OpenRaiser/Intern-Atlas huggingface dataset:
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA
Venue: arXiv cs.AR 2017
Year: 2017

Deep convolutional neural networks (CNN) based solutions are the current state- of-the-art for computer vision tasks. Due to the large size of these models, they are typically run on clusters of CPUs or GPUs. However, power requirements and cost budgets can be a major hindrance in adoption of CNN for IoT applications. Recent research highlights that CNN contain significant redundancy in their structure and can be quantized to lower bit-width parameters and activations, while maintaining acceptable accuracy. Low bit-width and especially single bit-width (binary) CNN are particularly suitable for mobile applications based on FPGA implementation, due to the bitwise logic operations involved in binarized CNN. Moreover, the transition to lower bit-widths opens new avenues for performance optimizations and model improvement. In this paper, we present an automatic flow from trained TensorFlow models to FPGA system on chip implementation of binarized CNN. This flow involves quantization of model parameters and activations, generation of network and model in embedded-C, followed by automatic generation of the FPGA accelerator for binary convolutions. The automated flow is demonstrated through implementation of binarized "YOLOV2" on the low cost, low power Cyclone- V FPGA device. Experiments on object detection using binarized YOLOV2 demonstrate significant performance benefit in terms of model size and inference speed on FPGA as compared to CPU and mobile CPU platforms. Furthermore, the entire automated flow from trained models to FPGA synthesis can be completed within one hour.
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.
The Saga of KPR: Theoretical and Experimental developments
Venue: arXiv cs.GT 2017
Year: 2017

In this article, we present a brief narration of the origin and the overview of the recent developments done on the Kolkata Paise Restaurant (KPR) problem, which can serve as a prototype for a broader class of resource allocation problems in the presence of a large number of competing agents, typically studied using coordination and anti-coordination games. We discuss the KPR and its several extensions, as well as its applications in many economic and social phenomena. We end the article with some discussions on our ongoing experimental analysis of the same problem. We demonstrate that this provides an interesting picture of how people analyze complex situations, and design their strategies or react to them.
Three IQs of AI Systems and their Testing Methods
Venue: arXiv cs.AI 2017
Year: 2017

The rapid development of artificial intelligence has brought the artificial intelligence threat theory as well as the problem about how to evaluate the intelligence level of intelligent products. Both need to find a quantitative method to evaluate the intelligence level of intelligence systems, including human intelligence. Based on the standard intelligence system and the extended Von Neumann architecture, this paper proposes General IQ, Service IQ and Value IQ evaluation methods for intelligence systems, depending on different evaluation purposes. Among them, the General IQ of intelligence systems is to answer the question of whether the artificial intelligence can surpass the human intelligence, which is reflected in putting the intelligence systems on an equal status and conducting the unified evaluation. The Service IQ and Value IQ of intelligence systems are used to answer the question of how the intelligent products can better serve the human, reflecting the intelligence and required cost of each intelligence system as a product in the process of serving human.
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
Venue: arXiv cs.CV 2017
Year: 2017

Segmentation of the levator hiatus in ultrasound allows to extract biometrics which are of importance for pelvic floor disorder assessment. In this work, we present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalising activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction and rest, all labelled by three operators, is used for training and evaluation in a leave-one-patient-out cross-validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams' index of 1.03), and outperforming a U-Net architecture without the need for batch normalisation. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semi-automatic approach.
Parallel Complexity of Forward and Backward Propagation
Venue: arXiv cs.LG 2017
Year: 2017

We show that the forward and backward propagation can be formulated as a solution of lower and upper triangular systems of equations. For standard feedforward (FNNs) and recurrent neural networks (RNNs) the triangular systems are always block bi-diagonal, while for a general computation graph (directed acyclic graph) they can have a more complex triangular sparsity pattern. We discuss direct and iterative parallel algorithms that can be used for their solution and interpreted as different ways of performing model parallelism. Also, we show that for FNNs and RNNs with $k$ layers and $τ$ time steps the backward propagation can be performed in parallel in O($\log k$) and O($\log k \log τ$) steps, respectively. Finally, we outline the generalization of this technique using Jacobians that potentially allows us to handle arbitrary layers.
Sum-Rate Analysis for High Altitude Platform (HAP) Drones with Tethered Balloon Relay
Venue: arXiv cs.IT 2017
Year: 2017

