OpenRaiser/Intern-Atlas huggingface dataset:
Constellation Shaping for WDM systems using 256QAM/1024QAM with Probabilistic Optimization
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, probabilistic shaping is numerically and experimentally investigated for increasing the transmission reach of wavelength division multiplexed (WDM) optical communication system employing quadrature amplitude modulation (QAM). An optimized probability mass function (PMF) of the QAM symbols is first found from a modified Blahut-Arimoto algorithm for the optical channel. A turbo coded bit interleaved coded modulation system is then applied, which relies on many-to-one labeling to achieve the desired PMF, thereby achieving shaping gain. Pilot symbols at rate at most 2% are used for synchronization and equalization, making it possible to receive input constellations as large as 1024QAM. The system is evaluated experimentally on a 10 GBaud, 5 channels WDM setup. The maximum system reach is increased w.r.t. standard 1024QAM by 20% at input data rate of 4.65 bits/symbol and up to 75% at 5.46 bits/symbol. It is shown that rate adaptation does not require changing of the modulation format. The performance of the proposed 1024QAM shaped system is validated on all 5 channels of the WDM signal for selected distances and rates. Finally, it was shown via EXIT charts and BER analysis that iterative demapping, while generally beneficial to the system, is not a requirement for achieving the shaping gain.
Clinical Informatics: Recursive Concurrence Intravenous Medication Administration Systems Protocols for Addressing the Potential Problem of Recessive Lethal Autonomy in Smart Infusion Systems
Venue: arXiv cs.CY 2016
Year: 2016

Smart Infusion Pumps are vital tools for use in administering a broad range of parenteral/intravenous medications. Safe and effective use of smart infusion pumps depends upon their integration with Pump Servers, Computerized Physician Order Entry Systems, Pharmacy Information Systems, DERS/MERS Dose/Medication Error Reduction Systems( and the digital Drug Libraries that they use), eMARS/electronic Medication Administration Records Systems, etc. More computer systems result in more computer network connections, with systems that run on ever more lines of computer software code. Resulting in more chaos and more complexity, and inevitably a greater likelihood for systems failure and/or malfunction - this in effect runs contrary to the objective of deploying clinical informatics solutions such as smart infusion systems, due to their inherent technical and chronic performance problems as well as error prone usage that may occasion Recessive(unintended but underlying) Lethal Autonomy. The Recursive Concurrence protocols suggested in this paper are a means for reducing if not eliminating the challenging phenomenon of Recessive Lethal Autonomy in clinical informatics devices and/or systems.
Face Recognition Using Deep Multi-Pose Representations
Venue: arXiv cs.CV 2016
Year: 2016

We introduce our method and system for face recognition using multiple pose-aware deep learning models. In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features. 3D rendering is used to generate multiple face poses from the input image. Sensitivity of the recognition system to pose variations is reduced since we use an ensemble of pose-specific CNN features. The paper presents extensive experimental results on the effect of landmark detection, CNN layer selection and pose model selection on the performance of the recognition pipeline. Our novel representation achieves better results than the state-of-the-art on IARPA's CS2 and NIST's IJB-A in both verification and identification (i.e. search) tasks.
A Comment on the Averseness of Random Serial Dictatorship to Stochastic Dominance Efficiency
Venue: arXiv cs.GT 2016
Year: 2016

Random Serial Dictatorship (RSD) is arguably the most well-known and widely used assignment rule. Although it returns an ex post efficient assignment, Bogomolnaia and Moulin (A new solution to the random assignment problem, J. Econ. Theory 100, 295--328) proved that RSD may not be SD-efficient (efficient with respect stochastic dominance). The result raises the following question: under what conditions is RSD not SD-efficient? In this comment, we give a detailed argument that the RSD assignment is not SD-efficient if and only if an ex post assignment exists that is not SD-efficient. Hence RSD can be viewed as being inherently averse to SD-efficiency. The characterization was proved by Manea (2009).
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training
Venue: arXiv cs.LG 2016
Year: 2016

