Miracle-dz/UniFAR-FTUs huggingface dataset:
IONIZATION MODELING ASTROPHYSICAL GASEOUS STRUCTURES. I. THE OPTICALLY THIN REGIME

We present a code for modeling the ionization conditions of optically thin astrophysical gas structures. Given the gas hydrogen density, equilibrium temperature, elemental abundances, and the ionizing spectrum, the code solves the equilibrium ionization fractions and number densities for all ions from hydrogen to zinc. The included processes are photoionization, Auger ionization, direct collisional ionization, excitation auto-ionizatio n, charge exchange ionization, two-body radiative recombination, dielectronic recombination, and charge exchange recombination. The ionizing spectrum can be generalized to include the ultraviolet background (UVB) and/or Starburst99 stellar populations of various masses, ages, metallicities, and distances. The ultimate goal with the code is to provide fast computation of the ionization conditions of gas in N-body + hydrodynamics cosmological simulations, in particular adaptive mesh refi nement codes, in order to facilitate absorption line analysis of the simulated gas for comparison with observations. To this end, we developed a method to linearize the rate equations and efficiently solve the rate matrix with a minimum number of iterations. Comparing the code to Cloudy 13.03 (Ferland), we find that the hydrogen and h elium ionization fractions and the metal species ionization corrections are in excellent agreement. We discuss the science drivers and plans for further development of the ionization code to a full radiative hydrodynamic routine that can be employed for processing the simulations. A stand-alone version of the code has been made publicly available. Subject headings:galaxies: formation, evolution, halos; (galaxies:) quasa rs: absorption lines; radiative transfer
Global Extinction in Spiral Galaxies

Magnitude-limited samples of spiral galaxies drawn from the Ursa Major and Pisces Clusters are used to determine their extinction properties as a function of inclination. Imaging photometry is available for 87 spirals in the B, R, I, and K′ bands. Extinction causes systematic scatter in color-magnitude plots. A strong luminosity dependence is found. Relative edge-on to face-on extinction of up to 1.7 mag is found at B for the most luminous galaxies but is unmeasurably small for faint galaxies. At R the differential absorption with inclination reaches 1.3 mag, at I it reaches 1.0 mag, and at K′ the differential absorption can in the extreme be as great as 0.3 mag. The luminosity dependence of reddening can be translated into a dependence on rotation rate, which is a distance-independent observable. Hence, corrections can be made that are useful for distance measurements. The strong dependence of the corrections on luminosity act to steepen luminosity–line width correlations. The effect is greatest toward the blue, with the consequence that luminosity–line width slope dependencies are now only weakly a function of color.
Spectrofluorimetric Determination of Atenolol and Carvedilol in Pharmaceutical Preparations after Optimization of Parameters using Response Surface Methodology

Background: The present work is aimed to study the effect of different parameters on the fluorescence intensity of atenolol (ATE) and carvedilol (CAR) and optimization by response surface methodology (RSM) to provide a simple analytical method for their quantification in pharmaceutical formulations. Methods: Various parameters affecting the fluorescence intensity, i.e., sodium dodecyl sulfate (SDS) concentration, pH, volume fraction of solvents were optimized using RSM. Then, the optimized parameters were applied to the validation of a method for fluorimetric determination of β-blockers in their pharmaceutical preparations. Results: It is obtained that under the optimum conditions for determination of ATE, the method provided a linear range between 130 to 750 ng/mL with a coefficient of correlation (r) of 0.9996. Also, the limit of detection and limit of quantification (LOD and LOQ) were 40 ng/mL and 130 ng/mL, respectively. Moreover, it is observed that, the linearity of method for determination of CAR was between 0.37 to 4.0 ng/mL and LOD and LOQ of method were 0.11 ng/mL and 0.37 ng/mL, respectively. Conclusion: An accurate, sensitive and reliable spectrofluorimetric method was developed anf successfully used to determine the (ATE) and carvedilol (CAR) in their pharmaceutical preparations.
Evaluation of Medicinally Important Constituents of Cotoneaster afghanicus G.Klotz Collected from Baluchistan Region of Pakistan

