Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Cloud-Edge-Device Adaptive Deep Learning Service for Cross-Platform Web

Deep learning shows great promise in providing more intelligence to the cross-platform web. However, insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning with low-performing web browsers. We propose DeepAdapter, an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower latency, lower mobile energy, and higher system throughput. DeepAdapter consists of context-aware pruning, service updating, and online scheduling. First, the offline pruning module provides a context-aware pruning algorithm that incorporates the latency, the network condition, and the device's computing capability to fit various contexts. Second, the service updating module optimizes branch model cache on the edge for massive mobile users and updates the new model pruning requirements. Third, the online scheduling module matches optimal branch models for mobile users. Also, a two-stage DRL-based online scheduling method named DeepScheduler can handle high concurrent requests between edge centers and remote cloud by designing the reward prediction model. Extensive experiments show that DeepAdapter can decrease average latency by 1.33x, reduce average mobile energy by 1.4x, and improve system throughput by 2.1x with considerable accuracy.
Teachers' Use of Technology: Examining the Domains of Laptop Competence

INTRODUCTION: The current coronavirus pandemic has forced the transition from face-to-face teaching and learning to fully online in Malaysia. OBJECTIVES: To examine the domains of laptop competence and its relationship with laptop use among teachers. METHODS: A quantitative descriptive survey design using questionnaires involved 133 Mathematics and Science secondary school teachers. RESULTS: Based on the results, the teachers are highly competent in word processor; basic laptop operation skills; telecommunication; spreadsheet; multimedia integration; and setup, maintenance, and troubleshooting of laptop but only moderately competent in media communication; and database. There was a significant positive relationship between teachers’ laptop competence and laptop use. CONCLUSION: Act as a guide to plan effectual training according to the needs of the teachers based on each item of the domains outlined to promote more rigorous use of laptops among the teachers.
Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma.

Background Head and neck squamous cell carcinoma (HNSCC) patients with a high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC. This study aimed to assess whether quantitative parameters derived from pretreatment dual-energy computed tomography (DECT) can predict the histopathological features of patients with HNSCC. Methods In this study, 72 consecutive patients with pathologically confirmed HNSCC were enrolled and underwent dual-phase (noncontrast-enhanced phase and contrast-enhanced phase) DECT examinations. Normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λHU), and normalized effective atomic number (NZeff) were calculated. The attenuation values on 40-140 keV noise-optimized virtual monoenergetic images [VMIs (+)] in the contrast-enhanced phase were recorded. The diagnostic performance of the quantitative parameters for predicting histopathological features, including tumor grade, LVI, and PNI, was assessed by receiver operating characteristic curves. Results The NIC, λHU, NZeff, and attenuation value on the VMIs (+) at 40 keV (A40) in the grade III group, LVI-positive group, and PNI-positive group were significantly higher than those in the grade I and II groups, the LVI-negative group, and the PNI-negative group (all P values <0.05). A multivariate logistic regression model combining these 4 quantitative parameters improved the diagnostic performance of the model in predicting tumor grade, LVI, and PNI (areas under the curve: 0.969, 0.944, and 0.931, respectively). Conclusions Quantitative parameters derived from pretreatment DECT, including NIC, λHU, NZeff, and A4,0 were found to be imaging markers for predicting the histopathological characteristics of HNSCC. Combining all these characteristics improved the predictive performance of the model.
The Impact of Specific Knowledge on Death Penalty Opposition: An Empirical Test of the Marshall Hypothesis

As we approach the 50-year anniversary of the landmark Furman decision, the time is ripe for a renewed empirical test of the hypotheses Justice Marshall presented in his concurring opinion. Most relevant to this study, he suggested in his second proposition that “people who were fully informed as to the purposes of the penalty and its liabilities would find the penalty, shocking, unjust and unacceptable. ” While there has been much empirical testing of this hypothesis, the intent of this study is to add to the existing literature by examining whether specific types of information about capital punishment is associated with opposition to capital punishment. Data were collected by an instructor who taught death penalty classes (n = 122 subjects) to test this hypothesis. Results provide qualified support for Marshall’s second hypothesis that knowledge is significantly related to an increase in death penalty opposition. Moreover, the following factors were significant predictors of overall change in participants’ opinion: cost, deterrence, race of victim, and evolving societies. Surprisingly, the innocence items did not appear statistically significant in altering opinions. Future research should continue to explore further aspects of the Marshall hypothesis such as the effect of different methods of information delivery.
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation

In this paper we propose the first correct poly-time algorithm for exact partitioning of high-order models (a worst case NP-hard problem). We define a general class of $m$-degree Homogeneous Polynomial Models, which subsumes several examples motivated from prior literature. Exact partitioning can be formulated as a tensor optimization problem. We relax this NP-hard problem to a convex conic form problem (poly-time solvable by interior point methods). To this end, we carefully define the positive semidefinite tensor cone, and show its convexity, and the convexity of its dual cone. This allows us to construct a primal-dual certificate to show that the solution of the convex relaxation is correct (equal to the unobserved true group assignment) under some sample complexity conditions.
AI-powered recommendations: the roles of perceived similarity and psychological distance on persuasion

Abstract Artificial intelligence (AI) plays various roles in our daily lives, such as personal assistant, salesperson, and virtual counselors; thus, it stands out in various fields as a recommendation agent. This study explored the effects of perceived similarity and psychological distance on the persuasion of AI recommendation agents through two experiments. Results of Experiment 1 elucidated that individuals feel more psychologically distant when they interact with AI recommendation agents than with human agents as a result of a different level of perceived similarity. Furthermore, psychological distance plays a mediating role in determining the effectiveness of desirability- vs. feasibility-focused messages in health-related issues. In Experiment 2, we manipulated the AI speaker's level of perceived similarity via anthropomorphism and found that the AI's recommendation with secondary (vs. primary) features is more effective when AI is humanized, and the reverse was found in non-humanized AI conditions. Both theoretical and managerial implications are provided.
Analysis of Beam Bending with an Elliptical Cross-Section

This paper calculates the stress function constants to determine and analyse the stress field of a beam with an elliptical cross-section under transverse loading. This was performed using linear elasticity principles. The Beltrami-Michell compatibility equations were used to derive the formulas used to calculate these parameters in the beam with the elliptical cross-section. This paper uses dimensionless analysis to comprehend the effect of each variable in the problem. The loading was applied at the centre of the right-end face of the elliptical beam. This loading configuration is the same as an existing linear elasticity problem; however, that problem models a cylindrical beam instead of an elliptical one. Thus, the existing parameters from the cylindrical model were used to verify the formulas, calculated in this paper, for the elliptical beam.
Constructing the hierarchy of predictive auditory sequences in the marmoset brain

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.
Large carbon cycle sensitivities to climate across a permafrost thaw gradient in subarctic Sweden

Abstract. Permafrost peatlands store large amounts of carbon potentially vulnerable to decomposition. However, the fate of that carbon in a changing climate remains uncertain in models due to complex interactions among hydrological, biogeochemical, microbial, and plant processes. In this study, we estimated effects of climate forcing biases present in global climate reanalysis products on carbon cycle predictions at a thawing permafrost peatland in subarctic Sweden. The analysis was conducted with a comprehensive biogeochemical model (ecosys) across a permafrost thaw gradient encompassing intact permafrost palsa with an ice core and a shallow active layer, partly thawed bog with a deeper active layer and a variable water table, and fen with a water table close to the surface, each with distinct vegetation and microbiota. Using in situ observations to correct local cold and wet biases found in the Global Soil Wetness Project Phase 3 (GSWP3) climate reanalysis forcing, we demonstrate good model performance by comparing predicted and observed carbon dioxide (CO2) and methane (CH4) exchanges, thaw depth, and water table depth. The simulations driven by the bias-corrected climate suggest that the three peatland types currently accumulate carbon from the atmosphere, although the bog and fen sites can have annual positive radiative forcing impacts due to their higher CH4 emissions. Our simulations indicate that projected precipitation increases could accelerate CH4 emissions from the palsa area, even without further degradation of palsa permafrost. The GSWP3 cold and wet biases for this site significantly alter simulation results and lead to erroneous active layer depth (ALD) and carbon budget estimates. Biases in simulated CO2 and CH4 exchanges from biased climate forcing are as large as those among the thaw stages themselves at a landscape scale across the examined permafrost thaw gradient. Future studies should thus not only focus on changes in carbon budget associated with morphological changes in thawing permafrost, but also recognize the effects of climate forcing uncertainty on carbon cycling.
The Impact of the Third Mission on Teaching and Research Performance: Evidence From Academic Scholars in an Emerging Country

