OpenRaiser/Intern-Atlas huggingface dataset:
Audience-Retention-Rate-Aware Caching and Coded Video Delivery with Asynchronous Demands
Venue: arXiv cs.IT 2018
Year: 2018

Most results on coded caching focus on a static scenario, in which a fixed number of users synchronously place their requests from a content library, and the performance is measured in terms of the latency in satisfying all of these demands. In practice, however, users start watching an online video content asynchronously over time, and often abort watching a video before it is completed. The latter behaviour is captured by the notion of audience retention rate, which measures the portion of a video content watched on average. In order to bring coded caching one step closer to practice, asynchronous user demands are considered in this paper, by allowing user demands to arrive randomly over time, and both the popularity of video files, and the audience retention rates are taken into account. A decentralized partial coded caching (PCC) scheme is proposed, together with two cache allocation schemes; namely the optimal cache allocation (OCA) and the popularity-based cache allocation (PCA), which allocate users' caches among different chunks of the video files in the library. Numerical results validate that the proposed PCC scheme, either with OCA or PCA, outperforms conventional uncoded caching as well as the state-of-the-art decentralized caching schemes, which consider only the file popularities, and are designed for synchronous demand arrivals. An information-theoretical lower bound on the average delivery rate is also presented.
Aspirational pursuit of mates in online dating markets
Venue: arXiv cs.SI 2018
Year: 2018

Romantic courtship is often described as taking place in a dating market where men and women compete for mates, but the detailed structure and dynamics of dating markets have historically been difficult to quantify for lack of suitable data. In recent years, however, the advent and vigorous growth of the online dating industry has provided a rich new source of information on mate pursuit. Here we present an empirical analysis of heterosexual dating markets in four large US cities using data from a popular, free online dating service. We show that competition for mates creates a pronounced hierarchy of desirability that correlates strongly with user demographics and is remarkably consistent across cities. We find that both men and women pursue partners who are on average about 25% more desirable than themselves by our measures and that they use different messaging strategies with partners of different desirability. We also find that the probability of receiving a response to an advance drops markedly with increasing difference in desirability between the pursuer and the pursued. Strategic behaviors can improve one's chances of attracting a more desirable mate, though the effects are modest.
A Scalable Data Science Platform for Healthcare and Precision Medicine Research
Venue: arXiv cs.DC 2018
Year: 2018

Objective: To (1) demonstrate the implementation of a data science platform built on open-source technology within a large, academic healthcare system and (2) describe two computational healthcare applications built on such a platform. Materials and Methods: A data science platform based on several open source technologies was deployed to support real-time, big data workloads. Data acquisition workflows for Apache Storm and NiFi were developed in Java and Python to capture patient monitoring and laboratory data for downstream analytics. Results: The use of emerging data management approaches along with open-source technologies such as Hadoop can be used to create integrated data lakes to store large, real-time data sets. This infrastructure also provides a robust analytics platform where healthcare and biomedical research data can be analyzed in near real-time for precision medicine and computational healthcare use cases. Discussion: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional data sets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Conclusion: Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to healthcare data for computational healthcare and precision medicine research.
Embedding Grammars
Venue: arXiv cs.CL 2018
Year: 2018

Classic grammars and regular expressions can be used for a variety of purposes, including parsing, intent detection, and matching. However, the comparisons are performed at a structural level, with constituent elements (words or characters) matched exactly. Recent advances in word embeddings show that semantically related words share common features in a vector-space representation, suggesting the possibility of a hybrid grammar and word embedding. In this paper, we blend the structure of standard context-free grammars with the semantic generalization capabilities of word embeddings to create hybrid semantic grammars. These semantic grammars generalize the specific terminals used by the programmer to other words and phrases with related meanings, allowing the construction of compact grammars that match an entire region of the vector space rather than matching specific elements.
Software Professionals are Not Directors: What Constitutes a Good Video?
Venue: arXiv cs.SE 2018
Year: 2018

Videos are one of the best documentation options for a rich and effective communication. They allow experiencing the overall context of a situation by representing concrete realizations of certain requirements. Despite 35 years of research on integrating videos in requirements engineering (RE), videos are not an established documentation option in terms of RE best practices. Several approaches use videos but omit the details about how to produce them. Software professionals lack knowledge on how to communicate visually with videos since they are not directors. Therefore, they do not necessarily have the required skills neither to produce good videos in general nor to deduce what constitutes a good video for an existing approach. The discipline of video production provides numerous generic guidelines that represent best practices on how to produce a good video with specific characteristics. We propose to analyze this existing know-how to learn what constitutes a good video for visual communication. As a plan of action, we suggest a literature study of video production guidelines. We expect to identify quality characteristics of good videos in order to derive a quality model. Software professionals may use such a quality model for videos as an orientation for planning, shooting, post-processing, and viewing a video. Thus, we want to encourage and enable software professionals to produce good videos at moderate costs, yet sufficient quality.
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Venue: arXiv cs.CV 2018
Year: 2018

