Asymptotic theory of microstructured surfaces: An asymptotic theory for waves guided by diffraction gratings or along microstructured surfaces

An effective surface equation, that encapsulates the detail of a microstructure, is developed to model microstructured surfaces. The equations deduced accurately reproduce a key feature of surface wave phenomena, created by periodic geometry, that are commonly called Rayleigh-Bloch waves, but which also go under other names such as Spoof Surface Plasmon Polaritons in photonics. Several illustrative examples are considered and it is shown that the theory extends to similar waves that propagate along gratings. Line source excitation is considered and an implicit long-scale wavelength is identified and compared to full numerical simulations. We also investigate non-periodic situations where a long-scale geometric variation in the structure is introduced and show that localised defect states emerge which the asymptotic theory explains

Altered rich club and frequency-dependent subnetworks organization in mild traumatic brain injury: A MEG resting-state study

Functional brain connectivity networks exhibit “small-world” characteristics and some of these networks follow a “rich-club” organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an “attack strategy” to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model’s hubs would reveal the “true” underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hypersynchronization among rich-club hubs compared to controls in the d band and the d-g1, "-g1, and b-g2 frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from " to g1 frequencies, and underrepresented in left occipital regions in the d-b, d-g1, "-b, and b-g2 frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery frommTBI. Furthermore, the proposed approachmight be used as a validation tool to assess patient recovery

Applying a two-stage Bayesian dynamic model to a short lived species, the anchovy in the Aegean Sea (Eastern Mediterranean). Comparison with an Integrated Catch at Age stock assessment model.

Two different stock assessment models were applied to the North Aegean Sea anchovy stock (Eastern Mediterranean Sea): an Integrated Catch at age Analysis and a Bayesian two-stage biomass based model. Commercial catch data over the period 2000-2008 as well as acoustics and Daily Egg Production Method estimates over the period 2003-2008 were used. Both models results were consistent, indicating that anchovy stock is exploited sustainably in relation to an exploitation rate reference point. Further, the stock biomass appears stable or increasing. However, the limitations in age-composition data, potential problems related to misinterpretation of age readings along with the existence of missing values in the survey data seem to favour the two-stage biomass method, which is based on a simplified age structure.

POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.Comment: The final version of this paper is going to appear in the ACM SIGSAC Conference on Computer and Communications Security (CCS'19), November 11-15, 2019, London, United Kingdo

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction: One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.Methods: We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.Results: Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.Discussion: It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces

Is inflation persistence different in reality?

This study examines the inflation persistence using both online and official price indexes in Argentina, Brazil, China, Japan, Germany, South Africa, the UK and the US, using fractional integration technique. The main results suggest that the degree of persistence, estimated by the long-memory parameter, is smaller when using online price indexes (believed to be a more realistic measure of inflation), mainly in the cases of Argentina, Brazil, China and the UK. Monetary policy implications are discussed.Juncal Cuñado gratefully acknowledges financial support from the Ministerio de Economía y Competitividad (ECO2014-55496). Luis A. Gil-Alana gratefully acknowledges financial support from the Ministerio de Economía y Competitividad (ECO2014-55496).http://www.elsevier.com/locate/ecolet2017-11-30hb2016Economic

Inflation-targeting and inflation volatility : international evidence from the cosine-squared cepstrum

Existing empirical evidence on the effect of inflation-targeting on inflation volatility is, at best, mixed. However, comparing inflation volatility across alternative monetary policy regimes, i.e., pre- and post-inflation-targeting, begs the question. The question is not whether the volatility of inflation has changed, but instead whether the volatility is different than it otherwise would have been. Given this, our paper uses the cosine-squared cepstrum to provide overwhelming international evidence that inflation targeting has indeed reduced inflation volatility in 22 out of the 24 countries considered in our sample of established inflation-targeters, than it would have been the case if the central banks in these countries did not decide to set a target for inflation.http://www.elsevier.com/locate/intecohj2022Economic

An asymptotic theory for waves guided by diffraction gratings or along microstructured surfaces

An effective surface equation, that encapsulates the detail of a microstructure, is developed to model microstructured surfaces. The equations deduced accurately reproduce a key feature of surface wave phenomena, created by periodic geometry, that are commonly called Rayleigh-Bloch waves, but which also go under other names such as Spoof Surface Plasmon Polaritons in photonics. Several illustrative examples are considered and it is shown that the theory extends to similar waves that propagate along gratings. Line source excitation is considered and an implicit long-scale wavelength is identified and compared to full numerical simulations. We also investigate non-periodic situations where a long-scale geometric variation in the structure is introduced and show that localised defect states emerge which the asymptotic theory explains

A haystack full of needles: scalable detection of IoT devices in the wild

Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers --all with sampled network data. In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences

Clust-IT:Clustering-Based Intrusion Detection in IoT Environments

Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS