bluuebunny/crossref_metadata_2025 huggingface dataset:
An Optimal Algorithm for Online Non-Convex Learning
Authors: Yang Lin , Deng Lei , Hajiesmaili Mohammad H. , Tan Cheng , Wong Wing Shing
Published: January 2019

In many online learning paradigms, convexity plays a central role in the derivation and analysis of online learning algorithms. The results, however, fail to be extended to the non-convex settings, which are necessitated by tons of recent applications. The Online Non-Convex Learning problem generalizes the classic Online Convex Optimization framework by relaxing the convexity assumption on the cost function (to a Lipschitz continuous function) and the decision set. The state-of-the-art result for ønco demonstrates that the classic Hedge algorithm attains a sublinear regret of O(√T log T). The regret lower bound for øco, however, is Omega(√T), and to the best of our knowledge, there is no result in the context of the ønco problem achieving the same bound. This paper proposes the Online Recursive Weighting algorithm with regret of O(√T), matching the tight regret lower bound for the øco problem, and fills the regret gap between the state-of-the-art results in the online convex and non-convex optimization problems.
Online Learning of Optimally Diverse Rankings
Authors: Magureanu Stefan , Proutiere Alexandre , Isaksson Marcus , Zhang Boxun
Published: January 2019

Search engines answer users' queries by listing relevant items (e.g. documents, songs, products, web pages, ...). These engines rely on algorithms that learn to rank items so as to present an ordered list maximizing the probability that it contains relevant item. The main challenge in the design of learning-to-rank algorithms stems from the fact that queries often have different meanings for different users. In absence of any contextual information about the query, one often has to adhere to the diversity principle, i.e., to return a list covering the various possible topics or meanings of the query. To formalize this learning-to-rank problem, we propose a natural model where (i) items are categorized into topics, (ii) users find items relevant only if they match the topic of their query, and (iii) the engine is not aware of the topic of an arriving query, nor of the frequency at which queries related to various topics arrive, nor of the topic-dependent click-through-rates of the items. For this problem, we devise LDR (Learning Diverse Rankings), an algorithm that efficiently learns the optimal list based on users' feedback only. We show that after T queries, the regret of LDR scales as O((N-L)log(T)) where N is the number of all items. This scaling cannot be improved, i.e., LDR is order optimal.
Multi-armed Bandit with Additional Observations
Authors: Yun Donggyu , Ahn Sumyeong , Proutiere Alexandre , Shin Jinwoo , Yi Yung
Published: January 2019

We study multi-armed bandit (MAB) problems with additional observations, where in each round, the decision maker selects an arm to play and can also observe rewards of additional arms (within a given budget) by paying certain costs. We propose algorithms that are asymptotic-optimal and order-optimal in their regrets under the settings of stochastic and adversarial rewards, respectively.
Online Learning in Weakly Coupled Markov Decision Processes
Authors: Wei Xiaohan , Yu Hao , Neely Michael J.
Published: January 2019

We consider multiple parallel Markov decision processes (MDPs) coupled by global constraints, where the time varying objective and constraint functions can only be observed after the decision is made. Special attention is given to how well the decision maker can perform in T slots, starting from any state, compared to the best feasible randomized stationary policy in hindsight. We develop a new distributed online algorithm where each MDP makes its own decision each slot after observing a multiplier computed from past information. While the scenario is significantly more challenging than the classical online learning context, the algorithm is shown to have a tight O(√T) regret and constraint violations simultaneously. To obtain such a bound, we combine several new ingredients including ergodicity and mixing time bound in weakly coupled MDPs, a new regret analysis for online constrained optimization, a drift analysis for queue processes, and a perturbation analysis based on Farkas' Lemma.
Why Some Like It Loud
Authors: Islam Mohammad A. , Yang Luting , Ranganath Kiran , Ren Shaolei
Published: January 2019

The common practice of power infrastructure oversubscription in data centers exposes dangerous vulnerabilities to well-timed power attacks (i.e., maliciously timed power loads), possibly creating outages and resulting in multimillion-dollar losses. In this paper, we focus on the emerging threat of power attacks in a multi-tenant data center, where a malicious tenant (i.e., attacker) aims at compromising the data center availability by launching power attacks and overloading the power capacity. We discover a novel acoustic side channel resulting from servers' cooling fan noise, which can help the attacker time power attacks at the moments when benign tenants' power usage is high. Concretely, we exploit the acoustic side channel by: (1) employing a high-pass filter to filter out the air conditioner's noise; (2) applying non-negative matrix factorization with sparsity constraint to demix the received aggregate noise and detect periods of high power usage by benign tenants; and (3) designing a state machine to guide power attacks. We run experiments in a practical data center environment as well as simulation studies, and demonstrate that the acoustic side channel can assist the attacker with detecting more than 50% of all attack opportunities, representing state-of-the-art timing accuracy.
ECI-Cache
Authors: Ahmadian Saba , Mutlu Onur , Asadi Hossein
Published: January 2019

In recent years, high interest in using Virtual Machines (VMs) in data centers and cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to providing a high-performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualized platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache size to provide the required performance level for each VM, while neglecting other important parameters such as cache write policy and request type, which can adversely affect both performance-per-cost and endurance.

