Abstract: We study expressive power of shallow and deep neural networks with piece-wise linear activation functions. We establish new rigorous upper and lower bounds for the network complexity in the setting of approximations in Sobolev spaces. In particular, we prove that deep ReLU networks more efficiently approximate smooth functions than shallow networks. In the case of approximations of 1D Lipschitz functions we describe adaptive depth-6 network architectures more efficient than the standard shallow architecture.

Abstract: Background The COVID-19 pandemic caused by SARS-CoV-2 coronavirus threatens global public health. Currently, neutralizing antibodies (NAbs) versus this virus are expected to correlate with recovery and protection of this disease. However, the characteristics of these antibodies have not been well studied in association with the clinical manifestations in patients. Methods Plasma collected from 175 COVID-19 recovered patients with mild symptoms were screened using a safe and sensitive pseudotyped-lentiviral-vector-based neutralization assay. Spike-binding antibody in plasma were determined by ELISA using RBD, S1, and S2 proteins of SARS-CoV-2. The levels and the time course of SARS-CoV-2-specific NAbs and the spike-binding antibodies were monitored at the same time. Findings SARS-CoV-2 NAbs were unable to cross-reactive with SARS-CoV virus. SARS-CoV-2-specific NAbs were detected in patients from day 10-15 after the onset of the disease and remained thereafter. The titers of NAb among these patients correlated with the spike-binding antibodies targeting S1, RBD, and S2 regions. The titers of NAbs were variable in different patients. Elderly and middle-age patients had significantly higher plasma NAb titers (P<0.0001) and spike-binding antibodies (P=0.0003) than young patients. Notably, among these patients, there were ten patients whose NAb titers were under the detectable level of our assay (ID50: < 40); while in contrast, two patients, showed very high titers of NAb, with ID50 :15989 and 21567 respectively. The NAb titers were positive correlated with plasma CRP levels but negative correlated with the lymphocyte counts of patients at the time of admission, indicating an association between humoral response and cellular immune response. Interpretation The variations of SARS-CoV-2 specific NAbs in recovered COVID-19 patients may raise the concern about the role of NAbs on disease progression. The correlation of NAb titers with age, lymphocyte counts, and blood CRP levels suggested that the interplay between virus and host immune response in coronavirus infections should be further explored for the development of effective vaccine against SARS-CoV-2 virus. Furthermore, titration of NAb is helpful prior to the use of convalescent plasma for prevention or treatment. Funding Ministry of Science and Technology of China, National Natural Science Foundation of China, Shanghai Municipal Health Commission, and Chinese Academy of Medical Sciences

Abstract: This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

Abstract: In this paper we address the problem of counting objects instances in images. Our models are able to precisely estimate the number of vehicles in a traffic congestion, or to count the humans in a very crowded scene. Our first contribution is the proposal of a novel convolutional neural network solution, named Counting CNN (CCNN). Essentially, the CCNN is formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps. Our second contribution consists in a scale-aware counting model, the Hydra CNN, able to estimate object densities in different very crowded scenarios where no geometric information of the scene can be provided. Hydra CNN learns a multiscale non-linear regression model which uses a pyramid of image patches extracted at multiple scales to perform the final density prediction. We report an extensive experimental evaluation, using up to three different object counting benchmarks, where we show how our solutions achieve a state-of-the-art performance.

Abstract: Rapid progress in identifying biomarkers that are hallmarks of disease has increased demand for high-performance detection technologies. Implementation of electrochemical methods in clinical analysis may provide an effective answer to the growing need for rapid, specific, inexpensive, and fully automated means of biomarker analysis. This Review summarizes advances from the past 5 years in the development of electrochemical sensors for clinically relevant biomolecules, including small molecules, nucleic acids, and proteins. Various sensing strategies are assessed according to their potential for reaching relevant limits of sensitivity, specificity, and degrees of multiplexing. Furthermore, we address the remaining challenges and opportunities to integrate electrochemical sensing platforms into point-of-care solutions.

