Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Recurrence Risk Factors in CT-Confirmed Acute Appendicitis in Adults Managed Non-operatively During the COVID-19 Pandemic

Background/Objective: Acute appendicitis (AA) is predominantly managed with appendectomy, but can be treated non-operatively, leading to a high risk of recurrence. Non-operative management has been more common since the COVID-19 pandemic affected the feasibility of performing surgery. This case-control study analyzed non-operatively managed patients in order to identify clinical and radiological factors associated with recurrence risk. Methods: Over 12 months, 48 adults with CT-proven AA managed non-operatively were identified, and followed up for at least six further months to assess them for recurrence (readmission to hospital more than 14 days after discharge and after symptom resolution, requiring treatment for appendicitis). Clinical and CT data were collected and a Cox regression survival analysis was performed to produce hazards ratios (HRs). Results: Of the 48 patients, 12 (25%) experienced a recurrence up until the end of the follow-up period, eight of whom were then treated operatively, and four treated non-operatively. On the univariate analysis, greater recurrence risk was observed in patients with diabetes mellitus, higher heart rate (on admission and maximum value during admission), lower white cell count and neutrophils and appendiceal wall thinning on CT. On the multivariate analysis, diabetes mellitus (HR=7.72, p=0.021) and higher heart rate (HR=1.08, p=0.018) were associated with statistically significant greater recurrence risk. Conclusions: Diabetes mellitus and higher heart rate on admission are associated with greater recurrence risk of AA managed non-operatively. No CT findings were associated with statistically significant greater risk. Clinicians should, therefore, consider DM and heart rate when making decisions on appendicitis management, especially during the COVID-19 pandemic but also beyond it.
A Robust Local Texture Descriptor in the Parametric Space of the Weibull Distribution

Research in texture feature approximation is still in the embryonic stage because of difficulties in developing a sound theoretical model to express the unique pattern in the intensity-variation of pixels in the neighbourhood of the pixel-of-interest so that it can sufficiently discriminate different textures. Local texture descriptors are widely used in image segmentation as they comprise pixel-wise features. The Weber local descriptor (WLD) with differential excitation and gradient orientation components, inspired by Weber's Law, has been leveraged in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique. However, WLD has inherent drawbacks in the formulation of the components that limit its discriminatory capability. This paper introduces a novel texture descriptor by directly modelling the distribution of intensity-variation in the parametric space of the Weibull distribution using its shape and scale parameters. A unified ‘joint scale’ texture property is introduced, which can discriminate textures better than the individual parameters while keeping the length of the descriptor shorter. Additionally, the accuracy of WLD's gradient orientation component is improved by using an extended Sobel operator and expressing gradients in $[-\pi /2,\pi /2)$ range. When incorporated in ICM, the proposed texture descriptor has consistently outperformed WLD and a recent enhancement with radial mean WLD (RM-WLD) on three benchmark datasets. It has also outperformed two other texture segmentation techniques and their deep learning based improvements.
Experimental Evaluation of Basic Similarity Measures and their Application in Visual Information Retrieval

—Searching for similar images is an important feature for image databases and decision support systems in various subject domains. However, it is essential that search results are sorted by degree of similarity in reverse order. This paper presents a comparative analysis of four existing similarity measures and experimentally tests whether they could be used to calculate similarity between images. Metrics could be evaluated by comparing their results to the cumulative human perception of similarity between the same images, obtained by real people. However, this introduces a lot of subjectivism due to non-uniform judgement and evaluation scales. The paper presents a more objective approach - checks which measure performs best in retrieving more images, containing objects of the same type. Results show all four measures could be used to calculate similarity between images, but Jaccard’s index performs best in most cases, because it compares features vectors positionally and thus indirectly consider shape, position, orientation and other features.
A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

As machine learning and artificial intelligence (ML/AI) are becoming more popular and advanced, there is a wish to turn sensitive data into valuable information via ML/AI techniques revealing only data that is allowed by concerned parties or without revealing any information about the data to third parties. Collaborative ML approaches like federated learning (FL) help tackle these needs and concerns, bringing a way to use sensitive data without disclosing critically sensitive features of that data. In this paper, we provide a detailed analysis of state of the art for collaborative ML approaches from a privacy perspective. A detailed threat model and security and privacy considerations are given for each collaborative method. We deeply analyze Privacy Enhancing Technologies (PETs), covering secure multi-party computation (SMPC), homomorphic encryption (HE), differential privacy (DP), and confidential computing (CC) in the context of collaborative ML. We introduce a guideline on the selection of the privacy preserving technologies for collaborative ML and privacy practitioners. This study constitutes the first survey to provide an in-depth focus on collaborative ML requirements and constraints for privacy solutions while also providing guidelines on the selection of PETs.
Sydney as ‘Sinoburbia’: Patterns of diversification across emerging Chinese ethnoburbs