In this paper, we propose a new high-Endurance and Cost-efficient I/O caching (ECI-Cache) scheme for virtualized platforms in large-scale data centers, which improves both performance-per-cost and endurance of the SSD cache. ECI-Cache dynamically assigns 1) an efficient cache size for each VM, to maximize the overall performance of the running VMs and 2) an effective write policy for each VM, to enhance the endurance and performance-per-cost of the storage subsystem.
Tomographic Node Placement Strategies and the Impact of the Routing Model
Authors: Pignolet Yvonne-Anne , Schmid Stefan , Tredan Gilles
Published: January 2019

Tomographic techniques can be used for the fast detection of link failures at low cost. Our paper studies the impact of the routing model on tomographic node placement costs. We present a taxonomy of path routing models and provide optimal and near-optimal algorithms to deploy a minimal number of asymmetric and symmetric tomography nodes for basic network topologies under different routing model classes. Intriguingly, we find that in many cases routing according to a more restrictive routing model gives better results: compared to a more general routing model, computing a good placement is algorithmically more tractable and does not entail high monitoring costs, a desirable trade-off in practice.
Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network
Authors: Yang Sen , Yan He , Ge Zihui , Wang Dongmei , Xu Jun
Published: January 2019

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.
Synthesis of Fault-Tolerant Distributed Router Configurations
Authors: Subramanian Kausik , D'Antoni Loris , Akella Aditya
Published: January 2019

Operators of modern networks require support for diverse and complex end-to-end policies, such as, middlebox traversals, isolation, and traffic engineering. While Software-defined Networking (SDN) provides centralized custom routing functionality in networks to realize these policies, many networks still deploy "legacy" control planes running distributed routing protocols like OSPF and BGP because these protocols are scalable and robust to failures. However, realization of policies by distributed control plane configurations is manual and error-prone. We present Zeppelin, a system for automatically generating policy-compliant control planes that also behave well under majority of small network failures. Zeppelin differs from existing approaches in that it uses policy-compliant paths to guide the synthesis process instead of directly generating policy-compliant configurations. We show that Zeppelin synthesizes highly resilient and policy-compliant configurations for real topologies with up to 80 routers.
Reinforcement with Fading Memories
Authors: Xu Kuang , Yun Se-Young
Published: January 2019

We study the effect of imperfect memory on decision making in the context of a stochastic sequential action-reward problem. An agent chooses a sequence of actions which generate discrete rewards at different rates. She is allowed to make new choices at rate β, while past rewards disappear from her memory at rate μ. We focus on a family of decision rules where the agent makes a new choice by randomly selecting an action with a probability approximately proportional to the amount of past rewards associated with each action in her memory. We provide closed-form formulae for the agent's steady-state choice distribution in the regime where the memory span is large (μ -> 0), and show that the agent's success critically depends on how quickly she updates her choices relative to the speed of memory decay. If β >> μ, the agent almost always chooses the best action, i.e., the one with the highest reward rate. Conversely, if β << μ, the agent chooses an action with a probability roughly proportional to its reward rate.
Distributed Statistical Machine Learning in Adversarial Settings
Authors: Chen Yudong , Su Lili , Xu Jiaming
Published: January 2019

