pinecone/core-2020-05-10-deduplication huggingface dataset:
Accelerated mapping of magnetic susceptibility using 3D planes-on-a-paddlewheel (POP) EPI at ultra-high field strength

With the advent of ultra-high field MRI scanners in clinical research, susceptibility based MRI has recently gained increasing interest because of its potential to assess subtle tissue changes underlying neurological pathologies/disorders. Conventional, but rather slow, three-dimensional (3D) spoiled gradient-echo (GRE) sequences are typically employed to assess the susceptibility of tissue. 3D echo-planar imaging (EPI) represents a fast alternative but generally comes with echo-time restrictions, geometrical distortions and signal dropouts that can become severe at ultra-high fields. In this work we assess quantitative susceptibility mapping (QSM) at 7T using non-Cartesian 3D EPI with a planes-on-a-paddlewheel (POP) trajectory, which is created by rotating a standard EPI readout train around its own phase encoding axis. We show that the threefold accelerated non-Cartesian 3D POP EPI sequence enables very fast, whole brain susceptibility mapping at an isotropic resolution of 1mm and that the high image quality has sufficient signal-to-noise ratio in the phase data for reliable QSM processing. The susceptibility maps obtained were comparable with regard to QSM values and geometric distortions to those calculated from a conventional 4min 3D GRE scan using the same QSM processing pipeline
Sensitivity of diffusion MRI to perilesional reactive astrogliosis in focal ischemia

Reactive astrogliosis is a response to injury in the central nervous system that plays an essential role in inflammation and tissue repair. It is characterized by hypertrophy of astrocytes, alterations in astrocyte gene expression and astrocyte proliferation. Reactive astrogliosis occurs in multiple neuropathologies, including stroke, traumatic brain injury and Alzheimer's disease, and it has been proposed as a possible source of the changes in diffusion magnetic resonance imaging (dMRI) metrics observed with these diseases. In this study, the sensitivity of dMRI to reactive astrogliosis was tested in an animal model of focal acute and subacute ischemia induced by the vasoconstricting peptide, endothelin-1. Reactive astrogliosis in perilesional cortex was quantified by calculating the astrocyte surface density as determined with a glial fibrillary acidic protein (GFAP) antibody, whereas perilesional diffusion changes were measured in vivo with diffusional kurtosis imaging. We found substantial changes in the surface density of GFAP-positive astrocyte processes and modest changes in dMRI metrics in the perilesional motor cortex following stroke. Although there are time point-specific correlations between dMRI and histological measures, there is no definitive evidence for a causal relationship.postprin
Tensor estimation for double-pulsed diffusional kurtosis imaging

Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.postprin
Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the ‘White Matter Modeling Challenge’ during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future
Clinical evaluation of time-of-flight MR angiography with sparse undersampling and iterative reconstruction for cerebral aneurysms

Compressed sensing (CS) MRI has just been introduced to research areas as an innovative approach to accelerate MRI. CS is expected to achieve higher k‐space undersampling by exploiting the underlying sparsity in an appropriate transform domain. MR angiography (MRA) provides high spatial resolution information on arteries; however, a relatively long acquisition time is necessary to cover a wide volume. Reduction of acquisition time by CS for time‐of‐flight (TOF) MR angiography (Sparse‐TOF) is beneficial in clinical examinations; therefore, the clinical validity of Sparse‐TOF needs to be investigated. The aim of this study was to compare the diagnostic capability of TOF MRA between parallel imaging (PI)‐TOF with an acceleration factor of 3 (annotated as 3×) and Sparse‐TOF (3× and 5×) in patients with cerebral aneurysms. PI‐TOF (3×) and Sparse‐TOF (3× and 5×) imaging were performed in 20 patients using a 3 T MRI system. Aneurysms in PI‐TOF (3×) and Sparse‐TOF (3× and 5×) were blindly rated as visible or scarcely visible by neuroradiologists. The neck, height and width of aneurysms were also measured. Twenty‐six aneurysms were visualized and rated as visible in PI‐TOF (3×) and Sparse‐TOF (3× and 5×), with excellent agreement between two raters. No significant differences were found in measured neck, height or width of aneurysms among them. Sparse‐TOF (3× and 5×) were acquired and reconstructed within 6 min, and cerebral aneurysms were visible in both of them with equivalent quality to PI‐TOF (3×). Sparse‐TOF (5×) is a good alternative to PI‐TOF (3×) to visualize cerebral aneurysms
Metabolic differences between primary and recurrent human brain tumors: a 1H NMR spectroscopic investigation

