Online Dynamic Acknowledgement with Learned Predictions

We revisit the online dynamic acknowledgment problem. In the problem, a sequence of requests arrive over time to be acknowledged, and all outstanding requests can be satisfied simultaneously by one acknowledgement. The goal of the problem is to minimize the total request delay plus acknowledgement cost. This elegant model studies the trade-off between acknowledgement cost and waiting experienced by requests. The problem has been well studied and the tight competitive ratios have been determined. For this well-studied problem, we focus on how to effectively use machine-learned predictions to have better performance. We develop algorithms that perform arbitrarily close to the optimum with accurate predictions while concurrently having the guarantees arbitrarily close to what the best online algorithms can offer without access to predictions, thereby achieving simultaneous optimum consistency and robustness. This new result is enabled by our novel prediction error measure. No error measure was defined for the problem prior to our work, and natural measures failed due to the challenge that requests with different arrival times have different effects on the objective. We hope our ideas can be used for other online problems with temporal aspects that have been resisting proper error measures.Comment: To appear in INFOCOM 202

Min-max Submodular Ranking for Multiple Agents

In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions to have value above a certain threshold as soon on average as possible, assuming we choose one element per time. The problem is flexible enough to capture various applications in machine learning, including decision trees. This paper considers the min-max version of SR where multiple instances share the ground set. With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents -- thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents.Comment: To appear in AAAI 202

Analysis of cybersecurity threats in Industry 4.0: the case of intrusion detection

Nowadays, industrial control systems are experiencing a new revolution with the interconnection of the operational equipment with the Internet, and the introduction of cutting-edge technologies such as Cloud Computing or Big data within the organization. These and other technologies are paving the way to the Industry 4.0. However, the advent of these technologies, and the innovative services that are enabled by them, will also bring novel threats whose impact needs to be understood. As a result, this paper provides an analysis of the evolution of these cyber-security issues and the requirements that must be satis ed by intrusion detection defense mechanisms in this context.Springer ; Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Growth factor restriction impedes progression of wound healing following cataract surgery: identification of VEGF as a putative therapeutic target

Secondary visual loss occurs in millions of patients due to a wound-healing response, known as posterior capsule opacification (PCO), following cataract surgery. An intraocular lens (IOL) is implanted into residual lens tissue, known as the capsular bag, following cataract removal. Standard IOLs allow the anterior and posterior capsules to become physically connected. This places pressure on the IOL and improves contact with the underlying posterior capsule. New open bag IOL designs separate the anterior capsule and posterior capsules and further reduce PCO incidence. It is hypothesised that this results from reduced cytokine availability due to greater irrigation of the bag. We therefore explored the role of growth factor restriction on PCO using human lens cell and tissue culture models. We demonstrate that cytokine dilution, by increasing medium volume, significantly reduced cell coverage in both closed and open capsular bag models. This coincided with reduced cell density and myofibroblast formation. A screen of 27 cytokines identified nine candidates whose expression profile correlated with growth. In particular, VEGF was found to regulate cell survival, growth and myofibroblast formation. VEGF provides a therapeutic target to further manage PCO development and will yield best results when used in conjunction with open bag IOL designs

Comparison of estrogens and estrogen metabolites in human breast tissue and urine

<p>Abstract</p> <p>Background</p> <p>An important aspect of the link between estrogen and breast cancer is whether urinary estrogen levels are representative of the intra-tissue levels of bioavailable estrogens.</p> <p>Methods</p> <p>This study compares 15 estrogen and estrogen metabolite levels in breast tissue and urine of 9 women with primary breast cancer using a quantitative liquid chromatography-mass spectrometry method.</p> <p>Results</p> <p>The average levels of estrogens (estrone, 17 beta-estradiol) were significantly higher in breast tissue than in urine. Both the 2 and the 16-hydroxylation pathways were less represented in breast tissue than urine; no components of the 4-hydroxypathway were detected in breast tissue, while 4-hydroxyestrone was measured in urine. However, the 2/16 ratio was similar in urine and breast tissue. Women carrying the variant CYP1B1 genotype (Leu/Val and Val/Val) showed significantly lower overall estrogen metabolite, estrogen, and 16-hydroxylation pathway levels in breast tissue in comparison to women carrying the wild type genotype. No effect of the CYP1B1 polymorphism was observed in urinary metabolites.</p> <p>Conclusions</p> <p>The urinary 2/16 ratio seems a good approximation of the ratio observed in breast tissue. Metabolic genes may have an important role in the estrogen metabolism locally in tissues where the gene is expressed, a role that is not readily observable when urinary measurements are performed.</p

Optical Magnetometry

Some of the most sensitive methods of measuring magnetic fields utilize interactions of resonant light with atomic vapor. Recent developments in this vibrant field are improving magnetometers in many traditional areas such as measurement of geomagnetic anomalies and magnetic fields in space, and are opening the door to new ones, including, dynamical measurements of bio-magnetic fields, detection of nuclear magnetic resonance (NMR), magnetic-resonance imaging (MRI), inertial-rotation sensing, magnetic microscopy with cold atoms, and tests of fundamental symmetries of Nature.Comment: 11 pages; 4 figures; submitted to Nature Physic

Linear magnetoresistance in commercial n-type silicon due to inhomogeneous doping

Free electron theory tells us that resistivity is independent of magnetic field. In fact, most observations match the semiclassical prediction of a magnetoresistance that is quadratic at low fields before saturating. However, a non-saturating linear magnetoresistance has been observed in exotic semiconductors such as silver chalcogenides, lightly-doped InSb, N-doped InAs, MnAs-GaAs composites, PrFeAsO, and epitaxial graphene. Here we report the observation of a large linear magnetoresistance in the ohmic regime in commonplace commercial n-type silicon wafer. It is well-described by a classical model of spatially fluctuating donor densities, and may be amplified by altering the aspect ratio of the sample to enhance current-jetting: increasing the width tenfold increased the magnetoresistance at 8 T from 445 % to 4707 % at 35 K. This physical picture may well offer insights into the large magnetoresistances recently observed in n-type and p-type Si in the non-ohmic regime.Comment: submitted to Nature Material

Stability boundary approximation of periodic dynamics

We develop here the method for obtaining approximate stability boundaries in the space of parameters for systems with parametric excitation. The monodromy (Floquet) matrix of linearized system is found by averaging method. For system with 2 degrees of freedom (DOF) we derive general approximate stability conditions. We study domains of stability with the use of fourth order approximations of monodromy matrix on example of inverted position of a pendulum with vertically oscillating pivot. Addition of small damping shifts the stability boundaries upwards, thus resulting to both stabilization and destabilization effects.Comment: 9 pages, 2 figure

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation