Miracle-dz/UniFAR-FTUs huggingface dataset:
The Art of Inquiry: Toward Robots that Infer Speech and Movement Characteristics

To improve user personalization of robots in social situations, robots can benefit from inferring information about the humans with whom they interact. Physical human behaviors and personality traits have previously been touted as possible sources of such information but often require complex processing or sensoring requirements. This paper investigates posing specific questions related to extrovert behaviors as an alternative source of this information. It aims to highlight significant relationships between questions aimed at behavioral reactions in specific scenarios and speech and movement attributes, obtained by a robot in a one-on-one social interaction. The paper used an experiment in which participants interacted with a robot through a non-scripted conversation. In it, the robot would gather information on the speech and movement characteristics, and estimated arousal/valence levels of the participant. The experiment was followed by a series of specific questions aimed at outlining the extroversion level of the participants. The results showed multiple significant but weak correlations $(\mathrm{p}\lt$.05) between the recorded attributes. These include correlations between the average determined valence and the average recorded velocity of speech, between the average answer reaction time and average answer length. The results also found correlations between arousal levels, average pause duration, and the answers recorded for individual questions of the questionnaire. The results suggest that introducing specific questions in human-robot interactions can potentially be used to decrease the processing and sensor demands of robots and offer user personalization using only a limited set of sensors.
The Influence of Construction Parameters on Radiometric Performances of Agricultural Nets

Plastic netting is most commonly used in agriculture to protect greenhouses, fruit-trees and ornamental plants from solar radiation, insects, birds, hail, wind, snow or heavy rainfall. There are many net typologies on the market with different construction parameters. It appears that there are no shared criteria for choosing the net typology which best fulfils a specific requirement, but that the choice generally depends on empirical or economic criteria, not on scientifically relevant considerations. In order to investigate the influence of net construction parameters on their radiometric properties, a set of field tests were performed using a spectroradiometer (GER2600) with a wavelength range of 250-2500 nm. Transmittance was calculated in field tests in the following wavelengths: UV (300-380 nm); Visible (380-760 nm); PAR (Photosynthetically active radiation 400-700 nm); NIR (760-1500 nm). Tests were carried out on 25 different kinds of commercial nets. An experimental setup of 120 x 120 x 50 cm was used to evaluate the radiometric properties of the nets tested. In addition, trials were performed on a full scale shade-house (9.60 x 4.40 x 30.00 m) covered with 4 different kinds of shading nets. Experimental test results made it possible to define the influence of net colour, porosity, shading factor and solar angle on the evaluation of transmittance in the UV, PAR and visible light wavelengths.
SSF: Accelerating Training of Spiking Neural Networks with Stabilized Spiking Flow

Surrogate gradient (SG) is one of the most effective approaches for training spiking neural networks (SNNs). While assisting SNNs to achieve classification performance comparable to artificial neural networks, SG suffers from the problem of time-consuming training, preventing it from efficient learning. In this paper, we formally analyze the backward process of classic SG and find that the membrane accumulation through time leads to exponential growth of training time. With this discovery, we propose Stabilized Spiking Flow (SSF), a simple yet effective approach to accelerate training of SG-based SNNs. For each spiking neuron, SSF averages its input and output activations over time to yield stabilized input and output, respectively. Then, instead of back propagating all errors that are related to current neuron and inherently entangled in time domain, the auxiliary gradient is directly propagated from the stabilized output to input through a devised relationship mapping. Additionally, SSF method is suitable to different neuron models. Extensive experiments on both static and neuromorphic datasets demonstrate that SNNs trained with SSF approach can achieve performance comparable to the original counterparts, while reducing the training time significantly. In particular, SSF speeds up the training process of state-of-the-art SNN models up to 10× when time steps equal to 80.
DEVELOPMENT OF PRODUCTIVITY OF PIG FARMS IN GERMANY

The development and change of productivity, as well as of its influencing factors, is of interest in economic research. In this paper we analyse the development of productivity in pig farms in Germany. Balanced farm panels are selected from the German Farm Accounting Data Network (FADN). As a productivity measure we use the Färe-Primont Index proposed by O’Donnell. Results shows a rather constant productivity level of pig fattening farms and decreasing productivity of farms specialized in piglet production. Due to cyclical pig prices the variation of productivity is rather high in pig farms. Significant scale effects are identified with highest TFP levels of large sized farms. The variation of income over time is much more pronounced than of productivity, which might partially be determined by the rather high aggregation of output and input variables used for the productivity calculations.
A STEPwise Approach for Oral Hygiene Behavior of Schoolchildren in Romania