Abstract: We propose that holographic entanglement entropy can be calculated at arbitrary orders in the bulk Planck constant using the concept of a “quantum extremal surface”: a surface which extremizes the generalized entropy, i.e. the sum of area and bulk entanglement entropy. At leading order in bulk quantum corrections, our proposal agrees with the formula of Faulkner, Lewkowycz, and Maldacena, which was derived only at this order; beyond leading order corrections, the two conjectures diverge. Quantum extremal surfaces lie outside the causal domain of influence of the boundary region as well as its complement, and in some spacetimes there are barriers preventing them from entering certain regions. We comment on the implications for bulk reconstruction.

Abstract: Background Platinum-based chemotherapy is standard-of-care first-line treatment for advanced urothelial carcinoma. However, progression-free survival and overall survival are limited by ch...

Abstract: We have argued that the evolutionary perspective to social psychology is not untestable, not reductionist, not a theory about rigid genetic determinism, not a justification for the status quo, and not incompatible with sociocultural or cognitive analyses. What it is, instead, is a set of ideas that have proved quite useful in generating novel hypotheses, and parsimoniously connecting findings from very different domains ranging from mate choice and family relationships to aggression and intergroup relations. Adopting an evolutionary perspective can help us appreciate not only the common threads that bind the people in our culture to those in other cultures, but also, beyond that, to the other species with which we share the earth. Taking this broad perspective, however, also makes us aware of the vast reaches of our own ignorance. As yet, we know very little about how evolved psychological mechanisnis inside individuals develop, or how they influence, and are influenced by, the complex cultures that humans construct. Bringing light to these questions will require a fuller integration of all the different theoretical perspectives on human social behavior.

Abstract: Background:The incidence of isolated anterior cruciate ligament (ACL) tears in the general population is not well defined.Purpose/Hypothesis:The purpose of this study was to define the population-based incidence of ACL tears, describe trends in ACL injuries over time, and evaluate changes in the rate of surgical management. The hypothesis was that the incidence of ACL injury and the rate of subsequent ACL reconstruction increase over time.Study Design:Cohort study; Level of evidence, 3.Methods:The study population included 1841 individuals who were diagnosed with new-onset, isolated ACL tears (without concomitant ligament injury that required surgery) between January 1, 1990, and December 31, 2010. The complete medical records were reviewed to confirm diagnosis and to extract injury and treatment details. Age- and sex-specific incidence rates were calculated and adjusted to the 2010 US population. Poisson regression analyses were performed to examine incidence trends by age, sex, and calendar period.Resul...

Abstract: This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method, where a projection plane orthogonal to the skin tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model.

Abstract: The intestinal epithelium forms an essential barrier between a host and its microbiota. Protozoa and helminths are members of the gut microbiota of mammals, including humans, yet the many ways that gut epithelial cells orchestrate responses to these eukaryotes remain unclear. Here we show that tuft cells, which are taste-chemosensory epithelial cells, accumulate during parasite colonization and infection. Disruption of chemosensory signaling through the loss of TRMP5 abrogates the expansion of tuft cells, goblet cells, eosinophils, and type 2 innate lymphoid cells during parasite colonization. Tuft cells are the primary source of the parasite-induced cytokine interleukin-25, which indirectly induces tuft cell expansion by promoting interleukin-13 production by innate lymphoid cells. Our results identify intestinal tuft cells as critical sentinels in the gut epithelium that promote type 2 immunity in response to intestinal parasites.

Abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a Watch, Listen, Attend and Spell (WLAS) network that learns to transcribe videos of mouth motion to characters, (2) a curriculum learning strategy to accelerate training and to reduce overfitting, (3) a Lip Reading Sentences (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that if audio is available, then visual information helps to improve speech recognition performance.