General purpose computing systems are used for a large variety of applications. Extensive supports for flexibility in these systems limit their energy efficiencies. Neural networks, including deep networks, are widely used for signal processing and pattern recognition applications. In this paper we propose a multicore architecture for deep neural network based processing. Memristor crossbars are utilized to provide low power high throughput execution of neural networks. The system has both training and recognition (evaluation of new input) capabilities. The proposed system could be used for classification, dimensionality reduction, feature extraction, and anomaly detection applications. The system level area and power benefits of the specialized architecture is compared with the NVIDIA Telsa K20 GPGPU. Our experimental evaluations show that the proposed architecture can provide up to five orders of magnitude more energy efficiency over GPGPUs for deep neural network processing.
Approximate Networking for Global Access to the Internet for All (GAIA)
Venue: arXiv cs.NI 2016
Year: 2016

Decades of experience have shown that there is no single one-size-fits-all solution that can be used to provision Internet globally and that invariably there are tradeoffs in the design of Internet. Despite the best efforts of networking researchers and practitioners, an ideal Internet experience is inaccessible to an overwhelming majority of people the world over, mainly due to the lack of cost efficient ways of provisioning high-performance global Internet. In this paper, we argue that instead of an exclusive focus on a utopian goal of universally accessible "ideal networking" (in which we have high throughput and quality of service as well as low latency and congestion), we should consider providing "approximate networking" through the adoption of context-appropriate tradeoffs. Approximate networking can be used to implement a pragmatic tiered global access to the Internet for all (GAIA) system in which different users the world over have different context-appropriate (but still contextually functional) Internet experience.
A Characterization of Cybersecurity Posture from Network Telescope Data
Venue: arXiv cs.CR 2016
Year: 2016

Data-driven understanding of cybersecurity posture is an important problem that has not been adequately explored. In this paper, we analyze some real data collected by CAIDA's network telescope during the month of March 2013. We propose to formalize the concept of cybersecurity posture from the perspectives of three kinds of time series: the number of victims (i.e., telescope IP addresses that are attacked), the number of attackers that are observed by the telescope, and the number of attacks that are observed by the telescope. Characterizing cybersecurity posture therefore becomes investigating the phenomena and statistical properties exhibited by these time series, and explaining their cybersecurity meanings. For example, we propose the concept of {\em sweep-time}, and show that sweep-time should be modeled by stochastic process, rather than random variable. We report that the number of attackers (and attacks) from a certain country dominates the total number of attackers (and attacks) that are observed by the telescope. We also show that substantially smaller network telescopes might not be as useful as a large telescope.
Deep Extreme Feature Extraction: New MVA Method for Searching Particles in High Energy Physics
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we present Deep Extreme Feature Extraction (DEFE), a new ensemble MVA method for searching $τ^{+}τ^{-}$ channel of Higgs bosons in high energy physics. DEFE can be viewed as a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations. This is achieved by adopting an implicit neural controller (not involved in feedforward compuation) that directly controls and distributes gradient flows from higher level deep prediction network. Such model-independent controller results in that every single local feature learned are used in the feature-to-output mapping stage, avoiding the blind averaging of features. DEFE makes the ensembles 'deep' in the sense that it allows deep post-process of these features that tries to learn to select and abstract the ensemble of neural feature learners. With the application of this model, a selection regions full of signal process can be obtained through the training of a miniature collision events set. In comparison of the Classic Deep Neural Network, DEFE shows a state-of-the-art performance: the error rate has decreased by about 37\%, the accuracy has broken through 90\% for the first time, along with the discovery significance has reached a standard deviation of 6.0 $σ$. Experimental data shows that, DEFE is able to train an ensemble of discriminative feature learners that boosts the overperformance of final prediction.
The method "Model Elimination" of D.W.Loveland explained
Venue: arXiv cs.LO 2016
Year: 2016

We present concisely the method "Model Elimination" of D.W.Loveland. Especially, we explain and prove the correctness of the lemmas generated by this method.
Semantics and Analysis of DMN Decision Tables
Venue: arXiv cs.SE 2016
Year: 2016

The Decision Model and Notation (DMN) is a standard notation to capture decision logic in business applications in general and business processes in particular. A central construct in DMN is that of a decision table. The increasing use of DMN decision tables to capture critical business knowledge raises the need to support analysis tasks on these tables such as correctness and completeness checking. This paper provides a formal semantics for DMN tables, a formal definition of key analysis tasks and scalable algorithms to tackle two such tasks, i.e., detection of overlapping rules and of missing rules. The algorithms are based on a geometric interpretation of decision tables that can be used to support other analysis tasks by tapping into geometric algorithms. The algorithms have been implemented in an open-source DMN editor and tested on large decision tables derived from a credit lending dataset.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.
Position-aided Large-scale MIMO Channel Estimation for High-Speed Railway Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