We consider the distributed statistical learning problem over decentralized systems that are prone to adversarial attacks. This setup arises in many practical applications, including Google's Federated Learning. Formally, we focus on a decentralized system that consists of a parameter server and m working machines; each working machine keeps N/m data samples, where N is the total number of samples. In each iteration, up to q of the m working machines suffer Byzantine faults -- a faulty machine in the given iteration behaves arbitrarily badly against the system and has complete knowledge of the system. Additionally, the sets of faulty machines may be different across iterations. Our goal is to design robust algorithms such that the system can learn the underlying true parameter, which is of dimension d, despite the interruption of the Byzantine attacks. In this paper, based on the geometric median of means of the gradients, we propose a simple variant of the classical gradient descent method. We show that our method can tolerate q Byzantine failures up to 2(1+ε)q łe m for an arbitrarily small but fixed constant ε>0. The parameter estimate converges in O(łog N) rounds with an estimation error on the order of max √dq/N, ~√d/N , which is larger than the minimax-optimal error rate √d/N in the centralized and failure-free setting by at most a factor of √q . The total computational complexity of our algorithm is of O((Nd/m) log N) at each working machine and O(md + kd log 3 N) at the central server, and the total communication cost is of O(m d log N). We further provide an application of our general results to the linear regression problem. A key challenge arises in the above problem is that Byzantine failures create arbitrary and unspecified dependency among the iterations and the aggregated gradients. To handle this issue in the analysis, we prove that the aggregated gradient, as a function of model parameter, converges uniformly to the true gradient function.
A Fine-grained Event-based Modem Power Model for Enabling In-depth Modem Energy Drain Analysis
Authors: Chen Xiaomeng , Meng Jiayi , Hu Y. Charlie , Gupta Maruti , Hasholzner Ralph , Ekambaram Venkatesan Nallampatti , Singh Ashish , Srikanteswara Srikathyayani
Published: January 2019

Cellular modems enable ubiquitous Internet connectivities to modern smartphones, but in doing so they become a major contributor to the smartphone energy drain. Understanding modem energy drain requires a detailed power model. The prior art, an RRC-state based power model, was developed primarily to model the modem energy drain of application data transfer. As such, it serves well its original purpose, but is insufficient to study detailed modem behavior, eg. activities in the control plane. In [2], we propose a new methodology of modeling modem power draw behavior at the event-granularity, and develop to our knowledge the first fine-grained modem power model that captures the power draw of all LTE modem radio-on events in different RRC modes. Second, we quantitatively demonstrate the advantages of the new model over the state-based power model under a wide variety of context via controlled experiments. Finally, using our fine-grained modem power model, we perform the first detailed modem energy drain in-the-wild study involving 12 Nexus 6 phones under normal usage by 12 volunteers spanning a total of 348 days. Our study provides the first quantitative analysis of energy drain due to modem control activities in the wild and exposes their correlation with context such as location and user mobility. In this abstracts, we introduce the essence of the methodology and the highlighted results from the in-the-wild study.
Degree of Queue Imbalance
Authors: Zhou Xingyu , Wu Fei , Tan Jian , Srinivasan Kannan , Shroff Ness
Published: January 2019

In this paper, we argue that heavy-traffic delay optimality is a coarse metric that does not necessarily imply good delay performance. Specifically, we show that any load balancing scheme is heavy-traffic delay optimal as long as it satisfies a fairly weak condition. This condition only requires that in the long-term the dispatcher favors, even slightly, shorter queues over longer queues. Hence, the empirical delay performance of heavy-traffic delay optimal schemes can range from very good (that of join-shortest-queue) to very bad (arbitrarily close to the performance of random routing).

To overcome this limitation, we introduce a new metric called degree of queue imbalance, which measures the queue length difference between all the servers in steady-state. Given a heavy-traffic delay optimal load balancing scheme, we can characterize the resultant degree of queue imbalance. This, in turn, allows us to explicitly differentiate between good and poor load balancing schemes. Thus, this paper implies that good load balancing should not only be designed to be heavy-traffic delay optimal, but also have a low degree of queue imbalance.
On a Class of Stochastic Multilayer Networks
Authors: Jiang Bo , Nain Philippe , Towsley Don , Guha Saikat
Published: January 2019

In this paper, we introduce a new class of stochastic multilayer networks. A stochastic multilayer network is the aggregation of M networks (one per layer) where each is a subgraph of a foundational network G . Each layer network is the result of probabilistically removing links and nodes from G . The resulting network includes any link that appears in at least K layers. This model is an instance of a non-standard site-bond percolation model. Two sets of results are obtained: first, we derive the probability distribution that the M -layer network is in a given configuration for some particular graph structures (explicit results are provided for a line and an algorithm is provided for a tree), where a configuration is the collective state of all links (each either active or inactive). Next, we show that for appropriate scalings of the node and link selection processes in a layer, links are asymptotically independent as the number of layers goes to infinity, and follow Poisson distributions. Numerical results are provided to highlight the impact of having several layers on some metrics of interest (including expected size of the cluster a node belongs to in the case of the line). This model finds applications in wireless communication networks with multichannel radios, multiple social networks with overlapping memberships, transportation networks, and, more generally, in any scenario where a common set of nodes can be linked via co-existing means of connectivity.
Designing Low-Complexity Heavy-Traffic Delay-Optimal Load Balancing Schemes
Authors: Zhou Xingyu , Wu Fei , Tan Jian , Sun Yin , Shroff Ness
Published: January 2019