High-resolution proton magnetic resonance spectroscopy was performed on tissue specimens from 33 patients with astrocytic tumors (22 astrocytomas, 11 glioblastomas) and 13 patients with meningiomas. For all patients, samples of primary tumors and their first recurrences were examined. Increased anaplasia, with respect to malignant transformation, resulting in a higher malignancy grade, was present in 11 recurrences of 22 astrocytoma patients. Spectroscopic features of tumor types, as determined on samples of the primary occurrences, were in good agreement with previous studies. Compared with the respective primary astrocytomas, characteristic features of glioblastomas were significantly increased concentrations of alanine (Ala) (pŒ0.005), increased metabolite ratios of glycine (Gly)/total creatine (tCr) (pŒ0.0001) and glutamate (Glu)/glutamine (Gln) (pŒ0.004). Meningiomas showed increased Ala (pŒ0.02) and metabolite ratios [Gly, total choline (tCho), Ala] over tCr (pŒ0.001) relative to astrocytomas, and N-acetylaspartate and myo-inositol were absent. Metabolic changes of an evolving tumor were observed in recurrent astrocytomas: owing to their consecutive assessments, more indicators of malignant degeneration were detected in astrocytoma recurrences (e.g. Gly, pŒ0.029; tCho, pŒ0.034; Glu, pŒ0.015; tCho/tCr, pŒ0.001) in contrast to the comparison of primary astrocytomas with primary glioblastomas. The present investigation demonstrated a correlation of the tCho-signal with tumor progression. Significantly elevated concentrations of Ala (pŒ0.037) and Glu (pŒ0.003) and metabolite ratio tCho/tCr (pŒ0.005) were even found in recurrent low-grade astrocytomas with unchanged histopathological grading (nŒ11). This may be related to an early stage of malignant transformation, not yet detectable morphologically, and emphasizes the high sensitivity of 1H NMR spectroscopy in elucidating characteristics of brain tumor metabolism
A hybrid association rule mining approach for characterizing network traffic behaviour

Understanding network traffic behaviour is crucial for managing and securing computer networks. One important technique is to mine frequent patterns or association rules from analysed traffic data. On the one hand, association rule mining usually generates a huge number of patterns and rules, many of them meaningless or user-unwanted; on the other hand, association rule mining can miss some necessary knowledge if it does not consider the hierarchy relationships in the network traffic data. Aiming to address such issues, this paper proposes a hybrid association rule mining method for characterizing network traffic behaviour. Rather than frequent patterns, the proposed method generates non-similar closed frequent patterns from network traffic data, which can significantly reduce the number of patterns. This method also proposes to derive new attributes from the original data to discover novel knowledge according to hierarchy relationships in network traffic data and user interests. Experiments performed on real network traffic data show that the proposed method is promising and can be used in real applications. Copyright2013 John Wiley & Sons, Ltd
Multi-granular, multi-purpose and multi-Gb/s monitoring on off-the-shelf systems

This is the accepted version of the following article: [Moreno, V., Santiago del Río, P. M., Ramos, J., Muelas, D., García-Dorado, J. L., Gomez-Arribas, F. J. and Aracil, J. (2014), Multi-granular, multi-purpose and multi-Gb/s monitoring on off-the-shelf systems. Int. J. Network Mgmt., 24: 221–234. doi: 10.1002/nem.1861, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/nem.1861/abstractAs an attempt to make network managers’ life easier, we present M3Omon, a system architecture that helps
to develop monitoring applications and perform network diagnosis. M3Omon behaves as an intermediate
layer between the traffic and monitoring applications that provides advanced features, high performance and
low cost. Such advanced features leverage a multi-granular and multi-purpose approach to the monitoring
problem. Multi-granular monitoring gives answer to tasks that use traffic aggregates to identify an event,
and requires either flow records or packet data or even both to understand it and, eventually, take the
convenient countermeasures. M3Omon provides a simple API to access traffic simultaneously at several different
granularities—i.e., packet-level, flow-level and aggregate statistics. The multi-purposed design of M3Omon
allows not only performing tasks in parallel that are specifically targeted to different traffic-related purposes
(e.g., traffic classification and intrusion detection) but also sharing granularities between applications—e.g.,
several concurrent applications fed from flow records that are provided by M3Omon. Finally, the low-cost
characteristic is brought by off-the-shelf systems (the combination of open-source software and commodity
hardware) and the high performance is achieved thanks to modifications in the standard NIC driver, low-level
hardware interaction, efficient memory management and programming optimization
On Detecting Termination in Cognitive Radio Networks

