Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble methods based on bias-variance analysis

Ensembles of classifiers represent one of the main research directions in machine learning. Two main theories are invoked to explain the success of ensemble methods. The first one consider the ensembles in the framework of large margin classifiers, showing that ensembles enlarge the margins, enhancing the generalization capabilities of learning algorithms. The second is based on the classical bias–variance decomposition of the error, and it shows that ensembles can reduce variance and/or bias. In accordance with this second approach, this thesis pursues a twofold purpose: on the one hand it explores the possibility of using bias–variance decomposition of the error as an analytical tool to study the properties of learning algorithms; on the other hand it explores the possibility of designing ensemble methods based on bias–variance analysis of the error. At first, bias–variance decomposition of the error is considered as a tool to analyze learning algorithms. This work shows how to apply Domingos and James theories on bias–variance decomposition of the error to the analysis of learning algorithms. Extended experiments with Support Vector Machines (SVMs) are presented, and the analysis of the relationships between bias, variance, kernel type and its parameters provides a characterization of the error decomposition, offering insights into the way SVMs learn. In a similar way bias–variance analysis is applied as a tool to explain the properties of ensembles of learners. A bias–variance analysis of ensembles based on resampling techniques is conducted, showing that, as expected, bagging is a variance reduction ensemble method, while the theoretical property of canceled variance holds only for Breiman’s random aggregated predictors. In addition to analyzing learning algorithms, bias–variance analysis can offer guidance to the design of ensemble methods. This work shows that it provides a theoretical and practical tool to develop new ensemble methods well-tuned to the characteristics of a specific base learner. On the basis of the analysis and experiments performed on SVMs and bagged ensembles of SVMs, new ensemble methods based on bias–variance analysis are proposed. In particular Lobag (Low bias bagging ) selects low bias base learners and then combines them through bootstrap aggregating techniques. This approach affects both bias, through the selection of low bias base learners, and variance, through bootstrap aggregation of the selected low bias base learners. Moreover a new potential class of ensemble methods (heterogeneous ensembles of SVMs), that aggregate different SVM models on the basis of their bias– variance characteristics, is introduced. From an applicative standpoint it is also shown that the proposed ensemble methods can be successfully applied to the analysis of DNA microarray data.
Expanding the capabilities of the pulsed plasma thruster for in-space and atmospheric operation

Expanding the Capabilities of the Pulsed Plasma Thruster for In-Space and Atmospheric Operation Ian Kronheim Johnson Chair of the Supervisory Committee: Dr. Robert Winglee Department of Earth and Space Sciences Of all in-space propulsion systems to date, the Pulsed Plasma Thruster (PPT) is unique in its simplicity and wide range of operational parameters. This study examined multiple uses of the thruster for in-space and atmospheric propulsion, as well as the creation of a CubeSat satellite and atmospheric airship as test beds for the thruster. The PPT was tested as a solid-propellant feed source for the High Power Helicon Thruster, a compact plasma source capable of generating order of magnitude higher plasma densities than comparable power level systems. Replacing the gaseous feed system reduced the thruster size and complexity, as well as allowing for extremely discrete discharges, minimizing the influence of wall effects. Teflon (C2F4) has been the traditional propellant for PPTs due to a high exhaust velocity and ability to ablate without surface modification over long durations. A number of alternative propellants, including minerals and metallics commonly found on asteroids, were tested for use with the PPT. Compounds with significant fractions of sulfur showed the highest performance increase, with specific thrusts double that of Teflon. A PPT with sulfur propellant designed for CubeSat operation, as well as the subsystems necessary for autonomous operation, was built and tested in the laboratory. The PPT was modified for use at atmospheric pressures where the impulse was well defined as a function of the discharge chamber volume, capacitor energy, and background pressure. To demonstrate that the air-breathing PPT was a viable concept the device was launched on two atmospheric balloon flights.
Seasonal Domain Shift in the Global South: Dataset and Deep Features Analysis

