OpenRaiser/Intern-Atlas huggingface dataset:
Learning Nonlinear Mixtures: Identifiability and Algorithm
Venue: arXiv cs.LG 2019
Year: 2019

Linear mixture models have proven very useful in a plethora of applications, e.g., topic modeling, clustering, and source separation. As a critical aspect of the linear mixture models, identifiability of the model parameters is well-studied, under frameworks such as independent component analysis and constrained matrix factorization. Nevertheless, when the linear mixtures are distorted by an unknown nonlinear functions -- which is well-motivated and more realistic in many cases -- the identifiability issues are much less studied. This work proposes an identification criterion for a nonlinear mixture model that is well grounded in many real-world applications, and offers identifiability guarantees. A practical implementation based on a judiciously designed neural network is proposed to realize the criterion, and an effective learning algorithm is proposed. Numerical results on synthetic and real-data corroborate effectiveness of the proposed method.
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
Venue: arXiv cs.CL 2019
Year: 2019

Neural networks (NNs) have become the state of the art in many machine learning applications, especially in image and sound processing [1]. The same, although to a lesser extent [2,3], could be said in natural language processing (NLP) tasks, such as named entity recognition. However, the success of NNs remains dependent on the availability of large labelled datasets, which is a significant hurdle in many important applications. One such case are electronic health records (EHRs), which are arguably the largest source of medical data, most of which lies hidden in natural text [4,5]. Data access is difficult due to data privacy concerns, and therefore annotated datasets are scarce. With scarce data, NNs will likely not be able to extract this hidden information with practical accuracy. In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4.3 above the architecture that won the competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms. To reach this state-of-the-art accuracy, our approach applies transfer learning to leverage on datasets annotated for other I2B2 tasks, and designs and trains embeddings that specially benefit from such transfer.
Vector representations of text data in deep learning
Venue: arXiv cs.CL 2019
Year: 2019

In this dissertation we report results of our research on dense distributed representations of text data. We propose two novel neural models for learning such representations. The first model learns representations at the document level, while the second model learns word-level representations. For document-level representations we propose Binary Paragraph Vector: a neural network models for learning binary representations of text documents, which can be used for fast document retrieval. We provide a thorough evaluation of these models and demonstrate that they outperform the seminal method in the field in the information retrieval task. We also report strong results in transfer learning settings, where our models are trained on a generic text corpus and then used to infer codes for documents from a domain-specific dataset. In contrast to previously proposed approaches, Binary Paragraph Vector models learn embeddings directly from raw text data. For word-level representations we propose Disambiguated Skip-gram: a neural network model for learning multi-sense word embeddings. Representations learned by this model can be used in downstream tasks, like part-of-speech tagging or identification of semantic relations. In the word sense induction task Disambiguated Skip-gram outperforms state-of-the-art models on three out of four benchmarks datasets. Our model has an elegant probabilistic interpretation. Furthermore, unlike previous models of this kind, it is differentiable with respect to all its parameters and can be trained with backpropagation. In addition to quantitative results, we present qualitative evaluation of Disambiguated Skip-gram, including two-dimensional visualisations of selected word-sense embeddings.
Information Systems Development and Evolution: A replication study on work distribution in Norwegian Organizations
Venue: arXiv cs.SE 2019
Year: 2019

The information systems landscape is at first sight very different from how it was 20 years ago. On the other hand, it seems that we are still struggling with many of the same problems, including late or abandoned projects and unfilled customer demands. In this article we present selected data from survey investigations from 1993, 1998, 2003, 2008 and 2013 among Norwegian organizations on how they conduct information systems development and maintenance. In particular we compare data from 2008 and 2013 in more detail. A major finding is that whereas main work distribution numbers was very stable between 2003 and 2008, we see some changes as for time used on maintenance and development between 2008 and 2013. Even if we witness large changes in the underlying implementation technology and methods used, a number of aspects such as application portfolio upkeep (the amount of work for keeping the application portfolio operational) though are still on the same level as it has been the last 15 years. On the other hand, because of the more complex infrastructures supporting the application portfolio, and the increasing number of in particular external users, an increasing amount of resources is used for other tasks such as operations and user-support than in the first investigations, although also this appears to have stabilised between the last investigations.
Multiple Set Matching and Pre-Filtering with Bloom Multifilters
Venue: arXiv cs.DS 2019
Year: 2019

