Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying genetic factors that increase cognitive reserve: A theoretical approach

Studies have demonstrated that some individuals display pathological hallmarks of Alzheimer's disease (AD) but are not afflicted with cognitive decline. The ability to maintain cognitive function despite the presence of pathology is referred to as cognitive reserve. This project aims to identify the molecular pathways involved in cognitive reserve using Drosophila melanogaster (Drosophila) models of AD. Specifically, a theoretical approach using experimental evolution to drive a population of AD-like Drosophila carrying a tau mutation to develop cognitive reserve is proposed. To accomplish this, a population of AD-like Drosophila will be placed in a single population cage along with wild-type flies and forced to compete for food and water. The first generation of AD-like Drosophila will be generated using random mutagenesis of the initially isogenic AD-like fly. The selected tau mutant displays a rough eye condition which allows for easy distinction between tau mutant and wild-type flies. It is hypothesised that AD-like flies with cognitive decline will be unable to survive because their limited cognitive abilities will prevent them from effectively competing for food and water. In contrast, AD-like flies with mutations that promote cognitive reserve will be better capable of survival. After 90-99% of mutant flies have died, the surviving mutant flies will be back-crossed to the P1 mutant to maintain tau mutation stability. It is expected that artificial selection will result in the creation of a generation of tau mutant flies that demonstrate cognitive abilities comparable to those of wild-type flies despite maintaining an AD-like tau mutation. This approach will monitor the successful trajectory of the evolution of increased cognitive reserve through survival curve analysis and measures of cognition. A limitation of the method is that only a dominant mutation or series of dominant mutations would be identified using this approach.
Automated Question Title Reformulation by Mining Modification Logs From Stack Overflow

In Stack Overflow, developers may not clarify and summarize the critical problems in the question titles due to a lack of domain knowledge or poor writing skills. Previous studies mainly focused on automatically generating the question titles by analyzing the posts’ problem descriptions and code snippets. In this study, we aim to improve title quality from the perspective of question title reformulation and propose a novel approach QETRA motivated by the findings of our formative study. Specifically, by mining modification logs from Stack Overflow, we first extract title reformulation pairs containing the original title and the reformulated title. Then we resort to multi-task learning by formalizing title reformulation for each programming language as separate but related tasks. Later we adopt a pre-trained model T5 to automatically learn the title reformulation patterns. Automated evaluation and human study both show the competitiveness of QETRA after compared with six state-of-the-art baselines. Moreover, our ablation study results also confirm that our studied question title reformulation task is more practical than the direct question title generation task for generating high-quality titles. Finally, we develop a browser plugin based on QETRA to facilitate the developers to perform title reformulation. Our study provides a new perspective for studying the quality of post titles and can further generate high-quality titles.
Sturm-Liouville Problem with Mixed Boundary Conditions for a Differential Equation with a Fractional Derivative and Its Application in Viscoelasticity Models

In this study, we obtained a system of eigenfunctions and eigenvalues for the mixed homogeneous Sturm-Liouville problem of a second-order differential equation containing a fractional derivative operator. The fractional differentiation operator was considered according to two definitions: Gerasimov-Caputo and Riemann-Liouville-Visualizations of the system of eigenfunctions, the biorthogonal system, and the distribution of eigenvalues on the real axis were presented. The numerical behavior of eigenvalues was studied depending on the order of the fractional derivative for both definitions of the fractional derivative.
On AI-Assisted Pneumoconiosis Detection from Chest X-rays

According to theWorld Health Organization, Pneumoconiosis affects millions of workers globally, with an estimated 260,000 deaths annually. The burden of Pneumoconiosis is particularly high in low-income countries, where occupational safety standards are often inadequate, and the prevalence of the disease is increasing rapidly. The reduced availability of expert medical care in rural areas, where these diseases are more prevalent, further adds to the delayed screening and unfavourable outcomes of the disease. This paper aims to highlight the urgent need for early screening and detection of Pneumoconiosis, given its significant impact on affected individuals, their families, and societies as a whole. With the help of low-cost machine learning models, early screening, detection, and prevention of Pneumoconiosis can help reduce healthcare costs, particularly in low-income countries. In this direction, this research focuses on designing AI solutions for detecting different kinds of Pneumoconiosis from chest X-ray data. This will contribute to the Sustainable Development Goal 3 of ensuring healthy lives and promoting well-being for all at all ages, and present the framework for data collection and algorithm for detecting Pneumoconiosis for early screening. The baseline results show that the existing algorithms are unable to address this challenge. Therefore, it is our assertion that this research will improve state-of-the-art algorithms of segmentation, semantic segmentation, and classification not only for this disease but in general medical image analysis literature.
Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning

Nitrogen (N) and phosphorus (P) are primary indicators of soil nutrients in agriculture. Accurate management of these nutrients is essential for ensuring food security. High-resolution, multi-spectral remote sensing images can provide crucial information for mapping soil nutrients at the field scale. This study compares the capabilities of ZH-1 and Sentinel-2 satellite data, along with different spectral indices, in mapping soil nutrients (total N and Olsen-P) using two machine learning algorithms, random forest (RF) and XGBoost (XGB). Two agricultural fields in Suihua City were selected as the study areas for this investigation. The results showed that Sentinel-2 data performed best in computing the total N content in soil using the RF model (R2 = 0.74, RMSE = 0.10 g/kg). However, for the soil Olsen-P content, the XGBoost model performed better with ZH-1 data (R2 = 0.75, RMSE = 9.79 mg/kg) than the RF model. This study demonstrates that both ZH-1 and Sentinel-2 satellite data perform well in terms of accurately mapping soil total N and Olsen-P contents using machine learning. Due to its higher spectral and spatial resolution, ZH-1 remote sensing data provides more detailed information on soil nutrient content during Olsen-P inversion and exhibits comparable accuracy.
An index of wind band literature analyses from periodicals and university research

The study of a composition in preparation for rehearsal and subsequent performance is one of the most important facets of the conductor’s art. A conductor will often begin the process of score study by examining existing research pertaining to the piece, if such research exists. Books, periodicals and university research have long provided information pertaining to works from a variety of genres. In particular, periodicals and university research have provided in-depth analyses of many musical genres, encompassing solo, chamber, and large ensemble works. Orchestral and choral conductors have at their disposal various such indices and compilations to locate analyses of compositions which have been studied. Before this index, no such work existed for wind band or wind ensemble conductors based on a scholarly research method. This index seeks to fill that void. The purpose of this index of wind band literature analyses is to provide band conductors immediate access to the location of wind literature analyses found in periodicals and university research. The current study is based on a systematic, scholarly method and is designed to be as thorough as possible. It contains 731 entries by 334 authors and encompasses 457 works by 203 composers.
Is the locus of control a predictor and/or mediator of emotional dysregulation and psychopathological symptoms?

Locus of control has been highlighted as a variable related to mental health and well-being, as well as being indicated as a predictor for psychopathologies such as depression and anxiety. To verify these relationships, the aim of this study was to present two path analysis models related to the associations between locus of control, depression, anxiety, and emotional dysregulation. A sample of 327 participants (Mage = 38.3 years, SD = 12.8), of both genders (Fem. = 75.2%), completed a sociodemographic questionnaire, the Locus of Control Scale (ELOCUS), the Baptista Depression Scale (EBADEP-A), the Emotional Dysregulation Scale (EDEA), and the Cognitive Anxiety Scale (ECOGA). Spearman correlations and path analyses were performed for the two proposed models. In the first model, the locus of control was a predictor of emotional dysregulation, symptoms of depression and anxiety. In the second model, the locus of control mediated the relationship between factors of emotional dysregulation (seeking strategies and pessimism) in relation to depression and anxiety. It was concluded that the locus of control is an important construct in the direct or indirect prediction of psychopathological symptoms.
Using Weather Pattern Recognition to Classify and Predict Summertime Heavy Rainfall Occurrence over the Upper Nan River Basin, Northwestern Thailand

In this study, self-organizing maps in combination with K-means clustering are used to objectively classify the anomalous weather patterns (WPs) associated with the summertime [May–June (MJ) and July–August–September (JAS)] heavy rainfall days during 1979–2007 over the Upper Nan River basin, northwestern Thailand. The results show that in MJ, intensive rains are mainly brought by the remarkable enhancement of the westerly summer monsoon. Meanwhile, westward-propagating tropical disturbances including tropical cyclones are the primary factors that reproduce heavy rainfall over the Upper Nan in JAS. These results also suggest that the occurrence time of local heavy rainfall is strongly related to the seasonal transition of the summer monsoon over the Indochina Peninsula. The classification results are then implemented with the perfect prognosis and analog method to predict the occurrence (yes/no) of heavy rainfall days over the studied basin in summer 2008–17 using prognostic WPs from the operational Japan Meteorological Agency Global Spectral Model (GSM). In general, the forecast skill of this approach up to 3-day lead times is significantly improved, in which the method not only outperforms GSM with the same forecast ranges, but also its 3-day forecast is better than the 1–2-day forecasts from GSM. However, the false alarms ratio is still high, particularly in JAS. Nevertheless, it is expected that the new approach will provide warning and useful guidance for decision-making by forecasters or end-users engaging in water management and disaster prevention activities.
Multi-Agent Advisor Q-Learning (Extended Abstract)