Domain shifts during seasonal variations are an important aspect affecting the robustness of aerial scene classification and so it is important that such variation is captured within aerial scene datasets. This is more evident in geographic locations in the global South, where aerial coverage is scarcer and the rural and semi-urban landscape varies dramatically between wet and dry seasons. As current datasets do not offer the ability to experiment with domain shifts due to seasonal variations, this work proposes a labelled dataset for classifying land use from aerial images, comprising both wet and dry season data from Ghaziabad in India. Moreover, we conduct a thorough investigation into how image features, namely colour, shape, and texture, influence the accuracy of scene classification. We demonstrate that a combination of an architecture that extracts salient features, with the implementation of a larger receptive field improves classification performance when applied to both shallow or deep architectures by extracting invariant feature representations across domains.
Investigating the impact of online language exchanges on second language speaking and willingness to communicate of Chinese EFL learners: a mixed methods study

Introduction The current study aims to investigate the impact of online language exchanges on the speaking skills and willingness to communicate (WTC) of Chinese postgraduate students in an advanced English program. The study compares two intact classes: e-tandem, who communicate with foreign English speakers through the Tandem language exchange application, and conventional, who participate in collaborative speaking tasks inside the class. The study also examines the attitudes and perceptions of the EFL learners toward the online language exchanges. Methods 58 Chinese postgraduate students were recruited from a second-year advanced English program and divided into two intact classes: e-tandem and conventional. The e-tandem group used the Tandem language exchange application to communicate with foreign English speakers online, while the conventional group participated in collaborative speaking tasks inside the class. The data were collected through the IELTS speaking module, WTC scale, and semi-structured interviews. The data were analyzed using descriptive and inferential statistics. Results Both groups showed improvement in their speaking skills and WTC. However, the e-tandem group outperformed the conventional group. The findings indicate that online language exchanges have a positive impact on the speaking skills and WTC of EFL learners. The EFL learners also had positive attitudes and perceptions toward the online language exchanges, although some expressed reservations. Discussion The study concludes that online language exchanges can be an effective tool for improving the speaking skills and WTC of EFL learners. The study also suggests that collaborative speaking courses in EFL settings should consider incorporating online language exchanges. However, the study also highlights the need to address the concerns and reservations expressed by some EFL learners regarding online language exchanges. Overall, the study has important pedagogical implications for EFL settings, suggesting that online language exchanges can enhance speaking skills and WTC.
A 6-year study on the mortality dynamics of sprouts germinated on Schima superba after a severe ice storm in southern China

Introduction Natural disturbances modify forest structure by affecting regeneration dynamics and can change main ecosystem functions. An ice storm unusually took place in southern China in early 2008, which caused huge damage to forests. Resprouting of woody plants in a subtropical forest has received little attention. The role of survival time and mortality has been assessed for newsprouts after an ice storm. Methods In this study, damage types, in addition to the annual number and mortality rates of sprouts for all tagged and sampled resprouted Chinese gugertree (Schima superba Gardner & Champ.) individuals more than or equal to 4 cm in basal diameter (BD), were monitored. A total of six plots (20 m × 20 m) wererecorded in a subtropical secondary forest dominated by S. superba in Jianglang Mountain, China. This investigation had been conducted for six consecutive years. Results The results showed that the survival rates of the sprouts were dependent on the year they sprouted. The earlier the year they boomed, the lower the mortality. The sprouts produced in 2008 were of the highest vitality and survival rates. Sprouts of the decapitated trees exhibited a better survival rate than those of uprooted or leaning trees. Sprouting position also plays a role in regeneration. Sprouts at the basal trunks of uprooted trees and the sprouts at the upper trunksof the decapitated trees exhibited the lowest mortality. The relationship between the accumulative mortality rate and the average diameter of new sprouts isaffected by damage types. Discussion We reported the mortality dynamics of sproutsin a subtropical forest after a rare natural disaster. This information could serve asa reference for the construction of a branch sprout dynamic model ormanagement of forest restoration after ice storms.
Big data analytics and smart cities: applications, challenges, and opportunities

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.
Increased thalamic gray matter volume induced by repetitive transcranial magnetic stimulation treatment in patients with major depressive disorder

