bluuebunny/crossref_metadata_2025 huggingface dataset:
A derivation of coincidence relations utilizing quaternion and matrix based on the hexagonal lattice for material engineers
Authors: Kumano Tomoji , Nakagawa Junichi
Published: January 2019

Grain-oriented silicon steel is mainly used as the core material of transformers, and it is manufactured by applying secondary recrystallization. The driving force of this process is the grain boundary energy, based on the nature of the grain boundary, which is determined by coincidence site lattice (CSL) relations. CSL relations are determined by the arrangement of lattice points in three-dimensional space and have already been shown mathematically by using advanced mathematics. However, their derivation processes are abstract, making them difficult for material engineers to understand. Therefore, in this study, a derivation of CSL relations is attempted in order to enable material engineers to easily understand the derivation. This study contributes to industrial mathematics by helping material engineers understand the essence of the mathematical method in order to use it appropriately. Specifically, a derivation method for coincidence relations is proposed using the hexagonal lattice (in the case of an axial ratio of [Formula: see text]) as an example that avoids the need for advanced mathematics. This method involves applying the scale rotation of a quaternion, and it is thus named the quaternion-matrix method. The matrix specifying the [Formula: see text] coincidence relation of a certain lattice system is expressed by a similarity transformation using the matrix comprising its primitive translation vectors and is given as the following transformation matrix: [Formula: see text]. Based on the rational number property of the transformation matrix elements, the following formula is derived: [Formula: see text], [Formula: see text], [Formula: see text] value. Here, ([Formula: see text]) is specified by the integrality (lattice point) and irreducibility (unit cell) among the elements of [Formula: see text], and the quaternion for the CSL formation is thus derived. Finally, based on the polar form of this quaternion, the coincidence relation can be derived.
Cross-Modality Feature Learning via Convolutional Autoencoder
Authors: Liu Xueliang , Wang Meng , Zha Zheng-Jun , Hong Richang
Published: January 2019

Learning robust and representative features across multiple modalities has been a fundamental problem in machine learning and multimedia fields. In this article, we propose a novel MUltimodal Convolutional AutoEncoder (MUCAE) approach to learn representative features from visual and textual modalities. For each modality, we integrate the convolutional operation into an autoencoder framework to learn a joint representation from the original image and text content. We optimize the convolutional autoencoders of different modalities jointly by exploiting the correlation between the hidden representations from the convolutional autoencoders, in particular by minimizing both the reconstructing error of each modality and the correlation divergence between the hidden feature of different modalities. Compared to the conventional solutions relying on hand-crafted features, the proposed MUCAE approach encodes features from image pixels and text characters directly and produces more representative and robust features. We evaluate MUCAE on cross-media retrieval as well as unimodal classification tasks over real-world large-scale multimedia databases. Experimental results have shown that MUCAE performs better than the state-of-the-arts methods.
Dense 3D-Convolutional Neural Network for Person Re-Identification in Videos
Authors: Liu Jiawei , Zha Zheng-Jun , Chen Xuejin , Wang Zilei , Zhang Yongdong
Published: January 2019

Person re-identification aims at identifying a certain pedestrian across non-overlapping multi-camera networks in different time and places. Existing person re-identification approaches mainly focus on matching pedestrians on images; however, little attention has been paid to re-identify pedestrians in videos. Compared to images, video clips contain motion patterns of pedestrians, which is crucial to person re-identification. Moreover, consecutive video frames present pedestrian appearance with different body poses and from different viewpoints, providing valuable information toward addressing the challenge of pose variation, occlusion, and viewpoint change, and so on. In this article, we propose a Dense 3D-Convolutional Network (D3DNet) to jointly learn spatio-temporal and appearance representation for person re-identification in videos. The D3DNet consists of multiple three-dimensional (3D) dense blocks and transition layers. The 3D dense blocks enlarge the receptive fields of visual neurons in both spatial and temporal dimensions, leading to discriminative appearance representation as well as short-term and long-term motion patterns of pedestrians without the requirement of an additional motion estimation module. Moreover, we formulate a loss function consisting of an identification loss and a center loss to minimize intra-class variance and maximize inter-class variance simultaneously, toward addressing the challenge of large intra-class variance and small inter-class variance. Extensive experiments on two real-world video datasets of person identification, i.e., MARS and iLIDS-VID, have shown the effectiveness of the proposed approach.
Structure-Aware Deep Learning for Product Image Classification
Authors: Chen Zhineng , Ai Shanshan , Jia Caiyan
Published: January 2019

