Cutting the traintracks: Cauchy, Schubert and Calabi-Yau

In this note we revisit the maximal-codimension residues, or leading singularities, of four-dimensional $L$-loop traintrack integrals with massive legs, both in Feynman parameter space and in momentum (twistor) space. We identify a class of "half traintracks" as the most general degenerations of traintracks with conventional (0-form) leading singularities, although the integrals themselves still have rigidity $\lfloor\frac{L-1}2\rfloor$ due to lower-loop "full traintrack'' subtopologies. As a warm-up exercise, we derive closed-form expressions for their leading singularities both via (Cauchy's) residues in Feynman parameters, and more geometrically using the so-called Schubert problems in momentum twistor space. For $L$-loop full traintracks, we compute their leading singularities as integrals of $(L{-}1)$-forms, which proves that the rigidity is $L{-}1$ as expected; the form is given by an inverse square root of an irreducible polynomial quartic with respect to each variable, which characterizes an $(L{-}1)$-dim Calabi-Yau manifold (elliptic curve, K3 surface, etc.) for any $L$. We also briefly comment on the implications for the "symbology" of these traintrack integrals.Comment: refs updated; 36 pages, 12 figure

Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection

Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets. Despite the challenges posed by the numerous interaction combinations, they also offer opportunities for multimodal learning of visual texts. In this paper, we present a systematic and unified framework (RmLR) that enhances HOI detection by incorporating structured text knowledge. Firstly, we qualitatively and quantitatively analyze the loss of interaction information in the two-stage HOI detector and propose a re-mining strategy to generate more comprehensive visual representation.Secondly, we design more fine-grained sentence- and word-level alignment and knowledge transfer strategies to effectively address the many-to-many matching problem between multiple interactions and multiple texts.These strategies alleviate the matching confusion problem that arises when multiple interactions occur simultaneously, thereby improving the effectiveness of the alignment process. Finally, HOI reasoning by visual features augmented with textual knowledge substantially improves the understanding of interactions. Experimental results illustrate the effectiveness of our approach, where state-of-the-art performance is achieved on public benchmarks. We further analyze the effects of different components of our approach to provide insights into its efficacy.Comment: ICCV202

Online human action recognition with spatial and temporal skeleton features using a distributed camera network

Online action recognition is an important task for human-centered intelligent services. However, it remains a highly challenging problem due to the high varieties and uncertainties of spatial and temporal scales of human actions. In this paper, the following core ideas are proposed to deal with the online action recognition problem. First, we combine spatial and temporal skeleton features to represent human actions, which include not only geometrical features, but also multiscale motion features, such that both spatial and temporal information of the actions are covered. We use an efficient one-dimensional convolutional neural network to fuse spatial and temporal features and train them for action recognition. Second, we propose a group sampling method to combine the previous action frames and current action frames, which are based on the hypothesis that the neighboring frames are largely redundant, and the sampling mechanism ensures that the long-term contextual information is also considered. Third, the skeletons from multiview cameras are fused in a distributed manner, which can improve the human pose accuracy in the case of occlusions. Finally, we propose a Restful style based client-server service architecture to deploy the proposed online action recognition module on the remote server as a public service, such that camera networks for online action recognition can benefit from this architecture due to the limited onboard computational resources. We evaluated our model on the data sets of JHMDB and UT-Kinect, which achieved highly promising accuracy levels of 80.1% and 96.9%, respectively. Our online experiments show that our memory group sampling mechanism is far superior to the traditional sliding window

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples

Forty years of reform and opening up:China’s progress toward a sustainable path

After 40 years of reform and “opening up,” China has made remarkable economic progress. Such economic prosperity, however, has been coupled with environmental degradation. We analyze diverse long-term data to determine whether China is experiencing a decoupling of economic growth and environmental impacts, and where China stands with respect to the Sustainable Development Goals (SDGs) in terms of reducing regional division, urban-rural gap, social inequality, and land-based impacts on oceans. The results highlight that China’s desire to achieve “ecological civilization” has resulted in a decoupling trend for major pollutants since 2015, while strong coupling remains with CO2 emissions. Progress has been made in health care provision, poverty reduction, and gender equity in education, while income disparity continues between regions and with rural-urban populations. There is a considerable way to go toward achieving delivery of the SDGs; however, China’s progress toward economic prosperity and concomitant sustainability provides important insights for other countries