Cotoneaster afghanicus G.Klotz, indigenous to mountainous parts of China, Afghanistan and Pakistan is also present in Baluchistan region of Pakistan and has medicinally important fixed oils and essential oils. The oils and chemical compounds extracted from C. afghanicus were subjected to chemical analyses and evaluation of antioxidant, antibacterial, biofilm inhibiting, thrombolytic and cytotoxic effects. Fixed and essential oils were characterized by gas chromatography-mass spectrometry, which enabled identification of 4 and 14 compounds. Fixed oil showed significant antioxidant (64.67 % scavenging of 2,2-diphenyl-1-picrylhydrazyl radical) and antibacterial (7 mm zone of inhibition for Gram-positive bacteria) activities. Ethanol extract (355 mg/100 g of sample) showed highest biofilm inhibitory activity (54.63 %) among the extracted compounds. Essential oil and ethanol extract also showed notable antioxidant effect with less cytotoxic effect indicating that these are good biologically active agents. The results of this study suggest to further investigate the use of extracted oils and chemical compounds as treatments for local ailments.
Assaying techniques to improve dry season plantings in eastern Madagascar

Rampant deforestation in eastern Madagascar highlights the urgency of protecting and restoring its unique rainforests. This experiment addresses a restoration bottleneck: the precipitous drop in seedling survival during dry season plantings. We assayed nine soil, water, and shade treatments that were codeveloped with local farmers to improve survival and growth of 17 native tree species planted on a denuded hillside during one dry season. Most treatments had no effect; however, rustic shade structures decreased transplant shock mortality (i.e. mortality within the first 2 months after planting) by 75%. This improvement is broadly accessible; it can be accomplished with materials freely and easily accessed in most farmlands in eastern Madagascar. Also, survival varied among species and seedling size, suggesting that carefully selecting species that are resistant to the harsh dry periods, improving shade availability, and planting larger seedlings of each species could lead to further gains in native tree performance in dry season plantings. Short‐lived dry season resistant species (e.g. Uapaca sp., Trema orientalis, Psiadia sp., Macaranga sp., and Pittosporum ochrosiifolium) could also provide shade as nurse trees for less resistant canopy trees, which have the potential to be established through enrichment plantings in years following the initial planting.
Equivocal associations between small‐scale shoreline restoration and subtidal fishes in an urban estuary

Restoration of degraded coastal and estuarine habitats owing to human activities is a major global concern. In Puget Sound, Washington, U.S.A. , removal of hard armor from beaches and intertidal zones has become a priority for state and local agencies. However, the effectiveness of these shoreline restoration programs for subtidal habitats and fish is unknown. We surveyed six restoration sites in Puget Sound over 2 years to evaluate associations between shoreline restoration and subtidal fish abundance. We measured the abundance of juvenile salmonids and forage fishes along armored, restored, and reference shorelines. Bayesian generalized linear models showed limited support for associations between shoreline restoration and these fishes in the 3–7 years since armor removal. Pacific herring were more abundant at reference shorelines; the shoreline effect for surf smelt varied by survey site. Shoreline restoration was not an important predictor of salmonid abundance; the best models for Chinook and chum salmon included predictors for survey site and eelgrass, respectively. The retention of survey site in several species' top models reveals the influence of the broader landscape context. We also found seasonal variation in abundance for chum salmon and surf smelt. Our results suggest that juvenile forage fish and salmonids in estuaries likely have unique responses to shoreline features, and that the positive effects of armor removal either do not extend into subtidal areas or are not detectable at local scales. To be most effective, coastal restoration programs should consider broader landscape patterns as well as species‐specific habitat needs when prioritizing investments.
The Impact of Thermal Cues on Affective Responses to Emotionally Resonant Vibrations

This paper investigates how presenting emotionally resonant vibrotactile stimuli at cool, neutral and warm temperature levels impacts mean ratings for emotional resonance and affective response. Affective vibrotactile stimuli can elicit pleasant or calming responses, making them applicable for emotion regulation. Evoking real-world sensations via emotional resonance can widen their affective range and improve their effectiveness, and allow them to enhance immersive multimodal experiences. Thermotactile cues have been shown to affect emotional responses, but have not been combined with emotionally resonant vibrations to see how they change responses to such cues. This study (n=20) assessed the impact of 3 temperature levels (24℃, 30℃, and 34℃) on 15 emotionally resonant vibrotactile cues and observed if emotionally resonant stimuli exceeded the affective range non-resonant vibrotactile stimuli. The findings suggest that presenting specific resonant vibrations at temperatures that are appropriate for the sensation they evoke can improve emotional resonance and vice versa. In addition, temperature had a positive effect on affective response and emotionally resonant vibrations were found to have a wider affective range than traditional vibrotactile cues. These findings support using emotionally resonant vibrations and thermal cues to elicit desirable emotional responses in emotion regulation and immersive media applications.
Touchless touch with biosignal transfer for online communication