Automatic product image classification is a task of crucial importance with respect to the management of online retailers. Motivated by recent advancements of deep Convolutional Neural Networks (CNN) on image classification, in this work we revisit the problem in the context of product images with the existence of a predefined categorical hierarchy and attributes, aiming to leverage the hierarchy and attributes to improve classification accuracy. With these structure-aware clues, we argue that more advanced deep models could be developed beyond the flat one-versus-all classification performed by conventional CNNs. To this end, novel efforts of this work include a salient-sensitive CNN that gazes into the product foreground by inserting a dedicated spatial attention module; a multiclass regression-based refinement that is expected to predict more accurately by merging prediction scores from multiple preceding CNNs, each corresponding to a distinct classifier in the hierarchy; and a multitask deep learning architecture that effectively explores correlations among categories and attributes for categorical label prediction. Experimental results on nearly 1 million real-world product images basically validate the effectiveness of the proposed efforts individually and jointly, from which performance gains are observed.
Brownout Approach for Adaptive Management of Resources and Applications in Cloud Computing Systems
Authors: Xu Minxian , Buyya Rajkumar
Published: January 2019

Cloud computing has been regarded as an emerging approach to provisioning resources and managing applications. It provides attractive features, such as an on-demand model, scalability enhancement, and management cost reduction. However, cloud computing systems continue to face problems such as hardware failures, overloads caused by unexpected workloads, or the waste of energy due to inefficient resource utilization, which all result in resource shortages and application issues such as delays or saturation. A paradigm, the brownout, has been applied to handle these issues by adaptively activating or deactivating optional parts of applications or services to manage resource usage in cloud computing system. Brownout has successfully shown that it can avoid overloads due to changes in workload and achieve better load balancing and energy saving effects. This article proposes a taxonomy of the brownout approach for managing resources and applications adaptively in cloud computing systems and carries out a comprehensive survey. It identifies open challenges and offers future research directions.
Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data
Authors: Zhao Liang , Chen Zhikui , Yang Laurence T. , Deen M. Jamal , Wang Z. Jane
Published: January 2019

Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employeddeepneuralnetworks (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparities. However, only a few articles consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this article, we propose adeep semantic mapping model forheterogeneous multimediatransferlearning (DHTL) using co-occurrence data. More specifically, we integrate the DNN withcanonicalcorrelationanalysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and it is superior to the several existing state-of-the-art methods for deep transfer learning.
Towards the Decentralised Cloud
Authors: Ferrer Ana Juan , Marquès Joan Manuel , Jorba Josep
Published: January 2019

Cloud computing emerged as a centralised paradigm that made “infinite” computing resources available on demand. Nevertheless, the ever-increasing computing capacities present on smart connected things and devices calls for the decentralisation of Cloud computing to avoid unnecessary latencies and fully exploit accessible computing capacities at the edges of the network. Whilst these decentralised Cloud models represent a significant breakthrough from a Cloud perspective, they are rooted in existing research areas such as Mobile Cloud Computing, Mobile Ad hoc Computing, and Edge computing. This article analyses the pre-existing works to determine their role in Decentralised Cloud and future computing development.
Deep Learning–Based Multimedia Analytics
Authors: Zhang Wei , Yao Ting , Zhu Shiai , Saddik Abdulmotaleb El
Published: January 2019

The multimedia community has witnessed the rise of deep learning–based techniques in analyzing multimedia content more effectively. In the past decade, the convergence of deep-learning and multimedia analytics has boosted the performance of several traditional tasks, such as classification, detection, and regression, and has also fundamentally changed the landscape of several relatively new areas, such as semantic segmentation, captioning, and content generation. This article aims to review the development path of major tasks in multimedia analytics and take a look into future directions. We start by summarizing the fundamental deep techniques related to multimedia analytics, especially in the visual domain, and then review representative high-level tasks powered by recent advances. Moreover, the performance review of popular benchmarks gives a pathway to technology advancement and helps identify both milestone works and future directions.
Linked Vocabulary Recommendation Tools for Internet of Things
Authors: Kolbe Niklas , Kubler Sylvain , Robert Jérémy , Le Traon Yves , Zaslavsky Arkady
Published: January 2019

