Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Specific Learning Disabilities (SLD)/Dyslexia Developments Over the Last Decade in Hong Kong

The condition of Specific Learning Disabilities (SLD) is basically a child health issue requiring the close cooperation and collaboration of medical, social and educational professionals working in a transdisciplinary team. For many years, numerous Hong Kong children and parents suffer unnecessarily from these disabilities due to poor awareness and professional support. Local research work recently confirms that the prevalence rate of SLD in Hong Kong children is comparable to the international data and that the incidence of Dyslexia is about 10% of the population. Numerous workshops and seminars have been held locally and keenly supported by professionals, parents, politicians, educators and administrators through the years. Consensus on definition and multidisciplinary interventions has been reached with the goal of formulating comprehensive management programme for children with SLD. This paper reviews the evolution of SLD in Hong Kong in the past ten years as well as major breakthroughs in recent developments in the practice of SLD. The admission of SLD into the Rehabilitation Programme Plan 2007 is a monumental milestone whereby SLD is officially recognised as a member of the Disability Category which also entitles individuals with SLD to have access to remediation, compensation, accommodation and resource support from the Hong Kong SAR Government at the family, school and community levels. These are key factors for success in habilitation. The SLD Summit 2007 hosted in Hong Kong in November 2007 summarises the most up-to-date progress, consolidates the consensus on SLD for Hong Kong and endeavours to highlight the way forward and action plans for the future.
Navigational Environmental Risk Evaluation of Breakwater Based on the Analytic Hierarchy Process

This paper introduces the analytic hierarchy process, and applies in the process of engineering project risk evaluation of navigational environment safety. This paper takes the breakwater project of a harbor as an example for analysis, through the recognition of each risk source and risk factor that the project influences on navigation safety to determine the risk indicators, and uses analytic hierarchy process to analyze the impact of various risk factors on navigation safety, thus gains the overall influence of project on navigation safety. This can be used as an auxiliary tool of engineering risk management.
How Structural Model Variability Influences Transonic Aeroelastic Stability

This paper considers the ways in which structural model parameter variability can influence aeroelastic stability. Previous work on formulating the stability calculation (with the Euler equations providing the aerodynamic predictions) is exploited to use Monte Carlo, interval, and perturbation calculations to allow this question to be investigated. Three routes are identified. The first involves variable normal-mode frequencies only. The second involves normal-mode frequencies and shapes. Finally, the third, in addition to normal-mode frequencies and shapes, also includes their influence on the static equilibrium. Previous work has suggested only considering the first route, which allows significant gains in computational efficiency if reduced-order models can be built for the aerodynamics. However, results in the current paper show that neglecting the mode-shape variation can give misleading results for the flutter-onset prediction, complicating the development of reduced aerodynamicmodels for variability analysis.
Discerning Free Driving from Car-Following State Based on Trajectory Data from Active Mode Car-Following Experiment

Discerning free driving from car-following state is an important issue in modeling driver's longitudinal behavior. Existing studies suggested that there is a turning point for correlation coefficient of leading and following vehicles' speed between free driving and car-following states. In this paper, by applying the adopted method to trajectory data, a longer 12 s time gap threshold between free driving and car-following is reported. Also a notable difference across experienced and beginner driver groups is found. This study could provide some evidence on driver behavior heterogeneity, and also raise the doubt for a universal “free” vehicle standard.
The podocyte-specific knockout of palladin in mice with a 129 genetic background affects podocyte morphology and the expression of palladin interacting proteins

Proper and size selective blood filtration in the kidney depends on an intact morphology of podocyte foot processes. Effacement of interdigitating podocyte foot processes in the glomeruli causes a leaky filtration barrier resulting in proteinuria followed by the development of chronic kidney diseases. Since the function of the filtration barrier is depending on a proper actin cytoskeleton, we studied the role of the important actin-binding protein palladin for podocyte morphology. Podocyte-specific palladin knockout mice on a C57BL/6 genetic background (PodoPalldBL/6-/-) were back crossed to a 129 genetic background (PodoPalld129-/-) which is known to be more sensitive to kidney damage. Then we analyzed the morphological changes of glomeruli and podocytes as well as the expression of the palladin-binding partners Pdlim2, Lasp-1, Amotl1, ezrin and VASP in 6 and 12 months old mice. PodoPalld129-/- mice in 6 and 12 months showed a marked dilatation of the glomerular tuft and a reduced expression of the mesangial marker protein integrin α8 compared to controls of the same age. Furthermore, ultrastructural analysis showed significantly more podocytes with morphological deviations like an enlarged sub-podocyte space and regions with close contact to parietal epithelial cells. Moreover, PodoPalld129-/- of both age showed a severe effacement of podocyte foot processes, a significantly reduced expression of pLasp-1 and Pdlim2, and significantly reduced mRNA expression of Pdlim2 and VASP, three palladin-interacting proteins. Taken together, the results show that palladin is essential for proper podocyte morphology in mice with a 129 background.
Vaping discussion in the COVID-19 pandemic: An observational study using Twitter data

