OpenRaiser/Intern-Atlas huggingface dataset:
Demystifying the Characteristics of 3D-Stacked Memories: A Case Study for Hybrid Memory Cube
Venue: arXiv cs.AR 2017
Year: 2017

Three-dimensional (3D)-stacking technology, which enables the integration of DRAM and logic dies, offers high bandwidth and low energy consumption. This technology also empowers new memory designs for executing tasks not traditionally associated with memories. A practical 3D-stacked memory is Hybrid Memory Cube (HMC), which provides significant access bandwidth and low power consumption in a small area. Although several studies have taken advantage of the novel architecture of HMC, its characteristics in terms of latency and bandwidth or their correlation with temperature and power consumption have not been fully explored. This paper is the first, to the best of our knowledge, to characterize the thermal behavior of HMC in a real environment using the AC-510 accelerator and to identify temperature as a new limitation for this state-of-the-art design space. Moreover, besides bandwidth studies, we deconstruct factors that contribute to latency and reveal their sources for high- and low-load accesses. The results of this paper demonstrates essential behaviors and performance bottlenecks for future explorations of packet-switched and 3D-stacked memories.
CortexNet: a Generic Network Family for Robust Visual Temporal Representations
Venue: arXiv cs.CV 2017
Year: 2017

In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks. These models have achieved super-human performance on object recognition, localisation, and detection in still images. However, there is a need to identify the best strategy to employ these networks with temporal visual inputs and obtain a robust and stable representation of video data. Inspired by the human visual system, we propose a deep neural network family, CortexNet, which features not only bottom-up feed-forward connections, but also it models the abundant top-down feedback and lateral connections, which are present in our visual cortex. We introduce two training schemes - the unsupervised MatchNet and weakly supervised TempoNet modes - where a network learns how to correctly anticipate a subsequent frame in a video clip or the identity of its predominant subject, by learning egomotion clues and how to automatically track several objects in the current scene. Find the project website at https://engineering.purdue.edu/elab/CortexNet/.
Summary Analysis of the 2017 GitHub Open Source Survey
Venue: arXiv cs.CY 2017
Year: 2017

This report is a high-level summary analysis of the 2017 GitHub Open Source Survey dataset, presenting frequency counts, proportions, and frequency or proportion bar plots for every question asked in the survey.
On the Development of Intelligent Agents for MOBA Games
Venue: arXiv cs.AI 2017
Year: 2017

Multiplayer Online Battle Arena (MOBA) is one of the most played game genres nowadays. With the increasing growth of this genre, it becomes necessary to develop effective intelligent agents to play alongside or against human players. In this paper we address the problem of agent development for MOBA games. We implement a two-layered architecture agent that handles both navigation and game mechanics. This architecture relies on the use of Influence Maps, a widely used approach for tactical analysis. Several experiments were performed using {\em League of Legends} as a testbed, and show promising results in this highly dynamic real-time context.
A Tutor Agent for MOBA Games
Venue: arXiv cs.AI 2017
Year: 2017

Digital games have become a key player in the entertainment industry, attracting millions of new players each year. In spite of that, novice players may have a hard time when playing certain types of games, such as MOBAs and MMORPGs, due to their steep learning curves and not so friendly online communities. In this paper, we present an approach to help novice players in MOBA games overcome these problems. An artificial intelligence agent plays alongside the player analyzing his/her performance and giving tips about the game. Experiments performed with the game {\em League of Legends} show the potential of this approach.
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
Venue: arXiv cs.CV 2017
Year: 2017

Overhead depth map measurements capture sufficient amount of information to enable human experts to track pedestrians accurately. However, fully automating this process using image analysis algorithms can be challenging. Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image. Many of the assumptions underpinning the hand-crafted solutions do not hold in these cases and the multitude of exceptions are hard to model precisely. Deep Learning (DL) algorithms, on the other hand, do not require hand crafted solutions and are the current state-of-the-art in object localization in images. However, they require exceeding amount of annotations to produce successful models. In the case of object localization these annotations are difficult and time consuming to produce. In this work we present an approach for developing pedestrian localization models using DL algorithms with efficient weak supervision from an expert. We circumvent the need for annotation of large corpus of data by annotating only small amount of patches and relying on synthetic data augmentation as a vehicle for injecting expert knowledge in the model training. This approach of weak supervision through expert selection of representative patches, suitable transformations and synthetic data augmentations enables us to successfully develop DL models for pedestrian localization efficiently.
A Maximum Matching Algorithm for Basis Selection in Spectral Learning
Venue: arXiv cs.LG 2017
Year: 2017