Bloom filter is a space-efficient probabilistic data structure for checking elements' membership in a set. Given multiple sets, however, a standard Bloom filter is not sufficient when looking for the items to which an element or a set of input elements belong to. In this article, we solve multiple set matching problem by proposing two efficient Bloom Multifilters called Bloom Matrix and Bloom Vector. Both of them are space efficient and answer queries with a set of identifiers for multiple set matching problems. We show that the space efficiency can be optimized further according to the distribution of labels among multiple sets: Uniform and Zipf. While both of them are space efficient, Bloom Vector can efficiently exploit Zipf distribution of data for further space reduction. Our results also highlight that basic ADD and LOOKUP operations on Bloom Matrix are faster than on Bloom Vector. However, Bloom Matrix does not meet the theoretical false positive rate of less than $10^{-2}$ for LOOKUP operations if the represented data or the labels are not uniformly distributed among the multiple sets. Consequently, we introduce \textit{Bloom Test} which uses Bloom Matrix as the pre-filter structure to determine which structure is suitable for improved performance with an arbitrary input dataset.
A Computational Framework for Motor Skill Acquisition
Venue: arXiv cs.AI 2019
Year: 2019

There have been numerous attempts in explaining the general learning behaviours by various cognitive models. Multiple hypotheses have been put further to qualitatively argue the best-fit model for motor skill acquisition task and its variations. In this context, for a discrete sequence production (DSP) task, one of the most insightful models is Verwey's Dual Processor Model (DPM). It largely explains the learning and behavioural phenomenon of skilled discrete key-press sequences without providing any concrete computational basis of reinforcement. Therefore, we propose a quantitative explanation for Verwey's DPM hypothesis by experimentally establishing a general computational framework for motor skill learning. We attempt combining the qualitative and quantitative theories based on a best-fit model of the experimental simulations of variations of dual processor models. The fundamental premise of sequential decision making for skill learning is based on interacting model-based (MB) and model-free (MF) reinforcement learning (RL) processes. Our unifying framework shows the proposed idea agrees well to Verwey's DPM and Fitts' three phases of skill learning. The accuracy of our model can further be validated by its statistical fit with the human-generated data on simple environment tasks like the grid-world.
Beyond socket options: making the Linux TCP stack truly extensible
Venue: arXiv cs.NI 2019
Year: 2019

The Transmission Control Protocol (TCP) is one of the most important protocols in today's Internet. Its specification and implementations have been refined for almost forty years. The Linux TCP stack is one of the most widely used TCP stacks given its utilisation on servers and Android smartphones and tablets. However, TCP and its implementations evolve very slowly. In this paper, we demonstrate how to leverage the eBPF virtual machine that is part of the recent versions of the Linux kernel to make the TCP stack easier to extend. We demonstrate a variety of use cases where the eBPF code is injected inside a running kernel to update or tune the TCP implementation. We first implement the TCP User Timeout Option. Then we propose a new option that enables a client to request a server to use a specific congestion control scheme. Our third extension is a TCP option that sets the initial congestion window. We then demonstrate how eBPF code can be used to tune the acknowledgment strategy.
Coresets for $(k,l)$-Clustering under the Fréchet Distance
Venue: arXiv cs.CG 2019
Year: 2019

