Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunities for Lattice QCD Thermodynamics Lattice QCD Executive Committee

Over the next decade high performance computing resources w ill reach the petaflops scale. Coupled with current and planned experiments at RHIC, FAIR, and the LHC, these computational resources will offer significant opportunities for t he advancement of our understanding of the properties of strongly interacting matter at high tem p ratures and densities. We describe expected quantitative and qualitative gains in (1) our know ledge of the equation of state at zero and nonzero density, (2) the phase diagram of QCD at zero and n o zero density, and (3) the structure of the plasma phase, including excited states and tr sport properties. We describe several key computational projects for achieving these gai ns nd estimate the computational cost in units of teraflops-years (TF-y). 1 QCD at nonzero temperature and density The properties of strongly interacting matter at nonzero te mp rature and baryon number density are being studied in heavy ion experiments at the Relati vistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL). In the near future th se experiments will be extended to even higher energies and temperatures at the Large Hadron Co llider (LHC). By contrast, at BNL and at the future European heavy ion facility FAIR, a series o f new low energy experiments is planned that will allow us to study such matter at moderate te mperatures, but high baryon number density. The former physical conditions occurred in the ear ly universe; the latter may approximate the environment in the interior of dense stellar objects suc h as neutron stars. Under extreme conditions of high temperature or high baryon number density strongly interacting matter is expected to have a rich phase structure as in dicated in Figure 1. Quantifying the drastic changes in the interaction among elementary par ticles that go along with such phase changes requires large scale numerical calculations. Numerical studies of lattice QCD can provide a wealth of new i nformation about properties of strongly interacting matter. Lattice QCD is likely to have a particularly strong impact on current and future experimental studies as well as the phenomenolog ica modeling of hot and dense matter in the following three areas: • Lattice calculations can provide detailed information abo ut basic bulk thermodynamic properties: the equation of state, energy and entropy density, t he pressure, and the velocity of
Data driven discovery of nonlinear dynamics

We demonstrate that sparse regression and compressive sensing techniques are capable of accurately determining a set of functions governing a nonlinear dynamical system. We analyze a technique introduced by Brunton, Proctor, and Kutz, 2016 [1] that builds a sparse representation of a dynamical system by computing sequential least squares fittings of the data to identify the governing equations. By computing the sparse regression in such a fashion, this method quickly identifies nonzero coefficients and allows them to converge to their respective weight. The result is a sparse representation of the dynamical system in the nonlinear function space. We look at the mechanics of the lasso, discuss the introduction of sparsity to a system by choice of the l1-norm, and investigate the algorithm used in sparse identification of nonlinear dynamics through a sequential least squares method.
3169 - CONTINUUM AND ATOMISTIC MODELING OF DYNAMIC FRACTURE AT NANOSCALE

Crack branching and instability phenomena are believed to be closely related to the circumferential or hoop stress in the vicinity of the crack tip. In this paper we show that the hoop stress around a mode I crack in a harmonic solid becomes bimodal at a critical speed of about 73 percent of the Rayleigh speed, in agreement with the continuum mechanics theory. Additionally, we compare the energy flow field predicted by continuum theory with the solution of molecular-dynamics simulations and show that the two approaches yield comparable results for the dynamic Poynting vector field. This study exemplifies joint atomistic and continuum modeling of nanoscale dynamic systems and yields insight into coupling of the atomistic scale with continuum mechanics concepts.
Distributed Coverage Maximization via Sketching

Coverage problems are central in optimization and have a wide range of applications in data mining and machine learning. While several distributed algorithms have been developed for coverage problems, the existing methods suffer from several limitations, e.g., they all achieve either suboptimal approximation guarantees or suboptimal space and memory complexities. In addition, previous algorithms developed for submodular maximization assume oracle access, and ignore the computational complexity of communicating large subsets or computing the size of the union of the subsets in this subfamily. In this paper, we develop an improved distributed algorithm for the $k$-cover and the set cover with $\lambda$ outliers problems, with almost optimal approximation guarantees, almost optimal memory complexity, and linear communication complexity running in only four rounds of computation. Finally, we perform an extensive empirical study of our algorithms on a number of publicly available real data sets, and show that using sketches of size $30$ to $600$ times smaller than the input, one can solve the coverage maximization problem with quality very close to that of the state-of-the-art single-machine algorithm.
Modeling Social Annotation Data with Content Relevance using a Topic Model

We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as web pages stored in social bookmarking services. In these services, since users can attach annotations freely, some annotations do not describe the semantics of the content, thus they are noisy, i.e. not content-related. The extraction of content-related annotations can be used as a preprocessing step in machine learning tasks such as text classification and image recognition, or can improve information retrieval performance. The proposed model is a generative model for content and annotations, in which the annotations are assumed to originate either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method by using synthetic data and real social annotation data for text and images.
Improvements to a Spherical Binaural Capture Model for Objective Measurement of Spatial Impression with Consideration of Head Movements