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting the most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both empirically and experimentally. Some of the key findings include that (i) many tools neglect to thoroughly address both the curation of a vocabulary collection and effective selection mechanisms, (ii) modern information retrieval techniques are underrepresented, and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet sufficiently extended to fit today’s IoT projects.
Mining Missing Assumptions from Counter-Examples
Authors: Plassan Guillaume , Morin-Allory Katell , Borrione Dominique
Published: January 2019

During the formal functional verification of Register-Transfer Level designs, a false failure is often observed. Most of the time, this failure is caused by an underconstrained model. The analysis of the root cause for the verification error and the creation of missing assumptions are a significant time burden. In this article, we present a methodology to automatically mine these missing assumptions from counter-examples. First, multiple counter-examples are generated for the same property. Then, relevant behaviors are mined from the counter-examples. Finally, corresponding assumptions are filtered and a small amount is returned to the user for review.
Effect of Recommendations on Serving Content with Unknown Demand
Authors: Gupta Samarth , Moharir Sharayu
Published: January 2019

We consider the task of content replication in distributed content delivery systems used by Video-on-Demand (VoD) services with large content catalogs. The prior work in this area focuses on the setting where each request is generated independent of all past requests. Motivated by the fact that most popular VoD services offer recommendations to users based on their viewing history, in a departure from existing studies, we study the setting with time-correlation in requests coming from each user.

We use a Markovian process to model each user’s request process. In addition to introducing time-correlation in user requests, our model is consistent with empirically observed properties of the request process for VoD services with recommendation engines. In the setting where the underlying Markov Chain is unknown and has to be learned from the very requests the system is trying to serve, we show that separating the task of estimating content popularity and using the estimates to design a static content replication policy is strictly sub-optimal. To prove this, we show that an adaptive policy, which jointly performs the task of estimation and content replication, outperforms all policies that separate the task of estimation and content replication.
A Survey on Agent-based Simulation Using Hardware Accelerators
Authors: Xiao Jiajian , Andelfinger Philipp , Eckhoff David , Cai Wentong , Knoll Alois
Published: January 2019

Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as Field Programmable Gate Arrays. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorisation of the literature according to the applied techniques. Since, at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still addressed in a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.
Unsupervised Joint PoS Tagging and Stemming for Agglutinative Languages
Authors: Bölücü Necva , Can Burcu
Published: January 2019

The number of possible word forms is theoretically infinite in agglutinative languages. This brings up the out-of-vocabulary (OOV) issue for part-of-speech (PoS) tagging in agglutinative languages. Since inflectional morphology does not change the PoS tag of a word, we propose to learn stems along with PoS tags simultaneously. Therefore, we aim to overcome the sparsity problem by reducing word forms into their stems. We adopt a Bayesian model that is fully unsupervised. We build a Hidden Markov Model for PoS tagging where the stems are emitted through hidden states. Several versions of the model are introduced in order to observe the effects of different dependencies throughout the corpus, such as the dependency between stems and PoS tags or between PoS tags and affixes. Additionally, we use neural word embeddings to estimate the semantic similarity between the word form and stem. We use the semantic similarity as prior information to discover the actual stem of a word since inflection does not change the meaning of a word. We compare our models with other unsupervised stemming and PoS tagging models on Turkish, Hungarian, Finnish, Basque, and English. The results show that a joint model for PoS tagging and stemming improves on an independent PoS tagger and stemmer in agglutinative languages.
Functional Characterization of Deformation Fields
Authors: Corman Etienne , Ovsjanikov Maks
Published: January 2019

In this article, we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric. In particular, our approach allows one to represent a deformation field in a coordinate-free way as a linear operator acting on real-valued functions defined on the shape. Such a representation provides both a way to relate deformation fields to other classical functional operators and enables analysis and processing of deformation fields using standard linear-algebraic tools. This opens the door to a wide variety of applications such as explicitly adding extrinsic information into the computation of functional maps, intrinsic shape symmetrization, joint deformation design through precise control of metric distortion, and coordinate-free deformation transfer without requiring pointwise correspondences. Our method is applicable to both surface and volumetric shape representations and we guarantee the equivalence between the operator-based and standard deformation field representation under mild genericity conditions in the discrete setting. We demonstrate the utility of our approach by comparing it with existing techniques and show how our representation provides a powerful toolbox for a wide variety of challenging problems.
Aggregation of Votes with Multiple Positions on Each Issue
Authors: Kirousis Lefteris , Kolaitis Phokion G. , Livieratos John
Published: January 2019

