Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-negative matrix factorization using constrained optimization with applications

This research work provides a new method for solving non-negative matrix factorization (NMF) problems, which has applications to image processing, text clustering and data mining. Matrix factorization has been a popular topic of research for some decades and several methods and algorithms have been developed for matrix factorization. NMF has received much attention recently, as many of the practical problems inhere the property of being non-negative naturally. In NMF, a high dimensional non-negative matrix is factorized into two lower dimensional non-negative matrices, sometimes referred to as the basis matrix and the co-efficient matrix respectively. NMF has applications in the field of parts-based learning of images, the analysis of hyper-spectral images, text and data mining, artificial neural networks, dimension reduction and many others, where a factorization of a data matrix is nonnegative by nature. Factorizing these data matrices provides important information and reduced dimensions of the data, which helps further analysis. Due to the lack of information about the factors with the exception of the property of non-negativity, it is hard to find a global solution for an NMF problem. However, local solutions are useful for numerous applications. Researchers have proposed different methods to factorize the original data matrix. The problem itself has non-convexity, and finding the solution alternately may introduce convexity in the sub-problems. To find a feasible solution for a particular problem, some specific constraints have been introduced in the previously-developed methods. Approaches include multiplicative updates of the factor matrices, non-negative alternative least squares, gradient-based solving methods and geometric approaches to the problem. In this thesis, a new alternating optimization method is proposed. Following from the revisited and new interpretations of the geometric properties of NMF, we i
Short-Term Clinical Results After ILM Removal and Inverted ILM Flap for Macular Hole Repair – A Single-Center Retrospective Experience

Purpose To compare results after ILM peeling and ILM inverted flap technique utilized the repair of full thickness macular holes, irrespective of their size. Patients and Methods Pre- and postoperative data of 109 patients who suffered from a full thickness macular hole were retrospectively analyzed. Forty-eight patients were treated with an inverted ILM flap technique, 61 patients were treated with ILM peeling. All patients received a gas tamponade. The primary endpoint was macular hole closure as demonstrated by OCT scanning. Secondary endpoints were best corrected visual acuity and clinical complication rates. Results For small and medium-sized macular holes the closure rates in the ILM flap technique group were 100% and 94%, respectively. For ILM peeling, the closure rate was identical (95%). For large macular holes, the closure rate was 100% in the flap versus 50% in the ILM peeling group, but visual acuity improved in both groups (ILM flap p=0.001, ILM peeling p=0.002). In both treatment groups, larger holes were associated with a reduced final visual outcome. For medium-sized macular holes, visual acuity significantly improved only in the ILM peeling group. Both techniques were associated with minimal and comparable side effects. Conclusion In our limited series, the inverted ILM flap technique for repair of macular holes demonstrated a high closure rate. For large MHs, we saw a trend towards a better closure rate in the flap technique compared to ILM peel only. However, final visual acuity showed no significant difference between the groups. Clinical results and complications appeared to be comparable in both groups.
Self-Attention Refinement for Actionness Temporal Action Localization

In this paper ,In this paper, for the problem of temporal action localisation, we propose a way to obtain the start and end times and types of actions based on actionness by aggregating action instance segmentation on a sequence of temporal features. In addition we believe that the context of actions is not only reflected in the results of convolution, but also the characteristics of inter class similarity and intra class consistency are essential. For this reason, we designed temporal self -attention mechanism (TSA) and temporal pyramid pooling module (TPP). Our results show that the single-stage model can achieve considerable accuracy after proper feature fusion.
A Finite State Aproach to Interactive Transcription

We describe a novel approach to transcribing morphologically complex, local, oral languages. The approach connects with local motivations for participating in language work which center on language learning, accessing the content of audio collections, and applying this knowledge in language revitalization and maintenance. We develop a constraint-based approach to interactive word completion, expressed using Optimality Theoretic constraints, implemented in a finite state transducer, and applied to an Indigenous language. We show that this approach suggests correct full word predictions on 57.9% of the test utterances, and correct partial word predictions on 67.5% of the test utterances. In total, 87% of the test utterances receive full or partial word suggestions which serve to guide the interactive transcription process.
Combining passive- and active-DTS measurements to locate and quantify groundwater discharge variability into a headwater stream

