ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

Modular and predictable assembly of porous organic molecular crystals

Nanoporous molecular frameworks are important in applications such as separation, storage and catalysis. Empirical rules exist for their assembly but it is still challenging to place and segregate functionality in three-dimensional porous solids in a predictable way. Indeed, recent studies of mixed crystalline frameworks suggest a preference for the statistical distribution of functionalities throughout the pores rather than, for example, the functional group localization found in the reactive sites of enzymes. This is a potential limitation for 'one-pot' chemical syntheses of porous frameworks from simple starting materials. An alternative strategy is to prepare porous solids from synthetically preorganized molecular pores. In principle, functional organic pore modules could be covalently prefabricated and then assembled to produce materials with specific properties. However, this vision of mix-and-match assembly is far from being realized, not least because of the challenge in reliably predicting three-dimensional structures for molecular crystals, which lack the strong directional bonding found in networks. Here we show that highly porous crystalline solids can be produced by mixing different organic cage modules that self-assemble by means of chiral recognition. The structures of the resulting materials can be predicted computationally, allowing in silico materials design strategies. The constituent pore modules are synthesized in high yields on gram scales in a one-step reaction. Assembly of the porous co-crystals is as simple as combining the modules in solution and removing the solvent. In some cases, the chiral recognition between modules can be exploited to produce porous organic nanoparticles. We show that the method is valid for four different cage modules and can in principle be generalized in a computationally predictable manner based on a lock-and-key assembly between modules

Spontaneous charging affects the motion of sliding drops

Water drops moving on surfaces are not only an everyday phenomenon seen on windows but also form an essential part of many industrial processes. Previous understanding is that drop motion is dictated by viscous dissipation and activated dynamics at the contact line. Here we demonstrate that these two effects cannot fully explain the complex paths of sliding or impacting drops. To accurately determine the forces experienced by moving drops, we imaged their trajectory when sliding down a tilted surface, and applied the relevant equations of motion. We found that drop motion on low-permittivity substrates is substantially influenced by electrostatic forces. Our findings confirm that electrostatics must be taken into consideration for the description of the motion of water, aqueous electrolytes and ethylene glycol on hydrophobic surfaces. Our results are relevant for improving the control of drop motion in many applications, including printing, microfluidics, water management and triboelectric nanogenerators

Profiling Mechanisms of Alkane Hydroxylase Activity In Vivo Using the Diagnostic Substrate Norcarane

SummaryMechanistically informative chemical probes are used to characterize the activity of functional alkane hydroxylases in whole cells. Norcarane is a substrate used to reveal the lifetime of radical intermediates formed during alkane oxidation. Results from oxidations of this probe with organisms that contain the two most prevalent medium-chain-length alkane-oxidizing metalloenzymes, alkane ω-monooxygenase (AlkB) and cytochrome P450 (CYP), are reported. The results—radical lifetimes of 1–7 ns for AlkB and less than 100 ps for CYP—indicate that these two classes of enzymes are mechanistically distinguishable and that whole-cell mechanistic assays can identify the active hydroxylase. The oxidation of norcarane by several recently isolated strains (Hydrocarboniphaga effusa AP103, rJ4, and rJ5, whose alkane-oxidizing enzymes have not yet been identified) is also reported. Radical lifetimes of 1–3 ns are observed, consistent with these organisms containing an AlkB-like enzyme and inconsistent with their employing a CYP-like enzyme for growth on hydrocarbons

Grassmannian flows and applications to nonlinear partial differential equations

We show how solutions to a large class of partial differential equations with nonlocal Riccati-type nonlinearities can be generated from the corresponding linearized equations, from arbitrary initial data. It is well known that evolutionary matrix Riccati equations can be generated by projecting linear evolutionary flows on a Stiefel manifold onto a coordinate chart of the underlying Grassmann manifold. Our method relies on extending this idea to the infinite dimensional case. The key is an integral equation analogous to the Marchenko equation in integrable systems, that represents the coodinate chart map. We show explicitly how to generate such solutions to scalar partial differential equations of arbitrary order with nonlocal quadratic nonlinearities using our approach. We provide numerical simulations that demonstrate the generation of solutions to Fisher--Kolmogorov--Petrovskii--Piskunov equations with nonlocal nonlinearities. We also indicate how the method might extend to more general classes of nonlinear partial differential systems.Comment: 26 pages, 2 figure

The Growth of Black Holes and Their Host Spheroids in (Sub)mm-loud QSOs at High Redshift

