Showing papers by "University of Texas at Austin published in 2019"
University of Jyväskylä1, University of California, Los Angeles2, California Polytechnic State University3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, Brigham Young University12, University of Texas at Austin13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
12,774 citations
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TL;DR: The center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors and performs competitively with sophisticated multi-stage methods and runs in real-time.
Abstract: Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.
1,899 citations
University of California, Santa Barbara1, University of Texas at Austin2, Dresden University of Technology3, University of Wrocław4, University of Tartu5, Gulu University6, Middle East University7, Stockholm University8, University of the Punjab9, University of Nigeria, Nsukka10, Istanbul University11, Franklin & Marshall College12, Norwegian University of Science and Technology13, University of Algiers14, Australian National University15, Russian Academy of Sciences16, Russian State University for the Humanities17, İzmir University of Economics18, University of Social Sciences and Humanities19, Université catholique de Louvain20, Ankara University21, Pontifical Catholic University of Peru22, Cumhuriyet University23, University of the Republic24, ISCTE – University Institute of Lisbon25, The Chinese University of Hong Kong26, National Autonomous University of Mexico27, University of Pécs28, University of Constantine the Philosopher29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, Institute of Molecular and Cell Biology36, University of Amsterdam37, Catholic University of the Sacred Heart38, VU University Amsterdam39, University of Granada40, University of Delhi41, University of Havana42, Pontifical Catholic University of Rio de Janeiro43, University of Vienna44, Universiti Utara Malaysia45, Vilnius University46, University of British Columbia47, University of Sussex48, Romanian Academy49, Comenius University in Bratislava50, Slovak Academy of Sciences51, University of Monterrey52, SAS Institute53, DHA Suffa University54, Pontifical Catholic University of Chile55, South-West University "Neofit Rilski"56, University of São Paulo57, Kyung Hee University58, University of Ljubljana59
TL;DR: This work combines this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets and finds that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
Abstract: Humans express a wide array of ideal mate preferences. Around the world, people desire romantic partners who are intelligent, healthy, kind, physically attractive, wealthy, and more. In order for these ideal preferences to guide the choice of actual romantic partners, human mating psychology must possess a means to integrate information across these many preference dimensions into summaries of the overall mate value of their potential mates. Here we explore the computational design of this mate preference integration process using a large sample of n = 14,487 people from 45 countries around the world. We combine this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets. Across cultures, people higher in mate value appear to experience greater power of choice on the mating market in that they set higher ideal standards, better fulfill their preferences in choice, and pair with higher mate value partners. Furthermore, we find that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
1,827 citations
TL;DR: Liu et al. as mentioned in this paper discuss crucial conditions needed to achieve a specific energy higher than 350 Wh kg−1, up to 500 Wh kg −1, for rechargeable Li metal batteries using high-nickel-content lithium nickel manganese cobalt oxides as cathode materials.
Abstract: State-of-the-art lithium (Li)-ion batteries are approaching their specific energy limits yet are challenged by the ever-increasing demand of today’s energy storage and power applications, especially for electric vehicles. Li metal is considered an ultimate anode material for future high-energy rechargeable batteries when combined with existing or emerging high-capacity cathode materials. However, much current research focuses on the battery materials level, and there have been very few accounts of cell design principles. Here we discuss crucial conditions needed to achieve a specific energy higher than 350 Wh kg−1, up to 500 Wh kg−1, for rechargeable Li metal batteries using high-nickel-content lithium nickel manganese cobalt oxides as cathode materials. We also provide an analysis of key factors such as cathode loading, electrolyte amount and Li foil thickness that impact the cell-level cycle life. Furthermore, we identify several important strategies to reduce electrolyte-Li reaction, protect Li surfaces and stabilize anode architectures for long-cycling high-specific-energy cells. Jun Liu and Battery500 Consortium colleagues contemplate the way forward towards high-energy and long-cycling practical batteries.
1,747 citations
TL;DR: The topic of exceptional points in photonics is reviewed and some of the possible exotic behavior that might be expected from engineering such systems are explored, as well as new angle of utilizing gain and loss as new degrees of freedom, in stark contrast with the traditional approach of avoiding these elements.
