Miracle-dz/UniFAR-FTUs huggingface dataset:
How Potential Tourists’ Perception of Interaction With Local People Impact Their Overall Experience And Destination Image in Emerging Destination?

This study examines the influence of potential tourists’ perceptions of their functional, hedonic, and expressive interactions with local people on their overall experience and their view of the destination. Also, this study examines how the potential tourists’ mental image of their destination increases their intention to visit it and encourages them to talk about the location with others. A video of tourism in Saudi Arabia was showed to participants and data were obtained from 460 respondents who had never visited Saudi Arabia at the time of the study. Saudi Arabia was selected because it is considered an emerging tourism destination. Structural equation modeling was used to test the proposed hypotheses The findings indicate that all dimensions of interaction are important in increasing the potential tourists’ positive perceived overall experience. Also, the study finds that potential tourists’ positive perception of their overall experience positively contributes to a positive image of the destination. Lastly, this study provides theoretical, practical, and recommendations for future studies.
Extended Differentiable Marching Cubes by Manifold-Preserving Shape Inflation

This paper introduces an extended differentiable marching cubes (DMC) method for end-to-end learning of precise 3D surface geometries using a neural network. The original DMC method extracts the isosurface using a fixed-size voxel grid, similar to the traditional marching cubes. Therefore, the original method involves a trade-off between output resolution and memory consumption. In contrast, there remains room to increase the output resolution without increasing the number of voxels because an output surface often exists over a limited number of voxels. According to this observation, our method deforms an input point cloud to occupy the voxel grid as widely as possible, thereby refining small parts of the target shape. To obtain such deformation, we apply normalizing flow (NF), typically used to transform probability density functions. Its invertibility allows us to reproduce a mesh for the input point cloud by cancelling the deformation of the mesh obtained for the deformed point cloud using DMC. To obtain appropriate deformation, NF is conditioned by a global shape feature and is trained by several loss functions to inflate the input shape while preserving its manifold structure. We tested the proposed method with two shape datasets and showed that our extended DMC achieves higher performance than the original DMC, even used with a simple deformation.
Design of an object sorting system using a vision-guided robotic arm

High precision arms for manufacturing use are typically large, massive, and expensive. Small low-cost arms made from budget servo motors suffer from precision and accuracy problems. This paper presents a visual feedback system along with a simple kinematic approach to control and allow a small robotic arm to detect and identify different objects when present in the field of view, as well as being grasp-guided by visual feedback. The real-time vision system provides visual feedback for the arm to compensate for the low precision and accuracy of budget servo motors of the robotic arm. The feedback ensures that the gripper of the arm is aligned with the object involved. This project was undertaken as a senior capstone design project.
Impact of Psychological Biases and Personality Traits on Investor Trading Behavior

Though, finance has been studied from several years, while behavioral finance is quite new field in financial studies which takes under consideration psychology of human being. Theories comes under behavior finance are based on the psychology, which tries to develop understanding how behavior of individual investors is being impacted by their emotions and cognitive errors. Present study is directed to examine how certain psychological biases and personality traits are influencing the investors’ trading behavior in Pakistan Commodity Market. Primary data was collected from investors of PMEX through survey questionnaires. The study has selected a sample size of 300 and after dispersion of survey questionnaires, 216 accurately filled questionnaires have been received back. Afterwards, collected data analyzed by utilizing SPSS and AMOS. The statistical tools utilized in present study included Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The outcomes of the study indicated that all exogenous latent variables which include psychological biases (overconfidence, loss-aversion, self-control) and personality traits (extraversion, openness to experience, agreeableness) have substantial impact on investors trading behavior. Therefore, the study concludes that investors at PMEX are alleged with psychological biases and personality traits during their trading activities. The outcomes of the study could be useful for individual investors, market regulators, commodity market advisors and financial educators in avoiding costly errors in trading. Present study also provided appropriate recommendations and directions for future studies.
REINFORCEMENT LEARNING FOR RECONFIGURATION MANEUVER DESIGN IN MULTI-BODY SYSTEMS

Reinforcement learning is used to design reconﬁguration maneuvers for a space-craft to adjust its position relative to another spacecraft located along an L 2 halo orbit in the Sun-Earth circular restricted three-body problem. This speciﬁc scenario is modeled after a starshade reconﬁguring to block starlight while a space telescope observes exoplanets. First, reconﬁguration maneuver design is translated into a reinforcement learning problem. Then, Proximal Policy Optimization is used to train a policy that generates sequences of impulsive reconﬁguration maneuvers. The trained policy is examined and used to produce reconﬁguration maneuver sequences that successfully achieve the reconﬁguration goals with low maneuver magnitudes.
Woody plant species diversity of Shello Giorgis dry Afromontane forest, Farita district, West Amhara, Ethiopia