High altitude platform (HAP) drones can provide broadband wireless connectivity to ground users in rural areas by establishing line-of-sight (LoS) links and exploiting effective beamforming techniques. However, at high altitudes, acquiring the channel state information (CSI) for HAPs, which is a key component to perform beamforming, is challenging. In this paper, by exploiting an interference alignment (IA) technique, a novel method for achieving the maximum sum-rate in HAP-based communications without CSI is proposed. In particular, to realize IA, a multiple-antenna tethered balloon is used as a relay between multiple HAP drones and ground stations (GSs). Here, a multiple-input multiple-output X network system is considered. The capacity of the considered M*N X network with a tethered balloon relay is derived in closed-form. Simulation results corroborate the theoretical findings and show that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs communications in absence of CSI. The results also show the existence of an optimal balloon's altitude for which the sum-rate is maximized.
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Venue: arXiv cs.AI 2017
Year: 2017

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.
Algorithms for low-distortion embeddings into arbitrary 1-dimensional spaces
Venue: arXiv cs.CG 2017
Year: 2017

We study the problem of finding a minimum-distortion embedding of the shortest path metric of an unweighted graph into a "simpler" metric $X$. Computing such an embedding (exactly or approximately) is a non-trivial task even when $X$ is the metric induced by a path, or, equivalently, into the real line. In this paper we give approximation and fixed-parameter tractable (FPT) algorithms for minimum-distortion embeddings into the metric of a subdivision of some fixed graph $H$, or, equivalently, into any fixed 1-dimensional simplicial complex. More precisely, we study the following problem: For given graphs $G$, $H$ and integer $c$, is it possible to embed $G$ with distortion $c$ into a graph homeomorphic to $H$? Then embedding into the line is the special case $H=K_2$, and embedding into the cycle is the case $H=K_3$, where $K_k$ denotes the complete graph on $k$ vertices. For this problem we give -an approximation algorithm, which in time $f(H)\cdot \text{poly} (n)$, for some function $f$, either correctly decides that there is no embedding of $G$ with distortion $c$ into any graph homeomorphic to $H$, or finds an embedding with distortion $\text{poly}(c)$; -an exact algorithm, which in time $f'(H, c)\cdot \text{poly} (n)$, for some function $f'$, either correctly decides that there is no embedding of $G$ with distortion $c$ into any graph homeomorphic to $H$, or finds an embedding with distortion $c$. Prior to our work, $\text{poly}(\mathsf{OPT})$-approximation or FPT algorithms were known only for embedding into paths and trees of bounded degrees.
A tight lower bound for an online hypercube packing problem and bounds for prices of anarchy of a related game
Venue: arXiv cs.DS 2017
Year: 2017

We prove a tight lower bound on the asymptotic performance ratio $ρ$ of the bounded space online $d$-hypercube bin packing problem, solving an open question raised in 2005. In the classic $d$-hypercube bin packing problem, we are given a sequence of $d$-dimensional hypercubes and we have an unlimited number of bins, each of which is a $d$-dimensional unit hypercube. The goal is to pack (orthogonally) the given hypercubes into the minimum possible number of bins, in such a way that no two hypercubes in the same bin overlap. The bounded space online $d$-hypercube bin packing problem is a variant of the $d$-hypercube bin packing problem, in which the hypercubes arrive online and each one must be packed in an open bin without the knowledge of the next hypercubes. Moreover, at each moment, only a constant number of open bins are allowed (whenever a new bin is used, it is considered open, and it remains so until it is considered closed, in which case, it is not allowed to accept new hypercubes). Epstein and van Stee [SIAM J. Comput. 35 (2005), no. 2, 431-448] showed that $ρ$ is $Ω(\log d)$ and $O(d/\log d)$, and conjectured that it is $Θ(\log d)$. We show that $ρ$ is in fact $Θ(d/\log d)$. To obtain this result, we elaborate on some ideas presented by those authors, and go one step further showing how to obtain better (offline) packings of certain special instances for which one knows how many bins any bounded space algorithm has to use. Our main contribution establishes the existence of such packings, for large enough $d$, using probabilistic arguments. Such packings also lead to lower bounds for the prices of anarchy of the selfish $d$-hypercube bin packing game. We present a lower bound of $Ω(d/\log d)$ for the pure price of anarchy of this game, and we also give a lower bound of $Ω(\log d)$ for its strong price of anarchy.
Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times
Venue: arXiv cs.SE 2017
Year: 2017