Abstract: BACKGROUND Singularities are critical points for which the behavior of a mathematical model governing a physical system is of a fundamentally different nature compared to the neighboring points. Exceptional points are spectral singularities in the parameter space of a system in which two or more eigenvalues, and their corresponding eigenvectors, simultaneously coalesce. Such degeneracies are peculiar features of nonconservative systems that exchange energy with their surrounding environment. In the past two decades, there has been a growing interest in investigating such nonconservative systems, particularly in connection with the quantum mechanics notions of parity-time symmetry, after the realization that some non-Hermitian Hamiltonians exhibit entirely real spectra. Lately, non-Hermitian systems have raised considerable attention in photonics, given that optical gain and loss can be integrated as nonconservative ingredients to create artificial materials and structures with altogether new optical properties. ADVANCES As we introduce gain and loss in a nanophotonic system, the emergence of exceptional point singularities dramatically alters the overall response, leading to a range of exotic functionalities associated with abrupt phase transitions in the eigenvalue spectrum. Even though such a peculiar effect has been known theoretically for several years, its controllable realization has not been made possible until recently and with advances in exploiting gain and loss in guided-wave photonic systems. As shown in a range of recent theoretical and experimental works, this property creates opportunities for ultrasensitive measurements and for manipulating the modal content of multimode lasers. In addition, adiabatic parametric evolution around exceptional points provides interesting schemes for topological energy transfer and designing mode and polarization converters in photonics. Lately, non-Hermitian degeneracies have also been exploited for the design of laser systems, new nonlinear optics phenomena, and exotic scattering features in open systems. OUTLOOK Thus far, non-Hermitian systems have been largely disregarded owing to the dominance of the Hermitian theories in most areas of physics. Recent advances in the theory of non-Hermitian systems in connection with exceptional point singularities has revolutionized our understanding of such complex systems. In the context of optics and photonics, in particular, this topic is highly important because of the ubiquity of nonconservative elements of gain and loss. In this regard, the theoretical developments in the field of non-Hermitian physics have allowed us to revisit some of the well-established platforms with a new angle of utilizing gain and loss as new degrees of freedom, in stark contrast with the traditional approach of avoiding these elements. On the experimental front, progress in fabrication technologies has allowed for harnessing gain and loss in chip-scale photonic systems. These theoretical and experimental developments have put forward new schemes for controlling the functionality of micro- and nanophotonic devices. This is mainly based on the anomalous parameter dependence in the response of non-Hermitian systems when operating around exceptional point singularities. Such effects can have important ramifications in controlling light in new nanophotonic device designs, which are fundamentally based on engineering the interplay of coupling and dissipation and amplification mechanisms in multimode systems. Potential applications of such designs reside in coupled-cavity laser sources with better coherence properties, coupled nonlinear resonators with engineered dispersion, compact polarization and spatial mode converters, and highly efficient reconfigurable diffraction surfaces. In addition, the notion of the exceptional point provides opportunities to take advantage of the inevitable dissipation in environments such as plasmonic and semiconductor materials, which play a key role in optoelectronics. Finally, emerging platforms such as optomechanical cavities provide opportunities to investigate exceptional points and their associated phenomena in multiphysics systems.
1,276 citations
TL;DR: Investigation of human transcriptomes before and during nivolumab therapy revealed that clinical benefits correlate with reduced expression of SLC3A2 and increased IFNγ and CD8 and targeting this pathway in combination with checkpoint blockade is a potential therapeutic approach.
Abstract: Cancer immunotherapy restores or enhances the effector function of CD8+ T cells in the tumour microenvironment1,2. CD8+ T cells activated by cancer immunotherapy clear tumours mainly by inducing cell death through perforin-granzyme and Fas-Fas ligand pathways3,4. Ferroptosis is a form of cell death that differs from apoptosis and results from iron-dependent accumulation of lipid peroxide5,6. Although it has been investigated in vitro7,8, there is emerging evidence that ferroptosis might be implicated in a variety of pathological scenarios9,10. It is unclear whether, and how, ferroptosis is involved in T cell immunity and cancer immunotherapy. Here we show that immunotherapy-activated CD8+ T cells enhance ferroptosis-specific lipid peroxidation in tumour cells, and that increased ferroptosis contributes to the anti-tumour efficacy of immunotherapy. Mechanistically, interferon gamma (IFNγ) released from CD8+ T cells downregulates the expression of SLC3A2 and SLC7A11, two subunits of the glutamate-cystine antiporter system xc-, impairs the uptake of cystine by tumour cells, and as a consequence, promotes tumour cell lipid peroxidation and ferroptosis. In mouse models, depletion of cystine or cysteine by cyst(e)inase (an engineered enzyme that degrades both cystine and cysteine) in combination with checkpoint blockade synergistically enhanced T cell-mediated anti-tumour immunity and induced ferroptosis in tumour cells. Expression of system xc- was negatively associated, in cancer patients, with CD8+ T cell signature, IFNγ expression, and patient outcome. Analyses of human transcriptomes before and during nivolumab therapy revealed that clinical benefits correlate with reduced expression of SLC3A2 and increased IFNγ and CD8. Thus, T cell-promoted tumour ferroptosis is an anti-tumour mechanism, and targeting this pathway in combination with checkpoint blockade is a potential therapeutic approach.
