Reviewerly/exHarmony huggingface dataset:
DDL: Deep Dictionary Learning for Predictive Phenotyping

Predictive phenotyping is about accurately predicting what phenotypes will occur in the next clinical visit based on longitudinal Electronic Health Record (EHR) data. Several deep learning (DL) models have demonstrated great performance in predictive phenotyping. However, these DL-based phenotyping models requires access to a large amount of labeled data, which are often expensive to acquire. To address this label-insufficient challenge, we propose a deep dictionary learning framework (DDL) for phenotyping, which utilizes unlabeled data as a complementary source of information to generate a better, more succinct data representation. With extensive experiments on multiple real-world EHR datasets, we demonstrated DDL can outperform the state of the art predictive phenotyping methods on a wide variety of clinical tasks that require patient phenotyping such as heart failure classification, mortality prediction, and sequential prediction. All empirical results consistently show that unlabeled data can indeed be used to generate better data representation, which helps improve DDL's phenotyping performance over existing baseline methods that only uses labeled data.
Transferring Ensemble Representations Using Deep Convolutional Neural Networks for Small-Scale Image Classification

The deep convolutional neural networks (DCNN) require large number of training data to avoid overfitting, which makes it unsuitable for processing small-scale image datasets. The transfer learning using DCNN (TCNN) reuses pre-trained layers to generate a mid-level image representation so that the optimization of more than millions CNN parameters can be avoided. By this way, overfitting problem in small-scale data can be alleviated. However, although now many public DCNNs have been trained and can be reused, the existing TCNNs are formed by only a single pre-trained DCNN structure and cannot make full use of multiple structures of pre-trained DCNNs. At the same time, the existing ensemble CNNs have not enough good representation ability. To address this problem, we combine the conventional ideas of ensemble CNNs and propose three ensemble TCNNs (TECNN). They are the voting method based on the combination of all TCNNs, the PickOver method by finding the optimal combination, and weighted method by finding weighted combination. Different from the existing ensemble CNNs, the proposed methods do not need to retrain the component CNNs and generate ensemble transferring representations by transferring the pre-trained mid-level parameters. The mathematical models of those three methods are also provided. Their versions of using fine-tuning are also compared in the experiments. In addition, we replace the Softmax classifier with ensemble linear classifiers in the full-connection layer. They outperform the current state of the art algorithms on Caltech ImageNet and some internet image data. All this research has released as an open source library called Transferring Image Ensemble Representations using Deep Convolutional Neural Networks (TECNN). The source codes and relevant datasets in different versions are available from: http://www.cquptshuyinxia.com/TECNN.html.
Dense-view synthesis for three-dimensional light-field display based on unsupervised learning

Three-dimensional (3D) light field display, as a potential future display method, has attracted considerable attention. However, there still exist certain issues to be addressed, especially the capture of dense views in real 3D scenes. Using sparse cameras associated with view synthesis algorithm has become a practical method. Supervised convolutional neural network (CNN) is used to synthesize virtual views. However, such a large amount of training target views is sometimes difficult to be obtained and the training position is relatively fixed. Novel views can also be synthesized by unsupervised network MPVN, but the method has strict requirements on capturing multiple uniform horizontal viewpoints, which is not suitable in practice. Here, a method of dense-view synthesis based on unsupervised learning is presented, which can synthesize arbitrary virtual views with multiple free-posed views captured in the real 3D scene based on unsupervised learning. Multiple posed views are reprojected to the target position and input into the neural network. The network outputs a color tower and a selection tower indicating the scene distribution along the depth direction. A single image is yielded by the weighted summation of two towers. The proposed network is end-to-end trained based on unsupervised learning by minimizing errors during reconstructions of posed views. A virtual view can be predicted in a high quality by reprojecting posed views to the desired position. Additionally, a sequence of dense virtual views can be generated for 3D light-field display by repeated predictions. Experimental results demonstrate the validity of our proposed network. PSNR of synthesized views are around 30dB and SSIM are over 0.90. Since multiple cameras are supported to be placed in free-posed positions, there are not strict physical requirements and the proposed method can be flexibly used for the real scene capture. We believe this approach will contribute to the wide applications of 3D light-field display in the future.
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner's goal is to match the teacher’s demonstrated behavior. In this paper, we consider the setting where the learner has its own preferences that it additionally takes into consideration. These preferences can for example capture behavioral biases, mismatched worldviews, or physical constraints. We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner’s preferences. We design learner-aware teaching algorithms and show that significant performance improvements can be achieved over learner-agnostic teaching.
Hindsight Credit Assignment