Exchanges between groundwater and surface water play a key role for ecosystem preservation, especially in headwater catchments where groundwater discharge into streams highly contributes to streamflow generation and maintenance. Despite several decades of research, investigating the spatial variability of groundwater discharge into streams 10 still remains challenging mainly because groundwater/surface water interactions are controlled by multi-scale processes. In this context, we evaluated the potential of using FO-DTS (Fiber Optic Distributed Temperature Sensing) technology to locate and quantify groundwater discharge at high resolution. To do so, we propose to combine, for the first time, long-term passive-DTS measurements and active-DTS measurements by deploying FO cables in the streambed sediments of a first-and second-order stream in gaining conditions. The passive-DTS experiment provided eight-month monitoring of streambed 15 temperature fluctuations along more than 530 m of cable, while the punctual active-DTS experiment, performed during few days, allowed a detailed and accurate investigation of groundwater discharge variability over a 60 m-length heated section. Results show that Llong-term passive-DTS measurements turn out to be an efficient method to detect and locate groundwater discharge along several hundreds of meters. The continuous eight-months monitoring allowed and to investigate the temporal pattern highlighting changes in the groundwater discharge dynamic in response to the hydrological 20 dynamic of the headwater catchment.of exchanges over the annual hydrological cycle. However, the quantification of fluxes with this approach remains limited given the high uncertainties associated to the on estimates, due to uncertainties on thermal properties and boundary conditions.especially due to the assumptions made about thermal properties. On the contrary, active-DTS measurements, which have seldom been performed in streambed sediments and never applied to quantify water fluxes, never been in streambed sediments up to now, allow estimating the spatial distribution of both thermal 25 conductivities and the groundwater fluxes at high resolution all along the 60-m heated section of FO cable with very low uncertainties all along the heated section of FO cable. The method allows describing the variability of streambed properties at an unprecedented scale and reveals the variability of groundwater inflows at small scale. In the end, this study shows the potential and the interest of the complementary use of passive- and active-DTS experiments to quantify groundwater discharge at different spatial and temporal scales. Thus, rResults show that groundwater discharges are mainly concentrated 30 in the upstream part of the watershed, where steepest slopes are observed, confirming the importance of the topography in
Using Intervention Research to Adopt Evidence-Based Management as a Practical Leadership Capability

Methods: A design-based case study employed an analytic workflow that included observations of team workshops to surface coded indicators of critical thinking. Additionally, heuristic-based questions during interviews with a purposeful sampling of leaders, managers, and employees explored the shared experiences throughout the intervention. Applied nexus analysis and interdiscursivity informed the content in the next phase with a deeper understanding of where shared perspectives aligned and where disconnects existed. The approach generated insights to inform the planning of subsequent workshops.
Study of Food Security in South Minahasa Regency Using System Dynamics

The definition of food security according to Law no. 18/2012 concerning Food, are: "The condition of the fulfillment of Food for the state to individuals, which is reflected in the availability of sufficient food, both in quantity and quality, safe, diverse, nutritious, equitable and affordable and does not conflict with religion, belief and community culture, to be able to live a healthy, active and productive life in a sustainable manner". Food security is thus a system with subsystems of availability, distribution, and consumption. System dynamics is a modeling method whose use is closely related to the dynamic tendencies of complex systems, namely the behavior patterns generated by the system over time. The main assumption in the system dynamics paradigm is that the dynamic tendencies that are continuous in any complex system originate from the causal structures that make up the system. In this study, the dynamic system modeling method is used to identify the behavior of the regional food security system in South Minahasa regency with the aim of analyzing the dynamics of the causal relationship of the variables that affect the food security system in terms of production, consumption, and rice stock in South Minahasa regency by using an approach systems and application of dynamic systems modeling techniques. In this study, a Causal Loop (CLD) and the Stock-Flow Diagram (SFD) were developed for the rice availability system based on the relationship between the production sub-system, consumption sub-system and the government's rice reserve sub-system involving the relationship of a number of variables to produce a model output in the form of projections of rice availability in South Minahasa regency for the period of 2020 to 2121. The results shows that food availability in period of terms of rice production in South Minahasa regency is very sufficient for the 2020 -2120 period, and there is even a surplus every year of 400% at the average
Tongue Twisters Detection in Ukrainian by Using TDA

