Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of target localization accuracy for image-guided radiation therapy by 3D and 4D cone-beam CT in the presence of respiratory motion: a phantom study

We evaluate the target definition accuracy of four-dimensional CT (4D-CT) simulation and target localization accuracy of 3D or 4D cone-beam CT (CBCT) in the presence of respiration. The target motion is modelled by a sine curve or a cos6 curve. The target volumes, shapes, and positions obtained from the 4D-CT simulation are compared with the static CT image and theoretical values of the phantom. Reference average intensity projection (AIP) and maximum intensity projection (MIP) images for target localization are generated from the 4D-CT simulation. Localization involves aligning the AIP/MIP to 3D cone-beam CT (3D-CBCT) or 4D cone-beam CT (4D-CBCT), and localization accuracy is evaluated from the difference in target position between the reference AIP/MIP image and 3D-CBCT/4D-CBCT measurements. 4D-CBCT also allows measurement of the target motion standard deviation (SD) and excursion (EX). The SD and EX errors are calculated with respect to the theoretical value of the phantom. The target volume and position accuracies obtained via 4D-CT at each phase are within 3.0% and 2.5 mm, respectively, of the static and theoretical values for the sine and cos6 curves. The target localization errors for 3D-CBCT are within 1.0 mm regardless of the EX variation and reference image for the sine curve, whereas the errors for the cos6 curve increase from 0.1 to 5.1 mm with increasing EX variation. In contrast, the 4D-CBCT localization errors are within 1.0 mm regardless of EX variation, reference image, and motion pattern. In addition, SD and EX errors, respectively, range from −1.3 to 0.1 mm and −2.2 to 0.1 mm (AIP) and from −4.4 to −2.7 mm and −13.5 to −4.2 mm (MIP). 4D-CBCT for AIP is more accurate than that for MIP. Target localization is simple and accurate for 3D-CBCT and 4D-CBCT with the AIP. However, 3D-CBCT is more inaccurate than 4D-CBCT when considering EX variations with the cos6 curve.
EXPLAINING AI: UNDERSTANDING DEEP LEARNING MODELS FOR HERITAGE POINT CLOUDS

Abstract. Deep Learning has been pivotal in many real-world applications (e.g., autonomous driving, medicine and retail). With the wide availability of consumer-grade depth sensors, acquiring 3D data has become more affordable and effective, and many 3D datasets are currently publicly available. 3D data provides a great opportunity for a better comprehension of the surrounding environment for machines. There is a growing need for innovative methods for the treatment and analysis of point clouds and for their classification. The complex hidden layers, which are at the basis of deep neural networks (DNNs), make it difficult to interpret these models, that up to a few years ago DNNs were considered and treated as black box operators. Still, with their increasing popularity, making them explainable and interpretable has become mandatory. A lot of efforts were devoted to developing an Explainable Artificial Intelligence (XAI) framework for explaining DNNs decisions with 2D data, while only a few studies have attempted to investigate the explainability of 3D DNNs and, even more, heritage scenarios. To overcome these limitations, it was proposed the BubblEX framework: a novel multimodal fusion framework to learn the 3D point features. In our work, BubblEX has been exploited to understand the decisions taken by DNNs for heritage point clouds. The approach has been applied to a Digital Cultural Heritage Dataset, which is publicly available: the ArCH (Architectural Cultural Heritage) Dataset.
Cosmetic Breast Implants and the Risk of Suicide: A Systematic Review and Meta-Analysis

Objective: Having cosmetic breast implants increases a woman's chance of suicide, which is now a global challenge. This systematic review evaluated the possible risk of suicide among women who undergo cosmetic breast implants. Method : This meta-analysis was done based on Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). In the current systematic review and meta-analysis, we systematically searched for all articles written in both English or Persian that estimated the prevalence of suicidal ideation in women who had cosmetic breast implants. We systematically searched different databases, including MEDLINE (PubMed), Web of Science, Embase, Cochrane, Library ProQuest, Scopus, and Google Scholar, from inception to March 2021. There was also a search for references. Suicidal ideation, a suicide plan, or suicide attempts were the outcomes. In order to determine the total pooled prevalence of suicidal ideation, we utilized a random-effects model. To examine the risks of bias in each study, we applied the Joanna Briggs Institute Critical Appraisal method. Results: We identified 218 citations in our initial search. After omitting duplicated citations and excluding irrelevant studies according to the title and abstract selection, 42 studies were chosen for the full text analysis. Finally, 11 research, examining a total of 324,332 women were incorporated into the systematic review and critical appraisal assessment. Eight of these studies were found to be eligible for meta-analysis. The frequency of suicide in women with cosmetic breast implant was 0.2% (95% CI: 0.1% to 0.4%; P < 0.001) (Q-value: 168.143, I2:95.83). Most of the included studies had moderate quality. Conclusion: There might be a correlation between cosmetic breast implants and suicide risk, which could be stronger in the presence of a history of mental illnesses. The evidence about the possible effects of breast implants on the risk of suicide is still inconclusive, and there is a need for future well-designed studies on this topic.
Inferring Trips and Origin-Destination Flows From Wi-Fi Probe Data: A Case Study of Campus Wi-Fi Network