Abstract: Natural antioxidants are widely distributed in food and medicinal plants. These natural antioxidants, especially polyphenols and carotenoids, exhibit a wide range of biological effects, including anti-inflammatory, anti-aging, anti-atherosclerosis and anticancer. The effective extraction and proper assessment of antioxidants from food and medicinal plants are crucial to explore the potential antioxidant sources and promote the application in functional foods, pharmaceuticals and food additives. The present paper provides comprehensive information on the green extraction technologies of natural antioxidants, assessment of antioxidant activity at chemical and cellular based levels and their main resources from food and medicinal plants.

Abstract: Multi-headed attention is a driving force behind recent state-of-the-art NLP models. By applying multiple attention mechanisms in parallel, it can express sophisticated functions beyond the simple weighted average. However we observe that, in practice, a large proportion of attention heads can be removed at test time without significantly impacting performance, and that some layers can even be reduced to a single head. Further analysis on machine translation models reveals that the self-attention layers can be significantly pruned, while the encoder-decoder layers are more dependent on multi-headedness.

Abstract: Pulsatile blood flow through the cavities of the heart and great vessels is time-varying and multidirectional. Access to all regions, phases and directions of cardiovascular flows has formerly been limited. Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) has enabled more comprehensive access to such flows, with typical spatial resolution of 1.5×1.5×1.5 – 3×3×3 mm3, typical temporal resolution of 30–40 ms, and acquisition times in the order of 5 to 25 min. This consensus paper is the work of physicists, physicians and biomedical engineers, active in the development and implementation of 4D Flow CMR, who have repeatedly met to share experience and ideas. The paper aims to assist understanding of acquisition and analysis methods, and their potential clinical applications with a focus on the heart and greater vessels. We describe that 4D Flow CMR can be clinically advantageous because placement of a single acquisition volume is straightforward and enables flow through any plane across it to be calculated retrospectively and with good accuracy. We also specify research and development goals that have yet to be satisfactorily achieved. Derived flow parameters, generally needing further development or validation for clinical use, include measurements of wall shear stress, pressure difference, turbulent kinetic energy, and intracardiac flow components. The dependence of measurement accuracy on acquisition parameters is considered, as are the uses of different visualization strategies for appropriate representation of time-varying multidirectional flow fields. Finally, we offer suggestions for more consistent, user-friendly implementation of 4D Flow CMR acquisition and data handling with a view to multicenter studies and more widespread adoption of the approach in routine clinical investigations.

Abstract: Purpose: To explore whether a cross-talk exists between PARP inhibition and PD-L1/PD-1 immune checkpoint axis, and determine whether blockade of PD-L1/PD-1 potentiates PARP inhibitor (PARPi) in tumor suppression.Experimental Design: Breast cancer cell lines, xenograft tumors, and syngeneic tumors treated with PARPi were assessed for PD-L1 expression by immunoblotting, IHC, and FACS analyses. The phospho-kinase antibody array screen was used to explore the underlying mechanism of PARPi-induced PD-L1 upregulation. The therapeutic efficacy of PARPi alone, PD-L1 blockade alone, or their combination was tested in a syngeneic tumor model. The tumor-infiltrating lymphocytes and tumor cells isolated from syngeneic tumors were analyzed by CyTOF and FACS to evaluate the activity of antitumor immunity in the tumor microenvironment.Results: PARPi upregulated PD-L1 expression in breast cancer cell lines and animal models. Mechanistically, PARPi inactivated GSK3β, which in turn enhanced PARPi-mediated PD-L1 upregulation. PARPi attenuated anticancer immunity via upregulation of PD-L1, and blockade of PD-L1 resensitized PARPi-treated cancer cells to T-cell killing. The combination of PARPi and anti-PD-L1 therapy compared with each agent alone significantly increased the therapeutic efficacy in vivoConclusions: Our study demonstrates a cross-talk between PARPi and tumor-associated immunosuppression and provides evidence to support the combination of PARPi and PD-L1 or PD-1 immune checkpoint blockade as a potential therapeutic approach to treat breast cancer. Clin Cancer Res; 23(14); 3711-20. ©2017 AACR.