We present a solution to scale spectral algorithms for learning sequence functions. We are interested in the case where these functions are sparse (that is, for most sequences they return 0). Spectral algorithms reduce the learning problem to the task of computing an SVD decomposition over a special type of matrix called the Hankel matrix. This matrix is designed to capture the relevant statistics of the training sequences. What is crucial is that to capture long range dependencies we must consider very large Hankel matrices. Thus the computation of the SVD becomes a critical bottleneck. Our solution finds a subset of rows and columns of the Hankel that realizes a compact and informative Hankel submatrix. The novelty lies in the way that this subset is selected: we exploit a maximal bipartite matching combinatorial algorithm to look for a sub-block with full structural rank, and show how computation of this sub-block can be further improved by exploiting the specific structure of Hankel matrices.
An Efficient Algorithm for Computing High-Quality Paths amid Polygonal Obstacles
Venue: arXiv cs.CG 2017
Year: 2017

We study a path-planning problem amid a set $\mathcal{O}$ of obstacles in $\mathbb{R}^2$, in which we wish to compute a short path between two points while also maintaining a high clearance from $\mathcal{O}$; the clearance of a point is its distance from a nearest obstacle in $\mathcal{O}$. Specifically, the problem asks for a path minimizing the reciprocal of the clearance integrated over the length of the path. We present the first polynomial-time approximation scheme for this problem. Let $n$ be the total number of obstacle vertices and let $\varepsilon \in (0,1]$. Our algorithm computes in time $O(\frac{n^2}{\varepsilon ^2} \log \frac{n}{\varepsilon})$ a path of total cost at most $(1+\varepsilon)$ times the cost of the optimal path.
Link Adaptation for Wireless Video Communication Systems
Venue: arXiv cs.IT 2017
Year: 2017

This PhD thesis considers the performance evaluation and enhancement of video communication over wireless channels. The system model considers hybrid automatic repeat request (HARQ) with Chase combining and turbo product codes (TPC). The thesis proposes algorithms and techniques to optimize the throughput, transmission power and complexity of HARQ-based wireless video communication. A semi-analytical solution is developed to model the performance of delay-constrained HARQ systems. The semi-analytical and Monte Carlo simulation results reveal that significant complexity reduction can be achieved by noting that the coding gain advantage of the soft over hard decoding is reduced when Chase combining is used, and it actually vanishes completely for particular codes. Moreover, the thesis proposes a novel power optimization algorithm that achieves a significant power saving of up to 80%. Joint throughput maximization and complexity reduction is considered as well. A CRC (cyclic redundancy check)-free HARQ is proposed to improve the system throughput when short packets are transmitted. In addition, the computational complexity/delay is reduced when the packets transmitted are long. Finally, a content-aware and occupancy-based HARQ scheme is proposed to ensure minimum video quality distortion with continuous playback.
A Physically Plausible Model for Rendering Highly Scattering Fluorescent Participating Media
Venue: arXiv cs.GR 2017
Year: 2017

We present a novel extension of the path tracing algorithm that is capable of treating highly scattering participating media in the presence of fluorescent structures. The extension is based on the formulation of the full radiative transfer equation when solved on a per-wavelength-basis, resulting in accurate model and unbiased algorithm for rendering highly scattering fluorescent participating media. The model accounts for the intrinsic properties of fluorescent dyes including their absorption and emission spectra, molar absorptivity and quantum yield and also their concentration. Our algorithm is applied to render highly scattering isotropic fluorescent solutions under different illumination conditions. The spectral performance of the model is validated against emission spectra of different fluorescent dyes that are of significance in spectroscopy.
Computational Coverage of TLG: Nonlinearity
Venue: arXiv cs.LO 2017
Year: 2017