Background With the spread of COVID-19, significant concerns have been raised about the potential increased risk for electronic cigarette (e-cigarette) users for COVID-19 infection and related syndromes. Social media is an increasingly popular source for health information dissemination and discussion, and can affect health outcomes. Objective This study aims to identify the topics in the public vaping discussion in COVID-19–related Twitter posts in order to get insight into public vaping-related perceptions, attitudes and concerns, and to discern possible misinformation and misconceptions around vaping in the COVID-19 pandemic. Methods Using the tweets ID database maintained by Georgia State University’s Panacea Lab, we downloaded the tweets related to COVID-19 from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to February 12, 2021. We used R to analyze the tweets that contained a list of 79 keywords related to vaping. After removing duplicates and tweets created by faked accounts or bots, the final data set consisted of 11,337 unique tweets from 7,710 different users. We performed the latent Dirichlet allocation (LDA) algorithm for topic modeling and carried out a sentiment analysis. Results Despite fluctuations, the number of daily tweets was relatively stable (average number of daily tweets = 33.4) with a sole conspicuous spike happening on a few days after August 11, 2020 when a research team published findings that teenagers and young adults who vape face a much higher risk of COVID-19 infection than their peers who do not vape. Topic modeling generated 8 topics: linkage between vaping and risk of COVID-19 infection, vaping pneumonia and the origin of COVID-19, vaping and spread of COVID-19, vaping regulation, calling for quitting vaping, protecting youth, similarity between e-cigarette or vaping-associated lung injury (EVALI) and COVID-19, and sales information. Daily sentiment scores showed that the public sentiment was predominantly negative, but became slightly more positive over the course of the study time period. Conclusions While some content in the public discourse on vaping before the COVID-19 pandemic continued in Twitter posts during the COVID-19 time period, new topics emerged. We found a substantial amount of anti-vaping discussion and dominantly negative sentiment around vaping during COVID-19, a sharp contrast to the predominantly pro-vaping voice on social media in the pre-COVID-19 period. Continued monitoring of social media conversations around vaping is needed, and the public health community may consider using social media platforms to actively convey scientific information around vaping and vaping cessation.
How to Draw a Correlation Function

We discuss connection between the XX0 Heisenberg spin chain and some aspects of enumerative combinatorics. The representation of the Bethe wave functions via the Schur functions allows to apply the theory of symmetric functions to the calculation of the correlation functions. We provide a combinatorial derivation of the dynamical auto-correlation functions and visualise them in terms of nests of self-avoiding lattice paths. Asymptotics of the auto-correlation functions are obtained in the double scaling limit provided that the evolution parameter is large.
Real Estate Economics

Basic information Code: ENG3074 Extent: 24-30 ECTS Language: English Organizing department: Department of Built Environment Teacher in charge: Ari Ekroos Students from other Master's Programmes at Aalto Target group: The general application procedure for minors at Aalto University, see Instructions for applying Application process: Max 10 students will be admitted yearly Quotas and restrictions: Prerequisites: Earlier studies must have included at least the basics of economics, law and statistics/econometrics. Any earlier studies in real estate, land management or other closely related field are regarded positive.
Measuring Wind Turbine Health Using Drifting Concepts

Time series processing is an essential aspect of wind turbine health monitoring. Despite the progress in this field, there is still room for new methods to improve modeling quality. In this paper, we propose two new approaches for the analysis of wind turbine health. Both approaches are based on abstract concepts, implemented using fuzzy sets, which summarize and aggregate the underlying raw data. By observing the change in concepts, we infer about the change in the turbine's health. Analyzes are carried out separately for different external conditions (wind speed and temperature). We extract concepts that represent relative low, moderate, and high power production. The first method aims at evaluating the decrease or increase in relatively high and low power production. This task is performed using a regression-like model. The second method evaluates the overall drift of the extracted concepts. Large drift indicates that the power production process undergoes fluctuations in time. Concepts are labeled using linguistic labels, thus equipping our model with improved interpretability features. We applied the proposed approach to process publicly available data describing four wind turbines. The simulation results have shown that the aging process is not homogeneous in all wind turbines.
Justifying the Dependability and Security of Business-Critical Blockchain-based Applications