Clustering is the task of partitioning a given set of geometric objects. This is thoroughly studied when the objects are points in the euclidean space. There are also several approaches for points in general metric spaces. In this thesis we consider clustering polygonal curves, i.e., curves composed of line segments, under the Fréchet distance. We obtain clusterings by minimizing an objective function, which yields a set of centers that induces a partition of the input. The objective functions we consider is the so called $(k,l)$-\textsc{center}, where we are to find the $k$ center-curves that minimize the maximum distance between any input-curve and a nearest center-curve and the so called $k$-\textsc{median}, where we are to find the $k$ center-curves that minimize the sum of the distances between the input-curves and a nearest center-curve. Given a set of $n$ polygonal curves, we are interested in reducing this set to an $ε$-coreset, i.e., a notably smaller set of curves that has a very similar clustering-behavior. We develop a construction method for such $ε$-coresets for the $(k,l)$-\textsc{center}, that yields $ε$-coresets of size of a polynomial of $\frac{1}ε$, in time linear in $n$ and a polynomial of $\frac{1}ε$, for line segments. Also, we develop a construction technique for the $(k,l)$-\textsc{center} that yields $ε$-coresets of size exponential in $m$ with basis $\frac{1}ε$, in time sub-quadratic in $n$ and exponential in $m$ with basis $\frac{1}ε$, for general polygonal curves. Finally, we develop a construction method for the $k$-\textsc{median}, that yields $ε$-coresets of size polylogarithmic in $n$ and a polynomial of $\frac{1}ε$, in time linear in $n$ and a polynomial of $\frac{1}ε$.
On the Capacity Region of the Layered Packet Erasure Broadcast Channel with Feedback
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, the capacity region of the Layered Packet Erasure Broadcast Channel (LPE-BC) with Channel Output Feedback (COF) available at the transmitter is investigated. The LPE-BC is a high-SNR approximation of the fading Gaussian BC recently proposed by Tse and Yates, who characterized the capacity region for any number of users and any number of layers when there is no COF. This paper derives capacity inner and outer bounds for the LPE-BC with COF for the case of two users and any number of layers. The inner bounds generalize past results for the two-user erasure BC, which is a special case of the LPE-BC with COF with only one layer. The novelty lies in the use of \emph{inter-user \& inter-layer network coding} retransmissions (for those packets that have only been received by the unintended user), where each random linear combination may involve packets intended for any user originally sent on any of the layers. Analytical and numerical examples show that the proposed outer bound is optimal for some LPE-BCs.
Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I)
Venue: arXiv cs.NE 2019
Year: 2019

Artificial Intelligence frameworks should allow for ever more autonomous and general systems in contrast to very narrow and restricted (human pre-defined) domain systems, in analogy to how the brain works. Self-constructive Artificial Intelligence ($SCAI$) is one such possible framework. We herein propose that $SCAI$ is based on three principles of organization: self-growing, self-experimental and self-repairing. Self-growing: the ability to autonomously and incrementally construct structures and functionality as needed to solve encountered (sub)problems. Self-experimental: the ability to internally simulate, anticipate and take decisions based on these expectations. Self-repairing: the ability to autonomously re-construct a previously successful functionality or pattern of interaction lost from a possible sub-component failure (damage). To implement these principles of organization, a constructive architecture capable of evolving adaptive autonomous agents is required. We present Schema-based learning as one such architecture capable of incrementally constructing a myriad of internal models of three kinds: predictive schemas, dual (inverse models) schemas and goal schemas as they are necessary to autonomously develop increasing functionality. We claim that artificial systems, whether in the digital or in the physical world, can benefit very much form this constructive architecture and should be organized around these principles of organization. To illustrate the generality of the proposed framework, we include several test cases in structural adaptive navigation in artificial intelligence systems in Paper II of this series, and resilient robot motor control in Paper III of this series. Paper IV of this series will also include $SCAI$ for problem structural discovery in predictive Business Intelligence.
Truncated nuclear norm regularization for low-rank tensor completion
Venue: arXiv cs.CV 2019
Year: 2019

Recently, low-rank tensor completion has become increasingly attractive in recovering incomplete visual data. Considering a color image or video as a three-dimensional (3D) tensor, existing studies have put forward several definitions of tensor nuclear norm. However, they are limited and may not accurately approximate the real rank of a tensor, and they do not explicitly use the low-rank property in optimization. It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix. In this paper, we propose a new method called the tensor truncated nuclear norm (T-TNN), which suggests a new definition of tensor nuclear norm. The truncated nuclear norm is generalized from the matrix case to the tensor case. With the help of the low rankness of TNN, our approach improves the efficacy of tensor completion. We adopt the definition of the previously proposed tensor singular value decomposition, the alternating direction method of multipliers, and the accelerated proximal gradient line search method in our algorithm. Substantial experiments on real-world videos and images illustrate that the performance of our approach is better than those of previous methods.
Modeling Data-Driven Dominance Traits for Virtual Characters using Gait Analysis
Venue: arXiv cs.GR 2019
Year: 2019