Automated builds are integral to the Continuous Integration (CI) software development practice. In CI, developers are encouraged to integrate early and often. However, long build times can be an issue when integrations are frequent. This research focuses on finding a balance between integrating often and keeping developers productive. We propose and analyze models that can predict the build time of a job. Such models can help developers to better manage their time and tasks. Also, project managers can explore different factors to determine the best setup for a build job that will keep the build wait time to an acceptable level. Software organizations transitioning to CI practices can use the predictive models to anticipate build times before CI is implemented. The research community can modify our predictive models to further understand the factors and relationships affecting build times.
Hierarchical Cross Network for Person Re-identification
Venue: arXiv cs.CV 2017
Year: 2017

Person re-identification (person re-ID) aims at matching target person(s) grabbed from different and non-overlapping camera views. It plays an important role for public safety and has application in various tasks such as, human retrieval, human tracking, and activity analysis. In this paper, we propose a new network architecture called Hierarchical Cross Network (HCN) to perform person re-ID. In addition to the backbone model of a conventional CNN, HCN is equipped with two additional maps called hierarchical cross feature maps. The maps of an HCN are formed by merging layers with different resolutions and semantic levels. With the hierarchical cross feature maps, an HCN can effectively uncover additional semantic features which could not be discovered by a conventional CNN. Although the proposed HCN can discover features with higher semantics, its representation power is still limited. To derive more general representations, we augment the data during the training process by combining multiple datasets. Experiment results show that the proposed method outperformed several state-of-the-art methods.
MUDA: A Truthful Multi-Unit Double-Auction Mechanism
Venue: arXiv cs.GT 2017
Year: 2017

In a seminal paper, McAfee (1992) presented a truthful mechanism for double auctions, attaining asymptotically-optimal gain-from-trade without any prior information on the valuations of the traders. McAfee's mechanism handles single-parametric agents, allowing each seller to sell a single unit and each buyer to buy a single unit. This paper presents a double-auction mechanism that handles multi-parametric agents and allows multiple units per trader, as long as the valuation functions of all traders have decreasing marginal returns. The mechanism is prior-free, ex-post individually-rational, dominant-strategy truthful and strongly-budget-balanced. Its gain-from-trade approaches the optimum when the market size is sufficiently large.
A comparison of web privacy protection techniques
Venue: arXiv cs.CR 2017
Year: 2017

A comparison of web privacy protection techniques
Uplink Sounding Reference Signal Coordination to Combat Pilot Contamination in 5G Massive MIMO
Venue: arXiv cs.IT 2017
Year: 2017

To guarantee the success of massive multiple-input multiple-output (MIMO), one of the main challenges to solve is the efficient management of pilot contamination. Allocation of fully orthogonal pilot sequences across the network would provide a solution to the problem, but the associated overhead would make this approach infeasible in practical systems. Ongoing fifth-generation (5G) standardisation activities are debating the amount of resources to be dedicated to the transmission of pilot sequences, focussing on uplink sounding reference signals (UL SRSs) design. In this paper, we extensively evaluate the performance of various UL SRS allocation strategies in practical deployments, shedding light on their strengths and weaknesses. Furthermore, we introduce a novel UL SRS fractional reuse (FR) scheme, denoted neighbour-aware FR (FR-NA). The proposed FR-NA generalizes the fixed reuse paradigm, and entails a tradeoff between i) aggressively sharing some UL SRS resources, and ii) protecting other UL SRS resources with the aim of relieving neighbouring BSs from pilot contamination. Said features result in a cell throughput improvement over both fixed reuse and state-of-the-art FR based on a cell-centric perspective.
ComboGAN: Unrestrained Scalability for Image Domain Translation
Venue: arXiv cs.CV 2017
Year: 2017

