OpenRaiser/Intern-Atlas huggingface dataset:
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Venue: arXiv cs.CV 2015
Year: 2015

Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning have shown promise in this direction but also highlighted the need for enormous quantity of supervised data --- performance increases in proportion to the amount of data used. However, this quickly becomes prohibitive when considering the manual labour needed to collect such data. In this work, we focus our attention on depth based semantic per-pixel labelling as a scene understanding problem and show the potential of computer graphics to generate virtually unlimited labelled data from synthetic 3D scenes. By carefully synthesizing training data with appropriate noise models we show comparable performance to state-of-the-art RGBD systems on NYUv2 dataset despite using only depth data as input and set a benchmark on depth-based segmentation on SUN RGB-D dataset. Additionally, we offer a route to generating synthesized frame or video data, and understanding of different factors influencing performance gains.
On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization
Venue: arXiv cs.LG 2015
Year: 2015

Recently diversity-inducing regularization methods for latent variable models (LVMs), which encourage the components in LVMs to be diverse, have been studied to address several issues involved in latent variable modeling: (1) how to capture long-tail patterns underlying data; (2) how to reduce model complexity without sacrificing expressivity; (3) how to improve the interpretability of learned patterns. While the effectiveness of diversity-inducing regularizers such as the mutual angular regularizer has been demonstrated empirically, a rigorous theoretical analysis of them is still missing. In this paper, we aim to bridge this gap and analyze how the mutual angular regularizer (MAR) affects the generalization performance of supervised LVMs. We use neural network (NN) as a model instance to carry out the study and the analysis shows that increasing the diversity of hidden units in NN would reduce estimation error and increase approximation error. In addition to theoretical analysis, we also present empirical study which demonstrates that the MAR can greatly improve the performance of NN and the empirical observations are in accordance with the theoretical analysis.
National, disciplinary and temporal variations in the extent to which articles with more authors have more impact: Evidence from a geometric field normalised citation indicator
Venue: arXiv cs.DL 2015
Year: 2015

The importance of collaboration in research is widely accepted, as is the fact that articles with more authors tend to be more cited. Nevertheless, although previous studies have investigated whether the apparent advantage of collaboration varies by country, discipline, and number of co-authors, this study introduces a more fine-grained method to identify differences: the geometric Mean Normalized Citation Score (gMNCS). Based on comparisons between disciplines, years and countries for two million journal articles, the average citation impact of articles increases with the number of authors, even when international collaboration is excluded. This apparent advantage of collaboration varies substantially by discipline and country and changes a little over time. Against the trend, however, in Russia solo articles have more impact. Across the four broad disciplines examined, collaboration had by far the strongest association with impact in the arts and humanities. Although international comparisons are limited by the availability of systematic data for author country affiliations, the new indicator is the most precise yet and can give statistical evidence rather than estimates.
Face Alignment Across Large Poses: A 3D Solution
Venue: arXiv cs.CV 2015
Year: 2015

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45 degree), lacking the ability to align faces in large poses up to 90 degree. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.
Synthesizing Omnidirectional Antenna Patterns, Received Power and Path Loss from Directional Antennas for 5G Millimeter-Wave Communications
Venue: arXiv cs.IT 2015
Year: 2015

Omnidirectional path loss models are vital for radiosystem design in wireless communication systems, as they allow engineers to perform network simulations for systems with arbitrary antenna patterns. At millimeter-wave frequencies, channel measurements are frequently conducted using steerable highgain directional antennas at both the transmitter and receiver to make up for the significant increase in free space path loss at these frequencies compared to traditional cellular systems that operate at lower frequencies. The omnidirectional antenna pattern, and resulting omnidirectional received power must therefore be synthesized from many unique pointing angles, where the transmit and receive antennas are rotated over many different azimuth and elevation planes. In this paper, the equivalent omnidirectional antenna pattern and omnidirectional received power are synthesized by summing the received powers from all measured unique pointing angles obtained at antenna halfpower beamwidth step increments in the azimuth and elevation planes, and this method is validated by demonstrating that the synthesized omnidirectional received power and path loss are independent of antenna beamwidth, through theoretical analyses and millimeter-wave propagation measurements using antennas with different beamwidths. The method in this paper is shown to provide accurate results while enhancing the measurement range substantially through the use of directional antennas.
Interpretable Two-level Boolean Rule Learning for Classification
Venue: arXiv cs.LG 2015
Year: 2015

