OpenRaiser/Intern-Atlas huggingface dataset:
Imagination improves Multimodal Translation
Venue: arXiv cs.CL 2017
Year: 2017

We decompose multimodal translation into two sub-tasks: learning to translate and learning visually grounded representations. In a multitask learning framework, translations are learned in an attention-based encoder-decoder, and grounded representations are learned through image representation prediction. Our approach improves translation performance compared to the state of the art on the Multi30K dataset. Furthermore, it is equally effective if we train the image prediction task on the external MS COCO dataset, and we find improvements if we train the translation model on the external News Commentary parallel text.
Trajectory Planning of Automated Vehicles in Tube-like Road Segments
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a method based on linear programming for trajectory planning of automated vehicles, combining obstacle avoidance, time scheduling for the reaching of waypoints and time-optimal traversal of tube-like road segments. System modeling is conducted entirely spatial-based. Kinematic vehicle dynamics as well as time are expressed in a road-aligned coordinate frame with path along the road centerline serving as the dependent variable. We elaborate on control rate constraints in the spatial domain. A vehicle dimension constraint heuristic is proposed to constrain vehicle dimensions inside road boundaries. It is outlined how friction constraints are accounted for. The discussion is extended to dynamic vehicle models. The benefits of the proposed method are illustrated by a comparison to a time-based method.
Redrawing the 'Color Line': Examining Racial Segregation in Associative Networks on Twitter
Venue: arXiv cs.SI 2017
Year: 2017

Online social spaces are increasingly salient contexts for associative tie formation. However, the racial composition of associative networks within most of these spaces has yet to be examined. In this paper, we use data from the social media platform Twitter to examine racial segregation patterns in online associative networks. Acknowledging past work on the role that social structure and agency play in influencing the racial composition of individuals' networks, we argue that Twitter blurs the influence of these forces and may invite users to generate networks that are both more or less segregated than what has been observed offline, depending on use. While we expect to find some level of racial segregation within this space, this paper unpacks the extent to which we observe same-race connectedness for black and white users, assesses whether these patterns are likely generated by opportunity or by choice, and contextualizes results by comparing them with patterns of same-race connectedness observed offline.
The Distributed MIMO Scenario: Can Ideal ADCs Be Replaced by Low-resolution ADCs?
Venue: arXiv cs.IT 2017
Year: 2017

This letter considers the architecture of distributed antenna system, which is made up of a massive number of single-antenna remote radio heads (RRHs), some with full-resolution but others with low-resolution analog-to-digital converter (ADC) receivers. This architecture is greatly motivated by its high energy efficiency and low-cost implementation. We derive the worst-case uplink spectral efficiency (SE) of the system assuming a frequency-flat channel and maximum-ratio combining (MRC), and reveal that the SE increases as the number of quantization bits for the low-resolution ADCs increases, and the SE converges as the number of RRHs with low-resolution ADCs grows. Our results furthermore demonstrate that a great improvement can be obtained by adding a majority of RRHs with low-resolution ADC receivers, if sufficient quantization precision and an acceptable proportion of high-to-low resolution RRHs are used.
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities
Venue: arXiv cs.CV 2017
Year: 2017

Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. On the other hand satellite imagery captures scenes that are diverse. This paper describes a deep learning approach that analyzes a geo referenced satellite image and efficiently detects built structures in it. A Fully Convolution Network (FCN) is trained on low resolution Google earth satellite imagery in order to achieve end result. The detected built communities are then correlated with the vaccination activity that has furnished some useful statistics.
Radio Resource Allocation for Multicarrier-Low Density Spreading Multiple Access
Venue: arXiv cs.IT 2017
Year: 2017

Multicarrier-low density spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near optimum multiuser detection. In MC-LDSMA, each user's symbol spread on a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging comparing to some well-known multiple access techniques. In this paper, we study the radio resource allocation for single-cell MC-LDSMA system. Firstly, we consider the single-user case, and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple access channel, we provide an optimal solution for MC-LDSMA that maximizes the users' weighted sum-rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with OFDMA.
Formalizing Timing Diagram Requirements in Discrete Duration Calulus
Venue: arXiv cs.LO 2017
Year: 2017