Ethiopia has grater geographical diversity ranging from 100m. a.s.l below sea level to 4600m.a.s.l. The highlands, defined as above 1500 m.a.s.l account 45% of the total area in the country1 and also Ethiopia has a significant portion of its land area in the Dry afromontane forest and grassland complex vegetation type (DAF). The DAF is found scattered as small fragments in many cases and extensive coverage in few cases in the north, northwest, northeast, central, south, southeast and southwest highlands of Ethiopia.2,3 While the low lands lies below 1500 account 55%. As a result 35% of the land might once have been covered by natural high forest was reduced to 2.7% in 1990s.4,5 Forest in these wide agro-ecological zones provide socio-cultural; economic, environmental and biodiversity conservation values to human.6,7 Tropical forests are vanishing and being degraded rapidly due to accelerated human population growth. Forest clearing, overgrazing and luck of proper policy framework are also the major factors that contribute to loss of forest resource in the country.8,9 Forest degradation in quality (species reduction) and quality (shrinkage in volume/area) are threatening the biodiversity resource of the country. The main cause for degradation of Shello Giorgis forests in general, the poor policy of the government related to forests. To use the forest in sustainable way, setting priority for the threatened species in vegetation types and conservation of the forest is crucial. Therefore, the main objective of the study was to undergo inventory of indigenous woody plant species and to assess the regeneration status and to set conservation priority in Shello Giorgis forest. Materials and methods
Assessment of Phasor Extraction Methods for Power System Co-Simulation Applications

The paper examines a number of methods for extracting dynamic phasors from samples of natural waveforms that are generated using electromagnetic transient (EMT) simulators. It delves into the theory underlying each phasor extraction method and the numerical routines used for their implementation. The paper performs an in-depth analysis of the properties of the extracted phasors for general power system signals that may include electromechanical oscillations, dc and harmonic components, imbalances, and arbitrary transients. Simulation results are presented to demonstrate any limitations of these methods and to assess the resulting harmonic spectra of the phasors. An EMT-dynamic phasor co-simulation example is also included, in which various phasor extraction methods are implemented. The paper’s findings are essential in selecting and implementing phasor extraction methods used in co-simulations of large power systems using EMT and dynamic phasors solvers. Keywords—Base-frequency dynamic phasors, generalized averaging, shifted frequency analysis, time-varying phasors.
Assessment of paper metrics as predictors of quality for inkjet printing

Recent developments in inkjet technology have enabled the development of high-speed inkjet presses with similar quality and performance to conventional printing presses. These inkjet presses can print on a wide range of papers. Prints made on some papers are of high quality whereas on others are unacceptable. These print results vary from one digital press to another. The focus of this publication is the development of techniques to predict print quality from measurements of papers. Two studies were conducted. In the first, a set of around 250 papers were measured, prints were made on a single digital press and the print quality assessed. In the second, a set of 20 papers were measured, prints were made on three digital presses from different manufacturers and the print quality assessed for each. The studies were unable to identify or develop a single metric that can be used to predict print quality, however, a set of techniques is presented that have been found to be effective predictors of print quality where multiple metrics are used in combination and these methods and results are presented. Both studies adopted a ‘black box’ approach where only the paper measurements and the result of assessment were used to make predictions.
Deep Learning-Based Long Term Mortality Prediction in the National Lung Screening Trial

In this study, the long-term mortality in the National Lung Screening Trial (NLST) was investigated using a deep learning-based method. Binary classification of the non-lung-cancer mortality (i.e. cardiovascular and respiratory mortality) was performed using neural network models centered around a 3D-ResNet. The models were trained on a participant age, gender, and smoking history matched cohort. Utilising both the 3D CT scan and clinical information, the models can achieve an AUC of 0.73 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.60 and 0.38 respectively. By interpreting the trained models with 3D saliency maps, we examined the features on the CT scans that correspond to the mortality signal. By extracting information from 3D CT volumes, we can highlight regions in the thorax region that contribute to mortality that might be overlooked by the clinicians. Therefore, this can help focus preventative interventions appropriately, particularly for under-recognised pathologies and thereby reducing patient morbidity.
Socio-economic inequalities in adolescent mental health in the UK: multiple socio-economic indicators and reporter effects

