Search

Several significant models have been developed that enable the study of diffusion of signals across biological, social and engineered networks. Within these established frameworks, the inverse problem of identifying the source of the propagated signal is challenging, owing to the numerous alternative possibilities for signal progression through the network. In real world networks, the challenge of determining sources is compounded as the true propagation dynamics are typically unknown, and when they have been directly measured, they rarely conform to the assumptions of any of the well-studied models. In this paper we introduce a method called Network Infusion (NI) that has been designed to circumvent these issues, making source inference practical for large, complex real world networks. The key idea is that to infer the source node in the network, full characterization of diffusion dynamics, in many cases, may not be necessary. This objective is achieved by creating a diffusion kernel that well-approximates standard diffusion models, but lends itself to inversion, by design, via likelihood maximization or error minimization. We apply NI for both single-source and multi-source diffusion, for both single-snapshot and multi-snapshot observations, and for both homogeneous and heterogeneous diffusion setups. We prove the mean-field optimality of NI for different scenarios, and demonstrate its effectiveness over several synthetic networks. Moreover, we apply NI to a real-data application, identifying news sources in the Digg social network, and demonstrate the effectiveness of NI compared to existing methods. Finally, we propose an integrative source inference framework that combines NI with a distance centrality-based method, which leads to a robust performance in cases where the underlying dynamics are unknown., Comment: 21 pages, 13 figures

Clonal expansion is a core aspect of T cell immunity. However, little is known with respect to the relationship between replicative history and the formation of distinct CD8+ memory T cell subgroups. To address this issue, we developed a genetic-tracing approach, termed the DivisionRecorder, that reports the extent of past proliferation of cell pools in vivo. Using this system to genetically ‘record’ the replicative history of different CD8+ T cell populations throughout a pathogen-specific immune response, we demonstrate that the central memory T cell (TCM) pool is marked by a higher number of prior divisions than the effector memory T cell pool, due to the combination of strong proliferative activity during the acute immune response and selective proliferative activity after pathogen clearance. Furthermore, by combining DivisionRecorder analysis with single cell transcriptomics and functional experiments, we show that replicative history identifies distinct cell pools within the TCM compartment. Specifically, we demonstrate that lowly divided TCM display enriched expression of stem-cell-associated genes, and that such lowly divided cells are superior in eliciting a proliferative recall response. The latter data provide the first evidence that a stem cell like memory T cell pool that reconstitutes the CD8+ T cell effector pool upon reinfection is marked by prior quiescence.

If queue-states are unknown to a dispatcher, a replication strategy has been proposed where job copies are sent randomly to servers. When the service of any copy is completed, redundant copies are removed. For jobs that can be algebraically manipulated and have linear servers, to get the response-time performance of sending d copies of n jobs via the replication strategy, with Maximum Distance Separable (MDS) codes we show that n + d jobs suffice. That is, while replication is multiplicative, MDS is linear.

We present the first fully-integrated universal Max- imum Likelihood decoder in 40 nm CMOS using the Guessing Random Additive Noise Decoding (GRAND) algorithm for low- power applications. The 0.83 mm2 multi-code multi-rate universal decoder can efficiently decode any code of length up to 128 bits with 1 μs latency at 68 MHz. Dynamic clock gating leveraging noise statistics reduces the average power dissipation to 3.75 mW at 1.1 V or 30.6 pJ/decoded bit with a throughput of 122.6 Mb/s. Universal decoding reduces hardware footprint, and the design allows seamless swapping between codebooks with no downtime, enabling use by multiple applications without switch-over.

