Query: Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language

Relevant passages:
Towards Zero Unknown Word in Neural Machine Translation <=SEP=> Neural Machine translation has shown promising results in recent years. In order to control the computational complexity, NMT has to employ a small vocabulary, and massive rare words outside the vocabulary are all replaced with a single unk symbol. Besides the inability to translate rare words, this kind of simple approach leads to much increased ambiguity of the sentences since meaningless unks break the structure of sentences, and thus hurts the translation and reordering of the in-vocabulary words. To tackle this problem, we propose a novel substitution-translation-restoration method. In substitution step, the rare words in a testing sentence are replaced with similar in-vocabulary words based on a similarity model learnt from monolingual data. In translation and restoration steps, the sentence will be translated with a model trained on new bilingual data with rare words replaced, and finally the translations of the replaced words will be substituted by that of original ones. Experiments on Chinese-to-English translation demonstrate that our proposed method can achieve more than 4 BLEU points over the attention-based NMT. When compared to the recently proposed method handling rare words in NMT, our method can also obtain an improvement by nearly 3 BLEU points.

OpenNMT: Open-source Toolkit for Neural Machine Translation <=SEP=> We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.

Neural Machine Translation of Rare Words with Subword Units <=SEP=> Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU, respectively.

ASPEC: Asian Scientific Paper Excerpt Corpus <=SEP=> In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain. ASPEC was constructed in the Japanese-Chinese machine translation project conducted between 2006 and 2010 using the Special Coordination Funds for Promoting Science and Technology. It consists of a Japanese-English scientific paper abstract corpus of approximately 3 million parallel sentences (ASPEC-JE) and a Chinese-Japanese scientific paper excerpt corpus of approximately 0.68 million parallel sentences (ASPEC-JC). ASPEC is used as the official dataset for the machine translation evaluation workshop WAT (Workshop on Asian Translation).

PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification <=SEP=> We present a new release of the Paraphrase Database. PPDB 2.0 includes a discriminatively re-ranked set of paraphrases that achieve a higher correlation with human judgments than PPDB 1.0’s heuristic rankings. Each paraphrase pair in the database now also includes finegrained entailment relations, word embedding similarities, and style annotations.

Optimizing Paxos with batching and pipelining <=SEP=> Paxos is probably the most popular state machine replication protocol. Two optimizations that can greatly improve its performance are batching and pipelining. However, tuning these two optimizations to achieve high-throughput can be challenging, as their effectiveness depends on many parameters like the network latency and bandwidth, the speed of the nodes, and the properties of the application. We address this question by presenting an analytical model of the performance of Paxos, and showing how it can be used to determine configuration values for the batching and pipelining optimizations that result in high-throughput. We then validate the model, by presenting the results of experiments where we investigate the interaction of these two optimizations both in LAN and WAN environments, and comparing these resultswith the prediction from themodel. The results confirm the accuracy of the model, with the predicted values being usually very close to the ones that provide the highest performance in the experiments. Furthermore, both the model and the experiments give useful insights into the relative effectiveness of batching and pipelining. They show that although batching by itself provides the largest gains in all scenarios, in most cases combining it with pipelining provides a very significant additional increase in throughput, with this being true not only on high-latencyWANbut also inmany LAN scenarios. © 2012 Elsevier B.V. All rights reserved.

Security in Infancy , Childhood , and Adulthood : A Move to the Level of Representation <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

OptoCOMM: Introducing a new optical underwater wireless communication modem <=SEP=> OptoCOMM aims at demonstrating the potential, at physical level, of a communication facility for the SUNRISE platform constituted by an Optical Underwater Wireless Communication (OUWC) module with target performance of 10 Mb/s transmission rate at 10 meters range in shallow medium/high turbidity harbour waters. The module, which is based on blue Light Emitting Diode (LED) units and common photodiodes, is an evolution of the proof-of-principle prototype already proven in laboratory (pool). It will constitute an additional node integrated in the Littoral Ocean Observatory Network (LOON) test-bed of the SUNRISE infrastructures, providing a high speed and short-range communication node, which will complete the capability of acoustic modems already present in the test-bed. Three modules (nodes) will be developed and experimentally demonstrated: one for direct integration with the LOON infrastructures, one, battery powered, to be potentially installed on buoys, Remotely Operated Vehicles (ROVs), etc., and one to be installed on the eFolaga Autonomous Underwater Vehicle (AUV) of the proponents. The paper describes in detail the development of the modems as well as the first lab experiments, where the core technology has been successfully tested.

