Showing papers by "École Polytechnique Fédérale de Lausanne published in 2018"

Present and future Köppen-Geiger climate classification maps at 1-km resolution

Abstract: We present new global maps of the Koppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen . Machine-accessible metadata file describing the reported data (ISA-Tab format)

Carbon capture and storage (CCS): the way forward

Abstract: Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Abstract: At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function (which maps input vectors to output vectors) follows the so-called kernel gradient associated with a new object, which we call the Neural Tangent Kernel (NTK). This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK. We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping. Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit.

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Abstract: Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozen 2D materials have been successfully synthesized or exfoliated. Here, we search for 2D materials that can be easily exfoliated from their parent compounds. Starting from 108,423 unique, experimentally known 3D compounds, we identify a subset of 5,619 compounds that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van der Waals density functional theory, validated against experimental structural data and calculated random phase approximation binding energies, further allowed the identification of 1,825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1,036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds, we explore vibrational, electronic, magnetic and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors.

Graph Signal Processing: Overview, Challenges, and Applications

Abstract: Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

Soft Robotic Grippers.

Abstract: Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

Transition Metal Oxides as Electrocatalysts for the Oxygen Evolution Reaction in Alkaline Solutions: An Application-Inspired Renaissance

Abstract: Water splitting is the essential chemical reaction to enable the storage of intermittent energies such as solar and wind in the form of hydrogen fuel. The oxygen evolution reaction (OER) is often considered as the bottleneck in water splitting. Though metal oxides had been reported as OER electrocatalysts more than half a century ago, the recent interest in renewable energy storage has spurred a renaissance of the studies of transition metal oxides as Earth-abundant and nonprecious OER catalysts. This Perspective presents major progress in several key areas of the field such as theoretical understanding, activity trend, in situ and operando characterization, active site determination, and novel materials. A personal overview of the past achievements and future challenges is also provided.

Dissipative Kerr Solitons in Optical Microresonators

Abstract: The development of compact, chip-scale optical frequency comb sources (microcombs) based on parametric frequency conversion in microresonators has seen applications in terabit optical coherent communications, atomic clocks, ultrafast distance measurements, dual-comb spectroscopy, and the calibration of astophysical spectrometers and have enabled the creation of photonic-chip integrated frequency synthesizers. Underlying these recent advances has been the observation of temporal dissipative Kerr solitons in microresonators, which represent self-enforcing, stationary, and localized solutions of a damped, driven, and detuned nonlinear Schrodinger equation, which was first introduced to describe spatial self-organization phenomena. The generation of dissipative Kerr solitons provide a mechanism by which coherent optical combs with bandwidth exceeding one octave can be synthesized and have given rise to a host of phenomena, such as the Stokes soliton, soliton crystals, soliton switching, or dispersive waves. Soliton microcombs are compact, are compatible with wafer-scale processing, operate at low power, can operate with gigahertz to terahertz line spacing, and can enable the implementation of frequency combs in remote and mobile environments outside the laboratory environment, on Earth, airborne, or in outer space.

Fully textured monolithic perovskite/silicon tandem solar cells with 25.2% power conversion efficiency.

Abstract: Tandem devices combining perovskite and silicon solar cells are promising candidates to achieve power conversion efficiencies above 30% at reasonable costs. State-of-the-art monolithic two-terminal perovskite/silicon tandem devices have so far featured silicon bottom cells that are polished on their front side to be compatible with the perovskite fabrication process. This concession leads to higher potential production costs, higher reflection losses and non-ideal light trapping. To tackle this issue, we developed a top cell deposition process that achieves the conformal growth of multiple compounds with controlled optoelectronic properties directly on the micrometre-sized pyramids of textured monocrystalline silicon. Tandem devices featuring a silicon heterojunction cell and a nanocrystalline silicon recombination junction demonstrate a certified steady-state efficiency of 25.2%. Our optical design yields a current density of 19.5 mA cm−2 thanks to the silicon pyramidal texture and suggests a path for the realization of 30% monolithic perovskite/silicon tandem devices.

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

Abstract: The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014-2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V.

Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations

Abstract: We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst-case distribution within this Wasserstein ball. The state-of-the-art methods for solving the resulting distributionally robust optimization problems rely on global optimization techniques, which quickly become computationally excruciating. In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over Wasserstein balls can in fact be reformulated as finite convex programs—in many interesting cases even as tractable linear programs. Leveraging recent measure concentration results, we also show that their solutions enjoy powerful finite-sample performance guarantees. Our theoretical results are exemplified in mean-risk portfolio optimization as well as uncertainty quantification.

