Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Tool Support for Cross-Device Development

As the number of consumer computing devices at our disposal has grown, we find ourselves in situations where we have access to more than a single device. Even though people have started using multiple devices both sequentially and in parallel, applications and operating systems offer limited support for these scenarios. Support is often limited to data synchronisation via the cloud or restricted to devices from the same manufacturer. The research community has been exploring how applications could adapt to the set of devices at hand and has devised new cross-device interaction techniques. Frameworks and tools have been developed for designing and prototyping such cross-device applications. However, less attention has been paid to what is needed to go beyond research prototypes and to engineer products. In this thesis, we investigate how we can support the development of cross-device applications across the whole engineering lifecycle from design, prototyping, and implementation to testing, debugging, and usage analysis. We start with an analysis of existing cross-device research as well as general purpose developer tools for the whole engineering lifecycle. We focus on tools for working with web technologies as we have chosen to use the web as a foundation for this thesis due to its cross-platform nature. We then introduce a set of novel tools for cross-device engineering that implement the requirements we identified in the analysis. For the design and prototyping phase, we contribute MultiMasher, a visual tool for creating functional cross-device mashups. The tool allows developers and designers to experiment with cross-device ideas based on existing applications without the need to write code. The implementation phase is addressed with XD-MVC, a cross-device library. XDMVC consists of a layered architecture to provide various levels of support. At the lower end, a JavaScript API offers basic functionality such as connection management, state synchronisation, and device roles. At the other end of the spectrum, we offer high level components for device pairing and UI distribution based on patterns. XD-Tools offers support for informal testing and debugging. It caters to the need of being able to test and debug many different device combinations and easily switch between different ones. This is achieved both with the integration of real devices as well as the emulation of devices on the developer machine. At the same time, the tool alleviates the issues of fragmentation of the code across devices by aggregating information for existing debugging tools such as the console. Formal, automated testing is addressed with XD-Testing. It allows developers to write parametrised UI tests in a new domain specific language. These tests can be repeated across multiple device combinations to test functionality independent of the set of devices used. At the same time, a developer can use explicit device selectors to test the distribution of the UI for a given device combination.
Extreme Insolation: Climatic Variation Shapes the Evolution of Thermal Tolerance at Multiple Scales

The climatic variability hypothesis (CVH) is a cornerstone of thermal ecology, predicting the evolution of wider organismal thermal tolerance ranges in more thermally variable environments. Thermal tolerance ranges depend on both upper and lower tolerance limits (critical thermal maxima [CTmax⁡] and critical thermal minima [CTmin⁡]), which may show different responses to environmental gradients. To delineate the relative effects of mean and extreme temperatures on thermal tolerances, we conducted a within-latitude comparative test of CVH predictions for army ants (Dorylinae) at multiple scales: across elevations, in seasonal versus aseasonal forests, and in subterranean versus surface microhabitats. Consistent with the CVH, thermally buffered subterranean species had narrower thermal tolerance ranges. Both CTmin⁡ and CTmax⁡ decreased with elevation for subterranean species. In contrast, aboveground species (those exposed to insolation) showed a decrease in CTmin⁡ but no change in CTmax⁡ across elevations. Furthermore, greater seasonal temperature variation in dry forests correlated with increased CTmax⁡ but not CTmin⁡. These patterns suggest that CTmax⁡ and CTmin⁡ respond to different abiotic selective forces: habitat-specific exposure to extreme insolation corresponds to CTmax⁡ differences but not to CTmin⁡ variation. We predict that increasingly frequent heat spikes associated with climate change will have habitat-specific physiological consequences for ectothermic animals. Models predicting climate change impacts should account for species microhabitat uses and within-latitude differences in temperature seasonality.
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network

