Miracle-dz/UniFAR-FTUs huggingface dataset:
Aneurysms.

ONE OF the outstanding achievements of surgery over the last two decades has been the successful development of techniques for treating aneurysms. Aneurysm, in its various forms, has long been recognized (Edwin Smith papyrus, circa 1600 B.C. (Breasted, 1930); Aetius of Amida, 6th century A.D.) as a threat to life or limb, but until recently little could be done to avert impending disaster. As so often this surgical advance has also brought an increased understanding of the condition being treated. This review will concentrate upon our knowledge and our ability to treat aneurysms as it stands today, but tribute must be paid to the contributions of Hunter (Home, 1786); Cooper (1818, 1836), Matas (1888, 1902), and many others who over the centuries have set the scene for the day when surgeons would have practical methods for replacing diseased arteries (Carrel, 1908; Voorhees, Jaretzki & Bakemore, 1952). Methods of ligation or induced thrombosis either brought disaster or at most a very limited success. The weakened degenerative walls of aneurysms, by their very nature, do not allow the successful repair by use of their own substance, and in spite ofmany attempts failure was the inevitable outcome (Fig. 1). It was only with the advent of cadaver homografts that it became possible to replace major arteries, and by 1953 an ever-increasing number of reports of successful replacement of aneurysms by homografts were appearing in the surgical literature (De Bakey & Cooley, 1953). Homografts had their short-comings and limitations, however, and were soon superseded by arterial prostheses made from knitted or woven plastic yarn. This method holds pride of place today and it is now possible to replace the aneurysm by an arterial prosthesis of Terylene (Dacron) which will easily outlast the patient. The value of this surgery is indisputable (Szilagyi et al., 1966; De Bakey et al., 1964) and it is perhaps a suitable stage, after nearly 20 years' experience with the techniques, to stand aside for a moment to consider the hard-won lessons that have brought this spectacular advance almost into the category of routine surgery. This review is not by any means comprehensive but merely an attempt to highlight the fundamental principles that FIG. 1. Surgical endeavour 30 years ago! An attempt was made to strengthen this aneurysm with kangaroo tendon (Professor Pybus, Newcastle-upon-Tyne).
Vectorial field propagation through high NA objectives using polarized Gaussian beam decomposition

Scalar fields can be propagated through non-paraxial systems using the Gaussian beam decomposition method. However, for high NA objectives, this scalar treatment is not sufficient to correctly describe the electromagnetic fields inside the focal region due to high ray bendings, which result in a significant change in the polarization state of light. To model these vectorial effects, the Gaussian beam decomposition method has to be extended to include the polarization state of light. In this work we have combined it with the three dimensional polarization ray tracing in order to propagate vectorial fields through high NA optical systems. During the Gaussian beam decomposition, the polarization state of each individual beamlet is represented by a polarization vector [𝐸𝑥, 𝐸𝑦, 𝐸𝑧 ] associated with its central ray. Individual Gaussian beams are then propagated through the system using the complex ray tracing method. The effect of the optical system on the polarization state of each beam is computed by applying the three dimensional polarization ray tracing of the corresponding central rays. Finally the individual beams are superposed coherently in the plane of interest resulting in the complete vectorial field. We apply the proposed method to compute the vectorial field inside the focal region of a high NA microscope objective lens and compare our result to the vectorial Debye integral method. We find that the Gaussian beam decomposition method overcomes serious limitations of algorithms relying on Fourier transforms, i.e. the field sampling requirements are less critical in high NA focusing and in the presence of large aberrations. However, sharp edges in the amplitude profile are difficult to handle as they should be replaced with smooth Gaussian edge.
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning

This paper presents a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL), and achieves systematic out-of-distribution generalisation in tasks that involve following a formally specified instruction. Specifically, the agent learns general notions of negation and disjunction, and successfully applies them to previously unseen objects without further training. To this end, we also introduce Task Temporal Logic (TTL), a learning-oriented formal language, whose atoms are designed to help the training of a DRL agent targeting systematic generalisation. To validate this combination of logic-based and neural-network techniques, we provide experimental evidence for the kind of neural-network architecture that most enhances the generalisation performance of the agent. Our findings suggest that the right architecture can significatively improve the ability of the agent to generalise in systematic ways, even with abstract operators, such as negation, which previous research have struggled with.
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients’ discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
ICSSIM-A Framework for Building Industrial Control Systems Security Simulation Testbeds

