bluuebunny/crossref_metadata_2025 huggingface dataset:
Regularity estimates for scalar conservation laws in one space dimension
Authors: Marconi Elio
Published: January 2019

We deal with the regularizing effect that, in scalar conservation laws in one space dimension, the nonlinearity of the flux function [Formula: see text] has on the entropy solution. More precisely, if the set [Formula: see text] is dense, the regularity of the solution can be expressed in terms of [Formula: see text] spaces, where [Formula: see text] depends on the nonlinearity of [Formula: see text]. If moreover the set [Formula: see text] is finite, under the additional polynomial degeneracy condition at the inflection points, we prove that [Formula: see text] for every [Formula: see text] and that this can be improved to [Formula: see text] regularity except an at most countable set of singular times. Finally, we present some examples that show the sharpness of these results and counterexamples to related questions, namely regularity in the kinetic formulation and a property of the fractional BV spaces.
Gneiss Dome Formation in the Himalaya and southern Tibet
Authors: Jessup Micah J. , Langille Jackie M. , Diedesch Timothy F. , Cottle John M.
Published: January 2019

Gneiss domes in the Himalaya and southern Tibet record processes of crustal thickening, metamorphism, melting, deformation and exhumation during the convergence between the Indian and Eurasian plates. We review two types of gneiss domes: North Himalayan gneiss domes (NHGD) and later domes formed by orogen-parallel extension. Located in the southern Tibetan Plateau, the NHGD are cored by granite and gneiss, and mantled by the Tethyan sedimentary sequence. The footwall of these were extruded southwards from beneath the Tibetan Plateau and subsequently warped into a domal shape. The second class of domes were formed during displacement on normal-sense shear zones and detachments that accommodated orogen-parallel extension during the Late Miocene. In some cases, formation of these domes involved an early stage of southwards-directed extrusion prior to doming. We review evidence for orogen-parallel extension to provide context for the formation of these gneiss domes. Compilations of pressure–temperature–time–deformation data and temperature–time paths indicate differences between dome types, and we accordingly propose new terminology. Type 1 domes are characterized by doming as an artefact of post-high-temperature exhumation processes in the Middle Miocene. Type 2 domes formed in response to exhumation during orogen-parallel extension in the Late Miocene that potentially post-dates south-directed extrusion.
The Cost of Uncertainty in Curing Epidemics
Authors: Hoffmann Jessica , Caramanis Constantine
Published: January 2019

Epidemic models are used across biological and social sciences, engineering, and computer science, and have had important impact in the study of the dynamics of human disease and computer viruses, but also trends rumors, viral videos, and most recently the spread of fake news on social networks. In this paper, we focus on epidemics propagating on a graph, as introduced by the seminal paper [5]. In particular, we consider so-called SI models (see below for a precise definition) where an infected node can only propagate the infection to its non-infected neighbor, as opposed to the fully mixed models considered in the early literature. This graph-based approach provides a more realistic model, in which the spread of the epidemic is determined by the connectivity of the graph, and accordingly some nodes may play a larger role than others in the spread of the infection.
The Price of Fragmentation in Mobility-on-Demand Services
Authors: Séjourné Thibault , Samaranayake Samitha , Banerjee Siddhartha
Published: January 2019

Mobility-on-Demand platforms are a fast growing component of the urban transit ecosystem. Though a growing literature addresses the question of how to make individual MoD platforms more efficient, less is known about the cost of market fragmentation, i.e., the impact on overall welfare due to splitting demand between multiple independent platforms. Our work aims to quantify how much platform fragmentation degrades the efficiency of the system. In particular, we focus on a setting where demand is exogenously split between multiple platforms, and study the increase in supply rebalancing costs incurred by each platform to meet this demand, vis-a-vis the cost incurred by a centralized platform serving the aggregate demand. We show under a large-market scaling, this Price-of-Fragmentation undergoes a phase transition, wherein, depending on the nature of the exogenous demand, the additional cost due to fragmentation either vanishes or grows unbounded. We provide conditions that characterize which regime applies to any given system, and discuss implications of these findings on how such platforms should be regulated.
Censored Demand Estimation in Retail
Authors: Amjad Muhammad J. , Shah Devavrat
Published: January 2019

In this paper, the question of interest is estimating true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. To address this question, we introduce a non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of "matrix completion" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [2], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to 0 as the number of store locations and time intervals increase to ınfty. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach.
A Whittle's Index Based Approach for QoE Optimization in Wireless Networks
Authors: Anand Arjun , de Veciana Gustavo
Published: January 2019