This research analyzes the dental hygiene habits of schoolchildren from parents’ perspectives, using the STEPS approach recommended by the World Health Organization. The key points of oral health care in children include the role of the family in encouraging and maintaining proper oral hygiene practices. This study aimed to assess the oral hygiene practices of schoolchildren with on-site dentists in Romania. Additionally, it sought to establish correlations between these behaviors and the educational levels of the adults with whom they live with. The participants were selected from the zero grade to the eighth grade, totaling 3843students. Statistical analysis involved the application of Fisher’s Exact Test and Z-tests with Bonferroni correction. Multinominal and binominal logistic regression models were employed to predict the impact of parents’ education on children’s oral health status and behavior. The oral health status of children evaluated by parents as poor was more frequent in children whose parents had a primary or gymnasium education (p < 0.001). Female adults with a university education evaluated the gum health of their children as very good to a much greater extent (41.7%, p < 0.001). Regarding the use of auxiliary means, the majority of parents with a university education mentioned that their children use dental floss (26.4% females/27.4% males) (p < 0.001), compared to those with primary education, where the percentage was only (1.2% males/3.5% females) (p < 0.001). The results of this study highlight that the education level of the adults with whom the children live with influences the perception of the teeth and gums health status, the frequency of oral hygiene, the use of fluoride toothpaste, and auxiliary brushing aids.
Spacetime Surface Regularization for Neural Dynamic Scene Reconstruction

We propose an algorithm, 4DRegSDF, for the spacetime surface regularization to improve the fidelity of neural rendering and reconstruction in dynamic scenes. The key idea is to impose local rigidity on the deformable Signed Distance Function (SDF) for temporal coherency. Our approach works by (1) sampling points on the deformed surface by taking gradient steps toward the steepest direction along SDF, (2) extracting differential surface geometry, such as tangent plane or curvature, at each sample, and (3) adjusting the local rigidity at different timestamps. This enables our dynamic surface regularization to align 4D spacetime geometry via 3D canonical space more accurately. Experiments demonstrate that our 4DRegSDF achieves state-of-the-art performance in both reconstruction and rendering quality over synthetic and real-world datasets. https://4dregsdf.github. io/
NEMS/MEMS integration in submicron CMOS Technologies

La reduccion de los dispositivos MEMS hasta la nano escala (NEMS) ha permitido el acceso a nuevos dominios de la fisica y promete revolucionar las aplicaciones de sensado. Sin embargo esta miniaturizacion ha sido conseguida a costa de procesos de fabicracion complicados y no reproducibles. Es por ello que esta tesis trata de obtener dichos dispositivos NEMS a partir de una tecnologia CMOS comercial (ST 65nm). Con este objetivo un estudio en detalle de la tecnologia ST 65nm es llevado a cabo para posteriormente definir en ella estructuras NEMS en sus diferentes capas (en polysilicio con un grosor y ancho de 60 nm x 100 nm y en metal 1, cobre , con unas dimensiones de 90nm x 100nm). Un nuevo post proceso de liberacion es presentado que nos permite liberar las estructuras, demostrando asi su correcta fabricacion. Sin embargo, fruto de esta miniaturizacion las senales electricas usadas para sensar su movimiento se reducen tambien. Como alternativa a un sensado capacitivo estudiamos la viabilidad de adaptar a nuestro proceso de fabricacion CMOS-MEMS a un metodo de transduccion basado en un transistor cuyo puerta resuena, su movimiento modula las cargas del canal y dicho desplazamiento puede ser leido en la corriente del puerta del transistor. Mediante dicho metodo de transduccion la respuesta en frecuencia de un resonador de polysilicio a 24 MHz fue leida y su funcionamiento como interruptor a bajos voltajes (2.25 V pull-in) fue validado. Ademas, proponemos el uso de interruptores mecanicos no solo como memorias o en aplicaciones logicas (gracias a su eficiencia energetica) sino como el elemento base para la implementacion de un oscilador en anillo, completamente mecanico. Con este oscilador ampliamos el rango de aplicacion de los interruptores N/MEMS a nuevos campos como el sensado de masa pero con el valor anadido de tener una senal digital. Para implementar esta nueva configuracion presentamos un modelo y desarrollamos interruptores mecanicos en diferentes tecnologias CMOS intentando siempre reducir sus dimensiones. Con estos interruptores mecanicos CMOS hemos conseguido voltajes de operacion bajos (5V), respuestas abruptas (4.3 mV/decada) y una buena relacion ION/IOFF (1.104).
Affective Computing as a Tool for Understanding Emotion Dynamics from Physiology: A Predictive Modeling Study of Arousal and Valence

