Miracle-dz/UniFAR-FTUs huggingface dataset:
GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation

Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform those in another, etc.? Answers to these questions require explanations of the model structure, the choice of parameters, and contextualization of the findings with respect to their geographic context. This is particularly true for local forms of regression models which are focused on the role of locational context in determining human behavior. In this paper, we present GeoExplainer, a visual analytics framework designed to support analysts in creating explanative documentation that summarizes and contextualizes their spatial analyses. As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results. As analysts explore the model results, all visualizations and annotations can be captured in an interactive report generation widget. We demonstrate our framework using a case study modeling the determinants of voting in the 2016 US Presidential Election.
Nonresponsiveness of the Rat Hypothalamo-Pituitary-Adrenocortical Axis to Parturition-Related Events: Inhibitory Action of Endogenous Opioids.

During the last 2 days of pregnancy in rats, basal corticosterone secretion is enhanced, although the response of the hypothalamo-pituitary-adrenocortical (HPA) axis to emotional and physical stressors is blunted, independent of the action of endogenous opioids. In this study, alterations in the reactivity of the HPA axis, which may accompany parturition-related stimuli, and the involvement of endogenous opioids were examined in chronically catheterized rats. In vehicle-treated controls (n = 9), ACTH and corticosterone secretion decreased in preparation for birth (P < 0.01) and further declined immediately after delivery of the second pup (P < 0.01), remaining low for 150 min. In contrast, in animals injected with the opiate antagonist naloxone (5 mg ml−1 kg−1, iv, n = 6) after delivery of the second pup, ACTH and corticosterone release were enhanced within 20 min (ACTH, 5.0-fold; corticosterone, 2.3-fold; P < 0.01 vs. controls) and returned to control levels after 90 min. In confirmation of previous repo.
General validity of the exponential law for the effect of concentration polarization in reverse osmosis in a stirred-cell geometry, including an activity correction for 1:1 salt solutions

Reverse osmosis (RO) is a method to desalinate water with membranes and an applied pressure. Very important in the theory of mass transport in RO is the concentration polarization (CP) layer, which develops on the upstream side of the membrane because of a combination of salt convection and diffusion. Because of the CP-effect, the salt concentration at the membrane surface is higher than in the channel, and this increases the osmotic pressure there, and thus transmembrane water flux is reduced (the osmotic pressure acts against water flux), while salt leakage through the membrane increases. So it is very important to understand and describe the CP-layer accurately. We analyze a one-dimensional geometry, which is of relevance for a typical lab-scale RO setup using small membrane coupons where the solution on the feed side of the membrane is stirred. For this geometry, the standard film layer approach is often used that assumes a stagnant film layer of a defined thickness, which however does not exist in reality. We set up a model without that assumption but including refreshment of solution because of the flow of water along the membrane due to stirring. We show that the `exponential law' for the CP-layer that is predicted by the the film model, also applies for this more accurate model. We further improve the model by including the activity coefficient of salt ions, as described by the Bjerrum theory that is based on ion-ion Coulombic interactions. We evaluate the original linearized Bjerrum theory as well as an extended Bjerrum equation that is valid up to 1.5 M salt concentration. We show how including this activity correction leads to a reduction of the diffusional driving force at high concentration, and thus the salt concentration at the membrane further increases. However, the effect can be easily included by reducing the CP-layer mass transfer coefficient by a fixed percentage.
Effect of Smelling Green Tea Rich in Aroma Components on EEG Activity and Memory Task Performance

: We investigated the memory task performance and the central nervous activity after smelling two kinds of pan-fired Japanese green tea to examine their physical and psychological effects. Twenty eight subjects participated in this study. We used Koushun and Kouju for test samples, which were made by different manufacturing processes. After smelling each odor sample, a memory task and an arithmetic task were used to test mental stress. Electroencephalogram (EEG) was recorded before and after smelling the test samples, and EEG activity was estimated for 4 frequency bands (alpha 1, alpha 2, beta 1 and beta 2). The profiles of mood states (POMS) and the visual analog scale (VAS) after mental stress task were completed for subjective assessments. The results showed that the odor of Kouju may induce a positive emotion. It may also affect the EEG band power of beta 1 at right frontal region and improve memory task performance.
Exploring the Effects of VR Activities on Stress Relief: A Comparison of Sitting-in-Silence, VR Meditation, and VR Smash Room