The design of schedulers to optimize heterogeneous users' Quality of Experience (QoE) remains a challenging and important problem for wireless systems. Our paper ([1]) explores three inter-related aspects of this problem: 1) non-linear relationships between a user's QoE and flow delays; 2) managing load dependent QoE trade-offs among heterogeneous application classes; and 3), striking a good balance between opportunistic scheduling and greedy QoE optimization. To that end we study downlink schedulers which minimize the expected cost modeled by convex functions of flow delays for users with heterogeneous channel rate variations. The essential features of this challenging problem are modeled as a Markov Decision Process to which we apply Whittle's relaxation, which in turn is shown to be indexable. Based on the Whittle's relaxation we develop a new scheduling policy, Opportunistic Delay Based Index Policy (ODIP). We then prove various structural properties for ODIP which result in closed form expressions for Whittle's indices under different scheduler scenarios. Using extensive simulations we show that ODIP scheduler provides a robust means to realize complex QoE trade-offs for a range of system loads.
Asymptotic Optimal Control of Markov-Modulated Restless Bandits
Authors: Duran Santiago , Verloop Ina Maria
Published: January 2019

This paper studies optimal control subject to changing conditions. This is an area that recently received a lot of attention as it arises in numerous situations in practice. Some applications being cloud computing systems with fluctuating arrival rates, or the time-varying capacity as encountered in power-aware systems or wireless downlink channels. To study this, we focus on a restless bandit model, which has proved to be a powerful stochastic optimization framework to model scheduling of activities. This paper is a first step to its optimal control when restless bandits are subject to changing conditions. We consider the restless bandit problem in an asymptotic regime, which is obtained by letting the population of bandits grow large, and letting the environment change relatively fast. We present sufficient conditions for a policy to be asymptotically optimal and show that a set of priority policies satisfies these. Under an indexability assumption, an averaged version of Whittle's index policy is proved to be inside this set of asymptotic optimal policies. The performance of the averaged Whittle's index policy is numerically evaluated for a multi-class scheduling problem.
Learning Proportionally Fair Allocations with Low Regret
Authors: Talebi Mohammad Sadegh , Proutiere Alexandre
Published: January 2019

We address the problem of learning Proportionally Fair (PF) allocations in parallel server systems with unknown service rates. We provide the first algorithms, to our knowledge, for learning such allocations with sub-linear regret.
LTERadar
Authors: Vlachou Christina , Pefkianakis Ioannis , Kim Kyu-Han
Published: January 2019

Major LTE hardware vendors (e.g. Qualcomm, Ericsson), mobile service providers (e.g. Verizon, T-Mobile), and standardization bodies (e.g. LTE-U forum, 3GPP) are extending LTE networks into unlicensed spectrum bands to boost the speeds and coverage of mobile networks. However, the deployment of LTE in unlicensed has raised serious concerns regarding their adverse impact on Wi-Fi networks in the same bands. We design LTERadar, a lightweight interference detector, that runs on Wi-Fi devices and accurately detects LTE interference in real time. LTERadar is a purely software-based solution that is independent of specific hardware or technology of the LTE interferer (e.g. LTE-U, LAA, the dominant LTE unlicensed protocols). Our implementation and evaluation with off-the-shelf Wi-Fi APs show that LTERadar achieves more than 90% of the interference detection accuracy in operational networks.
On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays
Authors: Doan Thinh T. , Beck Carolyn L. , Srikant R.
Published: January 2019

Motivated by applications in machine learning and statistics, we study distributed optimization problems over a network of processors, where the goal is to optimize a global objective composed of a sum of local functions. In these problems, due to the large scale of the data sets, the data and computation must be distributed over multiple processors resulting in the need for distributed algorithms. In this paper, we consider a popular distributed gradient-based consensus algorithm, which only requires local computation and communication. An important problem in this area is to analyze the convergence rate of such algorithms in the presence of communication delays that are inevitable in distributed systems. We prove the convergence of the gradient-based consensus algorithm in the presence of uniform, but possibly arbitrarily large, communication delays between the processors. Moreover, we obtain an upper bound on the rate of convergence of the algorithm as a function of the network size, topology, and the inter-processor communication delays.
Towards Optimality in Parallel Job Scheduling
Authors: Berg Benjamin , Dorsman Jan-Pieter , Harchol-Balter Mor
Published: January 2019

To keep pace with Moore's law, chip designers have focused on increasing the number of cores per chip. To effectively leverage these multi-core chips, one must decide how many cores to assign to each job. Given that jobs receive sublinear speedups from additional cores, there is a tradeoff: allocating more cores to an individual job reduces the job's runtime, but decreases the efficiency of the overall system. We ask how the system should assign cores to jobs so as to minimize the mean response time over a stream of incoming jobs. To answer this question, we develop an analytical model of jobs running on a multi-core machine. We prove that EQUI, a policy which continuously divides cores evenly across jobs, is optimal when all jobs follow a single speedup curve and have exponentially distributed sizes. We also consider a class of "fixed-width" policies, which choose a single level of parallelization, k, to use for all jobs. We prove that, surprisingly, fixed-width policies which use the optimal fixed level of parallelization, k*, become near-optimal as the number of cores becomes large. In the case where jobs may follow different speedup curves, finding a good scheduling policy is even more challenging. In particular, EQUI is no longer optimal, but a very simple policy, GREEDY*, performs well empirically.
Fork and Join Queueing Networks with Heavy Tails
Authors: Zeng Yun , Tan Jian , Xia Cathy Honghui
Published: January 2019

