Miracle-dz/UniFAR-FTUs huggingface dataset:
When attractiveness demands longer looks: the effects of situation and gender

We investigated how aesthetics guides our exploration of the environment. We embedded attractive and nonattractive faces into complex, real-world scenes and measured eye movements during scene viewing. We examined whether attractive faces would elicit longer looks, which would suggest that the aesthetic response orients people toward the rewarding and pleasing aspects of the environment. Experiment 1 showed that mean fixation, mean first fixation, and total fixation durations were longer to attractive faces, and fixations were longest to female faces and by female perceivers. In Experiment 2, we examined whether these effects of attractiveness are sensitive to situational factors. When perceivers were subjected to a threat or social approach manipulation prior to viewing the scenes, we confirmed specific hypotheses concerning the two manipulations. In accordance with the hypothesis that males have higher aggression potential than females, there were no differences in fixation durations between attractive and nonattractive male faces in the threat condition. On the other hand, in the social approach condition, both female and male attractive faces received longer looks. These results suggest that the aesthetic response orients people not only to the pleasing aspects of their environment, but also to those features that are adaptively relevant.
Recent advances in microfluidic technologies for cell-to-cell interaction studies

Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the ethical guidelines, outlined in our author and reviewer resource centre, still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. Accepted Manuscript
Developing Website for Belimbing Tourism Village, Tabanan, Bali

Purpose: The purpose of this research is to evaluate the Belimbing Village website in development efforts website by model information, communication, transaction, relationship and techical-merit (ICTRT) carried out to support the Belimbing Tourism Village produces dimensions of effectiveness website the maximum in helping the promotion of Belimbing Tabanan Tourism Village. Research methods: The data collection methods used were observation, interviews, questionnaires and interviews. In this study using qualitative and quantitative approaches or better known as mixed method research by using SWOT analysis techniques and Importance Performance Analysis. Findings: The results of the research using the interview method show that the application website in Belimbing Village is still very minimal because there are many features and items that need to be developed, one of which is the information dimension, transaction dimension and communication dimension. The results of the study using SWOT analysis showed that Belimbing Village has utilized ICT, but even though it has implemented ICT there are still many aspects that are lacking in website, so that more in-depth research is needed to assess its effectiveness in website Belimbing Village. Based on analysis Importance Performance Analysis produce effectiveness attributes. The researcher adopted the ICTRT model which consisted of 47 evaluation instruments which were divided into 5 evaluation dimensions. Implication: Site evaluation website expected to maximize promotion through website in Belimbing Village because website is an effective solution to introduce and market products/services by utilizing the internet as an information medium.
How a Realistic Linear Rh = ct Model of Cosmology Could Present the Illusion of Late Cosmic Acceleration

Realistic FLRW cosmic coasting models which contain matter now appear to be a reasonable alternative in explaining the accumulated Supernova Cosmology Project data since 1998. In sharp contrast to the unrealistic original classic Milne universe, which was entirely devoid of matter, these modified Milne-type models containing matter, often referred to as realistic linear Rh = ct models, have rapidly become the primary competition with standard cosmology. This paper compares the expected relative luminosity distances and relative angular diameter distances for given magnitudes of redshift within these two competing models. A simple ratio formula is derived, which explains how expected luminosity distances and angular diameter distances for given magnitudes of redshift within a realistic Milne-type cosmic expansion could create the illusion (for standard model proponents) of cosmic acceleration where none exists.
Why should I switch on my camera? Developing the cognitive skills of compassionate communications for online group/teamwork management

Introduction Associated with learning and social isolation from each other during the pandemic-driven transition to online platforms in Higher Education (HE), many students were, and remain, reluctant to turn on their video cameras to be present with each other during their online meetings. Using the Compassionate Mind Foundation's definition of compassion, not as an emotion, but as a psychobiological motivation to take wise action to help when self or others struggle, this comparative study examined (a) the deployment by students during online, task-focused group/team meetings, of taught verbal and non-verbal communication strategies that were explicitly compassionate and (b) the effects of these strategies on each other's social and learning experiences in these meetings, compared to when they did not use them. Methods Twenty-four STEM students from a sample of five Sri Lankan universities, were mixed, then divided into six groups of four students per group. This mixed-methods study, video-recorded and analyzed each group's task-focused group meetings before, then after, an online interactive 90-min training session (the intervention) in the Cognitive Skills of Compassionate Communications (CSCC) for groups/teams. Results Using R, SPSS and Microsoft Excel to analyse the quantitative data, a statistically significant improvement in students' screen-gaze attentiveness was identified after the CSCC intervention. The qualitative data analysis explained this and other behavioral changes that were shown to enhance students' social and learning experiences in their online meetings. Given the strong historical and political drivers of current divisions across Sri Lankan student communities, these findings call for more urgent research on compassion as a cognitive competence for accelerating group/team cohesion and criticality across HE, and beyond.
Coarse-to-Fine Depth Super-Resolution With Adaptive RGB-D Feature Attention

