Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Management in Nursing – Hour of need

Time is free and priceless, it is not we have it but it is how we make use of it. Nursing is profession in which nurses need to work under pressurized working conditions many at times. Time management is about how one manages self. One cannot manage the time; one can only manage how he/she can use it Organizing and prioritizing the patient care activities is of prime importance for providing quality care and to maintain the personal and professional balance. Nursing students need to cultivate the skills of effective time management along with other life skills for the ease of transition to a graduate nurse. Time management is not only setting and achieving the goals but also accomplishing in minimum possible time. Good time management techniques and skills are essential for functioning more effectively and to focus on results. Good time management benefits in several ways including greater productivity, less stress, improved efficiency, more opportunities for professional advancement and greater opportunities to achieve career and life goals. Nursing is a profession requiring the skills in performing different tasks during the course of day for coping with time limitations and pressure. Understanding the importance of time management and skills in gaining it is very essential.
ScorePlus: An integrated scalable cyber-physical experiment environment for Smart Grid

Smart Grid is a complex cyber-physical system that modernizes the traditional electric power infrastructure by sensing, control, computation and communication. Validating the functionality, security and reliability of Smart Grid applications within such a system requires the modeling and emulation of both power networks and communication networks, as well as the interactions between them. In this paper, we present the design, implementation and evaluation of an integrated scalable cyber-physical experiment environment for Smart Grid, called ScorePlus. Compared with previous related works, ScorePlus fills the gap by: 1) Creating and integrating both software emulator and hardware testbed, such that they all follow the same architecture and interface, and the same Smart Grid application program can be tested on either of them without any modification; 2) Providing remote access to the hardware testbed such that users can configure physical devices of the hardware testbed through Internet; 3) Supporting scalable distributed experiments such that multiple software emulators and hardware testbeds running at different locations are able to connect and form a larger Smart Grid system.
Modular processes in mind and brain

One approach to understanding a complex process starts with an attempt to divide it into modules·, sub- processes that are independent in some sense, and have distinct functions. In this paper, I discuss an approach to the modular decomposition of neural and mental processes. Several examples of process decomposition are presented, together with discussion of inferential requirements. Two examples are of well-established and purely behavioural realizations of the approach (signal detection theory applied to discrimination data; the method of additive factors applied to reaction-time data), and lead to the identification of mental modules. Other examples, leading to the identification of modular neural processes, use brain measures, including the fMRI signal, the latencies of electrophysiological events, and their amplitudes. Some measures are pure (reflecting just one process), while others are composite. Two of the examples reveal mental and neural modules that correspond. Attempts to associate brain regions with be- haviourally defined processing modules that use a brain manipulation (transcranial magnetic stimulation, TMS) are promising but incomplete. I show why the process-decomposition approach discussed here, in which the criterion for modularity is separate modifiability, is superior for modular decomposition to the more frequently used task comparison procedure (often used in cognitive neuropsychology) and to its associated subtraction method. To demonstrate the limitations of task comparison, I describe the erroneous conclusion to which it has led about sleep deprivation, and the interpretive difficulties in a TMS study.
Periodic global parameterization

We present a new globally smooth parameterization method for the triangulated surfaces of arbitrary topology. Given two orthogonal piecewise linear vector fields defined over the input mesh (typically the estimated principal curvature directions), our method computes two piecewise linear periodic functions, aligned with the input vector fields, by minimizing an objective function. The bivariate function they define is a smooth parameterization almost everywhere on the surface, except in the vicinity of singular vertices, edges, and triangles, where the derivatives of the parameterization vanish. We extract a quadrilateral chart layout from the parameterization function and propose an automatic procedure to detect the singularities, and fix them by splitting and reparameterizing the containing charts. Our method can construct both quasiconformal (angle preserving) and quasi-isometric (angle and area preserving) parameterizations. The more restrictive class of quasi-isometric parameterizations is constructed at the expense of introducing more singularities. The constructed parameterizations can be used for a variety of geometry processing applications. Since we can align the parameterization with the principal curvature directions, our result is particularly suitable for surface fitting and remeshing.
Speaker tracking audio-video system

In this paper the system for tracking of a moving speaker is reported. The system utilizes audio visual information acquired from a camera and microphones. Using the camera a face of a speaker is detected. Two pairs of microphones allow for estimation of direction of sound arrival using general cross-correlation method. The preliminary results of experiments are described.
Indoor RFID localization improved by motion segmentation

A new method for indoor localization of moving objects based on RFID localization improved by motion segmentation in video sequence captured with static or PTZ video camera is presented. Data acquired by an RFID reader are used for region of interest extraction. Motion vectors are estimated for blocks within the region of interest and used for moving object segmentation. Segmentation is improved by morphological post processing. Centroid of segmented moving object is calculated and used for its position estimation. The conducted experiments show that the localization accuracy is increased significantly using the proposed method.
The College Completion Agenda: 2010 Progress Report.