The cognitive radio networks are an emerging wireless communication and
computing paradigm. The cognitive radio nodes execute computations on multiple
heterogeneous channels in the absence of licensed users (a.k.a. primary users)
of those bands. Termination detection is a fundamental and non-trivial problem
in distributed systems. In this paper, we propose a termination detection
protocol for multi-hop cognitive radio networks where the cognitive radio nodes
are allowed to tune to channels that are not currently occupied by primary
users and to move to different locations during the protocol execution. The
proposed protocol applies credit distribution and aggregation approach and
maintains a new kind of logical structure, called the virtual tree-like
structure. The virtual tree-like structure helps in decreasing the latency
involved in announcing termination. Unlike conventional tree structures, the
virtual tree-like structure does not require a specific node to act as the root
node that has to stay involved in the computation until termination
announcement; hence, the root node may become idle soon after finishing its
computation. Also, the protocol is able to detect the presence of licensed
users and announce strong or weak termination, whichever is possible.Comment: Accepted in Wiley International Journal of Network Management
  (Wiley-IJNM) 2014, 12 figure
Business-driven policy optimization for service management

The performance of services offered by network operators has a direct impact on its reputation, on its revenue due to new customer subscriptions, and also on penalties that can apply when services are not provided to an acceptable quality level. Previous research on business-oriented network and service optimization has mainly focused on optimizing individual business indicators, such as profit and revenue, in isolation without analyzing the effect on network configurations and the subsequent impact on other indicators. Given that different business objectives are usually incompatible, a single network configuration cannot optimize them simultaneously. Determining the configuration and the associated trade-offs that satisfy multiple objectives is a complex task. This paper addresses this gap and presents a framework that derives policy configurations that optimize the business value of the network infrastructure. We describe a methodology to quantify\ud
business functions considering the dynamics of network events, the dynamics of end-user service usage, the nature of the business indicators, and their relationships with the underlying control methods. The proposed approach addresses the complexity of the target problem through a surrogate-based optimization approach properly tailored to match our application domain needs. We evaluate the effectiveness of the proposed approach through experimentation in a simulation environment we developed over OPNETPeer ReviewedPostprint (author’s final draft
Detecting malicious activities with user-agent-based profiles

Hypertext transfer protocol (HTTP) has become the main protocol to carry out malicious activities. Attackers typically use HTTP for communication with command-and-control servers, click fraud, phishing and other malicious activities, as they can easily hide among the large amount of benign HTTP traffic. The user-agent (UA) field in the HTTP header carries information on the application, operating system (OS), device, and so on, and adversaries fake UA strings as a way to evade detection. Motivated by this, we propose a novel grammar-guided UA string classification method in HTTP flows. We leverage the fact that a number of ‘standard' applications, such as web browsers and iOS mobile apps, have well-defined syntaxes that can be specified using context-free grammars, and we extract OS, device and other relevant information from them. We develop association heuristics to classify UA strings that are generated by ‘non-standard' applications that do not contain OS or device information. We provide a proof-of-concept system that demonstrates how our approach can be used to identify malicious applications that generate fake UA strings to engage in fraudulent activitie
Performance evaluation of legacy QCN for multicast and multiple unicast traffic transmission

