Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units

In silico analysis of biological systems represents a valuable alternative and complementary approach to experimental research. Computational methodologies, indeed, allow to mimic some conditions of cellular processes that might be difficult to dissect by exploiting traditional laboratory techniques, therefore potentially achieving a thorough comprehension of the molecular mechanisms that rule the functioning of cells and organisms. In spite of the benefits that it can bring about in biology, the computational approach still has two main limitations: first, there is often a lack of adequate knowledge on the biological system of interest, which prevents the creation of a proper mathematical model able to produce faithful and quantitative predictions; second, the analysis of the model can require a massive number of simulations and calculations, which are computationally burdensome. The goal of the present thesis is to develop novel computational methodologies to efficiently tackle these two issues, at multiple scales of biological complexity (from single molecular structures to networks of biochemical reactions). The inference of the missing data — related to the three-dimensional structures of proteins, the number and type of chemical species and their mutual interactions, the kinetic parameters — is performed by means of novel methods based on Evolutionary Computation and Swarm Intelligence techniques. General purpose GPU computing has been adopted to reduce the computational time, achieving a relevant speedup with respect to the sequential execution of the same algorithms. The results presented in this thesis show that these novel evolutionary-based and GPU-accelerated methodologies are indeed feasible and advantageous from both the points of view of inference quality and computational performances.
A TCP Test to Allow Senders to Identify Receiver Non-Compliance

The TCP protocol relies on receivers sending accurate and timely feedback to the sender. Currently the sender has no means to verify that a receiver is correctly sending this feedback according to the protocol. A receiver that is non-compliant has the potential to disrupt a sender's resource allocation, increasing its transmission rate on that connection which in turn could adversely affect the network itself. This document presents a two stage test process that can be used to identify whether a receiver is non-compliant. The tests enshrine the principle that one shouldn't attribute to malice that which may be accidental. The first stage test causes minimum impact to the receiver but raises a suspicion of non-compliance. The second stage test can then be used to verify that the receiver is non- compliant. This specification does not modify the core TCP protocol - the tests can either be implemented as a test suite or as a stand- alone test through a simple modification to the sender implementation.
Least Pair-Wise Collision Beam Schedule for mmWave Inter-Cell Interference Suppression

The narrow beamwidth characteristic in millimeter wave (mmWave) makes the level of inter-cell interference depend on the beams used by adjacent cells, which indicates that a well-designed schedule of the beams in neighboring cells can reduce the inter-cell interference. In this paper, we investigate a time-domain mmWave beam scheduling problem for interference suppression in a two-cell scenario, where each cell may transmit one or multiple beams simultaneously. Specifically, the beams are one-to-one paired between two adjacent cells, and the simultaneous transmission of two beams in one pair can cause severe inter-cell interference, which is referred to as a pair-wise collision. Instead of optimizing the sum rate of network, we minimize the number of pair-wise collision time slots under the constraint of the service demand of each beam. In this way, the channel state information is not required, and the global optimal solution can be found by our proposed least pair-wise collision (LPC) algorithm, which is a recursive algorithm with linear computational complexity. After that, we extend LPC algorithm to more complicated models, including an asymmetric model and a full-dimension model. Finally, simulations are conducted to verify the efficiency of our methodology.
Necklacelike solitons formed by manipulating vortex beams in a synthetic structure

We embed a helical lattice into a portion of a bulk photorefractive medium to form a synthetic structure and numerically investigate the nonlinear transmission of a probe vortex beam in the structure. Numerical demonstrations confirm that the structure can stabilize multisoliton states in the form of nonrotating and nonexpanding necklacelike solitons. Lattice structure and the amplitude, width, and topological charge of the probe vortex beam can influence the structure of necklacelike solitons. Interestingly, an increase in beam intensity may lead to multiplication of the number of bright “spots. ”
Variants of the Transverse Plane Block and Quadratus Lumborum Block (Review)

Eﬀective implementation of the fast track concept after surgical treatment is impossible without control of pain in the early postoperative period. Regional methods might signiﬁcantly improve the quality of postoperative analgesia and accelerate patient recovery as a component of multimodal pain therapy. The implementation of ultrasonic navigation in peripheral nerve blockade protocols has signiﬁcantly increased the eﬃciency and safety of the blockade. Nevertheless, each method of regional anesthesia has its own indications, contraindications, and complications. The purpose of this review is to help with the right choice of the type of blockade of the anterior abdominal wall, depending on the surgery performed.
An MRT Extension to the Multigrid Lattice Boltzmann Method

