OpenRaiser/Intern-Atlas huggingface dataset:
SciTokens: Capability-Based Secure Access to Remote Scientific Data
Venue: arXiv cs.DC 2018
Year: 2018

The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. In this paper, we introduce SciTokens, open source software to help scientists manage their security credentials more reliably and securely. We describe the SciTokens system architecture, design, and implementation addressing use cases from the Laser Interferometer Gravitational-Wave Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey Telescope (LSST) projects. We also present our integration with widely-used software that supports distributed scientific computing, including HTCondor, CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for capability-based secure access to remote scientific data. The access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems.
Probabilistic verification of all languages
Venue: arXiv cs.CC 2018
Year: 2018

We present three protocols for verifying all languages: (i) For any unary (binary) language, there is a log-space (linear-space) interactive proof system (IPS); (ii) for any language, there is a constant-space weak-IPS (the non-members may not be rejected with high probability); and, (iii) for any language, there is a constant-space IPS with two provers where the verifier reads the input once. Additionally, we show that uncountably many binary (unary) languages can be verified in constant space and in linear (quadratic) expected time.
Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation
Venue: arXiv cs.CV 2018
Year: 2018

Reliable motion estimation and strain analysis using 3D+time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose an unsupervised autoencoder network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated both the autoencoder and semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarcted regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.
Self-Assembly of 3-D Structures Using 2-D Folding Tiles
Venue: arXiv cs.CG 2018
Year: 2018

Self-assembly is a process which is ubiquitous in natural, especially biological systems. It occurs when groups of relatively simple components spontaneously combine to form more complex structures. While such systems have inspired a large amount of research into designing theoretical models of self-assembling systems, and even laboratory-based implementations of them, these artificial models and systems often tend to be lacking in one of the powerful features of natural systems (e.g. the assembly and folding of proteins), namely the dynamic reconfigurability of structures. In this paper, we present a new mathematical model of self-assembly, based on the abstract Tile Assembly Model (aTAM), called the Flexible Tile Assembly Model (FTAM). In the FTAM, the individual components are 2-dimensional square tiles as in the aTAM, but in the FTAM, bonds between the edges of tiles can be flexible, allowing bonds to flex and entire structures to reconfigure, thus allowing 2-dimensional components to form 3-dimensional structures. We analyze the powers and limitations of FTAM systems by (1) demonstrating how flexibility can be controlled to carefully build desired structures, and (2) showing how flexibility can be beneficially harnessed to form structures which can "efficiently" reconfigure into many different configurations and/or greatly varying configurations. We also show that with such power comes a heavy burden in terms of computational complexity of simulation and prediction by proving that, for important properties of FTAM systems, determining their existence is intractable, even for properties which are easily computed for systems in less dynamic models.
Fast Modular Subset Sum using Linear Sketching
Venue: arXiv cs.DS 2018
Year: 2018

Given n positive integers, the Modular Subset Sum problem asks if a subset adds up to a given target t modulo a given integer m. This is a natural generalization of the Subset Sum problem (where m=+\infty) with ties to additive combinatorics and cryptography. Recently, in [Bringmann, SODA'17] and [Koiliaris and Xu, SODA'17], efficient algorithms have been developed for the non-modular case, running in near-linear pseudo-polynomial time. For the modular case, however, the best known algorithm by Koiliaris and Xu [Koiliaris and Xu, SODA'17] runs in time O~(m^{5/4}). In this paper, we present an algorithm running in time O~(m), which matches a recent conditional lower bound of [Abboud et al.'17] based on the Strong Exponential Time Hypothesis. Interestingly, in contrast to most previous results on Subset Sum, our algorithm does not use the Fast Fourier Transform. Instead, it is able to simulate the "textbook" Dynamic Programming algorithm much faster, using ideas from linear sketching. This is one of the first applications of sketching-based techniques to obtain fast algorithms for combinatorial problems in an offline setting.
Dating with Scambots: Understanding the Ecosystem of Fraudulent Dating Applications
Venue: arXiv cs.CY 2018
Year: 2018

