Showing papers by "Cornell University published in 2017"
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21 Jul 2017TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Abstract: Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.
27,821 citations
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21 Jul 2017TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Abstract: Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.
16,727 citations
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07 Aug 2017TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
Abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.
12,161 citations
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Theo Vos1, Amanuel Alemu Abajobir, Kalkidan Hassen Abate2, Cristiana Abbafati3 +775 more•Institutions (305)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.
10,401 citations
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Cornell University1, University of Colorado Denver2, Aix-Marseille University3, The Royal Marsden NHS Foundation Trust4, University of Michigan5, Swansea University6, German Cancer Research Center7, Cross Cancer Institute8, University of Zurich9, Princess Margaret Cancer Centre10, Netherlands Cancer Institute11, University of Sydney12, Katholieke Universiteit Leuven13, Complutense University of Madrid14, Peter MacCallum Cancer Centre15, University of Paris16, University of Melbourne17, Northwestern University18, Bristol-Myers Squibb19, University of Duisburg-Essen20
TL;DR: Among patients with advanced melanoma, significantly longer overall survival occurred with combination therapy with nivolumab plus ipilimumab or with n ivolumAB alone than with ipil optimumab alone.
Abstract: BackgroundNivolumab combined with ipilimumab resulted in longer progression-free survival and a higher objective response rate than ipilimumab alone in a phase 3 trial involving patients with advanced melanoma. We now report 3-year overall survival outcomes in this trial. MethodsWe randomly assigned, in a 1:1:1 ratio, patients with previously untreated advanced melanoma to receive nivolumab at a dose of 1 mg per kilogram of body weight plus ipilimumab at a dose of 3 mg per kilogram every 3 weeks for four doses, followed by nivolumab at a dose of 3 mg per kilogram every 2 weeks; nivolumab at a dose of 3 mg per kilogram every 2 weeks plus placebo; or ipilimumab at a dose of 3 mg per kilogram every 3 weeks for four doses plus placebo, until progression, the occurrence of unacceptable toxic effects, or withdrawal of consent. Randomization was stratified according to programmed death ligand 1 (PD-L1) status, BRAF mutation status, and metastasis stage. The two primary end points were progression-free survival a...
3,794 citations
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University of Udine1, École Polytechnique Fédérale de Lausanne2, University of Lugano3, Leipzig University4, University of Paris5, University of North Texas6, Princeton University7, National Research Council8, International School for Advanced Studies9, Cornell University10, University of Lincoln11, University of Milan12, École Polytechnique13, International Centre for Theoretical Physics14, University of Paderborn15, University of Oxford16, Jožef Stefan Institute17, University of Padua18, Sapienza University of Rome19, Vietnam Academy of Science and Technology20, University of British Columbia21, University of Lorraine22, Centre national de la recherche scientifique23, University of Zurich24, École Normale Supérieure25, Université Paris-Saclay26, Wake Forest University27, Temple University28
TL;DR: Recent extensions and improvements are described, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.
Abstract: Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudopotential and projector-augmented-wave approaches Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement their ideas In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software
3,638 citations
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Simon I. Hay, Amanuel Alemu Abajobir1, Kalkidan Hassen Abate2, Cristiana Abbafati3 +800 more•Institutions (32)
TL;DR: At a global level, DALYs and HALE continue to show improvements and the importance of continued health interventions, which has changed in most locations in pace with the gross domestic product per person, education, and family planning.
3,029 citations
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University of Udine1, University of Lugano2, École Polytechnique Fédérale de Lausanne3, Leipzig University4, University of Paris5, University of North Texas6, Princeton University7, National Research Council8, International School for Advanced Studies9, Cornell University10, University of Lincoln11, University of Milan12, École Polytechnique13, International Centre for Theoretical Physics14, University of Paderborn15, University of Oxford16, Jožef Stefan Institute17, University of Padua18, Sapienza University of Rome19, Vietnam Academy of Science and Technology20, University of British Columbia21, University of Lorraine22, Centre national de la recherche scientifique23, University of Zurich24, École Normale Supérieure25, Université Paris-Saclay26, Wake Forest University27, Temple University28
TL;DR: Quantum ESPRESSO as discussed by the authors is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density functional theory, density functional perturbation theory, and many-body perturbations theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches.
Abstract: Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches. Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement theirs ideas. In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.
2,818 citations
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01 Oct 2017TL;DR: In this paper, adaptive instance normalization (AdaIN) is proposed to align the mean and variance of the content features with those of the style features, which enables arbitrary style transfer in real-time.
