Miracle-dz/UniFAR-FTUs huggingface dataset:
The Norm Residue Theorem in Motivic Cohomology

This book presents the complete proof of the Bloch–Kato conjecture and several related conjectures of Beilinson and Lichtenbaum in algebraic geometry. Brought together here for the first time, these conjectures describe the structure of étale cohomology and its relation to motivic cohomology and Chow groups. Although the proof relies on the work of several people, it is credited primarily to Vladimir Voevodsky. The book draws on a multitude of published and unpublished sources to explain the large-scale structure of Voevodsky's proof and introduces the key figures behind its development. It proceeds to describe the highly innovative geometric constructions of Markus Rost, including the construction of norm varieties, which play a crucial role in the proof. It then addresses symmetric powers of motives and motivic cohomology operations. The book unites various components of the proof that until now were scattered across many sources of varying accessibility, often with differing hypotheses, definitions, and language.
A search for top-antitop quark resonances with the ATLAS detector at the LHC

Various extensions to the Standard Model postulate the existence of heavy particles that decay to a top-antitop quark pair. If one of these particles exists with a mass of a few TeV then it may be produced during proton-proton collisions at the Large Hadron Collider (LHC). The analysis presented in this thesis searches 36 fb−1 of √s = 13 TeV proton- proton collision data collected by the ATLAS detector for evidence of a top-antitop quark resonance, and sets upper limits on the production cross section of new heavy particles in a set of benchmark models. The observed top-antitop quark invariant mass spectrum agrees well with the spectrum expected under the Standard Model and no evidence of a top-antitop quark resonance is found. A 1% width Z′ boson in a topcolour-assisted technicolour model is observed to be excluded at masses below 3.0 TeV, a 30% (15%) width Kaluza-Klein gluon in a Randall-Sundrum model is observed to be excluded at masses below 3.7 TeV (3.8 TeV), and a 3-6% width Kaluza-Klein graviton in a Randall-Sundrum model is observed to be excluded in the mass interval 0.45 < mGKK < 0.65 TeV. Some techniques developed to improve the sensitivity of the analysis are described: a new method of electron-in-jet overlap removal and a reconstruction of semi-boosted and very boosted top-antitop quark decay topologies. A study evaluating the prospects of this search at the High Luminostiy LHC is also presented. It estimates that a search of 3000 fb−1 of proton-proton collision data collected using an upgraded ATLAS detector will be able to exclude a Z′ boson at masses below 4 TeV.
Numerical simulation of propagation problems on the sphere with a compact scheme

We consider propagation problems on the sphere and their approximation by a compact finite difference scheme. The scheme used in this study uses the Cubed Sphere, a particular spherical grid with logically Cartesian structure. A central role is played by the standard one dimensional Hermitian derivative [22]. This compact scheme operates along great circles, thus avoiding any one sided compact scheme. [10, 11]. The scheme is centered. A simple high frequency filter is added to reinforce the stability. The final scheme is reminiscent of compact schemes in Computational Aeroacoustics or in turbulence Direct Numerical Simulation. Numerical results on a broad series of numerical test cases in climatology are presented, including linear convection problems, the linearized shallow water equations and the non linear shallow water equations. The results demonstrate the interest of the present approach in a variety of situations arising in numerical climatology.
7. Virtual Environments With Chroma-Keying Video Capture In Psychological Therapy

: A novel system of virtual environments (VEs) was developed and evaluated in the context of cognitive behaviour therapy (CBT) for socially anxious patients recovering from psychosis. The system uses chroma-keying video-capture to allow the person to see a life-size projection of their self interacting with specially-scripted and digitally-edited filmed environments played in real time on a screen in front of them. Two feasibility studies collected self-reported ratings of anxiety and paranoia along with narrative feedback from 32 patients before and after using three non-interactive VEs. Six patients also used a variety of interactive VEs to carry out exposure-type behavioural experiments during a single therapy session. About two-thirds of patients found that the VEs variably evoked responses that were similar to real life. These types of VEs can potentially be used as a therapy tool to help patients understand psychological processes, practise social skills, and gain the impetus to carry out real life behavioural experiments. Patients experience VEs in different ways - albeit as surreal, strange, or weird - depending on what is relevant and meaningful to them, what extraneous factors they find stressful or distracting (e.g. the presence of the researcher in the room or using the equipment), whether they perceive the VEs as realistic or immersive, and whether they use safety behaviours and avoidance strategies while in the VEs to control anxiety or paranoia.
Post-Laspeyres; The Case for a New Formula for Compiling Consumer Price Indexes