1,222 citations
TL;DR: In this paper, multiple interlayer exciton resonances with either positive or negative circularly polarized emission were observed in a molybdenum diselenide/tungsten diselinide (MoSe2/WSe2) heterobilayer with a small twist angle.
Abstract: Recent advances in the isolation and stacking of monolayers of van der Waals materials have provided approaches for the preparation of quantum materials in the ultimate two-dimensional limit1,2. In van der Waals heterostructures formed by stacking two monolayer semiconductors, lattice mismatch or rotational misalignment introduces an in-plane moire superlattice3. It is widely recognized that the moire superlattice can modulate the electronic band structure of the material and lead to transport properties such as unconventional superconductivity4 and insulating behaviour driven by correlations5–7; however, the influence of the moire superlattice on optical properties has not been investigated experimentally. Here we report the observation of multiple interlayer exciton resonances with either positive or negative circularly polarized emission in a molybdenum diselenide/tungsten diselenide (MoSe2/WSe2) heterobilayer with a small twist angle. We attribute these resonances to excitonic ground and excited states confined within the moire potential. This interpretation is supported by recombination dynamics and by the dependence of these interlayer exciton resonances on twist angle and temperature. These results suggest the feasibility of engineering artificial excitonic crystals using van der Waals heterostructures for nanophotonics and quantum information applications. Multiple interlayer exciton resonances in a MoSe2/WSe2 heterobilayer with a small twist angle are attributed to excitonic ground and excited states confined within the moire potential.
973 citations
TL;DR: In this paper, the authors report the fabrication of magic-angle twisted bilayer graphene devices with highly uniform twist angles, which enables the observation of new superconducting domes, orbital magnets and Chern insulating states.
Abstract: Superconductivity can occur under conditions approaching broken-symmetry parent states1. In bilayer graphene, the twisting of one layer with respect to the other at ‘magic’ twist angles of around 1 degree leads to the emergence of ultra-flat moire superlattice minibands. Such bands are a rich and highly tunable source of strong-correlation physics2–5, notably superconductivity, which emerges close to interaction-induced insulating states6,7. Here we report the fabrication of magic-angle twisted bilayer graphene devices with highly uniform twist angles. The reduction in twist-angle disorder reveals the presence of insulating states at all integer occupancies of the fourfold spin–valley degenerate flat conduction and valence bands—that is, at moire band filling factors ν = 0, ±1, ±2, ±3. At ν ≈ −2, superconductivity is observed below critical temperatures of up to 3 kelvin. We also observe three new superconducting domes at much lower temperatures, close to the ν = 0 and ν = ±1 insulating states. Notably, at ν = ± 1 we find states with non-zero Chern numbers. For ν = −1 the insulating state exhibits a sharp hysteretic resistance enhancement when a perpendicular magnetic field greater than 3.6 tesla is applied, which is consistent with a field-driven phase transition. Our study shows that broken-symmetry states, interaction-driven insulators, orbital magnets, states with non-zero Chern numbers and superconducting domes occur frequently across a wide range of moire flat band fillings, including close to charge neutrality. This study provides a more detailed view of the phenomenology of magic-angle twisted bilayer graphene, adding to our evolving understanding of its emergent properties. The fabrication of magic-angle twisted bilayer graphene devices with highly uniform twist angles enables the observation of new superconducting domes, orbital magnets and Chern insulating states.
