Efficient people counting with limited manual interferences

© 2014 IEEE. People counting is a topic with various practical applications. Over the last decade, two general approaches have been proposed to tackle this problem: a) counting based on individual human detection; b)counting by measuring regression relation between the crowd density and number of people. Because the regression based method can avoid explicit people detection which faces several well-known challenges, it has been considered as a robust method particularly on a complicated environments. An efficient regression based method is proposed in this paper, which can be well adopted into any existing video surveillance system. It adopts color based segmentation to extract foreground regions in images. Regression is established based on the foreground density and the number of people. This method is fast and can deal with lighting condition changes. Experiments on public datasets and one captured dataset have shown the effectiveness and robustness of the method

O(αsv2)O(\alpha_s v^2) correction to pseudoscalar quarkonium decay to two photons

We investigate the $O(\alpha_s v^2)$ correction to the process of pseudoscalar quarkonium decay to two photons in nonrelativistic QCD (NRQCD) factorization framework. The short-distance coefficient associated with the relative-order $v^2$ NRQCD matrix element is determined to next-to-leading order in $\alpha_s$ through the perturbative matching procedure. Some technical subtleties encountered in calculating the {O(\alpha_s) QCD amplitude are thoroughly addressed.Comment: v2, 28 pages, 2 figures and 2 tables, matching the published version; typos corrected, references added, as well as a "Note added in the proof

Deformation of the Fermi surface in the extended Hubbard model

The deformation of the Fermi surface induced by Coulomb interactions is investigated in the t-t'-Hubbard model. The interplay of the local U and extended V interactions is analyzed. It is found that exchange interactions V enhance small anisotropies producing deformations of the Fermi surface which break the point group symmetry of the square lattice at the Van Hove filling. This Pomeranchuck instability competes with ferromagnetism and is suppressed at a critical value of U(V). The interaction V renormalizes the t' parameter to smaller values what favours nesting. It also induces changes on the topology of the Fermi surface which can go from hole to electron-like what may explain recent ARPES experiments.Comment: 5 pages, 4 ps figure

Thermal width and gluo-dissociation of quarkonium in pNRQCD

The thermal width of heavy-quarkonium bound states in a quark-gluon plasma has been recently derived in an effective field theory approach. Two phenomena contribute to the width: the Landau damping phenomenon and the break-up of a colour-singlet bound state into a colour-octet heavy quark-antiquark pair by absorption of a thermal gluon. In the paper, we investigate the relation between the singlet-to-octet thermal break-up and the so-called gluo-dissociation, a mechanism for quarkonium dissociation widely used in phenomenological approaches. The gluo-dissociation thermal width is obtained by convoluting the gluon thermal distribution with the cross section of a gluon and a 1S quarkonium state to a colour octet quark-antiquark state in vacuum, a cross section that at leading order, but neglecting colour-octet effects, was computed long ago by Bhanot and Peskin. We will, first, show that the effective field theory framework provides a natural derivation of the gluo-dissociation factorization formula at leading order, which is, indeed, the singlet-to-octet thermal break-up expression. Second, the singlet-to-octet thermal break-up expression will allow us to improve the Bhanot--Peskin cross section by including the contribution of the octet potential, which amounts to include final-state interactions between the heavy quark and antiquark. Finally, we will quantify the effects due to final-state interactions on the gluo-dissociation cross section and on the quarkonium thermal width.Comment: 17 pages, 6 figure

A global method for coupling transport with chemistry in heterogeneous porous media

Modeling reactive transport in porous media, using a local chemical equilibrium assumption, leads to a system of advection-diffusion PDE's coupled with algebraic equations. When solving this coupled system, the algebraic equations have to be solved at each grid point for each chemical species and at each time step. This leads to a coupled non-linear system. In this paper a global solution approach that enables to keep the software codes for transport and chemistry distinct is proposed. The method applies the Newton-Krylov framework to the formulation for reactive transport used in operator splitting. The method is formulated in terms of total mobile and total fixed concentrations and uses the chemical solver as a black box, as it only requires that on be able to solve chemical equilibrium problems (and compute derivatives), without having to know the solution method. An additional advantage of the Newton-Krylov method is that the Jacobian is only needed as an operator in a Jacobian matrix times vector product. The proposed method is tested on the MoMaS reactive transport benchmark.Comment: Computational Geosciences (2009) http://www.springerlink.com/content/933p55085742m203/?p=db14bb8c399b49979ba8389a3cae1b0f&pi=1

The first microbial colonizers of the human gut: composition, activities, and health implications of the infant gut microbiota

The human gut microbiota is engaged in multiple interactions affecting host health during the host's entire life span. Microbes colonize the neonatal gut immediately following birth. The establishment and interactive development of this early gut microbiota are believed to be (at least partially) driven and modulated by specific compounds present in human milk. It has been shown that certain genomes of infant gut commensals, in particular those of bifidobacterial species, are genetically adapted to utilize specific glycans of this human secretory fluid, thus representing a very intriguing example of host-microbe coevolution, where both partners are believed to benefit. In recent years, various metagenomic studies have tried to dissect the composition and functionality of the infant gut microbiome and to explore the distribution across the different ecological niches of the infant gut biogeography of the corresponding microbial consortia, including those corresponding to bacteria and viruses, in healthy and ill subjects. Such analyses have linked certain features of the microbiota/microbiome, such as reduced diversity or aberrant composition, to intestinal illnesses in infants or disease states that are manifested at later stages of life, including asthma, inflammatory bowel disease, and metabolic disorders. Thus, a growing number of studies have reported on how the early human gut microbiota composition/development may affect risk factors related to adult health conditions. This concept has fueled the development of strategies to shape the infant microbiota composition based on various functional food products. In this review, we describe the infant microbiota, the mechanisms that drive its establishment and composition, and how microbial consortia may be molded by natural or artificial interventions. Finally, we discuss the relevance of key microbial players of the infant gut microbiota, in particular bifidobacteria, with respect to their role in health and disease

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award