In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. We provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning-based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
Hidden Markov Model-Based Control for Cooperative Output Regulation of Heterogeneous Multi-Agent Systems under Switching Network Topology

This paper investigates the problem of stochastically cooperative output regulation of heterogeneous multi-agent systems (MASs) subject to hidden Markov jumps using observer-based distributed control. In order to address a more realistic situation than prior studies, this paper focuses on the following issues: (1) asynchronous phenomena in the system mode’s transmission to the controller; (2) the impact of system mode switching on network topology; and (3) the emergence of coupled terms between the mode-dependent Lyapunov matrix and the control gain in control design conditions. Specifically, to reduce the complexity arising from the asynchronous controller-side mode, the leader–state observer is developed so that the solution pair of regulator equations can be integrated into the observer. Furthermore, a linear decoupling method is proposed to handle the emergence of the aforementioned coupled terms; this provides sufficient LMI conditions to achieve stochastically cooperative output regulation for heterogeneous MASs. Finally, the validity of the proposed method is shown through two illustrative examples.
Causal Inference Analysis of Family Educational Expectations and Students’ Cognitive Abilities ——Based on CEPS Data

Based on data from the China Education Panel Survey (CEPS), this paper used the SCM (structural causal model, SCM) to study the causal inferences between family educational expectations and students’ cognitive abilities. The SCM model, originally proposed by Judy Perl, is effective in inferring causal relationships between variables to help policy makers develop appropriate policies and measures. We found that there is a direct causal relationship between family educational expectations and students’ cognitive ability. Family characteristics variables such as: family economic level, family cultural capital, and parental education level are similarly causally related to family educational expectations and students’ cognitive ability. The study result can be used to improve students’ cognitive ability.
Lamination of wood boards with modified amino-formaldehyde resins

This paper focuses on the development of the formulation of modified amino-formaldehyde oligomers, the study of their properties, obtaining impregnation compositions based on them and the study of the properties of the finished product -reinforced wood particle board. As a modifying component we chose ethylene glycol, salts of organic acids (modifier CN) and alloying additive “OLIGO LD”. Ethylene glycol at the stage of synthesis was used to improve stability and elasticity of oligomers. The CN modifier was introduced during the synthesis of oligomers, which allowed to influence the properties of the obtained products in a directed way.
Inferring white-tailed deer (Odocoileus virginianus) population dynamics from wildlife collisions in the City of Ottawa

Concerns associated with growing white-tailed deer (Odocoileus virginianus) numbers in Ottawa, Ontario have motivated several studies related to the distribution and ecology of deer in the Ottawa-Carleton region. This project infers deer-population trends from deer-vehicle collisions in Ottawa, Ontario, and considers the influence of traffic volume on estimates of population dynamics from deer-vehicle collision data. Traffic volume and collision data for various road segments across suburban Ottawa were analyzed to answer questions related to the characteristics and spatial distribution of deer collisions and traffic volume in the city. Deer-vehicle collisions are increasing at a faster rate than traffic volume, suggesting that the deer population is increasing. The distribution of collisions supports the boundaries previously suggested for the location of one deerherd summer range, but not the other. Deer-collision numbers east and west of the Rideau River, a likely barrier to deer movement, were very similar, even though research and concern related to deer numbers has been concentrated west of the Rideau. More collisions occurred on 400-series highways than on other roads, suggesting that highways are a higher risk for deer collisions than other roads. The number of deer-vehicle collisions is much higher on recently constructed 400-series highways than on older 400-series highways, indicating that new highways represent high-risk areas for collisions. This research suggests that deer-vehicle collisions could be a very useful data source for inferring deer population dynamics of suburban deer, but it is imperative that significant factors affecting the number and distribution of collisions, such as category of road and traffic volume, are considered during any analyses.
A New Method for Magnetic Gain Modulation of Chemical Oxygen Iodine Laser Based on Flat-Top Pulsed Magnetic Field