In this work, we are focusing on a new and yet uncovered way for malicious apps to gain profit. They claim to be dating apps. However, their sole purpose is to lure users into purchasing premium/VIP services to start conversations with other (likely fake female) accounts in the app. We call these apps as fraudulent dating apps. This paper performs a systematic study to understand the whole ecosystem of fraudulent dating apps. Specifically, we have proposed a three-phase method to detect them and subsequently comprehend their characteristics via analyzing the existing account profiles. Our observation reveals that most of the accounts are not managed by real persons, but by chatbots based on predefined conversation templates. We also analyze the business model of these apps and reveal that multiple parties are actually involved in the ecosystem, including producers who develop apps, publishers who publish apps to gain profit, and the distribution network that is responsible for distributing apps to end users. Finally, we analyze the impact of them to users (i.e., victims) and estimate the overall revenue. Our work is the first systematic study on fraudulent dating apps, and the results demonstrate the urge for a solution to protect users.
Want to bring a community together? Create more sub-communities
Venue: arXiv cs.SI 2018
Year: 2018

Understanding overlapping community structures is crucial for network analysis and prediction. AGM (Affiliation Graph Model) is one of the favorite models for explaining the densely overlapped community structures. In this paper, we thoroughly re-investigate the assumptions made by the AGM model on real datasets. We find that the AGM model is not sufficient to explain several empirical behaviors observed in popular real-world networks. To our surprise, all our experimental results can be explained by a parameter-free hypothesis, leading to more straightforward modeling than AGM which has many parameters. Based on these findings, we propose a parameter-free Jaccard-based Affiliation Graph (JAG) model which models the probability of edge as a network specific constant times the Jaccard similarity between community sets associated with the individuals. Our modeling is significantly simpler than AGM, and it eliminates the need of associating a parameter, the probability value, with each community. Furthermore, JAG model naturally explains why (and in fact when) overlapping communities are densely connected. Based on these observations, we propose a new community-driven friendship formation process, which mathematically recovers the JAG model. JAG is the first model that points towards a direct causal relationship between tight connections in the given community with the number of overlapping communities inside it. Thus, \emph{the most effective way to bring a community together is to form more sub-communities within it.} The community detection algorithm based on our modeling demonstrates a significantly simple algorithm with state-of-the-art accuracy on six real-world network datasets compared to the existing link analysis based methods.
Self-dual cyclic codes over $M_2(\mathbb{Z}_4)$
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study the codes over the matrix ring over $\mathbb{Z}_4$, which is perhaps the first time the ring structure $M_2(\mathbb{Z}_4)$ is considered as a code alphabet. This ring is isomorphic to $\mathbb{Z}_4[w]+U\mathbb{Z}_4[w]$, where $w$ is a root of the irreducible polynomial $x^2+x+1 \in \mathbb{Z}_2[x]$ and $U\equiv$ ${11}\choose{11}$. We first discuss the structure of the ring $M_2(\mathbb{Z}_4)$ and then focus on algebraic structure of cyclic codes and self-dual cyclic codes over $M_2(\mathbb{Z}_4)$. We obtain the generators of the cyclic codes and their dual codes. Few examples are given at the end of the paper.
Convexity Analysis of Optimization Framework of Attitude Determination from Vector Observations
Venue: arXiv cs.SY 2018
Year: 2018

In the past several years, there have been several representative attitude determination methods developed using derivative-based optimization algorithms. Optimization techniques e.g. gradient-descent algorithm (GDA), Gauss-Newton algorithm (GNA), Levenberg-Marquadt algorithm (LMA) suffer from local optimum in real engineering practices. A brief discussion on the convexity of this problem is presented recently \cite{Ahmed2012} stating that the problem is neither convex nor concave. In this paper, we give analytic proofs on this problem. The results reveal that the target loss function is convex in the common practice of quaternion normalization, which leads to non-existence of local optimum.
Knowledge Management Strategies and Processes in Agile Software Development: A Systematic Literature Review
Venue: arXiv cs.SE 2018
Year: 2018