This work introduces an alternative solution to costly conventional approaches for large-scale travel behavior data collection by utilizing an opportunistic sensing data source i.e., Wi-Fi probe data. Through our case study of Chiang Mai University campus as a city, we developed a framework for inferring and visualizing Wi-Fi data-based travel behavior by demonstrating how a Wi-Fi probe data can be analyzed to infer trips and origin-destination flows. Specifically, our contributions include algorithms developed for inferring spatial presence, residence, stay, trip, and trip distribution among places in the campus, as well as campus inflow and outflow. Moreover, to handle the Wi-Fi access point data for the analysis, and visualize the inferred trips and flows, an online visual analytics tool called Wi-Flow is developed as part of this work. Our framework differs from the other studies with our residence and trip detection algorithms that produce the result at the individual level as opposed to the overall network. The experimental results are intuitive and insightful, providing useful information for area management. Our research underscores the significance of utilizing Wi-Fi probe data in mobility modeling. Additionally, it introduces an opportunistic sensing approach for estimating mobility flows, which not only contributes to our understanding of transportation dynamics but also holds significance in comprehending the implications for carbon capture efforts.
Target-Driven Visual Navigation by Using Causal Intervention

Target-driven visual navigation presents great potentials in scientific and industrial fields. It takes the target and environment observations as input. However, during training, we found that the agent sometimes got stuck in specific locations. Based on the analysis on visual information from a novel causal perspective, one of the most critical hurdles is the neglect of confounders in environments, which often leads to spurious correlations. Mitigating the confounding effect helps to discover the real causality and therefore are taken into consideration in other fields such as object detection. In this article, we propose Causal Intervention Visual Navigation (CIVN), based on deep reinforcement learning (DRL) and causal intervention. We realize causal intervention using front-door adjustment as most confounders are hard to model explicitly. Specifically, CIVN is implemented by Causal Attention, which is a reasonable approximation of causal intervention for visual navigation. Causal attention provides high-quality representation, which is leveraged by DRL and reduces the number of “stuck”. It is worth mentioned that causal intervention is for the first time applied by us in solving the confounding effect in target-driven visual navigation. Extensive experiments on AI2-THOR demonstrate that CIVN achieves better performance than prior arts. Specifically, the generalization for unknown targets and scenes is improved by a large margin, which is a basic research topic in visual navigation. Moreover, to obtain better generalization, we propose a novel experiment utilizing pre-trained models firstly.
An efficient preconditioning bi-CGSTAB algorithm to compute flows in chemical nonequilibrium

An efficient method for solving the chemical nonequilibrium Navier-Stokes equations including all the species conservation and energy production equations are developed. The algorithm is embodied in a fully coupled, implicit, large block structure. Van Leer flux splitting for invisid terms and central differencing for viscous terms in the explicit operators are applied in the numerical algorithm. The fully-coupled system is solved implicitly and the Bi-Conjugate Gradient Stable (Bi-CGSTAB) method with a preconditioner of incomplete LU-factorization (ILU) is used for solving large block-structure and diagonal dominate matrix equations. The computations are performed for the hypersonic inflow over blunt bodies including half cylinder, double ellipse, and blunt nose with cylindrical portion following 15 deg inclined plane. Computational results in the present study are compared with other calculated data and exhibit good agreement. Convergence histories of the mean flow variables and species equations demonstrate that the fast convergent rate can be achieved by the preconditioned Bi-CGSTAB method. (Author)
The Establishment of the Cut-off Value of Weight-for-Height for Preschool Children in China and an Empirical Study on the Influence of Overweight and Obesity on Physical Performance

Background: We aimed to establish the cut-off value of weight-for-height for preschool children in China, and to explore the influence of overweight and obesity on the physical performance of preschool children. Methods: Data from 31 provinces in China monitored in 2010 and 2014, the standard deviation unit curves of WFH were established by using GAMLSS (generalized additive model for location, scale, and shape) method preschool children in China, and then the cut-off values of normal weight, overweight and obesity were divided. The research group tested 5 physical performance indicators of 5154 preschool children from April 2018 to June 2019 in Beijing and Zhejiang in China. We then compared physical performance differences between the normal-weight group and the overweight-obesity group. Results: A smooth and effective standard deviation unit curve of WFH was established. The overweight and obesity of Chinese preschool children were at a high level. Being overweight and obese reduced preschool children’s relative strength, speed, sensitivity, coordination, and balance ability, but did not reduce preschool children’s flexibility. In general, the physical performance of preschool children in the normal weight group was better than that in the overweight and obesity group. Conclusion: It is suggested to use the WFH criteria in this study to evaluate overweight and obesity in Chinese preschool children, and strengthen physical activity and take in energy substances properly of preschool children, to control the spread of overweight and obesity, to improve physical performance level.
Microdochium sichuanense sp. nov. (Microdochiaceae, Xylariales), from a Poaceae host in Sichuan, China