968 citations
TL;DR: It is shown that CeH9 can be synthesized at 80-100 GPa with laser heating, and is characterized by a clathrate structure with a dense 3-dimensional atomic hydrogen sublattice, which shed a significant light on the search for superhydrides in close similarity with atomic hydrogen within a feasible pressure range.
Abstract: Hydrogen-rich superhydrides are believed to be very promising high-Tc superconductors. Recent experiments discovered superhydrides at very high pressures, e.g. FeH5 at 130 GPa and LaH10 at 170 GPa. With the motivation of discovering new hydrogen-rich high-Tc superconductors at lowest possible pressure, here we report the prediction and experimental synthesis of cerium superhydride CeH9 at 80–100 GPa in the laser-heated diamond anvil cell coupled with synchrotron X-ray diffraction. Ab initio calculations were carried out to evaluate the detailed chemistry of the Ce-H system and to understand the structure, stability and superconductivity of CeH9. CeH9 crystallizes in a P63/mmc clathrate structure with a very dense 3-dimensional atomic hydrogen sublattice at 100 GPa. These findings shed a significant light on the search for superhydrides in close similarity with atomic hydrogen within a feasible pressure range. Discovery of superhydride CeH9 provides a practical platform to further investigate and understand conventional superconductivity in hydrogen rich superhydrides. Hydrogen-rich superhydrides are promising high-temperature superconductors which have been observed only at pressures above 170 GPa. Here the authors show that CeH9 can be synthesized at 80-100 GPa with laser heating, and is characterized by a clathrate structure with a dense 3-dimensional atomic hydrogen sublattice.
926 citations
15 Jun 2019
TL;DR: The proposed method performs on-par with the state-of-the-art region based detection methods, with a bounding box AP of 43.7% on COCO test-dev and extreme point guided segmentation further improves this to 34.6% Mask AP.
Abstract: With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem. State of the art algorithms enumerate a near-exhaustive list of object locations and classify each into: object or not. In this paper, we show that bottom-up approaches still perform competitively. We detect four extreme points (top-most, left-most, bottom-most, right-most) and one center point of objects using a standard keypoint estimation network. We group the five keypoints into a bounding box if they are geometrically aligned. Object detection is then a purely appearance-based keypoint estimation problem, without region classification or implicit feature learning. The proposed method performs on-par with the state-of-the-art region based detection methods, with a bounding box AP of 43.7% on COCO test-dev. In addition, our estimated extreme points directly span a coarse octagonal mask, with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding boxes. Extreme point guided segmentation further improves this to 34.6% Mask AP.
811 citations
TL;DR: The opportunities, progress and challenges of integrating atomically thin materials with silicon-based nanosystems are reviewed, and the prospects for computational and non-computational applications are considered.
Abstract: The development of silicon semiconductor technology has produced breakthroughs in electronics—from the microprocessor in the late 1960s to early 1970s, to automation, computers and smartphones—by downscaling the physical size of devices and wires to the nanometre regime. Now, graphene and related two-dimensional (2D) materials offer prospects of unprecedented advances in device performance at the atomic limit, and a synergistic combination of 2D materials with silicon chips promises a heterogeneous platform to deliver massively enhanced potential based on silicon technology. Integration is achieved via three-dimensional monolithic construction of multifunctional high-rise 2D silicon chips, enabling enhanced performance by exploiting the vertical direction and the functional diversification of the silicon platform for applications in opto-electronics and sensing. Here we review the opportunities, progress and challenges of integrating atomically thin materials with silicon-based nanosystems, and also consider the prospects for computational and non-computational applications. Progress in integrating atomically thin two-dimensional materials with silicon-based technology is reviewed, together with the associated opportunities and challenges, and a roadmap for future applications is presented.
TL;DR: Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes.
TL;DR: This column introduces the dilemma of that seemingly easy task of teaching reference, and includes examples of course assignments to aid in that effort.
Abstract: Reference is the balance of employing a process to use materials to locate information. A key feature of teaching reference is to help students understand what a reference source is. This column introduces the dilemma of that seemingly easy task. It includes examples of course assignments to aid in that effort.
Daniel Taliun1, Daniel N. Harris2, Michael D. Kessler2, Jedidiah Carlson3 +191 more•Institutions (61)
TL;DR: The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation as well as resources and early insights from the sequence data.
