OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Control and Quality-of-Service in Multihop Wireless Networks
Venue: arXiv cs.NI 2017
Year: 2017

Control of wireless multihop networks, while simultaneously meeting end-to-end mean delay requirements of different flows is a challenging problem. Additionally, distributed computation of control parameters adds to the complexity. Using the notion of discrete review used in fluid control of networks, a distributed algorithm is proposed for control of multihop wireless networks with interference constraints. The algorithm meets end-to-end mean delay requirements by solving an optimization problem at review instants. The optimization incorporates delay requirements as weights in the function being maximized. The weights are dynamic and vary depending on queue length information. The optimization is done in a distributed manner using an incremental gradient ascent algorithm. The stability of the network under the proposed policy is analytically studied and the policy is shown to be throughput optimal.
Refounding legitimacy towards Aethogenesis
Venue: arXiv cs.CY 2017
Year: 2017

The fusion of humans and technology takes us into an unknown world described by some authors as populated by quasi living species that would relegate us - ordinary humans - to the rank of alienated agents emptied of our identity and consciousness. I argue instead that our world is woven of simple though invisible perspectives which - if we become aware of them - may renew our ability for making judgments and enhance our autonomy. I became aware of these invisible perspectives by observing and practicing a real time collective net art experiment called the Poietic Generator. As the perspectives unveiled by this experiment are invisible I have called them anoptical perspectives i.e. non-optical by analogy with the optical perspective of the Renaissance. Later I have come to realize that these perspectives obtain their cognitive structure from the political origins of our language. Accordingly it is possible to define certain cognitive criteria for assessing the legitimacy of the anoptical perspectives just like some artists and architects of the Renaissance defined the geometrical criteria that established the legitimacy of the optical one.
Artificial Delay Based ARC of a Class of Uncertain EL Systems with Only Position Feedback
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, the tracking control problem of an Euler-Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control strategy, christened Time-Delayed Adaptive Robust Control (TARC), is presented. TARC approximates the unknown dynamics through the time-delayed estimation, and the adaptive-robust control provides robustness against the approximation error. The novel adaptation law of TARC, in contrast to the conventional adaptive-robust control methodologies, requires neither complete model of the system nor any knowledge of predefined uncertainty bounds to compute the switching gain, and circumvents the over- and underestimation problems of the switching gain. Moreover, TARC only utilizes position feedback and approximates the velocity and acceleration terms from the past position data. The adopted state-derivatives estimation method in TARC avoids any explicit requirement of external low pass filters for the removal of measurement noise. A new stability notion in continuous-time domain is proposed considering the time delay, adaptive law, and state-derivatives estimation which in turn provides a selection criterion for gains and sampling interval of the controller.
Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things
Venue: arXiv cs.IT 2017
Year: 2017

Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.
Accelerating Training of Deep Neural Networks via Sparse Edge Processing
Venue: arXiv cs.NE 2017
Year: 2017

We propose a reconfigurable hardware architecture for deep neural networks (DNNs) capable of online training and inference, which uses algorithmically pre-determined, structured sparsity to significantly lower memory and computational requirements. This novel architecture introduces the notion of edge-processing to provide flexibility and combines junction pipelining and operational parallelization to speed up training. The overall effect is to reduce network complexity by factors up to 30x and training time by up to 35x relative to GPUs, while maintaining high fidelity of inference results. This has the potential to enable extensive parameter searches and development of the largely unexplored theoretical foundation of DNNs. The architecture automatically adapts itself to different network sizes given available hardware resources. As proof of concept, we show results obtained for different bit widths.
Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark localization step within this bounded region. Both steps are based on a 3D extension of the recently introduced ENet. We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
Venue: arXiv cs.IR 2017
Year: 2017

Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns similar to traditional sponsored search ads. Click-Through Rate (CTR) prediction is an integral part of online search advertising systems where it is utilized as an input to auctions which determine the final ranking of promoted listings to a particular user for each query. In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings. We obtain representations from texts and images by utilizing state-of-the-art deep learning techniques and employ multimodal learning to combine these different signals. We compare the system to non-trivial baselines on a large-scale real world dataset from Etsy, demonstrating the effectiveness of the model and strong correlations between offline experiments and online performance. The paper is also the first technical overview to this kind of product in e-commerce context.
Predicting Discharge Medications at Admission Time Based on Deep Learning
Venue: arXiv cs.CL 2017
Year: 2017

Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay. It also facilitates medication reconciliation process with easy detection of medication discrepancy at discharge time to improve patient safety. However, since the information available upon admission is limited and patients' condition may evolve during an inpatient stay, these predictions could be a difficult decision for physicians to make. In this work, we investigate how to leverage deep learning technologies to assist physicians in predicting discharge medications based on information documented in the admission note. We build a convolutional neural network which takes an admission note as input and predicts the medications placed on the patient at discharge time. Our method is able to distill semantic patterns from unstructured and noisy texts, and is capable of capturing the pharmacological correlations among medications. We evaluate our method on 25K patient visits and compare with 4 strong baselines. Our methods demonstrate a 20% increase in macro-averaged F1 score than the best baseline.
Timing Aware Dummy Metal Fill Methodology
Venue: arXiv cs.AR 2017
Year: 2017