College Completion Agenda 2011 Progress Report The College Board is a mission-driven not-for-profit organization that connects students to college success and opportunity. Founded in 1900, the College Board was created to expand access to higher education. Today, the membership association is made up of more than 5,900 of the world’s leading educational institutions and is dedicated to promoting excellence and equity in education. Each year, the College Board helps more than seven million students prepare for a successful transition to college through programs and services in college readiness and college success — including the SAT® and the Advanced Placement Program®. The organization also serves the education community through research and advocacy on behalf of students, educators and schools.
The speaker partitioning problem

We give a unification of several different speaker recognition problems in terms of the general speaker partitioning problem, where a set of N inputs has to be partitioned into subsets according to speaker. We show how to solve this problem in terms of a simple generative model and demonstrate performance on NIST SRE 2006 and 2008 data. Our solution yields probabilistic outputs, which we show how to evaluate with a cross-entropy criterion. Finally, we show improved accuracy of the generative model via a discriminatively trained re-calibration transformation of log-likelihoods.
Mathematical Properties of Option Pricing Under Gamma Distribution Process

A problem of risk neutral probability density function estimation for prices of risky assets is discussed when the asset pricing model uses exponential random process with independent increments. The structure of increments consists of two components: systematic drift and a random gamma distributed component. To determine the risk neutral probability density Escher transform is used. Obtained results help to define a formula for European call option pricing. Also we compare the found risk neutral density with the density, when the random component is defined by a Wiener process, driven by gamma process.
Simplifying Complicated Simplicial Complexes: Discrete Morse Theory and its Applications

Acknowledgements Many thanks to my thesis advisor, Professor Michael Hopkins, for providing invaluable guidance, advice, and candy throughout the course of this project. I am also very grateful to Zachary Abel for his help throughout the year, and to those who read and commented on my drafts: Professor Thanks to my family for their support, both within the realm of mathematics and outside of it; in particular, thank you to my mother Marcia for passing along the genes for passing the AP Calculus exam, to my father David for looking at my thesis, to my brother Ben for reading my thesis, and to my brother Aryeh for reading at least a few pages of my thesis and providing a comment – more than anyone might expect from a 15-year-old. I am deeply indebted to my instructors in mathematics: my high school teachers; Professors Anil Nerode and Allen Hatcher at Cornell; and my instructors in college: Professors Noam Elkies (also my advisor for the past three years), am likewise grateful to my instructors in theoretical computer science, who instilled in me not only an appreciation for computer science but a deeper understanding of mathematics as well: Professors Salil Vadhan, Michael Mitzenmacher, and Michael Rabin. Finally, my thanks go out to Randall Munroe of xkcd, who has, throughout my college career, provided an incentive for me to work on math until late in the evenings by posting a new comic strip online at midnight three nights a week.
Acquired hypofibrinogenemia: current perspectives

Acquired hypofibrinogenemia is most frequently caused by hemodilution and consumption of clotting factors. The aggressive replacement of fibrinogen has become one of the core principles of modern management of massive hemorrhage. The best method for determining the patient’s fibrinogen level remains controversial, and particularly in acquired dysfibrinogenemia, could have major therapeutic implications depending on which quantification method is chosen. This review introduces the available laboratory and point-of-care methods and discusses the relative advantages and limitations. It also discusses current strategies for the correction of hypofibrinogenemia.
Imaging a photodynamic therapy photosensitizer in vivo with a time-gated fluorescence tomography system

Abstract. We report the tomographic imaging of a photodynamic therapy (PDT) photosensitizer, 2-(1-hexyloxyethyl)-2-devinyl pyropheophorbide-a (HPPH) in vivo with time-domain fluorescence diffuse optical tomography (TD-FDOT). Simultaneous reconstruction of fluorescence yield and lifetime of HPPH was performed before and after PDT. The methodology was validated in phantom experiments, and depth-resolved in vivo imaging was achieved through simultaneous three-dimensional (3-D) mappings of fluorescence yield and lifetime contrasts. The tomographic images of a human head-and-neck xenograft in a mouse confirmed the preferential uptake and retention of HPPH by the tumor 24-h post-injection. HPPH-mediated PDT induced significant changes in fluorescence yield and lifetime. This pilot study demonstrates that TD-FDOT may be a good imaging modality for assessing photosensitizer distributions in deep tissue during PDT monitoring.
ON THE SYMMETRIES OF INTEGRABILITY