Computer Vision for Analyzing and Classifying cells and tissues often require rigorous lab procedures and so automated Computer Vision solutions have been sought. Most work in such field usually requires Feature Extractions before the analysis of such features via Machine Learning and Machine Vision algorithms. We developed a Convolutional Neural Network that classifies 5 types of epithelial breast cell lines comprised of two human cancer lines, 2 normal immortalized lines, and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12A and HC11) without requiring feature extraction. The Multiclass Cell Line Classification Convolutional Neural Network extends our earlier work on a Binary Breast Cancer Cell Line Classification model. CellLineNet is 31-layer Convolutional Neural Network trained, validated and tested on a 3,252 image dataset of 5 types of Epithelial Breast cell Lines (MDA-MB-468, MCF7, 10A, 12A and HC11) in an end-to-end fashion. End-to-End Learning enables CellLineNet to identify and learn on its own, visual features and regularities most important to Breast Cancer Cell Line Classification from the dataset of images. Using Transfer Learning, the 28-layer MobileNet Convolutional Neural Network architecture with pre-trained ImageNet weights is extended and fine tuned to the Multiclass Epithelial Breast cell Line Classification problem. CellLineNet simply requires an imaged Cell Line as input and it outputs the type of breast epithelial cell line (MDA-MB-468, MCF7, 10A, 12A or HC11) as predicted probabilities for the 5 classes. CellLineNet scored a 96.67% Accuracy.
flowPloidy: An R package for genome size and ploidy assessment of flow cytometry data

Premise of the Study Despite advantages in terms of reproducibility, histogram analysis based on nonlinear regression is rarely used in genome size assessments in plant biology. This is due in part to the lack of a freely available program to implement the procedure. We have developed such a program, the R package flowPloidy. Methods and Results flowPloidy builds on the existing statistical tools provided with the R environment. This base provides tools for importing flow cytometry data, fitting nonlinear regressions, and interactively visualizing data. flowPloidy adds tools for building flow cytometry models, fitting the models to histogram data, and producing visual and tabular summaries of the results. Conclusions flowPloidy fills an important gap in the study of plant genome size. This package will enable plant scientists to apply a more powerful statistical technique for assessing genome size. flowPloidy improves on existing software options by providing a no‐cost workflow streamlined for genome size and ploidy determination.
A Novel Technique for Mapping the Vascularity of Labia Minora Prior to Labiaplasty: Cold Light Illumination

Abstract Objective Aim of the study was to assess the feasibility of a novel technique to determine the vascularity of labia minora prior to labiaplasty. Methods A cold light source employed in laparoscopic procedures was used to illuminate the outer surface of the labia minora as described in this prospective descriptive study. Results Of the patients, 46.1% (n = 41) had upper third prominence, 36% (n = 32) had middle third prominence, and 18% (n = 16) had lower third prominence according to the Banwell classification of morphologies. Right labia minora width was 0 – 2 cm in 51.7% (n = 46), 2 – 4 cm in 47.2% (n = 42) and > 4 cm in 1.1% (n = 1) of cases. Left labia minora width was 0 – 2 cm in 52.8% (n = 47), 2 – 4 cm in 41.6% (n = 37) and > 4 cm in 5.6% (n = 5) of cases. The incidence of Anterior 2 and Posterior 1 vessels in the different morphologies were statistically significantly different (p = 0.007, p = 0.018). The Anterior 2 vessel was higher in the lower morphology group whereas the Posterior 1 vessel was higher in the upper morphology group. A central vessel was observed in 93.3% (n = 83) of patients in the left labium minus and the right labium minus. The incidence of the Posterior 1 vessel was significantly higher in the left labium minus than in the right labium minus (p = 0.021). Discussion This novel technique to assess labial vascularity using a cold light source could be very useful to reduce dehiscence by avoiding excessive resection of highly vascularized tissue. Most units can easily access a cold light source, which can be used to assess labial vascularity prior to labiaplasty.
Efficient bivariate image denoising technique using new orthogonal CWT filter design

