MultiIoT: Towards Large-scale Multisensory Learning for the Internet of Things

The Internet of Things (IoT), the network integrating billions of smart physical devices embedded with sensors, software, and communication technologies for the purpose of connecting and exchanging data with other devices and systems, is a critical and rapidly expanding component of our modern world. The IoT ecosystem provides a rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, video, and audio for prediction tasks involving the pose, gaze, activities, and gestures of humans as well as the touch, contact, pose, 3D of physical objects. Machine learning presents a rich opportunity to automatically process IoT data at scale, enabling efficient inference for impact in understanding human wellbeing, controlling physical devices, and interconnecting smart cities. To develop machine learning technologies for IoT, this paper proposes MultiIoT, the most expansive IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 tasks. MultiIoT introduces unique challenges involving (1) learning from many sensory modalities, (2) fine-grained interactions across long temporal ranges, and (3) extreme heterogeneity due to unique structure and noise topologies in real-world sensors. We also release a set of strong modeling baselines, spanning modality and task-specific methods to multisensory and multitask models to encourage future research in multisensory representation learning for IoT

IoT-LM: Large Multisensory Language Models for the Internet of Things

The Internet of Things (IoT) network integrating billions of smart physical devices embedded with sensors, software, and communication technologies is a critical and rapidly expanding component of our modern world. The IoT ecosystem provides a rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio to recognize the states of humans and physical objects. Machine learning presents a rich opportunity to automatically process IoT data at scale, enabling efficient inference for understanding human wellbeing, controlling physical devices, and interconnecting smart cities. To realize this potential, we introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem. IoT-LM is enabled by two technical contributions: the first is MultiIoT, the most expansive unified IoT dataset to date, encompassing over 1.15 million samples from 12 modalities and 8 tasks prepared for multisensory pre-training and instruction-tuning. The second is a new multisensory multitask adapter layer to condition pre-trained large language models on multisensory IoT data. Not only does IoT-LM yield substantial improvements on 8 supervised IoT classification tasks, but it also demonstrates new interactive question-answering, reasoning, and dialog capabilities conditioned on IoT sensors. We release IoT-LM's data sources and new multisensory language modeling framework.Comment: arXiv admin note: text overlap with arXiv:2311.0621

How is the local-scale gravitational instability influenced by the surrounding large-scale structure formation?

We develop the formalism to investigate the relation between the evolution of the large-scale (quasi) linear structure and that of the small-scale nonlinear structure in Newtonian cosmology within the Lagrangian framework. In doing so, we first derive the standard Friedmann expansion law using the averaging procedure over the present horizon scale. Then the large-scale (quasi) linear flow is defined by averaging the full trajectory field over a large-scale domain, but much smaller than the horizon scale. The rest of the full trajectory field is supposed to describe small-scale nonlinear dynamics. We obtain the evolution equations for the large-scale and small-scale parts of the trajectory field. These are coupled to each other in most general situations. It is shown that if the shear deformation of fluid elements is ignored in the averaged large-scale dynamics, the small-scale dynamics is described by Newtonian dynamics in an effective Friedmann-Robertson-Walker (FRW) background with a local scale factor. The local scale factor is defined by the sum of the global scale factor and the expansion deformation of the averaged large-scale displacement field. This means that the evolution of small-scale fluctuations is influenced by the surrounding large-scale structure through the modification of FRW scale factor. The effect might play an important role in the structure formation scenario. Furthermore, it is argued that the so-called {\it optimized} or {\it truncated} Lagrangian perturbation theory is a good approximation in investigating the large-scale structure formation up to the quasi nonlinear regime, even when the small-scale fluctuations are in the non-linear regime.Comment: 15pages, Accepted for publication in Gravitation and General Relativit

Lithium and GSK3-β promoter gene variants influence white matter microstructure in bipolar disorder

Lithium is the mainstay for the treatment of bipolar disorder (BD) and inhibits glycogen synthase kinase 3-β (GSK3-β). The less active GSK3-β promoter gene variants have been associated with less detrimental clinical features of BD. GSK3-β gene variants and lithium can influence brain gray matter structure in psychiatric conditions. Diffusion tensor imaging (DTI) measures of white matter (WM) integrity showed widespred disruption of WM structure in BD. In a sample of 70 patients affected by a major depressive episode in course of BD, we investigated the effect of ongoing long-term lithium treatment and GSK3-β promoter rs334558 polymorphism on WM microstructure, using DTI and tract-based spatial statistics with threshold-free cluster enhancement. We report that the less active GSK3-β rs334558*C gene-promoter variants, and the long-term administration of the GSK3-β inhibitor lithium, were associated with increases of DTI measures of axial diffusivity (AD) in several WM fiber tracts, including corpus callosum, forceps major, anterior and posterior cingulum bundle (bilaterally including its hippocampal part), left superior and inferior longitudinal fasciculus, left inferior fronto-occipital fasciculus, left posterior thalamic radiation, bilateral superior and posterior corona radiata, and bilateral corticospinal tract. AD reflects the integrity of axons and myelin sheaths. We suggest that GSK3-β inhibition and lithium could counteract the detrimental influences of BD on WM structure, with specific benefits resulting from effects on specific WM tracts contributing to the functional integrity of the brain and involving interhemispheric, limbic, and large frontal, parietal, and fronto-occipital connections

Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at √s=8 Tev

Peer reviewe

Measurement of Higgs boson production and properties in the WW decay channel with leptonic final states

Peer reviewe

Study of double parton scattering using W+2-jet events in proton-proton collisions at √s=7 TeV

Peer reviewe

Measurements of the tt¯ charge asymmetry using the dilepton decay channel in pp collisions at √s=7 TeV

Peer reviewe

Temporal trends in serum concentrations of polychlorinated dioxins, furans, and PCBs among adult women living in Chapaevsk, Russia: a longitudinal study from 2000 to 2009

<p>Abstract</p> <p>Background</p> <p>The present study assessed the temporal trend in serum concentrations of polychlorinated dibenzo-<it>p</it>-dioxins, dibenzofurans, and biphenyls (PCBs) among residents of a Russian town where levels of these chemicals are elevated due to prior industrial activity.</p> <p>Methods</p> <p>Two serum samples were collected from eight adult women (in 2000 and 2009), and analyzed with gas chromatography-high-resolution mass spectrometry.</p> <p>Results</p> <p>The average total toxic equivalency (TEQ) decreased by 30% (from 36 to 25 pg/g lipid), and the average sum of PCB congeners decreased by 19% (from 291 to 211 ng/g lipid). Total TEQs decreased for seven of the eight women, and the sum of PCBs decreased for six of eight women. During this nine year period, larger decreases in serum TEQs and PCBs were found in women with greater increases in body mass index.</p> <p>Conclusions</p> <p>This study provides suggestive evidence that average serum concentrations of dioxins, furans, and PCBs are decreasing over time among residents of this town.</p

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities