Differentiating complex network models: An engineering perspective

AbstractNetwork models that can capture the underlying network’s topologies and functionalities are crucial for the development of complex network algorithms and protocols. In the engineering community, the performances of network algorithms and protocols are usually evaluated by running them on a network model. In most if not all reported work, the criteria used to determine such a network model rely on how close it matches the network data in terms of some basic topological characteristics. However, the intrinsic relations between a network topology and its functionalities are still unclear. A question arises naturally: For a network model which can reproduce some topological characteristics of the underlying network, is it reasonable and valid to use this model to be a test-bed for evaluating the network’s performances? To answer this question, we take a close look at several typical complex network models of the AS-level Internet as examples of study. We find that although a model can represent the Internet in terms of topological metrics, it cannot be used to evaluate the Internet performances. Our findings reveal that the approaches using topological metrics to discriminate network models, which have been widely used in the engineering community, may lead to confusing or even incorrect conclusions

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 202

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures

Many transport systems in the real world can be modeled as networked systems. Due to limited resources, only a few nodes can be selected as seeds in the system, whose role is to spread required information or control signals as widely as possible. This problem can be modeled as the influence maximization problem. Most of the existing selection strategies are based on the invariable network structure and have not touched upon the condition that the network is under structural failures. Related studies indicate that such strategies may not completely tackle complicated diffusion tasks in reality, and the robustness of the information diffusion process against perturbances is significant. To give a numerical performance criterion of seeds under structural failure, a measure has been developed to define the robust influence maximization (RIM) problem. Further, a memetic optimization algorithm (MA) which includes several problem-orientated operators to improve the search ability, termed RIMMA, has been presented to deal with the RIM problem. Experimental results on synthetic networks and real-world networks validate the effectiveness of RIMMA, its superiority over existing approaches is also shown

Relationships between ionospheric parameters derived from ionosonde observations and characteristics of post-sunset GHz scintillation during high solar activities (2012−2013) at Sanya (18.3°N, 109.6°E), China

In this study, we present characteristics of post-sunset GHz scintillation occurrence and their correlations with ionospheric parameters derived from ionosonde observations in high solar activity years (2012−2013) of solar cycle 24 at Sanya (18.3°N, 109.6°E; dip lat.: 12.8°N), China. The analyzed data include the F2-layer’s critical frequency (foF2), peak height (hmF2), and minimum virtual height (h’F), as well as the scale height around the F2-layer peak (Hm), and virtual height (h’F5) and true height (hF5) measured at 5 MHz.We have investigated relationships between the equinoctial asymmetry of these scintillations and these ionospheric parameters. In addition, we calculate the growth rates of Rayleigh−Taylor instability on the basis of the ionosonde measurements and theoretical models, respectively. We find that the equinoctial asymmetry of scintillation onset time is associated with the scale length of the vertical electron density gradient (L), which has been shown to affect the growth of Rayleigh−Taylor instability at the bottom of the F-layer. The seasonal variations of foF2, Hm and scale length of vertical electron density gradient appear to cause the seasonal variations of scintillation occurrence; the equinoctial asymmetry of scintillation occurrence rate over low latitudes appears to be related to background electron density and vertical drifts in the F-layer around time of sunset. Further study is required to explain the observed correlational weakness in low latitudes between scintillation strength, represented by the daily maximum S4, and daily maximum values of foF2, hmF2, h’F, Hm, and also the drifts