Purpose Repetitive transcranial magnetic stimulation (rTMS) is an effective therapy in improving depressive symptoms in MDD patients, but the intrinsic mechanism is still unclear. In this study, we investigated the influence of rTMS on brain gray matter volume for alleviating depressive symptoms in MDD patients using structural magnetic resonance imaging (sMRI) data. Methods Patients with first episode, unmedicated patients with MDD (n = 26), and healthy controls (n = 31) were selected for this study. Depressive symptoms were assessed before and after treatment by using the HAMD-17 score. High-frequency rTMS treatment was conducted in patients with MDD over 15 days. The rTMS treatment target is located at the F3 point of the left dorsolateral prefrontal cortex. Structural magnetic resonance imaging (sMRI) data were collected before and after treatment to compare the changes in brain gray matter volume. Results Before treatment, patients with MDD had significantly reduced gray matter volumes in the right fusiform gyrus, left and right inferior frontal gyrus (triangular part), left inferior frontal gyrus (orbital part), left parahippocampal gyrus, left thalamus, right precuneus, right calcarine fissure, and right median cingulate gyrus compared with healthy controls (P < 0.05). After rTMS treatment, significant growth in gray matter volume of the bilateral thalamus was observed in depressed patients (P < 0.05). Conclusion Bilateral thalamic gray matter volumes were enlarged in the thalamus of MDD patients after rTMS treatment and may be the underlying neural mechanism for the treatment of rTMS on depression.
A comparison of altered white matter microstructure in youth born with congenital heart disease or born preterm

Introduction Alterations to white matter microstructure as detected by diffusion tensor imaging have been documented in both individuals born with congenital heart disease (CHD) and individuals born preterm. However, it remains unclear if these disturbances are the consequence of similar underlying microstructural disruptions. This study used multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) and neurite orientation dispersion and density imaging (NODDI) to characterize and compare alterations to three specific microstructural elements of white matter – myelination, axon density, and axon orientation – in youth born with CHD or born preterm. Methods Participants aged 16 to 26 years with operated CHD or born ≤33 weeks gestational age and a group of healthy peers of the same age underwent a brain MRI including mcDESPOT and high angular resolution diffusion imaging acquisitions. Using tractometry, average values of myelin water fraction (MWF), neurite density index (NDI), and orientation dispersion index (ODI) were first calculated and compared between groups for 30 white matter bundles. Afterwards, bundle profiling was performed to further characterize the topology of the detected microstructural alterations. Results The CHD and preterm groups both presented with widespread bundles and bundle segments with lower MWF, accompanied by some occurrences of lower NDI, relative to controls. While there were no differences in ODI between the CHD and control groups, the preterm group presented with both higher and lower ODI compared to the control group and lower ODI compared to the CHD group. Discussion While youth born with CHD or born preterm both presented with apparent deficits in white matter myelination and axon density, youth born preterm presented with a unique profile of altered axonal organization. Future longitudinal studies should aim to better understand the emergence of these common and distinct microstructural alterations, which could orient the development of novel therapeutic approaches.
The large-scale impact of anthropogenic mixing by offshore wind turbine foundations in the shallow North Sea

Structure drag from offshore wind turbines and its physical impacts on the marine environment of the German Bight are investigated in this study. The flow past vertical cylinders, such as wind turbine foundations, and associated turbulent mixing has long been studied, but questions remain about anticipated regional implications of offshore wind infrastructure on physical and biogeochemical conditions. Here, we present two existing modeling approaches for simulating wind turbine foundation effects in regional ocean models and discuss the problematic use of very high resolution in hydrostatic modeling. By implementing a low-resolution structure drag parameterization in an unstructured-grid model, we demonstrate the impacts of monopile drag on hydrodynamic conditions, validated against recent in-situ measurements. Although the anthropogenic mixing is confined at wind farm sites, our simulations show that structure-induced mixing affects much larger, regional scales. The additional turbulence production emerges as the driving mechanism behind the monopile impacts, leading to changes in both the current velocities and stratification, with magnitudes of about 10%, similar in magnitude to regional annual and interannual variabilities. This study provides new insights into the hydrodynamic impact of offshore wind farms at their current development levels and emphasizes the need for further research in view of potential restructuring of the future coastal environment.
Sleep and chronotype influence aerobic performance in young soccer players