The widespread of audio-visual online communication highlights the importance of enhancing their ability to transmit emotional color and personal experience. Non-verbal biometric cues and signals were recently recognized to convey such missing emotional context when added to virtual interactions. Motivated by that, we present a haptic system allowing for a biometric signal transfer in a fully touchless and seamless way. We utilize camera-based heart-rate signal readings and ultrasonic mid-air haptic technology to affect the audience with a temporal tactile pattern representing the speaker’s heart rate. We assess the usability and engagement enhancement of such a system in two user-studies involving one-way communication, i.e., watching a short emotional video. Our analysis of biometric data and subjective responses hints toward changed values of arousal and valence as well as physiological responses when the haptic feedback was applied in a group of participants. Finally, we outline a further research agenda to confirm our observations with different emotions and communication scenarios.
Evaluating Just-In-Time Vibrotactile Feedback for Communication Anxiety

Wrist-worn vibrotactile feedback has been heralded as a promising intervention for reducing state anxiety during stressor events. However, current work has focused on the continuous delivery of the vibrotactile stimulus, which entails the risk of habituation to the potentially relieving effects of the feedback. This paper examines the just-in-time administration of vibrotactile feedback during a public speaking task in an effort to reduce communication apprehension. We evaluate two types of vibrotactile feedback delivery mechanisms compared to a control in a between-subjects design – one that delivers stimulus over random time points and one that delivers stimulus during moments of heightened physiological reactivity, as determined by changes in electrodermal activity. The results from these interventions indicate that vibrotactile feedback administered during high physiological arousal improves stress-related physiological measures (e.g., heart rate) and self-reported stress annotations early on in the intervention, and contributes to increased vocal stability during the public speaking task, but these effects diminish over time. Delivering the vibrotactile feedback over random points in time appears to worsen stress-related measures overall.
Predicting Backchannel Signaling in Child-Caregiver Multimodal Conversations

Conversation requires cooperative social interaction between interlocutors. In particular, active listening through backchannel signaling (hereafter BC) i.e., showing attention through verbal (short responses like “Yeah”) and non-verbal behaviors (e.g. smiling or nodding) is crucial to managing the flow of a conversation and it requires sophisticated coordination skills. How does BC develop in childhood? Previous studies were either conducted in highly controlled experimental settings or relied on qualitative corpus analysis, which does not allow for a proper understanding of children’s BC development, especially in terms of its collaborative/coordinated use. This paper aims at filling this gap using a machine learning model that learns to predict children’s BC production based on the interlocutor’s inviting cues in child-caregiver naturalistic conversations. By comparing BC predictability across children and adults, we found that, contrary to what has been suggested in previous in-lab studies, children between the ages of 6 and 12 can actually produce and respond to backchannel inviting cues as consistently as adults do, suggesting an adult-like form of coordination.
Debugging Procedural Level Designs with Mental Maps

Procedural Level Generation provides tools and techniques for generating many game levels from a single specification. Instead of creating levels by hand, level designers make use of generators that automate the creation process. However, iteratively improving a level’s design requires encoding generators of adventures, puzzles and encounters in notations that bear little resemblance to generated content. Raising the level quality is difficult, because it is hard to reason about bugs that can manifest inside generated content. We take the position that debugging requires special attention. We argue that advancing the area of PCG calls for tools and debugging techniques that speed up procedural level design and empower level designers. We propose exploring how Domain-Specific Languages can help in authoring a level’s design, validating the generator’s code, and debugging issues in generated content. We introduce Mental Maps, a visual language that expresses the spacial relations between rooms, objects and paths. We discuss how Mental Maps can serve as generator blueprints before the generation happens, and as debugging lenses for projecting issues afterwards.
3rd Workshop on Social Affective Multimodal Interaction for Health (SAMIH)