Consumer price indexes (CPIs) are compiled at the higher (weighted) level using Laspeyres-type arithmetic averages. This paper questions the suitability of such formulas and considers two counterpart alternatives that use geometric averaging, the Geometric Young and the (price-updated) Geometric Lowe. The paper provides a formal decomposition and understanding of the differences between the two. Empirical results are provided using United States CPI data. The findings lead to an advocacy of variants of a hybrid formula suggested by Lent and Dorfman (2009) that substantially reduces bias from Laspeyres-type indexes.
Fitting Probability Distribution Function to Malaria Incidence Data

Abstract: Malaria remains a major infectious disease that affects millions of people. Once infected with Plasmodium parasites, a host can develop a broad range of clinical presentations, which result from complex interactions between factors derived from the host, the parasite and the environment. Malaria has historically been a very serious health problem and currently poses the most significant threat to the health of Masinde Muliro University of Science and Technology students, data shows that more than 70% percent of pediatric cases are due to malaria. Methodology: Hence, the study aimed to fit malaria incidences dataset for the period 1st January, 2013 to 31st December, 2015. Data on monthly malaria incidence was obtained from the Masinde Muliro University of Science and Technology health service. Gamma, Weibull and Lognormal Distributions were employed to fit the malaria incidence dataset using R-software. Results: High malaria incidences were observed in the months of August, September and November. AIC values results showed that lognormal distribution had the lowest AIC value of 185.9875 followed by the Gamma distribution with a value of 187.8815 and then the Weibull distribution with a value of 188.7271. This confirmed the lognormal distribution to be the best fitting distribution for the malaria incidence dataset Conclusion: The Poisson regression model did not accurately fit the data on malaria incidences due to over dispersion in the data but lognormal distribution was a better fit compared to gamma and Weibull distribution.
Using Music Videos

As part of IFTE 2011, I presented some activities based around music videos. The activities have been part of our Foundation English course, which has been designed to cater for students who struggle with English for a wide variety of reasons. However, the main thing these students have in common is that they need more time to complete assessments.
Surface Roughness-Induced Spectral Degradation of Multi-Spaceborne Solar Diffusers Due to Space Radiation Exposure

Solar diffusers (SDs) have often been used as the onboard calibrators for the radiometric calibration of reflective solar band imaging sensors. After being spaceborne, the reflectance of SDs is observed to degrade with spectral dependence due to exposure to solar UV and energetic particle radiation. Long-term spectral reflectance data of SDs onboard multiple LEO imaging sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) on SNPP, are analyzed. The reflectance of SDs on these three instruments degrades faster for the shorter wavelength (0.4– $0.6~\mu \text{m}$ ) bands than the longer wavelength bands. The Surface Roughness-induced Rayleigh Scattering (SRRS) model is applied to simulate the SD degradation on these instruments, and the growth of the surface roughness parameter of the SDs is derived. It is determined that the change of surface roughness scale length is ~tens of nanometers. To show the consistency of roughness growth rates among the SDs on Terra/Aqua MODIS and SNPP VIIRS instruments, the functional dependences of the growth rates are characterized according to the SD exposure time and the stage of surface roughness. It is also found that the flattening or reverse in the growth trend of the surface roughness for these three SDs occurred around the same interval between October 2013 and October 2015. The confirmation of the applicability of SRRS model with the long-term spectral reflectance data from three independent spaceborne SDs facilitates a better understanding of the origin and physical processes of the SD degradation.
Empirical Pilot Study Variable Selection Using Value Tree Analysis

The selection of variables to collect in empirical pilot studies is critical due to time, financial and sample size limitations. The collection of patient data in the healthcare system poses additional concerns due to patient privacy, incomplete data and clinical feasibility. The existence of these intrinsic pilot study and healthcare constraints requires a strategic selection of study variables to maximize the variability explained in the patient population and knowledge gained from data analysis. We propose a value tree analysis to select data collection strategies (combination of study variables) that produce the highest preference value for the researcher while also taking into account the interests of other study stakeholders (patient, provider, clinical staff, Institutional Review Board, etc.). In the first stage (problem structuring), the decision context is framed to define the pilot study environment and stakeholder values that may impact the study design. The study objectives, decision alternatives and attributes are then generated to consider tradeoffs among conflicting criteria and stakeholder interests. In the second stage (preference elicitation), decision attributes are weighted using Analytical Hierarchy Processing and a value function is used to elicit the decision maker‟s preference for each variable combination. Value tree analysis is a low cost and low computation approach for time-sensitive variable selection in empirical pilot healthcare studies. The proposed approach provides a structured and strategic decision-making process that accounts for pilot study and patient data constraints and maximizes value for study stakeholders.
Robust 3D reconstruction method based on image sequence

