Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian statistical models of shape and appearance for subcortical brain segmentation

Our motivation is to develop an automated technique for segmentation of sub-cortical human brain structures from MR images. To this purpose, models of shape-andappearance are constructed and fit to new image data. The statistical models are trained from 317 manually labelled T1-weighted MR images. Shape is modelled using a surface-based point distribution model (PDM) such that the shape space is constrained to the linear combination of the mean shape and eigenvectors of the vertex coordinates. In addition, to model intensity at the structural boundary, intensities are sampled along the surface normal from the underlying image. We propose a novel Bayesian appearance model whereby the relationship between shape and intensity are modelled via the conditional distribution of intensity given shape. Our fully probabilistic approach eliminates the need for arbitrary weightings between shape and intensity as well as for tuning parameters that specify the relative contribution between the use of shape constraints and intensity information. Leave-one-out crossvalidation is used to validate the model and fitting for 17 structures. The PDM for shape requires surface parameterizations of the volumetric, manual labels such that vertices retain a one-to-one correspondence across the training subjects. Surface parameterizations with correspondence are generated through the use of deformable models under constraints that embed the correspondence criterion within the deformation process. A novel force that favours equal-area triangles throughout the mesh is introduced. The force adds stability to the mesh such that minimal smoothing or within-surface motion is required. The use of the PDM for segmentation across a series of subjects results in a set surfaces that retain point correspondence. The correspondence facilitates landmarkbased shape analysis. Amongst other metrics, vertex-wise multivariate statistics and discriminant analysis are used to investigate local and global size and shape differences between groups. The model is fit, and shape analysis is applied to two clinical datasets. Thesis submitted for the degree Doctorate of Philosophy at the University of Oxford Trinity Term, 2007
Fully static processor-optimal assignment of data-flow graphs

The data-flow graph (DFG) is an important graph-theoretic model for multiprocessor implementation of real-time digital signal processing (DSP) algorithms. Given a time schedule for a DFG, we consider the problem of the processor-optimal assignment for a fully static schedule. Previously, the solution of this problem was found by solving an integer programming problem. We propose a linear programming approach to solving the problem.
Fast acquisition of burst and transient signals using a predictive adaptive beamformer

The author presents a simple prediction algorithm for blindly adapting a narrowband beamformer to rapidly acquire burst or transient signals by exploiting only the abrupt ON/OFF transitions of the transient waveforms, that is without using knowledge of the frequency or DOA (direction of arrival) of the signal of interest or knowledge of the manifold, geometry, or noise covariance of the array. It is shown via analysis and computer simulation that the algorithm is able to detect burst and transient signals, and to capture those signals with maximum attainable output SINR (signal-to-interference and -noise ratio) in environments containing severe stationary cochannel interference. In particular, it is shown that the algorithm can adapt a four-element antenna to blindly detect and capture FSK (frequency shift keying) bursts with 10-20-ms burst durations in less than 2 ms, even at low power levels ( approximately 4-dB signal-to-white-noise ratio) and in the presence of strong ( approximately 50-dB SIR) stationary interference. <<ETX>>
VLSI implementation of digit-recurrent CORDIC with constant scaling factor

A new high-speed redundant and on-line CORDIC processor with constant scaling factor is presented based on a variant of the original CORDIC algorithm. The new processor has the advantages of high throughput rate, low hardware cost, sample controls and full functionality. The radix-2 signed-digit (SD) redundant arithmetic is used to reduce the carry-propagation delay in the conventional binary adders. The pipelined structure is adopted to increase the throughput rate while the on-line (digit-serial) arithmetic reduces the hardware cost and I/O requirement. Compared to previously proposed methods, the new redundant and on-line CORDIC preserves the constant scaling factor, an important merit of the original CORDIC, and thus does not require any complicated division or variable scaling factor calculation. Furthermore, it can perform both the CORDIC evaluation for angle calculation and the CORDIC application for rotations. VLSI implementation of the processor using Compass 0.6 /spl mu/m standard cell library is also included.
In-Vehicle False Information Attack Detection and Mitigation Framework using Machine Learning and Software Defined Networking

