OpenRaiser/Intern-Atlas huggingface dataset:
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning
Venue: arXiv cs.CL 2017
Year: 2017

Linguistic resources such as part-of-speech (POS) tags have been extensively used in statistical machine translation (SMT) frameworks and have yielded better performances. However, usage of such linguistic annotations in neural machine translation (NMT) systems has been left under-explored. In this work, we show that multi-task learning is a successful and a easy approach to introduce an additional knowledge into an end-to-end neural attentional model. By jointly training several natural language processing (NLP) tasks in one system, we are able to leverage common information and improve the performance of the individual task. We analyze the impact of three design decisions in multi-task learning: the tasks used in training, the training schedule, and the degree of parameter sharing across the tasks, which is defined by the network architecture. The experiments are conducted for an German to English translation task. As additional linguistic resources, we exploit POS information and named-entities (NE). Experiments show that the translation quality can be improved by up to 1.5 BLEU points under the low-resource condition. The performance of the POS tagger is also improved using the multi-task learning scheme.
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Venue: arXiv cs.CV 2017
Year: 2017

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.
Graph-based Features for Automatic Online Abuse Detection
Venue: arXiv cs.IR 2017
Year: 2017

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach.
Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran
Venue: arXiv cs.CV 2017
Year: 2017

The SLEUTH model, based on the Cellular Automata (CA), can be applied to city development simulation in metropolitan areas. In this study the SLEUTH model was used to model the urban expansion and predict the future possible behavior of the urban growth in Tehran. The fundamental data were five Landsat TM and ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to simulate the spatial pattern. The first scenario assumed historical urbanization mode would persist and the only limitations for development were height and slope. The second one was a compact scenario which makes the growth mostly internal and limited the expansion of suburban areas. The last scenario proposed a polycentric urban structure which let the little patches grow without any limitation and would not consider the areas beyond the specific buffer zone from the larger patches for development. Results showed that the urban growth rate was greater in the first scenario in comparison with the other two scenarios. Also it was shown that the third scenario was more suitable for Tehran since it could avoid undesirable effects such as congestion and pollution and was more in accordance with the conditions of Tehran city.
Long range forces in a performance portable Molecular Dynamics framework
Venue: arXiv cs.DC 2017
Year: 2017

Molecular Dynamics (MD) codes predict the fundamental properties of matter by following the trajectories of a collection of interacting model particles. To exploit diverse modern manycore hardware, efficient codes must use all available parallelism. At the same time they need to be portable and easily extendible by the domain specialist (physicist/chemist) without detailed knowledge of this hardware. To address this challenge, we recently described a new Domain Specific Language (DSL) for the development of performance portable MD codes based on a "Separation of Concerns": a Python framework automatically generates efficient parallel code for a range of target architectures. Electrostatic interactions between charged particles are important in many physical systems and often dominate the runtime. Here we discuss the inclusion of long-range interaction algorithms in our code generation framework. These algorithms require global communications and careful consideration has to be given to any impact on parallel scalability. We implemented an Ewald summation algorithm for electrostatic forces, present scaling comparisons for different system sizes and compare to the performance of existing codes. We also report on further performance optimisations delivered with OpenMP shared memory parallelism.
Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization
Venue: arXiv cs.NE 2017
Year: 2017

In evolutionary algorithms, a preselection operator aims to select the promising offspring solutions from a candidate offspring set. It is usually based on the estimated or real objective values of the candidate offspring solutions. In a sense, the preselection can be treated as a classification procedure, which classifies the candidate offspring solutions into promising ones and unpromising ones. Following this idea, we propose a classification based preselection (CPS) strategy for evolutionary multiobjective optimization. When applying classification based preselection, an evolutionary algorithm maintains two external populations (training data set) that consist of some selected good and bad solutions found so far; then it trains a classifier based on the training data set in each generation. Finally it uses the classifier to filter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution. In such cases, it is not necessary to estimate or evaluate the objective values of the candidate offspring solutions. The classification based preselection is applied to three state-of-the-art multiobjective evolutionary algorithms (MOEAs) and is empirically studied on two sets of test instances. The experimental results suggest that classification based preselection can successfully improve the performance of these MOEAs.
Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities
Venue: arXiv cs.IR 2017
Year: 2017

Over the last decade we have made great progress in entity linking (EL) systems, but performance may vary depending on the context and, arguably, there are even principled limitations preventing a "perfect" EL system. This also suggests that there may be applications for which current "imperfect" EL is already very useful, and makes finding the "right" application as important as building the "right" EL system. We investigate the Digital Humanities use case, where scholars spend a considerable amount of time selecting relevant source texts. We developed WideNet; a semantically-enhanced search tool which leverages the strengths of (imperfect) EL without getting in the way of its expert users. We evaluate this tool in two historical case-studies aiming to collect a set of references to historical periods in parliamentary debates from the last two decades; the first targeted the Dutch Golden Age, and the second World War II. The case-studies conclude with a critical reflection on the utility of WideNet for this kind of research, after which we outline how such a real-world application can help to improve EL technology in general.
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery
Venue: arXiv cs.CV 2017
Year: 2017