We show that the Yang-Baxter equations for two-dimensional models admit as a group of symmetry the infinite discrete group . The existence of this symmetry explains the presence of a spectral parameter in the solutions of the equations. We show that similarly, for three-dimensional vertex models and the associated tetrahedron equations, there also exists an infinite discrete group of symmetry. Although generalizing naturally the previous one, it is a much bigger hyperbolic Coxeter group. We indicate how this symmetry can help to resolve the Yang-Baxter equations and their higher-dimensional generalizations and initiate the study of three-dimensional vertex models. These symmetries are naturally represented as birational projective transformations. They may preserve non-trivial algebraic varieties, and lead to proper parametrizations of the models, be they integrable or not. We mention the relation existing between spin models and the Bose-Messner algebras of algebraic combinatorics. Our results also yield the generalization of the condition qn=1 so often mentioned in the theory of quantum groups, when no q parameter is available.
Elastic proton-proton and proton-antiproton scattering: analysis of complete set of helicity amplitudes

The differential cross-sections are calculated for proton-proton and proton-antiproton elastic scattering using the phenomenological model based on the analytic parameterizations for global scattering parameters (total cross-section and $\rho$ - parameter), crossing symmetry and derivative relations. We confront our model predictions with experimental data in wide range of energy and momentum transfer. The suggested method may be useful for PAX Program (GSI) as well as for high-energy experiments at RHIC and LHC.
Semantic annotation of geodata based on linked-open data

There are several research works using web semantic to improve integration and retrieval of geospatial data provided by spatial data infrastructures (SDI) and geoportals. However, an important open issue that deserves more research efforts is how to implement the process of semantic annotation, in which geospatial data are associated to concepts formally defined in application ontologies. Since the volume of the data provided by a SDI is usually very large, it is necessary to provide mechanisms that are able to perform semantic annotation automatically (or semi-automatically). Aiming to solve this limitation, this article proposes a method that uses the open data provided by DBpedia, as well ideas proposed in the classic information retrieval, to provide automatic semantic annotation of feature types provided by SDIs. We used geospatial data gathered from the catalog service of a real SDI in order to evaluate our proposed method. The results obtained from initial experiments showed that our approach is viable, since it can generate semantic annotations to many feature types with an acceptable precision rate.
Genetic Analysis of Macrophage Characteristics as a Tool to Identify Tumor Susceptibility Genes

Genetic predisposition to cancer is influenced by allelic variation in tumor susceptibility genes (TSGs) as present in the germline. We previously demonstrated in the mouse that TSGs frequently participate in genetic interactions, indicating that they represent molecular networks. Inflammation may constitute one of the molecular networks underlying susceptibility to cancer by influencing the tumor microenvironment. Because macrophages play a key role in inflammation and are often associated with tumors, we argue that a subset of TSGs can be identified by examining the genetics of macrophage characteristics. A panel of inflammation-related assays was established to phenotype mouse bone marrow-derived macrophages, which included stimulation with lipopolysaccharides followed by measurement of secretion of tumor necrosis factor α and the p40 chain of interleukin-12 and of expression of inducible nitric oxide synthase and cyclooxygenase-2. This panel of assays was used for linkage analysis and applied to bone marrow-derived macrophages derived from individual mice of segregating crosses between inbred strain O20 and the highly related strains NTX-10 and NTX-20, which differed from O20 in only 10% of their genome, to reduce genetic complexity. Three macrophage-associated risk inflammatory factors were mapped—Marif1, Marif2, and Marif3—that each affected several inflammation-related assays, confirming that they function within molecular networks. Moreover, Marif1 and Marif2 were localized in regions with established linkage for both quantitative and qualitative aspects of lung cancer susceptibility. These studies provide a novel approach to investigate the genetics of microenvironmental influence on predisposition to tumorigenesis, thereby contributing to development of new strategies that aim to prevent or treat cancer.
Konrad-zuse-zentrum F ¨ Ur Informationstechnik Berlin Barrier Methods for Optimal Control Problems with Convex Nonlinear Gradient Constraints Barrier Methods for Optimal Control Problems with Convex Nonlinear Gradient Constraints †