We consider the problem of aggregating votes cast by a society on a fixed set of issues, where each member of the society may vote for one of several positions on each issue, but the combination of votes on the various issues is restricted to a set of feasible voting patterns. We follow the aggregation framework used by Dokow and Holzman [Aggregation of non-binary evaluations,
Advances in Applied Mathematics
, 45:4, 487--504, 2010], in which both preference aggregation and judgment aggregation can be cast. We require the aggregation to be independent on each issue, and also supportive, i.e., for every issue, the corresponding component of every aggregator, when applied to a tuple of votes, must take as value one of the votes in that tuple. We prove that, in such a setup, non-dictatorial aggregation of votes in a society of an arbitrary size is possible if and only if either there is a non-dictatorial aggregator for two voters or there is an aggregator for three voters such that, for each issue, the corresponding component of the aggregator, when restricted to two-element sets of votes, is a majority operation or a minority operation. We then introduce a notion of a uniform non-dictatorial aggregator, which is an aggregator such that on every issue, and when restricted to arbitrary two-element subsets of the votes for that issue, it differs from all projection functions. We first give a characterization of sets of feasible voting patterns that admit a uniform non-dictatorial aggregator. After this and by making use of Bulatov’s dichotomy theorem for conservative constraint satisfaction problems, we connect social choice theory with the computational complexity of constraint satisfaction by proving that if a set of feasible voting patterns has a uniform non-dictatorial aggregator of some arity, then the multi-sorted conservative constraint satisfaction problem on that set (with each issue representing a different sort) is solvable in polynomial time; otherwise, it is NP-complete.
A Variational Inference-Based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling
Authors: Wang Wenjing , Chen Nan , Chen Xi , Yang Linchang
Published: January 2019

In this article, we propose a variational Bayesian inference-based Gaussian process metamodeling approach (VBGP) that is suitable for the design and analysis of stochastic simulation experiments. This approach enables statistically and computationally efficient approximations to the mean and variance response surfaces implied by a stochastic simulation, while taking into full account the uncertainty in the heteroscedastic variance; furthermore, it can accommodate the situation where either one or multiple simulation replications are available at every design point. We demonstrate the superior performance of VBGP compared with existing simulation metamodeling methods through two numerical examples.
Joint User Association, Content Caching and Recommendations in Wireless Edge Networks
Authors: Chatzieleftheriou L. E. , Darzanos G. , Karaliopoulos M. , Koutsopoulos I.
Published: January 2019

In this paper, we investigate the performance gains that are achievable when jointly controlling (i) in which Small-cell Base Stations (SBSs) mobile users are associated to, (ii) which content items are stored at SBS co-located caches and (iii) which content items are recommended to the mobile users who are associated to different SBSs. We first establish a framework for the joint user association, content caching and recommendations problem, by specifying a set of necessary conditions for all three component functions of the system. Then, we provide a concrete formulation of the joint problem when the objective is to maximize the total hit ratio over all caches. We analyze the problems that emerge as special cases of the joint problem, when one of the three functions is carried out independently, and use them to characterize its complexity. Finally, we propose a heuristic that tackles the joint problem. Proof-of-concept simulations demonstrate that even this simple heuristic outperforms an optimal algorithm that takes only caching and recommendation decisions into account and provide evidence of the achievable performance gains when decisions over all three functions are jointly optimized.
Information Market for Web Browsing
Authors: Kakhki Arash Molavi , Erramilli Vijay , Gill Phillipa , Chaintreau Augustin , Krishnamurthy Balachander
Published: January 2019

Browsing privacy solutions are faced with an uphill battle to deployment. Many operate counter to the economic objectives of popular online services (e.g., by completely blocking ads) and do not provide enough incentive for users who may be subject to performance degradation for deploying them. In this study, we take a step towards realizing a system for online privacy that is mutually beneficial to users and online advertisers: an information market. This system not only maintains economic viability for online services, but provides users with financial compensation to encourage them to participate. We prototype and evaluate an information market that provides privacy and revenue to users while preserving and sometimes improving their Web performance. We evaluate feasibility of the market via a one month field study with 63 users and find that users are indeed willing to sell their browsing information. We also use Web traces of millions of users to drive a simulation study to evaluate the system at scale. We find that the system can indeed be profitable to both users and online advertisers.
Size Expansions of Mean Field Approximation
Authors: Gast Nicolas , Bortolussi Luca , Tribastone Mirco
Published: January 2019

