Query: Cosine Siamese Models for Stance Detection

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NLTK: The Natural Language Toolkit <=SEP=> NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.

Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection <=SEP=> Paraphrase detection is the task of examining two sentences and determining whether they have the same meaning. In order to obtain high accuracy on this task, thorough syntactic and semantic analysis of the two statements is needed. We introduce a method for paraphrase detection based on recursive autoencoders (RAE). Our unsupervised RAEs are based on a novel unfolding objective and learn feature vectors for phrases in syntactic trees. These features are used to measure the wordand phrase-wise similarity between two sentences. Since sentences may be of arbitrary length, the resulting matrix of similarity measures is of variable size. We introduce a novel dynamic pooling layer which computes a fixed-sized representation from the variable-sized matrices. The pooled representation is then used as input to a classifier. Our method outperforms other state-of-the-art approaches on the challenging MSRP paraphrase corpus.

A Comparison of Vector-based Representations for Semantic Composition <=SEP=> In this paper we address the problem of modeling compositional meaning for phrases and sentences using distributional methods. We experiment with several possible combinations of representation and composition, exhibiting varying degrees of sophistication. Some are shallow while others operate over syntactic structure, rely on parameter learning, or require access to very large corpora. We find that shallow approaches are as good as more computationally intensive alternatives with regards to two particular tests: (1) phrase similarity and (2) paraphrase detection. The sizes of the involved training corpora and the generated vectors are not as important as the fit between the meaning representation and compositional method.

Learning Discriminative Projections for Text Similarity Measures <=SEP=> Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We propose a novel discriminative training method that projects the raw term vectors into a common, low-dimensional vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the projected vectors, and is able to efficiently handle a large number of training examples in the highdimensional space. Evaluated on two very different tasks, cross-lingual document retrieval and ad relevance measure, our method not only outperforms existing state-of-the-art approaches, but also achieves high accuracy at low dimensions and is thus more efficient.

Ground-Penetrating Radar Theory and Application of Thin-Bed Offset-Dependent Reflectivity <=SEP=> Offset-dependent reflectivity or amplitude-variationwith-offset AVO analysis of ground-penetrating radar GPR data may improve the resolution of subsurface dielectric permittivity estimates. A horizontally stratified medium has a limiting layer thickness below which thin-bed AVO analysis is necessary. For a typical GPR signal, this limit is approximately 0.75 of the characteristic wavelength of the signal. Our approach to modeling the GPR thin-bed response is a broadband, frequency-dependent computation that utilizes an analytical solution to the three-interface reflectivity and is easy to implement for either transverse electric TE or transverse magnetic TM polarizations. The AVO curves for TE and TM modes differ significantly. In some cases, constraining the interpretation using both TE and TM data is critical. In two field examples taken from contaminated-site characterization data, we find quantitative thin-bed modeling agrees with the GPR field data and available characterization data.

Combining Labeled and Unlabeled Data for MultiClass Text Categorization <=SEP=> Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amount of labeled data required is to develop algorithms that can learn e ectively from a small number of labeled examples augmented with a large number of unlabeled examples. Current text learning techniques for combining labeled and unlabeled, such as EM and Co-Training, are mostly applicable for classi cation tasks with a small number of classes and do not scale up well for large multiclass problems. In this paper, we develop a framework to incorporate unlabeled data in the Error-Correcting Output Coding (ECOC) setup by rst decomposing multiclass problems into multiple binary problems and then using Co-Training to learn the individual binary classi cation problems. We show that our method is especially useful for text classi cation tasks involving a large number of categories and outperforms other semi-supervised learning techniques such as EM and Co-Training. In addition to being highly accurate, this method utilizes the hamming distance from ECOC to provide high-precision results. We also present results with algorithms other than co-training in this framework and show that co-training is uniquely suited to work well within ECOC.

Multi-fiber Networks for Video Recognition <=SEP=> In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks. To this end, we present the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network. To facilitate information flow between fibers we further incorporate multiplexer modules and end up with an architecture that reduces the computational cost of 3D networks by an order of magnitude, while increasing recognition performance at the same time. Extensive experimental results show that our multi-fiber architecture significantly boosts the efficiency of existing convolution networks for both image and video recognition tasks, achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics datasets. Our proposed model requires over 9× and 13× less computations than the I3D [1] and R(2+1)D [2] models, respectively, yet providing higher accuracy.

