Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Impact of Coding Depth in Sliding Window Random Linear Network Coding Schemes

Sliding Window Random Linear Network Coding (RLNC) offers a clear path towards achieving ultra-high reliability and low latency at the same time. Such requirements are pivotal for a wide range of applications in the future Internet as well as in 5G and beyond networks. While traditional RLNC has been extensively used for some years now, its Sliding Window flavor is rather recent and extremely promising because of its implementation advantages and the high degree of customization. Probably the most essential parameter of Sliding Window RLNC is the coding depth, i.e., the extent of non-coded packets protected by a coded one. In this work, for the first time, we elaborate on properly choosing the coding depth and shed light on the related trade-offs. We, first, show, experimentally, that significant performance gains can be obtained by fine-tuning the coding depth. Then, we propose and validate an analytical framework that allows us to decide the coding depth based on a channel’s reliability profile. Finally, we introduce a dynamic algorithm that, based on our analytical findings, can improve the performance of sliding window RLNC in the presence of bursts of errors.
Better Encoders for Neural Process Family Models

As research into machine learning matures into its 8th decade it has become increasingly the case that the most celebrated advances have been in areas with the greatest abundance of data. From reinforcement learning models that achieve super-human game-play performance (Silver et al., 2016) to massive language models that are able to generate convincing news stories in the style of specific authors1 (Brown et al., 2020), the path of progress has often been to make models bigger and supply them with more information. Whilst in many cases this is a suitable approach, we must not neglect the areas where it is unfeasible. When data is scarce or sampled from many different environments we must turn to methods which are much more efficient in the way that they extract information and do not overfit during training to the data that is available to them. The purpose of this thesis is to explore these problems, and in particular, a recent family of models that have been developed to work well in them, coined Neural Processes. At their root, neural processes are meta-learners that provide a mapping from a dataset to the predictive posterior distribution of an underlying stochastic processes. In this way they can be thought of as both meta-learning models and stochastic process approximators. In this thesis we start by exploring the strengths and weaknesses of the existing models in the neural process family (NPF), and build a new model called the Correlated Convolutional Neural Process (CorrConvNP) to give more robustness and flexibility to changes in task distributions. We show that the CorrConvNP exhibits strong performance over a spectrum of synthetic tasks that reflect real use-cases, and evaluation shows it to perform statistically better than the next best existing model in certain scenarios. Finally, we show how the CorrConvNP can be extended to different data regimes, such as classifying categories and zero-inflated regression, via a simple change of likelihood function. We then test it on both synthetic classification data sets and a practical rainfall prediction scenario. 1See https://newsyoucantuse.com/ to see the results!
A deep Natural Language Inference predictor in Italian without Italian training data

We present an application in the sphere of Natural Language Processing to tackle the problem of inference relation (NLI) between pairs of sentences in Italian without an Italian NLI training dataset. The model has been developed making use of the Knowledge Distillation technique. The model may be exploited in industrial scenarios to analyse any kind of user-generated content. Specifically, applications may include product or service review analysis, brand reputation analysis, or social media sentiment analysis. To support this claim, we evaluate the proposed architecture over the native Italian ABSITA dataset on different tasks, such as Aspect-Based Sentiment Analysis, Sentiment Analysis, and Topic Recognition. We balanced the dataset used for each task to obtain a 50/50 division, and we respectively achieve the performances in terms of accuracy of 94.03%, 88.12%, and 71.19%. By applying the model over different scenarios, we empirically demonstrate the generality and exploitability of the NLI technique. While the developed model has still room for improvement, it is suitable to operate unsupervised free-text information extraction — mainly over reviews — in a production environment.
Exploring Consumers’ Second-hand Apparel Consumption Intention and Main Influential Factors

Fast fashion has been the main business model of the fashion industry over the past years. However, the second-hand market is growing and becoming more prominent, especially among consumers from Gen Z. Despite young people tending to be more concerned and to show a higher level of environmental responsibility, this attitude to be more sustainable often does not translate into actual behaviour. In this sense, the main purpose of the present study was to analyse the consumers' intention towards purchasing, swapping, borrowing, or receiving second-hand apparel and to examine the main influential factors – especially the role of influencers, which has not been investigated in this specific context – that contribute to the (mis)alignment between consumers’ positive attitude and their behaviour, using the theory of planned behaviour. Data collected from a sample of 268 participants, through a Portuguese and an English survey, was analysed by applying the structural equation modelling (SEM) conducted on IBM SPSS AMOS. The findings revealed that consumption intention is positively and directly influenced by the factors: attitude, subjective norms, and perceived behavioural control. Moreover, the attitude is positively and directly influenced by both the environmental knowledge and the environmental concern, as well as the factor subjective norms is by the influencers’ role and the perceived behavioural control is by the perceived quality. Key-words: Fast Fashion, Second-hand Apparel, Influential Factors, Influencers JEL-Codes: D16 and L67
Running head: DECOMPOSING AB Decomposing the Attentional Blink