The complex wavelet transforms (CWTs) are known for their excellent edge preserving together with nearly shift invariant features. This study presents new orthogonal CWT filter designs. The proposed designs guarantee the perfect reconstruction feature of the CWT system while satisfying in a least squares sense, the CWT Hilbert constraints over the filter's pass-band. The study also describes a new image denoising technique based on bivariate thresholding of the noisy CWT coefficients. In this respect, a simple model is proposed to model the dependence between the magnitudes of the children and parent wavelet coefficient at every sub-band. This allows the derivation of a closed-form expression for the clean thresholded magnitudes. Several illustrative examples are given to verify the superior denoising performance especially in salt & pepper case.
Anticipated and imagined futures: prospective cognition and depressed mood following brain injury

Objectives Depression, which is common following acquired brain injury (ABI), has been shown to predict cognitive impairment, rehabilitation outcome, and quality of life. Whilst many studies have examined links between depression and cognitive–affective processing in the non‐ABI population, their applicability to this important clinical group, where cognitive difficulties can be marked, remains unknown. Here, we investigated biases in prospective cognition, which is known to be disrupted in (non‐ABI) depression yet important for well‐being. Design Cross‐sectional design with three groups (depressed ABI, non‐depressed ABI, and non‐ABI control participants). Continuous data were additionally analysed in correlation analyses. Methods Individuals with ABI varying in extent of self‐reported depression and matched non‐ABI control participants completed assessments of mood and prospective cognition (anticipating and imagining future events), alongside background tests of executive function and fluid intelligence. Results Relative to non‐depressed ABI and control participants, depressed ABI individuals demonstrated a reduced positive bias in prospective cognition: whereas non‐depressed ABI and control participants generated more examples of likely or possible positive versus negative future events, there was no evidence for such a positive bias in depressed ABI participants. Non‐depressed ABI and control participants also reported more vivid mental imagery for positive versus negative future scenarios, whereas such a pattern was not evident in depressed ABI participants. This pattern emerged despite background impairments in fluid intelligence and executive function associated with ABI. Conclusions These findings (1) elucidate depression‐linked cognitive–affective processes following ABI, where cognitive difficulties are common, and (2) highlight psychological processes associated with depression that are common to ABI and non‐ABI populations. Practitioner points Clinical implications A relative negative bias in future‐directed cognition is associated with depressed mood in individuals with chronic ABI. Such processes may contribute to the onset and maintenance of depression following ABI. These findings suggest it may be important to consider a role for prospective cognition in psychological interventions for depression following ABI. Limitations of the study The extent to which depressed mood following ABI is associated with biases in other cognitive domains remains unclear. Whether similar patterns would be observed in acute patients with more profound cognitive difficulties requires further investigation. Despite large effect sizes, our sample size is modest; these effects thus require replication in larger groups.
Estimation of Load-Pull Reflection Coefficients for Modulated Signals

A method to estimate reflection coefficients in a loadpull environment with modulated signals is proposed, which can be applied during the design stage and characterization of devices and components for next generation systems. The technique is based on least-square estimation of the impulse response of a finite impulse response filter. The proposed technique along with the conventional method are described and analyzed theoretically. The two approaches are experimentally demonstrated, verified, and compared using a load pull measurement setup with 6W GaN power amplifier as the device under test. The proposed technique shows better performance for both in-band and out-of-band estimation of reflection coefficients.
Real-time assessment of high resolution vital signs recording for calculation of perioperative clinical parameters

Clinical monitoring and recording of vital signs is essential for the therapy and outcome of critically ill patients in the perioperative setting. Most hospital systems are capable of displaying standard clinical parameters and storage of data at low temporal resolution. However, the assessment of novel clinical parameters, e.g. cumulative time of desaturation or hypotension, or the acquisition of highly sampled vital signs is not routinely provided. This limits the possibilities of anesthesiologists and intensivists to proactively tailor patient care and thus limits patient safety. We developed VREACT, a system for real-time assessment of novel clinical parameters and high resolution vital signs recording, which is easy-to-use and applicable in the clinical routine as well as during complex experimental measurements. VREACT was already successfully utilized for one clinical pilot trial and one experimental pig study. We are currently working on the integration of standard and novel clinical parameters into perioperative care.
Worst-Case Latency Analysis for IEEE 802.1Qbv Time Sensitive Networks Using Network Calculus

Distributed safety-critical applications in industrial automation, aerospace, and automotive, require worst-case end-to-end latency analysis for critical communication flows in order to prove their correct behavior in the temporal domain. With the advent of time sensitive networks (TSNs), distributed applications can be built on top of standard Ethernet technologies without sacrificing real-time characteristics. The time-based transmission selection and clock synchronization mechanism defined in the TSN enable the real-time transmission of frames based on a global schedule configured through so-called gate control lists (GCLs). This paper has an enhancement of allowing a mixture of the priority-based scheduling and time-triggered, which expand the solution space for the GCLs. Then, it is necessary to analyze the latency bounds for the critical traffic in the TSN network. In this paper, we start from the assumption that the GCLs, i.e., the communication schedules, and the traffic class (priority) assignment for critical flows are given for each output port and derive, using network calculus, an analysis of the worst-case delays that individual critical flows can experience along the hops from sender to receiver(s). Our method can be employed for the analysis of the TSNs where the GCLs have been created in advance, as well as for driving the GCL synthesis that explores a larger solution space than previous methods, which required a complete isolation of transmission events from different traffic classes. We validate our model and analysis by performing experiments on both synthetic and real-world use-cases, showing the scalability of our implementation as well as the impact of certain GCL properties (gate overlapping and traffic class assignments) on the worst-case latency of critical communication flows.
Cryogenic Characterization of 28-nm FD-SOI Ring Oscillators With Energy Efficiency Optimization

Extensive electrical characterization of ring oscillators (ROs) made in high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> metal gate 28-nm fully depleted silicon-on-insulator technology is presented for a set of temperatures between 296 and 4.3 K. First, delay per stage (<inline-formula> <tex-math notation="LaTeX">$\tau _{P}$ </tex-math></inline-formula>), static current (<inline-formula> <tex-math notation="LaTeX">${I} _{\textsf {STAT}}$ </tex-math></inline-formula>), and dynamic current (<inline-formula> <tex-math notation="LaTeX">${I} _{\textsf {DYN}}$ </tex-math></inline-formula>) are analyzed for the case of the increase of threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\textsf {TH}}$ </tex-math></inline-formula>) observed at low temperature. Then, the same analysis is performed by compensating <inline-formula> <tex-math notation="LaTeX">${V} _{\textsf {TH}}$ </tex-math></inline-formula> to a constant, temperature-independent value through forward body biasing (FBB). Energy efficiency optimization is proposed for different supply voltages (<inline-formula> <tex-math notation="LaTeX">${V} _{\textsf {DD}}$ </tex-math></inline-formula>) in order to find an optimal operating point combining both high RO frequencies and low-power dissipation. We show that the Energy-Delay product can be significantly reduced at low temperature by applying an FBB voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\textsf {FBB}}$ </tex-math></inline-formula>). We demonstrate that outstanding performance of RO in terms of speed (<inline-formula> <tex-math notation="LaTeX">$\tau _{P} = \textsf {37}$ </tex-math></inline-formula> ps) and static current (7nA/stage) can be achieved at 4.3 K with <inline-formula> <tex-math notation="LaTeX">${V} _{\textsf {DD}}$ </tex-math></inline-formula> reduced down to 0.325 V.
Noise Tolerant Localization for Sensor Networks

Most range-based localization approaches for wireless sensor networks (WSNs) rely on accurate and sufficient range measurements, yet noise and data missing are inevitable in distance ranging. Existing localization approaches often suffer from unsatisfied accuracy in the co-existence of incomplete and corrupted range measurements. In this paper, we propose LoMaC, a noise-tolerant localization scheme, to address this problem. Specifically, we first employ Frobenius-norm and $L_{1}$ -norm to formulate the reconstruction of noisy and missing Euclidean distance matrix (EDM) as a norm-regularized matrix completion (NRMC) problem. Second, we design an efficient algorithm based on alternating direction method of multiplier to solve the NRMC problem. Third, based on the completed EDM, we further employ a multi-dimension scaling method to localize unknown nodes. Meanwhile, to accelerate our algorithm, we also adopt some acceleration techniques to reduce the computation cost. Finally, extensive experimental results show that our algorithm not only achieves significantly better localization performance than prior algorithms but also provides an accurate position prediction of outlier, which is useful for malfunction diagnosis in WSNs.
ONLINE IDENTIFICATION OF DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC AND NONLINEAR DEPENDENCIES†