There are socio-economic inequalities in the experience of mental ill-health. However, less is known about the extent of inequalities by different indicators of socio-economic position (SEP). This is relevant for insights into the mechanisms by which these inequalities arise. For young people's mental health there is an additional layer of complexity provided by the widespread use of proxy reporters. Using data from the UK Millennium Cohort Study (N=10,969), we investigated the extent to which five objective SEP indicators (parent education, household income, household wealth, parent occupational status, and relative neighbourhood deprivation) predict adolescent internalising mental health and how this varies as a function of reporter. Both parent report and adolescent self-report were considered. Regression models demonstrated that whilst all five SEP indicators were associated with parent-reported adolescent mental health (regression coefficients for the most disadvantaged groups and adolescent mental health: parent education {beta}=0.53 [0.44;0.62], household income {beta}=0.56 [0.50;0.62], household wealth {beta}=0.18 [0.10;0.27], parent occupational status {beta}=0.40 [0.35;0.46], and relative neighbourhood deprivation {beta}=0.41 [0.33;0.49]), only income ({beta}=0.11 [0.04;0.17]), wealth ({beta}=0.12 [0.02;0.21]), and occupational status ({beta}=0.08 [0.03;0.13]) were associated with self-reported mental health. The magnitude of these effects was greater for parent-reported than self-reported adolescent internalising symptoms: SEP indicators jointly predicted 5.2% of the variance in parent-reported compared to 1.4% of the variance in self-reported internalising mental health. Income predicted the most variance in both parent (4.2% variance) and self-reported internalising symptoms (0.5% variance). Interestingly, the gradient of parent-reported adolescent mental health across SEP indicators mirrors that of parent's own mental health (for example, income predicted 7.3% variance). Our findings highlight that the relevance of different SEP indicators to adolescent internalising mental health differs between parent and adolescent reports. Therefore, it is important to consider the various perspectives of mental health inequalities gained from different types of reporters.
Indoor Air Quality Intervention in Schools; Effectiveness of a Portable HEPA Filter Deployment in Five Schools Impacted by Roadway and Aircraft Pollution Sources

The Healthy Air, Healthy Schools Study was established in January 2020 to better understand the impact of ultrafine particles (UFP) on indoor air quality in communities surrounding Seattle-Tacoma (Sea-Tac) International Airport. The study team took multipollutant measurements indoor and outdoor air pollution at five participating school locations to infiltration indoors. The schools participating in this project were located within a 7-mile radius of Sea-Tac Airport and within 0.5 miles of an active flight path. Based on experimental measures in an unoccupied classroom, infiltration rates of a) Ultrafine particles of aircraft origin b) Ultrafine particles of traffic origin and c) Wildfire smoke or other outdoor pollutants were characterized before and after the introduction of a classroom based portable HEPA filter intervention. The portable HEPA cleaners were an effective short-term intervention to improve the air quality in classroom environments, reducing the ultrafine particles to approximately 1/10th of that measured outside. Before the HEPA filter deployment, approximately one-half of all outdoor UFPs were measured indoors. This study is unique in focusing on UFP in school settings and demonstrating through multivariate methods that the UFP measured in the classroom space is primarily of outdoor origin. Although existing research suggests that improvements to indoor air quality in homes can significantly improve asthma outcomes, further research is necessary to establish the benefit to student health and academic performance of improved air quality in schools.
Variation in US Drug Overdose Mortality Within and Between Hispanic/Latine Subgroups: A Disaggregation of National Data

