OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Mode Inference Engine for Convolutional Neural Networks
Venue: arXiv cs.AR 2017
Year: 2017

During the past few years, interest in convolutional neural networks (CNNs) has risen constantly, thanks to their excellent performance on a wide range of recognition and classification tasks. However, they suffer from the high level of complexity imposed by the high-dimensional convolutions in convolutional layers. Within scenarios with limited hardware resources and tight power and latency constraints, the high computational complexity of CNNs makes them difficult to be exploited. Hardware solutions have striven to reduce the power consumption using low-power techniques, and to limit the processing time by increasing the number of processing elements (PEs). While most of ASIC designs claim a peak performance of a few hundred giga operations per seconds, their average performance is substantially lower when applied to state-of-the-art CNNs such as AlexNet, VGGNet and ResNet, leading to low resource utilization. Their performance efficiency is limited to less than 55% on average, which leads to unnecessarily high processing latency and silicon area. In this paper, we propose a dataflow which enables to perform both the fully-connected and convolutional computations for any filter/layer size using the same PEs. We then introduce a multi-mode inference engine (MMIE) based on the proposed dataflow. Finally, we show that the proposed MMIE achieves a performance efficiency of more than 84% when performing the computations of the three renown CNNs (i.e., AlexNet, VGGNet and ResNet), outperforming the best architecture in the state-of-the-art in terms of energy consumption, processing latency and silicon area.
Training Ensembles to Detect Adversarial Examples
Venue: arXiv cs.LG 2017
Year: 2017

We propose a new ensemble method for detecting and classifying adversarial examples generated by state-of-the-art attacks, including DeepFool and C&W. Our method works by training the members of an ensemble to have low classification error on random benign examples while simultaneously minimizing agreement on examples outside the training distribution. We evaluate on both MNIST and CIFAR-10, against oblivious and both white- and black-box adversaries.
Learning Robust Dialog Policies in Noisy Environments
Venue: arXiv cs.CL 2017
Year: 2017

Modern virtual personal assistants provide a convenient interface for completing daily tasks via voice commands. An important consideration for these assistants is the ability to recover from automatic speech recognition (ASR) and natural language understanding (NLU) errors. In this paper, we focus on learning robust dialog policies to recover from these errors. To this end, we develop a user simulator which interacts with the assistant through voice commands in realistic scenarios with noisy audio, and use it to learn dialog policies through deep reinforcement learning. We show that dialogs generated by our simulator are indistinguishable from human generated dialogs, as determined by human evaluators. Furthermore, preliminary experimental results show that the learned policies in noisy environments achieve the same execution success rate with fewer dialog turns compared to fixed rule-based policies.
Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of high-dimensional misspecified phase retrieval. This is where we have an $s$-sparse signal vector $\mathbf{x}_*$ in $\mathbb{R}^n$, which we wish to recover using sampling vectors $\textbf{a}_1,\ldots,\textbf{a}_m$, and measurements $y_1,\ldots,y_m$, which are related by the equation $f(\left<\textbf{a}_i,\textbf{x}_*\right>) = y_i$. Here, $f$ is an unknown link function satisfying a positive correlation with the quadratic function. This problem was analyzed in a recent paper by Neykov, Wang and Liu, who provided recovery guarantees for a two-stage algorithm with sample complexity $m = O(s^2\log n)$. In this paper, we show that the first stage of their algorithm suffices for signal recovery with the same sample complexity, and extend the analysis to non-Gaussian measurements. Furthermore, we show how the algorithm can be generalized to recover a signal vector $\textbf{x}_*$ efficiently given geometric prior information other than sparsity.
200x Low-dose PET Reconstruction using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in PET imaging raises concerns due to the risk of radiation exposure. To minimize this potential risk in PET imaging, efforts have been made to reduce the amount of radio-tracer usage. However, lowing dose results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of which will heavily affect clinical diagnosis. Besides, the ill-conditioning of low-dose PET image reconstruction makes it a difficult problem for iterative reconstruction algorithms. Previous methods proposed are typically complicated and slow, yet still cannot yield satisfactory results at significantly low dose. Here, we propose a deep learning method to resolve this issue with an encoder-decoder residual deep network with concatenate skip connections. Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose. Different cost functions for training model are explored. Multi-slice input strategy is introduced to provide the network with more structural information and make it more robust to noise. Evaluation on ultra-low-dose clinical data shows that the proposed method can achieve better result than the state-of-the-art methods and reconstruct images with comparable quality using only 0.5% of the original regular dose.
Tracing a Loose Wordhood for Chinese Input Method Engine
Venue: arXiv cs.CL 2017
Year: 2017