Depth maps suffer from multiple kinds of degradation such as noise and low resolution, due to the limitations of sensors. To improve the spatial resolution and quality of depth maps, RGB-D-based depth super-resolution (SR) methods utilize the corresponding color image to provide extra structure information. However, the inconsistency between the color texture and depth structure can lead to texture-copying artifacts if the two kinds of features are fused without selection. In this article, we propose a novel coarse-to-fine framework for RGB-D-based depth SR, which consists of two sub-networks, i.e., CONet for coarse SR and RFNet for refinement. Through the proposed coarse supervision strategy, CONet can alleviate multiple degradations in depth maps and assist with further SR in the refinement stage. Moreover, the branch attention module (BAM) is incorporated in the RFNet to adaptively select important information from RGB-D features and suppress the texture-copying artifact. Additionally, we propose an edge-aware spatial attention module (ESAM) to further locate and restore the depth discontinuity in the fused RGB-D features. Extensive experiments on multiple benchmarks demonstrate that compared to the state-of-the-art methods, the proposed method achieves improved results both quantitatively and qualitatively.
Interactive Occlusion-Free System for Accessible Volume Exploration

When exploring a 3D medical dataset, data occlusion creates difficulties for users to understand the dataset in detail since many internal structures are occluded. Techniques such as transfer function design, isosurface extraction, etc., have been proposed in the past to address the occlusion problem and assist expert users in exploring volumetric data. However, these techniques may not be feasible for some non-expert users’ scenarios, such as science museum visitors exploring a brain CT image data volume on a large touch screen or high school students learning the human body structure on a tablet because they have no need to conduct long-term data exploration and rigorous scientific analysis. To address the non-expert users’ need for an easy-to-use data exploration tool, we propose an interactive data exploration system for non-expert users to interact with the dataset with a short learning time and simple inputs. Non-expert users can remove the obstructing material to reveal the occluded structures by simply clicking on the obstructing material. Our system also preserves the context for non-expert users to understand the dataset after the obstructing material is removed easily. Additionally, for data providers such as the engineers in a museum, our system provides a semi-automatic workflow to set up a dataset into our system for non-expert users. In this work, we conduct two user studies to evaluate our system’s usability.
CloudSentry: Two-Stage Heavy Hitter Detection for Cloud-Scale Gateway Overload Protection

The cloud vendors provide sharing resources for millions of tenants across the world to achieve economies of scale. At the same time, the cloud network keeps the performance isolation between different tenants as if they use their private dedicated resources. However, heavy hitters caused by a single tenant at cloud gateways will break such isolation, undermining the predictable performance expected by other cloud tenants. To prevent it, heavy hitter detection becomes a key concern at the performance-critical cloud gateways but faces the dilemma between fine granularity and low overhead. In this work, we present CloudSentry, a scalable two-stage heavy hitter detection system dedicated to multi-tenant cloud gateways against such a dilemma. CloudSentry uses CPU utilization as an indicator of heavy hitters and conducts a lightweight coarse-grained detection running 24/7 to detect such CPU spikes. Then it invokes a fine-grained detection to precisely dump and analyze the potential heavy-hitter packets at the CPU spikes. After that, a more comprehensive analysis is conducted to associate heavy hitters with the cloud service scenarios and invoke a corresponding backpressure procedure. CloudSentry significantly reduces memory, computation and storage overhead compared with existing approaches. In a gateway cluster under an average traffic throughput of 251 Gbps, CloudSentry consumes only a fraction of 2%–5% CPU utilization with 8 KB run-time memory, producing only 10 MB heavy hitter logs during one month. Additionally, as it has been deployed in Alibaba Cloud for over two years, we share case studies and a lot of deployment experiences in this article.
A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government