A 3-D Camera With Adaptable Background Light Suppression Using Pixel-Binning and Super-Resolution <=SEP=> This paper presents a CMOS time-of-flight (TOF) 3-D camera employing a column-level background light (BGL) suppression scheme for high-resolution outdoor imaging. The use of the column-level approach minimizes a pixel size for high-density pixel arrays. Pixel-binning and super-resolution can be adaptably applied for an optimal BGL suppression at given spatiotemporal resolutions. A prototype sensor has been fabricated by using 0.11 μm CMOS processes. The sensor achieved a fill factor of 24% in a pixel pitch of 5.9 μm which is the smallest among all the reported TOF cameras up to date. Measurement results showed the temporal noise of 1.47 cm-rms with a 100 ms integration time at a demodulation frequency of 12.5 MHz using a white target at 1 m distance. The non-linearity was measured as 1% over the range of 0.75 m ~ 4 m. The BGL suppression over 100 klx was achieved from indoor and outdoor experiments, while the BGL-induced offset was maintained less than 2.6 cm under 0 ~ 100 klx.

Map Matching with Inverse Reinforcement Learning <=SEP=> We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.

Market power and efficiency in a computational electricity market with discriminatory double-auction pricing <=SEP=> This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.

Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. <=SEP=> Statistical power analysis provides the conventional approach to assess error rates when designing a research study. However, power analysis is flawed in that a narrow emphasis on statistical significance is placed as the primary focus of study design. In noisy, small-sample settings, statistically significant results can often be misleading. To help researchers address this problem in the context of their own studies, we recommend design calculations in which (a) the probability of an estimate being in the wrong direction (Type S [sign] error) and (b) the factor by which the magnitude of an effect might be overestimated (Type M [magnitude] error or exaggeration ratio) are estimated. We illustrate with examples from recent published research and discuss the largest challenge in a design calculation: coming up with reasonable estimates of plausible effect sizes based on external information.

Commutation Torque Ripple Reduction of Brushless DC Motor in Braking Operation <=SEP=> In this paper, the commutation torque ripple is studied in the brushless DC motor (BLDCM) braking operation. As the phase currents flow in the reversed directions in the braking operation, the current circuits in the braking operation are different from those in the motor operation. For this reason, it is found that the commutation torque ripple can be reduced in the speed range from the rated speed to zero just by the common braking pulse width modulation strategies, where the three-phase modulation is not needed. However, it is impossible in motor operation. Under the common braking pulse width modulation strategies, the speed ranges where commutation torque ripple can be reduced are derived and compared. Based on the analysis of these speed ranges, a simple braking method is proposed. With this method, the BLDCM can be slowed down without commutation torque ripple in the whole braking process from the rated speed to zero. Experiment results verify the effectiveness of the method and the correctness of the theory.

A geometry-based soft shadow volume algorithm using graphics hardware <=SEP=> Most previous soft shadow algorithms have either suffered from aliasing, been too slow, or could only use a limited set of shadow casters and/or receivers. Therefore, we present a strengthened soft shadow volume algorithm that deals with these problems. Our critical improvements include robust penumbra wedge construction, geometry-based visibility computation, and also simplified computation through a four-dimensional texture lookup. This enables us to implement the algorithm using programmable graphics hardware, and it results in images that most often are indistinguishable from images created as the average of 1024 hard shadow images. Furthermore, our algorithm can use both arbitrary shadow casters and receivers. Also, one version of our algorithm completely avoids sampling artifacts which is rare for soft shadow algorithms. As a bonus, the four-dimensional texture lookup allows for small textured light sources, and, even video textures can be used as light sources. Our algorithm has been implemented in pure software, and also using the GeForce FX emulator with pixel shaders. Our software implementation renders soft shadows at 0.5--5 frames per second for the images in this paper. With actual hardware, we expect that our algorithm will render soft shadows in real time. An important performance measure is bandwidth usage. For the same image quality, an algorithm using the accumulated hard shadow images uses almost two orders of magnitude more bandwidth than our algorithm.

