OpenRaiser/Intern-Atlas huggingface dataset:
A deep representation for depth images from synthetic data
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach.
Making Public Safety Data Accessible in the Westside Atlanta Data Dashboard
Venue: arXiv cs.CY 2016
Year: 2016

Individual neighborhoods within large cities can benefit from independent analysis of public data in the context of ongoing efforts to improve the community. Yet existing tools for public data analysis and visualization are often mismatched to community needs, for reasons including geographic granularity that does not correspond to community boundaries, siloed data sets, inaccurate assumptions about data literacy, and limited user input in design and implementation phases. In Atlanta this need is being addressed through a Data Dashboard developed under the auspices of the Westside Communities Alliance (WCA), a partnership between Georgia Tech and community stakeholders. In this paper we present an interactive analytic and visualization tool for public safety data within the WCA Data Dashboard. We describe a human-centered approach to understand the needs of users and to build accessible mapping tools for visualization and analysis. The tools include a variety of overlays that allow users to spatially correlate features of the built environment, such as vacant properties with criminal activity as well as crime prevention efforts. We are in the final stages of developing the first version of the tool, with plans for a public release in fall of 2016.
Detecting Cognitive Appraisals from Facial Expressions for Interest Recognition
Venue: arXiv cs.HC 2016
Year: 2016

Interest makes one hold her attention on the object of interest. Automatic recognition of interest has numerous applications in human-computer interaction. In this paper, we study the facial expressions associated with interest and its underlying and closely related components, namely, curiosity, coping potential, novelty and complexity. To this end, we conducted an experiment in which participants watched images and micro-videos while a front-facing camera recorded their expressions. After watching each item they self-reported their level of interest, curiosity, coping potential and perceived novelty and complexity. Using an automated method, we tracked facial action units (AU) and studied the relationship between the presence of facial movements with interest and its related components. We then tracked the facial landmarks, e.g., corners of lips, and extracted features from each response. We trained random forests regression models to detect the level of interest, curiosity, and appraisals. We found a large difference between the way people report and react to interesting visual content. The expressions in response to images and micro-videos were not always pronounced depending on the participants. This makes the direct detection of interest from facial expressions a challenging problem. With this work, for the first time, we demonstrate the feasibility of detecting cognitive appraisals from facial expressions which will open the door for appraisal-driven emotion recognition methods.
An Overview of Sustainable Green 5G Networks
Venue: arXiv cs.IT 2016
Year: 2016

The stringent requirements of a 1,000 times increase in data traffic and one millisecond round trip latency have made limiting the potentially tremendous ensuing energy consumption one of the most challenging problems for the design of the upcoming fifth-generation (5G) networks. To enable sustainable 5G networks, new technologies have been proposed to improve the system energy efficiency and alternative energy sources are introduced to reduce our dependence on traditional fossil fuels. In particular, various 5G techniques target the reduction of the energy consumption without sacrificing the quality-of-service. Meanwhile, energy harvesting technologies, which enable communication transceivers to harvest energy from various renewable resources and ambient radio frequency signals for communi- cation, have drawn significant interest from both academia and industry. In this article, we provide an overview of the latest research on both green 5G techniques and energy harvesting for communication. In addition, some technical challenges and potential research topics for realizing sustainable green 5G networks are also identified.
Regular and almost universal hashing: an efficient implementation
Venue: arXiv cs.DS 2016
Year: 2016

Random hashing can provide guarantees regarding the performance of data structures such as hash tables---even in an adversarial setting. Many existing families of hash functions are universal: given two data objects, the probability that they have the same hash value is low given that we pick hash functions at random. However, universality fails to ensure that all hash functions are well behaved. We further require regularity: when picking data objects at random they should have a low probability of having the same hash value, for any fixed hash function. We present the efficient implementation of a family of non-cryptographic hash functions (PM+) offering good running times, good memory usage as well as distinguishing theoretical guarantees: almost universality and component-wise regularity. On a variety of platforms, our implementations are comparable to the state of the art in performance. On recent Intel processors, PM+ achieves a speed of 4.7 bytes per cycle for 32-bit outputs and 3.3 bytes per cycle for 64-bit outputs. We review vectorization through SIMD instructions (e.g., AVX2) and optimizations for superscalar execution.
Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection
Venue: arXiv cs.AI 2016
Year: 2016

Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy. Although a number of structured sparsity models have been explored, such as trees, groups, clusters, and paths, connected subgraphs have been rarely explored in the current literature. One of the main technical challenges is that there is no structured sparsity-inducing norm that can directly model the space of connected subgraphs, and there is no exact implementation of a projection oracle for connected subgraphs due to its NP-hardness. In this paper, we explore efficient approximate projection oracles for connected subgraphs, and propose two new efficient algorithms, namely, Graph-IHT and Graph-GHTP, to optimize a generic nonlinear objective function subject to connectivity constraint on the support of the variables. Our proposed algorithms enjoy strong guarantees analogous to several current methods for sparsity-constrained optimization, such as Projected Gradient Descent (PGD), Approximate Model Iterative Hard Thresholding (AM-IHT), and Gradient Hard Thresholding Pursuit (GHTP) with respect to convergence rate and approximation accuracy. We apply our proposed algorithms to optimize several well-known graph scan statistics in several applications of connected subgraph detection as a case study, and the experimental results demonstrate that our proposed algorithms outperform state-of-the-art methods.
Discriminating Similar Languages: Evaluations and Explorations
Venue: arXiv cs.CL 2016
Year: 2016

We present an analysis of the performance of machine learning classifiers on discriminating between similar languages and language varieties. We carried out a number of experiments using the results of the two editions of the Discriminating between Similar Languages (DSL) shared task. We investigate the progress made between the two tasks, estimate an upper bound on possible performance using ensemble and oracle combination, and provide learning curves to help us understand which languages are more challenging. A number of difficult sentences are identified and investigated further with human annotation.
Low Complexity Channel Estimation for Millimeter Wave Systems with Hybrid A/D Antenna Processing
Venue: arXiv cs.IT 2016
Year: 2016

The availability of large bandwidth at millimeter wave (mmWave) frequencies is one of the major factors that rendered very high frequencies a promising candidate enabler for fifth generation (5G) mobile communication networks. To confront with the intrinsic characteristics of signal propagation at frequencies of tens of GHz and being able to achieve data rates of the order of gigabits per second, mmWave systems are expected to employ large antenna arrays that implement highly directional beamforming. In this paper, we consider mmWave wireless systems comprising of nodes equipped with large antenna arrays and being capable of performing hybrid analog and digital (A/D) processing. Intending at realizing channel-aware transmit and receive beamforming, we focus on designing low complexity compressed sensing channel estimation. In particular, by adopting a temporally correlated mmWave channel model, we present two compressed sensing algorithms that exploit the temporal correlation to reduce the complexity of sparse channel estimation, one being greedy and the other one being iterative. Our representative performance evaluation results offer useful insights on the interplay among some system and operation parameters, and the accuracy of channel estimation.
Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I and II) requirements and operate in difficult signal-jamming environments, with intensive signal-like jamming and a variety of changing possible signal portraits of possible recognized events. To address these issues, we have developed a twolevel event detection architecture, where the primary classifier is based on an ensemble of deep convolutional networks, can recognize 7 classes of signals and receives time-space data frames as input. Using real-life data, we have shown that the applied methods result in efficient and robust multiclass detection algorithms that have a high degree of adaptability.
Accelerating Deep Convolutional Networks using low-precision and sparsity
Venue: arXiv cs.LG 2016
Year: 2016

We explore techniques to significantly improve the compute efficiency and performance of Deep Convolution Networks without impacting their accuracy. To improve the compute efficiency, we focus on achieving high accuracy with extremely low-precision (2-bit) weight networks, and to accelerate the execution time, we aggressively skip operations on zero-values. We achieve the highest reported accuracy of 76.6% Top-1/93% Top-5 on the Imagenet object classification challenge with low-precision network\footnote{github release of the source code coming soon} while reducing the compute requirement by ~3x compared to a full-precision network that achieves similar accuracy. Furthermore, to fully exploit the benefits of our low-precision networks, we build a deep learning accelerator core, dLAC, that can achieve up to 1 TFLOP/mm^2 equivalent for single-precision floating-point operations (~2 TFLOP/mm^2 for half-precision).
Traffic Predictive Control from Low-Rank Structure
Venue: arXiv cs.SY 2016
Year: 2016