Quantitative phase imaging in biomedicine

Abstract: Quantitative phase imaging (QPI) has emerged as a valuable method for investigating cells and tissues. QPI operates on unlabelled specimens and, as such, is complementary to established fluorescence microscopy, exhibiting lower phototoxicity and no photobleaching. As the images represent quantitative maps of optical path length delays introduced by the specimen, QPI provides an objective measure of morphology and dynamics, free of variability due to contrast agents. Owing to the tremendous progress witnessed especially in the past 10–15 years, a number of technologies have become sufficiently reliable and translated to biomedical laboratories. Commercialization efforts are under way and, as a result, the QPI field is now transitioning from a technology-development-driven to an application-focused field. In this Review, we aim to provide a critical and objective overview of this dynamic research field by presenting the scientific context, main principles of operation and current biomedical applications. Over the past 10–15 years, quantitative phase imaging has moved from a research-driven to an application-focused field. This Review presents the main principles of operation and representative basic and clinical science applications.

Learning Word Vectors for 157 Languages

Abstract: Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.

Observation of a phononic quadrupole topological insulator

Abstract: The modern theory of charge polarization in solids is based on a generalization of Berry’s phase. The possibility of the quantization of this phase arising from parallel transport in momentum space is essential to our understanding of systems with topological band structures. Although based on the concept of charge polarization, this same theory can also be used to characterize the Bloch bands of neutral bosonic systems such as photonic or phononic crystals. The theory of this quantized polarization has recently been extended from the dipole moment to higher multipole moments. In particular, a two-dimensional quantized quadrupole insulator is predicted to have gapped yet topological one-dimensional edge modes, which stabilize zero-dimensional in-gap corner states. However, such a state of matter has not previously been observed experimentally. Here we report measurements of a phononic quadrupole topological insulator. We experimentally characterize the bulk, edge and corner physics of a mechanical metamaterial (a material with tailored mechanical properties) and find the predicted gapped edge and in-gap corner states. We corroborate our findings by comparing the mechanical properties of a topologically non-trivial system to samples in other phases that are predicted by the quadrupole theory. These topological corner states are an important stepping stone to the experimental realization of topologically protected wave guides in higher dimensions, and thereby open up a new path for the design of metamaterials.

Homogeneous Catalysis for Sustainable Hydrogen Storage in Formic Acid and Alcohols.

Abstract: Hydrogen gas is a storable form of chemical energy that could complement intermittent renewable energy conversion. One of the main disadvantages of hydrogen gas arises from its low density, and therefore, efficient handling and storage methods are key factors that need to be addressed to realize a hydrogen-based economy. Storage systems based on liquids, in particular, formic acid and alcohols, are highly attractive hydrogen carriers as they can be made from CO2 or other renewable materials, they can be used in stationary power storage units such as hydrogen filling stations, and they can be used directly as transportation fuels. However, to bring about a paradigm change in our energy infrastructure, efficient catalytic processes that release the hydrogen from these molecules, as well as catalysts that regenerate these molecules from CO2 and hydrogen, are required. In this review, we describe the considerable progress that has been made in homogeneous catalysis for these critical reactions, namely, the hy...

Equivalent-accuracy accelerated neural-network training using analogue memory

Abstract: Neural-network training can be slow and energy intensive, owing to the need to transfer the weight data for the network between conventional digital memory chips and processor chips. Analogue non-volatile memory can accelerate the neural-network training algorithm known as backpropagation by performing parallelized multiply-accumulate operations in the analogue domain at the location of the weight data. However, the classification accuracies of such in situ training using non-volatile-memory hardware have generally been less than those of software-based training, owing to insufficient dynamic range and excessive weight-update asymmetry. Here we demonstrate mixed hardware-software neural-network implementations that involve up to 204,900 synapses and that combine long-term storage in phase-change memory, near-linear updates of volatile capacitors and weight-data transfer with 'polarity inversion' to cancel out inherent device-to-device variations. We achieve generalization accuracies (on previously unseen data) equivalent to those of software-based training on various commonly used machine-learning test datasets (MNIST, MNIST-backrand, CIFAR-10 and CIFAR-100). The computational energy efficiency of 28,065 billion operations per second per watt and throughput per area of 3.6 trillion operations per second per square millimetre that we calculate for our implementation exceed those of today's graphical processing units by two orders of magnitude. This work provides a path towards hardware accelerators that are both fast and energy efficient, particularly on fully connected neural-network layers.