Abstract: Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.
2,266 citations
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22 May 2017TL;DR: This work quantitatively investigates how machine learning models leak information about the individual data records on which they were trained and empirically evaluates the inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon.
Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.
2,059 citations
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30 Oct 2017TL;DR: In this paper, the authors proposed a secure aggregation of high-dimensional data for federated deep neural networks, which allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner without learning each user's individual contribution.
Abstract: We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.
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TL;DR: It is discovered that modern neural networks, unlike those from a decade ago, are poorly calibrated, and on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
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17 Jul 2017TL;DR: This article found that depth, width, weight decay, and batch normalization are important factors influencing confidence calibration of neural networks, and that temperature scaling is surprisingly effective at calibrating predictions.
Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
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TL;DR: Current knowledge on the mechanisms that underlie the activation of immune responses against dying cells and their pathophysiological relevance are reviewed.
Abstract: Immunogenicity depends on two key factors: antigenicity and adjuvanticity. The presence of exogenous or mutated antigens explains why infected cells and malignant cells can initiate an adaptive immune response provided that the cells also emit adjuvant signals as a consequence of cellular stress and death. Several infectious pathogens have devised strategies to control cell death and limit the emission of danger signals from dying cells, thereby avoiding immune recognition. Similarly, cancer cells often escape immunosurveillance owing to defects in the molecular machinery that underlies the release of endogenous adjuvants. Here, we review current knowledge on the mechanisms that underlie the activation of immune responses against dying cells and their pathophysiological relevance.
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The Nature Conservancy1, James Madison University2, Woods Hole Research Center3, Ohio State University4, Institute of Ecosystem Studies5, Resources For The Future6, University of Aberdeen7, Cornell University8, Colorado State University9, World Resources Institute10, Swedish University of Agricultural Sciences11, University of Minnesota12, University of Maryland, College Park13, University of Florida14, Wetlands International15, University of Vermont16
TL;DR: It is shown that NCS can provide over one-third of the cost-effective climate mitigation needed between now and 2030 to stabilize warming to below 2 °C.
Abstract: Better stewardship of land is needed to achieve the Paris Climate Agreement goal of holding warming to below 2 °C; however, confusion persists about the specific set of land stewardship options available and their mitigation potential. To address this, we identify and quantify "natural climate solutions" (NCS): 20 conservation, restoration, and improved land management actions that increase carbon storage and/or avoid greenhouse gas emissions across global forests, wetlands, grasslands, and agricultural lands. We find that the maximum potential of NCS-when constrained by food security, fiber security, and biodiversity conservation-is 23.8 petagrams of CO2 equivalent (PgCO2e) y-1 (95% CI 20.3-37.4). This is ≥30% higher than prior estimates, which did not include the full range of options and safeguards considered here. About half of this maximum (11.3 PgCO2e y-1) represents cost-effective climate mitigation, assuming the social cost of CO2 pollution is ≥100 USD MgCO2e-1 by 2030. Natural climate solutions can provide 37% of cost-effective CO2 mitigation needed through 2030 for a >66% chance of holding warming to below 2 °C. One-third of this cost-effective NCS mitigation can be delivered at or below 10 USD MgCO2-1 Most NCS actions-if effectively implemented-also offer water filtration, flood buffering, soil health, biodiversity habitat, and enhanced climate resilience. Work remains to better constrain uncertainty of NCS mitigation estimates. Nevertheless, existing knowledge reported here provides a robust basis for immediate global action to improve ecosystem stewardship as a major solution to climate change.
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TL;DR: It is shown here that patients with depression can be subdivided into four neurophysiological subtypes defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks, which may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.
Abstract: Biomarkers have transformed modern medicine but remain largely elusive in psychiatry, partly because there is a weak correspondence between diagnostic labels and their neurobiological substrates. Like other neuropsychiatric disorders, depression is not a unitary disease, but rather a heterogeneous syndrome that encompasses varied, co-occurring symptoms and divergent responses to treatment. By using functional magnetic resonance imaging (fMRI) in a large multisite sample (n = 1,188), we show here that patients with depression can be subdivided into four neurophysiological subtypes (‘biotypes’) defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. Clustering patients on this basis enabled the development of diagnostic classifiers (biomarkers) with high (82–93%) sensitivity and specificity for depression subtypes in multisite validation (n = 711) and out-of-sample replication (n = 477) data sets. These biotypes cannot be differentiated solely on the basis of clinical features, but they are associated with differing clinical-symptom profiles. They also predict responsiveness to transcranial magnetic stimulation therapy (n = 154). Our results define novel subtypes of depression that transcend current diagnostic boundaries and may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.
