Miracle-dz/UniFAR-FTUs huggingface dataset:
Representation Learning of Next Shot Selection for Vlog Editing

As vlog has become increasingly popular on video-sharing platforms, more amateurs have participated in vlog creation. One critical step of attractive vlog creation is multi-shot assembly, which is to compose shots to form a coherent and engaging narrative. The process highly requires cinematographic knowledge and remains challenging for inexperienced users. Therefore, we aim to achieve automatic cinematographic-aware shot assembly for vlog editing. Our key idea is to automatically mine the cues of shot assembly from large-scale well-edited vlog data. To this end, based on cinematographic theories and experiments, we find that semantics and scale are crucial for shot assembly. Accordingly, we propose a Two-stream Cinematographic-aware Contrastive (TCC) model to learn the representation that discriminates between the good next shot choice against other options. Quantitative results clearly demonstrate the effectiveness of the proposed meth-ods against other alternative baselines.
Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112 th Congress

We introduce the Hierarchical Ideal Point Topic Model , which provides a rich picture of policy issues, framing, and voting behavior using a joint model of votes, bill text, and the language that legislators use when debating bills. We use this model to look at the relationship between Tea Party Republicans and “establishment” Republicans in the U.S. House of Representatives during the 112 th Congress.
A comparison of drift between sea ice and oil spills in the Baltic Sea: The runner 4 case

The drift of the oil spills due to the sink of the Dominica-flagged cargo ship Runner 4 on 5 March 2006 in the Gulf of Finland, Baltic Sea is analyzed along with the corresponding weather and ice conditions. A numerical sea ice model is employed to investigate the drift of sea ice. The preliminary comparison of the drift between the sea ice and the oil spills shows a noticeable difference.
A quantized-diffusion model for rendering translucent materials

We present a new BSSRDF for rendering images of translucent materials. Previous diffusion BSSRDFs are limited by the accuracy of classical diffusion theory. We introduce a modified diffusion theory that is more accurate for highly absorbing materials and near the point of illumination. The new diffusion solution accurately decouples single and multiple scattering. We then derive a novel, analytic, extended-source solution to the multilayer search-light problem by quantizing the diffusion Green's function. This allows the application of the diffusion multipole model to material layers several orders of magnitude thinner than previously possible and creates accurate results under high-frequency illumination. Quantized diffusion provides both a new physical foundation and a variable-accuracy construction method for sum-of-Gaussians BSSRDFs, which have many useful properties for efficient rendering and appearance capture. Our BSSRDF maps directly to previous real-time rendering algorithms. For film production rendering, we propose several improvements to previous hierarchical point cloud algorithms by introducing a new radial-binning data structure and a doubly-adaptive traversal strategy.
Log RGB Images Provide Invariance to Intensity and Color Balance Variation for Convolutional Networks

The interaction of light and matter follows physical rules that have been well-modeled in the vision community. These rules should be available to deep networks when learning vision tasks. However, typical signal processing pipelines, conversion to sRGB, and JPEG compression break the rules and make them unavailable for learning. This, in turn, makes color and intensity unreliable as features and more difﬁcult to use. Using linear or log RGB images that preserve the rules of the physics of reﬂection should make certain visual tasks simpler to learn and increase robustness to certain types of visual variation. We demonstrate that using linear RGB or log RGB improves the performance of a deep network on an image classiﬁcation task when the same network architecture is trained on the same images but in different formats. Furthermore, the linear and log RGB networks are more robust to intensity and color balance variation. In particular, the network trained on log RGB inputs shows invariance to intensity and color balance variation when that variation is not included in the training set, while the network trained on the same images in JPEG format shows severe reductions in performance. We further explore why this difference exists by visualizing low-level features in log RGB, linear RGB, and JPEG data and show that log space preserves certain types of features across intensity and color balance variation.
eCommons@AKU eCommons@AKU

Introduction Children’s early development is affected by caregiving experiences, with lifelong health and well-being implications. Governments and civil societies need population-based measures to monitor children’s early development and ensure that children receive the care needed to thrive. To this end, the WHO developed the Global Scales for Early Development (GSED) to measure children’s early development up to 3 years of age. The GSED includes three measures for population and programmatic level measurement: (1) short form (SF) (caregiver report), (2) long form (LF) (direct administration) and (3) psychosocial form (PF) (caregiver report). The primary aim of this protocol is to validate the GSED SF and LF. Secondary aims are to create preliminary reference scores for the GSED SF and LF, validate an adaptive testing algorithm and assess the feasibility and preliminary validity of the GSED PF. Methods and analysis We will conduct the validation in seven countries (Bangladesh, Brazil, Côte d’Ivoire, Pakistan, The Netherlands, People's Republic of China, United Republic of Tanzania), varying in geography, language, culture and income through a 1-year prospective design, combining cross-sectional and longitudinal methods with 1248 children per site, stratified by age and sex. The GSED generates an innovative common metric (Developmental Score: D-score) using the Rasch model and a Development for Age Z-score (DAZ). We will evaluate six psychometric properties of the GSED SF and LF: concurrent validity, predictive validity at 6 months, convergent and discriminant validity, and test–retest and inter-rater reliability. We will evaluate measurement invariance by comparing differential item functioning and differential test functioning across sites
Multi-AUV Underwater Cooperative Search Algorithm based on Biological Inspired Neurodynamics Model and Velocity Synthesis

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM.
Comprehensive analysis of phospholipids in the brain, heart, Comprehensive analysis of phospholipids in the brain, heart, kidney, and liver: brain phospholipids are least enriched with kidney, and liver: brain phospholipids are least enriched with polyunsaturated fatty acids polyunsaturated fatty ac

It is commonly accepted that brain phospholipids are highly enriched with long chain polyunsaturated fatty acids (PUFAs). However, the evidence for this remains unclear. We used HPLC-MS to analyze the content and composition of phospholipids in rat brain and compared it to the heart, kidney, and liver. Phospholipids typically contain one PUFA, such as 18:2, 20:4, or 22:6, and one saturated fatty acid, such as 16:0 or 18:0. However, we found that brain phospholipids containing monounsaturated fatty acids in the place of PUFAs are highly elevated compared to phospholipids in the heart, kidney, and liver. The relative content of phospholipid containing PUFAs is ∼ 60% in the brain whereas it is over 90% in other tissues. The most abundant species of phosphatidylcholine (PC) is PC(16:0/18:1) in the brain, whereas PC(18:0/20:4) and PC(16:0/20:4) are predominated in other tissues. Moreover, several major species of plasmanyl and plasmenyl phosphatidylethanolamine are found to contain monounsaturated fatty acid in the brain only. Overall, our data clearly show that brain phospholipids are the least enriched with PUFAs of the four major organs, challenging the common belief that the brain is highly enriched with PUFAs.
Expenditure Patterns by Travel Party Size: British and German Tourists on Crete, Greece

ABSTRACT Inbound visitors are the largest group of tourists to Crete, Greece. Given the importance of inbound pleasure tourism, a deeper understanding of expenditure patterns is vital for policy planners and destination marketers. Using the 2003 Exit Survey data, this study investigates the spending behaviour of British and German pleasure visitors to Crete. The analysis of two key variables—nationality and travel party size—were found to make a significant contribution to explaining expenditure level variations (p<.05), and have a considerable influence on expenditure distribution and spending patterns. These findings could be used for expenditure-based segmentation; policy and marketing implications are also derived.
Coupled analysis of heat transport and water flow with hysteresis in saturated-unsaturated porous media

A two-dimensional mathematical model for simulating coupled problems of water flow and heat transport in saturated-unsaturated porous media is presented. The model is composed of two submodels; that is, a saturated-unsaturated water flow submodel and a heat transport submodel. The saturatedunsaturated water flow submodel can take into consideration thermal convection in saturated zone due to temperature gradient, soil water hysteresis accompanied by unsaturated flow, and temperature effect on the density and viscosity of water together with the typical forced convection. The heat transport submodel can handle heat conduction, heat dispersion, and forced and thermal convection in saturatedunsaturated porous media. The numerical method proposed in this paper is based on the finite elements for water flow and the characteristic finite elements for heat transport. The model was applied to two experimental problems with laboratory and field data. First, water flow in saturated-unsaturated soil column was analyzed under hysteretic and isothermal condition. Then, the model was applied to a problem of coupled transport of water and heat in saturated-unsaturated porous media. The latter problem is accommodated to account for an aquifer thermal energy storage experiment conducted in Sanrihama dune sand area where serial tests of artificial recharge of warm and cold water and recovery for agricultural use have been taken place for several years. Although it was not possible to compare the simulation and experimental results in a strict sense due to the lack of adequate experimental data, it was qualitatively proved that the model can be successfully applied to this type of coupled problems.
Predicting wetted width in any river at any discharge