This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs; whereas our model requires only 14 generator/discriminator pairs.
Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

The WSDM Cup 2017 is a binary classification task for classifying Wikidata revisions into vandalism and non-vandalism. This paper describes our method using some machine learning techniques such as under-sampling, feature selection, stacking and ensembles of models. We confirm the validity of each technique by calculating AUC-ROC of models using such techniques and not using them. Additionally, we analyze the results and gain useful insights into improving models for the vandalism detection task. The AUC-ROC of our final submission after the deadline resulted in 0.94412.
Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. As it can be edited by anyone, entries frequently get vandalized, leading to the possibility that it might spread of falsified information if such posts are not detected. The WSDM 2017 Wiki Vandalism Detection Challenge requires us to solve this problem by computing a vandalism score denoting the likelihood that a revision corresponds to an act of vandalism and performance is measured using the ROC-AUC obtained on a held-out test set. This paper provides the details of our submission that obtained an ROC-AUC score of 0.91976 in the final evaluation.
Mining Smart Card Data for Travelers' Mini Activities
Venue: arXiv cs.AI 2017
Year: 2017

In the context of public transport modeling and simulation, we address the problem of mismatch between simulated transit trips and observed ones. We point to the weakness of the current travel demand modeling process; the trips it generates are over-optimistic and do not reflect the real passenger choices. We introduce the notion of mini activities the travelers do during the trips; they can explain the deviation of simulated trips from the observed trips. We propose to mine the smart card data to extract the mini activities. We develop a technique to integrate them in the generated trips and learn such an integration from two available sources, the trip history and trip planner recommendations. For an input travel demand, we build a Markov chain over the trip collection and apply the Monte Carlo Markov Chain algorithm to integrate mini activities in such a way that the selected characteristics converge to the desired distributions. We test our method in different settings on the passenger trip collection of Nancy, France. We report experimental results demonstrating a very important mismatch reduction.
Laguerre-Intersection Method for Implicit Solvation
Venue: arXiv cs.CG 2017
Year: 2017

Laguerre tessellations of macromolecules capture properties such as molecular interface surfaces, volumes and cavities. Explicit solvent molecular dynamics simulations of a macromolecule are slow as the number of solvent atoms considered typically increases by order of magnitude. Implicit methods model the solvent via continuous corrections to the force field based on estimates of the solvent exposed surface areas of individual atoms, gaining speed at the expense of accuracy. However, Laguerre cells of exterior atoms tend to be overly large or unbounded. Our method, the Laguerre-Intersection method, caps cells in a physically accurate manner by considering the intersection of the space-filling diagram with the Laguerre tessellation. This method optimizes an adjustable parameter, the weight, to ensure the areas and volumes of capped cells exposed to solvent are as close as possible, on average, to those computed from equilibrated explicit solvent simulations. The contact planes are radical planes, meaning that as the solvent weight is varied, cells remain constant. We test the consistency of our model using a high-quality trajectory of HIV-protease, a dimer with flexible loops and open-close transitions. We also compare our results with interval-arithmetic Gauss-Bonnet based method. Optimal solvent parameters quickly converge, which we use to illustrate the increased accuracy of the Laguerre-Intersection method over two recently proposed methods as compared to the explicit model.
Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study
Venue: arXiv cs.CL 2017
Year: 2017

Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram features when learning from textual data. They are also compatible with the use of word embeddings so that word similarities can be accounted for. While the original any-gram kernels are implemented on top of tree kernels, we propose a new approach which is independent of tree kernels and is more efficient. We also propose a more effective way to make use of word embeddings than the original any-gram formulation. When applied to the task of sentiment classification, our new formulation achieves significantly better performance.