Abstract: This review covers literature data summarizing, on one hand, the chemistry of essential oils and, on the other hand, their most important activities. Essential oils, which are complex mixtures of volatile compounds particularly abundant in aromatic plants, are mainly composed of terpenes biogenerated by the mevalonate pathway. These volatile molecules include monoterpenes (hydrocarbon and oxygenated monoterpens), and also sesquiterpenes (hydrocarbon and oxygenated sesquiterpens). Furthermore, they contain phenolic compounds, which are derived via the shikimate pathway. Thanks to their chemical composition, essential oils possess numerous biological activities (antioxidant, anti-inflammatory, antimicrobial, etc…) of great interest in food and cosmetic industries, as well as in the human health field.

Abstract: Water is the most abundant liquid on earth and also the substance with the largest number of anomalies in its properties. It is a prerequisite for life and as such a most important subject of current research in chemical physics and physical chemistry. In spite of its simplicity as a liquid, it has an enormously rich phase diagram where different types of ices, amorphous phases, and anomalies disclose a path that points to unique thermodynamics of its supercooled liquid state that still hides many unraveled secrets. In this review we describe the behavior of water in the regime from ambient conditions to the deeply supercooled region. The review describes simulations and experiments on this anomalous liquid. Several scenarios have been proposed to explain the anomalous properties that become strongly enhanced in the supercooled region. Among those, the second critical-point scenario has been investigated extensively, and at present most experimental evidence point to this scenario. Starting from very low ...

Abstract: In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model. Our formulation scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent. The proposed approach is evaluated in two environments: VizDoom and Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where curiosity allows for far fewer interactions with the environment to reach the goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent to explore more efficiently; and 3) generalization to unseen scenarios (e.g. new levels of the same game) where the knowledge gained from earlier experience helps the agent explore new places much faster than starting from scratch. Demo video and code available at this https URL

Abstract: Importance Non–small cell lung cancer remains the leading cause of cancer death in the United States. Until the last decade, the 5-year overall survival rate for patients with metastatic non–small cell lung cancer was less than 5%. Improved understanding of the biology of lung cancer has resulted in the development of new biomarker–targeted therapies and led to improvements in overall survival for patients with advanced or metastatic disease. Observations Systemic therapy for metastatic non–small cell lung cancer is selected according to the presence of specific biomarkers. Therefore, all patients with metastatic non–small cell lung cancer should undergo molecular testing for relevant mutations and expression of the protein PD-L1 (programmed death ligand 1). Molecular alterations that predict response to treatment (eg,EGFRmutations,ALKrearrangements,ROS1rearrangements, andBRAFV600E mutations) are present in approximately 30% of patients with non–small cell lung cancer. Targeted therapy for these alterations improves progression-free survival compared with cytotoxic chemotherapy. For example, somatic activating mutations in theEGFRgene are present in approximately 20% of patients with advanced non–small cell lung cancer. Tyrosine kinase inhibitors such as gefitinib, erlotinib, and afatinib improve progression-free survival in patients with susceptibleEGFRmutations. In patients with overexpression of ALK protein, the response rate was significantly better with crizotinib (a tyrosine kinase inhibitor) than with the combination of pemetrexed and either cisplatin or carboplatin (platinum-based chemotherapy) (74% vs 45%, respectively;P Conclusions and Relevance Improved understanding of the biology and molecular subtypes of non–small cell lung cancer have led to more biomarker-directed therapies for patients with metastatic disease. These biomarker-directed therapies and newer empirical treatment regimens have improved overall survival for patients with metastatic non–small cell lung cancer.