During our investigation of ascomycetes fungi on Poaceae, a microdochium-like fungus was collected from dead grass leaves from a terrestrial habitat in Chengdu, Sichuan Province, China. Based on morphological characterization and phylogenetic analyses, we introduce this fungus as Microdochium sichuanense sp. nov. Maximum likelihood, and Bayesian analyses of combined genes (LSU, ITS and rpb2) were performed to clarify the phylogenetic affinities of the new species. Microdochium sichuanense is phylogenetically closely related to M. hainanense and morphologically it fits the generic characteristics of Microdochium species in having ostiolate ascomata, peridium cells of textura angularis to textura prismatica, unitunicate, clavate to oblong, 8-spored asci and hyaline, oval to fusiform ascospores. The new species was compared with phylogenetically and morphologically closely related species, and detailed descriptions, micrographs, and phylogenetic trees of the new species are provided.
Moisture transport axes and their relation to atmospheric rivers and warm moist intrusions

12 The water vapor transport in the extratropics is mainly organized in narrow elongated 13 filaments. These filaments are referred to with a variety of names depending on the con14 texts. When making landfall on a coastline, they are generally referred to as atmospheric 15 rivers; when occurring at high latitudes, many authors regard them as warm moist in16 trusions; when occurring along a cold front and near a cyclone core, the most commonly 17 used term is warm conveyor belt. Here, we propose an algorithm that detects these var18 ious lines of moisture transport in instantaneous maps of the vertically integrated wa19 ter vapor transport. The detection algorithm extracts well-defined maxima in the wa20 ter vapor transport and connects them to lines that we refer to as moisture transport 21 axes. By only requiring a well-defined maximum in the vapor transport, we avoid im22 posing a threshold in the absolute magnitude of this transport (or the total column wa23 ter vapor). Consequently, the algorithm is able to pick up moisture transport axes at all 24 latitudes without requiring region-specific tuning or normalization. We demonstrate that 25 the algorithm can detect both atmospheric rivers and warm moist intrusions. Atmospheric 26 rivers sometimes consist of several distinct moisture transport axes, indicating the merg27 ing of several moisture filaments into one atmospheric river. Finally, we showcase the 28 synoptic situations and precipitation patterns associated with the occurrence of the iden29 tified moisture transport axes in example regions in the low, mid, and high latitudes. 30 Plain Language Summary 31 The transport of moisture from warmer and moister regions towards colder and drier 32 regions mainly occurs in brief and narrow bursts that can lead to extreme amounts of 33 precipitation upon landfall. In the mid-latitudes, such bursts are generally referred to 34 as atmospheric rivers, whereas in the Arctic they are often referred to as warm moist 35 intrusions. We introduce a new and more general definition of what constitutes such bursts 36 of moisture transport, which is based primarily on their narrow, elongated structure. Us37 ing this more general definition, we show that similar bursts in moisture transport oc38 cur frequently across all climate zones, including polar and tropical regions. In the Trop39 ics, for example, we identify seasonal variations in moisture transport, such as those as40 sociated with the Asian monsoon. 41
Voice Coded Lock

Accessing secure areas often requires some form of physical key, which can be lost or cumbersome to operate when a user’s hands are full. To fix this issue, we designed a hands-free, automatic door lock that opens when a user gives the correct audio password. This paper details the design and verification of our door lock. Our lock was able to consistently (with around 85% accuracy) and quickly (with around one second of processing time) classify live audio inputs given to the door lock system, and automatically unlock the door when provided with the correct password.
HOW VARIABLE ARE ENGLISH SIBILANTS?