Mean field approximation is a powerful tool to study the performance of large stochastic systems that is known to be exact as the system's size N goes to infinity. Recently, it has been shown that, when one wants to compute expected performance metric in steady-state, mean field approximation can be made more accurate by adding a term in 1/N to the original approximation. This is called the refined mean field approximation in [7]. In this paper, we show how to obtain the same result for the transient regime and we provide a further refinement by expanding the term in 1/N2 (both for transient and steady-state regime). Our derivations are inspired by moment-closure approximation. We provide a number of examples that show this new approximation is usable in practice for systems with up to a few tens of dimensions.
Degree-Greedy Algorithms on Large Random Graphs
Authors: Bermolen Paola , Jonckheere Matthieu , Larroca Federico , Saenz Manuel
Published: January 2019

Computing the size of maximum independent sets is an NPhard problem for fixed graphs. Characterizing and designing efficient algorithms to compute (or approximate) this independence number for random graphs are notoriously difficult and still largely open issues. In this paper, we show that a low complexity degree-greedy exploration is actually asymptotically optimal on a large class of sparse random graphs. Encouraged by this result, we present and study two variants of sequential exploration algorithms: static and dynamic degree-aware explorations. We derive hydrodynamic limits for both of them, which in turn allow us to compute the size of the resulting independent set. Whereas the former is simpler to compute, the latter may be used to arbitrarily approximate the degree-greedy algorithm. Both can be implemented in a distributed manner. The corresponding hydrodynamic limits constitute an efficient method to compute or bound the independence number for a large class of sparse random graphs. As an application, we then show how our method may be used to compute (or approximate) the capacity of a large 802.11-based wireless network.
Efficiency of Best Response Dynamics with High Playing Rates in Potential Games
Authors: Durand Stephane , Garin Federica , Gaujal Bruno
Published: January 2019

In this paper we design and analyze distributed best response dynamics to compute Nash equilibria in potential games. This algorithm uses local Poisson clocks for each player and does not rely on the usual but unrealistic assumption that players take no time to compute their best response. If this time (denotedδ) is taken into account, distributed best response dynamics (BRD) may suffer from overlaps: one player starts to play while another player has not changed its strategy yet. An overlap may lead to a decrease of the potential but we can show that they do not jeopardize eventual convergence to a Nash equilibrium. Our main result is to use a Markovian approach to show that the average execution time of the algorithm E[TBRD] can be bounded: 2δn log n/ log log n + O(n) ≤E[TBRD] ≤ 4e δn log n/ log log n+O(n), where ' is the Euler constant, n is the number of players and δ is the time taken by one player to compute its best response. These bounds are obtained by using an asymptotically optimal playing rate λ. Our analytic bound shows that 2δλ = log log n - log C, where C is a constant. This induces a large probability of overlap (p = 1 - C/ log1/2 n). In practice, numerical simulations also show that using high playing rates is efficient, with an optimal probability of overlap popt ≈ 0.78, for n up to 250. This shows that best response dynamics are unexpectedly efficient to compute Nash equilibria, even in a distributed setting.
Minimal-Variance Distributed Deadline Scheduling in a Stationary Environment
Authors: Nakahira Yorie , Ferragut Andres , Wierman Adam
Published: January 2019

Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, variability in service capacity often incurs operational and infrastructure costs. In this paper, we propose distributed algorithms that minimize service capacity variability when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes service capacity variance subject to strict demand and deadline requirements under stationary Poisson arrivals. We also characterize the optimal distributed policies for more general settings with soft demand requirements, soft deadline requirements, or both. Additionally, we show how close the performance of the optimal distributed policy is to that of the optimal centralized policy by deriving a competitive-ratio-like bound.
Fairness in Online Social Network Timelines
Authors: Hargreaves Eduardo , Agosti Claudio , Menasche Daniel , Neglia Giovanni , Reiffers-Masson Alexandre , Altman Eitan
Published: January 2019

Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.
Delta probing policies for redundancy
Authors: Raaijmakers Y. , Borst S.C. , Boxma O.J.
Published: January 2019