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TL;DR: A perspective on the roles of class I PI3Ks in the regulation of cellular metabolism and in immune system functions is provided, two topics closely intertwined with cancer biology.
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03 May 2017TL;DR: This work used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords and labels a sample of these tweets into three categories: those containinghate speech, only offensive language, and those with neither.
Abstract: A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.
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Queen's University1, European Organisation for Research and Treatment of Cancer2, Merck & Co.3, Columbia University Medical Center4, NewYork–Presbyterian Hospital5, Mayo Clinic6, Harvard University7, National Institutes of Health8, VU University Medical Center9, Memorial Sloan Kettering Cancer Center10, Genentech11, Ludwig Institute for Cancer Research12, Cornell University13, University Medical Center Groningen14
TL;DR: This guideline describes a standard approach to solid tumour measurements and definitions for objective change in tumour size for use in trials in which an immunotherapy is used and defines the minimum datapoints required from future trials to facilitate the compilation of a data warehouse to later validate iRECIST.
Abstract: Tumours respond differently to immunotherapies compared with chemotherapeutic drugs, raising questions about the assessment of changes in tumour burden-a mainstay of evaluation of cancer therapeutics that provides key information about objective response and disease progression. A consensus guideline-iRECIST-was developed by the RECIST working group for the use of modified Response Evaluation Criteria in Solid Tumours (RECIST version 1.1) in cancer immunotherapy trials, to ensure consistent design and data collection, facilitate the ongoing collection of trial data, and ultimate validation of the guideline. This guideline describes a standard approach to solid tumour measurements and definitions for objective change in tumour size for use in trials in which an immunotherapy is used. Additionally, it defines the minimum datapoints required from future trials and those currently in development to facilitate the compilation of a data warehouse to use to later validate iRECIST. An unprecedented number of trials have been done, initiated, or are planned to test new immune modulators for cancer therapy using a variety of modified response criteria. This guideline will allow consistent conduct, interpretation, and analysis of trials of immunotherapies.
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TL;DR: Although screening for rarer atypical forms of diabetic neuropathy may be warranted, DSPN and autonomic neuropathy are the most common forms encountered in practice and the strongest available evidence regarding treatment pertains to these forms.
Abstract: Diabetic neuropathies are the most prevalent chronic complications of diabetes. This heterogeneous group of conditions affects different parts of the nervous system and presents with diverse clinical manifestations. The early recognition and appropriate management of neuropathy in the patient with diabetes is important for a number of reasons:
1. Diabetic neuropathy is a diagnosis of exclusion. Nondiabetic neuropathies may be present in patients with diabetes and may be treatable by specific measures.
2. A number of treatment options exist for symptomatic diabetic neuropathy.
3. Up to 50% of diabetic peripheral neuropathies may be asymptomatic. If not recognized and if preventive foot care is not implemented, patients are at risk for injuries to their insensate feet.
4. Recognition and treatment of autonomic neuropathy may improve symptoms, reduce sequelae, and improve quality of life.
Among the various forms of diabetic neuropathy, distal symmetric polyneuropathy (DSPN) and diabetic autonomic neuropathies, particularly cardiovascular autonomic neuropathy (CAN), are by far the most studied (1–4). There are several atypical forms of diabetic neuropathy as well (1–4). Patients with prediabetes may also develop neuropathies that are similar to diabetic neuropathies (5–10). Table 1 provides a comprehensive classification scheme for the diabetic neuropathies.
View this table:
Table 1
Classification for diabetic neuropathies
Due to a lack of treatments that target the underlying nerve damage, prevention is the key component of diabetes care. Screening for symptoms and signs of diabetic neuropathy is also critical in clinical practice, as it may detect the earliest stages of neuropathy, enabling early intervention. Although screening for rarer atypical forms of diabetic neuropathy may be warranted, DSPN and autonomic neuropathy are the most common forms encountered in practice. The strongest available evidence regarding treatment pertains to these forms.
This Position Statement is based on several recent technical reviews, to which the reader is referred for detailed discussion …
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TL;DR: A synthetic strategy to grow Janus monolayers of transition metal dichalcogenides breaking the out-of-plane structural symmetry of MoSSe by means of scanning transmission electron microscopy and energy-dependent X-ray photoelectron spectroscopy is reported.
Abstract: Structural symmetry-breaking plays a crucial role in determining the electronic band structures of two-dimensional materials. Tremendous efforts have been devoted to breaking the in-plane symmetry of graphene with electric fields on AB-stacked bilayers or stacked van der Waals heterostructures. In contrast, transition metal dichalcogenide monolayers are semiconductors with intrinsic in-plane asymmetry, leading to direct electronic bandgaps, distinctive optical properties and great potential in optoelectronics. Apart from their in-plane inversion asymmetry, an additional degree of freedom allowing spin manipulation can be induced by breaking the out-of-plane mirror symmetry with external electric fields or, as theoretically proposed, with an asymmetric out-of-plane structural configuration. Here, we report a synthetic strategy to grow Janus monolayers of transition metal dichalcogenides breaking the out-of-plane structural symmetry. In particular, based on a MoS2 monolayer, we fully replace the top-layer S with Se atoms. We confirm the Janus structure of MoSSe directly by means of scanning transmission electron microscopy and energy-dependent X-ray photoelectron spectroscopy, and prove the existence of vertical dipoles by second harmonic generation and piezoresponse force microscopy measurements.
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TL;DR: Evidence supports a conceptual shift in the mechanisms of neurovascular coupling, from a unidimensional process involving neuronal-astrocytic signaling to local blood vessels to a multidimensional one in which mediators released from multiple cells engage distinct signaling pathways and effector systems across the entire cerebrovascular network in a highly orchestrated manner.
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TL;DR: This paper presents a simple yet effective approach that for the first time enables arbitrary style transfer in real-time, comparable to the fastest existing approach, without the restriction to a pre-defined set of styles.
Abstract: Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.
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01 Jan 2017TL;DR: Some of the ways in which key notions of fairness are incompatible with each other are suggested, and hence a framework for thinking about the trade-offs between them is provided.
Abstract: Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.
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TL;DR: Blood profiling of peripheral blood from patients with stage IV melanoma before and after treatment with the PD-1-targeting antibody pembrolizumab is used to identify pharmacodynamic changes in circulating exhausted-phenotype CD8 T cells (Tex cells) and identify a clinically accessible potential on-treatment predictor of response to PD- 1 blockade.
Abstract: Despite the success of monotherapies based on blockade of programmed cell death 1 (PD-1) in human melanoma, most patients do not experience durable clinical benefit. Pre-existing T-cell infiltration and/or the presence of PD-L1 in tumours may be used as indicators of clinical response; however, blood-based profiling to understand the mechanisms of PD-1 blockade has not been widely explored. Here we use immune profiling of peripheral blood from patients with stage IV melanoma before and after treatment with the PD-1-targeting antibody pembrolizumab and identify pharmacodynamic changes in circulating exhausted-phenotype CD8 T cells (Tex cells). Most of the patients demonstrated an immunological response to pembrolizumab. Clinical failure in many patients was not solely due to an inability to induce immune reinvigoration, but rather resulted from an imbalance between T-cell reinvigoration and tumour burden. The magnitude of reinvigoration of circulating Tex cells determined in relation to pretreatment tumour burden correlated with clinical response. By focused profiling of a mechanistically relevant circulating T-cell subpopulation calibrated to pretreatment disease burden, we identify a clinically accessible potential on-treatment predictor of response to PD-1 blockade.
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TL;DR: In this article, the authors used Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period to provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during this time.
Abstract: Using Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, we provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during this time. By 2010, conventional human capital variables taken together explained little of the gender wage gap, while gender differences in occupation and industry continued to be important. Moreover, the gender pay gap declined much more slowly at the top of the wage distribution than at the middle or bottom and by 2010 was noticeably higher at the top. We then survey the literature to identify what has been learned about the explanations for the gap. We conclude that many of the traditional explanations continue to have salience. Although human-capital factors are now relatively unimportant in the aggregate, women's work force interruptions and shorter hours remain significant in high-skilled occupations, possibly due to compensating differentials. Gender differences in occupations and industries, as well as differences in gender roles and the gender division of labor remain important, and research based on experimental evidence strongly suggests that discrimination cannot be discounted. Psychological attributes or noncognitive skills comprise one of the newer explanations for gender differences in outcomes. Our effort to assess the quantitative evidence on the importance of these factors suggests that they account for a small to moderate portion of the gender pay gap, considerably smaller than, say, occupation and industry effects, though they appear to modestly contribute to these differences.