We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rather than employing foresight. Somewhat surprisingly, we show that value functions can be rewritten through this lens, yielding a new family of algorithms. We study the properties of these algorithms, and empirically show that they successfully address important credit assignment challenges, through a set of illustrative tasks.
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas

Stylistic variation in text needs to be studied with different aspects including the writer's personal traits, interpersonal relations, rhetoric, and more.Despite recent attempts on computational modeling of the variation, the lack of parallel corpora of style language makes it difficult to systematically control the stylistic change as well as evaluate such models.We release PASTEL, the parallel and annotated stylistic language dataset, that contains ≈ 41K parallel sentences (8.3K parallel stories) annotated across different personas.Each persona has different styles in conjunction: gender, age, country, political view, education, ethnic, and time-of-writing.The dataset is collected from human annotators with solid control of input denotation: not only preserving original meaning between text, but promoting stylistic diversity to annotators.We test the dataset on two interesting applications of style language, where PASTEL helps design appropriate experiment and evaluation.First, in predicting a target style (e.g., male or female in gender) given a text, multiple styles of PASTEL make other external style variables controlled (or fixed), which is a more accurate experimental design.Second, a simple supervised model with our parallel text outperforms the unsupervised models using nonparallel text in style transfer.Our dataset is publicly available 1 .
An analysis of training and generalization errors in shallow and deep networks

This paper is motivated by an open problem around deep networks, namely, the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case of regression problems when each unit evaluates a periodic activation function. We argue that the minimal expected value of the square loss is inappropriate to measure the generalization error in approximation of compositional functions in order to take full advantage of the compositional structure. Instead, we measure the generalization error in the sense of maximum loss, and sometimes, as a pointwise error. We give estimates on exactly how many parameters ensure both zero training error as well as a good generalization error. We prove that a solution of a regularization problem is guaranteed to yield a good training error as well as a good generalization error and estimate how much error to expect at which test data.
Emergent Systematic Generalization in a Situated Agent

The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn.
Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing

Automatic liver segmentation plays a vital role in computer-aided diagnosis or treatment. Manual segmentation of organs is a tedious and challenging task and is prone to human errors. In this paper, we propose innovative pre-processing and adaptive 3D region growing methods with subject-specific conditions. To obtain strong edges and high contrast, we propose effective contrast enhancement algorithm then we use the atlas intensity distribution of most probable voxels in probability maps along with location before designing conditions for our 3D region growing method. We also incorporate the organ boundary to restrict the region growing. We compare our method with the label fusion of 13 organs on state-of-the-art Deeds registration method and achieved Dice score of 92.56%.
Robust Contact Angle Determination for Needle-in-Drop Type Measurements

One of the main approaches for contact angle determination using sessile drops with a missing apex (e.g., because of the presence of the needle tip) is the polynomial drop-profile fitting method. The major disadvantage of this fitting procedure is that the derived contact angle is highly sensitive to the polynomial order and the number of pixels involved in the actual fit. In the present work, an easily implementable method is introduced to effectively tackle these drawbacks. Instead of fitting the drop-profile itself, the polynomial fitting is applied to the difference between the drop profile and the circumcircle, independently for both sides of the drop. The derivative value of this difference at the contact point is used to correct the slope obtained analytically from the circumcircle. It is shown that this approach allows the robust determination of the contact angle with high (≤0.6°) accuracy in a straightforward manner, and the results are not affected by the actual contact angle, drop volume, or the resolution of the captured image. Validation of this new approach is also given in the contact angle range of 20°-150° by comparing the results to the values calculated by the Young-Laplace fit.
Heralded single-photon and correlated-photon-pair generation via spontaneous four-wave mixing in tapered optical fibers