This paper takes a corpus-phonetic approach to consider variability in word-initial, prevocalic English sibilants in 5k speakers, 235k tokens, from 27 geo-social-ethnic regions of North America and the British Isles. We analyse ERB-transformed, spectral peak measures calculated from amplitude-normalised multitaper spectra using a ‘distributional’ Bayesian mixed-effects regression which explicitly models token, speaker, and region-level variability. Following previous phonetic and sociolinguistic research we expected English /s/ to be more variable than / S /. The results, however, differ according to the level at which we consider variability. Across English regions, /s/ and / S / show a similar degree of variability. Across speakers within-region, /s/ is generally more variable than / S /, and within speakers, /s/ is generally more variable by token than / S /, both results likely reflecting linguistic and social-indexical sources of variation—such as gender, which has a greater effect on spectral peak for /s/ than for / S /. 1
Validating an infrastructure for online chatter monitoring

In today’s day and age, the number of people in the third stage of their life is increasing, and so are the expenses required to look after them and care for them. This increase has called for massive growth in the demand for technology to assist the elderly or help others to take care of them. But regardless of the money being invested and the efforts made, an essential factor that needs to be determined is how well the older generation would adapt to these technologies. Support groups have dominated all the research surrounding the interaction of older people and technology. But we live in an age of social media. COVID-19 forced more senior people to come out of their shells and get their hands dirty with technology since technology remained the sole communication medium during the social lockdown. And with the development of advanced text analytics techniques, it will be interesting to see whether the results found by the previous researchers in this field are still valid. And since society plays a significant factor in influencing the elderly to use technology, we will also look at the emotions of people talking about older people. We created a pipeline that can be used by anyone trying to fetch data from specific social media platforms, which gave an accuracy of around 80% on three different platforms. We then used Part of Speech Tagging techniques to find out what parts of speech were used the most by either older people or people talking about older people. We saw increased use of Possessive Pronouns and compared the same with data extracted using keywords not related to older people to confirm the same. We then tried to mine the emotion behind the texts and found that for nearly half of the keywords used across the three social media websites (17 out of 34), the primary feeling shown was that of being scared. The second most widely exhibited feeling across the dataset was the feeling of joy when the keywords were related to welfare schemes of the government for older people. For a few keywords, the emotion was sadness and angriness. Our research showed contradicting results to the outcomes delivered by previous researchers who used focus groups to get an idea of the emotions of older people towards technology.
TRAINING WITHIN INDUSTRY (TWI-JS ) PROGRAM AS A TOOL TO IMPROVE WORK SAFETY IN THE STEEL INDUSTRY IN POLAND

Accidents at work related to the operation of technological machinery are recorded in the steel industry in Poland. A reduction of accidents is possible through the implementation of technical and organizational measures. As regards the elimination of hazards at workplaces, it is possible to apply the Training Within Industry (TWI) program. One of its modules, i.e., Safety (JS), is aimed at creating a safe workplace by the acquisition of the skills of identification and elimination of the causes of hazards leading to accidents. This article aims to present the possibilities of the use of the TWI-JS method to prevent accidents at work in the steel industry.
Neural Progressive Meshes

The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Internet. Detailed high-resolution assets can pose a challenge to storage as well as transmission bandwidth, and level-of-detail techniques are often used to transmit an asset using an appropriate bandwidth budget. It is especially desirable for these methods to transmit data progressively, improving the quality of the geometry with more data. Our key insight is that the geometric details of 3D meshes often exhibit similar local patterns even across different shapes, and thus can be effectively represented with a shared learned generative space. We learn this space using a subdivision-based encoder-decoder architecture trained in advance on a large collection of surfaces. We further observe that additional residual features can be transmitted progressively between intermediate levels of subdivision that enable the client to control the tradeoff between bandwidth cost and quality of reconstruction, providing a neural progressive mesh representation. We evaluate our method on a diverse set of complex 3D shapes and demonstrate that it outperforms baselines in terms of compression ratio and reconstruction quality.
CAMBRIDGE WORKING PAPERS IN ECONOMICS The Impact of Qualitative Reviews on Racial Statistical Discrimination: Evidence from Airbnb

This paper analyses the effect of qualitative reviews on racial statistical discrimination. Using a finetuned Bidirectional Encoder Representations from Transformers (BERT) language model that was developed specifically for this task, I include the effects of recent qualitative reviews on the log listing price difference between Black and White hosts on Airbnb. For properties without guest reviews, I find a 4% log listing price difference between Black and White hosts for comparable properties. Once review information becomes available, this pricing difference reduces to 1%, providing evidence against the persistence of racial listing price differences on Airbnb, and furthermore, suggesting that race is used as the primary signal of property quality only in the absence of better information. Beyond its applications within the context of Airbnb, this paper aims to explain how the early provision of detailed qualitative information can reduce the effects of statistical discrimination against minorities. Reference Details CWPE 2331 Published 22 March 2023
Culture Awareness in Intelligent Systems (short paper)

Understanding how the data used to train intelligent systems affects their behaviour is a critical task in the Artificial Intelligence field. It is also known that making artificial agents capable of adapting their actions according to the culture improves their interaction with humans. For this reason, it may be crucial to know how the cultural component inside data affects the prediction of intelligent systems. In this paper, we propose a method to acknowledge the cultural factor inside data, and we show some preliminary results obtained by using Random Forest and Support Vector Machine models on two publicly available datasets.