OpenRaiser/Intern-Atlas huggingface dataset:
Designing for Reproducibility: A Qualitative Study of Challenges and Opportunities in High Energy Physics
Venue: arXiv cs.HC 2019
Year: 2019

Reproducibility should be a cornerstone of scientific research and is a growing concern among the scientific community and the public. Understanding how to design services and tools that support documentation, preservation and sharing is required to maximize the positive impact of scientific research. We conducted a study of user attitudes towards systems that support data preservation in High Energy Physics, one of science's most data-intensive branches. We report on our interview study with 12 experimental physicists, studying requirements and opportunities in designing for research preservation and reproducibility. Our findings suggest that we need to design for motivation and benefits in order to stimulate contributions and to address the observed scalability challenge. Therefore, researchers' attitudes towards communication, uncertainty, collaboration and automation need to be reflected in design. Based on our findings, we present a systematic view of user needs and constraints that define the design space of systems supporting reproducible practices.
More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018
Venue: arXiv cs.DC 2019
Year: 2019

We identify hardware that is optimal to produce molecular dynamics trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more towards the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift towards GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware.
Computing the Scope of Applicability for Acquired Task Knowledge in Experience-Based Planning Domains
Venue: arXiv cs.AI 2019
Year: 2019

Experience-based planning domains have been proposed to improve problem solving by learning from experience. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend previous work to generate a set of conditions that determine the scope of applicability of an activity schema. The inferred scope is a bounded representation of a set of problems of potentially unbounded size, in the form of a 3-valued logical structure, which is used to automatically find an applicable activity schema for solving task problems. We validate this work in two classical planning domains.
Dynamics Concentration of Large-Scale Tightly-Connected Networks
Venue: arXiv cs.SY 2019
Year: 2019

The ability to achieve coordinated behavior --engineered or emergent-- on networked systems has attracted widespread interest over several fields. This has led to remarkable advances on the development of a theoretical understanding of the conditions under which agents within a network can reach agreement (consensus) or develop coordinated behaviors such as synchronization. However, fewer advances have been made toward explaining another commonly observed phenomena in tightly-connected networks systems: output responses of nodes in the networks are almost identical to each other despite heterogeneity in their individual dynamics. In this paper, we leverage tools from high-dimensional probability to provide an initial answer to this phenomena. More precisely, we show that for linear networks of nodal random transfer functions, as the network size and connectivity grows, every node in the network follows the same response to an input or disturbance --irrespectively of the source of this input. We term this behavior as dynamics concentration since it stems from the fact that the network transfer matrix uniformly converges in probability, i.e., it concentrates, to a unique dynamic response determined by the distribution of the random transfer function of each node. We further discuss the implications of our analysis in the context of model reduction and robustness, and provide numerical evidence that similar phenomena occur in small deterministic networks over a properly defined frequency band.
What Makes Research Software Sustainable? An Interview Study With Research Software Engineers
Venue: arXiv cs.SE 2019
Year: 2019

Software is now a vital scientific instrument, providing the tools for data collection and analysis across disciplines from bioinformatics and computational physics, to the humanities. The software used in research is often home-grown and bespoke: it is constructed for a particular project, and rarely maintained beyond this, leading to rapid decay, and frequent `reinvention of the wheel'. Understanding how to develop sustainable research software, such that it is suitable for future reuse, is therefore of interest to both researchers and funders, but how to achieve this remains an open question. Here we report the results of an interview study examining how research software engineers -- the people actively developing software in an academic research environment -- subjectively define software sustainability. Thematic analysis of the data reveals two interacting dimensions: \emph{intrinsic sustainability}, which relates to internal qualities of software, such as modularity, encapsulation and testability, and \emph{extrinsic sustainability}, concerning cultural and organisational factors, including how software is resourced, supported and shared. Research software engineers believe an increased focus on quality and discoverability are key factors in increasing the sustainability of academic research software.
Routing-Based Delivery in Combination-Type Networks with Random Topology
Venue: arXiv cs.IT 2019
Year: 2019