Abstract: Summary paragraph The Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency
TL;DR: The authors' first-in-humans open-label pilot supports study feasibility and provides initial evidence that senolytics may alleviate physical dysfunction in IPF, warranting evaluation of DQ in larger randomized controlled trials for senescence-related diseases.
TL;DR: In this paper, the current research status of microstructure, properties and heat treatment of SLM processing aluminum alloys is systematically reviewed respectively and a future outlook is given at the end of this review paper.
Population Health Research Institute1, Brigham and Women's Hospital2, Katholieke Universiteit Leuven3, University of Grenoble4, King's College London5, Hospital Universitario La Paz6, Memorial Hospital of South Bend7, University of Texas at Austin8, Guy's and St Thomas' NHS Foundation Trust9, Dresden University of Technology10, Autonomous University of Madrid11, University of Calgary12
TL;DR: In patients with acute major bleeding associated with the use of a factor Xa inhibitor, treatment with andexanet markedly reduced anti–factor Xa activity, and 82% of patients had excellent or good hemostatic efficacy at 12 hours, as adjudicated according to prespecified criteria.
Abstract: Background Andexanet alfa is a modified recombinant inactive form of human factor Xa developed for reversal of factor Xa inhibitors. Methods We evaluated 352 patients who had acute major b...
University of Texas at Austin1, University of California, Irvine2, Stanford University3, Northwestern University4, Michigan State University5, University of Virginia6, University of Chicago7, Paradigm8, ICF International9, Arizona State University10, Pennsylvania State University11, Harvard University12, University of Pennsylvania13
TL;DR: A US national experiment showed that a short, online, self-administered growth mindset intervention can increase adolescents’ grades and advanced course-taking, and identified the types of school that were poised to benefit the most.
Abstract: A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention-which teaches that intellectual abilities can be developed-improved grades among lower-achieving students and increased overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. Notably, the study identified school contexts that sustained the effects of the growth mindset intervention: the intervention changed grades when peer norms aligned with the messages of the intervention. Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.
TL;DR: The role of diet quality, carbohydrate intake, fermentable FODMAPs, and prebiotic fiber in maintaining healthy gut flora is reviewed and the implications are discussed for various conditions including obesity, diabetes, irritable bowel syndrome, inflammatory bowel disease, depression, and cardiovascular disease.
Abstract: The gut microbiome plays an important role in human health and influences the development of chronic diseases ranging from metabolic disease to gastrointestinal disorders and colorectal cancer. Of increasing prevalence in Western societies, these conditions carry a high burden of care. Dietary patterns and environmental factors have a profound effect on shaping gut microbiota in real time. Diverse populations of intestinal bacteria mediate their beneficial effects through the fermentation of dietary fiber to produce short-chain fatty acids, endogenous signals with important roles in lipid homeostasis and reducing inflammation. Recent progress shows that an individual’s starting microbial profile is a key determinant in predicting their response to intervention with live probiotics. The gut microbiota is complex and challenging to characterize. Enterotypes have been proposed using metrics such as alpha species diversity, the ratio of Firmicutes to Bacteroidetes phyla, and the relative abundance of beneficial genera (e.g., Bifidobacterium, Akkermansia) versus facultative anaerobes (E. coli), pro-inflammatory Ruminococcus, or nonbacterial microbes. Microbiota composition and relative populations of bacterial species are linked to physiologic health along different axes. We review the role of diet quality, carbohydrate intake, fermentable FODMAPs, and prebiotic fiber in maintaining healthy gut flora. The implications are discussed for various conditions including obesity, diabetes, irritable bowel syndrome, inflammatory bowel disease, depression, and cardiovascular disease.
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TL;DR: This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
Abstract: Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. We propose a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself, and benchmark a series of innovations for the low-light image enhancement problem, including a global-local discriminator structure, a self-regularized perceptual loss fusion, and attention mechanism. Through extensive experiments, our proposed approach outperforms recent methods under a variety of metrics in terms of visual quality and subjective user study. Thanks to the great flexibility brought by unpaired training, EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world images from various domains. The code is available at \url{this https URL}
15 Jun 2019
TL;DR: A Pixel-wise Voting Network (PVNet) is introduced to regress pixel-wise vectors pointing to the keypoints and use these vectors to vote for keypoint locations, which creates a flexible representation for localizing occluded or truncated keypoints.