With the advent of smart industry, Industrial Control Systems (ICS) are in-creasingly using Cloud, IoT, and other services to meet Industry 4.0 targets. The connectivity inherent in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems and intrusion prevention systems empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research intrusion detection systems due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their intrusion detection algorithms in a controlled environment. Although various ICS testbeds have been developed, researchers’ access to a low-cost, adaptable, and customizable testbed that can accurately simulate industrial control systems and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for simulating customized virtual ICS security testbeds cyber threats and attacks can be investigated, and mitigations evaluated. ICSSIM aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high ﬁdelity. ICSSIM is built on top of the Docker container technology, which provides realistic network emulation and runs ICS components on isolated pri-Email vate operating system kernels. ICSSIM reduces the time for developing ICS components and oﬀers physical process modelling using software and hardware in the loop simulation. We demonstrated ICSSIM by creating a testbed and val-idating its functionality by showing how diﬀerent cyberattacks can be applied.
Focused-ion-beam-based nano-kirigami: from art to photonics

Abstract Kirigami, i.e. the cutting and folding of flat objects to create versatile shapes, is one of the most traditional Chinese arts that has been widely used in window decorations, gift cards, festivals, and various ceremonies, and has recently found intriguing applications in modern sciences and technologies. In this article, we review the newly developed focused-ion-beam-based nanoscale kirigami, named nano-kirigami, as a powerful three-dimensional (3D) nanofabrication technique. By utilizing the topography-guided stress equilibrium induced by ion-beam irradiation on a free-standing gold nanofilm, versatile 3D shape transformations such as upward buckling, downward bending, complex rotation, and twisting of nanostructures are precisely achieved. It is shown that the generated 3D nanostructures possess exceptional geometries and promising photonic functionalities, including strongly interacting multiple Fano resonances, giant optical chirality, clear photonic spin Hall effects, and diffractive phase/polarization effects. The studies of such structures can build up novel platforms for versatile manufacturing techniques and be helpful to establish new areas in plasmonics, nanophotonics, optomechanics, MEMS/NEMS, etc., with the generation of exotic but functional nanostructures.
PENGEMBANGAN PERANGKAT PEMBELAJARAN DENGAN MEDIA SCREENCAST-O-MATIC MATA KULIAH KALKULUS 2 MENGGUNAKAN MODEL 4-D THIAGARAJAN

Trigatra perguruan tinggi menerangkan aspek penelitian sebagai kepeloporan, konsepsi pendekatan masalah yang didukung oleh integritas ilmu dan iklim kerjasama yang kondusif. Pendidikan matematika merupakan bagian dari ilmu pengetahuan dan teknologi diharapkan dapat menjangkau dimensi ruang dan waktu. Media Pembelajaran merupakan saluran komunikasi yang dapat membantu menyusun perencanaan program pengajaran dan mempermudah dalam menyiapkan perangkat pembelajaran. Penelitian ini mengkaji pengembangan perangkat pembelajaran dengan media screencast-o-matic pada mata kuliah kalkulus 2. Penelitian ini bermaksud untuk mengembangkan perangkat pembelajaran dengan media screencast-o-matic pada mata kuliah kalkulus 2 melalui model pengembangan 4-D ( define, design, develop, dessimination ) yang dikemukakan oleh Thiagarajan. Penggunaan media tersebut diharapkan mampu membangkitkan motivasi dalam melakukan inovasi untuk meningkatkan proses pembelajaran sehingga lebih bervariatif. Metode yang digunakan dalam penelitian ini adalah metode analisis deskriptif kualitatif, karena penelitian ini bertujuan untuk mendeskripsikan proses pengembangan perangkat pembelajaran dan keefektifan pembelajaran dengan menggunakan screencast-o-matic pada mata kuliah kalkulus 2 melalui model 4-D Thiagarajan. Perangkat pembelajaran yang dikembangkan adalah Rencana Pelaksanaan Pembelajaran (RPP), Lembar Kerja Mahasiswa (LKM), dan Tes Hasil Belajar (THB) menunjukkan hasil yang efektif, positif dan valid. Penelitian ini menghasilkan luaran berupa perangkat pembelajaran yang dapat digunakan pada mata kuliah Kalkulus 2, Media Pembelajaran Screencast-o-matic dalam Pembelajaran Kalkulus 2 dan diharapkan menghasilkan publikasi ilmiah yang diterbitkan pada Jurnal Nasional Terakreditasi. Kata Kunci : Perangkat Pembelajaran, Screencast-o-matic, Kalkulus 2, Model 4-D Thiagarajan
An algorithm for calculating the contour Voigt and its improvement and refinement for some ranges of parameters