The potential of Augmented Reality (AR) technology to assist minimally invasive surgeries (MIS) lies in its computational performance and accuracy in dealing with challenging MIS scenes. Even with the latest hardware and software technologies, achieving both real-time and accurate augmented information overlay in MIS is still a formidable task. In this paper, we present a novel real-time AR framework for MIS that achieves interactive geometric aware augmented reality in endoscopic surgery with stereo views. Our framework tracks the movement of the endoscopic camera and simultaneously reconstructs a dense geometric mesh of the MIS scene. The movement of the camera is predicted by minimising the re-projection error to achieve a fast tracking performance, while the 3D mesh is incrementally built by a dense zero mean normalised cross correlation stereo matching method to improve the accuracy of the surface reconstruction. Our proposed system does not require any prior template or pre-operative scan and can infer the geometric information intra-operatively in real-time. With the geometric information available, our proposed AR framework is able to interactively add annotations, localisation of tumours and vessels, and measurement labelling with greater precision and accuracy compared with the state of the art approaches.
Image reconstruction with imperfect forward models and applications in deblurring
Venue: arXiv cs.NA 2017
Year: 2017

We present and analyse an approach to image reconstruction problems with imperfect forward models based on partially ordered spaces - Banach lattices. In this approach, errors in the data and in the forward models are described using order intervals. The method can be characterised as the lattice analogue of the residual method, where the feasible set is defined by linear inequality constraints. The study of this feasible set is the main contribution of this paper. Convexity of this feasible set is examined in several settings and modifications for introducing additional information about the forward operator are considered. Numerical examples demonstrate the performance of the method in deblurring with errors in the blurring kernel.
Viewing Robot Navigation in Human Environment as a Cooperative Activity
Venue: arXiv cs.RO 2017
Year: 2017

We claim that navigation in human environments can be viewed as cooperative activity especially in constrained situations. Humans concurrently aid and comply with each other while moving in a shared space. Cooperation helps pedestrians to efficiently reach their own goals and respect conventions such as the personal space of others. To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space. In this work, we present a navigation planner that is able to plan such cooperative trajectories, simultaneously enforcing the robot's kinematic constraints and avoiding other non-human dynamic obstacles. Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior not only in open spaces but also in confined areas. Besides adapting the robot trajectory, the planner is also able to proactively propose co-navigation solutions by jointly computing human and robot trajectories within the same optimization framework. We demonstrate richness and performance of the cooperative planner with simulated and real world experiments on multiple interactive navigation scenarios.
MPI Streams for HPC Applications
Venue: arXiv cs.DC 2017
Year: 2017

Data streams are a sequence of data flowing between source and destination processes. Streaming is widely used for signal, image and video processing for its efficiency in pipelining and effectiveness in reducing demand for memory. The goal of this work is to extend the use of data streams to support both conventional scientific applications and emerging data analytic applications running on HPC platforms. We introduce an extension called MPIStream to the de-facto programming standard on HPC, MPI. MPIStream supports data streams either within a single application or among multiple applications. We present three use cases using MPI streams in HPC applications together with their parallel performance. We show the convenience of using MPI streams to support the needs from both traditional HPC and emerging data analytics applications running on supercomputers.
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Venue: arXiv cs.CL 2017
Year: 2017

We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new domain, using only bandit feedback: the system receives a German sentence to translate, produces an English sentence, and only gets a scalar score as feedback. Targeting these two challenges (adaptation and bandit learning), we built a standard neural machine translation system and extended it in two ways: (1) robust reinforcement learning techniques to learn effectively from the bandit feedback, and (2) domain adaptation using data selection from a large corpus of parallel data.
On balanced 4-holes in bichromatic point sets
Venue: arXiv cs.CG 2017
Year: 2017

Let $S=R\cup B$ be a point set in the plane in general position such that each of its elements is colored either red or blue, where $R$ and $B$ denote the points colored red and the points colored blue, respectively. A quadrilateral with vertices in $S$ is called a $4$-hole if its interior is empty of elements of $S$. We say that a $4$-hole of $S$ is balanced if it has $2$ red and $2$ blue points of $S$ as vertices. In this paper, we prove that if $R$ and $B$ contain $n$ points each then $S$ has at least $\frac{n^2-4n}{12}$ balanced $4$-holes, and this bound is tight up to a constant factor. Since there are two-colored point sets with no balanced {\em convex} $4$-holes, we further provide a characterization of the two-colored point sets having this type of $4$-holes.
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
Venue: arXiv cs.CL 2017
Year: 2017

The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task. This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test set (also attaining a 74.9% accuracy), demonstrating that the model generalizes well to the cross-domain data. Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance. In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy of 85.5%, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.
Pilot Reuse Among D2D Users in D2D Underlaid Massive MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

