Miracle-dz/UniFAR-FTUs huggingface dataset:
The representational space of observed actions

Categorizing and understanding other people’s actions is a key human capability. Whereas there exists a growing literature regarding the organization of objects, the representational space underlying the organization of observed actions remain largely unexplored. Here we examined the organizing principles of a large set of actions and the corresponding neural representations. Using multiple-regression representational similarity analysis of fMRI data, in which we accounted for variability due to major action-related features (body parts, scenes, movements, objects), we found that the semantic dissimilarity structure was best captured by patterns of activation in the lateral occipitotemporal cortex (LOTC) and the left posterior inferior parietal lobe (IPL). Together, our results demonstrate that the organization of observed actions in the LOTC and the IPL resembles the organizing principles used by participants to classify actions behaviorally, in line with the view that these regions are crucial for accessing the meaning of actions.
Nuclear medicine imaging for bone metastases assessment: what else besides bone scintigraphy in the era of personalized medicine?

Accurate detection and reliable assessment of therapeutic responses in bone metastases are imperative for guiding treatment decisions, preserving quality of life, and ultimately enhancing overall survival. Nuclear imaging has historically played a pivotal role in this realm, offering a diverse range of radiotracers and imaging modalities. While the conventional bone scan using 99mTc marked bisphosphonates has remained widely utilized, its diagnostic performance is hindered by certain limitations. Positron emission tomography, particularly when coupled with computed tomography, provides improved spatial resolution and diagnostic performance with various pathology-specific radiotracers. This review aims to evaluate the performance of different nuclear imaging modalities in clinical practice for detecting and monitoring the therapeutic responses in bone metastases of diverse origins, addressing their limitations and implications for image interpretation.
Joint Laplace- and Fourier-Domain Seismic Inversion for Orthorhombic Medium Parameter Estimates

Two sets of rotationally invariant, horizontal, and vertical fractures permeated in a homogeneous isotropic background rock can yield a long-wavelength effective orthorhombic medium. Amplitude variation with angle and azimuth (AVAZ) inversion is an effective tool to invert azimuthal seismic data for orthorhombic medium parameters. Given the inherently band-limited nature of real seismic data, orthorhombic AVAZ inversion without reasonable model constraints is a challenging task to obtain stable and reliable estimated results. In this article, we have developed a novel joint Laplace- and Fourier-domain seismic inversion method to estimate P- and S-wave velocities, density, and horizontal and vertical fracture densities. The orthorhombic AVAZ seismic forward modeling in the Laplace domain is established by introducing the Fourier and damping operators. We present a two-step strategy for joint Laplace- and Fourier-domain seismic inversion in a Bayesian inference framework. First, we perform the Laplace-domain seismic inversion to obtain long-wavelength models of elastic parameters and fracture parameters by exploiting the low-frequency components of the damped azimuthal seismic data. Second, with the Laplace-domain inversion results as initial models, the Fourier-domain seismic inversion is implemented to obtain complete inversion results, where all frequency components of original azimuthal seismic data are employed. We demonstrate the feasibility of our method through a synthetic data example and an application of a field dataset acquired over a fractured shale reservoir. Meanwhile, we compare our method with the Fourier-domain seismic inversion, results show that our method can yield more reasonable estimated results of orthorhombic medium parameters than the Fourier-domain seismic inversion.
The Spatio-Temporal Distribution Characteristics of Carbon Dioxide Derived from the Trajectory Mapping of Ground Observation Network Data in Shanxi Province, One of China’s Largest Emission Regions

In this study, the trajectory mapping domain-filling technology, which can provide more reliable statistical estimates of long-lived gas concentrations in a broader geographical area based on limited station data, is used to map the CO2 concentration data of six ground observation stations to the entire Shanxi Province. The technology combines a dynamical model of the atmosphere with trace gas observations, combining forward and backward trajectories to greatly expand the information on long-lived CO2 gas concentrations over a trajectory path. The mapped results show good agreement with the observation results, which reveals the generalizability of the trajectory mapping domain-filling technology. The results show that the spatio-temporal distribution characteristics of CO2 concentration in the entire Shanxi region is significant: during the five years, the provincial average CO2 concentration exhibits an overall increasing trend. The CO2 concentration increases from the north to the south across the province. Influenced by the economic growth rate and COVID-19, there are differences in the annual variation characteristics of the CO2 concentration across the entire province, with the highest year-on-year growth in 2019 and a year-on-year decrease in 2020. The increasing rate of the CO2 concentration in the northern low-value areas is faster than that in the southern high-value areas. Overall, there is a decreasing trend in the CO2 concentration growth from the north to the south in the entire province. There are seasonal differences in the CO2 concentration distribution across the entire province. The CO2 concentration and amplitude are higher in autumn and winter than they are in spring and summer. This study can provide scientific support and methodological reference for the spatio-temporal distribution characteristics analysis of GHGs at the provincial–regional scale, as well as at the national and global scales.
NDPmulator: Enabling Full-System Simulation for Near-Data Accelerators From Caches to DRAM