Several temporal logics have been proposed to formalise timing diagram requirements over hardware and embedded controllers. These include LTL, discrete time MTL and the recent industry standard PSL. However, succintness and visual structure of a timing diagram are not adequately captured by their formulae. Interval temporal logic QDDC is a highly succint and visual notation for specifying patterns of behaviours. In this paper, we propose a practically useful notation called SeCeCntnl which enhances negation free fragment of QDDC with features of nominals and limited liveness. We show that timing diagrams can be naturally (compositionally) and succintly formalized in SeCeCntnl as compared with PSL and MTL. We give a linear time translation from timing diagrams to SeCeCntnl. As our second main result, we propose a linear time translation of SeCeCntnl into QDDC. This allows QDDC tools such as DCVALID and DCSynth to be used for checking consistency of timing diagram requirements as well as for automatic synthesis of property monitors and controllers. We give examples of a minepump controller and a bus arbiter to illustrate our tools. Giving a theoretical analysis, we show that for the proposed SeCeCntnl, the satisfiability and model checking have elementary complexity as compared to the non-elementary complexity for the full logic QDDC.
Spatial-Temporal Recurrent Neural Network for Emotion Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Emotion analysis is a crucial problem to endow artifact machines with real intelligence in many large potential applications. As external appearances of human emotions, electroencephalogram (EEG) signals and video face signals are widely used to track and analyze human's affective information. According to their common characteristics of spatial-temporal volumes, in this paper we propose a novel deep learning framework named spatial-temporal recurrent neural network (STRNN) to unify the learning of two different signal sources into a spatial-temporal dependency model. In STRNN, to capture those spatially cooccurrent variations of human emotions, a multi-directional recurrent neural network (RNN) layer is employed to capture longrange contextual cues by traversing the spatial region of each time slice from multiple angles. Then a bi-directional temporal RNN layer is further used to learn discriminative temporal dependencies from the sequences concatenating spatial features of each time slice produced from the spatial RNN layer. To further select those salient regions of emotion representation, we impose sparse projection onto those hidden states of spatial and temporal domains, which actually also increases the model discriminant ability because of this global consideration. Consequently, such a two-layer RNN model builds spatial dependencies as well as temporal dependencies of the input signals. Experimental results on the public emotion datasets of EEG and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.
sOFTDP: Secure and Efficient Topology Discovery Protocol for SDN
Venue: arXiv cs.NI 2017
Year: 2017

Topology discovery is one of the most critical tasks of Software-Defined Network (SDN) controllers. Current SDN controllers use the OpenFlow Discovery Protocol (OFDP) as the de-facto protocol for discovering the underlying network topology. In a previous work, we have shown the functional, performance and security limitations of OFDP. In this paper, we introduce and detail a novel protocol called secure and efficient OpenFlow Discovery Protocol sOTDP. sOFTDP requires minimal changes to OpenFlow switch design, eliminates major vulnerabilities in the topology discovery process and improves its performance. We have implemented sOFTDP as a topology discovery module in Floodlight for evaluation. The results show that our implementation is more secure than OFDP and previous security workarounds. Also, sOFTDP reduces the topology discovery time several orders of magnitude compared to the original OFDP and existing OFDP improvements.
A natural approach to studying schema processing
Venue: arXiv cs.NE 2017
Year: 2017

The Building Block Hypothesis (BBH) states that adaptive systems combine good partial solutions (so-called building blocks) to find increasingly better solutions. It is thought that Genetic Algorithms (GAs) implement the BBH. However, for GAs building blocks are semi-theoretical objects in that they are thought only to be implicitly exploited via the selection and crossover operations of a GA. In the current work, we discover a mathematical method to identify the complete set of schemata present in a given population of a GA; as such a natural way to study schema processing (and thus the BBH) is revealed. We demonstrate how this approach can be used both theoretically and experimentally. Theoretically, we show that the search space for good schemata is a complete lattice and that each generation samples a complete sub-lattice of this search space. In addition, we show that combining schemata can only explore a subset of the search space. Experimentally, we compare how well different crossover methods combine building blocks. We find that for most crossover methods approximately 25-35% of building blocks in a generation result from the combination of the previous generation's building blocks. We also find that an increase in the combination of building blocks does not lead to an increase in the efficiency of a GA. To complement this article, we introduce an open source Python package called schematax, which allows one to calculate the schemata present in a population using the methods described in this article.
Complexity and Inapproximability Results for Parallel Task Scheduling and Strip Packing
Venue: arXiv cs.CC 2017
Year: 2017