Chemical oxygen iodine laser (COIL) is widely used in material processing and industrial medicine due to the extremely high coupling efficiency to metals. COIL's pulse mode can greatly increase peak power. Magnetic gain modulation is the important technique to achieve COIL pulse mode. This article proposes a new method of combining flat-top pulsed magnetic field with permanent magnets to achieve magnetic gain modulation of COIL. The permanent magnets provide a static magnetic field, which is superimposed on the flat-top pulsed magnetic field for fast magnetic field removal to achieve ideal magnetic gain modulation. A new circuit structure for fast generation of flat-top pulsed magnetic field has been developed. The experimental results show that the flat-top pulsed circuit discharges the coil to produce a flat-top magnetic field that approximates a square wave. The 405 G static magnetic field can be cleared instantly in 50 × 50 × 30 mm3 resonant cavity. The drop time and clearing time of the magnetic field is 1.5 and 3 μs, respectively.
Gait Recognition from Fisheye Images

Gait recognition has been a hot topic of extensive research in video-based surveillance and forensics. Compared with traditional rectilinear cameras mainly used in existing studies, fisheye cameras have a wider field of view, and hence are more suitable for gait recognition applications in navigation robots, which enables more flexible and free surveillance scenarios. In this paper, to the best of our knowledge, we propose the first framework for gait recognition from images captured by fisheye cameras. To deal with severe image distortion and partial body occlusions induced by fisheye cameras set at lower heights, we combine a set of preprocessing procedures with a state-of-the-art model-based gait recognition method. Specifically, an input fisheye image is first expanded into a panoramic view before pedestrian detection. A person-dependent gnomonic projection is then applied to the detected human region for distortion correction. Next, background regions are attenuated to improve human model fitting accuracy in complex outdoor scenes. The resulting rectified image sequence is finally fed into the gait recognition network for human model estimation and gait feature extraction. To validate the performance, we collect a real fisheye image gait dataset with various views and capture scenarios, including simplified indoor and challenging outdoor scenes. Various experiments on the collected dataset demonstrate the effectiveness of the proposed method.
Beyond Homophily: Robust Graph Anomaly Detection via Neural Sparsification

Recently, graph-based anomaly detection (GAD) has attracted rising attention due to its effectiveness in identifying anomalies in relational and structured data. Unfortunately, the performance of most existing GAD methods suffers from the inherent structural noises of graphs induced by hidden anomalies connected with considerable benign nodes. In this work, we propose SparseGAD, a novel GAD framework that sparsifies the structures of target graphs to effectively reduce noises and collaboratively learns node representations. It then robustly detects anomalies by uncovering the underlying dependency among node pairs in terms of homophily and heterophily, two essential connection properties of GAD. Extensive experiments on real-world datasets of GAD demonstrate that the proposed framework achieves significantly better detection quality compared with the state-of-the-art methods, even when the graph is heavily attacked. Code will be available at https://github.com/KellyGong/SparseGAD.git.
Towards automatic identification of middle ear structures in endoscopic OCT

Endoscopic Optical Coherence Tomography (OCT) enables the assessment of the eardrum and the middle ear in vivo. However, revealing the ossicles is often limited due to shadowing effects of preceding structures and the 3D impression is difficult to interpret. To compare the identified middle ear structures, OCT and cone-beam CT of a patient were spatially aligned and showed a good agreement in locating malleus and the promontory wall. As CT imaging uses ionizing radiation and is thus limited in application, we furthermore provide a concept how radiology can be utilized as a priori knowledge for OCT imaging. Therefore, a statistical shape model derived from μCT data of temporal bone specimens was fitted to in vivo OCT measurements, potentially providing a real-time augmentation of endoscopic OCT for middle ear diagnostics in the future.
Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework

Recent years have seen a rise in smartphone applications promoting health and well being. We argue that there is a large and unexplored ground within the field of recommender systems (RS) for applications that promote good personal health. During the COVID-19 pandemic, with gyms being closed, the demand for at-home fitness apps increased as users wished to maintain their physical and mental health. However, maintaining long-term user engagement with fitness applications has proved a difficult task. Personalisation of the app recommendations that change over time can be a key factor for maintaining high user engagement. In this work we propose a reinforcement learning (RL) based framework for recommending sequences of body-weight exercises to home users over a mobile application interface. The framework employs a user simulator, tuned to feedback a weighted sum of realistic workout rewards, and trains a neural network model to maximise the expected reward over generated exercise sequences. We evaluate our framework within the context of a large 15 week live user trial, showing that an RL based approach leads to a significant increase in user engagement compared to a baseline recommendation algorithm.