The accurate simulation and performance assessment of Near-Data Accelerators (NDAccs) is a complex challenge as it must consider the operation of the entire processing system, the impact of the Operating System (OS) overheads, and the memory contention caused by concurrent processes. While recent proposals have attempted to repurpose and extend existing tools, the offered support for the development and evaluation of NDAccs is limited and full-system simulation is rarely provided. To mitigate this problem, the NDPmulator simulation framework, based on the widely established gem5 architectural simulator, is herein proposed. NDPmulator provides System Emulation (SE) and Full System (FS) support for the development and evaluation of novel NDAccs deployed at multiple levels of the memory hierarchy. To demonstrate its versatility and performance-efficiency, the proposed NDPmulator is used to model three existing NDAccs, showing that it can accurately estimate and anticipate the results of the evaluation performed by the original authors while requiring a significantly smaller implementation effort and a fraction of the simulation time. Furthermore, NDPmulator offers the possibility to conduct complex experiments where the NDAcc is coupled to a real system featuring an OS. Hence it allows modeling all overheads related to the NDAcc device driver, the OS, and the contention caused by concurrent and background processes.
Contextualizing the standard maternal continuum of care in Pakistan: an application of revised recommendation of the World Health Organization

Objective This study utilizes recent nationally representative data to contextualize the standard maternal continuum of care (SMCoC) in Pakistan. The revised SMCoC framework encompasses at least eight antenatal care visits, skilled birth attendants during delivery, and postnatal care within 48 h of childbirth. Methods The study used a sample of 3,887 ever-married women aged 15–49 from the latest Pakistan Demographic and Health Survey (PDHS) conducted in 2017–18. Several statistical methods were employed: descriptive statistics, bivariate, multilevel logistic regression models, and Fairlie decomposition analysis. Results Only 12% of women had accessed full SMCoC services in Pakistan. Education and the wealth quintile emerged as pivotal factors influencing the utilization of SMCoC. The likelihood of full SMCC utilization was more likely among higher educated women (OR: 3.37; 95% CI: 2.16–5.25) and those belonging to the wealthiest household wealth quintile (OR: 4.95; 95% CI: 2.33–5.51). Media exposure, autonomy, healthcare accessibility, residence, and region were also identified as significant predictors of SMCoC utilization among women. Conclusion In conclusion, while most women did not utilize full SMCoC services in Pakistan, the pattern is substantially varied by background characteristics. Education, wealth quintile, mass media exposure, and autonomy were significant factors, along with geographical aspects such as healthcare accessibility and region. The study underscores the need for a multifaceted approach to ensure equitable access to full SMCoC services for women in Pakistan, addressing individual, socioeconomic, and geographical factors.
Project-based Collaborative Learning for Making Tie-dye to Improve the Skills of Vocational High School Students

Developing the world of creative industries requires humane and creative human resources (HR). Educational institutions have a strategic role in developing creative human resources through learning that facilitates students to be active, wise, and collaborative. Therefore, it is necessary to implement project-based collaborative learning in learning activities, especially learning competencies related to the creative industry; one is about dyeing cloth with the popular tie-dye technique. This study was conducted at SMK N 1 Pengasih in the Fashion Design expertise program, using a project-based collaborative learning direct learning approach. The steps are; need assessment, learning preparation, tool and material preparation, learning implementation, and evaluation. The learning activity was declared successful because the number of targets present reached 100% with an activity level of 90%. The level of participation or activity in the learning process is grouped in the high category, namely 80%. This activity has a good impact and benefits students if the score is 80%. The evaluation design for increasing knowledge and skills was determined by the service team, as seen from the results of the tie-dye shirt product, with a score of 80% in the "good" category.
Low-Complexity Joint Antenna Selection and Robust Multi-Group Multicast Beamforming for Massive MIMO

