Concept: Open problem
How to determine the community structure of complex networks is an open question. It is critical to establish the best strategies for community detection in networks of unknown structure. Here, using standard synthetic benchmarks, we show that none of the algorithms hitherto developed for community structure characterization perform optimally. Significantly, evaluating the results according to their modularity, the most popular measure of the quality of a partition, systematically provides mistaken solutions. However, a novel quality function, called Surprise, can be used to elucidate which is the optimal division into communities. Consequently, we show that the best strategy to find the community structure of all the networks examined involves choosing among the solutions provided by multiple algorithms the one with the highest Surprise value. We conclude that Surprise maximization precisely reveals the community structure of complex networks.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 5 years ago
There is a rising concern regarding the accumulation of floating plastic debris in the open ocean. However, the magnitude and the fate of this pollution are still open questions. Using data from the Malaspina 2010 circumnavigation, regional surveys, and previously published reports, we show a worldwide distribution of plastic on the surface of the open ocean, mostly accumulating in the convergence zones of each of the five subtropical gyres with comparable density. However, the global load of plastic on the open ocean surface was estimated to be on the order of tens of thousands of tons, far less than expected. Our observations of the size distribution of floating plastic debris point at important size-selective sinks removing millimeter-sized fragments of floating plastic on a large scale. This sink may involve a combination of fast nano-fragmentation of the microplastic into particles of microns or smaller, their transference to the ocean interior by food webs and ballasting processes, and processes yet to be discovered. Resolving the fate of the missing plastic debris is of fundamental importance to determine the nature and significance of the impacts of plastic pollution in the ocean.
Biologically inspired multilevel approach for multiple moving targets detection from airborne forward-looking infrared sequences
- Journal of the Optical Society of America. A, Optics, image science, and vision
- Published over 5 years ago
In this paper, a biologically inspired multilevel approach for simultaneously detecting multiple independently moving targets from airborne forward-looking infrared (FLIR) sequences is proposed. Due to the moving platform, low contrast infrared images, and nonrepeatability of the target signature, moving targets detection from FLIR sequences is still an open problem. Avoiding six parameter affine or eight parameter planar projective transformation matrix estimation of two adjacent frames, which are utilized by existing moving targets detection approaches to cope with the moving infrared camera and have become the bottleneck for the further elevation of the moving targets detection performance, the proposed moving targets detection approach comprises three sequential modules: motion perception for efficiently extracting motion cues, attended motion views extraction for coarsely localizing moving targets, and appearance perception in the local attended motion views for accurately detecting moving targets. Experimental results demonstrate that the proposed approach is efficient and outperforms the compared state-of-the-art approaches.
- IEEE transactions on pattern analysis and machine intelligence
- Published about 4 years ago
In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical methods have been developed for solving these problems, it remains an open problem to find a numerical method which is not only empirically fast, but also has mathematically guaranteed strong convergence. In this paper, we propose an alternating iteration scheme for solving such problems. A rigorous convergence analysis shows that the proposed method satisfies the global convergence property: the whole sequence of iterates is convergent and converges to a critical point. Besides the theoretical soundness, the practical benefit of the proposed method is validated in applications including image restoration and recognition. Experiments show that the proposed method achieves similar results with less computation when compared to widely used methods such as K-SVD.
- IEEE transactions on pattern analysis and machine intelligence
- Published almost 4 years ago
Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in  based on hand-crafted features on the VOC 2012 dataset.
Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets -or clusters- and then generating in an automatic way a new song which is somehow “inspired” in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is.
Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.
Quantum theory has linked microscopic currents and macroscopic polarizations of ferroelectrics, but the interplay of lattice excitations and charge dynamics on atomic length and time scales is an open problem. Upon phonon excitation in the prototypical ferroelectric ammonium sulfate [(NH4)2SO4], we determine transient charge density maps by femtosecond x-ray diffraction. A newly discovered low frequency-mode with a 3 ps period and sub-picometer amplitudes induces periodic charge relocations over some 100 pm, a hallmark of soft-mode behavior. The transient charge density allows for deriving the macroscopic polarization, showing a periodic reversal of polarity.
We summarize content from the opening thematic session of the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM). Scientific discoveries from the past 20 years of research are covered, highlighting the impacts of rapid technological, computational, and financial growth on motor control research. We discuss spinal-level communication mechanisms, relationships between muscle structure and function, and direct cortical movement representations that can be decoded in the control of neuroprostheses. In addition to summarizing the rich scientific ideas shared during the session, we reflect on research infrastructure and capacity that contributed to progress in the field, and outline unresolved issues and remaining open questions.
Discovering new medicines is difficult and increasingly expensive. The pharmaceutical industry has responded to this challenge by embracing open innovation to access external ideas. Historically, partnerships were usually bilateral, and the drug discovery process was shrouded in secrecy. This model is rapidly changing. With the advent of the Internet, drug discovery has become more decentralised, bottom-up, and scalable than ever before. The term open innovation is now accepted as just one of many terms that capture different but overlapping levels of openness in the drug discovery process. Many pharmaceutical companies recognise the advantages of revealing some proprietary information in the form of results, chemical tools, or unsolved problems in return for valuable insights and ideas. For example, such selective revealing can take the form of openly shared chemical tools to explore new biological mechanisms or by publicly admitting what is not known in the form of an open call. The essential ingredient for addressing these problems is access to the wider scientific crowd. The business of crowdsourcing, a form of outsourcing in which individuals or organisations solicit contributions from Internet users to obtain ideas or desired services, has grown significantly to fill this need and takes many forms today. Here, we posit that open-innovation approaches are more successful when they establish a reliable framework for converting creative ideas of the scientific crowd into practice with actionable plans.