This research aims to develop an objective evaluation system of spatial impression, incorporating the finding from a previous study that head movements are naturally made in the subjective evaluation of spatial impression. Research already conducted indicated the limitations of a spherical binaural capture model for the objective measurement and evaluation of spatial impression. Further research has minimised the differences in interaural time and level differences, and cross-correlation coefficient between this spherical model and a head and torso simulator by implementing the effects of a torso and simplified pinnae. Further analysis of the head movements made by listeners in a range of listening situations is undertaken in order to determine the range of positions that needs to be taken into account. Analysis of these results together with the required microphone spacing is undertaken to derive the optimum positioning of the microphones around the sphere model to take head movements into account.
Class name : String Attribute name : String Type type : String t Class Attribute type : String Element name : String attr Class MM 0 Class MM 1 a 4 . Inline Class 6 . Rename Reference 3

Evolving metamodels are in the center of ModelDriven Engineering, necessitating the co-evolution of dependent artifacts like models and transformations. While model co-evolution has been extensively studied, transformation coevolution has received less attention up to now. Current approaches for transformation co-evolution provide a fixed, restricted set of metamodel (MM) changes, only. Furthermore, composite changes are treated as monolithic units, which may lead to inconsistent co-evolution for overlapping atomic changes and prohibits extensibility. Finally, transformation co-evolution is considered in isolation, possibly inducing inconsistencies between model and transformation co-evolution. To overcome these limitations, we propose a complete set of atomic MM changes being able to describe arbitrary MM evolutions. Reusability and extensibility are supported by means of change composition, ensuring an intra-artifact consistent co-evolution. Furthermore, each change provides resolution actions for both, models and transformations, ensuring an inter-artifact consistent co-evolution. Based on our conceptual approach, a prototypical implementation is presented.
A Survey of Methods and Strategies in Character Segmentation

Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the "classical" approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called "dissection. " The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.
The Inversion Algorithm For Digital Simulation

The Inversion Algorithm is an event-driven algorithm, whose performance rivals or exceeds that of Levelized Compiled code simulation, even at activity rates of 50% or more. The Inversion Algorithm has several unique features, the most remarkable of which is the size of the run-time code. The basic Algorithm can be implemented using no more than a page of run-time code, although in practice it is more efficient to provide several different variations of the basic algorithm. The run-time code is independent of the circuit under test, so the algorithm can be implemented either as a compiled code or an interpreted simulator with little variation in performance. Because of the small size of the run-time code, the run-time portions of the Inversion Algorithm can be implemented in assembly language for peak efficiency, and still be retargeted for new platforms with little effort.
Hepatocellular Carcinoma in the World and the Middle East

Hepatocellular carcinoma (HCC) is a major worldwide public health concern. Despite recent advances, there has been little success in improving the survival of HCC patients. Due to advances in diagnostic modalities and the increasing incidence of chronic viral hepatitis and nonalcoholic fatty liver disease (NAFLD), both of which are well known risk factors of HCC, the prevalence of HCC is increasing in developed countries and it is expected that this trend will continue in the future. In Middle Eastern countries the prevalence of this cancer is lower compared to sub-Saharan Africa and some Far East countries; however it is documented that the prevalence of this cancer in some parts of the Middle East is also increasing. In the current review, the prevalence and burden of this disease worldwide, particularly in Middle Eastern countries, and risk factors for HCC are discussed.
Audio-based Relative Positioning System for Multiple Micro Air Vehicle Systems

Employing a group of independently controlled flying micro air vehicles (MAVs) for aerial coverage missions, instead of a single flying robot, increases the robustness and efficiency of the missions. Designing a group of MAVs requires addressing new challenges, such as inter-robot collision avoidance and formation control, where individual's knowledge about the relative location of their local group members is essential. A relative positioning system for a MAV needs to satisfy severe constraints in terms of size, weight, processing power, power consumption, three-dimensional coverage and price. In this paper we present an on-board audio based system that is capable of providing individuals with relative positioning information of their neighbouring sound emitting MAVs. We propose a method based on coherence testing among signals of a small onboard microphone array to obtain relative bearing measurements, and a particle filter estimator to fuse these measurements with information about the motion of robots throughout time to obtain the desired relative location estimates. A method based on fractional Fourier transform (FrFT) is used to identify and extract sounds of simultaneous chirping robots in the neighbourhood. Furthermore, we evaluate our proposed method in a real world experiment with three simultaneously flying micro air vehicles.
Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data

Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods <100 ms. Based on the scale free properties of EEG microstates and their correlation with resting state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained from fMRI more meaningful to typically occurring fast neuronal processes in the sub-100 ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling (TR = 200 ms) using multiband EPI (MB EPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEG to fMRI temporal resolution. Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100 ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.
Dynamic decentralized control for protocentric aerial manipulators