Evolved GANs for generating pareto set approximations <=SEP=> In machine learning, generative models are used to create data samples that mimic the characteristics of the training data. Generative adversarial networks (GANs) are neural-network based generator models that have shown their capacity to produce realistic samples in different domains. In this paper we propose a neuro-evolutionary approach for evolving deep GAN architectures together with the loss function and generator-discriminator synchronization parameters. We also propose the problem of Pareto set (PS) approximation as a suitable benchmark to evaluate the quality of neural-network based generators in terms of the accuracy of the solutions they generate. The covering of the Pareto front (PF) by the generated solutions is used as an indicator of the mode-collapsing behavior of GANs. We show that it is possible to evolve GANs that generate good PS approximations. Our method scales to up to 784 variables and that it is capable to create architecture transferable across dimensions and functions.

Where Do Creative Interactions Come From? The Role of Tie Content and Social Networks <=SEP=> U the determinants of creativity at the individual and organizational level has been the focus of a long history of research in various disciplines from the social sciences, but little attention has been devoted to studying creativity at the dyadic level. Why are some dyadic interactions more likely than others to trigger the generation of novel and useful ideas in organizations? As dyads conduit both knowledge and social forces, they offer an ideal setting to disentangle the effects of knowledge diversity, tie strength, and network structure on the generation of creative thoughts. This paper not only challenges the current belief that sporadic and distant dyadic relationships (weak ties) foster individual creativity but also argues that diverse and strong ties facilitate the generation of creative ideas. From a knowledge viewpoint, our results suggest that ties that transmit a wide (rather than narrow) set of knowledge domains (within the same tie) favor creative idea generation if exchanges occur with sufficient frequency. From a social perspective, we find that strong ties serve as effective catalysts for the generation of creative ideas when they link actors who are intrinsically motivated to work closely together. Finally, this paper also shows that dyadic network cohesion (i.e., the connections from the focal dyad to common contacts) does not always hinder the generation of creative ideas. Our empirical evidence suggests that when cohesion exceeds its average levels, it becomes detrimental to creative idea generation. Hypotheses are tested in a sociometric study conducted within the development department of a software firm.

A lightweight method for virtual machine introspection <=SEP=> A method for the introspection of virtual machines is proposed. The main distinctive feature of this method is that it makes it possible to obtain information about the system operation using the minimum knowledge about its internal organization. The proposed approach uses rarely changing parts of the application binary interface, such as identifiers and parameters of system calls, calling conventions, and the formats of executable files. The lightweight property of the introspection method is due to the minimization of the knowledge about the system and by its high performance. The introspection infrastructure is based on the QEMU emulator, version 2.8. Presently, monitoring the file operations, processes, and API function calls are implemented. The available introspection tools (RTKDSM, Panda, and DECAF) get data for the analysis using kernel structures. All the data obtained (addresses of structures, etc.) is written to special profiles. Since the addresses and offsets strongly depend not only on the version of the operating system but also on the parameters of its assembly, these tools have to store a large number of profiles. We propose to use parts of the application binary interface because they are rarely modified and it is often possible to use one profile for a family of OSs. The main idea underlying the proposed method is to intercept the system and library function calls and read parameters and returned values. The processor provides special instructions for calling system and user defined functions. The capabilities of QEMU are extended by an instrumentation mechanism to enable one to track each executed instruction and find the instructions of interest among them. When a system call occurs, the control is passed to the system call detector that checks the number of the call and decides to which module the job should be forwarded. In the case of an API function call, the situation is similar, but the API function detector checks the function address. An introspection tool consisting of a set of modules is developed. These modules are dynamic libraries that are plugged in QEMU. The modules can interact by exchanging data.

Validation, verification, and testing techniques throughout the life cycle of a simulation study <=SEP=> Life cycle validation, verification, and testing (VV&T) is extremely important for the success of a simulation study. This paper surveys current software VV&T techniques and current simulation model VV&T techniques and describes how they can all be applied throughout the life cycle of a simulation study. The processes and credibility assessment stages of the life cycle are described and the applicability of the VV&T techniques for each stage is stated.