We study the Parallel Task Scheduling problem $Pm|size_j|C_{\max}$ with a constant number of machines. This problem is known to be strongly NP-complete for each $m \geq 5$, while it is solvable in pseudo-polynomial time for each $m \leq 3$. We give a positive answer to the long-standing open question whether this problem is strongly $NP$-complete for $m=4$. As a second result, we improve the lower bound of $\frac{12}{11}$ for approximating pseudo-polynomial Strip Packing to $\frac{5}{4}$. Since the best known approximation algorithm for this problem has a ratio of $\frac{4}{3} + \varepsilon$, this result narrows the gap between approximation ratio and inapproximability result by a significant step. Both results are proven by a reduction from the strongly $NP$-complete problem 3-Partition.
Rate-Memory Trade-off for the Two-User Broadcast Caching Network with Correlated Sources
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies the fundamental limits of caching in a network with two receivers and two files generated by a two-component discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. First, a lower bound on the optimal peak rate-memory trade-off is provided. Then, in order to leverage the correlation among the library files to alleviate the load over the shared link, a two-step correlation-aware cache-aided coded multicast (CACM) scheme is proposed. The first step uses Gray-Wyner source coding to represent the library via one common and two private descriptions, such that a second correlation-unaware multiple-request CACM step can exploit the additional coded multicast opportunities that arise. It is shown that the rate achieved by the proposed two-step scheme matches the lower bound for a significant memory regime and it is within half of the conditional entropy for all other memory values.
Proof Mining with Dependent Types
Venue: arXiv cs.PL 2017
Year: 2017

Several approaches exist to data-mining big corpora of formal proofs. Some of these approaches are based on statistical machine learning, and some -- on theory exploration. However, most are developed for either untyped or simply-typed theorem provers. In this paper, we present a method that combines statistical data mining and theory exploration in order to analyse and automate proofs in dependently typed language of Coq.
The Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning
Venue: arXiv cs.RO 2017
Year: 2017

Nearly all autonomous robotic systems use some form of motion planning to compute reference motions through their environment. An increasing use of autonomous robots in a broad range of applications creates a need for efficient, general purpose motion planning algorithms that are applicable in any of these new application domains. This thesis presents a resolution complete optimal kinodynamic motion planning algorithm based on a direct forward search of the set of admissible input signals to a dynamical model. The advantage of this generalized label correcting method is that it does not require a local planning subroutine as in the case of related methods. Preliminary material focuses on new topological properties of the canonical problem formulation that are used to show continuity of the performance objective. These observations are used to derive a generalization of Bellman's principle of optimality in the context of kinodynamic motion planning. A generalized label correcting algorithm is then proposed which leverages these results to prune candidate input signals from the search when their cost is greater than related signals. The second part of this thesis addresses admissible heuristics for kinodynamic motion planning. An admissibility condition is derived that can be used to verify the admissibility of candidate heuristics for a particular problem. This condition also characterizes a convex set of admissible heuristics. A linear program is formulated to obtain a heuristic which is as close to the optimal cost-to-go as possible while remaining admissible. This optimization is justified by showing its solution coincides with the solution to the Hamilton-Jacobi-Bellman equation. Lastly, a sum-of-squares relaxation of this infinite-dimensional linear program is proposed for obtaining provably admissible approximate solutions.
Scalable and Efficient Construction of Suffix Array with MapReduce and In-Memory Data Store System
Venue: arXiv cs.DC 2017
Year: 2017

Suffix Array (SA) is a cardinal data structure in many pattern matching applications, including data compression, plagiarism detection and sequence alignment. However, as the volumes of data increase abruptly, the construction of SA is not amenable to the current large-scale data processing frameworks anymore due to its intrinsic proliferation of suffixes during the construction. That is, ameliorating the performance by just adding the resources to the frameworks becomes less cost- effective, even having the severe diminishing returns. At issue now is whether we can permit SA construction to be more scalable and efficient for the everlasting accretion of data by creating a radical shift in perspective. Regarding TeraSort [1] as our baseline, we first demonstrate the fragile scalability of TeraSort and investigate what causes it through the experiments on the sequence alignment of a grouper (i.e., the SA construc- tion used in bioinformatics). As such, we propose a scheme that amalgamates the distributed key-value store system into MapReduce to leverage the in-memory queries about suffixes. Rather than handling the communication of suffixes, MapReduce is in charge of the communication of their indexes, which means better capacity for more data. It significantly abates the required disk space for constructing SA and better utilizes the memory, which in turn improves the scalability radically. We also examine the efficiency of our scheme in terms of memory and show it outperforms TeraSort. At last, our scheme can complete the pair- end sequencing and alignment with two input files without any degradation on scalability, and can accommodate the suffixes of nearly 6.7 TB in a small cluster composed of 16 nodes and Gigabit Ethernet without any compression.
Towards an augmented reality fourth generation social networks
Venue: arXiv cs.SI 2017
Year: 2017