We present an approximation Voigt contour for some parameters intervals such as the interval with y less than 0.02 and absolute value x less than 1.6 gives a simple formula for calculating and relative error less than 0.1%, and for some of the intervals suggetsted to use Hermite quadrature.
Kent Academic Repository

We consider whether techniques from concurrency theory can be applied in the area of Cognitive Neuroscience. We focus on two potential applications. The first of these explores structural decomposition, which is effectively assumed by the localisation of function metaphor that so dominates current Cognitive Neuroscience. We take concurrency theory methods, especially Process Calculi, as canonical illustrations of system description notations that support structural decomposition and, in particular, encapsulation of behaviour. We argue that carrying these behavioural and notational properties over to the Cognitive Neuroscience setting is difficult, since neural networks (the modelling method of choice) are not naturally encapsulable. Our second application presents work on verifying stability properties of neural network learning algorithms using model checking. We thereby present evidence that a particular learning algorithm, the Generalised Recirculation algorithm, exhibits an especially severe form of instability, whereby it forgets what it has learnt, while continuing to be trained on the same pattern set.
Kent Academic Repository

This paper revisits the problem of determinacy inference addressing the problem of how to uniformly handle cut . To this end a new semantics is introduced for cut , which is abstracted to systematically derive a backward analysis that derives conditions sufficient for a goal to succeed at most once. The method is conceptionally simpler and easier to implement than existing techniques, whilst improving the latter’s handling of cut . Formal arguments substantiate correctness and experimental work, and a tool called ’RedAlert’ demonstrates the method’s generality and applicability.
RN / 12 / 03 Evolving Human Competitive Spectra-Based Fault Localisation Techniques 08 / 05 / 2012

Spectra-Based Fault Localisation (SBFL) aims to assist debugging by applying risk evaluation formulæ (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming approach for evolving risk assessment formulæ. The empirical evaluation using 92 faults from four Unix utilities produces promising results. GP-evolved equations can consistently outperform many of the human-designed formulæ, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 5.9 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures. 1 The program spectra data used in the paper, as well as the complete empirical results, are available from: http://www.cs. ucl.ac. uk/staff/s.yoo/evolving-sbfl.html. UCL DEPARTMENT OF COMPUTER SCIENCE Research Note RN/12/08 Initial experiences of the Emerald: e-Infrastructure South GPU supercomputer
RN / 12 / 03 Evolving Human Competitive Spectra-Based Fault Localisation Techniques 08 / 05 / 2012

Spectra-Based Fault Localisation (SBFL) aims to assist debugging by applying risk evaluation formulæ (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming approach for evolving risk assessment formulæ. The empirical evaluation using 92 faults from four Unix utilities produces promising results. GP-evolved equations can consistently outperform many of the human-designed formulæ, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 5.9 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures. 1 The program spectra data used in the paper, as well as the complete empirical results, are available from: http://www.cs. ucl.ac. uk/staff/s.yoo/evolving-sbfl.html. UCL DEPARTMENT OF COMPUTER SCIENCE Research Note RN/12/03 Evolving Human Competitive Spectra-Based Fault Localisation Techniques
Journal of Education and Learning (EduLearn)

Received Feb 21, 2021 Revised Apr 13, 2021 Accepted Jan 22, 2022 People in social life often underestimated the presence of migrant workers caused by low skills and lack of education. The decision to continue higher education while working in the host country had tremendous challenges. This study aimed to develop a deeper understanding of the reasons, the problem encountered, and the learning strategies of migrant workers pursuing higher education at Indonesian Open Learning University of Taiwan (Universitas Terbuka Taiwan). There were 11 students participated in this study. This study was descriptive qualitative research, and the interactive model was applied in the analysis method. The data were gathered through in-depth interviews, participant observation, and documentation. The finding of this study revealed that: i) The migrant workers had the high motivation to pursue higher education because they wanted to change the stereotype that they were left behind and to seek a better life when they came back to Indonesia; ii) Inadequate time was the main problem to manage the study time due to the high workload; iii) The learning strategies of migrant workers were peer learning, group discussion, and note-taking. However, Indonesian Open Learning University of Taiwan was the excellent choice for the migrant workers in Taiwan to pursue higher education due to the flexibility of learning.
Calibration and temperature correction of a V-block refractometer

V-block refractometers have been used since the 1940s to retrieve the refractive index values of substances or optical glasses. When used outside laboratories, they are submitted to temperature variations which degrade their accuracy by varying the refractive index of the glasses and the length of the prisms. This paper proposes a method to calibrate a double-prism V-block refractometer by retrieving the values of two coefficients at a constant temperature and by applying corrections to these coefficients when the instrument is used at different temperatures. This method is applied to calibrate in salinity a NOSS instrument which can be used at sea on drifting floats, and the results show that measurement errors can be reduced by a factor of 5.8.
Modeling Task Effects in Human Reading with Neural Attention