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
Venue: arXiv cs.AI 2018
Year: 2018

Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. There is much research that focuses on applied RL in RTS games, and novel advances are therefore anticipated in the not too distant future. However, there are to date few environments for testing RTS AIs. Environments in the literature are often either overly simplistic, such as microRTS, or complex and without the possibility for accelerated learning on consumer hardware like StarCraft II. This paper introduces the Deep RTS game environment for testing cutting-edge artificial intelligence algorithms for RTS games. Deep RTS is a high-performance RTS game made specifically for artificial intelligence research. It supports accelerated learning, meaning that it can learn at a magnitude of 50 000 times faster compared to existing RTS games. Deep RTS has a flexible configuration, enabling research in several different RTS scenarios, including partially observable state-spaces and map complexity. We show that Deep RTS lives up to our promises by comparing its performance with microRTS, ELF, and StarCraft II on high-end consumer hardware. Using Deep RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of the time. Deep RTS is publicly available at https://github.com/cair/DeepRTS.
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions
Venue: arXiv cs.LG 2018
Year: 2018

From self-driving vehicles and back-flipping robots to virtual assistants who book our next appointment at the hair salon or at that restaurant for dinner - machine learning systems are becoming increasingly ubiquitous. The main reason for this is that these methods boast remarkable predictive capabilities. However, most of these models remain black boxes, meaning that it is very challenging for humans to follow and understand their intricate inner workings. Consequently, interpretability has suffered under this ever-increasing complexity of machine learning models. Especially with regards to new regulations, such as the General Data Protection Regulation (GDPR), the necessity for plausibility and verifiability of predictions made by these black boxes is indispensable. Driven by the needs of industry and practice, the research community has recognised this interpretability problem and focussed on developing a growing number of so-called explanation methods over the past few years. These methods explain individual predictions made by black box machine learning models and help to recover some of the lost interpretability. With the proliferation of these explanation methods, it is, however, often unclear, which explanation method offers a higher explanation quality, or is generally better-suited for the situation at hand. In this thesis, we thus propose an axiomatic framework, which allows comparing the quality of different explanation methods amongst each other. Through experimental validation, we find that the developed framework is useful to assess the explanation quality of different explanation methods and reach conclusions that are consistent with independent research.
Multiparty Classical Choreographies
Venue: arXiv cs.PL 2018
Year: 2018

We present Multiparty Classical Choreographies (MCC), a language model where global descriptions of communicating systems (choreographies) implement typed multiparty sessions. Typing is achieved by generalising classical linear logic to judgements that explicitly record parallelism by means of hypersequents. Our approach unifies different lines of work on choreographies and processes with multiparty sessions, as well as their connection to linear logic. Thus, results developed in one context are carried over to the others. Key novelties of MCC include support for server invocation in choreographies, as well as logic-driven compilation of choreographies with replicated processes.
Energy-Efficient Multi-View Video Transmission with View Synthesis-Enabled Multicast
Venue: arXiv cs.IT 2018
Year: 2018

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of heavy load to wireless networks. Except for further improving viewing experience, view synthesis can create multicast opportunities for efficient transmission of MVVs in multiuser wireless networks, which has not been recognized in existing literature. In this paper, we would like to exploit view synthesis-enabled multicast opportunities for energy-efficient MVV transmission in a multiuser wireless network. Specifically, we first establish a mathematical model to characterize the impact of view synthesis on multicast opportunities and energy consumption. Then, we consider the optimization of view selection, transmission time and power allocation to minimize the weighted sum energy consumption for view transmission and synthesis, which is a challenging mixed discrete-continuous optimization problem. We propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. To further reduce computational complexity, we also propose two low-complexity algorithms to obtain two suboptimal solutions, based on continuous relaxation and Difference of Convex (DC) programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.
Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures
Venue: arXiv cs.LG 2018
Year: 2018

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.
Vis4DD: A visualization system that supports Data Quality Visual Assessment
Venue: arXiv cs.DB 2018
Year: 2018

Data quality assessment process is essential to ensure reliable analytical outcomes. This process depends on human supervision-driven approaches since it is impossible to determine a defect based only on data. Visualization systems belong to a class of supervised tools that can make data defect pattern visible. However, their considerable design knowledge encodings and imple- mentations provide little support design to data quality visual assessment. To cover this gap, this work reports the design approach of V is4DD visualization system based on patterns of data defects structures and assessment tasks. An exploratory case study used this web-based system to explore which and how visual-interactive properties facilitate visual detection of data defect.
Edge Coloring Technique to Remove Small Elementary Trapping Sets from Tanner Graph of QC-LDPC Codes with Column Weight 4
Venue: arXiv cs.IT 2018
Year: 2018