We establish a unified analytical framework for designing load balancing algorithms that can simultaneously achieve low latency, low complexity, and low communication overhead. We first propose a general class ¶ of load balancing policies and prove that they are both throughput optimal and heavy-traffic delay optimal. This class ¶ includes popular policies such as join-shortest-queue (JSQ) and power-of- d as special cases, but not the recently proposed join-idle-queue (JIQ) policy. In fact, we show that JIQ is not heavy-traffic delay optimal even for homogeneous servers. By exploiting the flexibility offered by the class ¶, we design a new load balancing policy called join-below-threshold (JBT-d), in which the arrival jobs are preferentially assigned to queues that are no greater than a threshold, and the threshold is updated infrequently. JBT-d has several benefits: (i) JBT-d belongs to the class ¶i and hence is throughput optimal and heavy-traffic delay optimal. (ii) JBT-d has zero dispatching delay, like JIQ and other pull-based policies, and low message overhead due to infrequent threshold updates. (iii) Extensive simulations show that JBT-d has good delay performance, comparable to the JSQ policy in various system settings.
Special Issue on The Workshop on MAthematical performance Modeling and Analysis (MAMA 2018)
Authors: Squillante Mark S.
Published: January 2019

The complexity of computer systems, networks and applications, as well as the advancements in computer technology, continue to grow at a rapid pace. Mathematical analysis, modeling and optimization have been playing, and continue to play, an important role in research studies to investigate fundamental issues and tradeoffs at the core of performance problems in the design and implementation of complex computer systems, networks and applications.
A Markov Decision Process Approach to Analyze Discount & Reputation Trade-offs in E-commerce Systems
Authors: Xie Hong , Lui John C.S
Published: January 2019

E-commerce systems, e.g., Amazon, eBay and Taobao, are becoming increasingly popular. We consider eBay (or Taobao) like E-commerce systems, where a large number of sellers and buyers transact online. To reflect the trustworthiness of sellers, a reputation system is maintained. In particular, the feedback-based reputation system [4] is the most widely deployed. Sellers of such systems are initialized with a low reputation and they must obtain a sufficiently large number of positive feedbacks from buyers to earn a reputable label. For example, eBay and Taobao use three-level feedbacks, i.e., (-1 (Negative), 0 (Neutral), 1 (Positive). Each seller is initialized with a reputation score of zero. A positive (or negative) rating increases (or decreases) the reputation score by one, while a neutral rating does not change the reputation score.
A Modeling Approach to Classifying Malicious Cloud Users via Shuffling
Authors: Yang Yudong , Misra Vishal , Rubenstein Dan
Published: January 2019

DDoS attacks are still a serious security issue on the Internet. We explore a distributed Cloud setting in which users are mapped to servers where malicious users mapped to the same server can thwart the performance of legitimate users. By periodically shuffling the mapping of users to servers and observing how this affects successfully attacked servers, the malicious users can be identified. We use simple models to understand how to best score these observations to identify malicious users with well-defined levels of confidence.
Join-Idle-Queue with Service Elasticity
Authors: Mukherjee Debankur , Stolyar Alexander
Published: January 2019

We consider the model of a token-based joint auto-scaling and load balancing strategy, proposed in a recent paper by Mukherjee, Dhara, Borst, and van Leeuwaarden [4] (SIGMETRICS '17), which offers an efficient scalable implementation and yet achieves asymptotically optimal steady-state delay performance and energy consumption as the number of servers N ! 1. In the above work, the asymptotic results are obtained under the assumption that the queues have fixed-size finite buffers, and therefore the fundamental question of stability of the proposed scheme with infinite buffers was left open. In this paper, we address this fundamental stability question. The system stability under the usual subcritical load assumption is not automatic. Moreover, the stability may not even hold for all N. The key challenge stems from the fact that the process lacks monotonicity, which has been the powerful primary tool for establishing stability in load balancing models. We develop a novel method to prove that the subcritically loaded system is stable for large enough N, and establish convergence of steady-state distributions to the optimal one, as N ! 1. The method goes beyond the state of the art techniques " it uses an induction-based idea and a "weak monotonicity" property of the model; this technique is of independent interest and may have broader applicability.
Failure Localization in Power Systems via Tree Partitions
Authors: Guo Linqi , Liang Chen , Zocca Alessandro , Low Steven H. , Wierman Adam
Published: January 2019

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion.
A Novel View of the Drift
Authors: Hurtado-Lange Daniela , Maguluri Siva Theja
Published: January 2019