In a device-to-device (D2D) underlaid massive MIMO system, D2D transmitters reuse the uplink spectrum of cellular users (CUs), leading to cochannel interference. To decrease pilot overhead, we assume pilot reuse (PR) among D2D pairs. We first derive the minimum-mean-square-error (MMSE) estimation of all channels and give a lower bound on the ergodic achievable rate of both cellular and D2D links. To mitigate pilot contamination caused by PR, we then propose a pilot scheduling and pilot power control algorithm based on the criterion of minimizing the sum mean-square-error (MSE) of channel estimation of D2D links. We show that, with an appropriate PR ratio and a well designed pilot scheduling scheme, each D2D transmitter could transmit its pilot with maximum power. In addition, we also maximize the sum rate of all D2D links while guaranteeing the quality of service (QoS) of CUs, and develop an iterative algorithm to obtain a suboptimal solution. Simulation results show that the effect of pilot contamination can be greatly decreased by the proposed pilot scheduling algorithm, and the PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of system spectral efficiency.
Constrained Receiver Scheduling in Flexible Time and Wavelength Division Multiplexed Optical
Venue: arXiv cs.NI 2017
Year: 2017

An increasing bandwidth demand has mandated a shift to the time and wavelength division multiplexing (TWDM) techniques in optical access networks (OAN). Typical TWDM scheduling schemes consider scheduling of the optical line terminal receiver only. In this paper we have identified an additional collision domain that is present in TWDM schemes that offer security, in addition to bandwidth flexibility. Scheduling of the identified collision domain is termed as group scheduling. We illustrate that consideration of receiver scheduling only (as done in typical TWDM schemes) severely affects their throughput when implemented on flexible and secure TWDM architectures. A novel media access control protocol has been proposed in this paper that considers the multiple collision domains. Through simulations, we are able to illustrate that the proposed scheme achieves a high throughput. A theoretical upper bound of throughput has also been derived to explain the simulation results. Complexity reduction of the proposed scheme has been illustrated, thereby making it an attractive proposal.
Performance Overhead Comparison between Hypervisor and Container based Virtualization
Venue: arXiv cs.DC 2017
Year: 2017

The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Along with the recent popularity of Docker, the container-based virtualization starts receiving more attention for being a promising alternative. Since both of the virtualization solutions are not resource-free, their performance overheads would lead to negative impacts on the quality of Cloud services. To help fundamentally understand the performance difference between these two types of virtualization solutions, we use a physical machine with "just-enough" resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization's performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Although the container-based solution is undoubtedly lightweight, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed.
Optimal Power Allocation Strategies in Full-duplex Relay Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we consider a dual-hop, decode-and-forward network where the relay can operate in full- duplex (FD) or half-duplex (HD) mode. We model the residual self-interference as an additive Gaussian noise with variance proportional to the relay transmit power, and we assume a Gaussian input distribution at the source. Unlike previous work, we assume that the source is only aware of the transmit power distribution adopted by the relay over a given time horizon, but not of the symbols that the relay is currently transmitting. This assumption better reflects the practical situation where the relay node also forwards signaling traffic, or data originated by other sources. Under these conditions, we identify the optimal power allocation strategy at the source and relay, which in some cases coincides with the half duplex transmission mode. In particular, we prove that such strategy implies either FD transmissions over an entire frame, or FD/HD transmissions over a variable fraction of the frame. We determine the optimal transmit power level at the source and relay for each frame, or fraction thereof. We compare the performance of the proposed scheme against reference FD and HD techniques, which assume that the source is aware of the symbols instantaneously transmitted by the relay. Our results highlight that our scheme closely approaches or outperforms such reference strategies.
Distributed Solution of Large-Scale Linear Systems via Accelerated Projection-Based Consensus
Venue: arXiv cs.LG 2017
Year: 2017

Solving a large-scale system of linear equations is a key step at the heart of many algorithms in machine learning, scientific computing, and beyond. When the problem dimension is large, computational and/or memory constraints make it desirable, or even necessary, to perform the task in a distributed fashion. In this paper, we consider a common scenario in which a taskmaster intends to solve a large-scale system of linear equations by distributing subsets of the equations among a number of computing machines/cores. We propose an accelerated distributed consensus algorithm, in which at each iteration every machine updates its solution by adding a scaled version of the projection of an error signal onto the nullspace of its system of equations, and where the taskmaster conducts an averaging over the solutions with momentum. The convergence behavior of the proposed algorithm is analyzed in detail and analytically shown to compare favorably with the convergence rate of alternative distributed methods, namely distributed gradient descent, distributed versions of Nesterov's accelerated gradient descent and heavy-ball method, the block Cimmino method, and ADMM. On randomly chosen linear systems, as well as on real-world data sets, the proposed method offers significant speed-up relative to all the aforementioned methods. Finally, our analysis suggests a novel variation of the distributed heavy-ball method, which employs a particular distributed preconditioning, and which achieves the same theoretical convergence rate as the proposed consensus-based method.