among nodes. The theoretical model is tested on the real-world instances of two important airport systems in the US air traffic network; Illinois (including the hub of Chicago) and New York states (with JFK airport).
Optimal Input Placement in Lattice Graphs
Venue: arXiv cs.SY 2018
Year: 2018

The control of dynamical, networked systems continues to receive much attention across the engineering and scientific research fields. Of particular interest is the proper way to determine which nodes of the network should receive external control inputs in order to effectively and efficiently control portions of the network. Published methods to accomplish this task either find a minimal set of driver nodes to guarantee controllability or a larger set of driver nodes which optimizes some control metric. Here, we investigate the control of lattice systems which provides analytical insight into the relationship between network structure and controllability. First we derive a closed form expression for the individual elements of the controllability Gramian of infinite lattice systems. Second, we focus on nearest neighbor lattices for which the distance between nodes appears in the expression for the controllability Gramian. We show that common control energy metrics scale exponentially with respect to the maximum distance between a driver node and a target node.
An overview of process model quality literature - The Comprehensive Process Model Quality Framework
Venue: arXiv cs.SE 2018
Year: 2018

The rising interest in the construction and the quality of (business) process models resulted in an abundancy of emerged research studies and different findings about process model quality. The lack of overview and the lack of consensus hinder the development of the research field. The research objective is to collect, analyse, structure, and integrate the existing knowledge in a comprehensive framework that strives to find a balance between completeness and relevance without hindering the overview. The Systematic Literature Review methodology was applied to collect the relevant studies. Because several studies exist that each partially addresses this research objective, the review was performed at a tertiary level. Based on a critical analysis of the collected papers, a comprehensive, but structured overview of the state of the art in the field was composed. The existing academic knowledge about process model quality was carefully integrated and structured into the Comprehensive Process Model Quality Framework (CPMQF). The framework summarizes 39 quality dimensions, 21 quality metrics, 28 quality (sub)drivers, 44 (sub)driver metrics, 64 realization initiatives and 15 concrete process model purposes related to 4 types of organizational benefits, as well as the relations between all of these. This overview is thus considered to form a valuable instrument for both researchers and practitioners that are concerned about process model quality. The framework is the first to address the concept of process model quality in such a comprehensive way.
Financial Aspect-Based Sentiment Analysis using Deep Representations
Venue: arXiv cs.CL 2018
Year: 2018

The topic of aspect-based sentiment analysis (ABSA) has been explored for a variety of industries, but it still remains much unexplored in finance. The recent release of data for an open challenge (FiQA) from the companion proceedings of WWW '18 has provided valuable finance-specific annotations. FiQA contains high quality labels, but it still lacks data quantity to apply traditional ABSA deep learning architecture. In this paper, we employ high-level semantic representations and methods of inductive transfer learning for NLP. We experiment with extensions of recently developed domain adaptation methods and target task fine-tuning that significantly improve performance on a small dataset. Our results show an 8.7% improvement in the F1 score for classification and an 11% improvement over the MSE for regression on current state-of-the-art results.
Enhancing POI testing approach through the use of additional information
Venue: arXiv cs.PL 2018
Year: 2018

Recently, a new approach to perform regression testing has been defined: the point of interest (POI) testing. A POI, in this context, is any expression of a program. The approach receives as input a set of relations between POIs from a version of a program and POIs from another version, and also a sequence of input functions, i.e. test cases. Then, a program instrumentation, an input test case generation and different comparison functions are used to obtain the final report which indicates whether the alternative version of the program behaves as expected, e.g. it produces the same values or it uses less CPU/memory. In this paper, we explain how we can improve the POI testing approach through the use of common stack traces and a more sophisticated tracing for calls. These enhancements of the approach allow users to identify errors earlier and easier. Additionally, they enable new comparison modes and new categories of reported unexpected behaviours.
Left ventricle quantification through spatio-temporal CNNs
Venue: arXiv cs.CV 2018
Year: 2018

Cardiovascular diseases are among the leading causes of death globally. Cardiac left ventricle (LV) quantification is known to be one of the most important tasks for the identification and diagnosis of such pathologies. In this paper, we propose a deep learning method that incorporates 3D spatio-temporal convolutions to perform direct left ventricle quantification from cardiac MR sequences. Instead of analysing slices independently, we process stacks of temporally adjacent slices by means of 3D convolutional kernels which fuse the spatio-temporal information, incorporating the temporal dynamics of the heart to the learned model. We show that incorporating such information by means of spatio-temporal convolutions into standard LV quantification architectures improves the accuracy of the predictions when compared with single-slice models, achieving competitive results for all cardiac indices and significantly breaking the state of the art (Xue et al., 2018, MedIA) for cardiac phase estimation.
Implementing Strassen's Algorithm with CUTLASS on NVIDIA Volta GPUs
Venue: arXiv cs.MS 2018
Year: 2018

Conventional GPU implementations of Strassen's algorithm (Strassen) typically rely on the existing high-performance matrix multiplication (GEMM), trading space for time. As a result, such approaches can only achieve practical speedup for relatively large, "squarish" matrices due to the extra memory overhead, and their usages are limited due to the considerable workspace. We present novel Strassen primitives for GPUs that can be composed to generate a family of Strassen algorithms. Our algorithms utilize both the memory and thread hierarchies on GPUs, reusing shared memory and register files inherited from GEMM, fusing additional operations, and avoiding extra workspace. We further exploit intra- and inter-kernel parallelism by batching, streaming, and employing atomic operations. We also develop a performance model for NVIDIA Volta GPUs to select the appropriate blocking parameters and predict the performance for GEMM and Strassen. Overall, our 1-level Strassen can achieve up to 1.11x speedup with a crossover point as small as 1,536 compared to cublasSgemm on a NVIDIA Tesla V100 GPU. With additional workspace, our 2-level Strassen can achieve 1.19x speedup with a crossover point at 7,680.