Parallel and distributed computing systems are foundational to the success of cloud computing and big data analytics. Fork-Join Queueing Networks with Blocking (FJQN/Bs) are natural models for such systems. While engineering solutions have long been made to build and scale such systems, it is challenging to rigorously characterize the throughput performance of ever-growing systems, especially in the presence of heavy-tailed delays. In this paper, we utilize an infinite sequence of FJQN/Bs to study the throughput limit and focus on regularly varying service times with index α>1. We introduce two novel geometric concepts - scaling dimension and extended metric dimension - and show that an infinite sequence of FJQN/Bs is throughput scalable if the extended metric dimension <α-1 and only if the scaling dimension łe α-1. These results provide new insights on the scalability of a rich class of FJQN/Bs.
Performance of Balanced Fairness in Resource Pools
Authors: Bonald Thomas , Comte Céline , Mathieu Fabien
Published: January 2019

Understanding the performance of a pool of servers is crucial for proper dimensioning. One of the main challenges is to take into account the complex interactions between servers that are pooled to process jobs. In particular, a job can generally not be processed by any server of the cluster due to various constraints like data locality. In this paper, we represent these constraints by some assignment graph between jobs and servers. We present a recursive approach to computing performance metrics like mean response times when the server capacities are shared according to balanced fairness. While the computational cost of these formulas can be exponential in the number of servers in the worst case, we illustrate their practical interest by introducing broad classes of pool structures that can be exactly analyzed in polynomial time. This extends considerably the class of models for which explicit performance metrics are accessible.
Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization
Authors: Wang Sinong , Shroff Ness
Published: January 2019

Distributed network optimization has been studied several years. However, we still do not have a good idea of how to design schemes that can simultaneously provide good performance across the dimensions of utility optimality, convergence speed, and delay. To address these challenges, in this paper, we propose a new algorithmic framework with all these metrics approaching optimality. The salient features of our new algorithm are three-fold: (i) fast convergence: it converges with only O(log(1/ε)) iterations, that is the fastest speed among all the existing algorithms; (ii) low delay: it guarantees optimal utility with finite queue length; (iii) simple implementation: the control variables of this algorithm are based on virtual queues that do not require maintaining per-flow information. The new technique builds on a kind of inexact Uzawa method in the Alternating Directional Method of Multiplier. A theoretical contribution of independent interest is a new pathway we provide to prove global and linear convergence rate of Uzawa-ADMM without requiring the full rank assumption of the constraint matrix.
The PDE Method for the Analysis of Randomized Load Balancing Networks
Authors: Aghajani Reza , Li Xingjie , Ramanan Kavita
Published: January 2019

We introduce a new framework for the analysis of large-scale load balancing networks with general service time distributions, motivated by applications in server farms, distributed memory machines, cloud computing and communication systems. For a parallel server network using the so-called $SQ(d)$ load balancing routing policy, we use a novel representation for the state of the system and identify its fluid limit, when the number of servers goes to infinity and the arrival rate per server tends to a constant. The fluid limit is characterized as the unique solution to a countable system of coupled partial differential equations (PDE), which serve to approximate transient Quality of Service parameters such as the expected virtual waiting time and queue length distribution. In the special case when the service time distribution is exponential, our method recovers the well-known ordinary differential equation characterization of the fluid limit.

Furthermore, we develop a numerical scheme to solve the PDE, and demonstrate the efficacy of the PDE approximation by comparing it with Monte Carlo simulations. We also illustrate how the PDE can be used to gain insight into the performance of large networks in practical scenarios by analyzing relaxation times in a backlogged network. In particular, our numerical approximation of the PDE uncovers two interesting properties of relaxation times under the SQ(2) algorithm. Firstly, when the service time distribution is Pareto with unit mean, the relaxation time decreases as the tail becomes heavier. This is a priori counterintuitive given that for the Pareto distribution, heavier tails have been shown to lead to worse tail behavior in equilibrium. Secondly, for unit mean light-tailed service distributions such as the Weibull and lognormal, the relaxation time decreases as the variance increases. This is in contrast to the behavior observed under random routing, where the relaxation time increases with increase in variance.
Safe Randomized Load-Balanced Switching by Diffusing Extra Loads
Authors: Yang Sen , Lin Bill , Xu Jun
Published: January 2019