In this paper, we analyzed parasitic coupling capacitance coming from dummy metal fill and its impact on timing. Based on the modeling, we proposed two approaches to minimize the timing impact from dummy metal fill. The first approach applies more spacing between critical nets and metal fill, while the second approach leverages the shielding effects of reference nets. Experimental results show consistent improvement compared to traditional metal fill method.
DDD17: End-To-End DAVIS Driving Dataset
Venue: arXiv cs.CV 2017
Year: 2017

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
Venue: arXiv cs.CV 2017
Year: 2017

Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision. Classical Structure from Motion (SfM) methods have attempted to solve this problem through projected point displacement \& bundle adjustment. More recently, deep methods have attempted to solve this problem by directly learning a relationship between geometry and appearance. There is, however, a significant gap between these two strategies. SfM tackles the problem from purely a geometric perspective, taking no account of the object shape prior. Modern deep methods more often throw away geometric constraints altogether, rendering the results unreliable. In this paper we make an effort to bring these two seemingly disparate strategies together. We introduce learned shape prior in the form of deep shape generators into Photometric Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by the shape prior within the optimization-based inference framework, demonstrating impressive results.
Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.
Diffusion Operator and Spectral Analysis for Directed Hypergraph Laplacian
Venue: arXiv cs.DM 2017
Year: 2017

In spectral graph theory, the Cheeger's inequality gives upper and lower bounds of edge expansion in normal graphs in terms of the second eigenvalue of the graph's Laplacian operator. Recently this inequality has been extended to undirected hypergraphs and directed normal graphs via a non-linear operator associated with a diffusion process in the underlying graph. In this work, we develop a unifying framework for defining a diffusion operator on a directed hypergraph with stationary vertices, which is general enough for the following two applications. 1. Cheeger's inequality for directed hyperedge expansion. 2. Quadratic optimization with stationary vertices in the context of semi-supervised learning. Despite the crucial role of the diffusion process in spectral analysis, previous works have not formally established the existence of the corresponding diffusion processes. In this work, we give a proof framework that can indeed show that such diffusion processes are well-defined. In the first application, we use the spectral properties of the diffusion operator to achieve the Cheeger's inequality for directed hyperedge expansion. In the second application, the diffusion operator can be interpreted as giving a continuous analog to the subgradient method, which moves the feasible solution in discrete steps towards an optimal solution.
Multi-label Dataless Text Classification with Topic Modeling
Venue: arXiv cs.IR 2017
Year: 2017

Manually labeling documents is tedious and expensive, but it is essential for training a traditional text classifier. In recent years, a few dataless text classification techniques have been proposed to address this problem. However, existing works mainly center on single-label classification problems, that is, each document is restricted to belonging to a single category. In this paper, we propose a novel Seed-guided Multi-label Topic Model, named SMTM. With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document. In SMTM, each category is associated with a single category-topic which covers the meaning of the category. To accommodate with multi-labeled documents, we explicitly model the category sparsity in SMTM by using spike and slab prior and weak smoothing prior. That is, without using any threshold tuning, SMTM automatically selects the relevant categories for each document. To incorporate the supervision of the seed words, we propose a seed-guided biased GPU (i.e., generalized Polya urn) sampling procedure to guide the topic inference of SMTM. Experiments on two public datasets show that SMTM achieves better classification accuracy than state-of-the-art alternatives and even outperforms supervised solutions in some scenarios.
Double Q($σ$) and Q($σ, λ$): Unifying Reinforcement Learning Control Algorithms
Venue: arXiv cs.AI 2017
Year: 2017

Temporal-difference (TD) learning is an important field in reinforcement learning. Sarsa and Q-Learning are among the most used TD algorithms. The Q($σ$) algorithm (Sutton and Barto (2017)) unifies both. This paper extends the Q($σ$) algorithm to an online multi-step algorithm Q($σ, λ$) using eligibility traces and introduces Double Q($σ$) as the extension of Q($σ$) to double learning. Experiments suggest that the new Q($σ, λ$) algorithm can outperform the classical TD control methods Sarsa($λ$), Q($λ$) and Q($σ$).
Scheduling Wireless Ad Hoc Networks in Polynomial Time Using Claw-free Conflict Graphs
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we address the scheduling problem in wireless ad hoc networks by exploiting the computational advantage that comes when such scheduling problems can be represented by claw-free conflict graphs. It is possible to formulate a scheduling problem of network coded flows as finding maximum weighted independent set (MWIS) in the conflict graph of the network. We consider activation of hyperedges in a hypergraph to model a wireless broadcast medium. We show that the conflict graph of certain wireless ad hoc networks are claw-free. It is known that finding MWIS of a general graph is NP-hard, but in a claw-free conflict graph, it is possible to apply Minty's or Faenza et al.'s algorithms in polynomial time. We discuss our approach on some sample networks.
Improving Power Flow Robustness via Circuit Simulation Methods
Venue: arXiv cs.SY 2017
Year: 2017