A modern vehicle contains many electronic control units (ECUs), which communicate with each other through the Controller Area Network (CAN) bus to ensure vehicle safety and performance. Emerging Connected and Automated Vehicles (CAVs) will have more ECUs and coupling between them due to the vast array of additional sensors, advanced driving features (such as lane keeping and navigation) and Vehicle-to-Everything (V2X) connectivity. As a result, CAVs will have more vulnerabilities within the in-vehicle network. In this study, we develop a software defined networking (SDN) based in-vehicle networking framework for security against false information attacks on CAN frames. We then created an attack model and attack datasets for false information attacks on brake-related ECUs in an SDN based in-vehicle network. We subsequently developed a machine-learning based false information attack/anomaly detection model for the real-time detection of anomalies within the in-vehicle network. Specifically, we utilized the concept of time-series classification and developed a Long Short-Term Memory (LSTM) based model that detects false information within the CAN data traffic. Additionally, based on our research, we highlighted policies for mitigating the effect of cyber-attacks using the SDN framework. The SDN-based attack detection model can detect false information with an accuracy, precision and recall of 95%, 95% and 87%, respectively, while satisfying the real-time communication and computational requirements.
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA

Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM - ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.
A Permit-Based Optimistic Byzantine Ledger

PermitBFT solves the byzantine consensus problem for n nodes tolerating up to $f < \frac{n}{3}$ byzantine nodes in the partially synchronous networking model; it is the first BFT protocol to achieve an optimistic latency of just 2 message delays despite tolerating byzantine failures throughout the "fast track". The design of PermitBFT relies on two fundamental concepts. First, in PermitBFT the participating nodes do not wait for a distinguished leader to act and subsequently confirm its actions, but send permits to the next designated block creator proactively. Second, PermitBFT achieves a separation of the decision powers that are usually concentrated on a single leader node. A block creator controls which transactions to include in a new block, but not where to append the block in the block graph.
Requirements and Recommendations for IoT/IIoT Models to automate Security Assurance through Threat Modelling, Security Analysis and Penetration Testing

The factories of the future require efficient interconnection of their physical machines into the cyber space to cope with the emerging need of an increased uptime of machines, higher performance rates, an improved level of productivity and a collective collaboration along the supply chain. With the rapid growth of the Internet of Things (IoT), and its application in industrial areas, the so called Industrial Internet of Things (IIoT)/Industry 4.0 emerged. However, further to the rapid growth of IoT/IIoT systems, cyber attacks are an emerging threat and simple manual security testing can often not cope with the scale of large IoT/IIoT networks. In this paper, we suggest to extract metadata from commonly used diagrams and models in a typical software development process, to automate the process of threat modelling, security analysis and penetration testing, without detailed prior security knowledge. In that context, we present requirements and recommendations for metadata in IoT/IIoT models that are needed as necessary input parameters of security assurance tools.
Micro Air Vehicle Link (MAVlink) in a Nutshell: A Survey

The micro air vehicle link (MAVLink in short) is a communication protocol for unmanned systems (e.g., drones and robots). It specifies a comprehensive set of messages exchanged between unmanned systems and ground stations. This protocol is used in major autopilot systems, mainly ArduPilot and PX4, and provides powerful features not only for monitoring and controlling unmanned systems missions but also for their integration into the Internet. However, there is no technical survey and/or tutorial in the literature that presents these features or explains how to make use of them. Most of the references are online tutorials and basic technical reports, and none of them presents comprehensive and systematic coverage of the protocol. In this paper, we address this gap, and we propose an overview of the MAVLink protocol, the difference between its versions, and it is potential in enabling Internet connectivity to unmanned systems. We also discuss the security aspects of the MAVLink. To the best of our knowledge, this is the first technical survey and tutorial on the MAVLink protocol, which represents an important reference for unmanned systems users and developers.
Antiforensic techniques deployed by custom developed malware in evading anti-virus detection

Both malware and antivirus detection tools advance in their capabilities. Malware aim is to evade the detection while antivirus is to detect the malware. Over time, the detection techniques evolved from simple static signature matching over antiheuristic analysis to machine learning assisted algorithms. This thesis describes several layers of anti-virus evasion deployed by the malware and conducts the analysis of the evasion success rate. The scientific contribution of this research is in the following techniques the malware used -- the new algorithm for identifying the Windows operating system functions, a new custom developed obfuscation and de-obfuscation routine and the usage of USB and sound devices enumeration in the anti-heuristic detection. The new PE mutation engine facilitates the malware static signature variation. In the next stage of the assessment, anti-virus engines then test the malware evasion capabilities. The locally installed antivirus applications and the two multi-scanner online engines inspect the submitted malware samples. The thesis examines the results and discusses the strengths and weaknesses of each evasion technique.
On the Upload versus Download Cost for Secure and Private Matrix Multiplication