Load-balanced switch architectures are known to be scalable in both size and speed, which is of interest due to the continued exponential growth in Internet traffic. However, the main drawback of load-balanced switches is that packets can depart out of order from the switch. Randomized load-balancing of application flows by means of hashing on the packet header is a well-known simple solution to this packet reordering problem in which all packets belonging to the same application flow are routed through the same intermediate port and hence the same path through the switch. Unfortunately, this method of load-balancing can lead to instability, depending on the mix of flow sizes and durations in the group of flows that gets randomly assigned to route through the same intermediate port. In this paper, we show that the randomized load-balancing of application flows can be enhanced to provably guarantee both stability and packet ordering by extending the approach with safety mechanisms that can uniformly diffuse packets across the switch whenever there is a build-up of packets waiting to route through some intermediate port. Although simple and intuitive, our experimental results show that our extended randomized load-balancing approach outperforms existing load-balanced switch architectures.
Diversity in Faculty Recruiting
Authors: Golubchik Leana , Redel Mallory
Published: January 2019

In this article, we share approaches and practices that we have found to be successful in supporting women in STEM fields, based on our experience with the University of Southern California's (USC) Women in Science and Engineering Program (WiSE). Specifically, we focus on faculty recruitment and retention, as faculty makeup and their success affects the entire pipeline, including graduate and undergraduate students.
A Simple Steady-State Analysis of Load Balancing Algorithms in the Sub-Halfin-Whitt Regime
Authors: Liu Xin , Ying Lei
Published: January 2019

This paper studies the steady-state performance of load balancing algorithms in many-server systems. We consider a system with N identical servers with buffer size b-1 such that b = o (√N), in other words, each server can hold at most b jobs, one job in service and b - 1 jobs in buffer.
A Refined Mean Field Approximation for Synchronous Population Processes
Authors: Gast Nicolas , Latella Diego , Massink Mieke
Published: January 2019

Mean field approximation is a popular method to study the behaviour of stochastic models composed of a large number of interacting objects. When the objects are asynchronous, the mean field approximation of a population model can be expressed as an ordinary differential equation. When the objects are synchronous the mean field approximation is a discrete time dynamical system. In this paper, we focus on the latter. We show that, similarly to the asynchronous case, the mean field approximation of a synchronous population can be refined by a term in 1/N. Our result holds for finite time-horizon and steady-state. We provide two examples that illustrate the approach and its limit.
Smoothed Online Convex Optimization via Online Balanced Descent
Authors: Goel Gautam
Published: January 2019

We study smoothed online convex optimization, a version of online convex optimization where the learner incurs a penalty for changing her actions between rounds. Given a Ω(pd) lower bound on the competitive ratio of any online algorithm, where d is the dimension of the action space, we ask under what conditions this bound can be beaten. We introduce a novel algorithmic framework for this problem, Online Balanced Descent (OBD), which works by iteratively projecting the previous point onto a carefully chosen level set of the current cost function so as to balance the switching costs and hitting costs. We demonstrate the generality of the OBD framework by showing how, with different choices of "balance," OBD can improve upon state-of-theart performance guarantees for both competitive ratio and regret; in particular, OBD is the first algorithm to achieve a dimension-free competitive ratio, 3 + O(1/α), for locally polyhedral costs, where - measures the "steepness" of the costs. We also prove bounds on the dynamic regret of OBD when the balance is performed in the dual space that are dimension-free and imply that OBD has sublinear static regret.
Cyber Risk in Critical Infrastructure
Authors: Kelic Andjelka
Published: January 2019

Existing approaches to evaluating cyber risk are summarized and explored for their applicability to critical infrastructure. The approaches cluster in three different spaces: network security, cyber-physical, and mission assurance. In all approaches, some form of modeling is utilized at varying levels of detail, while the ability to understand consequence varies, as do interpretations of risk. A hybrid approach can account for cyber risk in critical infrastructure and allow for allocation of limited resources across the entirety of the risk spectrum.
DeepXplore
Authors: Pei Kexin , Cao Yinzhi , Yang Junfeng , Jana Suman
Published: January 2019

Over the past few years, Deep Learning (DL) has made tremendous progress, achieving or surpassing human-level performance for a diverse set of tasks, including image classification, speech recognition, and playing games like Go. These advances have led to widespread adoption and deployment of DL in security- and safety-critical systems, such as selfdriving cars, malware detection, and aircraft collision avoidance systems.
Salton Award Keynote
Authors: Järvelin Kalervo
Published: January 2019

This article is a lightly edited text of the author's Salton Award Keynote: Information Interaction in Context, presented at the 41st SIGIR conference in Ann Arbor, July 9th, 2019. It first gives some personal background and then discusses some important areas of information seeking and IR in the author's research work. These include task-based information behavior and interaction, natural language processing to improve document ranking in mono- and cross-language IR, and IR evaluation metrics. Finally, the article proposes a way to organize research on information interaction.
Fairness and Transparency in Ranking
Authors: Castillo Carlos
Published: January 2019

Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the extent to which the system provides "the right information, to the right people, in the right way, at the right time." However, people in current IR systems are not only the ones issuing search queries, but increasingly they are also the ones being searched. This raises several new problems in IR that have been addressed in recent research, particularly with respect to fairness/non-discrimination, accountability, and transparency. This is a summary of some these initial developments.
Report on the DATA
Authors: Koesten Laura , Mayr Philipp , Groth Paul , Simperl Elena , de Rijke Maarten
Published: January 2019