This paper proposes algorithms for learning two-level Boolean rules in Conjunctive Normal Form (CNF, i.e. AND-of-ORs) or Disjunctive Normal Form (DNF, i.e. OR-of-ANDs) as a type of human-interpretable classification model, aiming for a favorable trade-off between the classification accuracy and the simplicity of the rule. Two formulations are proposed. The first is an integer program whose objective function is a combination of the total number of errors and the total number of features used in the rule. We generalize a previously proposed linear programming (LP) relaxation from one-level to two-level rules. The second formulation replaces the 0-1 classification error with the Hamming distance from the current two-level rule to the closest rule that correctly classifies a sample. Based on this second formulation, block coordinate descent and alternating minimization algorithms are developed. Experiments show that the two-level rules can yield noticeably better performance than one-level rules due to their dramatically larger modeling capacity, and the two algorithms based on the Hamming distance formulation are generally superior to the other two-level rule learning methods in our comparison. A proposed approach to binarize any fractional values in the optimal solutions of LP relaxations is also shown to be effective.
Minimizing Total Busy Time for Energy-Aware Virtual Machine Allocation Problems
Venue: arXiv cs.DC 2015
Year: 2015

This paper investigates the energy-aware virtual machine (VM) allocation problems in clouds along characteristics: multiple resources, fixed interval time and non-preemption of virtual machines. Many previous works have been proposed to use a minimum number of physical machines, however, this is not necessarily a good solution to minimize total energy consumption in the VM placement with multiple resources, fixed interval time and non-preemption. We observed that minimizing the sum of total busy time of all physical machines implies minimizing total energy consumption of physical machines. In addition to, if mapping of a VM onto physical machines have the same total busy time then the best mapping has physical machine's remaining available resource minimizing. Based on these observations, we proposed heuristic-based EM algorithm to solve the energy-aware VM allocation with fixed starting time and duration time. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or latest finishing time, or the longest duration time first, etc.) to allocate VM. We evaluate the EM using CloudSim toolkit and jobs log-traces in the Feitelson's Parallel Workloads Archive. Simulation's results show that all of EM-ST, EM-LFT and EM-LDTF algorithms could reduce total energy consumption compared to state-of-the-art of power-aware VM allocation algorithms. (e.g. Power-Aware Best-Fit Decreasing (PABFD) [7])).
Distributed Energy Beamforming with One-Bit Feedback
Venue: arXiv cs.IT 2015
Year: 2015

Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they are constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of transmit waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain in practical WET systems. In this paper, we propose a new channel training scheme to achieve optimal EB gain in a distributed WET system, where multiple separated ETs adjust their transmit phases to collaboratively send power to a single ER in an iterative manner, based on one-bit feedback from the ER per training interval which indicates the increase/decrease of the received power level from one particular ET over two preassigned transmit phases. The proposed EB algorithm can be efficiently implemented in practical WET systems even with a large number of distributed ETs, and is analytically shown to converge quickly to the optimal EB design as the number of feedback intervals per ET increases. Numerical results are provided to evaluate the performance of the proposed algorithm as compared to other distributed EB designs.
A Symbolic Logic with Concrete Bounds for Cryptographic Protocols
Venue: arXiv cs.LO 2015
Year: 2015

We present a formal logic for quantitative reasoning about security properties of network protocols. The system allows us to derive concrete security bounds that can be used to choose key lengths and other security parameters. We provide axioms for reasoning about digital signatures and random nonces, with security properties based on the concrete security of signature schemes and pseudorandom number generators (PRG). The formal logic supports first-order reasoning and reasoning about protocol invariants, taking concrete security bounds into account. Proofs constructed in our logic also provide conventional asymptotic security guarantees because of the way that concrete bounds accumulate in proofs. As an illustrative example, we use the formal logic to prove an authentication property with concrete bounds of a signature-based challenge-response protocol.
Deploying Multiple Antennas on High-speed Trains: Equidistant Strategy v.s. Fixed-Interval Strategy
Venue: arXiv cs.IT 2015
Year: 2015

Deploying multiple antennas on high speed trains is an effective way to enhance the information transmission performance for high speed railway (HSR) wireless communication systems. However, how to efficiently deploy multiple antennas on a train? This problem has not been studied yet. In this paper, we shall investigate efficient antenna deployment strategies for HSR communication systems where two multi-antenna deployment strategies, i.e., the equidistant strategy and the fixed-interval strategy, are considered. To evaluate the system performance, mobile service amount and outage time ratio are introduced. Theoretical analysis and numerical results show that, when the length of the train is not very large, for two-antenna case, by increasing the distance of neighboring antennas in a reasonable region, the system performance can be enhanced. It is also shown that the two strategies have much difference performance behavior in terms of instantaneous channel capacity, and the fixed-interval strategy may achieve much better performance than the equidistant one in terms of service amount and outage time ratio when the antenna number is much large.
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Venue: arXiv cs.CV 2015
Year: 2015

We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.
Case Study on Cloud Based Library Software as a Service: Evaluating EZproxy
Venue: arXiv cs.CY 2015
Year: 2015

