Sleep architecture in neonatal and infantile onset epilepsies in the first six months of life: A scoping review

AIM: Epilepsy occurs in approximately 80 per 100,000 infants in the first year of life, ranging in severity from self-limited and likely to spontaneously resolve, to severe developmental and epileptic encephalopathies. Sleep plays a key role in early brain development and the reciprocal relationship between sleep and seizures is not yet fully understood, particularly in young children. We conducted a Scoping Review to synthesise current knowledge of sleep architecture in neonates and infants with epilepsy. METHODS: Peer-reviewed publications from 2005 to 2022 describing sleep architecture in infants up to six months of age with unprovoked seizures were included. The analysis set was derived from EMBASE, Web of Science and PubMED using key terms “sleep, epilepsy and infant” and related descriptors. Inclusion criteria were prospectively described in a Scoping Review protocol. Sleep architecture was assessed as macro- and micro-structural elements. RESULTS: 21 publications were included in the qualitative analysis. In self-limited familial and genetic epilepsy, sleep macrostructure was generally preserved. In DEEs and in epileptic encephalopathies of genetic or structural aetiology, sleep architecture was significantly disrupted. INTERPRETATION: Early identification of infants with epilepsy is important to ensure early and effective treatment. In the DEE spectrum, sleep architecture is significantly impacted, and abnormal sleep architecture may be associated with compromised developmental outcome. Further research is needed to identify the sequence of events in abnormal brain development, epilepsy and sleep disruption and potentially help to predict the course of epilepsy towards a self-limited epilepsy versus a DEE

Zone-based Federated Learning for Mobile Sensing Data

Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy. We propose Zone-based Federated Learning (ZoneFL) to address these requirements. ZoneFL divides the physical space into geographical zones mapped to a mobile-edge-cloud system architecture for good model accuracy and scalability. Each zone has a federated training model, called a zone model, which adapts well to data and behaviors of users in that zone. Benefiting from the FL design, the user data privacy is protected during the ZoneFL training. We propose two novel zone-based federated training algorithms to optimize zone models to user mobility behavior: Zone Merge and Split (ZMS) and Zone Gradient Diffusion (ZGD). ZMS optimizes zone models by adapting the zone geographical partitions through merging of neighboring zones or splitting of large zones into smaller ones. Different from ZMS, ZGD maintains fixed zones and optimizes a zone model by incorporating the gradients derived from neighboring zones' data. ZGD uses a self-attention mechanism to dynamically control the impact of one zone on its neighbors. Extensive analysis and experimental results demonstrate that ZoneFL significantly outperforms traditional FL in two models for heart rate prediction and human activity recognition. In addition, we developed a ZoneFL system using Android phones and AWS cloud. The system was used in a heart rate prediction field study with 63 users for 4 months, and we demonstrated the feasibility of ZoneFL in real-life

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

This paper presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system that supports deep learning models for mobile apps. FLSys is a key component toward creating an open ecosystem of FL models and apps that use these models. FLSys is designed to work with mobile sensing data collected on smart phones, balance model performance with resource consumption on the phones, tolerate phone communication failures, and achieve scalability in the cloud. In FLSys, different DL models with different FL aggregation methods in the cloud can be trained and accessed concurrently by different apps. Furthermore, FLSys provides a common API for third-party app developers to train FL models. FLSys is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) in the wild FL model. HAR sensing data was collected in two areas from the phones of 100+ college students during a five-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed (non-IID) data that affects FL model training in the wild. A sentiment analysis (SA) model is used to demonstrate how FLSys effectively supports concurrent models, and it uses a dataset with 46,000+ tweets from 436 users. We conducted extensive experiments on Android phones and emulators showing that FLSys achieves good model utility and practical system performance.Comment: The first two authors contributed equally to this wor

Long Lived Fourth Generation and the Higgs

A chiral fourth generation is a simple and well motivated extension of the standard model, and has important consequences for Higgs phenomenology. Here we consider a scenario where the fourth generation neutrinos are long lived and have both a Dirac and Majorana mass term. Such neutrinos can be as light as 40 GeV and can be the dominant decay mode of the Higgs boson for Higgs masses below the W-boson threshold. We study the effect of the Majorana mass term on the Higgs branching fractions and reevaluate the Tevatron constraints on the Higgs mass. We discuss the prospects for the LHC to detect the semi-invisible Higgs decays into fourth generation neutrino pairs. Under the assumption that the lightest fourth generation neutrino is stable, it's thermal relic density can be up to 20% of the observed dark matter density in the universe. This is in agreement with current constraints on the spin dependent neutrino-neutron cross section, but can be probed by the next generation of dark matter direct detection experiments.Comment: v1: 19 pages, 5 figures; v2: References added; v3: version to appear in JHE