We consider channel estimation for high-speed railway communication systems, where both the transmitter and the receiver are equipped with large-scale antenna arrays. It is known that the throughput of conventional training schemes monotonically decreases with the mobility. Assuming that the moving terminal employs a large linear antenna array, this paper proposes a position-aided channel estimation scheme whereby only a portion of the transmit antennas send pilot symbols and the full channel matrix can be well estimated by using these pilots together with the antenna position information based on the joint spatial-temporal correlation. The relationship between mobility and throughput/DoF is established. Furthermore, the optimal selections of transmit power and time interval partition between the training and data phases as well as the antenna size are presented accordingly. Both analytical and simulation results show that the system throughput with the position-aided channel estimator does not deteriorate appreciably as the mobility increases, which is sharply in contrast with the conventional one.
On Communication through a Gaussian Channel with an MMSE Disturbance Constraint
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers a Gaussian channel with one transmitter and two receivers. The goal is to maximize the communication rate at the intended/primary receiver subject to a disturbance constraint at the unintended/secondary receiver. The disturbance is measured in terms of minimum mean square error (MMSE) of the interference that the transmission to the primary receiver inflicts on the secondary receiver. The paper presents a new upper bound for the problem of maximizing the mutual information subject to an MMSE constraint. The new bound holds for vector inputs of any length and recovers a previously known limiting (when the length of vector input tends to infinity) expression from the work of Bustin $\textit{et al.}$ The key technical novelty is a new upper bound on the MMSE. This bound allows one to bound the MMSE for all signal-to-noise ratio (SNR) values $\textit{below}$ a certain SNR at which the MMSE is known (which corresponds to the disturbance constraint). This bound complements the `single-crossing point property' of the MMSE that upper bounds the MMSE for all SNR values $\textit{above}$ a certain value at which the MMSE value is known. The MMSE upper bound provides a refined characterization of the phase-transition phenomenon which manifests, in the limit as the length of the vector input goes to infinity, as a discontinuity of the MMSE for the problem at hand. For vector inputs of size $n=1$, a matching lower bound, to within an additive gap of order $O \left( \log \log \frac{1}{\sf MMSE} \right)$ (where ${\sf MMSE}$ is the disturbance constraint), is shown by means of the mixed inputs technique recently introduced by Dytso $\textit{et al.}$
Inferring Uncertain Trajectories from Partial Observations
Venue: arXiv cs.DB 2016
Year: 2016

The explosion in the availability of GPS-enabled devices has resulted in an abundance of trajectory data. In reality, however, majority of these trajectories are collected at a low sampling rate and only provide partial observations on their actually traversed routes. Consequently, they are mired with uncertainty. In this paper, we develop a technique called InferTra to infer uncertain trajectories from network-constrained partial observations. Rather than predicting the most likely route, the inferred uncertain trajectory takes the form of an edge-weighted graph and summarizes all probable routes in a holistic manner. For trajectory inference, InferTra employs Gibbs sampling by learning a Network Mobility Model (NMM) from a database of historical trajectories. Extensive experiments on real trajectory databases show that the graph-based approach of InferTra is up to 50% more accurate, 20 times faster, and immensely more versatile than state-of-the-art techniques.
Descending Price Optimally Coordinates Search
Venue: arXiv cs.GT 2016
Year: 2016

Investigating potential purchases is often a substantial investment under uncertainty. Standard market designs, such as simultaneous or English auctions, compound this with uncertainty about the price a bidder will have to pay in order to win. As a result they tend to confuse the process of search both by leading to wasteful information acquisition on goods that have already found a good purchaser and by discouraging needed investigations of objects, potentially eliminating all gains from trade. In contrast, we show that the Dutch auction preserves all of its properties from a standard setting without information costs because it guarantees, at the time of information acquisition, a price at which the good can be purchased. Calibrations to start-up acquisition and timber auctions suggest that in practice the social losses through poor search coordination in standard formats are an order of magnitude or two larger than the (negligible) inefficiencies arising from ex-ante bidder asymmetries.
Predictive Analytics Using Smartphone Sensors for Depressive Episodes
Venue: arXiv cs.CY 2016
Year: 2016