In our lives, we encounter various stressors that may cause negative mental and bodily reactions to make us feel frustrated, angry, or irritated. Effective methods to manage or reduce stress and anxiety are essential for a healthy life, and several stress-management approaches are found to be useful for stress relief, such as meditation, taking a rest, walking around nature, or even breaking things in a smash room. Previous research has revealed that certain experiences in virtual reality (VR) are effective for reducing stress as traditional real-world methods. However, it is still unclear how the stress relief effects are associated with other factors like individual user profile in terms of different treatment activities. In this paper, we report our findings from a formal user study that investigates the effects of two virtual activities: (1) VR Meditation and (2) VR Smash Room experience, compared with a traditional Sitting-in-Silence method. Our results show that VR Meditation has a better stress relief effect compared to VR Smash Room and Sitting-in-Silence, and the effects of the treatments are correlated with the participants’ personalities. We discuss the findings and implications addressing potential benefits/impacts of different stress-relief activities in VR.
Neighborhoods and health

1. Introduction 2. Neighbourhoods and Health: An Overview PART I: METHODOLOGICAL AND CONCEPTUAL APPROACHES TO STUDYING NEIGHBOURHOOD EFFECTS ON HEALTH 3. The examination of neighbourhood effects on health: Conceptual and methodological issues related to the presence of multiple levels of organization 4. Multilevel methods for public health research 5. The quantitative assessment of neighbourhood social environments 6. Neighbourhood-level context and health: lessons from sociology 7. Geocoding and measurement of neighbourhood socioeconomic position: A U.S. perspective 8. Area-based deprivation measures: A UK perspective PART II: NEIGHBOURHOODS AND HEALTH OUTCOMES 9. Neighbourhoods and infectious diseases 10. Infant health: Race, risk, and residence 11. Putting asthma into context: community influences on risk, behaviour, and intervention PART III: THE CONTOURS OF NEIGHBOURHOOD EFFECTS ON HEALTH 12. Residential segregation and health 13. Neighbourhoods and networks: The construction of safe places and bridges 14. Neighbourhoods, aging and functional limitations 15. Neighbourhoods, health research, and its relevance to public policy
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions

With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users demanding access to data from various modalities. To address this, cross-modal retrieval has emerged, enabling interaction across modalities, facilitating semantic matching, and leveraging complementarity and consistency between different modal data. Although prior literature undertook a review of the cross-modal retrieval field, it exhibits numerous deficiencies pertaining to timeliness, taxonomy, and comprehensiveness. This paper conducts a comprehensive review of cross-modal retrieval's evolution, spanning from shallow statistical analysis techniques to vision-language pre-training models. Commencing with a comprehensive taxonomy grounded in machine learning paradigms, mechanisms, and models, the paper then delves deeply into the principles and architectures underpinning existing cross-modal retrieval methods. Furthermore, it offers an overview of widely used benchmarks, metrics, and performances. Lastly, the paper probes the prospects and challenges that confront contemporary cross-modal retrieval, while engaging in a discourse on potential directions for further progress in the field. To facilitate the research on cross-modal retrieval, we develop an open-source code repository at https://github.com/BMC-SDNU/Cross-Modal-Retrieval.
ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events

Autonomous vehicles are advanced driving systems that are well known to be vulnerable to various adversarial attacks, compromising vehicle safety and posing a risk to other road users. Rather than actively training complex adversaries by interacting with the environment, there is a need to first intelligently find and reduce the search space to only those states where autonomous vehicles are found to be less confident. In this paper, we propose a black-box testing framework ReMAV that uses offline trajectories first to analyze the existing behavior of autonomous vehicles and determine appropriate thresholds to find the probability of failure events. To this end, we introduce a three-step methodology which i) uses offline state action pairs of any autonomous vehicle under test, ii) builds an abstract behavior representation using our designed reward modeling technique to analyze states with uncertain driving decisions, and iii) uses a disturbance model for minimal perturbation attacks where the driving decisions are less confident. Our reward modeling technique helps in creating a behavior representation that allows us to highlight regions of likely uncertain behavior even when the standard autonomous vehicle performs well. We perform our experiments in a high-fidelity urban driving environment using three different driving scenarios containing single- and multi-agent interactions. Our experiment shows an increase in 35, 23, 48, and 50% in the occurrences of vehicle collision, road object collision, pedestrian collision, and offroad steering events, respectively by the autonomous vehicle under test, demonstrating a significant increase in failure events. We compare ReMAV with two baselines and show that ReMAV demonstrates significantly better effectiveness in generating failure events compared to the baselines in all evaluation metrics.
"A 9000-Year Old Wapiti (Cervus elaphus) Skeleton from Northern Alberta, and its Implications