The increasing availability of structured data on the web makes searching for data an important and timely topic. This report presents the motivation, output, and research challenges of the second DATA:SEARCH workshop which was held in conjunction with SIGIR 2018, in Ann Arbor, Michigan. This workshop explored challenges in data search, with a particular focus on data on the web. The aim was to share and establish links between different perspectives on search and discovery for different kinds of structured data, which can potentially inform the design of a wide range of information retrieval technologies. The DATA:SEARCH workshop tries to bring together communities interested in making the web of data more discoverable, easier to search and more user friendly.
Report on EARS'18
Authors: Zhang Yongfeng , Zhang Yi , Zhang Min
Published: January 2019

This is a report on the first edition of the International Workshop on ExplainAble Recommendation and Search (EARS 2018), co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) held in Ann Arbor, Michigan, USA on July 8-12, 2018. The workshop was held on July 12, 2018, co-chaired by Yongfeng Zhang, Yi Zhang, and Min Zhang, with invited keynote speeches delivered by Prof. Paul Resnick from University of Michigan, and Dr. Qingsong Hua from Alibaba Inc. An invited panelist committee including Professors Matthew Lease, Paul Resnick, Mark Sanderson, Wlodek Zadrozny and Dr. Qingsong Hua made fruitful discussions about the past, present, and future on the research of explainable recommendation and search. The workshop accepted six contributed papers, and attracted over 80 registered participants. The scope of the workshop spans from technical approaches to explainable recommendation and search, to policy debates on the "principle of transparency" and the "right to explanation" of algorithmic decisions implied in recent EU General Data Protection Regulation (GDPR) and other similar regulations such as The California Consumer Privacy Act of 2018.
On Biases in Information Retrieval Models and Evaluation
Authors: Lipani Aldo
Published: January 2019

The advent of the modern information technology has benefited society as the digitization of content increased over the last half-century. While the processing capability of our species has remained unchanged, the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating relevant information from the rest. This separation, however, is a difficult task rooted in the complexity of understanding of what is and what is not relevant. To manage this complexity, IR has developed a strong empirical nature, which has led to the development of grounded retrieval models, resulting in the development of retrieval systems empirically designed to be biased towards relevant information. However, other biases have been observed, which counteract retrieval performance. In this thesis, the reduction of retrieval systems to filters of information, or sampling processes, has allowed us to systematically investigate these biases.
Exploiting User Signals and Stochastic Models to Improve Information Retrieval Systems and Evaluation
Authors: Maistro Maria
Published: January 2019

Progress and innovation are driven by experiments, but experimentation is useless without an objective evaluation measure that allow researchers to detect the improvements and identify the successful strategies. Due to the experimental nature of Information Retrieval (IR), accurately interpreting the result of a system in terms of user satisfaction is fundamental to push the research in the correct direction. Therefore, measuring systems effectiveness continues to be an active area of research and discussion in the scientific community. It is also the case of this thesis, whose leitmotiv is an investigation of effectiveness measures exploited in different aspects of IR.
Inferring User Needs & Tasks from User Interactions
Authors: Mehrotra Rishabh
Published: January 2019

The need for search often arises from a broad range of complex information needs or tasks (such as booking travel, buying a house, etc.) which lead to lengthy search processes characterised by distinct stages and goals. While existing search systems are adept at handling simple information needs, they offer limited support for tackling complex tasks. Accurate task representations could be useful in aptly placing the user in the task-subtask space and enable systems to contextually target the user, provide them better query suggestions, personalization and recommendations and help in gauging satisfaction.
Text Representation, Retrieval, and Understanding with Knowledge Graphs
Authors: Xiong Chenyan
Published: January 2019

This dissertation aims to improve text representation, retrieval, and understanding with knowledge graphs. Previous information retrieval systems were mostly built upon bag-ofwords representations and frequency-based retrieval models. Effective as they are, wordbased representations and frequency signals only provide shallow text understanding and have various intrinsic challenges. Utilizing entities and their structured semantics from knowledge graphs, this dissertation goes beyond bag-of-words and improves search with richer text representations, customized semantic structures, sophisticated ranking models and neural networks.
Biofilms in Food Processing Environments: Challenges and Opportunities
Authors: Alvarez-Ordóñez Avelino , Coughlan Laura M. , Briandet Romain , Cotter Paul D.
Published: January 2019

This review examines the impact of microbial communities colonizing food processing environments in the form of biofilms on food safety and food quality. The focus is both on biofilms formed by pathogenic and spoilage microorganisms and on those formed by harmless or beneficial microbes, which are of particular relevance in the processing of fermented foods. Information is presented on intraspecies variability in biofilm formation, interspecies relationships of cooperativism or competition within biofilms, the factors influencing biofilm ecology and architecture, and how these factors may influence removal. The effect on the biofilm formation ability of particular food components and different environmental conditions that commonly prevail during food processing is discussed. Available tools for the in situ monitoring and characterization of wild microbial biofilms in food processing facilities are explored. Finally, research on novel agents or strategies for the control of biofilm formation or removal is summarized.
Decreased miR-29b expression is associated with airway inflammation in chronic obstructive pulmonary disease
Authors: Tang Kun , Zhao Jianping , Xie Jungang , Wang Jianmiao
Published: January 2019

Chronic obstructive pulmonary disease (COPD) is a common chronic airway inflammatory disease. MicroRNAs are shown to be involved in the regulation of inflammation. We investigated the role of microRNA-29b (miR-29b) in the airway inflammation in COPD. The expression of miR-29b in the lung and plasma was examined. The target of miR-29b, bromodomain protein 4 (BRD4), was predicted by online algorithms and verified in human bronchial epithelial (HBE) cells. The expression of BRD4, interleukin (IL)-8, and IL-6 in the lung was also examined. The role of miR-29b in the inflammatory cytokine expression of airway epithelial cells was studied using an in vitro model system. In total, 60 subjects were recruited, including 10 nonsmokers, 24 smokers, and 26 patients with COPD. Both lung and plasma miR-29b are decreased in patients with COPD, and miR-29b expression levels are correlated with pulmonary function and inflammation. BRD4 is increased in the lung of patients with COPD and is correlated with miR-29b and IL-8 expression. miR-29b regulates cigarette smoke extract (CSE)-induced IL-8 expression by targeting BRD4 in HBE cells. The antioxidant N-acetylcysteine prevents CSE-induced miR-29b downregulation and BRD4 and IL-8 upregulation. Our findings indicate that miR-29b may participate in the airway inflammation in COPD by regulating inflammatory cytokine expression through targeting BRD4, plasma miR-29b may serve as a biomarker for disease severity in COPD, and oxidative stress may contribute to the decrease of miR-29b induced by cigarette smoke.
Local arginase inhibition does not modulate cutaneous vasodilation or sweating in young and older men during exercise
Authors: Meade Robert D. , Fujii Naoto , McGarr Gregory W. , Alexander Lacy M. , Boulay Pierre , Sigal Ronald J. , Kenny Glen P.
Published: January 2019

Age-related impairments in cutaneous vascular conductance (CVC) and sweat rate (SR) during exercise may result from increased arginase activity, which can attenuate endogenous nitric oxide (NO) production. We therefore evaluated whether arginase inhibition modulates these heat-loss responses in young ( n = 9, 23 ± 3 yr) and older ( n = 9, 66 ± 6 yr) men during two 30-min bouts of moderate-intensity cycling (Ex1 and Ex2) in the heat (35°C). CVC and SR were measured at forearm skin sites perfused with 1) lactated Ringer’s (control), 2) NG-nitro-L-arginine methyl ester (L-NAME; NO synthase-inhibited), or 3) Nω-hydroxy-nor-arginine and S-(2-boronoethyl)-l-cysteine (Nor-NOHA + BEC; arginase-inhibited). In both groups, CVC was reduced at L-NAME relative to control and Nor-NOHA + BEC (both P < 0.01). Likewise, SR was attenuated with L-NAME compared with control and Nor-NOHA + BEC during each exercise bout in the young men (all P ≤ 0.05); however, no influence of treatment on SR in the older men was observed ( P = 0.14). Based on these findings, we then evaluated responses in 7 older men (64 ± 7 yr) during passively induced elevations in esophageal temperature (∆Tes) equal to those in Ex1 (0.6°C) and Ex2 (0.8°C). L-NAME reduced CVC by 18 ± 20% CVCmaxat a ∆Tesof 0.8°C ( P = 0.03) compared with control, whereas Nor-NOHA + BEC augmented CVC by 20 ± 18% CVCmax, on average, throughout heating (both P ≤ 0.03). SR was not influenced by either treatment ( P = 0.80) Thus, arginase inhibition does not modulate CVC or SR during exercise in the heat but, consistent with previous findings, does augment CVC in older men during passive heating.