Decision feedback equalization <=SEP=> As real world communication channels are stressed with higher data rates, intersymbol interference (ISI) becomes a dominant limiting factor. One way to combat this effect that has recently received considerable attention is the use of a decision feedback equalizer (DFE) in the receiver. The action of the DFE is to feed back a weighted sum of past decision to cancel the ISI they cause in the present signaling interval. This paper summarizes the work in this area beginning with the linear equalizer. Three performance criteria have been used to derive optimum systems; 1) minimize the noise variance under a "zero forcing" (ZF) constraint i.e., insist that all intersymbol interference is cancelled, 2) minimize the mean-square error (MMSE) between the true sample and the observed signal just prior to the decision threshold, and 3) minimize the probability of error (Min Pe). The transmitter can be fixed and the receiver optimized or one can obtain the joint optimum transmitter and receiver. The number of past decisions used in the feedback equalization can be finite or infinite. The infinite case is easier to handle analytically. In addition to reviewing the work done in the area, we show that the linear equalizer is in fact a portion of the DFE receiver and that the processing done by the DFE is exactly equivalent to the general problem of linear prediction. Other similarities in the various system structures are also shown. The effect of error propagation due to incorrect decisions is discussed, and the coaxial cable channel is used as an example to demonstrate the improvement available using DFE.

Perceived qualities of smart wearables: determinants of user acceptance <=SEP=> Wearable computers are one of the new technologies that are expected to be a part of users' lives extensively in near future. While some of the users have positive attitudes towards these new products, some users may reject to use them due to different reasons. User experience is subjective, and effected by various parameters. Among these the first impression, namely the perceived qualities has an important impact on product acceptance. This paper aims to explore the perceived qualities of wearables and define the relations between them. An empirical study is conducted, to find out the hierarchy and meaningful relationships between the perceived qualities of smart wearables. The study is based on personal construct theory and data is presented by Cross-Impact Analysis. The patterns behind affection and affected qualities are explored to understand the design requirements for the best integration of wearables into daily lives.

Punching shear capacity of continuous slabs <=SEP=> Provisions for punching shear design of reinforced concrete slabs are usually calibrated on the basis of results from tests on isolated specimens that simulate the slab zone within the points of contraflexure around a column. However, the punching behavior of interior slab-column connections in actual continuous slabs without transverse reinforcement may be influenced by the effects of moment redistribution and compressive membrane action, which can lead to higher punching strengths and lower deformation capacities compared to those in isolated specimens. This paper discusses these behavioral differences on the basis of experiments performed on symmetric edge-restrained slabs and investigates available test data by means of a numerical model. A simplified calculation method (based on the Critical Shear Crack Theory) that accounts for these effects is also proposed. The calculation model shows consistent agreement with the results of the numerical evaluation and is sufficiently simple to be used in design and assessment.

Let's talk about it: evaluating contributions through discussion in GitHub <=SEP=> Open source software projects often rely on code contributions from a wide variety of developers to extend the capabilities of their software. Project members evaluate these contributions and often engage in extended discussions to decide whether to integrate changes. These discussions have important implications for project management regarding new contributors and evolution of project requirements and direction. We present a study of how developers in open work environments evaluate and discuss pull requests, a primary method of contribution in GitHub, analyzing a sample of extended discussions around pull requests and interviews with GitHub developers. We found that developers raised issues around contributions over both the appropriateness of the problem that the submitter attempted to solve and the correctness of the implemented solution. Both core project members and third-party stakeholders discussed and sometimes implemented alternative solutions to address these issues. Different stakeholders also influenced the outcome of the evaluation by eliciting support from different communities such as dependent projects or even companies. We also found that evaluation outcomes may be more complex than simply acceptance or rejection. In some cases, although a submitter's contribution was rejected, the core team fulfilled the submitter's technical goals by implementing an alternative solution. We found that the level of a submitter's prior interaction on a project changed how politely developers discussed the contribution and the nature of proposed alternative solutions.

To Kill a Centrifuge A Technical Analysis of What Stuxnet ’ s Creators Tried to Achieve <=SEP=> 

Fixed-Rate Compressed Floating-Point Arrays <=SEP=> Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4d values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.