Introduction Since the outbreak of the COVID-19 pandemic, the Chinese government has implemented a series of strict prevention and control policies to prevent the spread of the virus. Recently, the Chinese government suddenly changed its approach and lifted all prevention and control measures. This sudden change in policy is expected to lead to a widespread outbreak of COVID-19 in China, and the public and local governments are not adequately prepared for the unknown impact on society. The change in the “emergency” prevention and control policy provides a unique research perspective for this study. Methods The purpose of this study is to analyze the public's attitudes and emotional responses to COVID-19 under the sudden opening policy, identify the key factors that contribute to these attitudes and emotions, and propose solutions. In response to this sudden situation, we conducted data mining on topics and discussions related to the opening of the epidemic on Sina Weibo, collecting 125,686 interactive comments. We used artificial intelligence technology to analyze the attitudes and emotions reflected in each data point, identify the key factors that contribute to these attitudes and emotions, explore the underlying reasons, and find corresponding solutions. Results The results of the study show that in the face of the sudden release of the epidemic, the public mostly exhibited negative emotions and behaviors, with many people experiencing anxiety and panic. However, the gradual resumption of daily life and work has also led some people to exhibit positive attitudes. Conclusion The significance of this study is to help the government and institutions understand the impact of policy implementation on users, and to enable them to adjust policies in a timely manner to respond to potential social risks. The government, emergency departments, and the public can all prepare for similar situations based on the conclusions of this study.
Measures of ǫ-complexity

AbstractWe study some measures which are related to the notion of the ǫ-complexity. We prove that measureof ǫ-complexity deﬁned on the base of the notion of ǫ-separability is equivalent to the dual measure thatis deﬁned through ǫ-nets. Keywords : Complexity, Separability, Bernoulli measure 1 Introduction The problems under consideration in this article were originated in the process of study of complexityof behavior of orbits in dynamical systems. While symbolic complexity (see, for instance [4]) deals withsymbolic systems and topological complexity ([2]) reﬂects pure topological features of dynamics, theǫ-complexity depends essentially on a distance in the phase space (see deﬁnition bellow). If one has adynamical system generated by a continuous map f : X → X where X is a metric space with a distanceρ, one can introduce the sequence of distances ([3])ρ n (x,y) = max 0≤i≤n−1 ρ(f i x,f i y), n ∈ N,and study the ǫ-complexity with respect to the distance ρ n as a function of “time” n. This functionreﬂects the evolution of instability of orbits in time [1]. But to study it in details, one needs to knowmore about general properties of the ǫ-complexity of a metric space (without dynamics). The goal this article is to introduce and study quantities which contain an essential informationabout ǫ-complexity, the measures of ǫ-complexity in an “abstract” metric space. The main results willbe related to the ǫ-complexity deﬁned on the base of the notion of ǫ-separability. The notion was usedﬁrst by Kolmogorov and Tikhomirov [9] in their study of solutions of PDE and realization of randomprocesses (Shannon suggested to pay attention to this notions in 1949, though). We will also studyǫ-complexities based on the notion of ǫ-nets. We prove that measure of ǫ-complexity deﬁned on the baseof the notion of ǫ-separability is equivalent to the dual measure that is deﬁned through ǫ-nets.It appeared naturally that some results and ideas from discrete mathematics are worth to be ex-ploited. We believe that we made the ﬁrst step in this direction.
Fully 4-D direct joint estimation of compartmental models and blood input function from dynamic SPECT projections

COMPARTMENTAL model analysis of dynamic cardiac single photon emission computed tomography (SPECT) data can provide a quantitative measure of myocardial perfusion and can potentially provide better contrast between healthy and diseased tissue, compared to static images [1]. Compartmental analysis may also be useful for assessing tissue viability [2]. In previous work, we formulated a nonlinear estimation problem in which linear and conditionally linear parameters are estimated directly from projection data with the use of least squares, given iteratively estimated values for nonlinear washout parameters for one-compartment kinetic models for segmented volumes [3, 4]. This approach removes parameter bias generated by artifacts that appear in conventional image reconstructions because of projection data inconsistency and data truncation by cone beam collimators. The methods can be applied to any collimator or orbit geometry, provided that the acquired data yield qualitatively correct images that can be used to segment the entire projected field of view. To reduce the large computational burden associated with straightforward solution of the embedded linear least squares subproblem, we also developed an accelerated “semidirect” approach in which B-splines are first used to model smooth time-activity curves for segmented volumes, and then compartmental models are fit to the curves [5,6]. In the present work, we generalize the semidirect methods to accelerate direct joint estimation of compartmental models for tissue volumes and B-spline time-activity curve models for the blood input function and other volumes that do not obey a compartmental model. We hypothesize that the additional temporal regularization provided by compartmental models will improve the accuracy and precision of uptake and washout parameters for small tissue volumes such as myocardial defects.
PENG light meets the event calculus