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Cornell University1, University of Porto2, Imperial College London3, National Institutes of Health4, Memorial Sloan Kettering Cancer Center5, Princeton University6, Lawrence Berkeley National Laboratory7, Garvan Institute of Medical Research8, Stanford University9, University of Copenhagen10, Fred Hutchinson Cancer Research Center11
TL;DR: This Review summarizes the main processes and new mechanisms involved in the formation of the pre-metastatic niche and describes the main mechanisms used to modify organs of future metastasis.
Abstract: It is well established that organs of future metastasis are not passive receivers of circulating tumour cells, but are instead selectively and actively modified by the primary tumour before metastatic spread has even occurred. Sowing the 'seeds' of metastasis requires the action of tumour-secreted factors and tumour-shed extracellular vesicles that enable the 'soil' at distant metastatic sites to encourage the outgrowth of incoming cancer cells. In this Review, we summarize the main processes and new mechanisms involved in the formation of the pre-metastatic niche.
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Cornell University1, Paris Descartes University2, University of Massachusetts Medical School3, Spanish National Research Council4, University of Rome Tor Vergata5, Boston Children's Hospital6, University of Pittsburgh7, National Scientific and Technical Research Council8, National University of Cuyo9, Albert Einstein College of Medicine10, University of California, San Francisco11, University of New Mexico12, University of Split13, Goethe University Frankfurt14, University of Helsinki15, University of Salento16, German Cancer Research Center17, Virginia Commonwealth University18, St. Jude Children's Research Hospital19, Discovery Institute20, Harvard University21, University of Tromsø22, Eötvös Loránd University23, Hungarian Academy of Sciences24, New York University25, University of Vienna26, Babraham Institute27, University of South Australia28, University of Texas Southwestern Medical Center29, Howard Hughes Medical Institute30, University of Oviedo31, University of Graz32, National Institutes of Health33, Queens College34, City University of New York35, University of Tokyo36, University of Zurich37, Novartis38, Austrian Academy of Sciences39, University of Groningen40, University of Cambridge41, University of Padua42, University of Oxford43, University of Bern44, University of Oslo45, Foundation for Research & Technology – Hellas46, University of Crete47, Francis Crick Institute48, Osaka University49, Icahn School of Medicine at Mount Sinai50
TL;DR: A panel of leading experts in the field attempts here to define several autophagy‐related terms based on specific biochemical features to formulate recommendations that facilitate the dissemination of knowledge within and outside the field of autophagic research.
Abstract: Over the past two decades, the molecular machinery that underlies autophagic responses has been characterized with ever increasing precision in multiple model organisms. Moreover, it has become clear that autophagy and autophagy-related processes have profound implications for human pathophysiology. However, considerable confusion persists about the use of appropriate terms to indicate specific types of autophagy and some components of the autophagy machinery, which may have detrimental effects on the expansion of the field. Driven by the overt recognition of such a potential obstacle, a panel of leading experts in the field attempts here to define several autophagy-related terms based on specific biochemical features. The ultimate objective of this collaborative exchange is to formulate recommendations that facilitate the dissemination of knowledge within and outside the field of autophagy research.
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Memorial Sloan Kettering Cancer Center1, Cornell University2, University of Texas MD Anderson Cancer Center3, University of Colorado Denver4, Harvard University5, NewYork–Presbyterian Hospital6, Northwestern University7, Sarah Cannon Research Institute8, University of Texas Southwestern Medical Center9, City of Hope National Medical Center10, Cleveland Clinic11, University of Miami12, Stanford University13, Institut Gustave Roussy14, Université Paris-Saclay15, University of Oxford16, Celgene17, Agios Pharmaceuticals18
TL;DR: Inducing differentiation of myeloblasts, not cytotoxicity, seems to drive the clinical efficacy of enasidenib, a first-in-class, oral, selective inhibitor of mutant-IDH2 enzymes.
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TL;DR: It is shown that the DNA exonuclease Trex1 is induced by radiation doses above 12–18 Gy in different cancer cells, and attenuates their immunogenicity by degrading DNA that accumulates in the cytosol upon radiation.
Abstract: Trex1 is an exonuclease that degrades cytosolic DNA and has been associated with modulation of interferon responses in autoimmunity and viral infections. Here, the authors show that Trex1 attenuates the immunogenicity of cancer cells treated with high radiation doses by d…