Literature Review of Teamwork Models <=SEP=> Both human collaboration and software agent collaboration have been thoroughly studied, but there is relatively little research on hybrid human-agent teamwork. Some research has identified the roles that agents could play in hybrid teams: supporting individual team members, being a teammate, or supporting the team as a whole [99]. Some other work [57] has investigated trust concepts as the fundamental building block for effective human-agent teamwork or posited the types of shared knowledge that promote mutual understanding between cooperating humans and agents [9, 68]. However, many of the facets of human agent teamwork models, such as communication protocols for forming mutual intelligibility, performing team monitoring to assess progress, forming joint goals, addressing task interdependencies in hybrid teamwork are still unexplored. In this report, we address the following questions: 1. what factors affect human team task performance and cognition? 2. how can agent coordination mechanisms be adapted for human-agent teams? 3. with current technologies, what roles can agents successfully fill in hybrid human-agent teams? 4. what are the barriers to human-agent interaction?

Transformational leadership in nursing: a concept analysis. <=SEP=> AIM
To analyse the concept of transformational leadership in the nursing context.


BACKGROUND
Tasked with improving patient outcomes while decreasing the cost of care provision, nurses need strategies for implementing reform in health care and one promising strategy is transformational leadership. Exploration and greater understanding of transformational leadership and the potential it holds is integral to performance improvement and patient safety.


DESIGN
Concept analysis using Walker and Avant's (2005) concept analysis method.


DATA SOURCES
PubMed, CINAHL and PsychINFO.


METHODS
This report draws on extant literature on transformational leadership, management, and nursing to effectively analyze the concept of transformational leadership in the nursing context.


IMPLICATIONS FOR NURSING
This report proposes a new operational definition for transformational leadership and identifies model cases and defining attributes that are specific to the nursing context. The influence of transformational leadership on organizational culture and patient outcomes is evident. Of particular interest is the finding that transformational leadership can be defined as a set of teachable competencies. However, the mechanism by which transformational leadership influences patient outcomes remains unclear.


CONCLUSION
Transformational leadership in nursing has been associated with high-performing teams and improved patient care, but rarely has it been considered as a set of competencies that can be taught. Also, further research is warranted to strengthen empirical referents; this can be done by improving the operational definition, reducing ambiguity in key constructs and exploring the specific mechanisms by which transformational leadership influences healthcare outcomes to validate subscale measures.

Expression of stressful experiences through writing: effects of a self-regulation manipulation for pessimists and optimists. <=SEP=> This study assessed the effectiveness of a writing task designed to foster self-regulatory coping with stressful experiences to reduce medical clinic visits and to promote adjustment. Students entering college (N = 122) who were classified as optimists or pessimists by using a dispositional optimism measure participated in a self-regulation task (expressing thoughts and feelings about entering college and then formulating coping plans), a disclosure task (expressing thoughts and feelings only), or a control task (writing about trivial topics) for 3 weekly writing sessions. Among optimists, both the self-regulation task and the disclosure task reduced illness-related clinic visits during the following month; among pessimists, only the self-regulation task reduced clinic visits. In general, the self-regulation task beneficially affected mood state and college adjustment whereas the disclosure task increased grade point averages.

Statistical Nested Sensor Array Signal Processing <=SEP=> OF THE DISSERTATION Statistical Nested Sensor Array Signal Processing by Keyong Han Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral xi information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods.

A Comprehensive Study for Software Testing and Test Cases Generation Paradigms <=SEP=> Software testing is accounted to be an essential part in software development life cycle in terms of cost and manpower, where its total cost is considerable high. Consequently, many studies [48] have been conducted to minimize the associated cost and human effort to fix bugs and errors, and to improve the quality of testing process by automatically generating test cases. Test cases can be generated from different phases (requirement phase, design phase and after development phase). Though, test case generation at early stages is more effective rather than that after development, where time and effort used for finding and fixing errors and bugs is less than that after development. At later stage, fixing errors results in enormous code correction, consuming a lot of time and effort. In this paper, we study the different paradigms of testing techniques for generating test cases, where we investigate their coverage and associated capabilities. We finally propose a preliminary model for a generic automated test cases generation.