In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix A, with a matrix $B_{\theta}$, where $\theta \in$ {1, …, M}. The set of candidate matrices {B1, …, BM} are public, and available at all the N servers. The goal of the user is to distributedly compute $AB_{\theta}$, such that (a) no information is leaked about the matrix A to any server; and (b) the index $\theta$ is kept private from each server. Our goal is to understand the fundamental tradeoff between the upload vs download cost for this problem. Our main contribution is to show that the lower convex hull of following (upload, download) pairs: (U,D) = (N/(K − 1), (K/(K − 1)) (1 + (K/N) + ··· + (K/N)M−1)) for K = 2, …, N is achievable. The scheme improves upon state-of-the-art existing schemes for this problem, and leverages ideas from secret sharing and coded private information retrieval.
Non-Asymptotic Pure Exploration by Solving Games

Pure exploration (aka active testing) is the fundamental task of sequentially gathering information to answer a query about a stochastic environment. Good algorithms make few mistakes and take few samples. Lower bounds (for multi-armed bandit models with arms in an exponential family) reveal that the sample complexity is determined by the solution to an optimisation problem. The existing state of the art algorithms achieve asymptotic optimality by solving a plug-in estimate of that optimisation problem at each step. We interpret the optimisation problem as an unknown game, and propose sampling rules based on iterative strategies to estimate and converge to its saddle point. We apply no-regret learners to obtain the first finite confidence guarantees that are adapted to the exponential family and which apply to any pure exploration query and bandit structure. Moreover, our algorithms only use a best response oracle instead of fully solving the optimisation problem.
DEM Generation from High Resolution Satellite Imagery

Digital height models (DHM) covering larger areas can be generated by means of optical or synthetic aperture radar (SAR) images taken from space. An overview of the sensors and the characteristics of generated height models is given. With very high resolution optical satellite stereo pairs a system accuracy of 1.0 ground sampling distance (GSD) standard deviation can be reached. Of course this is not the accuracy of a DEM, which is also influenced by interpolation and includes areas with limited contrast and vegetation, leading to lower quality. In addition, the difference between digital surface models (DSMs), describing the visible surface, and digital terrain models (DTMs), describing the bare ground, has to be respected. The same problem exists for SAR images used by either interferometric SAR (InSAR) or by radargrammetry if no InSAR configuration is available. It has to be ascertained whether existing regional or nearly worldwide DEMs can be used instead of especially produced elevation models. The SRTM DEM and ASTER GDEM are both available, free of charge via the Internet, but their resolution and accuracy are limited. Higher resolution DEMs, such as the SPOT DEM (also named Reference 3D) or NextMap, are not free of charge and they do not cover the whole earth. This will also be the case for the TanDEM-X height model, which will be available in 2014. If more detailed DEMs are required, they can be determined by automatic image matching of very high resolution satellite imagery.
Discovery of an equal-mass ‘twin’ binary population reaching 1000 + au separations

We use a homogeneous catalogue of 42 000 main-sequence wide binaries identified by Gaia to measure the mass ratio distribution, p(q), of binaries with primary masses 0.1 < M1/M⊙ < 2.5, mass ratios 0.1 ≲ q < 1, and separations $50 \lt s/{\rm au} \lt 50\, 000$. A well-understood selection function allows us to constrain p(q) in 35 independent bins of primary mass and separation, with hundreds to thousands of binaries in each bin. Our investigation reveals a sharp excess of equal-mass ‘twin’ binaries that is statistically significant out to separations of 1000–10 000 au, depending on primary mass. The excess is narrow: a steep increase in p(q) at 0.95 ≲ q < 1, with no significant excess at q ≲ 0.95. A range of tests confirm the signal is real, not a data artefact or selection effect. Combining the Gaia constraints with those from close binaries, we show that the twin excess decreases with increasing separation, but its width (q ≳ 0.95) is constant over $0.01 \lt a/{\rm au} \lt 10\, 000$. The wide twin population would be difficult to explain if the components of all wide binaries formed via core fragmentation, which is not expected to produce strongly correlated component masses. We conjecture that wide twins formed at closer separations (a ≲ 100 au), likely via accretion from circumbinary discs, and were subsequently widened by dynamical interactions in their birth environments. The separation-dependence of the twin excess then constrains the efficiency of dynamical widening and disruption of binaries in young clusters. We also constrain p(q) across 0.1 ≲ q < 1. Besides changes in the twin fraction, p(q) is independent of separation at fixed primary mass over $100 \lesssim s/{\rm au} \lt 50\, 000$. It is flatter than expected for random pairings from the initial mass function but more bottom-heavy for wide binaries than for binaries with a ≲100 au.
Traffic Analysis on Encrypted Web Application