The drift method has been recently developed to study queueing systems in heavy traffic [3]. This method was successfully used to obtain the heavy traffic scaled sum queue lengths of several systems, even when the so-called complete resource pooling condition is not satisfied. In this poster, we present a novel view of the drift method to explain why this methodworks.We believe that this view can be exploited to obtain the joint distribution of the steady-state heavy-traffic scaled queue lengths.
Observe Before Play
Authors: Zuo Jinhang , Zhang Xiaoxi , Joe-Wong Carlee
Published: January 2019

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can, at a cost, pre-observe one or multiple arms before playing one of them in each round. Apart from the classic trade-off between exploration (trying out more arms to find the best one) and exploitation (sticking with the arm believed to offer the highest reward), we encounter an additional dilemma in each single round, i.e., pre-observing more arms gives a higher chance to play the best one, but incurs a larger cost which decreases the overall reward. We design an Observe-Before-Play (OBP) policy for arms with Bernoulli rewards, which could be generalized to any i.i.d. reward distributions bounded in [0, 1]. Our strategy could enable a better policy for secondary spectrum access in Cognitive Ratio Networks, where users can sense multiple channels' occupancies before choosing one on which to transmit. To evaluate our policy, we define the regret as the gap between the expected overall reward gained by our OBP policy and that obtained by the expected optimum, which always chooses an optimal sequence of arms to pre-observe based on the perfect knowledge of the arm distributions. Experiments show that our OBP policy has sub-linear regret and can outperform the classical MAB algorithm when the cost of pre-observations is relatively low.
Mobile Augmented Reality
Authors: Litts Breanne K. , Lewis Whitney E.
Published: January 2019

The proliferation of smartphones provides easy access to experience augmented reality (AR), which has fundamentally shifted the conversation around educational technology both in and out of classrooms. Early research on this new genre of teaching and learning afforded by AR has shown to improve and/or increase learning performance, learning motivation, student engagement, and positive attitudes (Bacca et al., 2014). As mobile technologies reach ubiquity, educators have become exceptionally concerned with designing tools and activities that equip young people to engage with these technologies as producers not just consumers. Of equal significance is the critical issue of ensuring that all young people have equitable access to not only the technologies but also to be able to participate in creating the technologies (Kafai & Burke, 2013). In this column, our goal is to present one approach by which we can empower all young people to produce with mobile augmented reality technologies.
Signpost
Authors: Adkins Joshua , Ghena Branden , Dutta Prabal
Published: January 2019

The world's population is flocking to city centers at an increasing rate, testing the ability of urban planners and local governments to address new problems in transportation, waste disposal, urban health, and public safety. While one could imagine solutions enabled by an emerging class of smart sensors, current attempts at such systems are difficult to deploy and single purpose in their design. If we expect researchers and citizen-scientists to participate in the data-driven rejuvenation of our urban spaces (and we should, since they are the ones experiencing the day-to-day problems), we must lower the bar to deploying sensors and accessing smart-city data. Towards this goal, we present Signpost, an infrastructurefree sensing platform that aims to enable easy, multi-use sensor deployments for citizens, and researchers who have little expertise in building smart and connected sensors.[1]
Improving Web Search Ranking by Incorporating User Behavior Information
Authors: Agichtein Eugene , Brill Eric , Dumais Susan
Published: January 2019

We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.
Report on CLEF 2018
Authors: Bellot Patrice , Cappellato Linda , Ferro Nicola , Mothe Josiane , Murtagh Fionn , Nie Jian-Yun , SanJuan Eric , Soulier Laure , Trabelsi Chiraz
Published: January 2019

This is a report on the ninth edition of the Conference and Labs of the Evaluation Forum (CLEF 2018), held in early September 2018, in Avignon, France. CLEF was a four day event combining a Conference and an Evaluation Forum. The Conference featured keynotes by Nicholas Belkin, Julio Gonzalo, and Gabriella Pasi, and presentation of 29 peer reviewed research papers covering a wide range of topics in addition to many posters. The Evaluation Forum consisted to ten Labs: CENTRE, CheckThat, DynSe, eRisk, eHealth, ImageCLEF, LifeCLEF, Cultural Microblog Contextualization, PAN, and PIR-CLEF, addressing a wide range of tasks, media, languages, and ways to go beyond standard test collections.
Report on the Quantum Information Access and Retrieval Theory Winter School
Authors: Di Buccio Emanuele
Published: January 2019