PZT-Actuated and -Sensed Resonant Micromirrors with Large Scan Angles Applying Mechanical Leverage Amplification for Biaxial Scanning <=SEP=> This article presents design, fabrication and characterization of lead zirconate titanate (PZT)-actuated micromirrors, which enable extremely large scan angle of up to 106° and high frequency of 45 kHz simultaneously. Besides the high driving torque delivered by PZT actuators, mechanical leverage amplification has been applied for the micromirrors in this work to reach large displacements consuming low power. Additionally, fracture strength and failure behavior of poly-Si, which is the basic material of the micromirrors, have been studied to optimize the designs and prevent the device from breaking due to high mechanical stress. Since comparing to using biaxial micromirror, realization of biaxial scanning using two independent single-axial micromirrors shows considerable advantages, a setup combining two single-axial micromirrors for biaxial scanning and the results will also be presented in this work. Moreover, integrated piezoelectric position sensors are implemented within the micromirrors, based on which closed-loop control has been developed and studied.

Streaming Hierarchical Video Segmentation <=SEP=> The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.

Design and Implementation of Power Converters for Hybrid Wind-Solar Energy Conversion System with an Implementation of MPPT <=SEP=> This paper presents the design and implementation of power converters for wind conversion systems. The power converter can not only transfer the power from a wind generator, but also improve the stability and safety of the system. The proposed system consists of a Permanent magnet synchronous generator (PMSG); a DC/DC boosts converter, a bi-directional DC/DC converter and a full-bridge DC/AC inverter. The wind generator is the main power source of the system, and the battery is used for energy storage and power compensation to recover the natural irregularity of the wind power. In this paper presents a new system configuration of the front-end rectifier stage for a hybrid wind or photo voltaic energy system. The configuration allows the two sources to supply the load separately or simultaneously, depending on the availability of energy sources. The inherent nature of this cuk-scpic fused converter, additional input filters are not necessary to filter out high frequency harmonic content is determinant for the generator life span, heating issue and efficiency. The fused multi-input rectifier stage also allows maximum power from the wind and sun. When it is available an adaptive MPPT algorithm will be used for photo voltaic (PV) system. Operational analysis of the proposed system, will discoursed in this paper simulation results are given to highlight the merit of the proposed circuit Index terms —Wind generator, PV and Fuel Cells, Bi-directional DC/DC converter, full-bridge DC/AC inverter, MPPT.

A Novel, Planar, and Compact Crossover Design for Dual-Band Applications <=SEP=> This paper presents, for the first time, the novel design of a dual-band crossover. The proposed circuit features planar implementation with moderate line impedance, compact layout, and widely separated frequency bands. Moreover, simple and explicit closed-form expressions are available for the exact evaluation of the line impedances. For verification, both simulated and measured results of a microstrip crossover operating at 800 MHz and 1.2 GHz are given.

Practical condition synchronization for transactional memory <=SEP=> Few transactional memory implementations allow for condition synchronization among transactions. The problems are many, most notably the lack of consensus about a single appropriate linguistic construct, and the lack of mechanisms that are compatible with hardware transactional memory. In this paper, we introduce a broadly useful mechanism for supporting condition synchronization among transactions. Our mechanism supports a number of linguistic constructs for coordinating transactions, and does so without introducing overhead on in-flight hardware transactions. Experiments show that our mechanisms work well, and that the diversity of linguistic constructs allows programmers to chose the technique that is best suited to a particular application.

Wasserstein Generative Adversarial Network <=SEP=> Recent advances in deep generative models give us new perspective on modeling highdimensional, nonlinear data distributions. Especially the GAN training can successfully produce sharp, realistic images. However, GAN sidesteps the use of traditional maximum likelihood learning and instead adopts an two-player game approach. This new training behaves very differently compared to ML learning. There are still many remaining problem of GAN training. In this thesis, we gives a comprehensive review of recently published methods or analysis on GAN training, especially the Wasserstein GAN and FlowGAN model. We also discuss the limitation of the later model and use this as the motivation to propose a novel generator architecture using mixture models. Furthermore, we also modify the discriminator architecture using similar ideas to allow ’personalized’ guidance. We refer the generator mixture model as Mixflow and mixture of discriminators as ’personalized GAN’ (PGAN). In experiment chapter, we demonstrate their performance advantages using toy examples compared to single flow model. In the end, we test their performance on MNIST dataset and the Mixflow model not only achieves the best log likelihood but also produce reasonable images compared to state-of-art DCGAN generation.

SparkClouds: Visualizing Trends in Tag Clouds <=SEP=> Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.