One of the phenomena that causes high decoding failure rates is trapping sets. Characterization of $(a,b)$ elementary trapping sets (ETSs), their graphical properties and the lower bounds on their size in variable regular LDPC codes with column weights 3, 4, 5 and 6, where $a$ is the size of the ETS and $b$ is the number of degree-one check nodes, have been an interesting subject among researchers. Although progressive-edge-growth method (PEG) to construct LDPC codes free of an specific ETS has been proposed in the literature, it is mostly applied to LDPC codes with column weight 3. In this paper, we focus on constructing QC-LDPC codes with column weight 4 whose Tanner graphs are free of small ETSs. Using coloring the edges of the variable node (VN) graph corresponding to an ETS, we provide the sufficient conditions to obtain QC-LDPC codes with column weight 4, girth 6 and free of $(5,b)$ ETSs, where $b\leq4$, and $(6,b)$ ETs, where $b\leq2$. Moreover, for $(4,n)$-regular QC-LDPC codes with girth 8, we present a method to remove $(7,4)$ ETSs from Tanner graphs.
Measuring Human Assessed Complexity in Synthetic Aperture Sonar Imagery Using the Elo Rating System
Venue: arXiv cs.CV 2018
Year: 2018

Performance of automatic target recognition from synthetic aperture sonar data is heavily dependent on the complexity of the beamformed imagery. Several mechanisms can contribute to this, including unwanted vehicle dynamics, the bathymetry of the scene, and the presence of natural and manmade clutter. To understand the impact of the environmental complexity on image perception, researchers have taken approaches rooted in information theory, or heuristics. Despite these efforts, a quantitative measure for complexity has not been related to the phenomenology from which it is derived. By using subject matter experts (SMEs) we derive a complexity metric for a set of imagery which accounts for the underlying phenomenology. The goal of this work is to develop an understanding of how several common information theoretic and heuristic measures are related to the SME perceived complexity in synthetic aperture sonar imagery. To achieve this, an ensemble of 10-meter x 10-meter images were cropped from a high-frequency SAS data set that spans multiple environments. The SME's were presented pairs of images from which they could rate the relative image complexity. These comparisons were then converted into the desired sequential ranking using a method first developed by A. Elo for establishing rankings of chess players. The Elo method produced a plausible rank ordering across the broad dataset. The heuristic and information theoretical metrics were then compared to the image rank from which they were derived. The metrics with the highest degree of correlation were those relating to spatial information, e.g. variations in pixel intensity, with an R-squared value of approximately 0.9. However, this agreement was dependent on the scale from which the spatial variation was measured. Results will also be presented for many other measures including lacunarity, image compression, and entropy.
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Venue: arXiv cs.CV 2018
Year: 2018

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.
Active Distribution Learning from Indirect Samples
Venue: arXiv cs.LG 2018
Year: 2018

This paper studies the problem of {\em learning} the probability distribution $P_X$ of a discrete random variable $X$ using indirect and sequential samples. At each time step, we choose one of the possible $K$ functions, $g_1, \ldots, g_K$ and observe the corresponding sample $g_i(X)$. The goal is to estimate the probability distribution of $X$ by using a minimum number of such sequential samples. This problem has several real-world applications including inference under non-precise information and privacy-preserving statistical estimation. We establish necessary and sufficient conditions on the functions $g_1, \ldots, g_K$ under which asymptotically consistent estimation is possible. We also derive lower bounds on the estimation error as a function of total samples and show that it is order-wise achievable. Leveraging these results, we propose an iterative algorithm that i) chooses the function to observe at each step based on past observations; and ii) combines the obtained samples to estimate $p_X$. The performance of this algorithm is investigated numerically under various scenarios, and shown to outperform baseline approaches.
Strong Converse for Hypothesis Testing Against Independence over a Two-Hop Network
Venue: arXiv cs.IT 2018
Year: 2018

By proving a strong converse, we strengthen the weak converse result by Salehkalaibar, Wigger and Wang (2017) concerning hypothesis testing against independence over a two-hop network with communication constraints. Our proof follows by judiciously combining two recently proposed techniques for proving strong converse theorems, namely the strong converse technique via reverse hypercontractivity by Liu, van Handel, and Verdú (2017) and the strong converse technique by Tyagi and Watanabe (2018), in which the authors used a change-of-measure technique and replaced hard Markov constraints with soft information costs. The techniques used in our paper can also be applied to prove strong converse theorems for other multiterminal hypothesis testing against independence problems.