Toward physical realizations of thermodynamic resource theories

Conventional statistical mechanics describes large systems and averages over many particles or over many trials. But work, heat, and entropy impact the small scales that experimentalists can increasingly control, e.g., in single-molecule experiments. The statistical mechanics of small scales has been quantified with two toolkits developed in quantum information theory: resource theories and one-shot information theory. The field has boomed recently, but the theorems amassed have hardly impacted experiments. Can thermodynamic resource theories be realized experimentally? Via what steps can we shift the theory toward physical realizations? Should we care? I present eleven opportunities in physically realizing thermodynamic resource theories.Comment: Publication information added. Cosmetic change

Effects of taper parameters on free spectral range of non-adiabatic tapered optical fibers for sensing applications

We investigated the effects of taper parameters on the free spectral range of transmission spectrum of non-adiabatic tapers. From experimental results, the optimum profile for the tapered fiber in terms of taper angle, waist diameter, and length are 11.500 mrad, 10 μm, and 20 mm, respectively

Electrical detection of magnetic skyrmions by non-collinear magnetoresistance

Magnetic skyrmions are localised non-collinear spin textures with high potential for future spintronic applications. Skyrmion phases have been discovered in a number of materials and a focus of current research is the preparation, detection, and manipulation of individual skyrmions for an implementation in devices. Local experimental characterization of skyrmions has been performed by, e.g., Lorentz microscopy or atomic-scale tunnel magnetoresistance measurements using spin-polarised scanning tunneling microscopy. Here, we report on a drastic change of the differential tunnel conductance for magnetic skyrmions arising from their non-collinearity: mixing between the spin channels locally alters the electronic structure, making a skyrmion electronically distinct from its ferromagnetic environment. We propose this non-collinear magnetoresistance (NCMR) as a reliable all-electrical detection scheme for skyrmions with an easy implementation into device architectures

Transformation of spin information into large electrical signals via carbon nanotubes

Spin electronics (spintronics) exploits the magnetic nature of the electron, and is commercially exploited in the spin valves of disc-drive read heads. There is currently widespread interest in using industrially relevant semiconductors in new types of spintronic devices based on the manipulation of spins injected into a semiconducting channel between a spin-polarized source and drain. However, the transformation of spin information into large electrical signals is limited by spin relaxation such that the magnetoresistive signals are below 1%. We overcome this long standing problem in spintronics by demonstrating large magnetoresistance effects of 61% at 5 K in devices where the non-magnetic channel is a multiwall carbon nanotube that spans a 1.5 micron gap between epitaxial electrodes of the highly spin polarized manganite La0.7Sr0.3MnO3. This improvement arises because the spin lifetime in nanotubes is long due the small spin-orbit coupling of carbon, because the high nanotube Fermi velocity permits the carrier dwell time to not significantly exceed this spin lifetime, because the manganite remains highly spin polarized up to the manganite-nanotube interface, and because the interfacial barrier is of an appropriate height. We support these latter statements regarding the interface using density functional theory calculations. The success of our experiments with such chemically and geometrically different materials should inspire adventure in materials selection for some future spintronicsComment: Content highly modified. New title, text, conclusions, figures and references. New author include

Laser-induced etching of few-layer graphene synthesized by Rapid-Chemical Vapour Deposition on Cu thin films

The outstanding electrical and mechanical properties of graphene make it very attractive for several applications, Nanoelectronics above all. However a reproducible and non destructive way to produce high quality, large-scale area, single layer graphene sheets is still lacking. Chemical Vapour Deposition of graphene on Cu catalytic thin films represents a promising method to reach this goal, because of the low temperatures (T < 900 Celsius degrees) involved during the process and of the theoretically expected monolayer self-limiting growth. On the contrary such self-limiting growth is not commonly observed in experiments, thus making the development of techniques allowing for a better control of graphene growth highly desirable. Here we report about the local ablation effect, arising in Raman analysis, due to the heat transfer induced by the laser incident beam onto the graphene sample.Comment: v1:9 pages, 8 figures, submitted to SpringerPlus; v2: 11 pages, PDFLaTeX, 9 figures, revised peer-reviewed version resubmitted to SpringerPlus; 1 figure added, figure 1 and 4 replaced,typos corrected, "Results and discussion" section significantly extended to better explain etching mechanism and features of Raman spectra, references adde