Knowledge-intensive companies that adopt Agile Software Development (ASD) relay on efficient implementation of Knowledge Management (KM) strategies to promotes different Knowledge Processes (KPs) to gain competitive advantage. This study aims to explore how companies that adopt ASD implement KM strategies utilizing practices that promote the KPs in the different organizational layers. Through a systematic literature review, we analyzed 32 primary studies, selected by automated search and snowballing in the extant literature. To analyze the data, we applied narrative synthesis. Most of the identified KM practices implement personalization strategies (81 %), supported by codification (19 %). Our review shows that the primary studies do not report KM practices in the strategic layer and two of them in the product portfolio layer; on the other hand, in the project layer, the studies report 33 practices that implement personalization strategy, and seven practices that implement codification. KM strategies in ASD promote mainly the knowledge transfer process with practices that stimulate social interaction to share tacit knowledge in the project layer. As a result of using informal communication, a significant amount of knowledge can be lost or not properly transferred to other individuals and, instead of propagating the knowledge, it remains inside a few individuals minds.
Facilitating information system development with Panoramic view on data
Venue: arXiv cs.SE 2018
Year: 2018

The increasing amount of information and the absence of an effective tool for assisting users with minimal technical knowledge lead us to use associative thinking paradigm for implementation of a software solution - Panorama. In this study, we present object recognition process, based on context + focus information visualization techniques, as a foundation for realization of Panorama. We show that user can easily define data vocabulary of selected domain that is furthermore used as the application framework. The purpose of Panorama approach is to facilitate software development of certain problem domains by shortening the Software Development Life Cycle with minimizing the impact of implementation, review and maintenance phase. Our approach is focused on using and updating data vocabulary by users without extensive programming skills. Panorama therefore facilitates traversing through data by following associations where user does not need to be familiar with the query language, the data structure and does not need to know the problem domain fully. Our approach has been verified by detailed comparison to existing approaches and in an experiment by implementing selected use cases. The results confirmed that Panorama fits problem domains with emphasis on data oriented rather than ones with process oriented aspects. In such cases the development of selected problem domains is shortened up to 25%, where emphasis is mainly on analysis, logical design and testing, while omitting physical design and programming, which is performed automatically by Panorama tool.
Exploring Hierarchy-Aware Inverse Reinforcement Learning
Venue: arXiv cs.AI 2018
Year: 2018

We introduce a new generative model for human planning under the Bayesian Inverse Reinforcement Learning (BIRL) framework which takes into account the fact that humans often plan using hierarchical strategies. We describe the Bayesian Inverse Hierarchical RL (BIHRL) algorithm for inferring the values of hierarchical planners, and use an illustrative toy model to show that BIHRL retains accuracy where standard BIRL fails. Furthermore, BIHRL is able to accurately predict the goals of `Wikispeedia' game players, with inclusion of hierarchical structure in the model resulting in a large boost in accuracy. We show that BIHRL is able to significantly outperform BIRL even when we only have a weak prior on the hierarchical structure of the plans available to the agent, and discuss the significant challenges that remain for scaling up this framework to more realistic settings.
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low- and high-precision unit in a cascade. A confidence evaluation unit is employed to identify misclassified cases from the excessively low-precision unit and forward them to the high-precision unit for re-processing. Experiments demonstrate that the proposed toolflow can achieve a performance boost up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy, without the need of retraining the model or accessing the training data.
Dynamic Density Estimation in Heterogeneous Cell Populations
Venue: arXiv cs.SY 2018
Year: 2018

Multicellular systems play a key role in bioprocess and biomedical engineering. Cell ensembles encountered in these setups show phenotypic variability like size and biochemical composition. As this variability may result in undesired effects in bioreactors, close monitoring of the cell population heterogeneity is important for maximum production output, and accurate control. However, direct measurements are mostly restricted to a few cellular properties. This motivates the application of model-based online estimation techniques for the reconstruction of non-measurable cellular properties. Population balance modeling allows for a natural description of cell-to-cell variability. In this contribution, we present an estimation approach that, in contrast to existing ones, does not rely on a finite-dimensional approximation through grid based discretization of the underlying population balance model. Instead, our so-called characteristics based density estimator employs sample approximations. With two and three-dimensional benchmark examples we demonstrate that our approach is superior to the grid based designs in terms of accuracy and computational demand.
A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation
Venue: arXiv cs.ET 2018
Year: 2018

Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state-dependence, non-linear conductance changes, and intrinsic variability in both their switching threshold and conductance values, that make them ideal devices for emulating the bio-physics of real synapses. In this paper we present a spiking neural network architecture that supports the use of memristive devices as synaptic elements, and propose mixed-signal analog-digital interfacing circuits which mitigate the effect of variability in their conductance values and exploit their variability in the switching threshold, for implementing stochastic learning. The effect of device variability is mitigated by using pairs of memristive devices configured in a complementary push-pull mechanism and interfaced to a current-mode normalizer circuit. The stochastic learning mechanism is obtained by mapping the desired change in synaptic weight into a corresponding switching probability that is derived from the intrinsic stochastic behavior of memristive devices. We demonstrate the features of the CMOS circuits and apply the architecture proposed to a standard neural network hand-written digit classification benchmark based on the MNIST data-set. We evaluate the performance of the approach proposed on this benchmark using behavioral-level spiking neural network simulation, showing both the effect of the reduction in conductance variability produced by the current-mode normalizer circuit, and the increase in performance as a function of the number of memristive devices used in each synapse.
Approximation Algorithms for Clustering via Weighted Impurity Measures
Venue: arXiv cs.DS 2018
Year: 2018

An impurity measures $I:{R}^k \to {R}^+$ maps a $k$-dimensional vector ${\bf v}$ to a non-negative value $I({\bf v})$ so that the more homogeneous ${\bf v}$, the larger its impurity. We study clustering based on impurity measures: given a collection $V$ of $n$ many $k$-dimensional vectors and an impurity measure $I$, the goal is to find a partition ${\cal P}$ of $V$ into $L$ groups $V_1,\ldots,V_L$ that minimizes the total impurities of the groups in ${\cal P}$, i.e., $I({\cal P})= \sum_{m=1}^{L} I(\sum_{{\bf v} \in V_m}{\bf v}).$ Impurity minimization is widely used as quality assessment measure in probability distribution clustering and in categorical clustering where it is not possible to rely on geometric properties of the data set. However, in contrast to the case of metric based clustering, the current knowledge of impurity measure based clustering in terms of approximation and inapproximability results is very limited. Our research contributes to fill this gap. We first present a simple linear time algorithm that simultaneously achieves $3$-approximation for the Gini impurity measure and $O(\log(\sum_{{\bf v} \in V} \| {\bf v} \|_1))$-approximation for the Entropy impurity measure. Then, for the Entropy impurity measure---where we also show that finding the optimal clustering is strongly NP-hard---we are able to design a polynomial time $O(\log^2(\min\{k,L\}))$-approximation algorithm. Our algorithm relies on a nontrivial characterization of a class of clusterings that necessarily includes a partition achieving $O(\log^2(\min\{k,L\}))$--approximation of the impurity of the optimal partition. Remarkably, this is the first polynomial time algorithm with approximation guarantee independent of the number of points/vector and not relying on any restriction on the components of the vectors for producing clusterings with minimum entropy.
The Dune Python Module
Venue: arXiv cs.MS 2018
Year: 2018

In this paper we present the new Dune-Python module which provides Python bindings for the Dune core, which is a C++ environment for solving partial differential equations. The aim of this new module is to firstly provide the general infrastructure for exporting realizations of statically polymorphic interfaces based on just-in-time compilation and secondly to provide bindings for the central interfaces of the dune core modules. In the first release we focus on the grid interface. Our aim is to only introduce a thin layer when passing objects into Python which can be removed when the object is passed back into a C++ algorithm. Thus no efficiency is lost and little additional code maintenance cost is incurred. To make the transition for Dune users to the Python environment straightforward the Python classes provide a very similar interface to their C++ counterparts. In addition, vectorized versions of many interfaces allow for more efficient code on the Python side. The infrastructure for exporting these interfaces and the resulting bindings for a Dune grid are explained in detail in this paper for both experienced Dune users and others interested in a flexible Python environment for implementing grid based schemes for solving partial differential equations.