We study the generation of correlated photon pairs and heralded single photons via strongly non-degenerate spontaneous four-wave mixing (SFWM) in a series of identical micro-/nano fibers (MNF). Joint spectral intensity of the biphoton field generated at the wavelength of about 880 nm and 1310 nm has been measured under excitation by 100 ps laser pulses demonstrating good agreement with the theoretical prediction. The measured zero-time second-order autocorrelation function was about 0.2 when the emission rate of the heralded photons was of 4 Hz. The MNF-based source perfectly matches standard single-mode fibers, which makes it compatible with the existing fiber communication networks. In addition, SFWM observation in a series of identical MNFs allows increasing generation rate of single photons via spatial multiplexing.
Robust Natural Language Inference Models with Example Forgetting.

We investigate whether example forgetting, a recently introduced measure of hardness of examples, can be used to select training examples in order to increase robustness of natural language understanding models in a natural language inference task (MNLI). We analyze forgetting events for MNLI and provide evidence that forgettable examples under simpler models can be used to increase robustness of the recently proposed BERT model, measured by testing an MNLI trained model on HANS, a curated test set that exhibits a shift in distribution compared to the MNLI test set. Moreover, we show that, the “large” version of BERT is more robust than its “base” version but its robustness can still be improved with our approach.
Assessing Sentiment of the Expressed Stance on Social Media

Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing. One may express a viewpoint towards a topic by using positive or negative language. This paper examines how the stance is being expressed in social media according to the sentiment polarity. There has been a noticeable misconception of the similarity between the stance and sentiment when it comes to viewpoint discovery, where negative sentiment is assumed to mean against stance, and positive sentiment means in-favour stance. To analyze the relation between stance and sentiment, we construct a new dataset with four topics and examine how people express their viewpoint with regards these topics. We validate our results by carrying a further analysis of the popular stance benchmark SemEval stance dataset. Our analyses reveal that sentiment and stance are not highly aligned, and hence the simple sentiment polarity cannot be used solely to denote a stance toward a given topic.
User experience as legitimacy trap

column User experience as legitimacy trap Share on Author: Paul Dourish UC Irvine UC IrvineView Profile Authors Info & Claims InteractionsVolume 26Issue 6November - December 2019 pp 46–49https://doi.org/10.1145/3358908Published:30 October 2019 6citation2,115DownloadsMetricsTotal Citations6Total Downloads2,115Last 12 Months1,253Last 6 weeks125 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Hyperspectral Image Denoising With Dual Deep CNN

A new hyperspectral image denoising algorithm, called the dual deep convolutional neural network (DD-CNN), is proposed in this paper. In contrast to internal denoising methods that utilize only the features from the target noisy image, the DD-CNN extensively explores the similarities between the target noisy image and the clean reference image from other bands. As external data, the reference images are selected based on the structural similarity index metric (SSIM). The DD-CNN is composed of two CNNs: one is responsible for extracting the features of the target image, and the other is responsible for extracting features from the reference image. A new activation function is proposed that activates the two types of features in the DD-CNN. Based on the dual structure and the new activation function, the external features extracted from the reference images are thoroughly integrated into the internal features of the target noise image. We experimented on different datasets with different noise levels; we also tested special cases for reference images with extra or undesirable features. The DD-CNN algorithm can effectively utilize the similarity between the external image and the target image. When the noise level is high, the advantages of the DD-CNN are obvious.
Demystifying Inter-Class Disentanglement

Learning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence. We present a unified formulation for class and content disentanglement and use it to illustrate the limitations of current methods. We therefore introduce LORD, a novel method based on Latent Optimization for Representation Disentanglement. We find that latent optimization, along with an asymmetric noise regularization, is superior to amortized inference for achieving disentangled representations. In extensive experiments, our method is shown to achieve better disentanglement performance than both adversarial and non-adversarial methods that use the same level of supervision. We further introduce a clustering-based approach for extending our method for settings that exhibit in-class variation with promising results on the task of domain translation.
Anomaly Detection for Univariate Time Series with Statistics and Deep Learning