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Abstract: The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.

Imaging-based molecular barcoding with pixelated dielectric metasurfaces.

Abstract: Metasurfaces provide opportunities for wavefront control, flat optics, and subwavelength light focusing. We developed an imaging-based nanophotonic method for detecting mid-infrared molecular fingerprints and implemented it for the chemical identification and compositional analysis of surface-bound analytes. Our technique features a two-dimensional pixelated dielectric metasurface with a range of ultrasharp resonances, each tuned to a discrete frequency; this enables molecular absorption signatures to be read out at multiple spectral points, and the resulting information is then translated into a barcode-like spatial absorption map for imaging. The signatures of biological, polymer, and pesticide molecules can be detected with high sensitivity, covering applications such as biosensing and environmental monitoring. Our chemically specific technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.

Cosmological Backgrounds of Gravitational Waves

Abstract: Gravitational waves (GWs) have a great potential to probe cosmology. We review early universe sources that can lead to cosmological backgrounds of GWs. We begin by presenting proper definitions of GWs in flat space-time and in a cosmological setting (section 2). Following, we discuss the reasons why early universe GW backgrounds are of a stochastic nature, and describe the general properties of a stochastic background (section 3). We recap current observational constraints on stochastic backgrounds, and discuss the basic characteristics of present and future GW detectors, including advanced LIGO, advanced Virgo, the Einstein telescope, KAGRA, and LISA (section 4). We then review in detail early universe GW generation mechanisms, as well as the properties of the GW backgrounds they give rise to. We classify the backgrounds in five categories: GWs from quantum vacuum fluctuations during standard slow-roll inflation (section 5), GWs from processes that operate within extensions of the standard inflationary paradigm (section 6), GWs from post-inflationary preheating and related non-perturbative phenomena (section 7), GWs from first order phase transitions related or not to the electroweak symmetry breaking (section 8), and GWs from general topological defects, and from cosmic strings in particular (section 9). The phenomenology of these early universe processes is extremely rich, and some of the GW backgrounds they generate can be within the reach of near-future GW detectors. A future detection of any of these backgrounds will provide crucial information on the underlying high energy theory describing the early universe, probing energy scales well beyond the reach of particle accelerators.

Real-Time Seamless Single Shot 6D Object Pose Prediction

Abstract: We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [10] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster - 50 fps on a Titan X (Pascal) GPU - and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [27, 28] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches [10, 25] when they are all used without postprocessing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.

Methylammonium-free, high-performance, and stable perovskite solar cells on a planar architecture

Abstract: Currently, perovskite solar cells (PSCs) with high performances greater than 20% contain bromine (Br), causing a suboptimal bandgap, and the thermally unstable methylammonium (MA) molecule. Avoiding Br and especially MA can therefore result in more optimal bandgaps and stable perovskites. We show that inorganic cation tuning, using rubidium and cesium, enables highly crystalline formamidinium-based perovskites without Br or MA. On a conventional, planar device architecture, using polymeric interlayers at the electron- and hole-transporting interface, we demonstrate an efficiency of 20.35% (stabilized), one of the highest for MA-free perovskites, with a drastically improved stability reached without the stabilizing influence of mesoporous interlayers. The perovskite is not heated beyond 100°C. Going MA-free is a new direction for perovskites that are inherently stable and compatible with tandems or flexible substrates, which are the main routes commercializing PSCs.

An optical-frequency synthesizer using integrated photonics

Abstract: Optical-frequency synthesizers, which generate frequency-stable light from a single microwave-frequency reference, are revolutionizing ultrafast science and metrology, but their size, power requirement and cost need to be reduced if they are to be more widely used. Integrated-photonics microchips can be used in high-coherence applications, such as data transmission1, highly optimized physical sensors2 and harnessing quantum states3, to lower cost and increase efficiency and portability. Here we describe a method for synthesizing the absolute frequency of a lightwave signal, using integrated photonics to create a phase-coherent microwave-to-optical link. We use a heterogeneously integrated III–V/silicon tunable laser, which is guided by nonlinear frequency combs fabricated on separate silicon chips and pumped by off-chip lasers. The laser frequency output of our optical-frequency synthesizer can be programmed by a microwave clock across 4 terahertz near 1,550 nanometres (the telecommunications C-band) with 1 hertz resolution. Our measurements verify that the output of the synthesizer is exceptionally stable across this region (synthesis error of 7.7 × 10−15 or below). Any application of an optical-frequency source could benefit from the high-precision optical synthesis presented here. Leveraging high-volume semiconductor processing built around advanced materials could allow such low-cost, low-power and compact integrated-photonics devices to be widely used. An optical-frequency synthesizer based on stabilized frequency combs has been developed utilizing chip-scale devices as key components, in a move towards using integrated photonics technology for ultrafast science and metrology.