Linear structural vector autoregressive models (SVARMs) have well-documented merits for topology inference of directional graphs emerging in diverse applications, including gene-regulatory, brain, and social networks. Although simple and tractable, linear SVARMs cannot capture nonlinearities that are inherent to complex systems, such as the human brain, that can also vary over time. Given nodal measurements, these considerations motivate the dynamic nonlinear SVARM approach developed here to track the possibly directed and dynamic nonlinear interactions among network nodes. For slow-varying topologies, nonlinear model parameters are estimated via functional stochastic gradient descent. Numerical tests showcase the effectiveness of the novel algorithms in unveiling sparse dynamically-evolving topologies.
Tactile Mesh Saliency: A Brief Synopsis

This work has previously been published [LDS* 16] and this extended abstract provides a synopsis for further discussion at the UK CGVC 2016 conference. We introduce the concept of tactile mesh saliency, where tactile salient points on a virtual mesh are those that a human is more likely to grasp, press, or touch if the mesh were a real-world object. We solve the problem of taking as input a 3D mesh and computing the tactile saliency of every mesh vertex. The key to solving this problem is in a new formulation that combines deep learning and learning-to-rank methods to compute a tactile saliency measure. Finally, we discuss possibilities for future work.
Effects of Rapid Thermal Annealing on Ar Inductively Coupled Plasma-Treated n-Type 4H-SiC Schottky and Ohmic Contacts

The effects of Ar inductively coupled plasma (ICP) treatment followed by a 600 °C–1000 °C rapid thermal annealing (RTA) on the n-type 4H-silicon carbide (SiC) Schottky-barrier diodes and n<sup>+</sup>-implanted ohmic contacts were investigated. The ICP treatment created a 3-nm-thick, sp<sup>2</sup>–C-rich, and amorphous layer at the SiC surface. The RTA repaired the bombardment-induced damages before metal deposition to avoid current degradation. This ICP + RTA-treated surface strongly pinned the Schottky-barrier height (SBH) at a minimum of 0.88 eV. In theory, the low SBH is beneficial to decrease the specific contact resistance (<inline-formula> <tex-math notation="LaTeX">$\rho _{C}$ </tex-math></inline-formula>). <inline-formula> <tex-math notation="LaTeX">$\rho _{C}$ </tex-math></inline-formula> of the ICP + RTA-treated Ti ohmic contacts decreased to lower than <inline-formula> <tex-math notation="LaTeX">$10^{-{5}} \,\,\Omega \cdot \text {cm}^{{2}}$ </tex-math></inline-formula> after 400 °C postmetal deposition annealing (PMDA). However, the additional O atoms, fixed in the amorphous layer by RTA, affected the <inline-formula> <tex-math notation="LaTeX">$\rho _{C}$ </tex-math></inline-formula> reduction. Fortunately, due to the chemical affinity for O of Ti, the in-diffused Ti could contend for the O atoms against the Si–O bonds during the PMDA. Therefore, the oxidized barricade was decomposed gradually, leading to the lowest <inline-formula> <tex-math notation="LaTeX">$\rho _{C}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.3\times 10^{-{6}} \,\,\Omega \cdot \text {cm}^{{2}}$ </tex-math></inline-formula>, after 600 °C PMDA. The lowest <inline-formula> <tex-math notation="LaTeX">$\rho _{C}$ </tex-math></inline-formula> is <inline-formula> <tex-math notation="LaTeX">$25\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> lower than that of the Ni silicide and the nontreated Ti contacts, respectively, at the same doping concentration.