A full approximation storage multigrid method has been shown to improve the convergence properties of the D2Q9 BGK lattice Boltzmann method [14]. In this paper we extend the method to the multiple relaxation time collision model due to its improved stability. The new method is validated using Poiseuille flow. The method is then extended to three dimensions. The efficiency of the V-cycle and W-cycle multigrid schedules and the under-relaxed Jacobi and Gauss Seidel multigrid smoothers are reported for the two and three dimensional models for lid driven cavity flow. AMS subject classifications: 76M25, 76D99
An improved model predictive control of low voltage ride through in a permanent magnet synchronous generator in wind turbine systems

This research investigates a wind energy conversion system based on a permanent magnet synchronous generator (PMSG). In addition, a model predictive control (MPC) is proposed for the PMSG in normal and fault conditions. The most efficient mode of the control algorithm is found for maximum power point tracking in normal conditions and fast dynamic response in fault conditions following the selection of the optimum voltage vector. This method prevents a sudden increase in the DC‐link voltage by storing the active power in the generator rotor inertia. Moreover, during the low voltage, the grid code adoption of the reactive current is injected into the grid side. The performance of the proposed control scheme is evaluated for a wind power generator using MATLAB software. The simulation results demonstrate that the proposed method can safeguard the DC‐link during the fault.
Detecting Small Objects in Urban Settings Using SlimNet Model

The automatic extraction of small objects such as roadside milestones, small traffic signs, and other urban furniture remains a technical challenge. This study focuses on methods of deep learning to detect small urban elements in mobile mapping system (MMS) images. Based on images obtained by an MMS in urban areas, we create an urban element detection (UED) data set containing several kinds of small objects found in a city. A simple feature extraction convolution neural network (CNN) called SlimNet is proposed and combined with an optimized faster R-CNN framework. The resulting deep learning method can automatically extract small objects commonly found in cities, including manhole covers, milestones, and license plates. Experiments on the UED data set show that SlimNet has the highest accuracy compared with other popular networks, including VGG, MobileNet, ResNet, and YOLOv3. The SlimNet model can achieve a mean average precision (AP) that is up to 12.3% higher than that of the lowest ResNet-152 network and can accelerate both training and detection owing to its relative simplicity. Moreover, $k$ -means clustering is used to choose the dimensions of the anchor box for detection. We ran $k$ -means clustering for different numbers of clusters, and the results show that at least four clusters are needed for detection using a small data set such as the UED. We also propose a method to use templates of different scales for anchors to further improve small object detection; this approach improved the AP by 3%–4% in our experiments.
Novel Data Conversion Method for Motion Capture

When calculating the rotational information of the skeleton joints,the traditional data conversion method can reduce the accuracy of the rotation data.Aiming at the problem,a data conversion method for motion capture is proposed.It establishes the human body tree structure skeleton model by using the structural relationship between their own frame human skeleton model,and designs the method of construction and decomposition to solve the three freedom rotation information of the key points,and uses this information to the drive human skeleton model.Experimental results demonstrate the effectiveness of this method.
Adaptive Data Dissemination Algorithm for Vehicular Opportunistic Networks

Vehicular Ad-hoc networks(VANETs) are promising information-sharing technologies for modern intelligent transportation systems(ITS),in which information can be disseminated and shared among a group of moving vehicles through wireless communication devices.Previous data dissemination schemes for VANETs are unable to meet the requirements of effective and efficient data dissemination in VANETs due to the dynamic nature of the framework such as the intermittent connection between vehicles.An Adaptive Copy and Spread(ACS) data dissemination algorithm was proposed for opportunistic VANETs to adaptively adjust the dissemination strategies.Through making use of the mo-ving parameters of vehicles(e.g.direction,velocity),ACS can dynamically calculate the number of message copies to be broadcasted so that vehicles can intelligently start or stop a message spread stage.In addition,ACS was designed to keep the message located at the effective area's boundary,inform the entering vehicles as soon as possible.Our simulation results show that ACS outperforms traditional Random Choice(RC) algorithm and Epidemic spreading(ED) algorithm with less network resource consumption in broad scenarios.
Desenvolvimento de um Chatbot baseado em Ontologia para Atendimento a Chamados de Suporte ao Cliente

Chatbots no contexto de suporte ao cliente permitem que dúvidas frequentes sejam resolvidas de forma rápida sem necessitar a alocação de um atendente e, consequentemente, podendo aumentar a quantidade de atendimentos por dia. Foi feito um estudo de caso de uma empresa sobre a senha de liberação do sistema, um pedido que causava sobrecarga na equipe de suporte. O objetivo do projeto foi desenvolver um chatbot capaz de, em um primeiro momento, responder os pedidos sobre senha de liberação e reduzir a quantidade de atendimentos do suporte humano. Foram realizados estudos de diversas ferramentas para desenvolvimento do chatbot e a ferramenta escolhida para o projeto foi a Dialogflow por possuir todas as funcionalidades necessárias para a implementação. Foram realizados experimentos com usuários, apresentando resultados satisfatórios.
An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons

In this paper, we develop an adaptive control algorithm for addressing security for a class of networked vehicles that comprise a formation of n^ human‐driven vehicles sharing kinematic data and an autonomous vehicle in the aft of the vehicle formation receiving data from the preceding vehicles through wireless vehicle‐to‐vehicle communication devices. Specifically, we develop an adaptive controller for mitigating time‐invariant state‐dependent adversarial sensor and actuator attacks while guaranteeing uniform ultimate boundedness of the closed‐loop networked system. Furthermore, an adaptive learning framework is presented for identifying the state space model parameters based on input‐output data. This learning technique utilizes previously stored data as well as current data to identify the system parameters using a relaxed persistence of excitation condition. The effectiveness of the proposed approach is demonstrated by an illustrative numerical example involving a platoon of connected vehicles.
A Load Balanced Algorithm for Anycast Routing Based on Genetic Algorithm

Anycast is a new network service defined in IPv6,and it can make a host communication with the most ap- propriate one of a group of servers.This paper presented a load balanced algorithm for anycast routing,and used ge- netic algorithm to get the hest result.The algorithm employed the delay,residual bandwidth and server load as routing metric to guarantee the quality of service.Emulation results indicate that system resource is adequately used and net- work efficiency is improved by the algorithm.
Analysis of serum cortisol levels by Fourier Transform Infrared Spectroscopy for diagnosis of stress in athletes

Introduction Fourier-transform infrared (FT-IR) spectroscopy is a technique with great potential for body fluids analyses. The aim of this study was to examine the impact of session training on cortisol concentrations in rugby players by means of infrared analysis of serum. Methods Blood collections were performed pre, post and 24 hours after of rugby training sessions. Serum cortisol was analyzed by FT-IR spectroscopy and chemiluminescent immunoassay. Results There was a significant difference between the integrated area, in the region of 1180-1102 cm-1, of the spectra for pre, post and post 24 h serums. The cortisol concentration obtained by chemiluminescent immunoassay showed no significant difference between pre, post and post 24 h. Positive correlations were obtained between the techniques (r = 0.75), post (r = 0.83) and post 24 h (r = 0.73). Conclusion The results showed no increase in cortisol levels of the players after the training sessions, as well as positive correlations indicating that FT-IR spectroscopy have produced promising results for the analysis of serum for diagnosis of stress.
A Full Asymptotic Series of European Call Option Prices in the SABR Model with Beta = 1

We develop two new pricing formulae for European options. The purpose of these formulae is to better understand the impact of each term of the model, as well as improve the speed of the calculations. We consider the SABR model (with β=1) of stochastic volatility, which we analyze by tools from Malliavin Calculus. We follow the approach of Alos et al. (2006) who showed that under stochastic volatility framework, the option prices can be written as the sum of the classic Hull-White (1987) term and a correction due to correlation. We derive the Hull-White term, by using the conditional density of the average volatility, and write it as a two-dimensional integral. For the correction part, we use two different approaches. Both approaches rely on the pairing of the exponential formula developed by Jin, Peng, and Schellhorn (2016) with analytical calculations. The first approach, which we call “Dyson series on the return’s idiosyncratic noise” yields a complete series expansion but necessitates the calculation of a 7-dimensional integral. Two of these dimensions come from the use of Yor’s (1992) formula for the joint density of a Brownian motion and the time-integral of geometric Brownian motion. The second approach, which we call “Dyson series on the common noise” necessitates the calculation of only a one-dimensional integral, but the formula is more complex. This research consisted of both analytical derivations and numerical calculations. The latter show that our formulae are in general more exact, yet more time-consuming to calculate, than the first order expansion of Hagan et al. (2002).
A convex-nonconvex variational method for the additive decomposition of functions on surfaces

We present a convex-nonconvex variational approach for the additive decomposition of noisy scalar fields defined over triangulated surfaces into piecewise constant and smooth components. The energy functional to be minimized is defined by the weighted sum of three terms, namely an fidelity term for the noise component, a Tikhonov regularization term for the smooth component and a total variation (TV)-like non-convex term for the piecewise constant component. The last term is parametrized such that the free scalar parameter allows to tune its degree of non-convexity and, hence, to separate the piecewise constant component more effectively than by using a classical convex TV regularizer without renouncing to convexity of the total energy functional. A method is also presented for selecting the two regularization parameters. The unique solution of the proposed variational model is determined by means of an efficient ADMM-based minimization algorithm. Numerical experiments show a nearly perfect separation of the different components.
PERFORMANCE COMPARISON OF STEPPED AND SMOOTH DIELECTRIC LENS-LOADED FLAT REFLECTORS