In this paper we are concerned with the application of interior point methods in function space to gradient constrained optimal control problems, governed by partial differential equations. We will derive existence of solutions together with first order optimality conditions. Afterwards we show continuity of the central path, together with convergence rates depending on the interior point parameter.
Gallic acid reduces cell viability, proliferation, invasion and angiogenesis in human cervical cancer cells

Gallic acid is a trihydroxybenzoic acid, also known as 3,4,5-trihydroxybenzoic acid, which is present in plants worldwide, including Chinese medicinal herbs. Gallic acid has been shown to have cytotoxic effects in certain cancer cells, without damaging normal cells. The objective of the present study was to determine whether gallic acid is able to inhibit human cervical cancer cell viability, proliferation and invasion and suppress cervical cancer cell-mediated angiogenesis. Treatment of HeLa and HTB-35 human cancer cells with gallic acid decreased cell viability in a dose-dependent manner. BrdU proliferation and tube formation assays indicated that gallic acid significantly decreased human cervical cancer cell proliferation and tube formation in human umbilical vein endothelial cells, respectively. Additionally, gallic acid decreased HeLa and HTB-35 cell invasion in vitro. Western blot analysis demonstrated that the expression of ADAM17, EGFR, p-Akt and p-Erk was suppressed by gallic acid in the HeLa and HTB-35 cell lines. These data indicate that the suppression of ADAM17 and the downregulation of the EGFR, Akt/p-Akt and Erk/p-Erk signaling pathways may contribute to the suppression of cancer progression by Gallic acid. Gallic acid may be a valuable candidate for the treatment of cervical cancer.
Resistance DPA of RSA on Smartcard

Smartcard could leak some power consumption during encryption or signature Differential power analysis (DPA) attackers can analysis encryption key with power information, it brings much more harm than traditional mathematics analysis. RSA is one of the most widely used public key algorithms nowadays. However when it is implemented in embedded devices such as smart cards, it is easy to be threatened by power analysis. In this paper, DPA methods based on algorithmic level of RSA and methods against DPA are studied and we propose a countermeasure of triple masking method against DPA roundly, implemented by simulation, effect is visible.
3D RRAM design and benchmark with 3d NAND FLASH

The monolithic 3D integration of resistive switching random access memory (RRAM) is one attractive approach to build high-density non-volatile memory. In this paper, the design considerations of 3D vertical RRAM architecture are presented from the device, circuit to system level. Due to the voltage drop and sneak path problem, the sub-array size of the 3D NAND is still limited as compared with that of the 3D NAND. To be cost-competitive with the 3D NAND, high on-state resistance, high I-V nonlinearity and low interconnect resistivity is required to enable Mb 3D RRAM sub-array. Although the 3D RRAM has disadvantage in array efficiency (consequently in cost per bit) than the 3D NAND, the 3D RRAM outperforms the 3D NAND in throughput performance at system-level.
Sparse Matrix-Vector Multiplication on GPGPUs

The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrix-vector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high-performance computing architectures. The introduction of General-Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem. With this article, we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and tradeoffs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.
ExpressQ: Identifying Keyword Context and Search Target in Relational Keyword Queries

Keyword search in relational databases has gained popularity due to its ease of use. However, the challenge to return query answers that satisfy users' information need remains. Traditional keyword queries have limited expressive capability and are ambiguous. In this work, we extend keyword queries to enhance their expressive power and describe an semantic approach to process these queries. Our approach considers keywords that match meta-data such as the names of relations and attributes, and utilizes them to provide the context of subsequent keywords in the query. Based on the ORM schema graph which captures the semantics of objects and relationships in the database, we determine the objects and relationships referred to by the keywords in order to infer the search target of the query. Then, we construct a set of minimal connected graphs called query patterns, to represent user's possible search intentions. Finally, we translate the top-k ranked query patterns into SQL statements in order to retrieve information that the user is interested in. We develop a system prototype called ExpressQ to process the extended keyword queries. Experimental results show that our system is able to generate SQL statements that retrieve user intended information effectively.
Making Sense of Hidden Layer Information in Deep Networks by Learning Hierarchical Targets

This paper proposes an architecture for deep neural networks with hidden layer branches that learn targets of lower hierarchy than final layer targets. The branches provide a channel for enforcing useful information in hidden layer which helps in attaining better accuracy, both for the final layer and hidden layers. The shared layers modify their weights using the gradients of all cost functions higher than the branching layer. This model provides a flexible inference system with many levels of targets which is modular and can be used efficiently in situations requiring different levels of results according to complexity. This paper applies the idea to a text classification task on 20 Newsgroups data set with two level of hierarchical targets and a comparison is made with training without the use of hidden layer branches.