‘Chinese Sydney’ has shifted away from its inner-city Chinatown towards new residential suburban concentrations with varied histories of progressive diversification. In some of these suburbs, where 40% or more of residents report Chinese heritage, older generations of diaspora Chinese intermingle with a substantial recent wave of China-born middle-class professionals – often distinguished as the ‘new Chinese’. This paper situates the localised, internal diversities of the modern arrival city within the geo-political conditions, urban development strategies and migration patterns that shape Sydney’s Chinese ethnoburbs (or ‘Sinoburbs’). Drawing on demographic analysis, site mapping of local infrastructure and site observations, we trace changing demographics and patterns of suburban development within three different case study suburbs. In doing so, we elucidate some emerging lines of inquiry that challenge the extant focus in both enclave and ethnoburb models of urban ethnic concentration and suggest a number of new interventions to future research on emerging Sinoburbia localities both in Australia and elsewhere.
Design of a Miniaturized Magnetic Actuation System for Motion Control of Micro/Nano Swimming Robots

Magnetically controlled microrobots for drug delivery and noninvasive treatment have great potential applications in the biomedical field in the future. The construction of the magnetic actuation system is an important step to realize the automated control of micro/nano swimmers. However, the construction of a magnetic actuation system still faces challenges; for example, the magnetic field cannot be turned off immediately, the distribution of the magnetic field in the workspace is not uniform, the working space is limited and the feedback is inconvenient. In view of the above problems, a design method based on an eight-axis electromagnetic coil magnetic control system is introduced in this paper, which can compositely actuate the microrobot and ensure movement with five degrees of freedom. In addition, the overall size of the system can be reduced as much as possible under the condition that the magnetic field in the workspace is sufficiently uniform and the magnetic field intensity is sufficiently large. Finally, in the experimental part, the magnetic field uniformity is verified by magnetic field simulation and measurement, and then the path following of the square trajectory is realized with the $75 \mu \mathrm{m}$ helical microswimmer as the operating object.
Practical consensus tracking of homogeneous sampled-data multi-agent systems

The aim of this article is to study the robustness of homogeneous sampled-data multi-agent systems including a leader whose dynamic evolution is independent of its followers. We explore the effects caused simultaneously by the sampling and the acceleration of the leader on the system, and show that practical consensus is achieved in this case. The results are illustrated in simulations.
Using Additional Information to Improve Classification of Clothing Item

Clothing classification can be a difficult task for clothing e-commerce sites. When it comes to distinguishing between two types of garments that look alike, discrimination of similar categories of clothing can be complicated. Even simply glancing at photos, categories like suit and tunic are difficult to interpret. Investigation of the effect of using additional information derived from photos shows that pose along with unsupervised latent space embedding (auto-encoders) improves the classification performance significantly. Validated on data obtained from a clothing e-commerce site, additional constraints provide better performance with similar model complexities. Especially human pose representing wearers gait (or advertisement attitude) increases the classification performance by 1% in accuracy.
Strategy of Livelihood among Persons Having Social Stigma in Sexual Orientation

This article aimed to expose the strategy of livelihood among the persons having social stigma in sexual orientation among heteronormative hegemonic system as Indonesia. This article focused on the pandemic time and showed the challenges and strategies of Christian bisexual groups to maintain their livelihoods before and after the pandemic. This study used a qualitative analysis with a case study of two Christian bisexuals. Data were collected by in-depth interviews through offline and online interviews. Data were analyzed with the perspective of Mary Virginia Lee Badgett's feminist economic theory, which included non-heteronormative people in her proposed feminist economic model. This study showed two main findings: first, gender and bisexual orientation have an impact on rejection, oppression, and job transfer in the period before and after the pandemic due to unequal power relations in heteronormative structures. These power relations also lead to the neglect of non-heteronormative contributions to the informal sector in a heteronormative economic structure. Secondly, bisexual individuals developed a particular resilience strategy during a pandemic, one of which is by utilizing informal networks with non-heteronormative groups. Another finding is the influence of religion which simultaneously provides support and oppression to the livelihoods of non-heteronormative groups.
Educating Students with Intellectual Disabilities in Nepal: Exploring Realities and Challenges fromTeachers’ Perspectives