The behaviors of patients with depression are usually difficult to predict because the patients demonstrate the symptoms of a depressive episode without a warning at unexpected times. The goal of this research is to build algorithms that detect signals of such unusual moments so that doctors can be proactive in approaching already diagnosed patients before they fall in depression. Each patient is equipped with a smartphone with the capability to track its sensors. We first find the home location of a patient, which is then augmented with other sensor data to identify sleep patterns and select communication patterns. The algorithms require two to three weeks of training data to build standard patterns, which are considered normal behaviors; and then, the methods identify any anomalies in day-to-day data readings of sensors. Four smartphone sensors, including the accelerometer, the gyroscope, the location probe and the communication log probe are used for anomaly detection in sleeping and communication patterns.
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we aim at learning simultaneously a discriminative dictionary and a robust projection matrix from noisy data. The joint learning, makes the learned projection and dictionary a better fit for each other, so a more accurate classification can be obtained. However, current prevailing joint dimensionality reduction and dictionary learning methods, would fail when the training samples are noisy or heavily corrupted. To address this issue, we propose a joint projection and dictionary learning using low-rank regularization and graph constraints (JPDL-LR). Specifically, the discrimination of the dictionary is achieved by imposing Fisher criterion on the coding coefficients. In addition, our method explicitly encodes the local structure of data by incorporating a graph regularization term, that further improves the discriminative ability of the projection matrix. Inspired by recent advances of low-rank representation for removing outliers and noise, we enforce a low-rank constraint on sub-dictionaries of all classes to make them more compact and robust to noise. Experimental results on several benchmark datasets verify the effectiveness and robustness of our method for both dimensionality reduction and image classification, especially when the data contains considerable noise or variations.
Secure cloud computations: Description of (fully)homomorphic ciphers within the P-adic model of encryption
Venue: arXiv cs.CR 2016
Year: 2016

In this paper we consider the description of homomorphic and fully homomorphic ciphers in the $p$-adic model of encryption. This model describes a wide class of ciphers, but certainly not all. Homomorphic and fully homomorphic ciphers are used to ensure the credibility of remote computing, including cloud technology. The model describes all homomorphic ciphers with respect to arithmetic and coordinate-wise logical operations in the ring of $p$-adic integers $Z_p$. We show that there are no fully homomorphic ciphers for each pair of the considered set of arithmetic and coordinate-wise logical operations on $Z_p$. We formulate the problem of constructing a fully homomorphic cipher as follows. We consider a homomorphic cipher with respect to operation "$*$" on $Z_p$. Then, we describe the complete set of operations "$G$", for which the cipher is homomorphic. As a result, we construct a fully homomorphic cipher with respect to the operations "$*$" and "$G$". We give a description of all operations "$G$", for which we obtain fully homomorphic ciphers with respect to the operations "$+$" and "$G$" from the homomorphic cipher constructed with respect to the operation "$+$". We also present examples of such "new" operations.
Analyzing the Targets of Hate in Online Social Media
Venue: arXiv cs.SI 2016
Year: 2016

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.
Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video
Venue: arXiv cs.CV 2016
Year: 2016

Understanding the camera wearer's activity is central to egocentric vision, yet one key facet of that activity is inherently invisible to the camera--the wearer's body pose. Prior work focuses on estimating the pose of hands and arms when they come into view, but this 1) gives an incomplete view of the full body posture, and 2) prevents any pose estimate at all in many frames, since the hands are only visible in a fraction of daily life activities. We propose to infer the "invisible pose" of a person behind the egocentric camera. Given a single video, our efficient learning-based approach returns the full body 3D joint positions for each frame. Our method exploits cues from the dynamic motion signatures of the surrounding scene--which changes predictably as a function of body pose--as well as static scene structures that reveal the viewpoint (e.g., sitting vs. standing). We further introduce a novel energy minimization scheme to infer the pose sequence. It uses soft predictions of the poses per time instant together with a non-parametric model of human pose dynamics over longer windows. Our method outperforms an array of possible alternatives, including deep learning approaches for direct pose regression from images.