We consider job dispatching in systems with N parallel servers, where jobs arrive according to a Poisson process of rate λ. In redundancy-d policies, replicas of an arriving job are assigned to d≤N servers selected uniformly at random (without replacement) with the objective to reduce the delay. We introduce a quite general workload model, in which job sizes have some probability distribution while the speeds (slowdown factors) of the various servers for a given job are allowed to be inter-dependent and non-identically distributed. This allows not only for inherent speed differences among different servers, but also for affinity relations. We further propose two novel redundancy policies, so-called deltaprobe- d policies, where d probes of a fixed, small, size ­ are created for each incoming job, and assigned to d servers selected uniformly at random. As soon as the first of these d probe tasks finishes, the actual job is assigned for execution with the same speed - to the corresponding server and the other probe tasks are abandoned. We also consider a delta-probe-d policy in which the probes receive preemptiveresume priority over regular jobs. The aim of these policies is to retain the benefits of redundancy-d policies while accounting for systematic speed differences and mitigating the risks of running replicas of the full job simultaneously for long periods of time.
On a unifying product form framework for redundancy models
Authors: Ayesta Urtzi , Bodas Tejas , Verloop Ina Maria
Published: January 2019

Using redundancy to minimize latency in parallel server systems has become very popular in recent years. While there are several variants of a redundancy-based system, the general notion of redundancy is to create multiple copies of the same job that will be sent to a subset of servers. By allowing for redundant copies, the aim is to minimize the system latency by exploiting the variability in the queue lengths of the different queues. Several recent works have both empirically and theoretically showed that redundancy can help in reducing the response time of a system.
Link Quality Optimization for Hybrid LEO-GSO Systems
Authors: Medina-Caballero Julio A. , Vazquez-Castro M. Angeles
Published: January 2019

In this paper, an optimised link design for LEO-GSO satellite systems is proposed so that a variable number of LEO satellites can maintain a required link quality when traversing GSO coverage. We identify the orbit, system and sata traffic parameters that are relevant, with which we obtain a parameterised optimal transmission energy function. Our results are useful for the design of energy-efficient hybrid satellite systems.
Learning Blockchain Delays
Authors: Ricci Saulo , Ferreira Eduardo , Menasche Daniel Sadoc , Ziviani Artur , Souza Jose Eduardo , Vieira Alex Borges
Published: January 2019

Despite the growing interest in cryptocurrencies, the delays incurred to confirm transactions are one of the factors that hamper the wide adoption of systems such as Bitcoin. Bitcoin transactions usually are confirmed in short periods (minutes), but still much larger than conventional credit card systems (seconds). In this work, we propose a framework encompassing machine learning and a queueing theory model to (i) identify which transactions will be confirmed; and (ii) characterize the confirmation time of confirmed transactions. The proposed queueing theory model accounts for factors such as the activity time of blocks and the mean time between transactions. We parameterize the model for transactions that are confirmed within minutes, suggesting that its integration into a more general framework is a step towards building scalability to Bitcoin.
On profitability of block withholding strategies
Authors: Grunspan C. , Perez-Marco R.
Published: January 2019

We present recent developments in the understanding of the profitability block withholding strategies in Bitcoin mining and other Proof-of-Work based blockchains. Block withholding strategies (like selfish, stubborn, trailing or catch-up mining) are rogue mining strategies that violate the rules of the Bitcoin protocol. The authors found recently the exact model based on iterative games to evaluate the profitability per unit time. With a novel application of martingale techniques and Doob's Stopping Time Theorem we compute their profitability in close-form. We can then compare in parameter space these strategies and honest mining, and decide which one is more profitable.
What Drives Cryptocurrency Prices?
Authors: Smuts Nico
Published: January 2019

The Google Trends
1
search analysis service and the Telegram
2
messaging platform are investigated to determine their respective relationships to cryptocurrency price behaviour. It is shown that, in contrast to earlier findings, the relationship between cryptocurrency price movements and internet search volumes obtained from Google Trends is no longer consistently positive, with strong negative correlations detected for Bitcoin and Ethereum during June 2018. Sentiment extracted from cryptocurrency investment groups on Telegram is found to be positively correlated to Bitcoin and Ethereum price movements, particularly during periods of elevated volatility. The number of messages posted on a Bitcoin-themed Telegram group is found to be an indicator of Bitcoin price action in the subsequent week. A long shortterm memory (LSTM) recurrent neural network is developed to predict the direction of cryptocurrency prices using data obtained from Google Trends and Telegram. It is shown that Telegram data is a better predictor of the direction of the Bitcoin market than Google Trends. The converse is true for Ethereum. The LSTM model produces the most accurate results when predicting price movements over a one-week period.
DeepSec meets RawPower - Deep Learning for Detection of Network Attacks Using Raw Representations
Authors: Marín Gonzalo , Casas Pedro , Capdehourat Germán
Published: January 2019