Many privacy enhancing technologies such as encryption and proxy have been proposed for protecting the privacy of network data. However, encryption and proxy do not obfuscate the patterns of network traffic which can be exploited by traffic analysis operators to detect users' private informations. Traffic analysis on encrypted network applications has been studied extensively, such as inferring the language or phrase of encrypted Voice over IP (VoIP) call, revealing login password over SSH tunnel, and identifying the application layer protocol. In this survey, we focus on traffic analysis on encrypted web application. First, we introduce the concept of traffic analysis. Then, we classify existing traffic analysis techniques on encrypted web application into two categories, namely website identification and web-user-input identification. Based on that, we make a comparison between each technique of traffic analysis on web application. Finally, we make a summary and draw the readers' interest to some open research topics in the area of web traffic analysis.
Postdetection selection diversity reception with correlated, unequal average power Rayleigh fading signals for pi /4-shift QDPSK mobile radio

The diversity gain degradations due to fading correlation and unequal average power are investigated for practical, two-branch postdetection selection diversity reception. The average bit error rate (BER) of pi /4-shift QDPSK is theoretically analyzed taking into account additive white Gaussian noise (AWGN), cochannel interference, and multipath channel delay spread; exact diversity gain degradations are calculated. Simple and useful approximate expressions for the gain degradations are also presented. >
Assessing the Validity of a a priori Patient-Trial Generalizability Score using Real-world Data from a Large Clinical Data Research Network: A Colorectal Cancer Clinical Trial Case Study

Existing trials had not taken enough consideration of their population representativeness, which can lower the effectiveness when the treatment is applied in real-world clinical practice. We analyzed the eligibility criteria of Bevacizumab colorectal cancer treatment trials, assessed their a priori generalizability, and examined how it affects patient outcomes when applied in real-world clinical settings. To do so, we extracted patient-level data from a large collection of electronic health records (EHRs) from the OneFlorida consortium. We built a zero-inflated negative binomial model using a composite patient-trial generalizability (cPTG) score to predict patients' clinical outcomes (i.e., number of serious adverse events, [SAEs]). Our study results provide a body of evidence that 1) the cPTG scores can predict patient outcomes; and 2) patients who are more similar to the study population in the trials that were used to develop the treatment will have a significantly lower possibility to experience serious adverse events.
A Unified Beamforming and A/D Self-Interference Cancellation Design for Full Duplex MIMO Radios

In this paper, we focus on reduced complexity full duplex Multiple-Input Multiple-Output (MIMO) systems and present a joint design of digital transmit and receive beamforming with Analog and Digital (A/D) selfinterference cancellation. We capitalize on a recently proposed multi-tap analog canceller architecture, whose number of taps does not scale with the number of transceiver antennas, and consider practical transmitter impairments for the full duplex operation. Particularly, transmitter IQ imbalance and nonlinear power amplification are assumed via relevant realistic models. Aiming at suppressing the residual linear and nonlinear self-interference signal below the noise floor, we propose a novel digital self-interference cancellation technique that is jointly designed with the configuration of the analog taps and digital beamformers. Differently from the state of the art, we design pilot- assisted estimation of all involved wireless channels. Our representative Monte Carlo simulation results demonstrate that our unified full duplex MIMO design exhibits higher self-interference cancellation capability with less analog taps compared to available techniques, which results in improved achievable rate and bit error performance.
Analyzing feature space via Pauli decomposition for quantum classifier

A method for analyzing the feature space used in the quantum classifier on the basis of Pauli decomposition is developed. In particular, for 2-dimensional input datasets, the method boils down to a general formula that easily computes a lower bound of the exact training accuracy, which eventually helps us to see whether the feature space constructed with the selected feature map is suitable for linearly separating the dataset. The effectiveness of this formula is demonstrated, with the special type of five feature maps and four 2-dimensional non-linear datasets.
Confluent conformal blocks and the Teukolsky master equation

Quasinormal modes of usual, four dimensional, Kerr black holes are described by certain solutions of a confluent Heun differential equation. In this work, we express these solutions in terms of the connection matrices for a Riemann-Hilbert problem, which was recently solved in terms of the Painleve V transcendent. We use this formulation to generate the small-frequency expansion for the angular spheroidal harmonic eigenvalue, and derive conditions on the monodromy properties for the radial modes. Using exponentiation, we relate the accessory parameter to a semi-classical conformal description and discuss the properties of the operators involved. For the radial equation, while the operators at the horizons have Liouville momenta proportional to the entropy intake, we find that spatial infinity is described by a Whittaker operator.