The current stage of development of the digital world requires solving many problems. In particular, the analysis of texts occupies one of the important places. The scope of such analysis is very wide, from the semantic analysis of resumes to the psychological portrait of a social network user based on his posts. The algorithm of tongue twisters detection in Ukrainian using the topological data analysis methods based on the consideration of a twister as a sample in the four-dimensional space with a construction simplicial structure on it, calculating its invariant and classifiers as machine learning methods to realize distinguishing a tongue twister from a simple narrative sentence is obtained by authors. A better rate of detection was obtained by using a support vector machine with a Gaussian RВF kernel.
Multi-scale Progressive Gated Transformer for Physiological Signal Classification

Physiological signal classification is of great significance for health monitoring and medical diagnosis. Deep learning-based methods (e.g. RNN and CNN) have been used in this domain to obtain reliable predictions. However, the performance of existing methods is constrained by the long-term dependence and irregular vibration of the univariate physiological signal sequence. To overcome these limitations, this paper proposes a Multi-scale Progressive Gated Transformer (MPGT) model to learn multi-scale temporal representations for better physiological signal classification. The key novelties of MPGT are the proposed Multi-scale Temporal Feature extraction (MTF) and Progressive Gated Trans-former (PGT). The former adopts coarse-and fine-grained feature extractors to project the input signal data into different temporal granularity embedding spaces and the latter integrates such multi-scale information for data representation. Classification task is then conducted on the learned representations. Experimental results on real-world datasets demonstrate the superiority of the proposed model.
Pulmonary Function Test and Obstructive Sleep Apnea Hypopnea Syndrome in Obese Adults: A Retrospective Study

Objective: We explore risk factors related to severe obstructive sleep apnea (OSA) in obese patients, including pulmonary ventilation function, diffusion function, and impulse oscillometry (IOS) data. Methods: The medical records of 207 obese patients who were prepared to undergo bariatric surgery in a hospital from May 2020 to September 2021 were retrospectively reviewed. Polysomnography (PSG), pulmonary ventilation function, diffusion function, and IOS parameters were collected according to the ethical standards of the institutional research committee (registration number: KYLL-202008-144). Logistic regression analysis was used to analyze the related independent risk factors. Results: There were significantly statistical difference in a number of pulmonary ventilation and diffusion function parameters among the non-OSAHS group, the mild-to-moderate OSA group, and the severe OSA group. However, only airway resistance parameters R5%, R10%, R15%, R20%, R25%, and R35% increased with increasing OSA severity and were positively correlated with apnea hypopnea index (AHI). Age ( P = 0.012, 1.104 (1.022, 1.192)), body mass index ( P < 0.0001, 1.12 (1.057, 1.187)), gender ( P = 0.003, 4.129 (1.625, 10.49)), and R25% ( P = 0.007, 1.018 (1.005, 1.031)) were independent risk factors for severe OSA. In patients aged 35 to 60, RV/TLC ( P = 0.029, 1.272 (1.025, 1.577)) is an independent risk factor for severe OSA. Conclusion: R25% was an independent risk factor for severe OSA in obese individuals, while RV/TLC was also an independent risk factor in those aged 35 to 60. Pulmonary function tests (PFTs), particularly IOS levels, are recommended to assess severe OSA in obese patients.
A Deep Learning Approach for Wireless Network Performance Classification Based on UAV Mobility Features

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios.
Aptamer Sensors for the Detection of Antibiotic Residues—A Mini-Review

Food security is a global issue, since it is closely related to human health. Antibiotics play a significant role in animal husbandry owing to their desirable broad-spectrum antibacterial activity. However, irrational use of antibiotics has caused serious environmental pollution and food safety problems; thus, the on-site detection of antibiotics is in high demand in environmental analysis and food safety assessment. Aptamer-based sensors are simple to use, accurate, inexpensive, selective, and are suitable for detecting antibiotics for environmental and food safety analysis. This review summarizes the recent advances in aptamer-based electrochemical, fluorescent, and colorimetric sensors for antibiotics detection. The review focuses on the detection principles of different aptamer sensors and recent achievements in developing electrochemical, fluorescent, and colorimetric aptamer sensors. The advantages and disadvantages of different sensors, current challenges, and future trends of aptamer-based sensors are also discussed.
Exploring the Needs of Informal Learners of Computational Skills: Probe-Based Elicitation for the Design of Self-Monitoring Interventions