People can be classified into three chronotypes (CT): morning-type (M-type), Neither-type (N-type) and Evening-type (E-type). M-types perform better in the morning, E-types in the evening. It seems that bad sleep worsens physical performance. The impact of sleep and CT on specific sports and populations is unclear. Therefore, we wanted to assess agility, strength and endurance in young soccer players in relation to their sleep and chronotype. 58 players (13–19 years) were recruited. Sleep and CT were assessed by questionnaires. The physical trial was performed at 8:30 a.m. and 6:00 p.m. , and included three tests to determine agility, strength and endurance. The sample was classified by CT as M-types (n = 11), N-types (n = 29) and E-types (n = 18). Furthermore, they were categorized as people with Good Sleep/Wake quality (GSW, n = 28) and people with Bad Sleep/Wake quality (BSW, n = 30). Comparing the three CTs in the aerobic test, M-types performed better in the morning (p = 0.01), while E-types in the evening (p < 0.001). GSW performed better than BSW (p = 0.019) in the aerobic test in the p.m. session. These results underline the difference in aerobic power between M-and E-types during the morning and evening session; moreover, they show a difference in p.m. aerobic performance according to sleep quality.
Landscape ecological risk assessment and influencing factor analysis of basins in suburban areas of large cities – A case study of the Fuchunjiang River Basin, China

Landscape ecological risk reflects the impact of human activities or natural hazards on landscape patterns, functions, and processes in a region. The scientific assessment of its evolutionary trends can play an essential role in maintaining and improving regional human wellbeing. This study focused on the Fuchunjiang River Basin in the suburbs of Hangzhou, a large city in China with a high degree of landscape heterogeneity and rapid economic growth. Based on land use data from 1990 to 2020, an ecological risk assessment model and geodetector tools were combined to study the spatiotemporal variation characteristics and influencing factors of landscape ecological risk in the basin. The results showed the following: (1) from 1990 to 2020, the landscape pattern of various land use types showed a general trend of an increasing degree of agglomeration and decreasing loss index; (2) the spatial distribution pattern of landscape ecological risk was “high in the northwest and low in the southeast. ” From basin to township scales, landscape ecological risk showed a decreasing trend on long time scales, although the changes were relatively drastic in short time periods; (3) GDP, human interference, area of urban and rural residential land, area of arable land transferred in, and area of arable land transferred out were the dominant influencing factors of landscape ecological risk; (4) the coupling between landscape ecological risk and GDP in 2020 exhibited an inverted “U” shaped relationship, indicating that the environmental Kuznets curve (EKC) may have the same applicability in the ecological risk management; (5) according to the main functions of different risk agglomerations, targeted ecological risk management strategies were proposed. In short, this study carried out the landscape ecological risk assessment at the township scale for the first time, quantitatively analyzed the relationship between economic development and ecological risk, and proposed targeted risk prevention strategies, which are instructive for other basin areas that are experiencing rapid urbanization in the world.
Long-term outcomes of the recovery approach in a high-security mental health setting: a 20 year follow-up study

Background This study examined the outcomes of a descriptive, longitudinal cohort consisting of 241 patients initially examined in a population study at the high secure State Hospital for Scotland and Northern Ireland in 1992–93. A partial follow-up focusing on patients with schizophrenia was conducted in 2000–01, followed by a comprehensive 20 year follow-up that began in 2014. Aims To explore what happens to patients who required high secure care during a 20 year follow-up period. Method Previously collected data were amalgamated with newly collected information to examine the recovery journey since baseline. Various sources were employed, including patient and keyworker interviews, case note reviews, and extraction from health and national records, and Police Scotland datasets. Results Over half of the cohort (56.0%) with available data resided outside secure services at some point during the follow-up period (mean 19.2 years), and only 12% of the cohort were unable to transition out of high secure care. The symptoms of psychosis improved, with statistically significant reductions observed in reported delusions, depression, and flattened affect. Reported sadness [according to the Montgomery–Åsberg Depression Rating Scale (MADRS)] at baseline, first, and 20 year follow-up interviews was negatively correlated with the questionnaire about the process of recovery (QPR) scores at the 20 year follow-up. However, qualitative data depicted progress and personal development. According to societal measures, there was little evidence of sustained social or functional recovery. The overall conviction rate post-baseline was 22.7%, with 7.9% violent recidivism. The cohort exhibited poor morbidity and mortality, with 36.9% of the cohort dying, primarily from natural causes (91%). Conclusions Overall, the findings showed positive outcomes in terms of movement out of high-security settings, symptom improvement, and low levels of recidivism. Notably, this cohort experienced a high rate of deaths and poor physical morbidity, along with a lack of sustained social recovery, particularly among those who had negotiated a path through services and who were current residents in the community. Social engagement, enhanced during residence in low secure or open ward settings, diminished significantly during the transition to the community. This is likely a result of self-protective measures adopted to mitigate societal stigma and the shift from a communal environment. Subjective depressive symptoms may impact broader aspects of recovery.