Multimodal federated learning on IoT data

Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with Internet-of-Things (IoT) devices, local data on clients are generated from different modalities such as sensory, visual, and audio data. Existing federated learning systems only work on local data from a single modality, which limits the scalability of the systems. In this paper, we propose a multimodal and semi-supervised federated learning framework that trains autoencoders to extract shared or correlated representations from different local data modalities on clients. In addition, we propose a multimodal FedAvg algorithm to aggregate local autoencoders trained on different data modalities. We use the learned global autoencoder for a downstream classification task with the help of auxiliary labelled data on the server. We empirically evaluate our framework on different modalities including sensory data, depth camera videos, and RGB camera videos. Our experimental results demonstrate that introducing data from multiple modalities into federated learning can improve its classification performance. In addition, we can use labelled data from only one modality for supervised learning on the server and apply the learned model to testing data from other modalities to achieve decent F1 scores (e.g., with the best performance being higher than 60%), especially when combining contributions from both unimodal clients and multimodal clients

A near-field scanning microwave microscope in a scanning electron microscope: design and challenges

International audienc

STAR: Secret sharing for private threshold aggregation reporting

Threshold aggregation reporting systems promise a practical, privacy-preserving solution for developers to learn how their applications are used in-the-wild. Unfortunately, proposed systems to date prove impractical for wide scale adoption, suffering from a combination of requiring: i) prohibitive trust assumptions; ii) high computation costs; or iii) massive user bases. As a result, adoption of truly-private approaches has been limited to only a small number of enormous (and enormously costly) projects. In this work, we improve the state of private data collection by proposing STAR, a highly efficient, easily deployable system for providing cryptographically-enforced κ-anonymity protections on user data collection. The STAR protocol is easy to implement and cheap to run, all while providing privacy properties similar to, or exceeding the current state-of-the-art. Measurements of our open-source implementation of STAR find that it is 1773x quicker, requires 62.4x less communication, and is 24x cheaper to run than the existing state-of-the-art

Evolution of the Magnetized, Neutrino-Cooled Accretion Disk in the Aftermath of a Black Hole Neutron Star Binary Merger

Black hole-torus systems from compact binary mergers are possible engines for gamma-ray bursts (GRBs). During the early evolution of the post-merger remnant, the state of the torus is determined by a combination of neutrino cooling and magnetically-driven heating processes, so realistic models must include both effects. In this paper, we study the post-merger evolution of a magnetized black hole-neutron star binary system using the Spectral Einstein Code (SpEC) from an initial post-merger state provided by previous numerical relativity simulations. We use a finite-temperature nuclear equation of state and incorporate neutrino effects in a leakage approximation. To achieve the needed accuracy, we introduce improvements to SpEC's implementation of general-relativistic magnetohydrodynamics (MHD), including the use of cubed-sphere multipatch grids and an improved method for dealing with supersonic accretion flows where primitive variable recovery is difficult. We find that a seed magnetic field triggers a sustained source of heating, but its thermal effects are largely cancelled by the accretion and spreading of the torus from MHD-related angular momentum transport. The neutrino luminosity peaks at the start of the simulation, and then drops significantly over the first 20\,ms but in roughly the same way for magnetized and nonmagnetized disks. The heating rate and disk's luminosity decrease much more slowly thereafter. These features of the evolution are insensitive to grid structure and resolution, formulation of the MHD equations, and seed field strength, although turbulent effects are not fully convergedComment: 17 pages, 18 figure

Three-Dimensional Dirac Electrons at the Fermi Energy in Cubic Inverse Perovskites: Ca_3PbO and its Family

The band structure of cubic inverse perovskites, Ca_3PbO and its family, are investigated with the first-principles method. A close observation of the band structure reveals that six equivalent Dirac electrons with a very small mass exist on the line connecting the Gamma- and X-points, and at the symmetrically equivalent points in the Brillouin zone. The discovered Dirac electrons are three-dimensional and remarkably located exactly at the Fermi energy. A tight-binding model describing the low-energy band structure is also constructed and used to discuss the origin of the Dirac electrons in this material. Materials related to Ca_3PbO are also studied, and some design principles for the Dirac electrons in this series of materials are proposed.Comment: 4.2 pages, refined versio

Genetic control of protein, oil and fatty acids content under partial drought stress and late sowing conditions in sunflower (Helianthus annuus)