There is a growing relationship between academic libraries and cloud computing. Therefore, understanding the beginnings and the current use of cloud base services in libraries is important. This will help understand the factors that libraries should consider in the future. The purpose of this paper is to better understand the future implementation of the cloud based software in academic settings. Using cloud based, web based, and other remote services may bring both advantages and disadvantages, some of which this paper will bring out. First, a brief literature review of the academic literature, and a review of available general-purpose cloud-based library products are conducted. Next, a real-life scenario for a mid-sized New Zealand institution of higher education is evaluated. This case involves moving from a locally hosted version of EZproxy to a cloud based version with support from the vendor. As this information system decision is an important one, this paper makes a contribution to the available literature and can be informative for librarians. In conclusion, academic libraries will gradually involve more pervasive use of cloud based systems. The examples of important factors to be considered in future decisions include timing and staffing.
A Python Engine for Teaching Artificial Intelligence in Games
Venue: arXiv cs.CY 2015
Year: 2015

Computer games play an important role in our society and motivate people to learn computer science. Since artificial intelligence is integral to most games, they can also be used to teach artificial intelligence. We introduce the Game AI Game Engine (GAIGE), a Python game engine specifically designed to teach about how AI is used in computer games. A progression of seven assignments builds toward a complete, working Multi-User Battle Arena (MOBA) game. We describe the engine, the assignments, and our experiences using it in a class on Game Artificial Intelligence.
QoE Optimization of Video Multicast with Heterogeneous Channels and Playback Requirements
Venue: arXiv cs.IT 2015
Year: 2015

We propose an application-layer forward error correction (AL-FEC) code rate allocation scheme to maximize the quality of experience (QoE) of a video multicast. The allocation dynamically assigns multicast clients to the quality layers of a scalable video bitstream, based on their heterogeneous channel qualities and video playback capabilities. Normalized mean opinion score (NMOS) is employed to value the client's quality of experience across various possible adaptations of a multilayer video, coded using mixed spatial-temporal-amplitude scalability. The scheme provides assurance of reception of the video layers using fountain coding and effectively allocates coding rates across the layers to maximize a multicast utility measure. An advantageous feature of the proposed scheme is that the complexity of the optimization is independent of the number of clients. Additionally, a convex formulation is proposed that attains close to the best performance and offers a reliable alternative when further reduction in computational complexity is desired. The optimization is extended to perform suppression of QoE fluctuations for clients with marginal channel qualities. The scheme offers a means to trade-off service utility for the entire multicast group and clients with the worst channels. According to the simulation results, the proposed optimization framework is robust against source rate variations and limited amount of client feedback.
Layered Downlink Precoding for C-RAN Systems with Full Dimensional MIMO
Venue: arXiv cs.IT 2015
Year: 2015

The implementation of a Cloud Radio Access Network (C-RAN) with Full Dimensional (FD)-MIMO is faced with the challenge of controlling the fronthaul overhead for the transmission of baseband signals as the number of horizontal and vertical antennas grows larger. This work proposes to leverage the special low-rank structure of FD-MIMO channel, which is characterized by a time-invariant elevation component and a time-varying azimuth component, by means of a layered precoding approach, so as to reduce the fronthaul overhead. According to this scheme, separate precoding matrices are applied for the azimuth and elevation channel components, with different rates of adaptation to the channel variations and correspondingly different impacts on the fronthaul capacity. Moreover, we consider two different Central Unit (CU) - Radio Unit (RU) functional splits at the physical layer, namely the conventional C-RAN implementation and an alternative one in which coding and precoding are performed at the RUs. Via numerical results, it is shown that the layered schemes significantly outperform conventional non-layered schemes, especially in the regime of low fronthaul capacity and large number of vertical antennas.
Exploring Person Context and Local Scene Context for Object Detection
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Venue: arXiv cs.AI 2015
Year: 2015

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through $k$-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.
A tight lower bound for Vertex Planarization on graphs of bounded treewidth
Venue: arXiv cs.DS 2015
Year: 2015

In the Vertex Planarization problem one asks to delete the minimum possible number of vertices from an input graph to obtain a planar graph. The parameterized complexity of this problem, parameterized by the solution size (the number of deleted vertices) has recently attracted significant attention. The state-of-the-art algorithm of Jansen, Lokshtanov, and Saurabh [SODA 2014] runs in time $2^{O(k \log k)} \cdot n$ on $n$-vertex graph with a solution of size $k$. It remains open if one can obtain a single-exponential dependency on $k$ in the running time bound. One of the core technical contributions of the work of Jansen, Lokshtanov, and Saurabh is an algorithm that solves a weighted variant of Vertex Planarization in time $2^{O(w \log w)} \cdot n$ on graphs of treewidth $w$. In this short note we prove that the running time of this routine is tight under the Exponential Time Hypothesis, even in unweighted graphs and when parameterizing by treedepth. Consequently, it is unlikely that a potential single-exponential algorithm for Vertex Planarization parameterized by the solution size can be obtained by merely improving upon the aforementioned bounded treewidth subroutine.