A robust 3D reconstruction method based on image sequence was presented.First,an optimal parameter estimation of two images was applied.Then,along with the number of images increasing,the sparse bundle adjustment was adopted to reduce the minimum geometric error of the measurements of image coordinates.Finally,the 3D structure and camera parameters were processed with a global optimization method to improve the robustness of the reconstruction.The experiment results demonstrate that the proposed method can effectively improve the accuracy and robustness of reconstruction,and factually represent 3D model of objects.
基于深度学习的车标识别方法研究 (Vehicle Logo Recognition Based on Deep Learning)

Identification of vehicles which have caused traffic accidents is an important issue in intelligent transportation system(ITS). However,due to the missing or the stains on the car license plates,it is difficult to locate the vehicles.Logos,as key features of vehicles,can also help to identify vehicles of interest.This paper proposed a method to detect and recognize vehicle logos based on deep learning.Compared with traditional approaches that extract features manually,this method uses original images as inputs to learn features automatically.Experimental results demonstrate that the proposed method has a high accuracy,and even in the conditions of illumination change and noise contamination,it is able to produce stable and accurate results.
Opacity Enforcement for Confidential Robust Control in Linear Cyber-Physical Systems

Opacity, a confidentiality property, is an increasing concern in cyber-physical systems (CPSs) that are vulnerable to an intruder which intends to reveal a “secret” of a system. This note presents a new framework for opacity and considers the problem of enforcing opacity in CPSs modeled as linear time-invariant systems. The confidential information involves the CPS’ interference attenuation capacity (IAC), called the secret. A system is opaque if the intruder never infers the secret IAC from an observation mapping of system output. To guarantee the confidentiality requirement, an effective algorithm is proposed for synthesizing opacity-enforcing controllers by using a new approximation-based Q-learning. A main advantage of this method is that it does not require any knowledge of the system dynamics matrices. A simulation example is presented to sustain the theoretical results.
Analysis and optimization of transaction level models for multi-processor system-on-chip design

The increasing complexity of modern embedded systems and systems-on-chip poses great challenges to the design process. An exploding number of alternatives has to be considered during the design process. Additionally, the amount of software with tight coupling to underlying hardware increases in current designs, adding another complexity dimension. System-Level Design addresses these challenges by using a unified approach for hardware and software design. Raising the level of abstraction, system-level design uses fewer, abstract models of hardware and software for system analysis, exploration, simulation, and implementation. Well-defined and efficient models are crucial for reliable design space exploration. In particular, fast yet accurate models are needed to reduce the design time and improve the end product. In this dissertation, we address the modeling of Multi-Processor System-on-Chip (MPSoC) with Transaction Level Models (TLM) for two essential system elements, communication busses and software processors. We contribute in three aspects. First, we systematically analyze communication models and quantify the speed/accuracy trade-off in TLM. We provide a classification of abstraction levels based on model granularity. In traditional models, each abstraction level improves the simulation speed by several orders of magnitude, however at a significant loss of accuracy. Second, we propose a novel modeling technique, Result Oriented Modeling (ROM), which removes the inaccuracy drawback of TLM, yet yields nearly the same speed. Third, we propose a fast alternative to traditional instruction set simulation, using a versatile processor model that shows speed gains of three orders of magnitude with only a few percent of error in accuracy. Overall, our work guides the system developer in choosing the proper model features and provides efficient techniques to model them. It also supports the designer in model selection, analysis and implementation. As a result, our system modeling research will influence the design of digital embedded systems, resulting in better and less expensive end products while reducing the time-to-market.
Size Effect in Grand-canonical Monte-Carlo Simulation of Solutions of Electrolyte

Abstract: A Grand-canonical Monte-Carlo simulation method is investigated. Due to charge neutrality requirement of electrolyte solutions, ions must be added to or removed from the system in groups. It is then implemented to simulate solution of 1:1, 2:1 and 2:2 salts at different concentrations using the primitive ion model. We investigate how the finite size of the simulation box can influence statistical quantities of the salt system. Remarkably, the method works well down to a system as small as one salt molecule. Although the fluctuation in the statistical quantities increases as the system gets smaller, their average values remain equal to their bulk value within the uncertainty error. Based on this knowledge, the osmotic pressures of the electrolyte solutions are calculated and shown to depend linearly on the salt concentrations within the concentration range simulated. Chemical potential of ionic salt that can be used for simulation of these salts in more complex system are calculated. Keywords: GCMC, electrolyte solution simulation, primitive ion model, finite size effect.
Improving the design of wireless sensor networks using QoS-aware opportunistic techniques