Abstract: Being secondary plant metabolites, polyphenols represent a large and diverse group of substances abundantly present in a majority of fruits, herbs and vegetables. The current contribution is focused on their bioavailability, antioxidative and anticarcinogenic properties. An overview of extraction methods is also given, with supercritical fluid extraction highlighted as a promising eco-friendly alternative providing exceptional separation and protection from degradation of unstable polyphenols. The protective role of polyphenols against reactive oxygen and nitrogen species, UV light, plant pathogens, parasites and predators results in several beneficial biological activities giving rise to prophylaxis or possibly even to a cure for several prevailing human diseases, especially various cancer types. Omnipresence, specificity of the response and the absence of or low toxicity are crucial advantages of polyphenols as anticancer agents. The main problem represents their low bioavailability and rapid metabolism. One of the promising solutions lies in nanoformulation of polyphenols that prevents their degradation and thus enables significantly higher concentrations to reach the target cells. Another, more practiced, solution is the use of mixtures of various polyphenols that bring synergistic effects, resulting in lowering of the required therapeutic dose and in multitargeted action. The combination of polyphenols with existing drugs and therapies also shows promising results and significantly reduces their toxicity.

Abstract: The relation between chiral edge modes and bulk Chern numbers of quantum Hall insulators is a paradigmatic example of bulk-boundary correspondence. We show that the chiral edge modes are not strictly tied to the Chern numbers defined by a non-Hermitian Bloch Hamiltonian. This breakdown of conventional bulk-boundary correspondence stems from the non-Bloch-wave behavior of eigenstates (non-Hermitian skin effect), which generates pronounced deviations of phase diagrams from the Bloch theory. We introduce non-Bloch Chern numbers that faithfully predict the numbers of chiral edge modes. The theory is backed up by the open-boundary energy spectra, dynamics, and phase diagram of representative lattice models. Our results highlight a unique feature of non-Hermitian bands and suggest a non-Bloch framework to characterize their topology.

Abstract: The broad-spectrum herbicide glyphosate (common trade name “Roundup”) was first sold to farmers in 1974. Since the late 1970s, the volume of glyphosate-based herbicides (GBHs) applied has increased approximately 100-fold. Further increases in the volume applied are likely due to more and higher rates of application in response to the widespread emergence of glyphosate-resistant weeds and new, pre-harvest, dessicant use patterns. GBHs were developed to replace or reduce reliance on herbicides causing well-documented problems associated with drift and crop damage, slipping efficacy, and human health risks. Initial industry toxicity testing suggested that GBHs posed relatively low risks to non-target species, including mammals, leading regulatory authorities worldwide to set high acceptable exposure limits. To accommodate changes in GBH use patterns associated with genetically engineered, herbicide-tolerant crops, regulators have dramatically increased tolerance levels in maize, oilseed (soybeans and canola), and alfalfa crops and related livestock feeds. Animal and epidemiology studies published in the last decade, however, point to the need for a fresh look at glyphosate toxicity. Furthermore, the World Health Organization’s International Agency for Research on Cancer recently concluded that glyphosate is “probably carcinogenic to humans.” In response to changing GBH use patterns and advances in scientific understanding of their potential hazards, we have produced a Statement of Concern drawing on emerging science relevant to the safety of GBHs. Our Statement of Concern considers current published literature describing GBH uses, mechanisms of action, toxicity in laboratory animals, and epidemiological studies. It also examines the derivation of current human safety standards. We conclude that: (1) GBHs are the most heavily applied herbicide in the world and usage continues to rise; (2) Worldwide, GBHs often contaminate drinking water sources, precipitation, and air, especially in agricultural regions; (3) The half-life of glyphosate in water and soil is longer than previously recognized; (4) Glyphosate and its metabolites are widely present in the global soybean supply; (5) Human exposures to GBHs are rising; (6) Glyphosate is now authoritatively classified as a probable human carcinogen; (7) Regulatory estimates of tolerable daily intakes for glyphosate in the United States and European Union are based on outdated science. We offer a series of recommendations related to the need for new investments in epidemiological studies, biomonitoring, and toxicology studies that draw on the principles of endocrinology to determine whether the effects of GBHs are due to endocrine disrupting activities. We suggest that common commercial formulations of GBHs should be prioritized for inclusion in government-led toxicology testing programs such as the U.S. National Toxicology Program, as well as for biomonitoring as conducted by the U.S. Centers for Disease Control and Prevention.

Abstract: This paper investigates a novel problem of generating images from visual attributes. We model the image as a composite of foreground and background and develop a layered generative model with disentangled latent variables that can be learned end-to-end using a variational auto-encoder. We experiment with natural images of faces and birds and demonstrate that the proposed models are capable of generating realistic and diverse samples with disentangled latent representations. We use a general energy minimization algorithm for posterior inference of latent variables given novel images. Therefore, the learned generative models show excellent quantitative and visual results in the tasks of attribute-conditioned image reconstruction and completion.

Abstract: Several studies have documented fish populations changing in response to long-term warming. Over the past decade, sea surface temperatures in the Gulf of Maine increased faster than 99% of the global ocean. The warming, which was related to a northward shift in the Gulf Stream and to changes in the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation, led to reduced recruitment and increased mortality in the region’s Atlantic cod (Gadus morhua) stock. Failure to recognize the impact of warming on cod contributed to overfishing. Recovery of this fishery depends on sound management, but the size of the stock depends on future temperature conditions. The experience in the Gulf of Maine highlights the need to incorporate environmental factors into resource management.

Abstract: The International Agency for Research on Cancer (IARC) has updated its 2002 guidelines on screening for breast cancer, drawing on data from studies completed in the past 15 years.

Abstract: Quantum teleportation of single-photon qubits from a ground observatory to a satellite in low-Earth orbit via an uplink channel is achieved with a fidelity that is well above the classical limit. The laws of quantum physics give rise to protocols for ultra-secure cryptography and quantum communications. However, to be useful in a global network, these protocols will have to function with satellites. Extending existing protocols to such long distances poses a tremendous experimental challenge. Researchers led by Jian-Wei Pan present a pair of papers in this issue that take steps toward a global quantum network, using the low-Earth-orbit satellite Micius. They demonstrate satellite-to-ground quantum key distribution, an integral part of quantum cryptosystems, at kilohertz rates over 1,200 kilometres, and report quantum teleportation of a single-photon qubit over 1,400 kilometres. Quantum teleportation is the transfer of the exact state of a quantum object from one place to another, without physical travelling of the object itself, and is a central process in many quantum communication protocols. These two experiments suggest that Micius could become the first component in a global quantum internet. An arbitrary unknown quantum state cannot be measured precisely or replicated perfectly1. However, quantum teleportation enables unknown quantum states to be transferred reliably from one object to another over long distances2, without physical travelling of the object itself. Long-distance teleportation is a fundamental element of protocols such as large-scale quantum networks3,4 and distributed quantum computation5,6. But the distances over which transmission was achieved in previous teleportation experiments, which used optical fibres and terrestrial free-space channels7,8,9,10,11,12, were limited to about 100 kilometres, owing to the photon loss of these channels. To realize a global-scale ‘quantum internet’13 the range of quantum teleportation needs to be greatly extended. A promising way of doing so involves using satellite platforms and space-based links, which can connect two remote points on Earth with greatly reduced channel loss because most of the propagation path of the photons is in empty space. Here we report quantum teleportation of independent single-photon qubits from a ground observatory to a low-Earth-orbit satellite, through an uplink channel, over distances of up to 1,400 kilometres. To optimize the efficiency of the link and to counter the atmospheric turbulence in the uplink, we use a compact ultra-bright source of entangled photons, a narrow beam divergence and high-bandwidth and high-accuracy acquiring, pointing and tracking. We demonstrate successful quantum teleportation of six input states in mutually unbiased bases with an average fidelity of 0.80 ± 0.01, well above the optimal state-estimation fidelity on a single copy of a qubit (the classical limit)14. Our demonstration of a ground-to-satellite uplink for reliable and ultra-long-distance quantum teleportation is an essential step towards a global-scale quantum internet.

Abstract: As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars are used for object detection, visual cameras as virtual mirrors, and LIDARs for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as DSRC and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This article makes the case that mmWave communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.