Background:Primary Sjögren’s syndrome (pSS) is a heterogeneous immune disorder with broad clinical phenotypes that can arise from a large number of genetic, hormonal, and environmental causes. B-cell hyperactivity is considered to be a pathogenic hallmark of pSS. However, whether B-cell hyperactivity in pSS patients is a result of polygenic, B cell-intrinsic factors, extrinsic factors, or both, is unclear. Despite controversies about the efficacy of rituximab, new B-cell targeting therapies are under investigation with promising early results. However, for such therapies to be successful, the etiology of B-cell hyperactivity in pSS needs to be clarified at the individual patient level.Objectives:To measure naïve B-cell function in pSS patients and healthy donors using quantitative immunology.Methods:We have developed standardised, quantitative functional assays of B-cell responses that measure division, death, differentiation and isotype switching, to reveal the innate programming of B cells in response to T-independent and dependent stimuli. This novel pipeline to measure B-cell health was developed to reveal the sum total of polygenic defects and underlying B-cell dysfunction at an individual level. For the current study, 25 pSS patients, fulfilling 2016 ACR-EULAR criteria, and 15 age-and gender-matched healthy donors were recruited. Standardized quantitative assays were used to directly measure B cell division, death and differentiation in response to T cell-independent (anti-Ig + CpG) and T-cell dependent (CD40L + IL-21) stimuli. Naïve B cells (IgD+CD27-) were sorted from peripheral blood mononuclear cells and were labeled with Cell Trace Violet at day 0 to track cell division until day 6. B cell differentiation was measured at day 5.Results:Application of our standardized assays, and accompanying parametric models, allowed us to study B cell-intrinsic defects in pSS patients to a range of stimuli. Strikingly, we demonstrated a hyperresponse of naïve B cells to combined B cell receptor (BCR) and Toll-like receptor (TLR)-9 stimulation in pSS patients. This hyperresponse was revealed by an increased mean division number (MDN) at day 5 in pSS patients compared with healthy donors (p=0.021). A higher MDN in pSS patients was observed at the cohort level and was likely attributed to an increased division burst (division destiny) time. The MDN upon BCR/TLR-9 stimulation correlated with serum IgG levels (rs=0.52; p=0.011). No difference in MDN of naïve B cells after T cell-dependent stimulation was observed between pSS patients and healthy donors. B cell differentiation capacity (e.g., plasmablast formation and isotype switching) after T cell-dependent stimulation was also assessed. At the cohort level, no difference in differentiation capacity between groups was observed, although some pSS patients showed higher plasmablast frequencies than healthy donors.Conclusion:Here, we demonstrate defects in B-cell responses both at the cohort level, as well as individual signatures of defective responses. Personalized profiles of B cell health in pSS patients reveal a group of hyperresponsive patients, specifically to combined BCR/TLR stimulation. These patients may benefit most from B-cell targeted therapies. Future studies will address whether profiles of B cell health might serve additional roles, such as prediction of disease trajectories, and thus accelerate early intervention and access to precision therapies.Disclosure of Interests:Gwenny M. Verstappen: None declared, Jessica Catherine Tempany: None declared, HoChan Cheon: None declared, Anthony Farchione: None declared, Sarah Downie-Doyle: None declared, Maureen Rischmueller Consultant of: Abbvie, Bristol-Meyer-Squibb, Celgene, Glaxo Smith Kline, Hospira, Janssen Cilag, MSD, Novartis, Pfizer, Roche, Sanofi, UCB, Ken R. Duffy: None declared, Frans G.M. Kroese Grant/research support from: Unrestricted grant from Bristol-Myers Squibb, Consultant of: Consultant for Bristol-Myers Squibb, Speakers bureau: Speaker for Bristol-Myers Squibb, Roche and Janssen-Cilag, Hendrika Bootsma Grant/research support from: Unrestricted grants from Bristol-Myers Squibb and Roche, Consultant of: Consultant for Bristol-Myers Squibb, Roche, Novartis, Medimmune, Union Chimique Belge, Speakers bureau: Speaker for Bristol-Myers Squibb and Novartis., Philip D. Hodgkin Grant/research support from: Medimmune, Vanessa L. Bryant Grant/research support from: CSL

Continuous mixture interpretation methods that employ probabilistic genotyping to compute the Likelihood Ratio (LR) utilize more information than threshold-based systems. The continuous interpretation schemes described in the literature, however, do not all use the same underlying probabilistic model and standards outlining which probabilistic models may or may not be implemented into casework do not exist; thus, it is the individual forensic laboratory or expert that decides which model and corresponding software program to implement. For countries, such as the United States, with an adversarial legal system, one can envision a scenario where two probabilistic models are used to present the weight of evidence, and two LRs are presented by two experts. Conversely, if no independent review of the evidence is requested, one expert using one model may present one LR as there is no standard or guideline requiring the uncertainty in the LR estimate be presented. The choice of model determines the underlying probability calculation, and changes to it can result in non-negligible differences in the reported LR or corresponding verbal categorization presented to the trier-of-fact. In this paper, we study the impact of model differences on the LR and on the corresponding verbal expression computed using four variants of a continuous mixture interpretation method. The four models were tested five times each on 101, 1-, 2- and 3-person experimental samples with known contributors. For each sample, LRs were computed using the known contributor as the person of interest. In all four models, intra-model variability increased with an increase in the number of contributors and with a decrease in the contributor's template mass. Inter-model variability in the associated verbal expression of the LR was observed in 32 of the 195 LRs used for comparison. Moreover, in 11 of these profiles there was a change from LR > 1 to LR < 1. These results indicate that modifications to existing continuous models do have the potential to significantly impact the final statistic, justifying the continuation of broad-based, large-scale, independent studies to quantify the limits of reliability and variability of existing forensically relevant systems.