A complete and fully articulated adult male wapiti (Cervus elaphus) skeleton was recovered from the 50 m terrace of Smoky River, near Watino, Alberta. A collagen-based date on antler gave an age of 9075 ± 305 yr BP (S-2614). The local terrace sequence features three benches, and two previously described fossil sites are placed within it. The importance of the find is that it indicates an environment at 9000 yr BP similar to the present. A brief review of local deglaciation precedes discussion of the paleontology.
Bootstrap Aggregating Multivariate Adaptive Regression Splines (Bagging MARS) for Poor Households Classification in Region of Jombang

Poverty is a major social problems of each country, especially developing countries, including Indonesia. The focus of this research is poverty in Jombang because of an increase Human Development Index and the economy is not accompanied by a decrease in the poverty rate in Jombang from 2009 until 2011, this is presumably because the provision of assistance to poor households in Jombang not on target, so needed classification method to help the poor households for assistance can be precisely targeted. MARS is a classification method that is focused to overcome the problems of high dimensions and discontinuities in the data. The accuracy of classification can be improved using the resampling methods, namely bagging. This research will use bagging MARS method to obtain classification models based aid poor households are expected in Jombang. MARS modeling results concluded that the probability of poor households in Jombang who need primary assistance is 0.789 and probability of secondary needs help is 0.211, and there are 14 variables that affect the expected help poor households in Jombang. The accuracy of MARS method for classification is 69.40 percent, while the accuracy of the bagging MARS method is 69.63 percent, this accuracy is the best among 25, 50, 100, 150, 200, 250, and 500 replication. Thus, in this research, a more precise method used to classify the expected help poor households in Jombang is bagging MARS.
Utilizing entropy to systematically quantify the resting-condition baroreflex regulation function

Baroreflex is critical to maintain the blood pressure homeostasis, and the quantification of the baroreflex regulation function (BRF) can provide guidance for disease diagnosis, treatment and healthcare. Current quantification of the BRF such as baroreflex sensitivity cannot represent the BRF systematically. From the perspective of complex systems, we regard that the BRF is the emergence result of the diverse states and interactions in the physiological mechanisms. Therefore, the three-layer emergence is constructed in this work, which is from the physiological mechanisms to the physiological indexes and then to the BRF. On this basis, since the entropy in statistical physics macroscopically measures the diversity of the system's states, a new index called the PhysioEnt is proposed to represent the BRF and quantify the physical relationships between the BRF and four physiological indexes, baroreflex sensitivity, heart rate, heart rate variability, and systolic blood pressure. Based on the proposed method, some new findings with medical significance are obtained, including the mechanisms that aging and obesity affect the resting-condition BRF are different, and the resting-condition BRFs of men and older people depend more on the physiological processes among organs/tissues. Based on the measurable indexes, the proposed method would support the individualized medicine prospectively.
Graph Edit Distance Learning via Different Attention

Recently, more and more research has focused on using Graph Neural Networks (GNN) to solve the Graph Similarity Computation problem (GSC), i.e., computing the Graph Edit Distance (GED) between two graphs. These methods treat GSC as an end-to-end learnable task, and the core of their architecture is the feature fusion modules to interact with the features of two graphs. Existing methods consider that graph-level embedding is difficult to capture the differences in local small structures between two graphs, and thus perform fine-grained feature fusion on node-level embedding can improve the accuracy, but leads to greater time and memory consumption in the training and inference phases. However, this paper proposes a novel graph-level fusion module Different Attention (DiffAtt), and demonstrates that graph-level fusion embeddings can substantially outperform these complex node-level fusion embeddings. We posit that the relative difference structure of the two graphs plays an important role in calculating their GED values. To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs. Based on DiffAtt, a new GSC method, named Graph Edit Distance Learning via Different Attention (REDRAFT), is proposed, and experimental results demonstrate that REDRAFT achieves state-of-the-art performance in 23 out of 25 metrics in five benchmark datasets. Especially on MSE, it respectively outperforms the second best by 19.9%, 48.8%, 29.1%, 31.6%, and 2.2%. Moreover, we propose a quantitative test Remaining Subgraph Alignment Test (RESAT) to verify that among all graph-level fusion modules, the fusion embedding generated by DiffAtt can best capture the structural differences between two graphs.
On the Number of Normalized Ground State Solutions for a class of Elliptic Equations with general nonlinearities and potentials