Abstract: This paper addresses the challenge of 6DoF pose estimation from a single RGB image under severe occlusion or truncation. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise vectors pointing to the keypoints and use these vectors to vote for keypoint locations. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large margin, while being efficient for real-time pose estimation. We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation. The code is available at https://zju3dv.github.io/pvnet/.
TL;DR: A community resource that includes ‘omics’ data from approximately 12,000 samples as part of the integrative Human Microbiome Project is reported, identifying harbingers of preterm birth in this cohort of women predominantly of African ancestry.
Abstract: The incidence of preterm birth exceeds 10% worldwide. There are significant disparities in the frequency of preterm birth among populations within countries, and women of African ancestry disproportionately bear the burden of risk in the United States. In the present study, we report a community resource that includes 'omics' data from approximately 12,000 samples as part of the integrative Human Microbiome Project. Longitudinal analyses of 16S ribosomal RNA, metagenomic, metatranscriptomic and cytokine profiles from 45 preterm and 90 term birth controls identified harbingers of preterm birth in this cohort of women predominantly of African ancestry. Women who delivered preterm exhibited significantly lower vaginal levels of Lactobacillus crispatus and higher levels of BVAB1, Sneathia amnii, TM7-H1, a group of Prevotella species and nine additional taxa. The first representative genomes of BVAB1 and TM7-H1 are described. Preterm-birth-associated taxa were correlated with proinflammatory cytokines in vaginal fluid. These findings highlight new opportunities for assessment of the risk of preterm birth.
TL;DR: A highly sensitive, flexible, and degradable pressure sensor fabricated by sandwiching porous MXene-impregnated tissue paper between a biodegradable polylactic acid (PLA) thin sheet and an interdigitated electrode-coated PLA thin sheet that exhibits high sensitivity with a low detection limit, broad range, fast response, and robust environmental degradability.
Abstract: Flexible and degradable pressure sensors have received tremendous attention for potential use in transient electronic skins, flexible displays, and intelligent robotics due to their portability, re...
TL;DR: It is demonstrated that enhancing the hydrability of the h-LAH could change the water state and partially activate the water, hence facilitating water evaporation, and raises the solar vapor generation to a record rate of ~3.6 kg m−2 hour−1 under 1 sun.
Abstract: Water purification by solar distillation is a promising technology to produce fresh water. However, solar vapor generation, is energy intensive, leading to a low water yield under natural sunlight. Therefore, developing new materials that can reduce the energy requirement of water vaporization and speed up solar water purification is highly desirable. Here, we introduce a highly hydratable light-absorbing hydrogel (h-LAH) consisting of polyvinyl alcohol and chitosan as the hydratable skeleton and polypyrrole as the light absorber, which can use less energy ( −2 hour −1 under 1 sun. The h-LAH-based solar still also exhibits long-term durability and antifouling functionality toward complex ionic contaminants.
TL;DR: It is demonstrated that radiotherapy induces tumor cell ferroptosis, an unappreciated mechanism and focus for the development of effective combinatorial cancer therapy.
Abstract: A challenge in oncology is to rationally and effectively integrate immunotherapy with traditional modalities, including radiotherapy. Here, we demonstrate that radiotherapy induces tumor-cell ferroptosis. Ferroptosis agonists augment and ferroptosis antagonists limit radiotherapy efficacy in tumor models. Immunotherapy sensitizes tumors to radiotherapy by promoting tumor-cell ferroptosis. Mechanistically, IFNγ derived from immunotherapy-activated CD8+ T cells and radiotherapy-activated ATM independently, yet synergistically, suppresses SLC7A11, a unit of the glutamate-cystine antiporter xc-, resulting in reduced cystine uptake, enhanced tumor lipid oxidation and ferroptosis, and improved tumor control. Thus, ferroptosis is an unappreciated mechanism and focus for the development of effective combinatorial cancer therapy. SIGNIFICANCE: This article describes ferroptosis as a previously unappreciated mechanism of action for radiotherapy. Further, it shows that ferroptosis is a novel point of synergy between immunotherapy and radiotherapy. Finally, it nominates SLC7A11, a critical regulator of ferroptosis, as a mechanistic determinant of synergy between radiotherapy and immunotherapy.This article is highlighted in the In This Issue feature, p. 1631.
