OpenRaiser/Intern-Atlas huggingface dataset:
UGAN: Untraceable GAN for Multi-Domain Face Translation
Venue: arXiv cs.CV 2019
Year: 2019

The multi-domain image-to-image translation is a challenging task where the goal is to translate an image into multiple different domains. The target-only characteristics are desired for translated images, while the source-only characteristics should be erased. However, recent methods often suffer from retaining the characteristics of the source domain, which are incompatible with the target domain. To address this issue, we propose a method called Untraceable GAN, which has a novel source classifier to differentiate which domain an image is translated from, and determines whether the translated image still retains the characteristics of the source domain. Furthermore, we take the prototype of the target domain as the guidance for the translator to effectively synthesize the target-only characteristics. The translator is learned to synthesize the target-only characteristics and make the source domain untraceable for the discriminator, so that the source-only characteristics are erased. Finally, extensive experiments on three face editing tasks, including face aging, makeup, and expression editing, show that the proposed UGAN can produce superior results over the state-of-the-art models. The source code will be released.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
Venue: arXiv cs.HC 2019
Year: 2019

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.
Achievable Rate Region for Iterative Multi-User Detection via Low-cost Gaussian Approximation
Venue: arXiv cs.IT 2019
Year: 2019

We establish a multiuser extrinsic information transfer (EXIT) chart area theorem for the interleave-division multiple access (IDMA) scheme, a special form of superposition coding, in multiple access channels (MACs). A low-cost multi-user detection (MUD) based on the Gaussian approximation (GA) is assumed. The evolution of mean-square errors (MSE) of the GA-based MUD during iterative processing is studied. We show that the K-dimensional tuples formed by the MSEs of K users constitute a conservative vector field. The achievable rate is a potential function of this conservative field, so it is the integral along any path in the field with value of the integral solely determined by the two path terminals. Optimized codes can be found given the integration paths in the MSE fields by matching EXIT type functions. The above findings imply that i) low-cost GA detection can provide near capacity performance, ii) the sum-rate capacity can be achieved independently of the integration path in the MSE fields; and iii) the integration path can be an extra degree of freedom for code design.
Evaluating the Impact of Using GRASP Framework on Clinicians and Healthcare Professionals Decisions in Selecting Clinical Predictive Tools
Venue: arXiv cs.CY 2019
Year: 2019

Background. When selecting predictive tools, clinicians and healthcare professionals are challenged with an overwhelming number of tools, most of which have never been evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (GRASP), based on the critical appraisal of published evidence. Methods. To examine GRASP impact on professionals decisions, a controlled experiment was conducted through an online survey. Randomising two groups of tools and two scenarios; participants were asked to select the best tools; most validated or implemented, with and without GRASP. A wide group of international participants were invited. Task completion time, rate of correct decisions, rate of objective vs subjective decisions, and level of decisional conflict were measured. Results. Valid responses received were 194. Compared to not using the framework, GRASP significantly increased correct decisions by 64% (T=8.53, p<0.001), increased objective decision making by 32% (T=9.24, p<0.001), and decreased subjective decision making; based on guessing and based on prior knowledge or experience by 20% (T=-5.47, p<0.001) and 8% (T=-2.99, p=0.003) respectively. GRASP significantly decreased decisional conflict; increasing confidence and satisfaction of participants with their decisions by 11% (T=4.27, p<0.001) and 13% (T=4.89, p<0.001) respectively. GRASP decreased task completion time by 52% (T=-0.87, p=0.384). The average system usability scale of GRASP was very good; 72.5%, and 88% of participants found GRASP useful. Discussion and Conclusions. Using GRASP has positively supported and significantly improved evidence-based decision making and increased accuracy and efficiency of selecting predictive tools.
Mapping Perceptions of Humanness in Speech-Based Intelligent Personal Assistant Interaction
Venue: arXiv cs.HC 2019
Year: 2019

Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.
Lexicographic Multiarmed Bandit
Venue: arXiv cs.LG 2019
Year: 2019