To extend established concepts of fair resource allocation in wired networks to wireless networks, wired model assumptions must be adapted to be relevant for wireless networks as for example, in wireless networks losses due to environmental conditions may occur even in the absence of queueing congestion. Thus fundamental questions of the existence and uniqueness of fair rate allocations must be reconsidered. We treat wireless networks characterized by lossy channels, spatial channel reuse, multiple routes and multiple frequencies. We establish the existence and uniqueness of utility fair and max-min fair solutions and that, as loss rates decrease, fair allocations converge to the loss-less ones.

There are three dominant contributing factors that distort short tandem repeat profile measurements, two of which, stutter and variations in the allelic peak heights, have been described extensively. Here we characterise the remaining component, baseline noise. A probabilistic characterisation of the non-allelic noise peaks is not only inherently useful for statistical inference but is also significant for establishing a detection threshold. We do this by analysing the data from 643 single person profiles for the Identifiler Plus kit and 303 for the PowerPlex 16 HS kit. This investigation reveals that although the dye colour is a significant factor, it is not sufficient to have a per-dye colour description of the noise. Furthermore, we show that at a per-locus basis, out of the Gaussian, log-normal, and gamma distribution classes, baseline noise is best described by log-normal distributions and provide a methodology for setting an analytical threshold based on that deduction. In the PowerPlex 16 HS kit, we observe evidence of significant stutter at two repeat units shorter than the allelic peak, which has implications for the definition of baseline noise and signal interpretation. In general, the DNA input mass has an influence on the noise distribution. Thus, it is advisable to study noise and, consequently, to infer quantities like the analytical threshold from data with a DNA input mass comparable to the DNA input mass of the samples to be analysed.

For forensic purposes, short tandem repeat allele signals are used as DNA fingerprints. The interpretation of signals measured from samples has traditionally been conducted by applying thresholding. More quantitative approaches have recently been developed, but not for the purposes of identifying an appropriate signal model. By analyzing data from 643 single person samples, we develop such a signal model. Three standard classes of two-parameter distributions, one symmetric (normal) and two right-skewed (gamma and log-normal), were investigated for their ability to adequately describe the data. Our analysis suggests that additive noise is well modeled via the log-normal distribution class and that variability in peak heights is well described by the gamma distribution class. This is a crucial step towards the development of principled techniques for mixed sample signal deconvolution., United States. Dept. of Justice. National Institute of Justice (2012-DN-BX-K050)

In this paper we establish the log-convexity of the rate region in 802.11 WLANs. This generalises previous results for Aloha networks and has immediate implications for optimisation based approaches to the analysis and design of 802.11 wireless networks.

A string is sent over a noisy channel that erases some of its characters. Knowing the statistical properties of the string's source and which characters were erased, a listener that is equipped with an ability to test the veracity of a string, one string at a time, wishes to fill in the missing pieces. Here we characterize the influence of the stochastic properties of both the string's source and the noise on the channel on the distribution of the number of attempts required to identify the string, its guesswork. In particular, we establish that the average noise on the channel is not a determining factor for the average guesswork and illustrate simple settings where one recipient with, on average, a better channel than another recipient, has higher average guesswork. These results stand in contrast to those for the capacity of wiretap channels and suggest the use of techniques such as friendly jamming with pseudo-random sequences to exploit this guesswork behavior., United States. Dept. of Defense (Air Force Contract FA8721-05-C-0002)

A finite load, large buffer model for the WLAN medium access protocol IEEE 802.11 is developed that gives throughput and delay predictions. This enables us to investigate the impact of buffering on resource allocation. In the presence of heterogeneous loads, 802.11 do not allocate transmission opportunities equally. It is shown that increased buffering can help this inequity, but only at the expense of possibly significantly increased delays

We introduce a tractable analytic model of throughput performance for general 802.11 multi-hop multi-radio networks subject to finite loads. The model’s accuracy and utility is illustrated by comparison with simulation.