NEW & NOTEWORTHY In the current study, we demonstrate that local arginase inhibition does not influence forearm cutaneous vasodilatory and sweating responses in young or older men during exercise-heat stress. Consistent with previous findings, however, we observed augmented cutaneous blood flow with arginase inhibition during whole-body passive heat stress. Thus, arginase differentially affects cutaneous vasodilation depending on the mode of heat stress but does not influence sweating during exercise or passive heating.
Gateway to Recovery: A Comparative Analysis of Stroke Patients’ Experiences of Change and Learning in Norway and Denmark
Authors: Pallesen Hanne , Aadal Lena , Moe Siri , Arntzen Cathrine
Published: January 2019

Objectives. The recovery process is reported by stroke survivors to be a change process fraught with crises and hazard. Interaction with health professionals and others may play a central role in establishing renewed control over life.Research Questions. (1) How do patients handle and overcome experienced changes after stroke? (2) How do they experience the support to handle these changes during the first year after stroke? (3) How do the similarities and differences transpire in Danish and Norwegian contexts?Methodology. A qualitative method was chosen. Six patients from Denmark and five patients from Norway (aged 25-66) were followed up until one year after stroke, by way of individual interviews. The data were analyzed (using NVivo 11) by means of phenomenological analysis.Findings. The participants described four main issues in the recovery process that impacted the experienced changes: (i) strategies and personal factors that promote motivation, (ii) the involvement of family, social network, and peers, (iii) professionals’ support, and (iv) social structures that limit the recovery process. There was a diversity of professional support and some interesting variations in findings about factors that affected recovery and the ability to manage a new life situation between Central Denmark and Northern Norway. Both Norwegian and Danish participants experienced positive changes and progress on the bodily level, as well as in terms of activity and participation. Furthermore, they learned how to overcome limitations, especially in bodily functions and daily activities at home. Unfortunately, progress or support related to psychosocial rehabilitation was almost absent in the Norwegian data.
Exendin-4 Exacerbates Burn-Induced Morbidity in Mice by Activation of the Sympathetic Nervous System
Authors: Ji Xiao-Jing , Hao Ji-Wei , Li Guang-Lei , Dong Ning , Wang Xin-Qi , Zhou Min , Zhang Qing-Hong , Yao Yong-Ming
Published: January 2019

Background. Although glucagon-like peptide 1- (GLP-1-) based therapy of hyperglycemia in burn injury has shown great potential in clinical trials, its safety is seldom evaluated. We hypothesize that exendin-4, a GLP-1 analogue, might affect the immune response via the activation of the sympathetic nervous system in burn injury.Methods. Male Balb/c mice were subjected to sham or thermal injury of 15% total body surface area. Exendin-4 on T cell functionin vitrowas examined in cultured splenocytes in the presence ofβ-adrenoceptor antagonist propranolol (1 nmol/L) or GLP-1R antagonist exendin (9-39) (1 μmol/L), whereas itsin vivoeffect was determined by i.p. injection of exendin-4 (2.4 nmol/kg) in mice. To further elucidate the sympathetic mechanism, propranolol (30 mg/kg) or vehicle was applied 30 min prior to injury.Results. Although the exacerbated burn-induced mortality by exendin-4 was worsened by propranolol pretreatment, the inhibition of T cell proliferation by exendin-4in vitrocould be restored by propranolol instead of exendin (9-39). However, a Th2 switch by exendin-4in vitrocould only be reversed by exendin (9-39). Likewise, the inhibition of splenic T cell function and NFAT activity by exendin-4in vivowas restored by propranolol. By contrast, the increased splenic NF-κB translocation by exendin-4in vivowas potentiated by propranolol in sham mice but suppressed in burn mice. Accordingly, propranolol abrogated the heightened inflammatory response in the lung and the accelerated organ injuries by exendin-4 in burn mice. On the contrary, a Th2 switch and higher serum levels of inflammatory mediators by exendin-4 were potentiated by propranolol in burn mice. Lastly, exendin-4 raised serum stress hormones which could be remarkably augmented by propranolol.Conclusions. Exendin-4 suppresses T cell function and promotes organ inflammation through the activation of the sympathetic nervous system, while elicits Th2 switch via GLP-1R in burn injury.
Corn Silk Tea for Hypertension: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Shi Shihua , Li Siming , Li Weihao , Xu Hao
Published: January 2019

Corn silk, a traditional Chinese medicine, has been found to exert an antihypertensive effect in clinical practice and trials. However, systematic review of current evidence on this topic was not available. Thus, this study aims to assess safety and efficacy of corn silk tea (CST) in improving clinical outcomes in patients with hypertension. A systematic literature search was conducted through popular electronic databases up to October 2018. Randomized controlled trials (RCTs) comparing CST plus conventional antihypertensive drugs with conventional antihypertensive drugs alone were included. The main outcome was total blood pressure lowering efficacy. The risk of bias assessment according to the Cochrane Handbook was used to evaluate the methodological quality of the included trials. Review Manager 5.3 software was used for data analyses. Five RCTs involving 567 participants were included. Due to the poor quality of methodologies of most trials, limited evidence showed that CST plus antihypertensive drugs might be more effective in lowering blood pressure compared with antihypertensive drugs alone (RR = 1.27; 95% CI: 1.17 to 1.38, P<0.00001; heterogeneity: P = 0.51, I2 = 0%, fixed‐effect model). However, there is no evidence that CST plus conventional antihypertensive drugs has less adverse events than conventional antihypertensive drugs.
Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems
Authors: Gao Zhaohui , Mu Dejun , Zhong Yongmin , Gu Chengfan , Ren Chengcai
Published: January 2019

This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear state estimation. This method adopts the concept of random weighting to address the problem that the cubature Kalman filter (CKF) performance is sensitive to system noise. It establishes random weighting theories to estimate system noise statistics and predicted state and measurement together with their associated covariances. Subsequently, it adaptively adjusts the weights of cubature points based on the random weighting estimations to improve the prediction accuracy, thus restraining the disturbances of system noises on state estimation. Simulations and comparison analysis demonstrate the improved performance of the proposed method for nonlinear state estimation.
A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters
Authors: Wang Sen-Hui , Li Hai-Feng , Zhang Yong-Jie , Zou Zong-Shu
Published: January 2019

As energy efficiency becomes increasingly important to the steel industry, the iron ore sintering process is attracting more attention since it consumes the second large amount of energy in the iron and steel making processes. The present work aims to propose a prediction model for the iron ore sintering characters. A hybrid ensemble model combined the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm is developed for regression problem. First, the factors that affect solid fuel consumption, gas fuel consumption, burn-through point (BTP), and tumbler index (TI) are ranked according to the attributes weightiness sequence by applying the RReliefF method. Second, the ELM network is selected as an ensemble predictor due to its fast learning speed and good generalization performance. Third, an improved AdaBoost.RT is established to overcome the limitation of conventional AdaBoost.RT by dynamically self-adjusting the threshold value. Then, an ensemble ELM is employed by using the improved AdaBoost.RT for better precision than individual predictor. Finally, this hybrid ensemble model is applied to predict the iron ore sintering characters by production data from No. 4 sintering machine in Baosteel. The results obtained show that the proposed model is effective and feasible for the practical sintering process. In addition, through analyzing the first superior factors, the energy efficiency and sinter quality could be obviously improved.
Assessing IEEE 802.11 and IEEE 802.16 as Backhauling Technologies for 3G Small Cells in Rural Areas of Developing Countries
Authors: Simó-Reigadas Javier , Figuera Carlos , Morgado Eduardo , Municio Esteban , Martínez-Fernández Andrés
Published: January 2019

Mobile networks are experiencing a great development in urban areas worldwide, and developing countries are not an exception. However, sparsely populated rural areas in developing regions usually do not have any access to terrestrial communications networks because operators cannot ensure enough revenues to justify the required investments. Therefore, alternative low-cost solutions are needed for both the access network and the backhaul network. In this sense, in order to provide rural 3G coverage in small villages, state-of-the-art approaches propose to use Small Cells in access networks and inexpensive multihop wireless networks based on WiFi for long distances (WiLD) or WiMAX for backhauling them. These technologies provide most of the required capabilities; however, there is no complete knowledge about the performance of WiFi and WiMAX in long-distance links under quality of service constraints. The aim of this work is to provide a detailed overview of the different alternatives for building rural wireless backhaul networks. We compare both IEEE 802.11n and IEEE 802.16 distance-aware analytical models and validate them by extensive simulations and field experiments. Also WiFi-based TDMA proprietary solutions are evaluated experimentally and compared. Finally, results are used to model a real study case in the Peruvian Amazon in order to illustrate that the performance provided by these technologies may be sufficient for the backhaul network of a rural 3G access network based on Small Cells.
Antioxidant Properties of Fullerene Derivatives Depend on Their Chemical Structure: A Study of Two Fullerene Derivatives on HELFs
Authors: Sergeeva Vasilina , Kraevaya Olga , Ershova Elizaveta , Kameneva Larisa , Malinovskaya Elena , Dolgikh Olga , Konkova Marina , Voronov Iliya , Zhilenkov Alexander , Veiko Natalia , Troshin Pavel , Kutsev Sergei , Kostyuk Svetlana
Published: January 2019

Oxidative stress is a major issue in a wide number of pathologies (neurodegenerative, cardiovascular, immune diseases, and cancer). Because of this, the search for new antioxidants is an important issue. One of the potential antioxidants that has been enthusiastically discussed in the past twenty years is fullerene and its derivatives. Although in aqueous solutions fullerene derivatives have shown to be antioxidants, their properties in this regard within the cells are controversially discussed. We have studied two different water-soluble fullerene C60 and C70 derivatives on human embryonic lung fibroblasts at a wide range of concentrations. Both of them cause a decrease in cellular ROS at short times of incubation (1 hour). Their prolonged action, however, is fundamentally different: derivative GI-761 causes secondary oxidative stress whereas derivative VI-419-P3K keeps ROS levels under control values. To gain a better understanding of this effect, we assessed factors that could play a role in the response of cells to fullerene derivatives. Increased ROS production occurred due to NOX4 upregulation by GI-761. Derivative VI-419-P3K activated the transcription of antioxidant master regulator NRF2 and caused its translocation to the nucleus. This data suggests that the antioxidant effect of fullerene derivatives depends on their chemical structure.