Affective computing has traditionally relied on predictive models that use summary annotations to understand emotions, an approach that often fails to capture the continuous nature of emotions. In this paper, we explore the previously unexamined possibility of understanding the temporal dynamics of emotions using the Continuously Annotated Signals of Emotion (CASE) dataset during the Emotion Physiology and Experience Collaboration (EPiC) 2023 competition. We present the first performance benchmark for predictive models using continuous annotations on this dataset, in which we achieve significantly better results than baseline models for specific scenarios. Our contributions include the development and comparison of predictive models for different affective dimensions, demonstrating that arousal models outperform valence models, a finding consistent with existing affective science literature. In addition, our analysis shows that predictions incorporating features from past data are more informative than those based on future data, suggesting that physiological activity precedes affective experience and subsequent annotation. These findings contribute to a deeper understanding of the temporal dynamics of emotion and have broad implications for both affective computing and affective science, highlighting the potential of this interdisciplinary approach.
Estimating in situ integrated soil moisture content using fiber-optic distributed temperature sensing (DTS) measurements in the field

The measurement of soil water content in the field is complicated by soil heterogeneity, as well as issues of temporal and spatial scales. A particular gap exists in our ability to observe water contents at the field scale, ranging from 0.1 to 80 km. This work explores the feasibility of using fiber-optic distributed temperature sensing (DTS) observations of the diel temperature cycle to estimate the volumetric water content of the soil profile. Relationships between the thermal properties of bulk soil and the water content of the soil are described, and analytical and numerical models of conductive heat transport are presented. Model results are compared to data collected using a fiber-optic DTS to measure temperature within the soil profile at Valley Vista Ranch, an agricultural site in northern Nevada. Observed temporal phase shifts and diel amplitudes at a depth of 15 cm in the soil profile are compared to the phase shifts and amplitudes predicted by the numerical simulations for the same surface temperature wave. While phase shifts and amplitudes measured in the field return repeatable estimates of the soil thermal properties, these estimates could not be correlated with the volumetric water content of the soil. Potential causes for this discrepancy, including advective heat fluxes, biased surface temperature measurements, and model deficiencies are explored, and recommendations for improvements to future experiments are presented.
Polyploid Cancer Cell Models in Drosophila

Cells with an abnormal number of chromosomes have been found in more than 90% of solid tumors, and among these, polyploidy accounts for about 40%. Polyploidized cells most often have duplicate centrosomes as well as genomes, and thus their mitosis tends to promote merotelic spindle attachments and chromosomal instability, which produces a variety of aneuploid daughter cells. Polyploid cells have been found highly resistant to various stress and anticancer therapies, such as radiation and mitogenic inhibitors. In other words, common cancer therapies kill proliferative diploid cells, which make up the majority of cancer tissues, while polyploid cells, which lurk in smaller numbers, may survive. The surviving polyploid cells, prompted by acute environmental changes, begin to mitose with chromosomal instability, leading to an explosion of genetic heterogeneity and a concomitant cell competition and adaptive evolution. The result is a recurrence of the cancer during which the tenacious cells that survived treatment express malignant traits. Although the presence of polyploid cells in cancer tissues has been observed for more than 150 years, the function and exact role of these cells in cancer progression has remained elusive. For this reason, there is currently no effective therapeutic treatment directed against polyploid cells. This is due in part to the lack of suitable experimental models, but recently several models have become available to study polyploid cells in vivo. We propose that the experimental models in Drosophila, for which genetic techniques are highly developed, could be very useful in deciphering mechanisms of polyploidy and its role in cancer progression.
Adaptive Switching Based Data-Communication Model for Internet of Healthcare Things Networks

The Internet of Things (IoT) is a network of smart sensory objects. These objects collect environmental data and communicate to exchange this gathered data without human intervention. IoT has evolved as a revolutionary technology for over a decade. It is currently, widely applied in a variety of applications like Internet of Vehicles (IoV), Internet of Healthcare Things (IoHT), and the Internet of Everything (IoE). IoHT is one of the most beneficial and significant IoT applications. The quality of the connection in an IoT network is governed by the application layer communication protocol. Application type has a significant impact on the preference of protocol selection. In this paper, the performance of CoAP and MQTT-SN is compared. Based on the performance analysis it was concluded that in case of high traffic conditions network congestion occurs on the medical broker of MQTT-SN, which lowers the network efficiency. A framework is proposed to achieve reliability and time optimization in larger sensor-based healthcare networks. An adaptive switching-based data communication model has been designed. In the proposed model, we have enabled IoHT network to run two application protocols in parallel. The adaptive switching algorithm allows to switch between the two available protocols based on the status of the network condition. Network simulation has been performed using NODE-RED. This tool is used to test the proposed framework in different scenarios. In the end it was concluded that during network congestion conditions at medical broker of MQTT-SN, the adaptive switching algorithm allows the network to switch to CoAP connection for the time efficient and reliable transmission in the IoHT network.
Lighting Every Darkness in Two Pairs : A Calibration-Free Pipeline for RAW Denoising

Calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods suffer from several main deficiencies: 1) the calibration procedure is laborious and time-consuming, 2) denoisers for different cameras are difficult to transfer, and 3) the discrepancy between synthetic noise and real noise is enlarged by high digital gain. To overcome the above shortcomings, we propose a calibration-free pipeline for Lighting Every Drakness (LED), regardless of the digital gain or camera sensor. Instead of calibrating the noise parameters and training repeatedly, our method could adapt to a target camera only with few-shot paired data and fine-tuning. In addition, well-designed structural modification during both stages alleviates the domain gap between synthetic and real noise without any extra computational cost. With 2 pairs for each additional digital gain (in total 6 pairs) and 0.5% iterations, our method achieves superior performance over other calibration-based methods.
Science Learning Motivation Among Students at Junior and Senior High Schools in Sungai Penuh: A Comparative Study

Student motivation in science learning has significantly changed during the pandemic. Consequently, it is crucial to trace how student motivation in science has evolved. This study uses a descriptive and comparative research approach to obtain a general overview of students' science learning motivation and compare the levels of student learning motivation, providing a comprehensive overview of student motivation at different educational levels. The total sample size for this study is 104 students, selected using purposive sampling techniques from two schools, junior high school (madrasa) 1 and senior high school 1 Sungai Penuh. A questionnaire on students' motivation for learning science, adapted from a previous study, was utilized to collect student motivation data. Student responses were converted into quantitative data using a Likert Scale of 1 to 5. The collected data were processed and analyzed using descriptive and Mann-Whitney U-test statistics. The results reveal a significant difference in the motivation for learning science between senior high school 1 and junior high school (madrasa) 1 students. The group of students from senior high school 1 exhibited a higher level of science learning motivation than those from junior high school (madrasa) 1. When comparing indicators, no significant differences were found in the motivation of students from junior high school (madrasa) 1 and senior high school 1 Sungai Penuh for learning goal orientation, self-efficacy, and self-regulation. The difference in motivation indicators was only significant in task value. These findings can guide classroom teachers in refocusing their teaching practices and assessing the effectiveness of intervention programs. Exploring other factors across various subjects and school levels is also recommended to gain a more comprehensive understanding of the variations in student learning experiences.
Dietary Marine Oils Selectively Decrease Obesogenic Diet-Derived Carbonylation in Proteins Involved in ATP Homeostasis and Glutamate Metabolism in the Rat Cerebellum

The regular intake of diets high in saturated fat and sugars increases oxidative stress and has been linked to cognitive decline and premature brain aging. The cerebellum is highly vulnerable to oxidative stress and thus, obesogenic diets might be particularly detrimental to this tissue. However, the precise molecular mechanisms behind obesity-related brain damage are still not clear. Since protein carbonylation, a biomarker of oxidative stress, influences protein functions and is involved in metabolic control, the current investigation addressed the effect of long-term high-fat and high-sucrose diet intake on the cerebellum of Sprague-Dawley rats by deciphering the changes caused in the carbonylated proteome. The antioxidant effects of fish oil supplementation on cerebellar carbonylated proteins were also investigated. Lipid peroxidation products and carbonylated proteins were identified and quantified using immunoassays and 2D-LC-MS/MS in the cerebellum. After 21 weeks of nutritional intervention, the obesogenic diet selectively increased carbonylation of the proteins that participate in ATP homeostasis and glutamate metabolism in the cerebellum. Moreover, the data demonstrated that fish oil supplementation restrained carbonylation of the main protein targets oxidatively damaged by the obesogenic diet, and additionally protected against carbonylation of several other proteins involved in amino acid biosynthesis and neurotransmission. Therefore, dietary interventions with fish oils could help the cerebellum to be more resilient to oxidative damage. The results could shed some light on the effect of high-fat and high-sucrose diets on redox homeostasis in the cerebellum and boost the development of antioxidant-based nutritional interventions to improve cerebellum health.