Consider the situation where a word is chosen probabilistically from a finite list. If an attacker knows the list and can inquire about each word in turn, then selecting the word via the uniform distribution maximizes the attacker's difficulty, its Guesswork, in identifying the chosen word. It is tempting to use this property in cryptanalysis of computationally secure ciphers by assuming coded words are drawn from a source's typical set and so, for all intents and purposes, uniformly distributed within it. By applying recent results on Guesswork, for i.i.d. sources it is this equipartition ansatz that we investigate here. In particular, we demonstrate that the expected Guesswork for a source conditioned to create words in the typical set grows, with word length, at a lower exponential rate than that of the uniform approximation, suggesting use of the approximation is ill-advised., Comment: ISIT 2013, with extended proof

We present a new information-theoretic definition and associated results, based on list decoding in a source coding setting. We begin by presenting list-source codes, which naturally map a key length (entropy) to list size. We then show that such codes can be analyzed in the context of a novel information-theoretic metric, \epsilon-symbol secrecy, that encompasses both the one-time pad and traditional rate-based asymptotic metrics, but, like most cryptographic constructs, can be applied in non-asymptotic settings. We derive fundamental bounds for \epsilon-symbol secrecy and demonstrate how these bounds can be achieved with MDS codes when the source is uniformly distributed. We discuss applications and implementation issues of our codes., Comment: Allerton 2012, 8 pages

The robustness to noise of the 802.11b/g 5.5 Mb/s and 11 Mb/s rates must be investigated experimentally as they cannot be predicted theoretically. In this paper we report on detailed outdoor and indoor measurements that lead us to the surprising conclusion that the 11 Mb/s 802.11g rate experiences fewer packet losses than the 6 Mb/s 802.11g rate at any given (symbol) SNR. This occurs due to the combination of modulation and physical layer coding schemes used by these rates and has serious implications for rate control algorithms. The practical implications of this, factoring in the interaction between packet loss and 802.11 MAC retries, is that 6 Mb/s is effectively redundant as a packet transmission rate if the 11 Mb/s rate is available.

Colouring a graph with its chromatic number of colours is known to be NP-hard. Identifying an algorithm in which decisions are made locally with no information about the graph's global structure is particularly challenging. In this article we analyse the complexity of a decentralised colouring algorithm that has recently been proposed for channel selection in wireless computer networks.

Analysis of the 802.11 CSMA/CA mechanism has received considerable attention recently. Bianchi presented an analytic model under a saturated traffic assumption. Bianchi's model is accurate, but typical network conditions are nonsaturated and heterogeneous. We present an extension of his model to a nonsaturated environment. The model's predictions, validated against simulation, accurately capture many interesting features of nonsaturated operation. For example, the model predicts that peak throughput occurs prior to saturation. Our model allows stations to have different traffic arrival rates, enabling us to address the question of fairness between competing flows. Although we use a specific arrival process, it encompasses a wide range of interesting traffic types including, in particular, VoIP.

Analysis of the 802.11 CSMA/CA mechanism has received considerable attention recently. Bianchi [3] presents an analytic model under a saturated traffic assumption. Bianchi's model is accurate, but typical network conditions are non-saturated heterogenous. We present an extension of his model to a non-saturated environment. The model's predictions, validated against simulation, accurately capture many interesting features of non-saturated operation. For example, the model predicts that peak throughput occurs prior to saturation. Our model also allows stations to have different traffic arrival rates, enabling us to address the question of fairness between competing flows.

In this paper we introduce a tractable analytic model of throughput performance for general 802.11 multi-hop networks. We use this model to explore a number of fairness issues in 802.11 multi-hop networks that have a significant impact on performance and capacity for realistic traffic. Schemes using the functionality provided by 802.11e are proposed to mitigate this unfairness and their efficacy demonstrated.

In this paper we consider voice calls in 802.11e infrastructure mode networks. We demonstrate that in standard 802.11 WLANs it is throttling of voice traffic at the access point that is the primary limitation on voice call capacity. We demonstrate that a straightforward 802.11e prioritisation strategy avoids this throttling and yields close to the theoretical maximum call capacity.