Fast Screening Algorithm for Rotation and Scale Invariant Template Matching <=SEP=> This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast prescreening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively. Extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match.

Psychological Stressors : Overview <=SEP=> Psychological stressors are social and physical environmental circumstances that challenge the adaptive capabilities and resources of an organism. These circumstances represent an extremely wide and varied array of different situations that possess both common and specific psychological and physical attributes. The challenge for theory, research, and practice is to abstract and understand the specific qualities and characteristics of environmental exposures that most strongly elicit noxious psychological and biological responses, which in turn can lead to serious mental and physical health problems over the life course. In the present article, historical perspectives and conceptual considerations are addressed first, which provides the context for the subsequent discussion of key issues for defining and assessing psychological stressors. Susceptibility to psychological stressors is subject to individual differences, which can alter the impact and adverse consequences of such environmental exposures, necessitating a discussion of these moderating influences as well. HISTORICAL AND GENERAL CONSIDERATIONS

The Love of Large Numbers: A Popularity Bias in Consumer Choice. <=SEP=> Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.

PubViz: Lightweight Visual Presentation of Publication Data <=SEP=> Publications play a central role in presenting the outcome of scientific research but are typically presented as textual lists, whereas related work in visualization of publication focuses on exploration – not presentation. To bridge this gap, we conducted a design study of an interactive visual representation of publication data in a BibTeX file. This paper reports our domain and problem characterization as well as our visualization design decisions in light of our user-centered design process including interviews, two user studies with a paper prototype and a d3.js prototype, and practical application at our group’s website.

Challenges in Shared-Environment Human-Robot Collaboration <=SEP=> We present and discuss four important yet underserved research questions critical to the future of sharedenvironment human-robot collaboration. We begin with a brief survey of research surrounding individual components required for a complete collaborative robot control system, discussing the current state of the art in Learning from Demonstration, active learning, adaptive planning systems, and intention recognition. We motivate the exploration of the presented research questions by relating them to existing work and representative use cases from the domains of construction and cooking.

Attribute-based encryption with non-monotonic access structures <=SEP=> We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.

Efficient deep learning for stereo matching with larger image patches <=SEP=> Stereo matching plays an important role in many applications, such as Advanced Driver Assistance Systems, 3D reconstruction, navigation, etc. However it is still an open problem with many difficult. Most difficult are often occlusions, object boundaries, and low or repetitive textures. In this paper, we propose a method for processing the stereo matching problem. We propose an efficient convolutional neural network to measure how likely the two patches matched or not and use the similarity as their stereo matching cost. Then the cost is refined by stereo methods, such as semiglobal maching, subpixel interpolation, median filter, etc. Our architecture uses large image patches which makes the results more robust to texture-less or repetitive textures areas. We experiment our approach on the KITTI2015 dataset which obtain an error rate of 4.42% and only needs 0.8 second for each image pairs.

Towards Internet of Things : Survey and Future Vision <=SEP=> Internet of things is a promising research due to its importance in many commerce, industry, and education applications. Recently, new applications and research challenges in numerous areas of Internet of things are fired. In this paper, we discuss the history of Internet of things, different proposed architectures of Internet of things, research challenges and open problems related to the Internet of things. We also introduce the concept of Internet of things database and discuss about the future vision of Internet of things. These are the manuscript preparation guidelines used as a standard template for all journal submissions. Author must follow these instructions while preparing/modifying these guidelines.

Automatic semantic classification of scientific literature according to the hallmarks of cancer <=SEP=> MOTIVATION
The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.


RESULTS
We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.


AVAILABILITY AND IMPLEMENTATION
The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html.


CONTACT
simon.baker@cl.cam.ac.uk.