We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with ~73% accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters.
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activation only from a global perspective, our research aims to clarify how a certain input unit (dimension) collaborates with other units (dimensions) to constitute inference patterns of the neural network and thus contribute to the network output. The analysis of local contextual effects w.r.t. certain input units is of special values in real applications. Explaining the logic of the alphaGo Zero model is a typical application. In experiments, our method successfully disentangled the rationale of each move during the Go game.
Interactive Narrative in Virtual Reality
Venue: arXiv cs.HC 2019
Year: 2019

Interactive fiction is a literary genre that is rapidly gaining popularity. In this genre, readers are able to explicitly take actions in order to guide the course of the story. With the recent popularity of narrative focused games, we propose to design and develop an interactive narrative tool for content creators. In this extended abstract, we show how we leverage this interactive medium to present a tool for interactive storytelling in virtual reality. Using a simple markup language, content creators and researchers are now able to create interactive narratives in a virtual reality environment. We further discuss the potential future directions for a virtual reality storytelling engine.
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Venue: arXiv cs.LG 2019
Year: 2019

We consider prediction with expert advice under the log-loss with the goal of deriving efficient and robust algorithms. We argue that existing algorithms such as exponentiated gradient, online gradient descent and online Newton step do not adequately satisfy both requirements. Our main contribution is an analysis of the Prod algorithm that is robust to any data sequence and runs in linear time relative to the number of experts in each round. Despite the unbounded nature of the log-loss, we derive a bound that is independent of the largest loss and of the largest gradient, and depends only on the number of experts and the time horizon. Furthermore we give a Bayesian interpretation of Prod and adapt the algorithm to derive a tracking regret.
Using offline metrics and user behavior analysis to combine multiple systems for music recommendation
Venue: arXiv cs.IR 2019
Year: 2019

There are many offline metrics that can be used as a reference for evaluation and optimization of the performance of recommender systems. Hybrid recommendation approaches are commonly used to improve some of those metrics by combining different systems. In this work we focus on music recommendation and propose a new way to improve recommendations, with respect to a desired metric of choice, by combining multiple systems for each user individually based on their expected performance. Essentially, our approach consists in predicting an expected error that each system will produce for each user based on their previous activity. To this end, we propose to train regression models for different metrics predicting the performance of each system based on a number of features characterizing previous user behavior in the system. We then use different fusion strategies to combine recommendations generated by each system. Following this approach one can optimize the final hybrid system with respect to the desired metric of choice. As a proof of concept, we conduct experiments combining two recommendation systems, a Matrix Factorization model and a popularity-based recommender. We use the data provided by Melon, a Korean music streaming service, to train and evaluate the performance of the systems.
Auto-weighted Mutli-view Sparse Reconstructive Embedding
Venue: arXiv cs.LG 2019
Year: 2019

With the development of multimedia era, multi-view data is generated in various fields. Contrast with those single-view data, multi-view data brings more useful information and should be carefully excavated. Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance the performances of many tasks. Especially for those high-dimensional data, how to develop a multi-view dimension reduction algorithm to obtain the low-dimensional representations is of vital importance but chanllenging. In this paper, we propose a novel multi-view dimensional reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse reconstructive correlations between features from multiple views. Furthermore, it is equipped with an auto-weighted technique to treat multiple views discriminatively according to their contributions. Various experiments have verified the excellent performances of the proposed AMSRE.
Efforts estimation of doctors annotating medical image
Venue: arXiv cs.LG 2019
Year: 2019

Accurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expense due to its high complexity and needing experienced doctors. To alleviate annotation cost, some active learning methods are proposed. But such methods just cut the number of annotation candidates and do not study how many efforts the doctor will exactly take, which is not enough since even annotating a small amount of medical data will take a lot of time for the doctor. In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image. First, by coming active learning and U-shape network, we employ a suggestive annotation strategy to choose the most effective annotation candidates. Then we exploit a fine annotation platform to alleviate annotating efforts on each candidate and first utilize a new criterion to quantitatively calculate the efforts taken by doctors. In our work, we take MR brain tissue segmentation as an example to evaluate the proposed method. Extensive experiments on the well-known IBSR18 dataset and MRBrainS18 Challenge dataset show that, using proposed strategy, state-of-the-art segmentation performance can be achieved by using only 60% annotation candidates and annotation efforts can be alleviated by at least 44%, 44%, 47% on CSF, GM, WM separately.