We consider a multiobjective multiarmed bandit problem with lexicographically ordered objectives. In this problem, the goal of the learner is to select arms that are lexicographic optimal as much as possible without knowing the arm reward distributions beforehand. We capture this goal by defining a multidimensional form of regret that measures the loss of the learner due to not selecting lexicographic optimal arms, and then, consider two settings where the learner has prior information on the expected arm rewards. In the first setting, the learner only knows for each objective the lexicographic optimal expected reward. In the second setting, it only knows for each objective near-lexicographic optimal expected rewards. For both settings we prove that the learner achieves expected regret uniformly bounded in time. The algorithm we propose for the second setting also attains bounded regret for the multiarmed bandit with satisficing objectives. In addition, we also consider the harder prior-free case, and show that the learner can still achieve sublinear in time gap-free regret. Finally, we experimentally evaluate performance of the proposed algorithms in a variety of multiobjective learning problems.
Fog Computing Applications: Taxonomy and Requirements
Venue: arXiv cs.DC 2019
Year: 2019

Fog computing was designed to support the specific needs of latency-critical applications such as augmented reality, and IoT applications which produce massive volumes of data that are impractical to send to faraway cloud data centers for analysis. However this also created new opportunities for a wider range of applications which in turn impose their own requirements on future fog computing platforms. This article presents a study of a representative set of 30 fog computing applications and the requirements that a general-purpose fog computing platform should support.
Training capsules as a routing-weighted product of expert neurons
Venue: arXiv cs.NE 2019
Year: 2019

Capsules are the multidimensional analogue to scalar neurons in neural networks, and because they are multidimensional, much more complex routing schemes can be used to pass information forward through the network than what can be used in traditional neural networks. This work treats capsules as collections of neurons in a fully connected neural network, where sub-networks connecting capsules are weighted according to the routing coefficients determined by routing by agreement. An energy function is designed to reflect this model, and it follows that capsule networks with dynamic routing can be formulated as a product of expert neurons. By alternating between dynamic routing, which acts to both find subnetworks within the overall network as well as to mix the model distribution, and updating the parameters by the gradient of the contrastive divergence, a bottom-up, unsupervised learning algorithm is constructed for capsule networks with dynamic routing. The model and its training algorithm are qualitatively tested in the generative sense, and is able to produce realistic looking images from standard vision datasets.
Adventures in Abstraction: Reachability in Hierarchical Drawings
Venue: arXiv cs.DS 2019
Year: 2019

We present algorithms and experiments for the visualization of directed graphs that focus on displaying their reachability information. Our algorithms are based on the concepts of the path and channel decomposition as proposed in the framework presented in GD 2018 (pp. 579-592) and focus on showing the existence of paths clearly. In this paper we customize these concepts and present experimental results that clearly show the interplay between bends, crossings and clarity. Additionally, our algorithms have direct applications to the important problem of showing and storing transitivity information of very large graphs and databases. Only a subset of the edges is drawn, thus reducing the visual complexity of the resulting drawing, and the memory requirements for storing the transitivity information. Our algorithms require almost linear time, $O(kn+m)$, where $k$ is the number of paths/channels, $n$ and $m$ is the number of vertices and edges, respectively. They produce progressively more abstract drawings of the input graph. No dummy vertices are introduced and the vertices of each path/channel are vertically aligned.
Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer
Venue: arXiv cs.DC 2019
Year: 2019

Common Midpoint (CMP) and Common Reflection Surface (CRS) are widely used methods for improving the signal-to-noise ratio in the field of seismic processing. These methods are computationally intensive and require high performance computing. This paper optimizes these methods on the Sunway many-core architecture and implements large-scale seismic processing on the Sunway Taihulight supercomputer. We propose the following three optimization techniques: 1) we propose a software cache method to reduce the overhead of memory accesses, and share data among CPEs via the register communication; 2) we re-design the semblance calculation procedure to further reduce the overhead of memory accesses; 3) we propose a vectorization method to improve the performance when processing the small volume of data within short loops. The experimental results show that our implementations of CMP and CRS methods on Sunway achieve 3.50x and 3.01x speedup on average compared to the-state-of-the-art implementations on CPU. In addition, our implementation is capable to run on more than one million cores of Sunway TaihuLight with good scalability.
Action Guidance with MCTS for Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency. In this paper, we focus on how to use action guidance by means of a non-expert demonstrator to improve sample efficiency in a domain with sparse, delayed, and possibly deceptive rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a new framework where even a non-expert simulated demonstrator, e.g., planning algorithms such as Monte Carlo tree search with a small number rollouts, can be integrated within asynchronous distributed deep reinforcement learning methods. Compared to a vanilla deep RL algorithm, our proposed methods both learn faster and converge to better policies on a two-player mini version of the Pommerman game.
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
Venue: arXiv cs.CV 2019
Year: 2019

Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions. In this paper, we present Quadtree Generating Networks (QGNs), a novel approach able to drastically reduce the memory footprint of modern semantic segmentation networks. The key idea is to use quadtrees to represent the predictions and target segmentation masks instead of dense pixel grids. Our quadtree representation enables hierarchical processing of an input image, with the most computationally demanding layers only being used at regions in the image containing boundaries between classes. In addition, given a trained model, our representation enables flexible inference schemes to trade-off accuracy and computational cost, allowing the network to adapt in constrained situations such as embedded devices. We demonstrate the benefits of our approach on the Cityscapes, SUN-RGBD and ADE20k datasets. On Cityscapes, we obtain an relative 3% mIoU improvement compared to a dilated network with similar memory consumption; and only receive a 3% relative mIoU drop compared to a large dilated network, while reducing memory consumption by over 4$\times$.
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning methods have achieved promising performance in many areas, but they are still struggling with noisy-labeled images during the training process. Considering that the annotation quality indispensably relies on great expertise, the problem is even more crucial in the medical image domain. How to eliminate the disturbance from noisy labels for segmentation tasks without further annotations is still a significant challenge. In this paper, we introduce our label quality evaluation strategy for deep neural networks automatically assessing the quality of each label, which is not explicitly provided, and training on clean-annotated ones. We propose a solution for network automatically evaluating the relative quality of the labels in the training set and using good ones to tune the network parameters. We also design an overfitting control module to let the network maximally learn from the precise annotations during the training process. Experiments on the public biomedical image segmentation dataset have proved the method outperforms baseline methods and retains both high accuracy and good generalization at different noise levels.
hood2vec: Identifying Similar Urban Areas Using Mobility Networks
Venue: arXiv cs.SI 2019
Year: 2019

Which area in NYC is the most similar to Lower East Side? What about the NoHo Arts District in Los Angeles? Traditionally this task utilizes information about the type of places located within the areas and some popularity/quality metric. We take a different approach. In particular, urban dwellers' time-variant mobility is a reflection of how they interact with their city over time. Hence, in this paper, we introduce an approach, namely hood2vec, to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins. We compare the pairwise similarities obtained from hood2vec with the ones obtained from comparing the types of venues in the different areas. The low correlation between the two indicates that the mobility dynamics and the venue types potentially capture different aspects of similarity between urban areas.
Temporal Data Fusion at the Edge
Venue: arXiv cs.DC 2019
Year: 2019

As an enabler technique, data fusion has gained great attention in the context of Internet of things (IoT). In traditional settings, data fusion is done at the cloud server. So the data to be fused should be transferred from the sensor nodes to the cloud server before data fusion. Such an application mode of data fusion inherits disturbing concerns from the cloud computing framework, e.g., privacy-leaking, large latency between data capture and computation, excessive ingress bandwidth consumption. We take into account how to do temporal data fusion at the edge to bypass the above issues. We present a Gaussian process based temporal data fusion (GPTDF) method targeted for the problem of sequential online prediction at the edge. The GPTDF method fits the edge computing framework and thus inherits desirable properties from edge computing, such as privacy-preserving, low latency between data capture and computation, and tiny bandwidth consumption. Through a real-data experiment using archived traffic datasets from the Caltrans Performance Measurement System (PeMS), we demonstrate that the application of GPTDF can provide more timely and accurate real-time predictions at the network edge.
Finite-Precision Implementation of Arithmetic Coding Based Distribution Matchers
Venue: arXiv cs.IT 2019
Year: 2019