Recent advances in power system simulation have included the use of complex rectangular current and voltage (I-V) variables for solving the power flow and three-phase power flow problems. This formulation has demonstrated superior convergence properties over conventional polar coordinate based formulations for three-phase power flow, but has failed to replicate the same advantages for power flow in general due to convergence issues with systems containing PV buses. In this paper, we demonstrate how circuit simulation techniques can provide robust convergence for any complex I-V formulation that is derived from our split equivalent circuit representation. Application to power grid test systems with up to 10000 buses demonstrates consistent global convergence to the correct physical solution from arbitrary initial conditions.
Beyond Profiling: Scaling Profiling Data Usage to Multiple Applications
Venue: arXiv cs.DC 2017
Year: 2017

Profiling techniques are used extensively at different parts of the computing stack to achieve many goals. One major goal is to make a piece of software execute more efficiently on a specific hardware platform, where efficiency spans criteria such as power, performance, resource requirements, etc. Researchers, both in academia and industry, have introduced many techniques to gather, and make use of, profiling data. However, one thing remains unchanged: making application A run more efficiently on machine 1. In this paper, we extend this criteria by asking: can profiling information of application A on machine 1 be used to make application B run more efficiently on machine 1? If so, then this means as machine 1 continues to execute more applications, it becomes better and more efficient. We present a generalized method for using profiling information gathered from the execution of programs from a limited corpus of applications to improve the performance of software from outside our corpus. As a proof of concept, we apply our technique to the specific problem of selecting the most efficient last-level-cache with which to execute an application. We were able to turn off an average of 19% of last-level-cache blocks for selected programs from PARSEC benchmark suite and only saw an average 2.8% increase in the rate of last-level cache misses.
Joining Local Knowledge to Communicate Reliably (Extended Abstract)
Venue: arXiv cs.DC 2017
Year: 2017

A fundamental primitive in distributed computing is Reliable Message Transmission (RMT), which refers to the task of correctly sending a message from a party (or player) to another, in a network where some intermediate relays might be controlled by an adversary. We address the problem under the realistic assumption that the topological knowledge of players is restricted to a certain subgraph and specifically study the role of local information exchange in the feasibility of RMT. We employ the General Adversary model of Hirt and Maurer and the recently introduced Partial Knowledge Model which subsume all known models for the adversary and local knowledge respectively. Tight feasibility conditions, naturally involving the network topology, the adversary and the local knowledge of players, are presented.
Language properties and Grammar of Parallel and Series Parallel Languages
Venue: arXiv cs.FL 2017
Year: 2017

In this paper we have defined the language theoretical properties of Parallel languages and series parallel languages. Parallel languages and Series parallel languages play vital roles in parallel processing and many applications in computer programming. We have defined regular expressions and context free grammar for parallel and series parallel languages based on sequential languages [2]. We have also discussed the recognizability of parallel and series parallel languages using regular expression and regular grammar.
Profile Matching Across Unstructured Online Social Networks: Threats and Countermeasures
Venue: arXiv cs.CR 2017
Year: 2017

In this work, we propose a profile matching (or deanonymization) attack for unstructured online social networks (OSNs) in which similarity in graphical structure cannot be used for profile matching. We consider different attributes that are publicly shared by users. Such attributes include both obvious identifiers such as the user name and non-obvious identifiers such as interest similarity or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to the profile matching in order to show the privacy threat in an extensive way. Our proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on two real-life datasets that are constructed by us. Our results indicate that profiles of the users in different OSNs can be matched with high probability by only using publicly shared attributes and without using the underlying graphical structure of the OSNs. We also propose possible countermeasures to mitigate this threat in the expense of reduction in the accuracy (or utility) of the attributes shared by the users. We formulate the tradeoff between the privacy and profile utility of the users as an optimization problem and show how slight changes in the profiles of the users would reduce the success of the attack. We believe that this work will be a valuable step to build a privacy-preserving tool for users against profile matching attacks between OSNs.
On Z2Z4[ξ]-Skew Cyclic Codes
Venue: arXiv cs.IT 2017
Year: 2017

Z2Z4-additive codes have been defined as a subgroup of Z2^{r} x Z4^{s} in [5] where Z2, Z4 are the rings of integers modulo 2 and 4 respectively and r and s positive integers. In this study, we define a new family of codes over the set Z2^{r}[\barξ] x Z4^{s}[ξ] where ξis the root of a monic basic primitive polynomial in Z4[x]. We give the standard form of the generator and parity-check matrices of codes over Z2^{r}[\barξ] x Z4^{s}[ξ] and also we introduce skew cyclic codes and their spanning sets over this set.