Chinese input methods are used to convert pinyin sequence or other Latin encoding systems into Chinese character sentences. For more effective pinyin-to-character conversion, typical Input Method Engines (IMEs) rely on a predefined vocabulary that demands manually maintenance on schedule. For the purpose of removing the inconvenient vocabulary setting, this work focuses on automatic wordhood acquisition by fully considering that Chinese inputting is a free human-computer interaction procedure. Instead of strictly defining words, a loose word likelihood is introduced for measuring how likely a character sequence can be a user-recognized word with respect to using IME. Then an online algorithm is proposed to adjust the word likelihood or generate new words by comparing user true choice for inputting and the algorithm prediction. The experimental results show that the proposed solution can agilely adapt to diverse typings and demonstrate performance approaching highly-optimized IME with fixed vocabulary.
How Better is Distributed SDN? An Analytical Approach
Venue: arXiv cs.DC 2017
Year: 2017

Distributed software-defined networks (SDN), consisting of multiple inter-connected network domains, each managed by one SDN controller, is an emerging networking architecture that offers balanced centralized control and distributed operations. Under such networking paradigm, most existing works focus on designing sophisticated controller-synchronization strategies to improve joint controller-decision-making for inter-domain routing. However, there is still a lack of fundamental understanding of how the performance of distributed SDN is related to network attributes, thus impossible to justify the necessity of complicated strategies. In this regard, we analyze and quantify the performance enhancement of distributed SDN architectures, influenced by intra-/inter-domain synchronization levels and network structural properties. Based on a generic weighted network model, we establish analytical methods for performance estimation under four synchronization scenarios with increasing synchronization cost. Moreover, two of these synchronization scenarios correspond to extreme cases, i.e., minimum/maximum synchronization, which are, therefore, capable of bounding the performance of distributed SDN with any given synchronization levels. Our theoretical results reveal how network performance is related to synchronization levels and inter-domain connections, the accuracy of which are confirmed by simulations based on both real and synthetic networks. To the best of our knowledge, this is the first work quantifying the performance of distributed SDN analytically, which provides fundamental guidance for future SDN protocol designs and performance estimation.
Mathematical Modeling and Analysis of ZigBee Node Battery Characteristics and Operation
Venue: arXiv cs.NI 2017
Year: 2017

ZigBee network technology has been used widely in different commercial, medical and industrial applications, and the importance of keeping the network operating at a longer time was the main objective of ZigBee manufacturers. In this paper, ZigBee battery characteristics and operation has been researched extensively and a mathematical modeling has been applied on existed practical data provided by Freescale semiconductors Inc [1], and Farnell [2]. As a result a mathematical optimized formula has been established which describes battery characteristic and voltage behavior as a function of time, and since ZigBee node batteries has been the core objective of this research work, a decline in battery voltage below 50% of battery capacity could influence and degrade ZigBee network performance.
A Low Cost ZigBee Sensor Network Architecture for Indoor Air Quality Monitoring
Venue: arXiv cs.NI 2017
Year: 2017

This paper presents a low-cost system architecture that has been proposed for automatically monitoring air quality indoors and continuously in real-time. The designed system is in pilot phase where 4 sensor nodes are deployed in indoor environment, and data over 4 weeks has been collected and performance analysis and assessment are performed. Environmental data from sensor nodes are sent through ZigBee communication protocol. The proposed system is low in cost, and achieves low power consumption. Hardware and network architecture are presented in addition to real-world deployment.
On the Energy-Efficient Deployment for Ultra-Dense Heterogeneous Networks with NLoS and LoS Transmissions
Venue: arXiv cs.NI 2017
Year: 2017

