Query: A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

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Building a Large Annotated Corpus of English: The Penn Treebank <=SEP=> In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389

Visualizing and Understanding Convolutional Networks <=SEP=> Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark (Krizhevsky et al., 2012). However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition <=SEP=> We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

Enhanced LSTM for Natural Language Inference <=SEP=> (1) Handcrafted features [BAPM15] 78.2 (2) LSTM [BGR+16] 80.6 (3) GRU [VKFU15] 81.4 (4) Tree CNN [MML+16] 82.1 (5) SPINN-PI [BGR+16] 83.2 (6) BiLSTM intra-Att [LSLW16] 84.2 (7) NSE [MY16a] 84.6 (8) Att-LSTM [RGH+15] 83.5 (9) mLSTM [WJ16] 86.1 (10) LSTMN [CDL16] 86.3 (11) Decomposable Att [PTDU16] 86.3 (12) Intra-sent Att+(11) [PTDU16] 86.8 (13) NTI-SLSTM-LSTM [MY16b] 87.3 (14) Re-read LSTM [SCSL16] 87.5 (15) btree-LSTM [PAD+16] 87.6

A Convolutional Neural Network for Modelling Sentences <=SEP=> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

The dynamics of organizational identity <=SEP=> While many organizational researchers make reference to Mead’s theory of social identity, none have explored how Mead’s ideas about the relationship between the “I” and the “me” might be extended to identity processes at the organizational level of analysis. In this paper we define organizational analogues for Mead’s “I” and “me” and explain how these two phases of organizational identity are related. In doing so we bring together existing theory concerning the links between organizational identities and images, with new theory concerning how reflection embeds identity in organizational culture and how identity expresses cultural understandings through symbols. We offer a model of organizational identity dynamics built on four processes linking organizational identity to culture and image. While the processes linking identity and image (mirroring and impressing) have been described in the literature before, the contribution of this paper lies in articulation of the processes linking identity and culture (reflecting and expressing), and of the interaction of all four processes working dynamically together to create, maintain and change organizational identity. We discuss the implications of our model in terms of two dysfunctions of organizational identity dynamics: narcissism and loss of culture.

Cross-modal plasticity: where and how? <=SEP=> Animal studies have shown that sensory deprivation in one modality can have striking effects on the development of the remaining modalities. Although recent studies of deaf and blind humans have also provided convincing behavioural, electrophysiological and neuroimaging evidence of increased capabilities and altered organization of spared modalities, there is still much debate about the identity of the brain systems that are changed and the mechanisms that mediate these changes. Plastic changes across brain systems and related behaviours vary as a function of the timing and the nature of changes in experience. This specificity must be understood in the context of differences in the maturation rates and timing of the associated critical periods, differences in patterns of transiently existing connections, and differences in molecular factors across brain systems.

A Machine Learning Approach to Twitter User Classification <=SEP=> This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.

A Weighted Finite-State Transducer (WFST)-Based Language Model for Online Indic Script Handwriting Recognition <=SEP=> Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.

#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter <=SEP=> Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-theart face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.

Feminism and interaction design <=SEP=> This workshop is aimed at exploring the issues at the intersection of feminist thinking and human computer interaction. Both feminism and HCI have made important contributions to social science in the past several decades, but though their potential for overlap seem high, they have not engaged each other directly until recently. In this workshop we will explore diverse--and contentious--ways that feminist perspectives can support user research, design ideation and problem framing, sketching and prototyping, and design criticism and evaluation. The workshop will include fast-moving mini-panels and hands-on group exercises emphasizing feminist interaction criticism and design ideation.

Scope+: a stereoscopic video see-through augmented reality microscope <=SEP=> During the usage of conventional stereo microscope, repetitive head movement are often inevitable for the users to retrieve information away from the microscope. Moreover, risks of surgeons loosing focus and increasing fatigue could also arise during microsurgeries. Therefore, Scope+, a stereoscopic video see-through augmented reality system was created not only to solve the problems mentioned above, but also to improve users stereo microscopic experience.

A New Approach to Linear Filtering and Prediction Problems <=SEP=> AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems

Radio Link Frequency Assignment <=SEP=> The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NP-hard) optimization problem. In 1993, the CELAR (the French “Centre d'Electronique de l'Armement”) built a suite of simplified versions of Radio Link Frequency Assignment Problems (RLFAP) starting from data on a real network Roisnel93. Initially designed for assessing the performances of several Constraint Logic Programming languages, these benchmarks have been made available to the public in the framework of the European EUCLID project CALMA (Combinatorial Algorithms for Military Applications). These problems should look very attractive to the CSP community: the problem is simple to represent, all constraints are binary and involve finite domain variables. They nevertheless have some of the flavors of real problems (including large size and several optimization criteria). This paper gives essential facts about the CELAR instances and also introduces the GRAPH instances which were generated during the CALMA project.