The application of machine learning models to the analysis of network traffic measurements has largely grown in recent years. In the networking domain, shallow models are usually applied, where a set of expert handcrafted features are needed to fix the data before training. There are two main problems associated with this approach: firstly, it requires expert domain knowledge to select the input features, and secondly, different sets of custom-made input features are generally needed according to the specific target (e.g., network security, anomaly detection, traffic classification). On the other hand, the power of machine learning models using deep architectures (i.e., deep learning) for networking has not been yet highly explored. In this paper we explore the power of deep learning models on the specific problem of detection of network attacks, using different representations for the input data. As a mayor advantage as compared to the state of the art, we consider raw measurements coming directly from the stream of monitored bytes as the input to the proposed models, and evaluate different raw-traffic feature representations, including packet and flow-level ones.
Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones
Authors: Wassermann Sarah , Wehner Nikolas , Casas Pedro
Published: January 2019

Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.
Framework for Dynamic‐Stiffness‐Based Free Vibration Analysis of Plate‐Like Structures
Authors: Marjanović Miroslav , Nefovska-Danilović Marija , Damnjanović Emilija
Published: January 2019

A framework for free vibration analysis of plate‐like structures is presented in the paper. Based on the previously formulated dynamic stiffness elements, FREEVIB object‐oriented software in Python environment has been created. Software design and structure as well as a wide range of possible structural problems that could be analyzed using the FREEVIB are presented. Through several illustrative examples including free vibration analysis of stepped, stiffened and folded plate structures, implying isotropic or orthotropic material formulations, the efficiency and accuracy of the FREEVIB is demonstrated. The possibilities of further extensions and improvements of the software are discussed.
A Case of Tumor of Follicular Infundibulum Involving the Vulva
Authors: Santiago Victor , Cartwright David , Khoshnoodi Pooria , Klein Molly , Giubellino Alessio
Published: January 2019

Tumor of the follicular infundibulum or infundibuloma is a relatively rare benign adnexal tumor usually solitary and located in the head, neck, and trunk. Here we present a 70-year-old woman with a tender vulvar lesion. Histopathologic exam shows a well-circumscribed lesion with a subepidermal horizontally oriented, plate-like proliferation of pale appearing squamous epithelial cells with numerous points of connections with the overlying epidermis and peripheral palisading. Overall these histopathologic features are consistent with the diagnosis of tumor of follicular infundibulum involving genital skin.
Local Delivery ofOx40l,Cd80, andCd86mRNA Kindles Global Anticancer Immunity
Authors: Haabeth Ole Audun Werner , Blake Timothy R. , McKinlay Colin J. , Tveita Anders A. , Sallets Adrienne , Waymouth Robert M. , Wender Paul A. , Levy Ronald
Published: January 2019

Localized expression of effector molecules can initiate antitumor responses through engagement of specific receptors on target cells in the tumor microenvironment. These locally induced responses may also have a systemic effect, clearing additional tumors throughout the body. In this study, to evoke systemic antitumor responses, we utilized charge-altering releasable transporters (CART) for local intratumoral delivery of mRNA coding for costimulatory and immune-modulating factors. Intratumoral injection of the CART–mRNA complexes resulted in mRNA expression at the site of administration, transfecting a substantial proportion of tumor-infiltrating dendritic cells, macrophages, and T cells in addition to the tumor cells, resulting in a local antitumor effect. Using a two-tumor model, we further show that mRNA therapy locally administered to one tumor stimulated a systemic antitumor response, curing both tumors. The combination of Ox40l-, Cd80-, and Cd86-encoding mRNA resulted in the local upregulation of proinflammatory cytokines, robust local T-cell activation, and migration of immune cells to local draining lymph node or to an anatomically distant tumor. This approach delayed tumor growth, facilitated tumor regression, and cured tumors in both A20 and CT26 tumor models. These results highlight mRNA-CART therapy as a viable approach to induce systemic antitumor immunity from a single localized injection.