TL;DR: The Gravity Recovery and Climate Experiment mission allows monitoring of changes in hydrology and the cryosphere with terrestrial and ocean applications and its contribution to the detection and quantification of climate change signals is focused on.
Abstract: Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations and understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends and improve service applications such as the U.S. Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi decadal record of mass variability in the Earth system is within reach.
TL;DR: A mitogenome toolkit MitoZ is developed, consisting of independent modules of de novo assembly, findMitoScaf (find Mitochondrial Scaffolds), annotation and visualization, that can generate mitogenomes assembly together with annotations and visualization results from HTS raw reads.
Abstract: Mitochondrial genome (mitogenome) plays important roles in evolutionary and ecological studies. It becomes routine to utilize multiple genes on mitogenome or the entire mitogenomes to investigate phylogeny and biodiversity of focal groups with the onset of High Throughput Sequencing (HTS) technologies. We developed a mitogenome toolkit MitoZ, consisting of independent modules of de novo assembly, findMitoScaf (find Mitochondrial Scaffolds), annotation and visualization, that can generate mitogenome assembly together with annotation and visualization results from HTS raw reads. We evaluated its performance using a total of 50 samples of which mitogenomes are publicly available. The results showed that MitoZ can recover more full-length mitogenomes with higher accuracy compared to the other available mitogenome assemblers. Overall, MitoZ provides a one-click solution to construct the annotated mitogenome from HTS raw data and will facilitate large scale ecological and evolutionary studies. MitoZ is free open source software distributed under GPLv3 license and available at https://github.com/linzhi2013/MitoZ.
TL;DR: In this paper, the mass, spin, and redshift distributions of binary black hole (BBH) mergers with LIGO and Advanced Virgo observations were analyzed using phenomenological population models.
Abstract: We present results on the mass, spin, and redshift distributions with phenomenological population models using the 10 binary black hole (BBH) mergers detected in the first and second observing runs completed by Advanced LIGO and Advanced Virgo. We constrain properties of the BBH mass spectrum using models with a range of parameterizations of the BBH mass and spin distributions. We find that the mass distribution of the more massive BH in such binaries is well approximated by models with no more than 1% of BHs more massive than 45 M and a power-law index of (90% credibility). We also show that BBHs are unlikely to be composed of BHs with large spins aligned to the orbital angular momentum. Modeling the evolution of the BBH merger rate with redshift, we show that it is flat or increasing with redshift with 93% probability. Marginalizing over uncertainties in the BBH population, we find robust estimates of the BBH merger rate density of R= (90% credibility). As the BBH catalog grows in future observing runs, we expect that uncertainties in the population model parameters will shrink, potentially providing insights into the formation of BHs via supernovae, binary interactions of massive stars, stellar cluster dynamics, and the formation history of BHs across cosmic time.
TL;DR: This Review looks at several key aspects of modern neuroevolution, including large-scale computing, the benefits of novelty and diversity, the power of indirect encoding, and the field’s contributions to meta-learning and architecture search.
Abstract: Much of recent machine learning has focused on deep learning, in which neural network weights are trained through variants of stochastic gradient descent. An alternative approach comes from the field of neuroevolution, which harnesses evolutionary algorithms to optimize neural networks, inspired by the fact that natural brains themselves are the products of an evolutionary process. Neuroevolution enables important capabilities that are typically unavailable to gradient-based approaches, including learning neural network building blocks (for example activation functions), hyperparameters, architectures and even the algorithms for learning themselves. Neuroevolution also differs from deep learning (and deep reinforcement learning) by maintaining a population of solutions during search, enabling extreme exploration and massive parallelization. Finally, because neuroevolution research has (until recently) developed largely in isolation from gradient-based neural network research, it has developed many unique and effective techniques that should be effective in other machine learning areas too. This Review looks at several key aspects of modern neuroevolution, including large-scale computing, the benefits of novelty and diversity, the power of indirect encoding, and the field’s contributions to meta-learning and architecture search. Our hope is to inspire renewed interest in the field as it meets the potential of the increasing computation available today, to highlight how many of its ideas can provide an exciting resource for inspiration and hybridization to the deep learning, deep reinforcement learning and machine learning communities, and to explain how neuroevolution could prove to be a critical tool in the long-term pursuit of artificial general intelligence. Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.