A three-stage decision framework for multi-subject emotion recognition using physiological signals <=SEP=> This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an emotion pool that an instance of the test subject can be assigned to; and (3) in the final stage, generating the predicted emotion from the given emotion pool for the test instance. In comparison with a series of alternative methods, the high accuracy of 70.04% achieved by our proposed method clearly demonstrates the potential of the three-stage decision method in multi-subject emotion recognition.

Multimodal deep learning for robust RGB-D object recognition <=SEP=> Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition. Our architecture is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network. We focus on learning with imperfect sensor data, a typical problem in real-world robotics tasks. For accurate learning, we introduce a multi-stage training methodology and two crucial ingredients for handling depth data with CNNs. The first, an effective encoding of depth information for CNNs that enables learning without the need for large depth datasets. The second, a data augmentation scheme for robust learning with depth images by corrupting them with realistic noise patterns. We present state-of-the-art results on the RGB-D object dataset [15] and show recognition in challenging RGB-D real-world noisy settings.

Replicating Milgram: Would people still obey today? <=SEP=> The author conducted a partial replication of Stanley Milgram's (1963, 1965, 1974) obedience studies that allowed for useful comparisons with the original investigations while protecting the well-being of participants. Seventy adults participated in a replication of Milgram's Experiment 5 up to the point at which they first heard the learner's verbal protest (150 volts). Because 79% of Milgram's participants who went past this point continued to the end of the shock generator's range, reasonable estimates could be made about what the present participants would have done if allowed to continue. Obedience rates in the 2006 replication were only slightly lower than those Milgram found 45 years earlier. Contrary to expectation, participants who saw a confederate refuse the experimenter's instructions obeyed as often as those who saw no model. Men and women did not differ in their rates of obedience, but there was some evidence that individual differences in empathic concern and desire for control affected participants' responses.

MaCow: Masked Convolutional Generative Flow <=SEP=> Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MACOW), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MACOW enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models.

Spaceborne MIMO Synthetic Aperture Radar for Multimodal Operation <=SEP=> In this paper, we introduce a novel multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) concept for multimodal operation. The proposed system employs waveforms based on the orthogonal frequency division multiplexing (OFDM) technique and digital beamforming (DBF) on receive. Thereby, it becomes feasible to maximize spatial degrees of freedom, which are necessary for the multimodal operation. The proposed MIMO SAR system produces multiple high-resolution wide-swath SAR imageries that are used for coherent postprocessing. Through this paper, we aim to open up a new perspective of using MIMO concept for a wide-swath SAR imaging with high resolution in interferometric and polarimetric modes, based on OFDM and DBF techniques. Therefore, this paper encompasses broad theoretical backgrounds and general system design issues as well as specific signal processing techniques and aspects.

A Different Approach to the Management of Osteoarthritis in the Knee: Ultrasound Guided Genicular Nerve Block. <=SEP=> A 61-year-old female was referred to our tertiary physical medicine and rehabilitation outpatient clinic with a 7-year history of severe left knee pain. The patient was a nonsmoker and obese, with no comorbidities, and reported that the left knee pain worsened when walking and descending stairs and particularly when climbing stairs. Radiography of the left knee revealed KellgrenLawrence grade 3 osteoarthritis (OA) with multiple osteophytes and definite joint space narrowing.

Qualitative comparison of graph-based and logic-based multi-relational data mining: a case study <=SEP=> The goal of this paper is to generate insights about the differences between graph-based and logic-based approaches to multi-relational data mining by performing a case study of graph-based system, Subdue and the inductive logic programming system, CProgol. We identify three key factors for comparing graph-based and logic-based multi-relational data mining; namely, the ability to discover structurally large concepts, the ability to discover semantically complicated concepts and the ability to effectively utilize background knowledge. We perform an experimental comparison of Subdue and CProgol on the Mutagenesis domain and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue.

Lung Nodule Detection Using Convolutional Neural Networks <=SEP=> We present a novel procedure to apply deep learning techniques to medical image classification. With increasing popularity of computed tomography (CT) lung screening, fully manual diagnosis of lung cancer puts a burden on the radiologists who need to spend hours reading through CT scanned images to identify Region of Interests (ROIs) to schedule follow-ups. Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists. However, lung cancer detection is challenging because of the varying size, location, shape, and density of nodules. Many studies have approached this problem using image-processing techniques with the intention of developing an optimal set of features. Convolutional neural network has demonstrated to learn discriminative visual features automatically and has beat many state-of-art algorithms in image-processing tasks, such as pattern recognition, object detection, segmentation, etc. In this report, we evaluate the feasibility of implementing deep learning algorithms for lung cancer diagnosis with the Lung Image Database Consortium (LIDC) database. We compare the performance of two 2.5D convolutional neural network models, two 3D convolutional neural network models, and one 3D convolutional neural networks with a 3D spatial transformer network module on the task of detecting lung nodules. The performance of our best model is comparable with the state-of-art results in the lung nodule detection task.