Target motion analysis based on peak power measurements using networked sensors

Target motion analysis (TMA) using a network of wireless sensors/receivers which measure the power from a mobile RF emitter is considered. Due to limited communication capability of each sensor node, only peak power measurements from sensor nodes are transmitted to the fusion center. We present two main results that yield the optimum sensors\u27 configuration such that the asymptotically achievable error variance of the target trajectory\u27s estimate is minimized, and we derive efficient numerical algorithms for computing the optimum estimates of the trajectory of the moving target, thus achieving the goal of TMA. © 2011 IEEE

The Core- and Pan-Genomic Analyses of the Genus Comamonas: From Environmental Adaptation to Potential Virulence

Comamonas is often reported to be one of the major members of microbial communities in various natural and engineered environments. Versatile catabolic capabilities of Comamonas have been studied extensively in the last decade. In contrast, little is known about the ecological roles and adaptation of Comamonas to different environments as well as the virulence of potentially pathogenic Comamonas strains. In this study, we provide genomic insights into the potential ecological roles and virulence of Comamonas by analysing the entire gene set (pangenome) and the genes present in all genomes (core genome) using 34 genomes of 11 different Comamonas species. The analyses revealed that the metabolic pathways enabling Comamonas to acquire energy from various nutrient sources are well conserved. Genes for denitrification and ammonification are abundant in Comamonas, suggesting that Comamonas plays an important role in the nitrogen biogeochemical cycle. They also encode sophisticated redox sensory systems and diverse c-di-GMP controlling systems, allowing them to be able to effectively adjust their biofilm lifestyle to changing environments. The virulence factors in Comamonas were found to be highly species-specific. The conserved strategies used by potentially pathogenic Comamonas for surface adherence, motility control, nutrient acquisition and stress tolerance were also revealed.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Published versio

Involvement in Denitrification is Beneficial to the Biofilm Lifestyle of Comamonas testosteroni: A Mechanistic Study and Its Environmental Implications

Comamonas is one of the most abundant microorganisms in biofilm communities driving wastewater treatment. Little has been known about the role of this group of organisms and their biofilm mode of life. In this study, using Comamonas testosteroni as a model organism, we demonstrated the involvement of Comamonas biofilms in denitrification under bulk aerobic conditions and elucidated the influence of nitrate respiration on its biofilm lifestyle. Our results showed that C. testosteroni could use nitrate as the sole electron acceptor for anaerobic growth. Under bulk aerobic condition, biofilms of C. testosteroni were capable of reducing nitrate, and intriguingly, nitrate reduction significantly enhanced viability of the biofilm-cells and reduced cell detachment from the biofilms. Nitrate respiration was further shown to play an essential role in maintaining high cell viability in the biofilms. RNA-seq analysis, quantitative polymerase chain reaction, and liquid chromatography-mass spectrometry revealed a higher level of bis(3′-5′)-cyclic dimeric guanosine monophosphate (c-di-GMP) in cells respiring on nitrate than those grown aerobically (1.3 × 10-4 fmol/cell vs 7.9 × 10-6 fmol/cell; P < 0.01). C-di-GMP is one universal signaling molecule that regulates the biofilm mode of life, and a higher c-di-GMP concentration reduces cell detachment from biofilms. Taking these factors together, this study reveals that nitrate reduction occurs in mature biofilms of C. testosteroni under bulk aerobic conditions, and the respiratory reduction of nitrate is beneficial to the biofilm lifestyle by providing more metabolic energy to maintain high viability and a higher level of c-di-GMP to reduce cell detachment.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Accepted versio