This study aims at exploring the realities and challenges that have been taking place for years while providing educational servicesto students with intellectual disabilities in Nepal. Interpretive-constructivist paradigm accompanied by phenomenological research design was usedto carry out this research. Eight teachers teaching to thestudents with intellectual disabilities were chosen from four special schools (two from each) located within Kathmandu valley by using purposive sampling method, and then all sample teachers were interviewed by using in-depth interview technique to collate credible and authenticinformation required for the study. Study results reveal that a number of attempts such as special education services in a separate setting, distinct structure of curriculum, community-based instruction, etc. had been made to optimize the access of these students to educational program. Nevertheless, thestudents with intellectual disabilitieswere not receiving quality educational services because of various challenges such as inadequate training to teachers, poor expectations from children, insufficient resources, etc. These students, therefore, should be taught in an inclusive educational setting byproviding community-based services based on the severity of their disability.
Distributed Cooperative Control from Position Motion to Interaction Synchronization

A distributed interaction control based on impedance frame is proposed in Euler-Lagrangian network system to achieve multi-agents interaction synchronization. Considering the different control objective of task space, a unified impedance dynamics is studied to realize two control modes transformation from position tracking to interaction synchronization by the impedance parameter regulation. The constrained conditions of variable impedance parameters is discussed to design a reasonable distributed controller by Lyapunov technique. Finally, The effectiveness of the proposed controller is validated by simulations on a multi-robots network system with 4 nodes.
Deep Learning Based Steatosis Quantification of Liver Histopathology Images Using Unsupervised Feature Extraction

Steatosis quantification is an essential issue for accurate diagnosis and donor transplantation. However, manually quantification processes of steatosis by a pathologist have some difficulties because of time-consuming and tiring processes that can vary in inter and intra-experts. In recent years, deep learning studies have emerged with promising performance on steatosis quantification. On the other hand, deep learning models require a large amount of data, yet the steatosis dataset is insufficient for deep models. Thus, we propose deep learning model consisting of two steps that showed high performance even on a small number of steatosis datasets. The first step is unsupervised feature extraction with UNet. The second step is classification by using extracted features as an input for classification models. ResNet-50, EfficientNet B1 and MobileNetV2 networks are used for classification. As a result, the proposed deep models enable fully automated steatosis quantification with high AUC.
Energy Efficiency Enhancement of SCM-Based Systems: Write-Friendly Coding

With the advent of the Internet of Things (IoT), more and more wearable devices have been developed and integrated into our daily lives. Energy efficiency is critical for these devices because they are typically run on energy-constrained resources like batteries or energy harvesters. The storage-class memory (SCM) technologies and data compression techniques could improve their energy efficiency via reducing data movements and squeezing the data size, respectively, where data compression is especially important for IoT and embedded systems to reduce the volume of data for the space-constrained memory/storage devices. Nevertheless, both of them cannot be aware of their inherent characteristics for further minimization of energy consumption. To this end, a write-friendly coding scheme is proposed in this work that jointly manages both techniques to yield energy-efficient SCM-based systems. Moreover, a novel design of ignorable bits is presented to partially skip write operations after completing data compression without sacrificing data accuracy. We evaluate the proposed scheme via a series of intensive experiments, the experimental results of which indicate that the proposed coding scheme reduces energy consumption by up to 45% under the investigated benchmarks.
Mobility-aware Software-Defined Service-Centric Networking

Future Internet applications, such as the vehicular use cases, impose requirements that challenge current networking paradigms. Hence, other networking paradigms have been proposed, such as Software-Defined Networking (SDN) and Information-Centric Networking (ICN), aiming to change network management and operation fundamentals to meet those requirements. Leveraging these networking paradigms, in this paper we present the Mobility-aware Service-Centric Networking (MSCN), an ICN-inspired solution to mitigate mobility-related networking issues by focusing on service provisioning rather than content. Due to the requirement of installing specialized hardware, ICN-based solutions face adoption issues. Still, SDN features can be used to emulate ICN behaviour without the requirement for deployment of new infrastructure. Therefore, we present an SDN-based implementation of MSCN (SD-MSCN), which relies on already installed SDN-enabled switches and the OpenFlow protocol. We evaluate the performance of our proposal by comparing SD-MSCN with other SDN-enabled ICN and IP protocols. Simulations show that our proposal outperforms IP-based solutions in the presence of user mobility events. Nevertheless, it is possible to produce proactive IP-based solutions that achieve similar performance as our proposal. However, our proposal has a significantly better performance than IP-based solutions in environments with frequent service mobility events.
Asymmetry in the harmonic generation from nonsymmetric molecules

We report a calculation of the harmonic emission from a one-electron heteronuclear nonsymmetric molecule (HeH2+) interacting with a few-cycle laser pulse linearly polarized along the molecular axis. We find that a 180° rotation of the molecule (or equivalently a 180° change in the carrier-envelope phase) leads to substantial changes in the harmonic emission of the molecule. Phase-dependent plateaux and cutoffs appear in the harmonic spectrum as a consequence of the phase-dependent electric field of few-cycle pulses. Asymmetries in the intensity of harmonics result from the phase dependence of ionization rates in nonsymmetric molecules, and from the fact that depending on the molecular orientation, the ionized electron wavepacket can be Coulomb focused as it visits the proton twice before the recollision leading to harmonic generation.
Formal specification and model checking of Saber lattice-based key encapsulation mechanism in Maude