This workshop discusses how interactive, multimodal technology such as virtual agents can be used in social skills training for measuring and training social-affective interactions. Sensing technology now enables analyzing user’s behaviors and physiological signals. Various signal processing and machine learning methods can be used for such prediction tasks. Such social signal processing and tools can be applied to measure and reduce social stress in everyday situations, including public speaking at schools and workplaces.
An Architecture Supporting Configurable Autonomous Multimodal Joint-Attention-Therapy for Various Robotic Systems

In this paper, we present a software-architecture for robot-assisted configurable and autonomous Joint-Attention-Training scenarios to support autism therapy. The focus of the work is the expandability of the architecture for the use of different robots, as well as the maximization of the usability of the interface for the therapeutic user. By evaluating the user-experience, we draw first conclusions about the usability of the system for computer and non-computer scientists. Both groups can solve different tasks without any major issues, and the overall usability of the system was rated as good.
Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market

For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.
Transnational development cultures: Navigating production, market, and cultural difference within European-Chinese game development teams

Discourse on the global games industry draws attention to the growth of China, in terms of both its consumer market and the games publishing landscape. Trade associations and government agencies have sought routes for Western games studios, publishers, and rights holders to connect with similar organisations in China. However, there has been limited focus on how Western and Chinese game developers understand and navigate the challenges of transnational collaboration and creativity in games production. This article presents a case study of five transnational game development teams, all of which were composed predominantly of developers from the UK and China who were tasked with producing game prototypes for both markets. Data gathered during game production included development diaries, interview data, game prototypes, and production documentation. Three themes emerged from analysis of the data: production practices and transnational working, navigating regulations and restrictions, and market and cultural differences. Findings highlight that developers encounter challenges around political and cultural difference that could inhibit production or lead to uncertainties in design decision making, but also that developers can quickly establish routes to collaboration and knowledge sharing that can help to overcome these barriers.
Towards Commensal Activities Recognition

Eating meals together is one of the most frequent human social experiences. When eating in the company of others, we talk, joke, laugh, and celebrate. In the paper, we focus on commensal activities, i.e., the actions related to food consumption (e.g., food chewing, in-taking) and the social signals (e.g., smiling, speaking, gazing) that appear during shared meals. We analyze the social interactions in a commensal setting and provide a baseline model for automatically recognizing such commensal activities from video recordings. More in detail, starting from a video dataset containing pairs of individuals having a meal remotely using a video-conferencing tool, we manually annotate commensal activities. We also compute several metrics, such as the number of reciprocal smiles, mutual gazes, etc., to estimate the quality of social interactions in this dataset. Next, we extract the participants’ facial activity information, and we use it to train standard classifiers (Support Vector Machines and Random Forests). Four activities are classified: chewing, speaking, food in-taking, and smiling. We apply our approach to more than 3 hours of videos collected from 18 subjects. We conclude the paper by discussing possible applications of this research in the field of Human-Agent Interaction.
Contextual modulation of affect: Comparing humans and deep neural networks

When inferring emotions, humans rely on a number of cues, including not only facial expressions, body posture, but also expressor-external, contextual information. The goal of the present study was to compare the impact of such contextual information on emotion processing in humans and two deep neural network (DNN) models. We used results from a human experiment in which two types of pictures were rated for valence and arousal: the first type depicted people expressing an emotion in a social context including other people; the second was a context-reduced version in which all information except for the target expressor was blurred out. The resulting human ratings of valence and arousal were systematically decreased in the context-reduced version, highlighting the importance of context. We then compared human ratings with those of two DNN models (one trained on face images only, and the other trained also on contextual information). Analyses of both categorical and the valence/arousal ratings showed that although there were some superficial similarities, both models failed to capture human rating patterns both in context-rich and context-reduced conditions. Our study emphasizes the importance of a more holistic, multi-modal training regime with richer human data to build better emotion-understanding systems in the area of affective computing.
Unitary evolution in Gowdy cosmology

Recent results on the nonunitary character of quantum time evolution in the family of Gowdy ${T}^{3}$ spacetimes bring the question of whether one should abandon the most sacred principle of unitary evolution in cosmology. In this work we show that the answer is in the negative. We put forward a full nonperturbative canonical quantization of the polarized Gowdy ${T}^{3}$ model that implements the dynamics while preserving unitarity. We discuss possible implications of this result.