The Attentional Blink (AB) refers to a deficit in reporting a second target (T2) embedded in a stream of distractors when presented 200-500 ms after a preceding target (T1). Several theories about the origin of the AB have been proposed; filter-based theories claim that the AB is the result of a temporarily closing of an attentional gate to avoid featural confusion for targets and distractors, while bottleneck theories propose that the AB is caused by a reduction in the capacity to either encode into or maintain information in visual shortterm memory. In three experiments, we systematically vary the exposure duration and composition of the T2 display allowing us to decompose the T2 deficit into well-established parameter estimates based on the Theory of Visual Attention (TVA). As the different AB theories make specific predictions regarding which parameters should be affected during the AB, we are able to test their plausibility. All three experiments consistently show a lower capacity to process T2 during the AB, supporting theories hypothesizing a bottleneck at the encoding stage. No evidence is found supporting filter-based theories or theories placing the bottleneck at the maintenance stage.
Older patients’ driving safety with the help of DRIVING SIMULATOR: Which cognitive test can predict better driving safety?

Introduction: Being able to drive is an important parameter of independence and self-sufficiency. The continued use of cars, which plays an important role in maintaining the mobility of the older individuals, is very important for the protection of the individual’s activity performance. Methods: Driving skills of 31 participants were tested with the help of a driving simulator and cognitive tests were applied to each participant. The study was aimed to reveal the relationship between the cognitive functions and safe driving skills of older patients using the driving simulator and to determine the cognitive test that predicts the safe driving skill best. Results: 31 participants was included in the study. All participants were male. The average age was 72.5±6.The median of MMSE was 29 (IQR; 28-30),the mean of MOCA was 25.52±2.6,the mean value of QMCI was 62.68±9.57, the median of trail making test A was 42.5 sec(22-97), and the median of trail making test B was 98.31 sec(38-313). MOCA test score correlated positively with “the skills expected before starting the vehicle “and driving parameters on the highway(“p: 0.0024, r:0.46”;”p:0.0024, r:0.46”,respectively). The QMCI test was found to have a statistically positive and significant correlation with operational skills and the skills expected before starting the vehicle(“p:0.041, r:0.43”; “p:0.015 r:0.50”,respectively). When the factors affecting operational skills and safe driving skills were analyzed by linear regression analysis, both skills were influenced by the QMCI-orientation step(p:0.001;CI:1.59-3.9). Conclusion: In our study, it is shown that driving skills decrease with aging. QMCI and MOCA, which are easy to apply in clinical practice, will be useful in patients driving vehicles aged 65 and over with the demonstration of a significant relationship with total driving score, safe driving and operational skills.
Deposition of droplets by pyroelectric field created by lithium tantalate with tailored domain structure

ABSTRACT A new concept of pyroelectric electrohydrodynamic system for deposition of the micron size droplets at the predetermined position on the substrate with high accuracy using pyroelectric field created by lithium tantalate with tailored domain structure has been proposed. The optimal geometry of the domain pattern has been obtained by computer simulation of the spatial distribution of surface pressure acting on liquid/vapor interface induced by pyroelectric field. The proposed technique was applied to dispense the silicon oil and the water solution of amino acid glycine.
4D monitoring of mountain areas using the UAV-PPK workflow

The mapping and three-dimensional modeling of mountain areas and the study of land cover change dynamics are current tasks in preserving and maintaining protected natural parks and forests. In this context, recent developments in digital photogrammetry using the SfM-MVS method to process captured imagery and the development of unmanned aerial systems (UAS) allow for reducing the costs, time, and the use of human resources and obtaining and repeatable 3D topographic data for moun-tainous regions. We will call this acquired 3D high-resolution topographic data (HRTD) 4D data in the context of an additional temporal component. The main objective of this study is to evaluate the applicability of PPK (Post-Processing Kinematic) direct georeferencing of images captured by UAVs and processed through the SfM-MVS method to obtain HRTDs for 4D land cover analysis. We analyze a 3D HRTD with an acquisition interval of two years for a mountain test area in Plana Mountain near Sofia. The test area has a diverse vegetation cover, including coniferous forest, grassland, hay meadows, shrubs, and single deciduous trees. We conducted multiple surveys of the test area with a budget PPK-UAV configuration (DJI Phantom 4 Pro with a single-frequency PPK-GNSS kit installed) from March 2020 to October 2022. Two autumn surveys from September 2020 and October 2022 were se-lected, which possess the most-good performance on numerical data accuracy. We performed 3D data analysis on 1) Assessment of the accuracy of PPK-SfM-MVS photogrammetry generated topographic data (3D clouds and DSM); 2) Investigation of the errors in the individual specific surfaces (for the individual isolated sections) using the M3C2 tool for comparing and evaluating dense point clouds; 3) Determining land cover changes in the demarcated areas using a surface of differences (DoD). Accuracy analysis showed that the PPK solution provides comparable accuracy (about RMSE3D = 0.067 m for the 2020 data, georeferencing (PPK+1GCP) and RMSE3D about 0.13 m for the 2022 data, georeferencing (PPK only)) like the GCP method. The multi-temporal topographic reconstructions based on UAV- PPK-SfM allowed us to quantify and qualitatively determine the land cover changes that occurred. The UAV-PPK-SfM workflow in the context of 4D land surface monitoring and the results suggested that even low-cost UAV-PPK systems can provide data suitable for measuring geomorphic change at the scale of the acquired data. The multi-temporal topographic reconstructions based on UAV- PPK-SfM allowed us to quantify and qualitatively determine the land cover changes that occurred. The UAV-PPK-SfM workflow in the context of 4D land surface monitoring and the results suggested that even low-cost UAV-PPK systems can provide data suitable for measuring geomorphic change at the scale of the acquired data.
Educational Game for Learning Computational Thinking in a Low Budget Virtual Reality Environment