Informal learners of computational skills often find it difficult to self-direct their learning pursuits, which may be spread across different mediums and study sessions. Inspired by self-monitoring interventions from domains such as health and productivity, we investigate key requirements for helping informal learners better self-reflect on their learning experiences. We carried out two elicitation studies with paper-based and interactive probes to explore a range of manual, automatic, and semi-automatic design approaches for capturing and presenting a learner’s data. We found that although automatically generated visual overviews of learning histories are initially promising for increasing awareness, learners prefer having controls to manipulate overviews through personally relevant filtering options to better reflect on their past, plan for future sessions, and communicate with others for feedback. Our findings have several implications for designing learner-centered self-monitoring interventions that can be both useful and engaging for informal learners.
An Automatic Parking Strategy Optimization Based on Open-drive and BIM

Aiming at the problem of path planning from vehicles entering the environment to discovering vacant parking spaces in large indoor environments such as parking buildings and underground garages, this paper proposes an automatic parking strategy optimization method based on OpenDRIVE+BIM. The optimal parking space selection method is proposed, and the fusion algorithm of the improved A* algorithm and DWA algorithm is used for path planning. The fusion algorithm is superior to the traversal algorithm in terms of search time, path length, turning angle, trajectory length and time consumption, and reduce the time and energy consumption problems in the process of traversing parking spaces.
Safe Reinforcement Learning through Phasic Safety-Oriented Policy Optimization

Exploration is an essential feature of Reinforcement Learning (RL) algorithms, as they attempt to learn the optimal policy through trial and error. In safety-constrained environments, safety violations during exploration are a significant challenge when the training is online. In this context, this paper proposes Phasic Safety-oriented Policy Optimization (PSPO), where the policy learning is divided into multiple phases with safety updates. This approach utilizes an adaptive safety shield to minimize repetitive unsafe explorations of the RL agent by action-masking, and at the same time learn an auxiliary policy which provides safety updates to the main policy. Such periodic updates reduce the number of safety infractions during training, without compromising rewards as in purely conservative safety shield based approaches. We have demonstrated the effectiveness of our approach in multiple safety-critical environments. Our experimental results exhibit fewer failures during training while demonstrating similar or faster convergence than prior methods.
Real-World Data Analysis of Survival Outcomes and Central Nervous System Relapses in Testicular Diffuse Large B Cell Lymphoma

Background: Primary testicular lymphoma is a rare type of non-Hodgkin lymphoma, mostly of the diffuse large B cell lymphoma (DLBCL). Although a consensus on standard treatment has been established, unresolved issues remain, such as recurrence in the central nervous system (CNS). Methods: We retrospectively analyzed the clinical characteristics and survival outcomes of 65 testicular DLBCL patients according to clinical settings and treatment modalities. Results: The median age of the patients in our study was 65 years, and two-thirds of them had disease limited to one testis. There was no right or left lateralization of testicular involvement. Over a median follow-up of 53.9 months (95% confidence interval 34.0–73.7 months), patients with stage I disease and a low international prognostic index score showed better survival outcomes than those in other categories. Orchiectomy, six cycles of chemotherapy, and radiation therapy (RT) to the contralateral testis demonstrated survival benefits, whereas CNS prophylaxis therapy did not reduce CNS recurrence. During the follow-up period, the survival curves showed continuous decline, mostly due to disease progression. CNS recurrence was observed in 15% of patients, and parenchymal involvement was dominant. However, no factors were associated with CNS recurrence in our analyses. Although our molecular analyses were performed in a small number of patients, MYD88, CD79B , and PIM1 mutations were frequent. Conclusion: In our study, treatment with orchiectomy, six cycles of immunochemotherapy, and contralateral RT was effective. However, because CNS prophylaxis is an essential part of testicular DLBCL management, better treatment strategies than intrathecal therapy are required.
Dynamic Scheduling of Maintenance by a Reinforcement Learning Approach - A Semiconductor Simulation Study

Scheduling in a semiconductor back-end factory is an extremely sophisticated and complex task. In semiconductor industry, more often than not, the scheduling of maintenance is underexposed to production scheduling. This is a missed opportunity as maintenance and production activities are deeply intertwined. This study considers the dynamic scheduling of maintenance activities on an assembly line. A policy is constructed to schedule a cleaning activity on the last machine of an assembly line such that the average production rate is maximized. The policy takes into account the given flexibility and the buffer content of the buffers in-between the machines in the assembly line. A Markov Decision Process is formulated for the problem and solved using Value Iteration and Reinforcement Learning Algorithms. In addition, for a real world case study, a simulation analysis is performed to evaluate the potential practical benefits.