We consider low-complexity design for joint antenna selection and robust multi-group multicast beamforming in massive multiple-input multiple-output (MIMO) systems. Relying on the estimated channel covariance and assuming a limited number of antennas for transmission at the base station, we aim to minimize the transmit power subject to the worst-case signal-to-interference-plus-noise-ratio (SINR) guarantee and per selected antenna power budget. Converting the worst-case SINR constraints to a set of non-convex constraints, we propose a two-phase approach to solve the problem efficiently: the antenna selection phase, followed by the robust multicast beamforming generation phase. We propose an SINR-based approach for antenna selection, where the challenging mixed-integer problem is converted into an approximate joint optimization problem via a sequence of transformation, relaxation, and SINR approximation. We develop a fast two-layered alternating direction method of multipliers (ADMM)-based algorithm to compute an approximate solution. In particular, with our ADMM construction, we obtain semi-closed-form solutions for antenna selection and beamforming subproblems at each ADMM iteration for fast updates. To further reduce the computational complexity, we propose a signal-to-leakage-ratio (SLR)-based approach using the SLR constraints in the joint optimization problem. This allows us to develop a two-layered ADMM-based algorithm, which can compute a solution more efficiently due to the SLR structure. The robust multicast beamforming solution for the selected antennas is computed using the fast algorithm we developed recently. Simulation results show the effectiveness of our two proposed approximated approaches for antenna selection and the overall two-phase approach in both overall performance and substantially low computational complexity in a massive MIMO setting.
Optimizing drug discovery for snakebite envenoming via a high-throughput phospholipase A2 screening platform

Snakebite envenoming is a neglected tropical disease that causes as many as 1.8 million envenomings and 140,000 deaths annually. To address treatment limitations that exist with current antivenoms, the search for small molecule drug-based inhibitors that can be administered as early interventions has recently gained traction. Snake venoms are complex mixtures of proteins, peptides and small molecules and their composition varies substantially between and within snake species. The phospholipases A2 (PLA2) are one of the main pathogenic toxin classes found in medically important viper and elapid snake venoms, yet varespladib, a drug originally developed for the treatment of acute coronary syndrome, remains the only PLA2 inhibitor shown to effectively neutralise venom toxicity in vitro and in vivo, resulting in an extremely limited drug portfolio. Here, we describe a high-throughput drug screen to identify novel PLA2 inhibitors for repurposing as snakebite treatments. We present method optimisation of a 384-well plate, colorimetric, high-throughput screening assay that allowed for a throughput of ∼2,800 drugs per day, and report on the screening of a ∼3,500 post-phase I repurposed drug library against the venom of the Russell’s viper, Daboia russelii. We further explore the broad-spectrum inhibitory potential and efficacy of the resulting top hits against a range of medically important snake venoms and demonstrate the utility of our method in determining drug EC50s. Collectively, our findings support the future application of this method to fully explore the chemical space to discover novel PLA2-inhibiting drugs of value for preventing severe pathology caused by snakebite envenoming.
Nexus Between Asset Class Volatility and the Output Gap in Nigeria: A Bayesian Var Approach

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">Excessive volatility in financial markets can disrupt economic activity, affect investor and consumer confidence, and potentially lead to financial crises in an economy. Due to this backdrop, this study examined the link between asset class volatility and the output gap in Nigeria. The asset classes were categorized into stock, crude, gold, and bitcoin. The study adopted the GARCH and Bayesian VAR approach and found that all share index has an initial negative impulse with output gap while other asset classes have a positive impulse on output gap. The outcome of this study revealed to both policymakers and economists the potential risks and vulnerabilities of asset class volatility in the economy. Based on this result, recommendations are made amongst which is the strengthening of the Nigerian stock market to help with the inflationary pressures this is because the Nigerian stock market hurt the output gap also, the government should prioritize investing in crude, gold, and bitcoin to push the actual output to full capacity, which brings about employment. </span></p>
WaveFormer: Spectral–Spatial Wavelet Transformer for Hyperspectral Image Classification

Transformers have proven effective for hyperspectral image classification (HSIC) but often incorporate average pooling that results in information loss. This letter presents WaveFormer, a novel transformer-based approach that leverages wavelet transforms for invertible downsampling. This preserves data integrity while enabling attention learning. Specifically, WaveFormer unifies downsampling with wavelet transforms to decompress feature maps without loss. This provides an efficient tradeoff between performance and computation. Furthermore, the wavelet decomposition enhances the interaction between structural and shape information in image patches and channel maps. To evaluate WaveFormer, we conducted extensive experiments on two benchmark hyperspectral datasets. Our results demonstrate that WaveFormer achieves state-of-the-art classification accuracy, obtaining overall accuracies of 95.66% and 96.54% on the Pavia University and the University of Houston datasets, respectively. By integrating wavelet transforms, WaveFormer presents a new transformer architecture for hyperspectral imagery that achieves superior classification without information loss from average pooling.
Heeding the Voices of Pre-Service Early Childhood Education Teachers: Their Beliefs about Early Childhood Science Education and Self-Efficacy for Teaching Science