Virtual reality (VR) is a technology that can create a simulation of real objects in a virtual environment more interactively. In terms of budget, VR devices can be categorized into low budget and high end. However, both of them can still carry out the same interactive simulations, such as educational games. This research aims to develop an educational game based in low-budget VR environment for learning computational thinking (CT), one of the 21st century skills that students need to learn. The method used is Design and Development Research (DDR) that consists of five stages: analysis, design, development, testing, and evaluation. Participants involved in this study are 30 students of vocational school (SMK) majoring Computer and Network Engineering. Evaluation conducted through Technology Acceptance Model (TAM) framework that consists of perceived usefulness (PU), perceived ease of use (PEU), attitude toward using (ATU), and intention to use (ITU). Based on the data obtained and the analysis performed, the Cronbach’s alpha score of PU and PEU were 0.714 and 0.614, respectively. Meanwhile, the Cronbach’s alpha score of ATU and ITU were 0.754 and 0.882, respectively. PU and PEU are positively correlated with ATU, while ATU is also positively correlated with ITU. It means that the ease of use of the application and the usability aspects of the developed application have a positive effect on user attitudes. This positive behavior is also positively correlated with the intention to use it again on another occasion. Thus it can be concluded that this educational game based in low-budget VR environment can be used for learning CT. It can be seen from the analysis and some positive comments from students, like participants wish they can use this learning approach in the other subject.
Do Scalp EEG Measurements Allow Causal Inference?

Rapidly developing methods of causal inference are being extensively applied to analyze electroencephalographic (EEG) measurements. However, there is considerable uncertainty surrounding the interpretation of the results. Is what the methods reflect from brain activity truly causality? In this study, we first simulate a ground-truth causal process in the cortex. Next, we project the cortex activity onto the surface of the head with a forward model, thereby obtaining data closely mimicking real noninvasive EEG signals. We argue and demonstrate that if the correct interpretation of the EEG measurement from any single electrode is a linear combination of all source cortical signals, a standard causal inference may fail on fundamental levels.
Blow flies (Diptera: Calliphoridae) ability to pollinate Hass avocado trees within paired tree enclosures

Avocado pollination remains a major focus for producers as fruit‐set is typically very low (less than 1%). Most avocado producers in Australia utilize honey bees (Apis mellifera) to improve yield; however, there are risks with reliance on one species for future pollination security. Flies regularly visit flowers including avocado flowers. However, their pollination ability is poorly understood. To address this, visual observations of flies foraging in avocado orchards along with releases of two species of blow fly (within replicate enclosures of paired avocado trees; Hass and Type B cultivars) were conducted at a commercial orchard in south‐western Western Australia. Visual observations during flowering identified that most non‐bee flower visits were by hover flies (Melangyna viridiceps and Sphaerophoria macrogaster) and blow flies (Calliphora vicina and C. albifrontalis). Hover flies mostly foraged on flowers in the morning while blow flies were generally more active in the afternoon. Pollination as measured by harvestable fruit was higher by C. dubia (45.6 ± 10.0) than by C. albifrontalis. (26.3 ± 6.6). Compared with open trees pollinated by bees and other insects present, trees pollinated by C. dubia produced nearly two‐thirds the number of fruit. When presented with equal numbers of avocado flowers, C. dubia fed three times more often than C. albifrontalis, which may explain their higher pollination rate. Both C. dubia and C. albifrontalis transferred on average one pollen grain/flower visit. Blow flies improved avocado yield above no insect pollinators (3 fruit/tree) by an average of 31 fruit/tree (across both fly species), but considerably less than trees in the open pollinated by honey bees (128 fruit/tree). Future trials with multi‐tree enclosures will assess avocado pollination by different fly species. This study demonstrated that at least one species of blow fly could pollinate avocado and provide potential as a managed pollinator in Australian horticulture.
Disentangling the intragroup HI in Compact Groups of galaxies by means of X3D visualization