In this paper I present an extension of the controlled natural language PENG Light that is motivated by the formal properties of the Event Calculus, a narrative-based formal language for events, uents, and the periods for which these uents hold. I show how this extension can be used to express domain-speci c axioms in a natural way and sketch how PENG Light and the Event Calculus can be brought together for reasoning about events and time.
Nonparametric methods for the estimation of the conditional distribution of an interval-censored lifetime given continuous covariates

This thesis contributes to the development of conditional nonparametric estimation of the survival function given a continuous time-fixed and time-varying covariate with censored data. It is based on three papers written with my Ph.D. supervisor, Professor Thierry Duchesne. The first paper, titled "A generalization of Turnbull's estimator for nonparametric estimation of the conditional survival function with interval-censored data," was published in 2011 in Lifetime Data Analysis , vol. 17, pp. 234-255. The second paper, titled "On the performance of some non-parametric estimators of the conditional survival function with interval-censored data", was published in 2011 in Computational Statistics & Data Analysis, vol. 55, pp. 3355-3364. The third paper, titled "Estimation of the conditional survival function of a failure time given a time-varying covariate with interval-censored observations ", will be submitted for publication in Statistica Sinica.
Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloud-network integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B\&B-CCRA and WF-CCRA, based on the Branch \&Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B\&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.
Aging, Parkinson’s Disease, and Models: What Are the Challenges?

Parkinson ’ s disease (PD) is a chronic, neurodegenerative condition characterized by motor symptoms such as bradykinesia, rigidity, and tremor, alongside multiple nonmotor symptoms. The appearance of motor symptoms is linked to progressive dopaminergic neuron loss within the substantia nigra. PD incidence increases sharply with age, suggesting a strong association between mechanisms driving biological aging and the development and progression of PD. However, the role of aging in the pathogenesis of PD remains understudied. Numerous models of PD, including cell models, toxin-induced models, and genetic models in rodents and nonhuman primates (NHPs), reproduce di ﬀ erent aspects of PD, but preclinical studies of PD rarely incorporate age as a factor. Studies using patient neurons derived from stem cells via reprogramming methods retain some aging features, but their characterization, particularly of aging markers and reproducibility of neuron type, is suboptimal. Investigation of age-related changes in PD using animal models indicates an association, but this is likely in conjunction with other disease drivers. The biggest barrier to drawing ﬁ rm conclusions is that each model lacks full characterization and appropriate time-course assessments. There is a need to systematically investigate whether aging increases the susceptibility of mouse, rat, and NHP models to develop PD and understand the role of cell models. We propose that a signi ﬁ cant investment in time and resources, together with the coordination and sharing of resources, knowledge, and data, is required to accelerate progress in understanding the role of biological aging in PD development and improve the reliability of models to test interventions.
Combination of observational and functional trait-based approaches in developing a polyculture design tool

ABSTRACT Models of species composition in diversified cropping systems utilize mostly functional trait-based or observation-based approaches. We argue that a combination of these two approaches makes polyculture design tools more robust. We assessed quantity, quality, and complementarity of information from multiple sources for designing diversified cropping systems with vegetables and spice crops for cold temperate climate. Trait and observational data were integrated from: (i) two grower-oriented and one academic crop database, (ii) a survey of farmers practicing community-supported agriculture, and (iii) a systematic literature review on the use of spice crops in vegetable farming. Survey results reveal that the farmers were capable of achieving medium to good levels of their main goals, but failed to reach desired multifunctionality with their polycultures, which can be potentially improved with computational tools. None of the analyzed data sources provided a comprehensive dataset for all target crops and functional traits. However, source combination allowed for design from known crop companions (farmers survey and grower-oriented databases), to addressing specific pest problems (literature review), and increasing functional complementarity and facilitation by trait matching (academic and grower-oriented trait databases). Integrating information from different sources increased the number of crop combination options but also planning and management complexity. GRAPHIC ABSTRACT Combination of functional trait-based and observation-based approaches within a polyculture design tool.