Coefficients describing at‐a‐station power‐law relationships between discharge and width were calculated by applying multilevel models to field data collected during routine hydrological monitoring at 326 gauging stations across New Zealand. These hydraulic geometry coefficients were then estimated for each of these stations using standard stepwise multiple‐linear regression models. Analysis was carried out to quantify how the relationship between width and discharge changed in relation to several available explanatory variables. All coefficients describing the at‐a‐station hydraulic geometry were found to have statistically significant relationships with catchment area. Statistically significant relationships between each of the coefficients were also found with the addition of catchment climate as an explanatory variable. Further statistically significant relationships were found when station elevation and channel slope, as well as hydrological source of flow and landcover of the upstream catchment were added to the explanatory variables. The level of confidence that can be associated with estimates of width at ungauged sites, and sites with limited data availability, was then assessed by comparing model predictions with independent paired data on observed width and discharge from 197 sites. When compared against these independent data, model predictions of width were improved with the addition of predictor variables of the hydraulic geometry coefficients. The greatest improvements were made when climate was added to catchment area as predictor variables. Minor improvements were made when all available information was used to predict width at these independent sites. Although the analysis was purely empirical, results describing relationships between hydraulic geometry coefficients and catchment characteristics corresponded well with knowledge of the processes controlling at‐a‐station hydraulic geometry of river width. Copyright © 2010 John Wiley & Sons, Ltd.
Focusing on the Future of Pterocarpus chrysothrix (Mukula) in Zambia: A Brief Review of Its Ecology, Distribution and Current Threats

Pterocopsis chrysothrix ( P. chrysothrix ) locally known as ‘Mukula’ is a multipurpose tree species extensively harvested and currently facing a threat from extinction in Zambia. It is as a result of its good utility properties such as quality timber and medicinal values that have triggered the illegal harvest and export of timber to developed countries. This paper focuses on briefly reviewing the ecology, distribution and current threats on P. chrysothryx from the on-going indiscriminate cutting in many parts of Zambia. This tree species is not very common in Zambia, however, it can be propagated by using different methods such as seeds, cuttings and tissue culture. The limited literature existing shows that it is illegally overharvested and exported in a semi processed form without undergoing formal legal procedures such as inspection and taxation. The problem of overharvesting of Mukula can be reversed by finding suitable propagation methods and developing sound legal strategies for the protection and management of this important tree species.
Integrating the Study of Family History in the Process of Learning the Latvian Language

Latvian language and literature, like the history of the nation and country, is an essential basis of national and cultural development, and an important part of civic education. One of the problems we face daily is related to the fact that many of young people do not perceive the Latvian language as a value, and do not want to learn some part of the curriculum, because it seems to them too far from reality, useless and irrelevant. To create more interest in language learning content, it should also include topics that may be personally meaningful to students or should be viewed in a context that is engaging to them. The purpose of the paper is to justify the need to include the study of family history in the learning content of the Latvian language, ensuring the connection between subjects, and to characterize the developed linguo-didactic material. The author’s experience in creating family trees and involving students in this work in interest education classes shows that this not only creates interest in young people in the set of personal names of their family and the motivation of their choice but also promotes the desire to more fully learn the history of the Latvian language and the language as a system, to develop their language and communication skills. The research was conducted in the 10th grade using the methods of document analysis, case analysis, pedagogical observation, and pupil’s survey. The research concluded that the study of family history can be successfully included in Latvian language lessons. The students were interested in lessons related to the topic of family history, admitting that they find the lessons more interesting, where it is possible to acquire not only theoretical knowledge of the Latvian language but also additional information about their family members. For the work to be more successful, students should already at the stage of primary education develop persistent habits, the skills of independent work, information acquisition, compilation, and analysis, as well as age-appropriate linguo-cultural tasks should be included in the curriculum, which would promote the study of language learning as a cultural phenomenon.
Path Planning for Automatic Berthing Using Ship-Maneuvering Simulation-Based Deep Reinforcement Learning

Despite receiving much attention from researchers in the field of naval architecture and marine engineering since the early stages of modern shipbuilding, the berthing phase is still one of the biggest challenges in ship maneuvering due to the potential risks involved. Many algorithms have been proposed to solve this problem. This paper proposes a new approach with a path-planning algorithm for automatic berthing tasks using deep reinforcement learning (RL) based on a maneuvering simulation. Unlike the conventional path-planning algorithm using the control theory or an advanced algorithm using deep learning, a state-of-the-art path-planning algorithm based on reinforcement learning automatically learns, explores, and optimizes the path for berthing performance through trial and error. The results of performing the twin delayed deep deterministic policy gradient (TD3) combined with the maneuvering simulation show that the approach can be used to propose a feasible and safe path for high-performing automatic berthing tasks.
Detection of citrus diseases in complex backgrounds based on image–text multimodal fusion and knowledge assistance