The coded caching scheme proposed by Maddah-Ali and Niesen (MAN) transmits coded multicast messages to users equipped with caches and it is known to be optimal within a constant factor. This work extends this caching scheme to two-hop relay networks with one main server with access to a library of $N$ files, and $H$ relays communicating with $K$ users with cache, each of which is connected to a random subset of relays. This topology can be considered as a generalized version of a celebrated family of networks, referred to as combination networks. Our approach is simply based on routing MAN packets through the network. The optimization of the routing can be formulated as a Linear Program (LP). In addition, to reduce the computation complexity, a dynamic algorithm is proposed to approach the LP solution. Numerical simulations show that the proposed scheme outperforms the existing caching schemes for this class of networks.
Deep Reinforcement Learning with Feedback-based Exploration
Venue: arXiv cs.LG 2019
Year: 2019

Deep Reinforcement Learning has enabled the control of increasingly complex and high-dimensional problems. However, the need of vast amounts of data before reasonable performance is attained prevents its widespread application. We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning. The uncertainty in the policy and the corrective feedback is combined directly in the action space as probabilistic conditional exploration. As a result, the greatest part of the otherwise ignorant learning process can be avoided. We demonstrate the proposed method, Predictive Probabilistic Merging of Policies (PPMP), in combination with DDPG. In experiments on continuous control problems of the OpenAI Gym, we achieve drastic improvements in sample efficiency, final performance, and robustness to erroneous feedback, both for human and synthetic feedback. Additionally, we show solutions beyond the demonstrated knowledge.
Inefficiency of K-FAC for Large Batch Size Training
Venue: arXiv cs.LG 2019
Year: 2019

In stochastic optimization, using large batch sizes during training can leverage parallel resources to produce faster wall-clock training times per training epoch. However, for both training loss and testing error, recent results analyzing large batch Stochastic Gradient Descent (SGD) have found sharp diminishing returns, beyond a certain critical batch size. In the hopes of addressing this, it has been suggested that the Kronecker-Factored Approximate Curvature (\mbox{K-FAC}) method allows for greater scalability to large batch sizes, for non-convex machine learning problems such as neural network optimization, as well as greater robustness to variation in model hyperparameters. Here, we perform a detailed empirical analysis of large batch size training %of these two hypotheses, for both \mbox{K-FAC} and SGD, evaluating performance in terms of both wall-clock time and aggregate computational cost. Our main results are twofold: first, we find that both \mbox{K-FAC} and SGD doesn't have ideal scalability behavior beyond a certain batch size, and that \mbox{K-FAC} does not exhibit improved large-batch scalability behavior, as compared to SGD; and second, we find that \mbox{K-FAC}, in addition to requiring more hyperparameters to tune, suffers from similar hyperparameter sensitivity behavior as does SGD. We discuss extensive results using ResNet and AlexNet on \mbox{CIFAR-10} and SVHN, respectively, as well as more general implications of our findings.
Object tracking in video signals using Compressive Sensing
Venue: arXiv cs.CV 2019
Year: 2019

Reducing the number of pixels in video signals while maintaining quality needed for recovering the trace of an object using Compressive Sensing is main subject of this work. Quality of frames, from video that contains moving object, are gradually reduced by keeping different number of pixels in each iteration, going from 45% all the way to 1%. Using algorithm for tracing object, results were satisfactory and showed mere changes in trajectory graphs, obtained from original and reconstructed videos.
The Parameterized Position Heap of a Trie
Venue: arXiv cs.DS 2019
Year: 2019

Let $Σ$ and $Π$ be disjoint alphabets of respective size $σ$ and $π$. Two strings over $Σ\cup Π$ of equal length are said to parameterized match (p-match) if there is a bijection $f:Σ\cup Π\rightarrow Σ\cup Π$ such that (1) $f$ is identity on $Σ$ and (2) $f$ maps the characters of one string to those of the other string so that the two strings become identical. We consider the p-matching problem on a (reversed) trie $\mathcal{T}$ and a string pattern $P$ such that every path that p-matches $P$ has to be reported. Let $N$ be the size of the given trie $\mathcal{T}$. In this paper, we propose the parameterized position heap for $\mathcal{T}$ that occupies $O(N)$ space and supports p-matching queries in $O(m \log (σ+ π) + m π+ \mathit{pocc}))$ time, where $m$ is the length of a query pattern $P$ and $\mathit{pocc}$ is the number of paths in $\mathcal{T}$ to report. We also present an algorithm which constructs the parameterized position heap for a given trie $\mathcal{T}$ in $O(N (σ+ π))$ time and working space.
Successive Refinement of Images with Deep Joint Source-Channel Coding
Venue: arXiv cs.IT 2019
Year: 2019