Despite the plethora of empirical evidence on the associations between the third mission of academic scholars (i.e., academic engagement, academic commercialization, and public engagement) and their performance regarding two other missions (teaching and research), we have not known much about such relationships in the context of emerging countries such as Vietnam. To examine this problem, we delivered a survey with 245 Vietnamese academic scholars. Findings extracted from exploratory factor analysis revealed that the third mission of Vietnamese academic scholars might be divided into two distinct factors: (i) academic engagement and commercialization and (ii) public engagement and extra work. Subsequently, results of multiple regression revealed that while both academic engagement and commercialization, public engagement and extra work, as well as the interaction of these two factors, have positive impacts on research performance, only the second factor has a positive impact on teaching performance. This study extended the current mixed understandings on the associations among the three missions of academic scholars with evidence from an emerging country such as Vietnam. This study also provides implications for stakeholders, including policymakers, university leaders, and industry practitioners.
Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA

Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74–0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17–0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53–0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.
Contributor Factors for Psychological Well-Being: A Qualitative Study of Teachers' Perspective in Malaysia

Universally, the educational systems are undergoing restructuring and transformations. The role of teachers has increased following this reformation and experiencing growing pressure to perform. Given that the nature of the teaching profession is both challenging and rewarding, it is vital to understand teachers' psychological well-being to support the mental health of this population. This study aimed to understand better the subjective perspectives regarding the contributor factors to psychological well-being among teachers. A consensual qualitative research approach was adopted in this study by collecting data through in-depth, semi-structured interviews. Interviews are used to understand the participants' narrative descriptions in-depth and uncover the meaning of their experiences, beliefs, and values. Seven Malaysian teachers were recruited from local school settings through purposive sampling. Data analysis involved an open-coding process in deriving themes from the participants' responses. Findings revealed five themes that indicate contributor factors for psychological well-being: positive attitudes, spiritual convictions, family and friends' support, personal growth, and personal responsibility. The practical implications of teachers' psychological well-being, mental health and psychotherapy practice, and future research were also discussed in this work.
The effect of different delivery modes of misinformation on false memories in adolescents and adults

ABSTRACT The current study examined the effects of different delivery modes of misinformation on false memory creation in adolescents and adults. Forty adolescents (14-15 year olds) and fifty-three adults (over 18) were instructed to watch a video. Following this, half of the participants were given direct-with interaction misinformation, in which misinformation was delivered by the experimenter and participants (co-witnesses) were able to interact before their memory was tested. The other half of the participants were given direct-without interaction misinformation, in which they listened to a narrative read aloud by the experimenter, without interacting. Lastly, all participants completed a memory test. We found that participants in the direct-without interaction group were more likely to report false memories compared to the direct-with interaction group. Furthermore, adolescents endorsed more misinformation details than adults. Our results provide a new perspective about the role of social factors in the occurrence of false memories.
Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model

Introduction Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10′N, longitude 102°18′E) was analysed with dengue data. Result A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.
Severe Stunting Symptoms upon Nepovirus Infection Are Reminiscent of a Chronic Hypersensitive-like Response in a Perennial Woody Fruit Crop

Virus infection of plants can result in various degrees of detrimental impacts and disparate symptom types and severities. Although great strides have been made in our understanding of the virus–host interactions in herbaceous model plants, the mechanisms underlying symptom development are poorly understood in perennial fruit crops. Grapevine fanleaf virus (GFLV) causes variable symptoms in most vineyards worldwide. To better understand GFLV-grapevine interactions in relation to symptom development, field and greenhouse trials were conducted with a grapevine genotype that exhibits distinct symptoms in response to a severe and a mild strain of GFLV. After validation of the infection status of the experimental vines by high-throughput sequencing, the transcriptomic and metabolomic profiles in plants infected with the two viral strains were tested and compared by RNA-Seq and LC-MS, respectively, in the differentiating grapevine genotype. In vines infected with the severe GFLV strain, 1023 genes, among which some are implicated in the regulation of the hypersensitive-type response, were specifically deregulated, and a higher accumulation of resveratrol and phytohormones was observed. Interestingly, some experimental vines restricted the virus to the rootstock and remained symptomless. Our results suggest that GFLV induces a strain- and cultivar-specific defense reaction similar to a hypersensitive reaction. This type of defense leads to a severe stunting phenotype in some grapevines, whereas others are resistant. This work is the first evidence of a hypersensitive-like reaction in grapevine during virus infection.