This study examined differences across Latine heritage groups (i.e., Mexican, Puerto Rican, Cuban, Dominican, Central American, South American) in rates of US drug overdose mortality. The study utilized 2015-2019 mortality data from the National Center for Health Statistics for 29,137 Hispanic individuals who died of drug overdose. Using population estimates from the American Community Survey, age-standardized drug overdose mortality rates were calculated by specific Latine heritage and sex, nativity, educational attainment, and geographic region. Standardized rate ratios (SRRs), incidence rate ratios (IRRs) from negative binomial regression models, and 95% Confidence Intervals (CIs) were calculated, and multiple imputation was used for missing Latine heritage group in select models. Drug overdose mortality rates in the Puerto Rican heritage population were more than three times as high as in the Mexican heritage population (IRR 3.61 [95% CI 3.02-4.30] in unadjusted model; IRR 3.70 [95% CI 3.31-4.15] in model adjusting for age, sex, nativity, educational attainment, and region; SRR 3.23 [95% CI, 3.15-3.32] in age-standardized model with missing Hispanic heritage imputed). Higher age-standardized rates of drug overdose mortality were observed in males than females across all Latine groups, yet the magnitude of the sex differential varied by Latine heritage. The relationship between drug overdose mortality and nativity differed by Latine heritage; in all groups except Puerto Rican, overdose mortality rates were significantly higher in the US-born than those not US-born. In contrast, overdose mortality rates were significantly lower in US-born Puerto Ricans than in Puerto Ricans who were not US-born (e.g., born in Puerto Rico; SRR, 0.84 [95% CI 0.80-0.88]). The relationship between drug overdose mortality and educational attainment (for ages 25+) also varied between Latine groups. The diverse subgroups comprising the US Latine population vary not only in rates of drug overdose mortality, but also in demographic risk factors for fatal drug overdose.
The Polyhedral Geometry of Pivot Rules and Monotone Paths

Motivated by the analysis of the performance of the simplex method we study the behavior of families of pivot rules of linear programs. We introduce normalized-weight pivot rules which are fundamental for the following reasons: First, they are memory-less, in the sense that the pivots are governed by local information encoded by an arborescence. Second, many of the most used pivot rules belong to that class, and we show this subclass is critical for understanding the complexity of all pivot rules. Finally, normalized-weight pivot rules can be parametrized in a natural continuous manner. We show the existence of two polytopes, the pivot rule polytopes and the neighbotopes, that capture the behavior of normalized-weight pivot rules on polytopes and linear programs. We explain their face structure in terms of multi-arborescences. We compute upper bounds on the number of coherent arborescences, that is, vertices of our polytopes. Beyond optimization, our constructions provide new perspectives on classical geometric combinatorics. We introduce a normalized-weight pivot rule, we call the max-slope pivot rule which generalizes the shadow-vertex pivot rule. The corresponding pivot rule polytopes and neighbotopes refine monotone path polytopes of Billera--Sturmfels. Moreover special cases of our polytopes yield permutahedra, associahedra, and multiplihedra. For the greatest improvement pivot rules we draw connections to sweep polytopes and polymatroids.
Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a 1 – 1/e factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.
Torus Actions on Quotients of Affine Spaces

We study the locus of fixed points of a torus action on a GIT quotient of a complex vector space by a reductive complex algebraic group which acts linearly. We show that, under the assumption that $G$ acts freely on the stable locus, the components of the fixed point locus are again GIT quotients of linear subspaces by Levi subgroups.
NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit

Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a data streaming pipeline, resulting in memory consumption that is orders of magnitude smaller than existing tools. NGG provides a massively-scalable solution for simulating social contact networks, enabling global-scale epidemic simulation studies.
MCAD: Beyond Basic-Block Throughput Estimation Through Differential, Instruction-Level Tracing

Estimating instruction-level throughput is critical for many applications: multimedia, low-latency networking, medical, automotive, avionic, and industrial control systems all rely on tightly calculable and accurate timing bounds of their software. Unfortunately, how long a program may run - or if it may indeed stop at all - cannot be answered in the general case. This is why state-of-the-art throughput estimation tools usually focus on a subset of operations and make several simplifying assumptions. Correctly identifying these sets of constraints and regions of interest in the program typically requires source code, specialized tools, and dedicated expert knowledge. Whenever a single instruction is modiﬁed, this process must be repeated, incurring high costs when iteratively developing timing sensitive code in practice. In this paper, we present MCAD, a novel and lightweight timing analysis framework that can identify the effects of code changes on the microarchitectural level for binary programs. MCAD provides accurate differential throughput estimates by emulating whole program execution using QEMU and forwarding traces to LLVM for instruction-level analysis. This allows developers to iterate quickly, with low overhead, using common tools: identifying execution paths that are less sensitive to changes over timing-critical paths only takes minutes within MCAD. To the best of our knowledge this represents an entirely new capability that reduces turnaround times for differential throughput estimation by several orders of magnitude compared to state-of-the-art tools. Our detailed evaluation shows that MCAD scales to real-world applications like FFmpeg and Clang with millions of instructions, achieving < 3% geo mean error compared to ground truth timings from hardware-performance counters on x86 and ARM machines.