A concept of fourth generation social network is described as one that, built on the features of augmented reality (AR), is able to implement an enriched layer of digital information that displays in People Augmented Reality (PAR) devices data shared by users in social networks. This PAR layer is accessed by the users in their devices through camera effects when targeting with a mobile phone to a user holding a mobile device with AGPS and with a profile in social media. The social network of fourth generation will be a combination between Facebook and Pokemon Go.
Annotating and Modeling Empathy in Spoken Conversations
Venue: arXiv cs.CL 2017
Year: 2017

Empathy, as defined in behavioral sciences, expresses the ability of human beings to recognize, understand and react to emotions, attitudes and beliefs of others. The lack of an operational definition of empathy makes it difficult to measure it. In this paper, we address two related problems in automatic affective behavior analysis: the design of the annotation protocol and the automatic recognition of empathy from spoken conversations. We propose and evaluate an annotation scheme for empathy inspired by the modal model of emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic spoken conversations. In the context of behavioral analysis, we designed an automatic segmentation and classification system for empathy. Given the different speech and language levels of representation where empathy may be communicated, we investigated features derived from the lexical and acoustic spaces. The feature development process was designed to support both the fusion and automatic selection of relevant features from high dimensional space. The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.
Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks
Venue: arXiv cs.IR 2017
Year: 2017

We tackle the novel problem of navigational voice queries posed against an entertainment system, where viewers interact with a voice-enabled remote controller to specify the program to watch. This is a difficult problem for several reasons: such queries are short, even shorter than comparable voice queries in other domains, which offers fewer opportunities for deciphering user intent. Furthermore, ambiguity is exacerbated by underlying speech recognition errors. We address these challenges by integrating word- and character-level representations of the queries and by modeling voice search sessions to capture the contextual dependencies in query sequences. Both are accomplished with a probabilistic framework in which recurrent and feedforward neural network modules are organized in a hierarchical manner. From a raw dataset of 32M voice queries from 2.5M viewers on the Comcast Xfinity X1 entertainment system, we extracted data to train and test our models. We demonstrate the benefits of our hybrid representation and context-aware model, which significantly outperforms models without context as well as the current deployed product.
Impact of Major RF Impairments on mm-wave Communications using OFDM Waveforms
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the joint impact of three major RF im-pairments, namely, oscillator phase noise, power amplifier non-linearity and I/Q imbalance on the performance of a mm-wave communication link based on OFDM modulation. General im-pairment models are first derived for describing the joint effects in each TX, each RX as well as a mm-wave communication link. Based on the obtained signal models and initial air interface de-sign from the mmMAGIC project, we numerically evaluate the impact of RF impairments on channel estimation in terms of channel-to-noise ratio (CNR) and also channel fluctuation due to common phase error (CPE) caused by phase noise within the channel coherence time. Then the impact on the link performance in terms of maximum sum rate is evaluated using extensive com-puter simulations. The simulation results show that the used air interface design is generally robust to the presence of RF impair-ments. With regard to the use of high order modulation alphabet and implementation of low-power and low-cost RF transceivers in mm-wave communication, special attention needs to be paid on phase noise where the inter-carrier-interference (ICI) can become a major limiting factor.
Sequential Hybrid Beamforming Design for Multi-Link mmwave Communication
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a sequential hybrid beamforming design for multi-link transmission over mmwave frequency bands. As a starting point, a baseline data communication link is established via traditional analog beamforming at both the BS and UE. If an extra RF chain is available at the UE, it can continue to probe the propagation environment at the same frequencies. In case the environment is favorable and system resources allow, a secondary data communication link is established to enable multi-stream transmission. In principle, the secondary link could be served by the same BS and/or one or several other BS(s). To initialize the secondary data communication link, a parallel beam search scheme is proposed, which helps the UE/BS to find a suitable beam pair with given optimization criteria without interrupting the baseline data communication. By applying the proposed two-step approach, hybrid beamforming becomes an add-on feature that can be easily switched on over an analog beamforming enabled system without interrupting its operation whenever system requires. Meanwhile, the information obtained by deploying the proposed parallel beam search scheme can also be used for deciding a back-up beam pair if signal blockage occurs to the baseline data communication link.