This paper presents the design of an X-band stepped lens-loaded flat reflector (STLR) using reflectarray unit cell approach. A flat reflector of size 15× 15 cm2, loaded with a 10× 10 dielectric unit cell array, centre-fed by a horn antenna, is evaluated by simulation and measurement. The simulated performance of the proposed structure is compared with a smooth lens loaded plane reflector (SMLR) and microstrip reflectarray (RA), of equivalent cross section. The results are in good agreement for SMLR, whereas a fair match is observed only in the main lobes of RA. It is worth to note that the reported STLR has a reduced thickness compared to SMLR. Furthermore, a simulation-based study is carried out on the effect of tapering of the dielectric structure in the proposed design, and a similar performance to when stepping is used is noted.
Cross Sections and Rate Coefficients for Rovibrational Excitation of HeH+ Isotopologues by Electron Impact

Cross sections and thermal rate coefficients for rotational and vibration excitation of the four stable isotopologues of the 4 HeH + ion by electron impact are presented. The data are calculated using a previously developed theoretical approach. The obtained rate coefficients are fitted to analytical formulas with the 10–10,000 K interval of applicability. These present results could be useful in tokamak plasma and astrophysical modeling and can help in the detection of these species in the interstellar medium.
System Design of an Indoor Mobile Robot Navigated by Infrared

The mobile robot sends radio to a destination of power supply device when it detects that the voltage of power is insufficient. The device sends infrared to guide the robot in moving towards it as soon as it receives the radio. The robot successively collects the signals of the infrared,and the two motors that drive the bicycle of the robot are real-time controlled,which is realized by the software that simulates pulse width modulation。 In doing this,the function of the robot to track infrared is achieved. Without image processing and precise environment model,the robot avoids collision and plans routes with many kinds of sensing technologies including ultrasonic,infrared,photoelectric,and with an embedded experienced expert system when it detects no or weak signals of the infrared,and then,it quickly leaves the local place and trails the infrared in the shortest distance,and is accurately located in the power supply device lastly.
A Generalized Dependency Tree Language Model for SMT

In this paper we describe a generalized dependency tree language model for machine translation. We consider in detail the question of how to deﬁne tree-based n -grams, or ‘ t -treelets’, and thoroughly explore the strengths and weaknesses of our approach by evaluating the eﬀect on translation quality for nine major languages. In addition, we show that it is possible to attain a signiﬁcant improvement in translation quality for even non-structured machine translation by reranking ﬁltered parses of k -best string output.
Do Leaders Matter?

Based on field research at a number of comprehensive schools, this paper explores how secondary headteachers contribute to the effectiveness of their schools. Although recent studies indicate that heads exercise only a small, indirect effect on performance, there is a widespread belief, shared by the government, OfSTED, governors and parents that leadership is a vital ingredient for success. Evidence about leadership is used to compare and contrast successful and less successful headteachers. Despite the complications of social context, internal politics and external pressure, strong heads seem to adopt similar, well-balanced leadership styles and strategies that correlate with well-motivated students and staff. In contrast, poor performers operate a limited range of styles and strategies and elicit a negative response from their colleagues. These findings match predictions arising from a classic experiment at Harvard Business School (Litwin & Stringer, 1966), on which the DfEE's Leadership Programme for Serving Heads is based. The paper describes how effective leaders motivate staff and students and indicates problematical influences limiting or constraining the performance of headteachers.
Alignment-free clustering and error correction of UMI tagged DNA molecules

The use of circulating tumour DNA (ctDNA) in cancer oncogenomics has the potential for rapid and non-invasive monitoring of patient-specific tumour progression. However, detection of low allele frequency variations in ctDNA raises many challenges, including the handling of sequencing errors. Tagging of DNA molecules with Unique Molecular Identifiers (UMI) attempts to mitigate sequencing errors; UMI tagged molecules are PCR amplified then sequenced independently. Analyzing UMI tagged sequencing data requires clustering reads originating from the same molecule then error-correcting sequencing errors in these clusters. Sizes of the current datasets require this process to be resource-efficient. To address this problem, we introduce Calib, a computational tool that clusters and error-corrects UMI tagged sequencing data. Calib is efficient and its parameters have been optimized to different dataset setups. On simulated datasets, Calib is highly accurate. On a real dataset, Calib results in significantly reduced false positive rates in downstream variation calling.