—The security of most cryptosystems currently in use today is threatened by advances in quantum computing. That is the reason why recently many researchers and industrial companies have spent lots of effort on constructing post-quantum cryptosystems, which are resistant to quantum attackers. A large number of post-quantum key encapsulation mechanisms (KEMs) have been proposed to provide secure key establishment - one of the most important building blocks in asymmetric cryptography. This paper presents a formal security analysis of Saber lattice-based KEM. We ﬁrst formally specify the mechanism in Maude, a rewriting logic-based speciﬁcation/programming language equipped with many functionalities, such as a reachability analyzer (or the search command) that can be used as an invariant model checker, and then conduct invariant model checking with the Maude search command, ﬁnding an attack.
“Quality Problems” in Swedish Municipal Adult Education : The Micropolitics of Quality Construed in the Audit Society

iii Sammanfattning . v Acknowledgement . vi List of papers . vii Table of contents . ix Introduction . 11 Aim and research questions . 14 The form and structure of the thesis . 15 Previous research . 16 Quality in the Audit society . 16 Quality audit processes . 19 Audits and audit concepts: indicators and transparency . 19 Outcomes and effects of quality audit processes . 21 Quality in (adult) education . 23 In summary: From Audit society to quality control . 26 Background . 28 Swedish adult education and governing by auditing . 28 The structure of MAE . 30 Flexibility and individualization . 31 MAE and the market . 32 The structure of SSI . 33 Regular supervisions and quality audits in MAE . 33 Theoretical considerations . 36 What’s the problem represented to be? 36 Situating the WPR approach in a wider theoretical discussion . 36 A post-structural approach to policy . 38 WPR and its three key assumptions . 39 Methodological considerations . 42 Introducing the three policy sites . 42 Site I: Policy and instructional documents . 43 Site II: Quality audit . 44 Site III: the SSI conference . 47 Methodological considerations: observing, audio recording and transcribing . 48 Ethical considerations . 49 Applying and operationalizing the WPR approach . 50 Q1: What’s the “problem” represented to be in, in a specific policy or policies? 52 Q2: What deep-seated presuppositions or assumptions underlie this representation of the “problem”? 53
Prescribed-Time Stabilization Robust to Measurement Disturbances

Prescribed-time stabilization employs time-varying gains that grow and multiply states that decay. Such feedback structures have unprecedented properties of regulation in user-prescribed finite time, independent of the initial condition, and with zero asymptotic gains to process right-hand side disturbances (perfect disturbance rejection), regardless of the disturbance size. However, when the state measurement is itself subject to a disturbance, the multiplication with growing gains threatens to result in unbounded control inputs. In this paper we present results—for linear systems in the controllable canonical form and for nonlinear high-dimensional Euler-Lagrange systems that describe high-degree-of-freedom robotic manipulators—which carry no such risk: the sum of the state and the measurement disturbance is still driven to zero, the input remains bounded, and a particular ISS property relative to the disturbance is guaranteed. The price we pay for such strong and fairly unexpected results is a structural condition we impose on the disturbance, which is met in practical applications that rely on accelerometer, gyroscope, or encoder measurements.
Estimation of Stellar Metal Abundance. II. A Recalibration of the Ca II K Technique, and the Autocorrelation Function Method

We have recalibrated a method for the estimation of stellar metal abundance, parameterized as [Fe/H], based on medium-resolution (1–2 Å) optical spectra (the majority of which cover the wavelength range 3700–4500 Å). The equivalent width of the Ca II K line (3933 Å) as a function of [Fe/H] and broadband B-V color, as predicted from spectrum synthesis and model atmosphere calculations, is compared with observations of 551 stars with high-resolution abundances available from the literature (a sevenfold increase in the number of calibration stars that were previously available). A second method, based on the Fourier autocorrelation function technique first described by Ratnatunga & Freeman, is used to provide an independent estimate of [Fe/H], as calibrated by comparison with 405 standard-star abundances. Metallicities based on a combination of the two techniques for dwarfs and giants in the color range 0.30 ≤ (B-V)0 ≤ 1.2 exhibit an external 1 σ scatter of approximately 0.10–0.20 dex over the abundance range -4.0 ≤ [Fe/H] ≤ 0.5. Particular attention has been given to the determination of abundance estimates at the metal-rich end of the calibration, where our previous attempt suffered from a considerable zero-point offset. Radial velocities, accurate to approximately 10 km s-1, are reported for all 551 calibration stars.