The operation of most signalized intersections is governed by predefined timing plans that are applied during specified times of the day. These plans are designed to accommodate average conditions and are unable to respond to large deviations in traffic flow. We propose a control approach that adjusts time-of-day signaling plans based on a prediction of future traffic flow. The prediction algorithm identifies correlated, low rank structure in historical measurement data and predicts future traffic flow from real-time measurements by determining which structural trends are prominent in the measurements. From this prediction, the controller then determines the optimal time of day to apply new timing plans. We demonstrate the potential benefits of this approach using eight months of high resolution data collected at an intersection in Beaufort, South Carolina.
Comparing 1D and 2D Real Time on Cellular Automata
Venue: arXiv cs.FL 2016
Year: 2016

We study the influence of the dimension of cellular automata (CA) for real time language recognition of one-dimensional languages with parallel input. Specifically, we focus on the question of determining whether every language that can be recognized in real time on a 2-dimensional CA working on the Moore neighborhood can also be recognized in real time by a 1-dimensional CA working on the standard two-way neighborhood. We show that 2-dimensional CA in real time can perform a linear number of simulations of a 1-dimensional real time CA. If the two classes are equal then the number of simulated instances can be polynomial.
5-State Rotation-Symmetric Number-Conserving Cellular Automata are not Strongly Universal
Venue: arXiv cs.FL 2016
Year: 2016

We study two-dimensional rotation-symmetric number-conserving cellular automata working on the von Neumann neighborhood (RNCA). It is known that such automata with 4 states or less are trivial, so we investigate the possible rules with 5 states. We give a full characterization of these automata and show that they cannot be strongly Turing universal. However, we give example of constructions that allow to embed some boolean circuit elements in a 5-states RNCA.
Security and Privacy Analysis of NSF Future Internet Architectures
Venue: arXiv cs.NI 2016
Year: 2016

The Internet Protocol (IP) is the lifeblood of the modern Internet. Its simplicity and universality have fueled the unprecedented and lasting global success of the current Internet. Nonetheless, some limitations of IP have been emerging in recent years. Its original design envisaged supporting perhaps tens of thousands of static hosts operating in a friendly academic-like setting, mainly in order to facilitate email communication and remote access to scarce computing resources. At present IP interconnects billions of static and mobile devices (ranging from supercomputers to IoT gadgets) with a large and dynamic set of popular applications. Starting in mid-1990s, the advent of mobility, wirelessness and the web substantially shifted Internet usage and communication paradigms. This accentuated long-term concerns about the current Internet architecture and prompted interest in alternative designs. The U.S. National Science Foundation (NSF) has been one of the key supporters of efforts to design a set of candidate next-generation Internet architectures. As a prominent design requirement, NSF emphasized "security and privacy by design" in order to avoid the long and unhappy history of incremental patching and retrofitting that characterizes the current Internet architecture. To this end, as a result of a competitive process, four prominent research projects were funded by the NSF in 2010: Nebula, Named-Data Networking (NDN), MobilityFirst (MF), and Expressive Internet Architecture (XIA). This paper provides a comprehensive and neutral analysis of salient security and privacy features (and issues) in these NSF-funded Future Internet Architectures. It also compares the four candidate designs with the current IP-based architecture and discusses similarities, differences, and possible improvements.
Reconstruction of weakly simple polygons from their edges
Venue: arXiv cs.CG 2016
Year: 2016

Given $n$ line segments in the plane, do they form the edge set of a \emph{weakly simple polygon}; that is, can the segment endpoints be perturbed by at most $\varepsilon$, for any $\varepsilon>0$, to obtain a simple polygon? While the analogous question for \emph{simple polygons} can easily be answered in $O(n\log n)$ time, we show that it is NP-complete for weakly simple polygons. We give $O(n)$-time algorithms in two special cases: when all segments are collinear, or the segment endpoints are in general position. These results extend to the variant in which the segments are \emph{directed}, and the counterclockwise traversal of a polygon should follow the orientation. We study related problems for the case that the union of the $n$ input segments is connected. (i) If each segment can be subdivided into several segments, find the minimum number of subdivision points to form a weakly simple polygon. (ii) If new line segments can be added, find the minimum total length of new segments that creates a weakly simple polygon. We give worst-case upper and lower bounds for both problems.
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value
Venue: arXiv cs.AI 2016
Year: 2016