A distribution matcher (DM) encodes a binary input data sequence into a sequence of symbols with a desired target probability distribution. Several DMs, including shell mapping and constant-composition distribution matcher (CCDM), have been successfully employed for signal shaping, e.g., in optical-fiber or 5G. The CCDM, like many other DMs, is typically implemented by arithmetic coding (AC). In this work we implement AC based DMs using finite-precision arithmetic (FPA). An analysis of the implementation shows that FPA results in a rate-loss that shrinks exponentially with the number of precision bits. Moreover, a relationship between the CCDM rate and the number of precision bits is derived.
A Lower Bound on Cycle-Finding in Sparse Digraphs
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of finding a cycle in a sparse directed graph $G$ that is promised to be far from acyclic, meaning that the smallest feedback arc set in $G$ is large. We prove an information-theoretic lower bound, showing that for $N$-vertex graphs with constant outdegree any algorithm for this problem must make $\tildeΩ(N^{5/9})$ queries to an adjacency list representation of $G$. In the language of property testing, our result is an $\tildeΩ(N^{5/9})$ lower bound on the query complexity of one-sided algorithms for testing whether sparse digraphs with constant outdegree are far from acyclic. This is the first improvement on the $Ω(\sqrt{N})$ lower bound, implicit in Bender and Ron, which follows from a simple birthday paradox argument.
It's All About The Scale -- Efficient Text Detection Using Adaptive Scaling
Venue: arXiv cs.CV 2019
Year: 2019

"Text can appear anywhere". This property requires us to carefully process all the pixels in an image in order to accurately localize all text instances. In particular, for the more difficult task of localizing small text regions, many methods use an enlarged image or even several rescaled ones as their input. This significantly increases the processing time of the entire image and needlessly enlarges background regions. If we were to have a prior telling us the coarse location of text instances in the image and their approximate scale, we could have adaptively chosen which regions to process and how to rescale them, thus significantly reducing the processing time. To estimate this prior we propose a segmentation-based network with an additional "scale predictor", an output channel that predicts the scale of each text segment. The network is applied on a scaled down image to efficiently approximate the desired prior, without processing all the pixels of the original image. The approximated prior is then used to create a compact image containing only text regions, resized to a canonical scale, which is fed again to the segmentation network for fine-grained detection. We show that our approach offers a powerful alternative to fixed scaling schemes, achieving an equivalent accuracy to larger input scales while processing far fewer pixels. Qualitative and quantitative results are presented on the ICDAR15 and ICDAR17 MLT benchmarks to validate our approach.
Iris Recognition for Personal Identification using LAMSTAR neural network
Venue: arXiv cs.CV 2019
Year: 2019

Iris recognition is one of the most important biometric recognition method. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it possible the differentiation among individuals. So during last years huge number of people have been trying to improve its performance. In this article first different common steps for the Iris recognition system is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
Faster asynchronous MST and low diameter tree construction with sublinear communication
Venue: arXiv cs.DC 2019
Year: 2019

Building a spanning tree, minimum spanning tree (MST), and BFS tree in a distributed network are fundamental problems which are still not fully understood in terms of time and communication cost. x The first work to succeed in computing a spanning tree with communication sublinear in the number of edges in an asynchronous CONGEST network appeared in DISC 2018. That algorithm which constructs an MST is sequential in the worst case; its running time is proportional to the total number of messages sent. Our paper matches its message complexity but brings the running time down to linear in $n$. Our techniques can also be used to provide an asynchronous algorithm with sublinear communication to construct a tree in which the distance from a source to each node is within an additive term of $\sqrt{n}$ of its actual distance. We can convert any asynchronous MST algorithm with time $T(n, m)$ and message complexity of $M(n, m)$ to an algorithm with time $O(n^{1 - 2ε} + T(n, n^{3/2 + ε}))$ and message complexity of $\tilde{O}(n^{3/2 + ε} + M(n, n^{3/2+ε}))$, for $ε\in [0, 1/4]$. Picking $ε= 0$ and using Awerbuch's algorithm \cite{awerbuch1987optimal}, this results in an MST algorithm with time $O(n)$ and message complexity $\tilde{O}(n^{3/2})$. However, if there were an asynchronous MST algorithm that takes time sublinear in $n$ and requires messages linear in $m$, by picking $ε> 0$ we could achieve sublinear time (in $n$) and sublinear communication (in $m$), simultaneously. To the best of our knowledge, there is no such algorithm. All the algorithms presented here are Monte Carlo and succeed with high probability, in the KT1 CONGEST asynchronous model.