The purpose of the present study was to map quantitative trait locus (QTLs) associated with percentage of seed protein, oil and fatty acids content under different conditions in a population of recombinant inbred lines (RILs) of sunflower. Three independent field experiments were conducted with well-, partial-irrigated and late-sowing conditions in randomized complete block design with three replications. High significant variation among genotypes is observed for the studied traits in all conditions. Several specific and non-specific QTLs for the aforementioned traits were detected. Under late-sowing condition, a specific QTL of palmitic acid content on linkage group 6 (PAC-LS.6) is located between ORS1233 and SSL66_1 markers. Common chromosomic regions are observed for percentage of seed oil and stearic acid content on linkage group 10 (PSO-PI.10 and SAC-WI.10) and 15 (PSO-PI.15 and SAC-LS.15). Overlapping occurs for QTLs of oleic and linoleic acids content on linkage groups 10, 11 and 16. Seven QTLs associated with palmitic, stearic, oleic and linoleic acids content are identified on linkage group 14. These common QTLs are linked to HPPD homologue, HuCL04260C001. Coincidence of the position for some detected QTLs and candidate genes involved in enzymatic and non-enzymatic antioxidants would be useful for the function of the respective genes in fatty acid stability.Key words: Sunflower, quantitative trait locus, simple sequence repeats, oil content, protein content, fatty acids

The case for retraining of ML models for IoT device identification at the edge

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, using resources available at the edge of the network. In this paper, we compare the accuracy of five different machine learning models (tree-based and neural network-based) for identifying IoT devices by using packet trace data from a large IoT test-bed, showing that all models need to be updated over time to avoid significant degradation in accuracy. In order to effectively update the models, we find that it is necessary to use data gathered from the deployment environment, e.g., the household. We therefore evaluate our approach using hardware resources and data sources representative of those that would be available at the edge of the network, such as in an IoT deployment. We show that updating neural network-based models at the edge is feasible, as they require low computational and memory resources and their structure is amenable to being updated. Our results show that it is possible to achieve device identification and categorization with over 80% and 90% accuracy respectively at the edge

Zest: REST over ZeroMQ

In this paper, we introduce Zest (REST over ZeroMQ), a middleware technology in support of an Internet of Things (IoT). Our work is influenced by the Constrained Application Protocol (CoAP) but emphasises systems that can support fine-grained access control to both resources and audit information, and can provide features such as asynchronous communication patterns between nodes. We achieve this by using a hybrid approach that combines a RESTful architecture with a variant of a publisher/subscriber topology that has enhanced routing support. The primary motivation for Zest is to provide inter-component communications in the Databox, but it is applicable in other contexts where tight control needs to be maintained over permitted communication patterns

Low-Energy Effective Hamiltonian and the Surface States of Ca_3PbO

The band structure of Ca_3PbO, which possesses a three-dimensional massive Dirac electron at the Fermi energy, is investigated in detail. Analysis of the orbital weight distributions on the bands obtained in the first-principles calculation reveals that the bands crossing the Fermi energy originate from the three Pb-p orbitals and three Ca-dx2y2 orbitals. Taking these Pb-p and Ca-dx2y2 orbitals as basis wave functions, a tight-binding model is constructed. With the appropriate choice of the hopping integrals and the strength of the spin-orbit coupling, the constructed model sucessfully captures important features of the band structure around the Fermi energy obtained in the first-principles calculation. By applying the suitable basis transformation and expanding the matrix elements in the series of the momentum measured from a Dirac point, the low-energy effective Hamiltonian of this model is explicitely derived and proved to be a Dirac Hamiltonain. The origin of the mass term is also discussed. It is shown that the spin-orbit coupling and the orbitals other than Pb-p and Ca-dx2y2 orbitals play important roles in making the mass term finite. Finally, the surface band structures of Ca_3PbO for several types of surfaces are investigated using the constructed tight-binding model. We find that there appear nontrivial surface states that cannot be explained as the bulk bands projected on the surface Brillouin zone. The relation to the topological insulator is also discussed.Comment: 11 page

Biodegradation of Phenol by Newly Isolated Phenol-degrading Bacterium Ralstonia sp. Strain PH-S1

A newly phenol-degrading bacterium, identified as Ralstonia sp. strain PH-S1, was isolated from oil-contaminated soil in Khark Island. It was isolated by a multistep enrichment and screening technique on mineral medium (MM) containing 100 mg.l-1 of phenol as the sole source of carbon. The bacterium was able to degrade up to 1100 mg.l-1 of phenol but the cell growth decreased with higher concentrations of phenol. The PH-S1 strain grew well in the pH range of 4 to 9 and in the temperature range of 30 to 40 °C. Different concentrations of NaCl ranging from 10 to 20% on the growth of bacteria was studied and it was found that this strain was able to grow well in 10% NaCl; but, higher concentrations of NaCl decreased the growth of the strain. The laboratory scale results indicated the potential application of the strain in the treatment of low saline industrial wastewaters. However, further investigations are required to confirm the ability of the strain