Significance:

The mRNA-CART system is a highly effective delivery platform for delivering immunostimulatory genes into the tumor microenvironment for potential therapeutic development.
A Pragmatic Testing-Eligibility Framework for Population Mutation Screening: The Example of BRCA1/2
Authors: Best Ana F. , Tucker Margaret A. , Frone Megan N. , Greene Mark H. , Peters June A. , Katki Hormuzd A.
Published: January 2019

Background:

Eligibility guidelines for genetic testing may be revisited, given technological advances, plummeting costs, and proposals for population mutation screening. A key property of eligibility criteria is the tradeoff between the number of mutation carriers identified versus population members tested. We assess the fractions of mutation carriers identified, versus women undergoing mutation testing, for BRCA1/2 founder mutation screening in U.S. Ashkenazi-Jewish women.

Methods:

BRCA1/2 carrier probabilities, based on personal/family history, were calculated using the risk-prediction tool BRCAPRO for 4,589 volunteers (102 mutation carriers) in the population-based Washington Ashkenazi Study. For each carrier-probability threshold between 0% and 10%, we compared the percentage of founder mutations detected versus the percentage of women requiring mutation testing. PCR mutation testing was conducted at the NIH for the 3 Ashkenazi-Jewish founder mutations (5382insC and 185delAG in BRCA1, and 6174delT in BRCA2).

Results:

Identifying 90% of BRCA1/2 founder mutations required testing the 60% of Ashkenazi-Jewish women with carrier probabilities exceeding 0.56%, potentially avoiding mutation testing for approximately 0.7 to 1.1 million U.S. Ashkenazi-Jewish women. Alternatively, testing the 44% whose carrier probability exceeded 0.78% identified 80% of mutation carriers, increasing to 89% of mutation carriers when accounting for cascade testing triggered after mutation-positive daughters were identified by screening. We present data on all carrier-probability thresholds, e.g., a 5% threshold identified 46% of mutation carriers while testing 10% of women.

Conclusions:

Different carrier-probability thresholds offered diverse tradeoffs between numbers of identified mutation carriers versus women tested. Low carrier-probability thresholds identified 90% of BRCA1/2 founder mutation carriers, without testing approximately 1 million U.S. Ashkenazi-Jewish women with lowest carrier probabilities.

Impact:

In general, this risk-based framework could provide useful new options to consider during eligibility-criteria development for population mutation screening.
The Mechanism of Action of the Anti-CD38 Monoclonal Antibody Isatuximab in Multiple Myeloma
Authors: Moreno Laura , Perez Cristina , Zabaleta Aintzane , Manrique Irene , Alignani Diego , Ajona Daniel , Blanco Laura , Lasa Marta , Maiso Patricia , Rodriguez Idoia , Garate Sonia , Jelinek Tomas , Segura Victor , Moreno Cristina , Merino Juana , Rodriguez-Otero Paula , Panizo Carlos , Prosper Felipe , San-Miguel Jesus F. , Paiva Bruno
Published: January 2019

Purpose:

Knowledge about the mechanism of action (MoA) of monoclonal antibodies (mAb) is required to understand which patients with multiple myeloma (MM) benefit the most from a given mAb, alone or in combination therapy. Although there is considerable research about daratumumab, knowledge about other anti-CD38 mAbs remains scarce.

Experimental Design:

We performed a comprehensive analysis of the MoA of isatuximab.

Results:

Isatuximab induces internalization of CD38 but not its significant release from MM cell surface. In addition, we uncovered an association between levels of CD38 expression and different MoA: (i) Isatuximab was unable to induce direct apoptosis on MM cells with CD38 levels closer to those in patients with MM, (ii) isatuximab sensitized CD38hi MM cells to bortezomib plus dexamethasone in the presence of stroma, (iii) antibody-dependent cellular cytotoxicity (ADCC) was triggered by CD38lo and CD38hi tumor plasma cells (PC), (iv) antibody-dependent cellular phagocytosis (ADCP) was triggered only by CD38hi MM cells, whereas (v) complement-dependent cytotoxicity could be triggered in less than half of the patient samples (those with elevated levels of CD38). Furthermore, we showed that isatuximab depletes CD38hi B-lymphocyte precursors and natural killer (NK) lymphocytes ex vivo—the latter through activation followed by exhaustion and eventually phagocytosis.

Conclusions:

This study provides a framework to understand response determinants in patients treated with isatuximab based on the number of MoA triggered by CD38 levels of expression, and for the design of effective combinations aimed at capitalizing disrupted tumor–stroma cell protection, augmenting NK lymphocyte–mediated ADCC, or facilitating ADCP in CD38lo MM patients.

See related commentary by Malavasi and Faini, p. 2946