We study nonlinear connectives (exponentials) in the context of Type Logical Grammar (TLG). We devise four conservative extensions of the displacement calculus with brackets, DbC, DbCM, DbCb and DbCbMr which contain the universal and existential exponential modalities of linear logic (LL). These modalities do not exhibit the same structural properties as in LL, which in TLG are especially adapted for linguistic purposes. The universal modality ! for TLG allows only the commutative and contraction rules, but not weakening, whereas the existential modality ? allows the so-called (intuitionistic) Mingle rule, which derives a restricted version of weakening. We provide a Curry-Howard labelling for both exponential connectives. As it turns out, controlled contraction by ! gives a way to account for the so-called parasitic gaps, and controlled Mingle ? iteration, in particular iterated coordination. Finally, the four calculi are proved to be Cut-Free, and decidability is proved for a linguistically sufficient special case of DbCbMr (and hence DbCb).
A linear-time algorithm for the maximum-area inscribed triangle in a convex polygon
Venue: arXiv cs.CG 2017
Year: 2017

Given the n vertices of a convex polygon in cyclic order, can the triangle of maximum area inscribed in P be determined by an algorithm with O(n) time complexity? A purported linear-time algorithm by Dobkin and Snyder from 1979 has recently been shown to be incorrect by Keikha, Löffler, Urhausen, and van der Hoog. These authors give an alternative algorithm with O(n log n) time complexity. Here we give an algorithm with linear time complexity.
Measurement-Adaptive Sparse Image Sampling and Recovery
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies. For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation. We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture. For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods. In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology. Simulation codes are available online for reproduction purposes.
AMPS: An Augmented Matrix Formulation for Principal Submatrix Updates with Application to Power Grids
Venue: arXiv cs.CE 2017
Year: 2017

We present AMPS, an augmented matrix approach to update the solution to a linear system of equations when the matrix is modified by a few elements within a principal submatrix. This problem arises in the dynamic security analysis of a power grid, where operators need to perform N - k contingency analysis, i.e., determine the state of the system when exactly k links from N fail. Our algorithms augment the matrix to account for the changes in it, and then compute the solution to the augmented system without refactoring the modified matrix. We provide two algorithms, a direct method, and a hybrid direct-iterative method for solving the augmented system. We also exploit the sparsity of the matrices and vectors to accelerate the overall computation. We analyze the time complexity of both algorithms, and show that it is bounded by the number of nonzeros in a subset of the columns of the Cholesky factor that are selected by the nonzeros in the sparse right-hand-side vector. Our algorithms are compared on three power grids with PARDISO, a parallel direct solver, and CHOLMOD, a direct solver with the ability to modify the Cholesky factors of the matrix. We show that our augmented algorithms outperform PARDISO (by two orders of magnitude), and CHOLMOD (by a factor of up to 5). Further, our algorithms scale better than CHOLMOD as the number of elements updated increases. The solutions are computed with high accuracy. Our algorithms are capable of computing N - k contingency analysis on a 778 thousand bus grid, updating a solution with k = 20 elements in 16 milliseconds on an Intel Xeon processor.
Upper Bounds on Number of Steals in Rooted Trees
Venue: arXiv cs.DC 2017
Year: 2017

Inspired by applications in parallel computing, we analyze the setting of work stealing in multithreaded computations. We obtain tight upper bounds on the number of steals when the computation can be modeled by rooted trees. In particular, we show that if the computation with $n$ processors starts with one processor having a complete $k$-ary tree of height $h$ (and the remaining $n-1$ processors having nothing), the maximum possible number of steals is $\sum_{i=1}^n(k-1)^i\binom{h}{i}$.
Human Mobility Mining through Head/Tail Breaks
Venue: arXiv cs.SI 2017
Year: 2017

Nowadays as the world population has become more interconnected and is relying on faster transportation methods, simplified connections and shorter commuting times, we witness a rapid increase in human mobility. In this situation unveiling and understanding human mobility patterns have become a crucial issue to support decisions and prediction activities when managing the complexity of the today social organization. In practice, the mobility pattern of each individual person consists of the sequence of visited places. Those places and their correlations represent the foundation of most modelling and activity researches for understanding human mobility. Even though visited places underpin almost the majority of works in this field, their features remain largely unknown because in previous works, they have been mainly considered as uncharacterized spot points in an area or social gathering places, without considering the roles and importance of places to the behavior of each single user.
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
Venue: arXiv cs.AI 2017
Year: 2017

Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent "learning-to-communicate" studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which can not generalize to changing environment or large collection of agents. In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving "learning-to-communicate" problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can efficiently learn the communication protocols even from scratch under partially observable environment. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations.
Models for the Displacement Calculus
Venue: arXiv cs.LO 2017
Year: 2017

The displacement calculus $\mathbf{D}$ is a conservative extension of the Lambek calculus $\mathbf{L1}$ (with empty antecedents allowed in sequents). $\mathbf{L1}$ can be said to be the logic of concatenation, while $\mathbf{D}$ can be said to be the logic of concatenation and intercalation. In many senses, it can be claimed that $\mathbf{D}$ mimics $\mathbf{L1}$ in that the proof theory, generative capacity and complexity of the former calculus are natural extensions of the latter calculus. In this paper, we strengthen this claim. We present the appropriate classes of models for $\mathbf{D}$ and prove some completeness results; strikingly, we see that these results and proofs are natural extensions of the corresponding ones for $\mathbf{L1}$.
An Online Learning Approach to Generative Adversarial Networks
Venue: arXiv cs.LG 2017
Year: 2017

We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training.
Fault Tolerant Consensus Agreement Algorithm
Venue: arXiv cs.DB 2017
Year: 2017

Recently a new fault tolerant and simple mechanism was designed for solving commit consensus problem. It is based on replicated validation of messages sent between transaction participants and a special dispatcher validator manager node. This paper presents a correctness, safety proofs and performance analysis of this algorithm.
Group-Server Queues
Venue: arXiv cs.PF 2017
Year: 2017

By analyzing energy-efficient management of data centers, this paper proposes and develops a class of interesting {\it Group-Server Queues}, and establishes two representative group-server queues through loss networks and impatient customers, respectively. Furthermore, such two group-server queues are given model descriptions and necessary interpretation. Also, simple mathematical discussion is provided, and simulations are made to study the expected queue lengths, the expected sojourn times and the expected virtual service times. In addition, this paper also shows that this class of group-server queues are often encountered in many other practical areas including communication networks, manufacturing systems, transportation networks, financial networks and healthcare systems. Note that the group-server queues are always used to design effectively dynamic control mechanisms through regrouping and recombining such many servers in a large-scale service system by means of, for example, bilateral threshold control, and customers transfer to the buffer or server groups. This leads to the large-scale service system that is divided into several adaptive and self-organizing subsystems through scheduling of batch customers and regrouping of service resources, which make the middle layer of this service system more effectively managed and strengthened under a dynamic, real-time and even reward optimal framework. Based on this, performance of such a large-scale service system may be improved greatly in terms of introducing and analyzing such group-server queues. Therefore, not only analysis of group-server queues is regarded as a new interesting research direction, but there also exists many theoretical challenges, basic difficulties and open problems in the area of queueing networks.
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
Venue: arXiv cs.CV 2017
Year: 2017

Purpose: To improve kidney segmentation in clinical ultrasound (US) images, we develop a new graph cuts based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. Methods: To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the GC based segmentation iteratively progresses until convergence. The proposed method has been evaluated and compared with state of the art image segmentation methods based on clinical kidney US images of 85 subjects. We randomly selected US images of 20 subjects as training data for tuning the parameters, and validated the methods based on US images of the remaining 65 subjects. The segmentation results have been quantitatively analyzed using 3 metrics, including Dice Index, Jaccard Index, and Mean Distance. Results: Experiment results demonstrated that the proposed method obtained segmentation results for bilateral kidneys of 65 subjects with average Dice index of 0.9581, Jaccard index of 0.9204, and Mean Distance of 1.7166, better than other methods under comparison (p<10-19, paired Wilcoxon rank sum tests). Conclusions: The proposed method achieved promising performance for segmenting kidneys in US images, better than segmentation methods that built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such progression chronic kidney disease.