We introduce deep learning based communication methods for successive refinement of images over wireless channels. We present three different strategies for progressive image transmission with deep JSCC, with different complexity-performance trade-offs, all based on convolutional autoencoders. Numerical results show that deep JSCC not only provides graceful degradation with channel signal-to-noise ratio (SNR) and improved performance in low SNR and low bandwidth regimes compared to state-of-the-art digital communication techniques, but can also successfully learn a layered representation, achieving performance close to a single-layer scheme. These results suggest that natural images encoded with deep JSCC over Gaussian channels are almost successively refinable.
Robust Decidability of Sampled-Data Control of Nonlinear Systems with Temporal Logic Specifications
Venue: arXiv cs.SY 2019
Year: 2019

This paper explores the theoretical limits of using discrete abstractions for nonlinear control synthesis. More specifically, we consider the problem of deciding continuous-time control with temporal logic specifications. We prove that sampled-data control of nonlinear systems with temporal logic specifications is robustly decidable in the sense that, given a continuous-time nonlinear control system and a temporal logic formula, one can algorithmically decide whether there exists a robust sampled-data control strategy to realize this specification when the right-hand side of the system is slightly perturbed by a small disturbance. If the answer is positive, one can then construct a (potentially less) robust sampled-data control strategy that realizes the same specification. The result is proved by constructing a robustly complete abstraction of the original continuous-time control system using sufficiently small discretization parameters. We illustrate the result with a nonlinear control example.
Identifying long-term periodic cycles and memories of collective emotion in online social media
Venue: arXiv cs.SI 2019
Year: 2019

Collective emotion has been traditionally evaluated by questionnaire survey on a limited number of people. Recently, big data of written texts on the Internet has been available for analyzing collective emotion for very large scales. Although short-term reflection between collective emotion and real social phenomena has been widely studied, long-term dynamics of collective emotion has not been studied so far due to the lack of long persistent data sets. In this study, we extracted collective emotion over a 10-year period from 3.6 billion Japanese blog articles. Firstly, we find that collective emotion shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied with pulses caused by natural disasters. For example, April is represented by high Tension, probably due to starting school in Japan. We also identified long-term memory in the collective emotion that is characterized by the power-law decay of the autocorrelation function over several months.
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks
Venue: arXiv cs.CL 2019
Year: 2019

With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, context-aware citation recommendation has been researched upon for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder, which uses Graph Convolutional Networks (GCN) layer and Bidirectional Encoder Representations from Transformers (BERT), which is a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, This dataset is the first well-organized dataset for context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision (MAP) and recall@k.
A Ground-Truth Data Set and a Classification Algorithm for Eye Movements in 360-degree Videos
Venue: arXiv cs.MM 2019
Year: 2019