Extreme Programming and Agile Software Development Methodologies <=SEP=> As a stakeholder of a software project, how does this sound to you? You can have releases as often as you like. The small number of defects is unprecedented. All of the features in the system are the most valuable ones to your business. At anytime, you have access to complete, accurate information as the status of any feature and of the quality of the system as a whole. The team developing your project works in an energized space with constant communication about the project. You are not dependent on any one or even two programmers for the continued success of the project. If your needs change, the development team welcomes the change of direction. As a developer of a software project, how does this sound to you? No one estimates your tasks but you, period! You always have access to a customer to clarify details about the features you are implementing. You are free (and required) to clean up the code whenever necessary. You complete a project every two weeks. You can work in any part of the system that you wish. You can pick who will help you on any given task. You are not required to work constantly long hours. Does this sound to good to be true? Teams are achieving these advantages using a relatively new set of software methodologies. Collectively, they are referred to as Agile Software Development Methodologies. The most pervasive is Extreme Programming. This chapter will introduce you to these exciting, popular, yet controversial new approaches to software development. ____________________________________________________________________________

Re-inventing PTPTN study loan with blockchain and smart contracts <=SEP=> The issue of default payments from borrowers of the National Higher Education Fund Corporation (PTPTN) is worrisome. Many borrowers fail to pay their loans and claims that the PTPTN has poor management and filing system. This study proposed a prototype for managing study loan repayment utilizing blockchain and smart contracts. Borrowers have full access toward their accounts and ledgers while corporation filing and management system get automatically up-to-date with the assistance of smart contracts.

Arousal-Biased Competition in Perception and Memory. <=SEP=> Our everyday surroundings besiege us with information. The battle is for a share of our limited attention and memory, with the brain selecting the winners and discarding the losers. Previous research shows that both bottom-up and top-down factors bias competition in favor of high priority stimuli. We propose that arousal during an event increases this bias both in perception and in long-term memory of the event. Arousal-biased competition theory provides specific predictions about when arousal will enhance memory for events and when it will impair it, which accounts for some puzzling contradictions in the emotional memory literature.

Innovation in Analogical Design: A Model-Based Approach <=SEP=> Analogical reasoning, it is commonly accepted, plays an important role in innovation and creativity. Since design often is innovative, and can be creative, the design task provides a good context for exploring the role of analogy in innovation and creativity. In the IDEAL project, we are exploring the processes of innovation and creativity in the context of conceptual (or preliminary, qualitative) design of physical devices. In particular, we are developing a model-based approach to innovation and creativity in analogical design: our core hypothesis is that innovative analogical design involves both reasoning from past experiences in designing devices (in the form of analogs or cases) and comprehension of how those devices work (in the form of device models or theories). In this paper, we focus on the issues of understanding feedback on the performance of a design, discovery of new design constraints, and reformulation of the design problem, and describe how the model-based approach addresses these issues.

Stronger generalization bounds for deep nets via a compression approach <=SEP=> Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds that’re orders of magnitude better in practice. These rely upon new succinct reparametrizations of the trained net — a compression that is explicit and efficient. These yield generalization bounds via a simple compression-based framework introduced here. Our results also provide some theoretical justification for widespread empirical success in compressing deep nets. Analysis of correctness of our compression relies upon some newly identified “noise stability”properties of trained deep nets, which are also experimentally verified. The study of these properties and resulting generalization bounds are also extended to convolutional nets, which had eluded earlier attempts on proving generalization.

Automatic Text Classification: A Technical Review <=SEP=> Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

Control of the power in induction heating systems with L-LC resonant voltage source inverters <=SEP=> This paper is a synthesis of the control circuit in an induction heating system with three-phase fully-controlled rectifier, single-phase voltage source inverter and L-LC resonant load. The attention is mainly directed on the control of the power transmitted to the induction coil. It is highlighted that the analytical design of a proportional integral derivative or proportional integral controller is practically impossible when the direct control of the current through inductor is desired. The two possibilities of power control taken into consideration are the inverter output current control and the inverter input voltage control. The design of the current and voltage controllers of proportional integral derivative type was carried out successfully based on Modulus Optimum criterion in Kessler variant. The performances of the control system in both variants of the power control were tested and validated by simulation under the Matlab-Simulink environment by using real parameters of induction coils and heated pipes from a leading Romanian manufacturer.

"Exercise to be fit, not skinny": The effect of fitspiration imagery on women's body image. <=SEP=> Fitspiration is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. The present study aimed to experimentally investigate the impact of fitspiration images on women's body image. Participants were 130 female undergraduate students who were randomly assigned to view either a set of Instagram fitspiration images or a control set of travel images presented on an iPad. Results showed that acute exposure to fitspiration images led to increased negative mood and body dissatisfaction and decreased state appearance self-esteem relative to travel images. Importantly, regression analyses showed that the effects of image type were mediated by state appearance comparison. Thus it was concluded that fitspiration can have negative unintended consequences for body image. The results offer support to general sociocultural models of media effects on body image, and extend these to "new" media.