This paper proposes an anomaly detection framework for univariate time series data. Based on the Dickey-Fuller test, fast Fourier transform (FFT), and Pearson product-moment correlation coefficient, data are classified into three classes, namely (1) stationary, (2) periodic and (3) non-stationary and non-periodic time series. Different schemes using statistics and gated recurrent unit (GRU) deep learning concepts are applied to different class of time series for performing anomaly detection. The proposed framework outperforms related methods, namely STL, SARIMA, LSTM, LSTM with STL, and ADSaS, in almost all measurements for five Numenta Anomaly Benchmark(NAB) datasets.
A CNN-based Approach to the Detection of SQL Injection Attacks

SQL injection has always been a major threat in the field of web application security. Traditional methods such as the rule-matching-based SQL injection detection solutions, which are inefficient to cope with the ever-changing SQL injection techniques and there is always a risk of bypassing variants. In this paper, we extract SQL injection attack related payloads from network flow and propose a SQL injection detection model based on Convolutional Neural Network (CNN), which can take the advantages of high-dimensional features of SQL injection behavior to deal with this issue. The proposed approach was tested in a real-traffic case study along with ModSecurity, which is the representative rule-matching-based method. The experimental results show that the CNN based model has higher accuracy, precision and recall rate, which validate its detection effectiveness and robustness against obfuscation of attacks.
Planning High-Quality Motions for Concentric Tube Robots in Point Clouds via Parallel Sampling and optimization

We present a method that plans motions for a concentric tube robot to automatically reach surgical targets inside the body while avoiding obstacles, where the patient's anatomy is represented by point clouds. Point clouds can be generated intra-operatively via endoscopic instruments, enabling the system to update obstacle representations over time as the patient anatomy changes during surgery. Our new motion planning method uses a combination of sampling-based motion planning methods and local optimization to efficiently handle point cloud data and quickly compute high quality plans. The local optimization step uses an interior point optimization method, ensuring that the computed plan is feasible and avoids obstacles at every iteration. This enables the motion planner to run in an anytime fashion, i.e., the method can be stopped at any time and the best solution found up until that point is returned. We demonstrate the method's efficacy in three anatomical scenarios, including two generated from endoscopic videos of real patient anatomy.
Speech Technology for Unwritten Languages

Speech technology plays an important role in our everyday life. Among others, speech is used for human-computer interaction, for instance for information retrieval and on-line shopping. In the case of an unwritten language, however, speech technology is unfortunately difficult to create, because it cannot be created by the standard combination of pre-trained speech-to-text and text-to-speech subsystems. The research presented in this article takes the first steps towards speech technology for unwritten languages. Specifically, the aim of this work was 1) to learn speech-to-meaning representations without using text as an intermediate representation, and 2) to test the sufficiency of the learned representations to regenerate speech or translated text, or to retrieve images that depict the meaning of an utterance in an unwritten language. The results suggest that building systems that go directly from speech-to-meaning and from meaning-to-speech, bypassing the need for text, is possible.
A Data-Driven Introduction to Authors, Readings, and Techniques in Visualization for the Digital Humanities

The newly rediscovered frontier between data visualization and the digital humanities has proven to be an exciting field of experimentation for scholars from both disciplines. This fruitful collaboration is attracting researchers from other areas of science who may be willing to create visual analysis tools that promote humanities research in its many forms. However, as the collaboration grows in complexity, it may become intimidating for these scholars to get engaged in the discipline. To facilitate this task, we have built an introduction to visualization for the digital humanities that sits on a data-driven stance adopted by the authors. In order to construct a dataset representative of the discipline, we analyze citations from a core corpus on 300 publications in visualization for the humanities obtained from recent editions of the InfoVis Vis4DH workshop, the ADHO Digital Humanities Conference, and the specialized digital humanities journal Digital Humanities Quarterly. From here, we extract referenced works and analyze more than 1900 publications in search of citation patterns, prominent authors in the field, and other interesting insights. Finally, following the path set by other researchers in the visualization and Human–Computer Interaction (HCI) communities, we analyze paper keywords to identify significant themes and research opportunities in the field.