Humans read by making a sequence of fixations and saccades. They often skip words, without apparent detriment to understanding. We offer a novel explanation for skipping: readers optimize a tradeoff between performing a language-related task and fixating as few words as possible. We propose a neural architecture that combines an attention module (deciding whether to skip words) and a task module (memorizing the input). We show that our model predicts human skipping behavior, while also modeling reading times well, even though it skips 40% of the input. A key prediction of our model is that different reading tasks should result in different skipping behaviors. We confirm this prediction in an eye-tracking experiment in which participants answers questions about a text. We are able to capture these experimental results using the our model, replacing the memorization module with a task module that performs neural question answering.
Unsupervised Person Re-Identification by Soft Multilabel Learning

Although unsupervised person re-identification (RE-ID) has drawn increasing research attentions due to its potential to address the scalability problem of supervised RE-ID models, it is very challenging to learn discriminative information in the absence of pairwise labels across disjoint camera views. To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID. The idea is to learn a soft multilabel (real-valued label likelihood vector) for each unlabeled person by comparing the unlabeled person with a set of known reference persons from an auxiliary domain. We propose the soft multilabel-guided hard negative mining to learn a discriminative embedding for the unlabeled target domain by exploring the similarity consistency of the visual features and the soft multilabels of unlabeled target pairs. Since most target pairs are cross-view pairs, we develop the cross-view consistent soft multilabel learning to achieve the learning goal that the soft multilabels are consistently good across different camera views. To enable effecient soft multilabel learning, we introduce the reference agent learning to represent each reference person by a reference agent in a joint embedding. We evaluate our unified deep model on Market-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-art unsupervised RE-ID methods by clear margins. Code is available at https://github.com/KovenYu/MAR.
Supplementary material for the paper : ” Adaptive Bandits : Towards the best history-dependent strategy “

In this document, we detail further some technical proofs not covered in the paper corresponding to this supplementary material. 1 Playing against an opponent using a known model 1.1 Regret upper bounds against the best history-class-based strategy Theorem 1 In the case of a Φ-constrained opponent, using the Φ-UCB algorithm with parameter α > 1/2, we have the distribution-dependent bound: R T ≤ ∑ c∈H/Φ;E(Ic(T ))>0 ∑ a∈A;∆c(a)>0 4α log(T ) ∆c(a) + ∆c(a)cα where Ic(T ) = ∑T t=1 I[h<t]=c, the per-class gaps ∆c(a) def = μc(a ∗ c) − μc(a), and the constant cα = 1 + 4 log(α+1/2) ( α+1/2 α−1/2 ) . We also have a distributionfree bound (i.e. which does not depend on the gaps): R T ≤ √ TAC ( 4α log(T ) + cα ) where C = |{c ∈ |H/Φ|;E(Ic(T )) > 0}| is the number of classes that may be activated during the run. Now, in the case of an arbitrary opponent, using ΦExp3 algorithm, we have: R̃ T ≤ 3 √ 2 √ TCA log(A). Proof: Φ-UCB: The distribution-dependent bound for Φ-UCB is a direct application of the result of [2] Appearing in Proceedings of the 14 International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, Fort Lauderdale, FL, USA. Volume 15 of JMLR: W&CP 15. Copyright 2011 by the authors. for the algorithm UCB about τa(t) def = ∑t s=1 Ias=a where at is played by UCB, that states that E(τa(t)) ≤ 4α log(t) ∆c(a) + cα. Indeed, we use the fact that R Φ T = ∑ c∈H/ΦRT (c) and thus remark that when a class c is visited, then we play according to a UCB algorithm for this class. Thus, for the distribution-free bound, we have: R T = ∑
: Performance Prediction for Semantic Segmentation by a Self-Supervised Image Reconstruction Decoder

If we consider x = (xi,c) to be given, then (3) can be understood as a constraint applied to distortion r̃ε. Thus, our unconstrained distortion r̃ε is indirectly subject to the constraint (3), resulting in the constrained distortion rε (2). With increasing ε, more and more pixels will be affected by (3), resulting in differences between r̃ε and rε. However, for ε ≪ 1 as is the case in our paper, this effect can typically be neglected, which we assume in the following mathematical description, i.e., r̃ε ≈ rε. 1.3. On Defining a Target Distortion Strength