A number of attempts have been made to improve accuracy and/or scalability of the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010; Kalisch and Buhlmann, 2007; 2008; Zhang, 2012, to give some examples). We add here one more tool to the toolbox: the simple observation that if one is forced to choose between a variety of possible conditioning sets for a pair of variables, one should choose the one with the highest p-value. One can use the CPC (Conservative PC, Ramsey et al., 2012) algorithm as a guide to possible sepsets for a pair of variables. However, whereas CPC uses a voting rule to classify colliders versus noncolliders, our proposed algorithm, PC-Max, picks the conditioning set with the highest p-value, so that there are no ambiguities. We combine this with two other optimizations: (a) avoiding bidirected edges in the orientation of colliders, and (b) parallelization. For (b) we borrow ideas from the PC-Stable algorithm (Colombo and Maathuis, 2014). The result is an algorithm that scales quite well both in terms of accuracy and time, with no risk of bidirected edges.
Seeing into Darkness: Scotopic Visual Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Images are formed by counting how many photons traveling from a given set of directions hit an image sensor during a given time interval. When photons are few and far in between, the concept of `image' breaks down and it is best to consider directly the flow of photons. Computer vision in this regime, which we call `scotopic', is radically different from the classical image-based paradigm in that visual computations (classification, control, search) have to take place while the stream of photons is captured and decisions may be taken as soon as enough information is available. The scotopic regime is important for biomedical imaging, security, astronomy and many other fields. Here we develop a framework that allows a machine to classify objects with as few photons as possible, while maintaining the error rate below an acceptable threshold. A dynamic and asymptotically optimal speed-accuracy tradeoff is a key feature of this framework. We propose and study an algorithm to optimize the tradeoff of a convolutional network directly from lowlight images and evaluate on simulated images from standard datasets. Surprisingly, scotopic systems can achieve comparable classification performance as traditional vision systems while using less than 0.1% of the photons in a conventional image. In addition, we demonstrate that our algorithms work even when the illuminance of the environment is unknown and varying. Last, we outline a spiking neural network coupled with photon-counting sensors as a power-efficient hardware realization of scotopic algorithms.
Energy Efficient Restoring of Barrier Coverage in Wireless Sensor Networks Using Limited Mobility Sensors
Venue: arXiv cs.DC 2016
Year: 2016

In Wireless Sensor Networks, sensors are used for tracking objects, monitoring health and observing a region/territory for different environmental parameters. Coverage problem in sensor network ensures quality of monitoring a given region. Depending on applications different measures of coverage are there. Barrier coverage is a type of coverage, which ensures all paths that cross the boundary of a region intersect at least one sensor's sensing region. The goal of the sensors is to detect intruders as they cross the boundary or as they penetrate a protected area. The sensors are dependent on their battery life. Restoring barrier coverage on sensor failure using mobile sensors with minimum total displacement is the primary objective of this paper. A centralized barrier coverage restoring scheme is proposed to increase the robustness of the network. We formulate restoring barrier coverage as bipartite matching problem. A distributed restoring of barrier coverage algorithm is also proposed, which restores it by first finding existing alternate barrier. If alternate barrier is not found, an alternate barrier is reconstructed by shifting existing sensors in a cascaded manner. Detailed simulation results are shown to evaluate the performance of our algorithms.
Quantified-Self 2.0: Using Context-Aware Services for Promoting Gradual Behaviour Change
Venue: arXiv cs.CY 2016
Year: 2016

The recent development of smartphone and wearable sensor technologies enable general public to carry self-tracking tasks more easily. Much work has been devoted to life data collection and visualisation to help people with better self-understanding. We believe that although (awareness/knowledge discovery is an important aspect of personal informatics, knowledge maintenance is more, or at least equally, important. In this paper, we propose a proactive approach that uses the knowledge mined from people's activity data to nudge them towards a good lifestyle (better knowledge maintenance). For demonstration purpose, a trial study was designed and implemented for good sleep maintenance. In the study, we first use smartphones as activity trackers to collect various features in a non-intrusive manner. We then use those data to learn users' activity patterns, including daily step amount, app usages, bedding time, wake-up time and sleep duration. Subsequently, we analyse correlations that may have the positive or negative impact on users' sleep qualities and finally we designed and implemented three proactive services that are able to generate customised advice in the "right" context to nudge users towards a better lifestyle. The experiments results are positive showing that with the use of the proposed services 1. daily step amount have been increased by 3.03% on average in a 10 days study and 2. sleep durations are increased by 7% for two subjects.