The design of a Wireless Sensor Network with Quality of Service (QoS) is a challenging and complex topic especially when the post-deployment corrections are expensive. This paper proposes a design methodology of Wireless Sensor Networks to estimate network performance in terms of end-to-end delay and reliability. It uses a probabilistic model to determine the needed node density, then adopting a variant of geographic routing it lets to calculate the number of path hops. The introduced opportunistic mechanism offers a trade-off between low end-to-end delay and reliable packets delivery. The modeled network with the adopted Geographic Opportunistic Routing has been evaluated through simulations and some guidelines about its design in order to obtain desired performance are given.
A Novel Dynamic Time Wrapping Similarity Algorithm Optimized by Multi-Granularity

Dynamic time warping algorithm (DTW) is a method of measuring the similarity of time series. Concerning the problem that DTW cannot keep high classification accuracy when the computation speed improved, a FG-DTW method based on the idea of naive granular computing is proposed. In this method, firstly, better temporal granularity is acquired by calculating temporal variance feature and it is used to replace original time series; Secondly, the elastic size of under comparing time series granularity allow dynamic adjustment through DTW algorithm and optimal time series corresponding granularity is obtained; Finally, DTW distance is calculated by optimal corresponding granularity model. At the same time, the early termination strategy of infimum function is introduced to improve the efficiency of FG-DTW algorithm. Experiments show that the proposed algorithm improves the running rate and accuracy effectively.
Face Annotation with Weakly Labeled Web Facial Images

Auto Face annotation is playing an important role in Many real-world applications. Search-based face (SBFA) annotation is aim to begin the automated face annotation task by employ the Content Based Image Retrieval (CBIR) techniques. CBIR is used as a query by image content and automatically detect the faces from photo, assign that face to corresponding human name. Searching and mining are massive weakly labeled facial images are available in World Wide Web. Weakly labeled facial images are often noisy and incomplete. To overcome this trouble move onto the unsupervised label refinement (ULR) and Clustering based approximation techniques (CBA), to improve the accuracy, performance and scalability. By combining textual and visual features, it manages to pick “good” features that reflect users’ perception, and therefore is effective for search.
Essential Molecular Biology A Practical Approach

Thank you very much for downloading essential molecular biology a practical approach. Maybe you have knowledge that, people have search numerous times for their favorite books like this essential molecular biology a practical approach, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their computer.
Security Considerations in the IP-based Internet of Things

A direct interpretation of the Internet of Things concept refers to the usage of standard Internet protocols to allow for human-to-thing or thing-to-thing communication. Although the security needs are well- recognized, it is still not fully clear how existing IP-based security protocols can be applied to this new setting. This Internet-Draft first provides an overview of security architecture, its deployment model and general security needs in the context of the lifecycle of a thing. Then, it presents challenges and requirements for the successful roll-out of new applications and usage of standard IP-based security protocols when applied to get a functional Internet of Things.
City based Traffic Management and Congestion Regulatory using Odd-Even policy in VANET

Vanet is a special category of adhoc-network in which vehicles and other stationary unit (RSU) form network for special need or purpose by inter-vehicle communication. Since in recently decades ,VANET gaining tremendous attention of research for making reliable communication among vehicles for easy navigation and better handling of traffic . Since due to success of VANET reliability , there is addition of large number of vehicles running on road . As a result large number of vehicles creates congestion(traffic) in city which further result in pollution ,speed factor and various other problem for managing traffic . In this paper , we introduced concept of odd-even policy for handling of traffic and smooth transportation and better communication among Vehicles in Vanet .
Research on the DSmT based model of the ultrasonic sensor detection for indoor environment contour

Due to the existence of the ultrasonic sensor distance error and angle error causing environment contour detection uncertainty, the ultrasonic distance measurement mode based on the foundation of the uncertainty representation was firstly proposed and the data uncertainty of ultrasonic measurement was reduced by the Dezert-Smarandache Theory (DSmT). Then Hough transform and random sample consensus algorithm are employed to identify the data fusion results to obtain environmental contour information. Finally, the experiment of ultrasonic sensors scanning the indoor environment are performed, and the contours information of indoor environment obtained by using the proposed method is consistent with the size of true environment. This result validly illustrates the feasibility and effectiveness of the proposed method. The proposed method has certain reference value for studying the localization and environment map detection of the mobile robot by using ultrasonic sensor.