Grid-connected photovoltaic systems with energy storage <=SEP=> There are different interesting ways that can be followed in order to reduce costs of grid-connected photovoltaic systems, i.e., by maximizing their energy production in every operating conditions, minimizing electrical losses on the plant, utilizing grid-connected photovoltaic systems not only to generate electrical energy to be put into the power system but also to implement some other important auxiliary services normally implemented by properly conceived apparatus (electrical power quality control, load demand peak control, …), making easy the integration of different kind of renewable electrical energy resources (wind plant, fuel-cells, …).

Ontology-based personalized search and browsing <=SEP=> As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.

Learning-to-Rank in Research Paper CBF recommendation: Leveraging Irrelevant Papers <=SEP=> Suggesting relevant literature to researchers has become an active area of study, typically relying on content-based filtering (CBF) over the rich textual features available. Given the high dimensionality and the sparsity of the training samples inherent to this domain, the focus has so far been on heuristic-based methods. In this paper, we argue for the model-based approach and propose a learning-to-rank method that leverages publicly available publications’ metadata to produce an effective prediction model. The proposed method is systematically evaluated on a scholarly paper recommendation dataset and compared against state-of-the-art model-based approaches as well as current, domain-specific heuristic methods. The results show that our approach clearly outperforms state-of-the-art research paper recommendations utilizing only publicly available meta-data.

Upper lateral cartilage fold-in flap: a combined spreader and/or splay graft effect without cartilage grafts. <=SEP=> Dorsal hump reduction almost always breaks the internal nasal valve and nasal obstruction is likely to occur postoperatively, unless reconstructed. One hundred eighty patients were operated using both open and closed rhinoplasty approaches. Upper lateral cartilages were meticulously separated from their junction with septum. Following bony and septal cartilaginous hump removal, upper lateral cartilages were folded inward. Either transcartilaginous horizontal mattress/simple sutures or perichondrial sutures were used depending of the desired width of the middle vault and the necessity for a splay-graft effect. In 7 patients unilateral, and in 1 patient bilateral, nasal synechia occurred and they were all treated under local anesthesia. All patients but 9 stated significantly improved nasal breathing. There was no inverted-V deformity or middle-vault narrowing observed. This technique is simple and physiologic, might be applicable for almost all primary rhinoplasty patients. Although it is possible with closed rhinoplasty approaches, it is easier with an open approach.

Light warping for enhanced surface depiction <=SEP=> Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance.
 Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios.
 Note: Third-Party Material Attribution  Third-party material used in ACM Transactions on Graphics 28(3), Article 25 - "Light Warping for Enhanced Surface Depiction," by Vergne, Pacanowski, Barla, Granier, and Schlick - was used without proper attribution.
 The 3D model used in Figures 1, 3, and 5, as well as in the cover image of this volume of the journal, was downloaded from the Shape Repository of AIM@SHAPE Project (http://shapes.aimatshape.net) and is the property of CNR-IMATI.
 We regret this oversight.

Fully Distributed Scrum: The Secret Sauce for Hyperproductive Offshored Development Teams <=SEP=> Scrum was designed to achieve a hyperproductive state where productivity increases 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshore teams can consistently achieve the hyperproductive state. In particular, can a team establish a localized velocity and then maintain or increase that velocity when distributing teams across continents. Since 2006, Xebia started projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show increasing velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model very similar to the SirsiDynix model (J. Sutherland, 2006) for high performance, distributed, offshore teams with outstanding quality.

Efficient mapping of quantum circuits to the IBM QX architectures <=SEP=> In March 2017, IBM launched the project IBM Q with the goal to provide access to quantum computers for a broad audience. This allowed users to conduct quantum experiments on a 5-qubit and, since June 2017, also on a 16-qubit quantum computer (called IBM QX2 and IBM QX3, respectively). In order to use these, the desired quantum functionality (e.g. provided in terms of a quantum circuit) has to properly be mapped so that the underlying physical constraints are satisfied — a complex task. This demands for solutions to automatically and efficiently conduct this mapping process. In this paper, we propose such an approach which satisfies all constraints given by the architecture and, at the same time, aims to keep the overhead in terms of additionally required quantum gates minimal. The proposed approach is generic and can easily be configured for future architectures. Experimental evaluations show that the proposed approach clearly outperforms IBM's own mapping solution with respect to runtime as well as resulting costs.