We study the growth of black holes and stellar population in spheroids at high redshift using several (sub)mm-loud QSO samples. Applying the same criteria established in an earlier work, we find that, similar to IR QSOs at low redshift, the far-infrared emission of these (sub)mm-loud QSOs mainly originates from dust heated by starbursts. By combining low-z IR QSOs and high-z (sub)mm-loud QSOs, we find a trend that the star formation rate (\Mstardot) increases with the accretion rate (\Mdot). We compare the values of \Mstardot/\Mdot for submm emitting galaxies (SMGs), far-infrared ultraluminous/hyperluminous QSOs and typical QSOs, and construct a likely evolution scenario for these objects. The (sub)mm-loud QSO transition phase has both high \Mdot and \Mstardot and hence is important for establishing the correlation between the masses of black holes and spheroids.Comment: 19 pages,3 figures,submitted to Chin. J. Astron. Astrophys. This paper was first prepared for publication on August 10th, 200

Identifying protein complexes directly from high-throughput TAP data with Markov random fields

<p>Abstract</p> <p>Background</p> <p>Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.</p> <p>Results</p> <p>We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes.</p> <p>Conclusion</p> <p>We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes.</p

A Multi-Proxy Assessment of the Impact of Environmental Instability on Late Holocene (4500-3800 BP) Native American Villages of the Georgia Coast

Circular shell rings along the South Atlantic Coast of North America are the remnants of some of the earliest villages that emerged during the Late Archaic (5000-3000 BP). Many of these villages, however, were abandoned during the Terminal Late Archaic (ca 3800-3000 BP). We combine Bayesian chronological modeling with mollusk shell geochemistry and oyster paleobiology to understand the nature and timing of environmental change associated with the emergence and abandonment of circular shell ring villages on Sapelo Island, Georgia. Our Bayesian models indicate that Native Americans occupied the three Sapelo shell rings at varying times with some generational overlap. By the end of the complex\u27s occupation, only Ring III was occupied before abandonment ca. 3845 BP. Ring III also consists of statistically smaller oysters harvested from less saline estuaries compared to earlier occupations. Integrating shell biochemical and paleobiological data with recent tree ring analyses shows a clear pattern of environmental fluctuations throughout the period in which the rings were occupied. We argue that as the environment became unstable around 4300 BP, aggregation at villages provided a way to effectively manage fisheries that are highly sensitive to environmental change. However, with the eventual collapse of oyster fisheries and subsequent rebound in environmental conditions ca. post-3800 BP, people dispersed from shell rings, and shifted to non-marine subsistence economies and other types of settlements. This study provides the most comprehensive evidence for correlations between large-scale environmental change and societal transformations on the Georgia coast during the Late Archaic period

Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scale of the dataset means that it can no longer be easily processed using standard software on commodity hardware. Secondly, the size of the research group means that replicability and consistency of key analytics used across multiple publications becomes an issue. We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset

V3 Loop Sequence Space Analysis Suggests Different Evolutionary Patterns of CCR5- and CXCR4-Tropic HIV

The V3 loop of human immunodeficiency virus type 1 (HIV-1) is critical for coreceptor binding and is the main determinant of which of the cellular coreceptors, CCR5 or CXCR4, the virus uses for cell entry. The aim of this study is to provide a large-scale data driven analysis of HIV-1 coreceptor usage with respect to the V3 loop evolution and to characterize CCR5- and CXCR4-tropic viral phenotypes previously studied in small- and medium-scale settings. We use different sequence similarity measures, phylogenetic and clustering methods in order to analyze the distribution in sequence space of roughly 1000 V3 loop sequences and their tropism phenotypes. This analysis affords a means of characterizing those sequences that are misclassified by several sequence-based coreceptor prediction methods, as well as predicting the coreceptor using the location of the sequence in sequence space and of relating this location to the CD4+ T-cell count of the patient. We support previous findings that the usage of CCR5 is correlated with relatively high sequence conservation whereas CXCR4-tropic viruses spread over larger regions in sequence space. The incorrectly predicted sequences are mostly located in regions in which their phenotype represents the minority or in close vicinity of regions dominated by the opposite phenotype. Nevertheless, the location of the sequence in sequence space can be used to improve the accuracy of the prediction of the coreceptor usage. Sequences from patients with high CD4+ T-cell counts are relatively highly conserved as compared to those of immunosuppressed patients. Our study thus supports hypotheses of an association of immune system depletion with an increase in V3 loop sequence variability and with the escape of the viral sequence to distant parts of the sequence space