In the industry, blockchains are increasingly used as the backbone of product and process traceability. Blockchain-based traceability participates in the demonstration of product and/or process compliance with existing safety standards or quality criteria. In this perspective, services and applications built on top of blockchains are business-critical applications, because an intended failure or corruption of the system can lead to an important reputation loss regarding the products or the processes involved. The development of a blockchain-based business-critical application must be then conducted carefully, requiring a thorough justification of its dependability and security. To this end, this paper encourages an engineering perspective rooted in well-understood tools and concepts borrowed from the engineering of safety-critical systems. Concretely, we use a justification frame-work, called CAE (Claim, Argument, Evidence), by following an approach based on assurance cases, in order to provide convincing arguments that a business-critical blockchain-based application is dependable and secure. The application of this approach is sketched with a case study based on the blockchain HYPERLEDGER FABRIC.
DP-GCN: Node Classification Based on Both Connectivity and Topology Structure Convolutions for Risky Seller Detection

A payment network contains transactions between sellers and buyers. Detecting risky (or bad) sellers on such a payment network is crucial to payment service providers for risk management and legal compliance. In this research, we formulate this task as a node classification task. Specifically, we aim to predict a label for each seller in a payment network, by analysing its properties and/or interactions. Nodes residing in different parts of a payment network can have similar local topology structures. Such local topology structures reveal sellers' business roles, eg. , supplier, drop-shipper, or retailer. We note that many existing solutions for graph-based node classification only consider node connectivity but not the similarity between node's local topology structure. Motivated by business need, we present a dual-path graph convolution network, named DP-GCN, for node classification. DP-GCN considers both node connectivity and topology structure similarity. The proposed model consists of three main modules: (i) a C-GCN module to capture connectivity relationships between nodes, (ii) a T-GCN module to capture topology structure similarities between nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, one of the leading payment service providers. Experimental results demonstrate DP-GCN's effectiveness and practicability.
Families and Clusters of Diffuse Interstellar Bands: a Data-Driven Correlation Analysis

More than 500 diffuse interstellar bands (DIBs) have been observed in astronomical spectra, and their signatures and correlations in different environments have been studied over the past decades to reveal clues about the nature of the carriers. We compare the equivalent widths of the DIBs, normalized to the amount of reddening, E_B-V, to search for anti-correlated DIB pairs using a data sample containing 54 DIBs measured in 25 sight lines. This data sample covers most of the strong and commonly detected DIBs in the optical region, and the sight lines probe a variety of ISM conditions. We find that 12.9% of the DIB pairs are anti-correlated, and the lowest Pearson correlation coefficient is r_norm ~ -0.7. We revisit correlation-based DIB families and are able to reproduce the assignments of such families for the well-studied DIBs by applying hierarchical agglomerative and k-means clustering algorithms. We visualize the dissimilarities between DIBs, represented by 1 - r_norm, using multi-dimensional scaling (MDS). With this representation, we find that the DIBs form a rather continuous sequence, which implies that some properties of the DIB carriers are changing gradually following this sequence. We also find at that least two factors are needed to properly explain the dissimilarities between DIBs. While the first factor may be interpreted as related to the ionization properties of the DIB carriers, a physical interpretation of the second factor is less clear and may be related to how DIB carriers interact with surrounding interstellar material.
ASET-B: Comparison of Model Predictions with Full-scale Test Data

Predictions of upper layer temperature and elevation made using the computer fire model ASET-B were compared to data from two full-scale test series. Although measurements of the upper layer temperature varied with elevation within the layer, ASET-B predictions agreed reasonably well with average smoke layer temperatures or temperatures measured at intermediate elevations within the smoke layer. ASET-B tended to underpredict layer temperatures measured at the top of the layer. ASET-B predictions were within 20% of measurements of the smoke layer elevation.
Long nonlinear internal waves and mixing in a three-layer stratified flow in the Boussinesq approximation

A one-dimensional long-wave model of an unsteady three-layer flow of a stratified fluid under a lid is proposed, taking into account turbulent mixing in the intermediate layer. In the Boussinesq approximation, the equations of motion are reduced to an evolutionary system of balance laws, which is hyperbolic for a small difference in velocities in the layers. Classes of stationary solutions are studied and the concept of subcritical (supercritical) three-layer stratified flow is introduced. Oscillating solutions are constructed that describe the spatial evolution of the mixing layer. The problem of transcritical flow over an obstacle is considered. Solutions are obtained that describe qualitatively different flow regimes on the leeward side of the obstacle. The proposed model is validated using experimental data and field observations on the entrainment of ambient fluid.
Maximal commutative unipotent subgroups and a characterization of affine spherical varieties

We describe maximal commutative unipotent subgroups of the automorphism group $\mathrm{Aut}(X)$ of an irreducible affine variety $X$. Further we show that a group isomorphism $\mathrm{Aut}(X) \to \mathrm{Aut}(Y)$ maps unipotent elements to unipotent elements, where $Y$ is irreducible and affine. Using this result, we show that the automorphism group detects sphericity and the weight-monoid. As an application, we show that an affine toric variety different from an algebraic torus is determined by its automorphism group among normal irreducible affine varieties and we show that a smooth affine spherical variety different from an algebraic torus is determined by its automorphism group (up to an automorphism of the base field) among smooth irreducible affine varieties.
Detecting Potentially Harmful and Protective Suicide-related Content on Twitter: A Machine Learning Approach

Research shows that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic large scale investigations are missing in general, and in particular for social media data. We apply machine learning methods to classify large quantities of Twitter data according to a novel annotation scheme that distinguishes 12 categories of suicide-related tweets. We then trained a benchmark of machine learning models including a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The two deep learning models achieved the best performance in two classification tasks: In the first task, we classified six main content categories, including personal stories about either suicidal ideation and attempts or coping, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these categories. The deep learning models reached accuracy scores above 73% on average across the six categories, and F1-scores in between 0.70 and 0.85 for all but the suicidal ideation and attempts category (0.51-0.55). In the second task, separating tweets referring to actual suicide from off-topic tweets, they correctly labeled around 88% of tweets, with BERT achieving F1-scores of 0.93 and 0.74 for the two categories, respectively. These classification performances are comparable to the state-of-the-art on similar tasks. By making data labeling more efficient, this work has enabled large-scale investigations on harmful and protective associations of social media content with suicide rates and help-seeking behavior.
The Standard Model and Dark Matter in a unified Einstein-Dirac system with discrete extra dimensions

The Dirac-Einstein system of quark-leptons coupled to gravity is constructed generalized in the space-time extended with chiral and dark discrete extra dimensions. The Einstein's gravity, all the Standard Model's gauge vector, and Higgs fields together with a dark photon, whose mass is generated by a dark Higgs scalar, can be incorporated in this framework as components of the generalized vielbein. The Standard Model derived from the extended Einstein-Hilbert action must satisfy some conditions leading to the predictions of Weinberg angle, Higgs and top quark masses in excellent agreement with experiments. Since, the Dirac operator extended by discrete derivatives has resulted in a non-diagonal mass matrix, which mixes each quark-lepton with its Kaluza-Klein partner. On one hand, the small mixing angle set a Stueckelberg mass to the quark-leptons' Kaluza-Klein partners at least in the hundreds TeV range. On the other hand, it implies weak couplings between the Kaluza-Klein partners of quark-leptons and the Standard Model's gauge vector fields. This feature indicates that these Kaluza-Klein modes can be considered as candidates of dark matters.
Transition from Lorentz to Fano Spectral Line Shapes in Nonrelativistic Quantum Electrodynamics

Spectroscopic signatures associated with symmetric Lorentzian and asymmetric Fano line shapes are ubiquitous. Distinct features of Fano resonances in contrast with conventional symmetric resonances have found several applications in photonics such as optical switching, sensing, lasing, and nonlinear and slow-light devices. Therefore, it is important to have control over the generation of these resonances. In this work, we show through ab initio simulations of coupled light-matter systems that Fano interference phenomena can be realized in a multimode photonic environment by strong coupling to the electromagnetic continuum. Specifically, we show that by effectively enhancing the light-matter coupling strength to the photon continuum in an experimentally feasible way, we can achieve a transition from Lorentzian to Fano lines shapes for both electronic and polaritonic excitations. An important outcome of switching between these spectral signatures is the possibility to control the Purcell enhancement of spontaneous emission alongside electromagnetically induced transparency which is a special case of Fano resonances. Switching from Fano back to a Lorentzian profile can be achieved by physically reducing the coupling strength to the continuum of modes. Our results hold potential for realizing tunable Fano resonances of molecules and materials interacting with the electromagnetic continuum within multimode photonic environments.