This cross-sectional survey study aimed to explore pre-service early childhood education teachers’ (PTs) beliefs about (a) the aims and scope of early childhood science education and (b) their self-efficacy beliefs in teaching science to preschool children. A survey consisting of open-ended questions was applied to PTs (N = 130). According to the results, PTs were aware of the importance of science education in early childhood. They mainly focused on the roles of early childhood science education in satisfying curiosity, developing questioning skills, and facilitating understanding of the environment and life. However, most PTs associated science education in early childhood with only natural sciences-related activities and concepts. Moreover, PTs reported low self-efficacy beliefs in teaching science to preschool children due to their lack of experience and theoretical knowledge. This study has some implications for PTs to view science education in early childhood from a comprehensive perspective and improve their self-efficacy.
A Methodology and an Empirical Analysis to Determine the Most Suitable Synthetic Data Generator

According to a report published by Gartner in 2021, a significant portion of Machine Learning (ML) training data will be artificially generated. This development has led to the emergence of various synthetic data generators (SDGs), particularly those based on Generative Adversarial Networks (GAN). All research endeavors so far have been exploratory, focused on specific objectives such as validating utility or disclosure control or assessing how generators can decrease or increase inherent bias with differential privacy. Hence, we aim to empirically identify an AI-based, data generator that can produce datasets that closely resemble real datasets, while also determining the hyper-parameters that enable a satisfactory balance between utility, privacy, and fairness in the datasets. To achieve this, we utilize the Synthetic Data Vault, Data Synthesizer, and Smartnoise-synth, which are three synthetic data generation packages that are accessible via Python. Different data generation models available within the package are presented with 13 tabular datasets iteratively as sample inputs to generate synthetic data. We generated synthetic data using every dataset and generator and investigated the goodness of the generator using five hypothetical scenarios. The utility and privacy offered by the generated data were compared with those of real data. The fairness in the ML model trained with synthetic data was used as a third metric for evaluation. Finally, we employ synthetic data to train regression and classification Machine Learning (ML) algorithms and evaluate their performance. After conducting experiments, analyzing metrics, and comparing ML scores across all 11 generators, we determined that the CTGAN from SDV and PATECTGAN from the SN-synth package were the most effective in mimicking real data for all 13 datasets utilized in our research.
The Role of Education in Strengthening Religious Moderation in Indonesia

Numerous scholarly articles have explored the relationship between education and its role in promoting religious moderation since the concept was introduced in Indonesia. However, only a few articles have been published in journals indexed by Scopus. Concerning Scopus as an accredited journal indexer recognized internationally, this paper's objective was to perform a systematic review of existing literature using the Scopus database from 2019 to 2023 to analyze the significance of education in strengthening religious moderation in Indonesia. The main steps are planning, conducting, and reporting the review. It includes 14 selected articles categorized based on five elements of educational systems: teachers, students, educational objectives, learning management, and educational environments. According to the systematic literature review, education has strengthened religious moderation in Indonesia. It is supported by sufficient teachers' capabilities in teaching religious moderation, students' role as the agents of religious moderation, educational objectives that accommodate religious moderation values, learning managements that use various learning methods and resources, higher thinking order skills, interreligious dialogue, and strategic partnership, and educational environments that provide sufficient learning systems, such as integrating Madrasah Diniyah and Pesantren Universities. However, the implementation still faces challenges requiring more attention from various parties.
Approximate Analytical Solutions for Strongly Coupled Systems of Singularly Perturbed Convection–Diffusion Problems

This work presents a reliable algorithm to obtain approximate analytical solutions for a strongly coupled system of singularly perturbed convection–diffusion problems, which exhibit a boundary layer at one end. The proposed method involves constructing a zero-order asymptotic approximate solution for the original system. This approximation results in the formation of two systems: a boundary layer system with a known analytical solution and a reduced terminal value system, which is solved analytically using an improved residual power series approach. This approach combines the residual power series method with Padé approximation and Laplace transformation, resulting in an approximate analytical solution with higher accuracy compared to the conventional residual power series method. In addition, error estimates are extracted, and illustrative examples are provided to demonstrate the accuracy and effectiveness of the method.