Design rules for minimizing voltage losses in high-efficiency organic solar cells.

Abstract: The open-circuit voltage of organic solar cells is usually lower than the values achieved in inorganic or perovskite photovoltaic devices with comparable bandgaps Energy losses during charge separation at the donor–acceptor interface and non-radiative recombination are among the main causes of such voltage losses Here we combine spectroscopic and quantum-chemistry approaches to identify key rules for minimizing voltage losses: (1) a low energy offset between donor and acceptor molecular states and (2) high photoluminescence yield of the low-gap material in the blend Following these rules, we present a range of existing and new donor–acceptor systems that combine efficient photocurrent generation with electroluminescence yield up to 003%, leading to non-radiative voltage losses as small as 021 V This study provides a rationale to explain and further improve the performance of recently demonstrated high-open-circuit-voltage organic solar cells Key optoelectronic properties for donor and acceptor organic semiconductors are identified to obtain organic solar cells with reduced open-circuit voltage losses and high power conversion efficiencies

Organoid profiling identifies common responders to chemotherapy in pancreatic cancer

Abstract: Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we find that PDO therapeutic profiles paralleled patient outcomes and that PDOs enable longitudinal assessment of chemo-sensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemo-sensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemo-refractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection.

Targeted neurotechnology restores walking in humans with spinal cord injury

Abstract: Spinal cord injury leads to severe locomotor deficits or even complete leg paralysis. Here we introduce targeted spinal cord stimulation neurotechnologies that enabled voluntary control of walking in individuals who had sustained a spinal cord injury more than four years ago and presented with permanent motor deficits or complete paralysis despite extensive rehabilitation. Using an implanted pulse generator with real-time triggering capabilities, we delivered trains of spatially selective stimulation to the lumbosacral spinal cord with timing that coincided with the intended movement. Within one week, this spatiotemporal stimulation had re-established adaptive control of paralysed muscles during overground walking. Locomotor performance improved during rehabilitation. After a few months, participants regained voluntary control over previously paralysed muscles without stimulation and could walk or cycle in ecological settings during spatiotemporal stimulation. These results establish a technological framework for improving neurological recovery and supporting the activities of daily living after spinal cord injury.

Lactate in the brain: from metabolic end-product to signalling molecule

Abstract: Lactate in the brain has long been associated with ischaemia; however, more recent evidence shows that it can be found there under physiological conditions. In the brain, lactate is formed predominantly in astrocytes from glucose or glycogen in response to neuronal activity signals. Thus, neurons and astrocytes show tight metabolic coupling. Lactate is transferred from astrocytes to neurons to match the neuronal energetic needs, and to provide signals that modulate neuronal functions, including excitability, plasticity and memory consolidation. In addition, lactate affects several homeostatic functions. Overall, lactate ensures adequate energy supply, modulates neuronal excitability levels and regulates adaptive functions in order to set the 'homeostatic tone' of the nervous system.

Progress and potential in organoid research.

Abstract: Tissue and organ biology are very challenging to study in mammals, and progress can be hindered, particularly in humans, by sample accessibility and ethical concerns. However, advances in stem cell culture have made it possible to derive in vitro 3D tissues called organoids, which capture some of the key multicellular, anatomical and even functional hallmarks of real organs at the micrometre to millimetre scale. Recent studies have demonstrated that organoids can be used to model organ development and disease and have a wide range of applications in basic research, drug discovery and regenerative medicine. Researchers are now beginning to take inspiration from other fields, such as bioengineering, to generate organoids that are more physiologically relevant and more amenable to real-life applications.

Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

Abstract: With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. This task now arises in a range of applications such as healthcare monitoring, fitness tracking or user-adaptive systems, where a general model capable of instantaneous activity recognition of an arbitrary user is needed. In this paper, we present a user-independent deep learning-based approach for online human activity classification. We propose using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series. Furthermore, we investigate the impact of time series length on the recognition accuracy and limit it up to 1 s that makes possible continuous real-time activity classification. The accuracy of the proposed approach is evaluated on two commonly used WISDM and UCI datasets that contain labeled accelerometer data from 36 and 30 users respectively, and in cross-dataset experiment. The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.