Abstract As an extreme kind of environment, Hickson Compact groups (HCGs) have shown to be very complex systems. HI-VLA observations revealed an intrincated network of HI tails and bridges, tracing pre-processing through extreme tidal interactions. We found HCGs to show a large HI deficiency supporting an evolutionary sequence where gas-rich groups transform via tidal interactions and ISM (interstellar medium) stripping into gas-poor systems. We detected as well a diffuse HI component in the groups, increasing with evolutionary phase, although with uncertain distribution. The complex net of detected HI as observed with the VLA seems hence so puzzling as the missing one. In this talk we revisit the existing VLA information on the HI distribution and kinematics of HCGs by means of X3D visualization. X3D constitutes a powerful tool to extract the most from HI data cubes and a mean of simplifying and easing the access to data visualization and publication via three-dimensional (3-D) diagrams.
Thermoforming, characterization, and in-house recycling of multi-layered laminated composites made with polyamide 6/recycled carbon fibers hybrid yarns-based plain weave fabrics

Carbon fiber reinforced plastics (CFRPs) in the form of laminated composites are widely used in various sectors, such as aeronautics and clean energy fields, due to their superior mechanical properties compared to traditional homogeneous composites. In this study, the authors developed a manufacturing process based on thermoforming a 7-layer stack of plain weave fabrics made from hybrid yarns (polyamide 6/recycled carbon fibers) to introduce a new method for reusing end-of-life (EoL) CFRPs and avoid landfilling. Several attempts were made to determine the optimal combination of process parameters, including forming temperature, forming pressure, and holding time. The manufactured multi-layered laminated composites underwent in-depth mechanical and thermal characterization tests to obtain information on their physical properties and identify potential applications for market exploitation. The developed materials exhibit very interesting mechanical properties, which are comparable to or greater than those of commercial carbon or glass fiber-reinforced polymers. Morphological analysis revealed good wettability of PA6 on rCFs and a very low degree of porosity. Finally, the re-use of yarn scraps and fabric remnants generated during the manufacturing process, as well as the recyclability of the manufactured composites once they reach the status of EoL materials, were studied through an in-house recycling process based on comminution and hot pressing of materials. Graphical Abstract
Distributed Cooperative Kalman Filter Constrained by Discretized Poisson Equation for Mobile Sensor Networks

This paper proposes cooperative Kalman filters for distributed mobile sensor networks where the mobile sensors are organized into cells that resemble a mesh grid to cover a spatial area. The mobile sensor networks are deployed to map an underlying spatial-temporal field modeled by the Poisson equation. After discretizing the Poisson equation with finite volume method, we found that the cooperative Kalman filters for the cells are subjected to a set of distributed constraints. The field value and gradient information at each cell center can be estimated by the constrained cooperative Kalman filter using measurements within each cell and information from neighboring cells. We also provide convergence analysis for the distributed constrained cooperative Kalman filter. Simulation results with a five cell network validates the proposed distributed filtering method.
Analysis of Mesoscale Structure of Snow Clouds by Stereoscopic Treatment of Radar Pictures

A stereoscopic treatment of successive radar pictures was devised for the analysis of movement of PPI radar echoes. The stereoscopic method was applied to the analysis of mesoscale wind structure in snow clouds in the winter monsoon season in Hokkaido. The results of the analysis showed that the method was highly useful for analyzing such mesoscale structures, and it was discovered that mesoscale convergence and divergence zones were alternatively distributed and that they were vertical to the prevailing wind direction at the echo level. The wave length of the alternative distribution of the zones was, about 20km, and it was considered that the distribution was originated from an internal gravitational wave disturbance. Considering the wave length, the disturbance analyzed, was of intermediate scale between cumulonimbus clouds and mesoscale disturbance of about 100km size previously found by Matsumoto, Ninomiya and Akiyama (1967) in snowy weather.
Graph-theoretic analysis of gene expression networks

We use graph theory to model a database of gene expression levels and provide a tool that can assist in designing biological experiments which could help to better understand the interactions between genes. The data was provided by StemBase (http://www.stembase.ca). We model a portion of this database as a graph, and study some parameters on it that may be biologically relevant. We include theoretical discussion of the parameters, and presentation of algorithms for their computation. The focus is on structural properties of the graph; thus, we investigate the graph’s bipartiteness, connected components, distance between vertices, radius, diameter, center, cut-vertices and blocks, and its maximal and maximum cliques. We develop the terminology and results used for modeling the database, and implement some of the algorithms in MATLAB. We analyze the results of running the code on the database and discuss their relevance.