The segmentation of a gaze trace into its constituent eye movements has been actively researched since the early days of eye tracking. As we move towards more naturalistic viewing conditions, the segmentation becomes even more challenging and convoluted as more complex patterns emerge. The definitions and the well-established methods that were developed for monitor-based eye tracking experiments are often not directly applicable to unrestrained set-ups such as eye tracking in wearable contexts or with head-mounted displays. The main contributions of this work to the eye movement research for 360-degree content are threefold: First, we collect, partially annotate, and make publicly available a new eye tracking data set, which consists of 13 participants viewing 15 video clips that are recorded in 360-degree. Second, we propose a new two-stage pipeline for ground truth annotation of the traditional fixations, saccades, smooth pursuits, as well as (optokinetic) nystagmus, vestibulo-ocular reflex, and pursuit of moving objects performed exclusively via the movement of the head. A flexible user interface for this pipeline is implemented and made freely accessible for use or modification. Lastly, we develop and test a simple proof-of-concept algorithm for automatic classification of all the eye movement types in our data set based on their operational definitions that were used for manual annotation. The data set and the source code for both the annotation tool and the algorithm are publicly available at https://web.gin.g-node.org/ioannis.agtzidis/360_em_dataset.
A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel ranking model that combines the Bradley-Terry-Luce probability model with a nonnegative matrix factorization framework to model and uncover the presence of latent variables that influence the performance of top tennis players. We derive an efficient, provably convergent, and numerically stable majorization-minimization-based algorithm to maximize the likelihood of datasets under the proposed statistical model. The model is tested on datasets involving the outcomes of matches between 20 top male and female tennis players over 14 major tournaments for men (including the Grand Slams and the ATP Masters 1000) and 16 major tournaments for women over the past 10 years. Our model automatically infers that the surface of the court (e.g., clay or hard court) is a key determinant of the performances of male players, but less so for females. Top players on various surfaces over this longitudinal period are also identified in an objective manner.
Wyner's Network on Caches: Combining Receiver Caching with a Flexible Backhaul
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we study a large linear interference network with an equal number of transmitters and receivers, where each transmitter is connected to two subsequent receivers. Each transmitter has individual access to a backhaul link (fetching the equivalent of $M_{T}$ files), while each receiver can cache a fraction $γ$ of the library. We explore the tradeoff between the communication rate, backhaul load, and caching storage by designing algorithms that can harness the benefits of cooperative transmission in partially connected networks, while exploiting the advantages of multicast transmissions attributed to user caching. We show that receiver caching and fetching content from the backhaul are two resources that can simultaneously increase the delivery performance in synergistic ways. Specifically, an interesting outcome of this work is that user caching of a fraction $γ$ of the library can increase the per-user Degrees of Freedom (puDoF) by $γ$. Further, the results reveal significant savings in the backhaul load, even in the small cache size region. For example, the puDoF achieved using the pair $(M_{T}=8, γ=0)$ can also be achieved with the pairs $(M_{T}=4,γ=0.035)$ and $(M_{T}=2,γ=0.1)$, showing that even small caches can provide significant savings in the backhaul load.
Extended framework of Hamilton's principle applied to Duffing oscillation
Venue: arXiv cs.NA 2019
Year: 2019

The paper begins with a novel variational formulation of Duffing equation using the extended framework of Hamilton's principle (EHP). This formulation properly accounts for initial conditions, and it recovers all the governing differential equations as its Euler-Lagrange equation. Thus, it provides elegant structure for the development of versatile temporal finite element methods. Herein, the simplest temporal finite element method is presented by adopting linear temporal shape functions. Numerical examples are included to verify and investigate performance of non-iterative algorithm in the developed method.
Age prediction using a large chest X-ray dataset
Venue: arXiv cs.CV 2019
Year: 2019

Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a persons age on Chest X-Rays. Specifically, we trained a CNN in regression fashion on a large publicly available dataset. Moreover, for interpretability, we explored activation maps to identify which areas of a CXR image are important for the machine (i.e. CNN) to predict a patients age, offering insight. Overall, amongst correctly predicted CXRs, we see areas near the clavicles, shoulders, spine, and mediastinum being most activated for age prediction, as one would expect biologically. Amongst incorrectly predicted CXRs, we have qualitatively identified disease patterns that could possibly make the anatomies appear older or younger than expected. A further technical and clinical evaluation would improve this work. As CXR is the most commonly requested imaging exam, a potential use case for estimating age may be found in the preventative counseling of patient health status compared to their age-expected average, particularly when there is a large discrepancy between predicted age and the real patient age.
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
Venue: arXiv cs.CV 2019
Year: 2019

A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data. In this paper we present a novel graph-based framework, which aims to tackle this problem in the presence of large scale data input. Our approach utilises a novel superpixel method, specifically designed for hyperspectral data, to define meaningful local regions in an image, which with high probability share the same classification label. We then extract spectral and spatial features from these regions and use these to produce a contracted weighted graph-representation, where each node represents a region rather than a pixel. Our graph is then fed into a graph-based semi-supervised classifier which gives the final classification. We show that using superpixels in a graph representation is an effective tool for speeding up graphical classifiers applied to hyperspectral images. We demonstrate through exhaustive quantitative and qualitative results that our proposed method produces accurate classifications when an incredibly small amount of labelled data is used. We show that our approach mitigates the major drawbacks of existing approaches, resulting in our approach outperforming several comparative state-of-the-art techniques.