We investigate network performance of ultra-dense heterogeneous networks (HetNets) and study the maximum energy-efficient base station (BS) deployment incorporating probabilistic non-line-of-sight (NLoS) and line-of-sight (LoS) transmissions. First, we develop an analytical framework with the maximum instantaneous received power (MIRP) and the maximum average received power (MARP) association schemes to model the coverage probability and related performance metrics, e.g., the potential throughput (PT) and the energy efficiency (EE). Second, we formulate two optimization problems to achieve the maximum energy-efficient deployment solution with specific service criteria. Simulation results show that there are tradeoffs among the coverage probability, the total power consumption, and the EE. To be specific, the maximum coverage probability with ideal power consumption is superior to that with practical power consumption when the total power constraint is small and inferior to that with practical power consumption when the total power constraint becomes large. Moreover, the maximum EE is a decreasing function with respect to the coverage probability constraint.
Interactive graph query language for multidimensional data in Collaboration Spotting visual analytics framework
Venue: arXiv cs.HC 2017
Year: 2017

Human reasoning in visual analytics of data networks relies mainly on the quality of visual perception and the capability of interactively exploring the data from different facets. Visual quality strongly depends on networks' size and dimensional complexity while network exploration capability on the intuitiveness and expressiveness of user frontends. The approach taken in this paper aims at addressing the above by decomposing data networks into multiple networks of smaller dimensions and building an interactive graph query language that supports full navigation across the sub-networks. Within sub-networks of reduced dimensionality, structural abstraction and semantic techniques can then be used to enhance visual perception further.
Directing Cinematographic Drones
Venue: arXiv cs.RO 2017
Year: 2017

Quadrotor drones equipped with high quality cameras have rapidely raised as novel, cheap and stable devices for filmmakers. While professional drone pilots can create aesthetically pleasing videos in short time, the smooth -- and cinematographic -- control of a camera drone remains challenging for most users, despite recent tools that either automate part of the process or enable the manual design of waypoints to create drone trajectories. This paper proposes to move a step further towards more accessible cinematographic drones by designing techniques to automatically or interactively plan quadrotor drone motions in 3D dynamic environments that satisfy both cinematographic and physical quadrotor constraints. We first propose the design of a Drone Toric Space as a dedicated camera parameter space with embedded constraints and derive some intuitive on-screen viewpoint manipulators. Second, we propose a specific path planning technique which ensures both that cinematographic properties can be enforced along the path, and that the path is physically feasible by a quadrotor drone. At last, we build on the Drone Toric Space and the specific path planning technique to coordinate the motion of multiple drones around dynamic targets. A number of results then demonstrate the interactive and automated capacities of our approaches on a number of use-cases.
Grasp that optimises objectives along post-grasp trajectories
Venue: arXiv cs.RO 2017
Year: 2017

In this article, we study the problem of selecting a grasping pose on the surface of an object to be manipulated by considering three post-grasp objectives. These objectives include (i) kinematic manipulation capability, (ii) torque effort \cite{mavrakis2016analysis} and (iii) impact force in case of a collision during post-grasp manipulative actions. In these works, the main assumption is that a manipulation task, i.e. trajectory of the centre of mass (CoM) of an object is given. In addition, inertial properties of the object to be manipulated is known. For example, a robot needs to pick an object located at point A and place it at point B by moving it along a given path. Therefore, the problem to be solved is to find an initial grasp pose that yields the maximum kinematic manipulation capability, minimum joint effort and effective mass along a given post-grasp trajectories. However, these objectives may conflict in some cases making it impossible to obtain the best values for all of them. We perform a series of experiments to show how different objectives change as the grasping pose on an object alters. The experimental results presented in this paper illustrate that these objectives are conflicting for some desired post-grasp trajectories. This indicates that a detailed multi-objective optimization is needed for properly addressing this problem in a future work.
In folly ripe. In reason rotten. Putting machine theology to rest
Venue: arXiv cs.AI 2017
Year: 2017

Computation has changed the world more than any previous expressions of knowledge. In its particular algorithmic embodiment, it offers a perspective, within which the digital computer (one of many possible) exercises a role reminiscent of theology. Since it is closed to meaning, algorithmic digital computation can at most mimic the creative aspects of life. AI, in the perspective of time, proved to be less an acronym for artificial intelligence and more of automating tasks associated with intelligence. The entire development led to the hypostatized role of the machine: outputting nothing else but reality, including that of the humanity that made the machine happen. The convergence machine called deep learning is only the latest form through which the deterministic theology of the machine claims more than what extremely effective data processing actually is. A new understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology.
Automated Cinematography with Unmanned Aerial Vehicles
Venue: arXiv cs.RO 2017
Year: 2017