QUARTZ (Quantum Information Access and Retrieval Theory) is an Innovative Training Network that aims to educate its Early Stage Researchers to adopt a novel theoretically and empirically motivated approach to Information Access and Retrieval based on the quantum mechanical framework that gives up the notions of unimodal features and classical ranking models disconnected from context. The present document reports on the QUARTZ Winter School, one of the activities of the QUARTZ training programme. The school was held at the Department of Information Engineering of the University of Padova, Padova, Italy, from February 7 to February 14, 2018. This report describes the goal of the school framed within the training activities of the QUARTZ project, how the school was structured in terms of lectures and activities devoted to the acquisition of transversal skills.
Report on the Second Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP 2018) at the Web Conference (WWW) 2018
Authors: Ghosh Saptarshi , Ghosh Kripabandhu , Ganguly Debasis , Chakraborty Tanmoy , Jones Gareth J. F. , Moens Marie-Francine
Published: January 2019

The Second Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP) was held in conjunction with The Web Conference (WWW) 2018 at Lyon, France. A primary aim of the workshop was to promote multi-modal and multi-view information retrieval from the social media content in disaster situations. The workshop programme included keynote talks, a peer-reviewed paper track, and a panel discussion on the relevant research problems in the scope of the workshop.
Text Summarization and Categorization for Scientific and Health-Related Data
Authors: Cohan Arman
Published: January 2019

The increasing amount of unstructured health-related data has created a need for intelligent processing, summarizing, and categorizing these data to extract knowledge from them. My research goal in this dissertation is to develop Natural Language Processing (NLP) and Information Retrieval (IR) methods for better processing and understanding health-related textual information to promote health care and wellbeing of individuals.
The Knowledge and Language Gap in Medical Information Seeking
Authors: Soldaini Luca
Published: January 2019

Interest in medical information retrieval has risen significantly in the last few years. The Internet has become a primary source for consumers looking for health information and advice; however, their lack of expertise causes a language and knowledge gap that affects their ability to properly formulate their information needs. Health experts also struggle to efficiently search the large amount of medical literature available to them, which impacts their ability of integrating the latest research findings in clinical practice. In this dissertation, I propose several methods to overcome these challenges, thus improving search outcomes.
Metagenomic comparison of tracheal aspirate and mini-bronchial alveolar lavage for assessment of respiratory microbiota
Authors: Kalantar Katrina L. , Moazed Farzad , Christenson Stephanie C. , Wilson Jenny , Deiss Thomas , Belzer Annika , Vessel Kathryn , Caldera Saharai , Jauregui Alejandra , Bolourchi Samaneh , DeRisi Joseph L. , Calfee Carolyn S. , Langelier Charles
Published: January 2019

Accurate and informative microbiological testing is essential for guiding diagnosis and management of pneumonia in patients who are critically ill. Sampling of tracheal aspirate (TA) is less invasive compared with mini-bronchoalveolar lavage (mBAL) and is now recommended as a frontline diagnostic approach in patients who are mechanically ventilated, despite the historical belief that TA was suboptimal due to contamination from oral microbes. Advancements in metagenomic next-generation sequencing (mNGS) now permit assessment of airway microbiota without a need for culture and, as such, provide an opportunity to examine differences between mBAL and TA at a resolution previously unachievable. Here, we engaged shotgun mNGS to assess quantitatively the airway microbiome in matched mBAL and TA specimens from a prospective cohort of critically ill adults. We observed moderate differences between sample types across all subjects; however, we found significant compositional similarity in subjects with bacterial pneumonia, whose microbial communities were characterized by dominant pathogens. In contrast, in patients with noninfectious acute respiratory illnesses, significant differences were observed between sample types. Our findings suggest that TA sampling provides a similar assessment of airway microbiota as more invasive testing by mBAL in patients with pneumonia.
Effect of helium preconditioning on neurological decompression sickness in rats
Authors: Zhang Rongjia , Yu Yongchao , Manaenko Anatol , Bi Hongda , Zhang Ning , Zhang Ling , Zhang Ting , Ye Zhouheng , Sun Xuejun
Published: January 2019

Decompression sickness (DCS) occurs because of an excessively rapid and extensive reduction of the ambient pressure. Bubble-induced spinal cord ischemia is generally considered as a part of neurological DCS pathogenesis. Because helium preconditioning (HPC) recently demonstrated beneficial properties against ischemic damage, we hypothesized that HPC may decrease the neurological deficits of DCS in rats. Seventy-five male Sprague-Dawley rats were divided into a non-HPC group ( n = 25) and a HPC group ( n = 25) and 25 naive animals that were euthanized for histological examination ( n = 5) or anesthetized for baseline somatosensory evoked potential (SSEP) recordings ( n = 20). To induce DCS, rats were compressed with air to a pressure of 709 kPa for 60 min and decompressed at a rate of 203 kPa/min. HPC was administered as three episodes of 79% helium-21% oxygen mixture inhalation for 5 min interspersed with 5 min of air breathing. We found that HPC resulted in significantly decreased DCS incidence and delay of DCS onset. HPC also improved animal performance on the grip test after decompression and significantly ameliorated decompression-induced decrease of platelet number. Furthermore, the incidence of abnormal SSEP waves and histological spinal lesions was significantly reduced by HPC. We conclude that HPC can decrease the occurrence of DCS and ameliorate decompression-induced neurological deficits.

