Linear response within the projection-based renormalization method: Many-body corrections beyond the random phase approximation

The explicit evaluation of linear response coefficients for interacting many-particle systems still poses a considerable challenge to theoreticians. In this work we use a novel many-particle renormalization technique, the so-called projector-based renormalization method, to show how such coefficients can systematically be evaluated. To demonstrate the prospects and power of our approach we consider the dynamical wave-vector dependent spin susceptibility of the two-dimensional Hubbard model and also determine the subsequent magnetic phase diagram close to half-filling. We show that the superior treatment of (Coulomb) correlation and fluctuation effects within the projector-based renormalization method significantly improves the standard random phase approximation results.Comment: 17 pages, 7 figures, revised versio

Study on Clinical, Histopathological Features and Evaluation Results of Skin Cancer Treatment in Can Tho Oncology Hospital

Skin cancer as the most common cancer diagnosis tend to be increasing. This condition is a particularly significant issue in developed countries. This study aimed to describe the clinical features, histopathological features, complications, and early surgical treatment outcomes of skin cancer in Can Tho Oncology Hospital from 2014 to 2015. This descriptive prospective study involved all patients with non-melanoma skin cancer that were examined and treated at Can Tho Oncology Hospital from July 2014 to March 2015. There were 78 cases selected. Skin cancer was found to be more common among older patients. The prevalence of basal cell carcinoma was found higher than squamous cell carcinoma with percentage worth 76.9% and 23.1% respectively. Worth 73.1% of all the patients in the study underwent surgery with wide resection and reconstruction. In this study, most patients were the elderly. The basal cell carcinoma was the most common. The main treatment was surgery with wide resection and reconstruction. The complication was rare 1.3% with skin flap necrosis

Major G-Quadruplex Form of HIV-1 LTR Reveals a (3 + 1) Folding Topology Containing a Stem-Loop

Nucleic acids can form noncanonical four-stranded structures called G-quadruplexes. G-quadruplex-forming sequences are found in several genomes including human and viruses. Previous studies showed that the G-rich sequence located in the U3 promoter region of the HIV-1 long terminal repeat (LTR) folds into a set of dynamically interchangeable G-quadruplex structures. G-quadruplexes formed in the LTR could act as silencer elements to regulate viral transcription. Stabilization of LTR G-quadruplexes by G-quadruplex-specific ligands resulted in decreased viral production, suggesting the possibility of targeting viral G-quadruplex structures for antiviral purposes. Among all the G-quadruplexes formed in the LTR sequence, LTR-III was shown to be the major G-quadruplex conformation in vitro. Here we report the NMR structure of LTR-III in K+ solution, revealing the formation of a unique quadruplex-duplex hybrid consisting of a three-layer (3 + 1) G-quadruplex scaffold, a 12-nt diagonal loop containing a conserved duplex-stem, a 3-nt lateral loop, a 1-nt propeller loop, and a V-shaped loop. Our structure showed several distinct features including a quadruplex-duplex junction, representing an attractive motif for drug targeting. The structure solved in this study may be used as a promising target to selectively impair the viral cycle

Temperature dependent graphene suspension due to thermal Casimir interaction

Thermal effects contributing to the Casimir interaction between objects are usually small at room temperature and they are difficult to separate from quantum mechanical contributions at higher temperatures. We propose that the thermal Casimir force effect can be observed for a graphene flake suspended in a fluid between substrates at the room temperature regime. The properly chosen materials for the substrates and fluid induce a Casimir repulsion. The balance with the other forces, such as gravity and buoyancy, results in a stable temperature dependent equilibrium separation. The suspended graphene is a promising system due to its potential for observing thermal Casimir effects at room temperature.Comment: 5 pages, 4 figures, in APL production 201

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

The software for oceanographic data management: VODC for PC 2.0

To manage and process a large amount of oceanographic data, users must have powerful tools that simplify these tasks. The VODC for PC is software designed to assist in managing oceanographic data. It based on 32 bits Windows operation system and used Microsoft Access database management system. With VODC for PC users can update data simply, convert to some international data formats, combine some VODC databases to one, calculate average, min, max fields for some types of data, check for valid data

Halliday\u27s Functional Grammar: Philosophical Foundation and Epistemology

It is difficult to track the philosophy foundation and epistemology of systemic functional grammar (SFG) formulated by Halliday in the 1980s as this kind of grammar views language as a systemic resource for meaning. Besides, it has had global impacts on linguistics and flourished in contemporary linguistic theory. Anyone who is familiar with Halliday\u27s work realizes that his SFG is an approach designed to analyze English texts. Halliday (1994: xv) explicitly states that “to construct a grammar for purposes of text analysis: one that would make it possible to say sensible and useful things about any text, spoken or written, in modern English.” The aim of this study is not about the applicability of SFG to text analysis as many researchers and scholars do. Our efforts are made to clarify the philosophical foundation of Halliday\u27s SFG. The paper presents on triangle: (i) language, mind and world; (ii) and empiricism in Halliday\u27s SFG