The rise of Unmanned Aerial Vehicles and their increasing use in the cinema industry calls for the creation of dedicated tools. Though there is a range of techniques to automatically control drones for a variety of applications, none have considered the problem of producing cinematographic camera motion in real-time for shooting purposes. In this paper we present our approach to UAV navigation for autonomous cinematography. The contributions of this research are twofold: (i) we adapt virtual camera control techniques to UAV navigation; (ii) we introduce a drone-independent platform for high-level user interactions that integrates cinematographic knowledge. The results presented in this paper demonstrate the capacities of our tool to capture live movie scenes involving one or two moving actors.
ex uno pluria: The Service-Infrastructure Cycle, Ossification, and the Fragmentation of the Internet
Venue: arXiv cs.NI 2017
Year: 2017

In this article I will first argue that a Service-Infrastructure Cycle is fundamental to networking evolution. Networks are built to accommodate certain services at an expected scale. New applications and/or a significant increase in scale require a rethinking of network mechanisms which results in new deployments. Four decades-worth of iterations of this process have yielded the Internet as we know it today, a common and shared global networking infrastructure that delivers almost all services. I will further argue, using brief historical case studies, that success of network mechanism deployments often hinges on whether or not mechanism evolution follows the iterations of this Cycle. Many have observed that this network, the Internet, has become ossified and unable to change in response to new demands. In other words, after decades of operation, the Service-Infrastructure Cycle has become stuck. However, novel service requirements and scale increases continue to exert significant pressure on this ossified infrastructure. The result, I will conjecture, will be a fragmentation, the beginnings of which are evident today, that will ultimately fundamentally change the character of the network infrastructure. By ushering in a ManyNets world, this fragmentation will lubricate the Service-Infrastructure Cycle so that it can continue to govern the evolution of networking. I conclude this article with a brief discussion of the possible implications of this emerging ManyNets world on networking research.
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
Venue: arXiv cs.CV 2017
Year: 2017

Emojis have become a very popular part of daily digital communication. Their appeal comes largely in part due to their ability to capture and elicit emotions in a more subtle and nuanced way than just plain text is able to. In line with recent advances in the field of deep learning, there are far reaching implications and applications that generative adversarial networks (GANs) can have for image generation. In this paper, we present a novel application of deep convolutional GANs (DC-GANs) with an optimized training procedure. We show that via incorporation of word embeddings conditioned on Google's word2vec model into the network, the generator is able to synthesize highly realistic emojis that are virtually identical to the real ones.
3D Object Classification via Spherical Projections
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we introduce a new method for classifying 3D objects. Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection. We introduce two complementary projections. The first captures depth variations of a 3D object, and the second captures contour-information viewed from different angles. Spherical projections combine key advantages of two main-stream 3D classification methods: image-based and 3D-based. Specifically, spherical projections are locally planar, allowing us to use massive image datasets (e.g, ImageNet) for pre-training. Also spherical projections are similar to voxel-based methods, as they encode complete information of a 3D object in a single neural network capturing dependencies across different views. Our novel network design can fully utilize these advantages. Experimental results on ModelNet40 and ShapeNetCore show that our method is superior to prior methods.
Encoding DNA sequences by integer chaos game representation
Venue: arXiv cs.CE 2017
Year: 2017

DNA sequences are fundamental for encoding genetic information. The genetic information may not only be understood by symbolic sequences but also from the hidden signals inside the sequences. The symbolic sequences need to be transformed into numerical sequences so the hidden signals can be revealed by signal processing techniques. All current transformation methods encode DNA sequences into numerical values of the same length. These representations have limitations in the applications of genomic signal compression, encryption, and steganography. We propose an integer chaos game representation (iCGR) of DNA sequences and a lossless encoding method DNA sequences by the iCGR. In the iCGR method, a DNA sequence is represented by the iterated function of the nucleotides and their positions in the sequence. Then the DNA sequence can be uniquely encoded and recovered using three integers from iCGR. One integer is the sequence length and the other two integers represent the accumulated distributions of nucleotides in the sequence. The integer encoding scheme can compress a DNA sequence by 2 bits per nucleotide. The integer representation of DNA sequences provides a prospective tool for sequence compression, encryption, and steganography. The Python programs in this study are freely available to the public at https://github.com/cyinbox/iCGR