We provide a precise description of the set of normalized ground state solutions (NGSS) for the class of elliptic equations: $$ -\Delta u - \lambda u + V (| x |) u - f (| x |, u) = 0,\quad\text{in}\quad \mathbb{R}^n,\ n\geq 1. $$ In particular, we show that under suitable assumptions on $V$ and $f$, the NGSS is unique for all the masses except for at most a finite number. Moreover, we prove that when unique, the NGSS $u_c$ is a smooth function of the mass $c. $ Our method is as follow: using the NGSS for a given mass $c$, we construct an exhaustive list of potential candidates to the minimization problem for masses close to $c$, and we develop a strategy how to pick the right one. In particular, if there is a unique NGSS for a given mass $c_0,$ then this uniqueness property is inherited for all the masses $c$ close to $c_0. $ Our method is general and applies to other equations provided that some key properties hold true.
survivalContour: Visualizing predicted survival via colored contour plots

Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for graphically illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time, and provide a Shiny app and R package as implementations of this tool. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks.
The Effect of an Exergame on the Shadow Play Skill Based on Muscle Memory for Young Female Participants: The Case of Forehand Drive in Table Tennis

Learning and practicing table tennis with traditional methods is a long, tedious process and may even lead to the internalization of incorrect techniques if not supervised by a coach. To overcome these issues, the presented study proposes an exergame with the aim of enhancing young female novice players’ performance by boosting muscle memory, making practice more interesting, and decreasing the probability of faulty training. Specifically, we propose an exergame based on skeleton tracking and a virtual avatar to support correct shadow practice to learn forehand drive technique without the presence of a coach. We recruited 44 schoolgirls aged between 8 and 12 years without a background in playing table tennis and divided them into control and experimental groups. We examined their stroke skills (via the Mott-Lockhart test) and the error coefficient of their forehand drives (using a ball machine) in the pre-test, post-test, and follow-up tests (10 days after the post-test). Our results showed that the experimental group had progress in the short and long term, while the control group had an improvement only in the short term. Further, the scale of improvement in the experimental group was significantly higher than in the control group. Given that the early stages of learning, particularly in girls children, are important in the internalization of individual skills in would-be athletes, this method could support promoting correct training for young females.
A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder

Turbo-Codes (TC) are a family of convolutional codes enabling Forward-Error-Correction (FEC) while approaching the theoretical limit of channel capacity predicted by Shannons theorem. One of the bottlenecks of a Turbo Encoder (TE) lies in the non-uniform interleaving stage. Interleaving algorithms require stalling the input vector bits before the bit rearrangement causing a delay in the overall process. This paper presents performance enhancement via a parallel algorithm for the interleaving stage of a Turbo Encoder application compliant with the DVB-RCS2 standard. The algorithm efficiently implements the interleaving operation while utilizing attributes of a given DSP. We will discuss and compare a serial model for the TE, with the presented parallel processed algorithm. Results showed a speed-up factor of up to 3.4 Total-Cycles, 4.8 Write and 7.3 Read.
Evidence for Unidirectional Flow through Plasmodesmata 1

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Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted Averages

The multi-armed bandit (MAB) problem is a classical problem that models sequential decision-making under uncertainty in reinforcement learning. In this study, we propose a new generalized upper confidence bound (UCB) algorithm (GWA-UCB1) by extending UCB1, which is a representative algorithm for MAB problems, using generalized weighted averages, and present an effective algorithm for various problem settings. GWA-UCB1 is a two-parameter generalization of the balance between exploration and exploitation in UCB1 and can be implemented with a simple modification of the UCB1 formula. Therefore, this algorithm can be easily applied to UCB-based reinforcement learning models. In preliminary experiments, we investigated the optimal parameters of a simple generalized UCB1 (G-UCB1), prepared for comparison and GWA-UCB1, in a stochastic MAB problem with two arms. Subsequently, we confirmed the performance of the algorithms with the investigated parameters on stochastic MAB problems when arm reward probabilities were sampled from uniform or normal distributions and on survival MAB problems assuming more realistic situations. GWA-UCB1 outperformed G-UCB1, UCB1-Tuned, and Thompson sampling in most problem settings and can be useful in many situations. The code is available at https://github.com/manome/python-mab.