Diseases pose a significant threat to the citrus industry, and the accurate detection of these diseases represent key factors for their early diagnosis and precise control. Existing diagnostic methods primarily rely on image models trained on vast datasets and limited their applicability due to singular backgrounds. To devise a more accurate, robust, and versatile model for citrus disease classification, this study focused on data diversity, knowledge assistance, and modal fusion. Leaves from healthy plants and plants infected with 10 prevalent diseases (citrus greening, citrus canker, anthracnose, scab, greasy spot, melanose, sooty mold, nitrogen deficiency, magnesium deficiency, and iron deficiency) were used as materials. Initially, three datasets with white, natural, and mixed backgrounds were constructed to analyze their effects on the training accuracy, test generalization ability, and classification balance. This diversification of data significantly improved the model’s adaptability to natural settings. Subsequently, by leveraging agricultural domain knowledge, a structured citrus disease features glossary was developed to enhance the efficiency of data preparation and the credibility of identification results. To address the underutilization of multimodal data in existing models, this study explored semantic embedding methods for disease images and structured descriptive texts. Convolutional networks with different depths (VGG16, ResNet50, MobileNetV2, and ShuffleNetV2) were used to extract the visual features of leaves. Concurrently, TextCNN and fastText were used to extract textual features and semantic relationships. By integrating the complementary nature of the image and text information, a joint learning model for citrus disease features was achieved. ShuffleNetV2 + TextCNN, the optimal multimodal model, achieved a classification accuracy of 98.33% on the mixed dataset, which represented improvements of 9.78% and 21.11% over the single-image and single-text models, respectively. This model also exhibited faster convergence, superior classification balance, and enhanced generalization capability, compared with the other methods. The image-text multimodal feature fusion network proposed in this study, which integrates text and image features with domain knowledge, can identify and classify citrus diseases in scenarios with limited samples and multiple background noise. The proposed model provides a more reliable decision-making basis for the precise application of biological and chemical control strategies for citrus production.
Vending Machine Product Demand Prediction Using Machine Learning Algorithms

The study aims to leverage the vending machine's historic product sales data to predict future demand using machine learning (ML) techniques with the goal of strategic inventory planning, reducing stock waste, and increasing operational efficiency and revenue. The study deploys different ML algorithms including XGBoost, FB Prophet, Autoregressive Integrated Moving Average and Support Vector Regression. This is among the first studies of its kind to explore ML for predictive restocking in the vending industry. The experiment is divided into two parts. In the first part, we used common historic sales data variables to make the prediction where XGBoost emerged as the best-performing model in terms of the mean absolute error. In the second part, we used additional features such as public holidays, day of the week, and sales deviation flags to make the prediction. Using additional variables slightly improved the performance of the ML algorithms with XGBoost having the lowest mean absolute error of 8. Overall, the study demonstrates the efficacy of utilising ML techniques and additional features in accurately predicting future product demand for vending machines, thus enabling strategic inventory planning, minimised stock waste, improved operational efficiency and increased revenue for the vending industry.
A Combination Strategy of Saturated Yaw Moment Control with Specialized Active Front Wheel Steering for Vehicle Assistance and Active Safety Control System

This paper introduces an assistance and active safety control system that integrates Direct Yaw Moment (DYM) control with a specialized Active Front-Wheel Steering (AFS) system to enhance vehicle maneuverability under normal driving conditions while blocking excessive wheel steering from developing instability in vehicle later motion. The proposed approach utilizes polytopic Linear Parameter Varying (LPV) technology to account for changes in vehicle speed. The control system is designed using the Linear Matrix Inequality (LMI) technique with input constraints, and further reinforced with a high-gain saturation method to achieve faster vehicle response to reference tracking demands. Simulation results demonstrate the advantages of the proposed control.
Riparian habitat and vegetation recovery of headwater streams after clearcut harvesting

In this thesis I examined two important aspects of clearcut harvesting along small headwater streams and their riparian zones. The objectives of the study were: 1) to investigate the impacts of clearcut harvesting on the morphological features of headwater systems and the pattern of recovery of those impacts over time, and 2) to explore the response of headwater riparian understory vegetation to clearcut harvesting