International audienceA multicast congestion control scheme is an interesting feature to control group communication applications such as teleconferencing tools and information dissemination services. This paper addresses a comparison between multiple unicast and multicast traffic congestion control for Carrier Ethernet. In this work, we proposed to study the Quantized Congestion Notification (QCN), which is a Layer 2 congestion control scheme, in the case of multicast traffic and multiple unicast traffic. Indeed, the QCN has recently been standardized as the IEEE 802.1Qau Ethernet Congestion Notification standard. This scheme is evaluated through simulation experiments, which are implemented by the OMNeT++ framework. This paper evaluates the Reaction Point (RP) start time congestion detection, feedback rate, loss rate, stability, fairness and scalability performance of the QCN for mul-ticast traffic transmission and multiple unicast traffic transmission. This paper also draws a parallel between QCN for multicast traffic transmission and that for multiple unicast traffic transmission. Despite the benefit of integrating the multi-cast traffic, results show that performance could degrade when the network scales up. The evaluation results also show that it is probable that the feedback implo-sion problem caused by the bottlenecks could be solved if we choose to set the queue parameter Qeq threshold value at a high value, 3/4 of the queue capacity for instance
Technological investment games among wireless telecommunications service providers

International audienceWith the development of new technologies in a competitive context, infrastructure investment and licence purchase as well as existing technology maintenance are crucial questions for current and emerging operators. This paper presents a three-level game analyzing this problem. At the highest level, the operators decide on which technologies to invest, given that some may already own licences or infrastructures. We limit ourselves to the realistic case where technologies are 3G, WiFi and WiMAX. At the intermediate level, with that set of operated technologies fixed, operators determine their service price. Finally, at the lowest level, customers choose their provider depending on the best combination of price and available quality of service. At each level, the best decision of actors depends on the actions of others, the interactions hence requiring to be studied as a (noncooperative) game. The model is analyzed by backward induction, meaning that decisions at a level depend on the equilibria reached at the lower levels. Different real-life cost scenarios are studied. Our model aims at helping both the operators to make their final decision on technological investments, and the regulator to determine a proper licence fee range for a better competition among providers
Isomorphisms of the De Bruijn digraph and free-space optical networks

International audienceThe de Bruijn digraph B(d, D) has degree d, diameter D, dD vertices, and dD+1 arcs. It is usually defined by words of size D on an alphabet of cardinality d, through a cyclic left-shift permutation on the words, after which the rightmost symbol is changed. In this paper, we show that any digraph defined on words of a given size, through an arbitrary permutation on the alphabet and an arbitrary permutation on the word indices, is isomorphic to the de Bruijn digraph, provided that this latter permutation is cyclic. We use this result to improve from O(dD+1) to the number of lenses required for the implementation of B(d, D) by the Optical Transpose Interconnection System proposed by Marsden et al. [Opt Lett 18 (1993), 1083-1085]
Multifacility ordered median problems on networks: a further analysis

In this paper, we address the ordered p-median problem, which includes as special cases most of the classical multifacility location problems discussed in the literature. Finite dominating sets (FDS) are known for particular instances of this problem: p-median, p-center, and p-centdian. We find an FDS for the ordered p-median problem. This set allows us to gain a better insight into the general FDS structure of network location problems. This FDS is later used to present the first polynomial time algorithm for p-facility ordered median problems on tree networks
Metaheuristics for the Vehicle Routing Problem with Loading Constraints

We consider a combination of the capacitated vehicle routing problem and a class of additional loading constraints involving a parallel machine scheduling problem. The work is motivated by a real-world transportation problem occurring to a wood-products retailer, which delivers its products to a number of customers in a specific region. We solve the problem by means of two different metaheuristics algorithms: a Tabu Search and an Ant Colony Optimization. Extensive computational results are given for both algorithms, on instances derived from the vehicle routing literature and on real-world instances
A tabu search heuristic for the vehicle routing problem with two-dimensional loading constraints

This article addresses the well-known Capacitated Vehicle Routing Problem (CVRP), in the special case where the demand of a customer consists of a certain number of two-dimensional weighted items. The problem calls for the minimization of the cost of transportation needed for the delivery of the goods demanded by the customers, and carried out by a fleet of vehicles based at a central depot. In order to accommodate all items on the vehicles, a feasibility check of the two-dimensional packing (2L) must be executed on each vehicle. The overall problem, denoted as 2L-CVRP, is NP-hard and particularly difficult to solve in practice. We propose a Tabu Search algorithm, in which the loading component of the problem is solved through heuristics, lower bounds, and a truncated branch-and-bound procedure. The effectiveness of the algorithm is demonstrated through extensivecomputational experiments