NEW & NOTEWORTHY Helium preconditioning ameliorates decompression-induced neurological deficits in rats. Helium breathing before air dives may prevent neurological deficit and attenuate symptoms after decompression.
Advanced glycation end-product cross-linking inhibits biomechanical plasticity and characteristic failure morphology of native tendon
Authors: Lee J. Michael , Veres Samuel P.
Published: January 2019

Advanced glycation end-products (AGEs) are formed in vivo from the nonenzymatic reaction between sugars and proteins. AGEs accumulate in long-lived tissues like tendons, cross-linking neighboring collagen molecules, and are in part complicit in connective tissue pathologies experienced in aging and with diabetes. We have previously described discrete plasticity: a characteristic form of nanoscale collagen fibril damage consisting of serial fibril kinking and collagen denaturation that occurs in some mechanically overloaded tendons. We suspect that this failure mechanism may be an adaptive trait of collagen fibrils and have published evidence that inflammatory cells may be able to recognize and digest the denatured collagen produced by overload. In this study, we treated bovine tail tendons with ribose to simulate long-term AGE cross-linking in vitro. We hypothesized that a high degree of cross-linking would inhibit the intermolecular sliding thought to be necessary for discrete plasticity to occur. Tendons were mechanically overloaded, and properties were investigated by differential scanning calorimetry and scanning election microscopy. Ribose cross-linking treatment altered the mechanical response of tendons after the yield point, significantly decreasing postyield extensibility and strain energy capacity before rupture. Coincident with altered mechanics, ribose cross-linking completely inhibited the discrete plasticity failure mechanism of tendon. Our results suggest that discrete plasticity, which may be an important physiological mechanism, becomes pathologically disabled by the formation of AGE cross-links in aging and diabetes.

NEW & NOTEWORTHY We have previously shown that mechanically overloaded collagen fibrils in mammalian tendons accrue nanoscaled damage. This includes development of a characteristic kinking morphology within a shell of denatured collagen: discrete plasticity. Here, using a ribose-incubation model, we show that advanced glycation end-product cross-linking associated with aging and diabetes completely inhibits this mechanism. Since discrete plasticity appears to cue cellular remodeling, this result has important implications for diabetic tendinopathy.
Prestrain and cholinergic receptor-dependent differential recruitment of mechanosensitive energy loss and energy release elements in airway smooth muscle
Authors: Hai Chi-Ming
Published: January 2019

We tested the hypothesis that oscillatory airway smooth muscle (ASM) mechanics is governed by mechanosensitive energy loss and energy release elements that can be recruited by prestrain and cholinergic stimulation. We measured mechanical energy loss and mechanical energy release in unstimulated and carbachol-stimulated bovine ASM held at prestrains ranging from 0.3 to 1.0 Lo(reference length) and subjected to sinusoidal length oscillation at 1 hz with oscillatory strain amplitudes ranging from 0.1 to 1.5% Lo. We found that oscillatory ASM mechanics during sinusoidal length oscillation is governed predominantly by one class of nonlinear mechanosensitive energy loss element and one class of nonlinear mechanosensitive energy release element with differential mechanosensitivities to oscillatory strain amplitude. The greater mechanosensitivity of the energy loss element than energy release element may explain the bronchodilatory effect of deep inspiration. Prestrain, an important determinant of ASM responsiveness, differentially increased energy loss and energy release in unstimulated and carbachol-stimulated ASM. Cholinergic stimulation, an important cause of bronchoconstriction and airway inflammation, also differentially increased energy loss and energy release. When prestrain and cholinergic stimulation were combined, we found that prestrain and cholinergic stimulation synergistically increased energy loss and energy release by ASM. The relationship between recruitment of energy loss elements and recruitment of energy release elements was nonlinear, suggesting that energy loss and energy release elements are not coupled in ASM cells. These findings imply that large lung volume and cholinergic ASM activation would synergistically increase mechanical energy expenditure during inspiration and mechanical recoil of ASM during expiration.

NEW & NOTEWORTHY We report for the first time that oscillatory airway smooth muscle mechanics is governed predominantly by one class of nonlinear mechanosensitive energy loss element and one class of nonlinear mechanosensitive energy release element with differential mechanosensitivities to oscillatory strain amplitude. Prestrain and cholinergic stimulation synergistically and differentially recruit energy loss and energy release elements. The greater mechanosensitivity of the energy loss element than the energy release element may explain the bronchodilatory effect of deep inspiration.
Nonlinear Hybrid Multipoint Model of High‐Speed Train with Traction/Braking Dynamic and Speed Estimation Law
Authors: Jia Chao , Xu Hongze , Wang Longsheng
Published: January 2019

This paper establishes a NHMPM (Nonlinear Hybrid Multipoint Model) for HST (High‐Speed Train) with the traction/braking dynamic and speed estimation law. Firstly, a full‐order flux observer is designed using regional pole assignment theory to calculate the electromagnetic torque. The traction and braking forces are obtained according to this electromagnetic torque. Then the basic running resistance force is reformulated by considering the aerodynamic drag distribution characteristics, and the nonlinear in‐train coupling force is analyzed as well. Next, the NHMPM including integer variables of running status and car types is built, where an adaptive parameter estimation algorithm and a speed estimation law are proposed to estimate unknown resistance coefficients and train speed, respectively. The effectiveness of the proposed algorithm, law, and NHMPM is verified through numerical simulations last.
Data Placement Algorithm for Improving I/O Load Balance without Using Popularity Information
Authors: Luo Xiangyu , Xin Gang , Gui Xiaolin
Published: January 2019

Data placement considerably affects the I/O performance of distributed storage systems such as HDFS. An ideal placement algorithm should keep the I/O load evenly distributed among different storage nodes. Most of the existing placement algorithms with I/O load balance guarantee depend on the information of data popularity to make the placement decisions. However, the popularity information is typically not available in the data placement phase. Furthermore, it usually varies during the data lifecycle. In this paper, we propose a new placement algorithm called Balanced Distribution for Each Age Group (BEAG), which makes data placement decisions in the absence of the popularity information. This algorithm maintains multiple counters for each storage node, with each counter representing the amount of data belonging to a certain age group. It ensures that the data in each age group are equally scattered among the different storage nodes. As the popularity variance of the data belonging to the same age group is considerably smaller than that of the entire data, BEAG significantly improves the I/O load balance. Experimental results show that compared to other popularity independent algorithms, BEAG decreases the I/O load standard deviation by 11.6% to 30.4%.
Improved Chebyshev Polynomials-Based Authentication Scheme in Client-Server Environment
Authors: Truong Toan-Thinh , Tran Minh-Triet , Duong Anh-Duc
Published: January 2019

With nonstop development of communication technologies, all aspects of social life continuously change and so do network systems. When establishing connection is easy, the convenience of online-service receives many users’ attentions, for example, the patients directly access medical system to be advised by doctors at any time. Therefore, user authentication scheme is necessary when we want to provide privacy and security for working sessions. Storing a password list for verification is an old method and not secure. This list can be easily leaked, and adversary can launch an offline password-guessing attack. In addition, information exchanged between user and server needs being prevented from attacker’s decryption. It can be said that current authentication schemes are unsuitable for new security standard. We need a strong user authentication scheme using new approach to overcome existing limitations and guarantee time efficiency. In this paper, we make a design with Chebyshev polynomial to achieve our goals and resist some kinds of attacks.
A Network Pharmacology Approach to Explore Mechanism of Action of Longzuan Tongbi Formula on Rheumatoid Arthritis
Authors: Huang An , Fang Gang , Pang Yuzhou , Pang Zongran
Published: January 2019

Longzuan Tongbi Formula (LZTB) is an effective proved prescription in Zhuang medicine for treating active rheumatoid arthritis (RA). However, its active ingredients, underlying targets, and pharmacological mechanism are still not clear in treating RA. We have applied network pharmacology to study LZTB and found that 8 herbs in LZTB and 67 compounds in the 8 herbs are involved in the regulation of RA-related genes; we have conducted pathway analysis of overlapping genes and found that 7 herbs participate in the regulations of 24 pathways associated with RA and that 5 herbs in the 7 herbs and 25 compounds in the 5 herbs participate in the regulation of hsa05323 (rheumatoid arthritis). The results indicated that all herbs in LZTB and some compounds in those herbs participate in the treatment of RA; 25 compounds are main active ingredients and hsa05323 (rheumatoid arthritis) is the major pathway in the treatment of RA. We have also found that three pathways (inflammatory mediator regulation of TRP channels, PPAR signaling pathway, and mTOR signaling pathway) might have some effect on the treatment of RA.