We present a control methodology for underactuated aerial manipulators that is both easy to implement on real systems and able to achieve highly dynamic behaviors. The method is composed by two parts: i) a nominal input/state trajectory generator that takes into account the full-body dynamics of the system exploiting its differential flatness property; ii) a decentralized feedback controller acting on the actuated degrees of freedom that confers the needed robustness to the closed-loop system. We demonstrate that the proposed controller is able to precisely track dynamic trajectories when implemented on a standard hardware. Comparative experiments clearly show the benefit of using the nominal input/state generator.
Downlink non-orthogonal multiple access system with limited feedback channel

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technology for fifth generation (5G) mobile communication system. However, the advantage of NOMA is only proven under the ideal condition of transmitter knowing the perfect knowledge of channel state information (CSI). In this paper, NOMA downlink multi-user system, in which the transmitter acquires the CSI through the limited feedback channel, is studied. Making use of the imperfect CSI feedback, channel direction information (CDI) and channel quality indicator (CQI), we propose the user selection scheme to reduce the interference between the NOMA users. Furthermore, a power allocation scheme is applied to improve the sum-rate of NOMA system. Numerical results show that NOMA system with limited feedback channel still gains higher system rate than traditional orthogonal multiple access system.
Sensors' Ground Reaction Force behavior for both Normal and Parkinson subjects - A qualitative study

Characterization of normal and abnormal Gait has been a major research field for decades, whether in fall prevention, sports biomechanics or even disease indication. In this paper, we assess time domain statistical properties of the Vertical Ground Reaction Force (VGRF) during moderate-pace walking, aiming eventually to create a reliable mathematical model of VGRF for normal and abnormal cases. For that endeavor, first order statistical analysis was performed upon signal segmentation in order to determine the degree of stationarity and base the model upon it. Furthermore, we performed curve fitting of the VGRF time series between present and past values, which led us to model the waveform with linear regression via Autoregressive Model for both Normal Walking Signals and Parkinson diseased patients' walking signals. However this is done only for one chosen sensor. However, it would be crucial to take the advantage of the array of sensors. Evaluating the cross-covariance between multi-sensor data of a given subject at different time lags capture the most important information. The seasonality in the values give a quite important indications of the behavior of data. The objective behind this analysis is to recommend a preliminary basis to create reliable mathematical model of normal walking signals versus pathological walking signals, that we will emphasize in a complementary work, in the simplest way available and creating fall prevention indicators for old patients.
Delivery of Myoglobin Polymersomes Results in Tumor Hemorrhagic Necrosis and Enhanced Radiation Response

Delivery of Myoglobin Polymersomes Results in Tumor Hemorrhagic Necrosis and Enhanced Radiation Response by Christina Lehmkuhl Hofmann Department of Biomedical Engineering Duke University Date:_______________________ Approved: ___________________________ Mark W. Dewhirst, Supervisor ___________________________ Gregory M. Palmer ___________________________ Michael J. Therien ___________________________ Ashutosh Chilkoti ___________________________ Fan Yuan An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biomedical Engineering in the Graduate School of Duke University 2015 Copyright by Christina Lehmkuhl Hofmann 2015
Oesophageal Doppler monitoring: costly and supported by weak evidence

cially available products, considering the quality of evidence used to support its use. A bold statement suggesting a saving of approximately £1100 per patient based on hospital stay by using the CardioQ over conventional central venous catheterisation is another worrying point. Venn et al. [2] found that there was a difference in the time until medically fit between patients who had goal directed fluid replacement with ODM guidance compared with the control group. However, they went on to say that they could not show a significant reduction in total hospital stay as there are a number of socio-economical factors that need to be taken into consideration. Sinclair et al. [3] studied the effects of intra-operative fluid management guided by ODM in a small number of patients and found that it corresponded to a shorter hospital stay and therefore could be shown to be cost-efficient. However, Sinclair et al. failed to mention the time lapse between admission and surgery; the pre-operative medical intervention for each patient was not standardised; and the study was conducted in 1997 with the inpatient stay data compared with an in-house audit carried out in 1990-91. We applaud those who conducted this landmark study, but with the design limitations, it is a concern that so much weight was attached to it for the NICE guidance. Patients with fractured femurs are a difficult group to study and many have regional anaesthesia for their surgery. We have found that ODM is particularly difficult to use in awake patients and especially so in those with any cognitive impairment, which accounts for approximately one third of the hip fracture population. Further work is needed on alternative methods of monitoring in this high-risk group of patients.
Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets

Type-2 fuzzy sets involve more uncertainties than type-1 fuzzy sets. They provide us additional degrees of freedom to represent the uncertainty and the fuzziness of the real-world. Type-2 fuzzy sets can be regarded as an extension of type-1 fuzzy sets. In this paper, we extend the TOPSIS method to propose a new method for handling fuzzy multiple attributes group decision-making problems based on the ranking values of interval type-2 fuzzy sets. The proposed method provides us a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner.