Miracle-dz/UniFAR-FTUs huggingface dataset:
The Left Amygdala and Right Frontoparietal Cortex Support Emotional Adaptation Aftereffects

Adaptation aftereffects—in which prolonged prior experience (adaptation) can bias the subsequent judgment of ambiguous stimuli—are a ubiquitous phenomenon. Numerous studies have found behaviorally stable adaptation aftereffects in a variety of areas. However, it is unclear which brain regions are responsible for this function, particularly in the case of high-level emotional adaptation aftereffects. To address this question, the present study used fMRI technology to investigate the neural mechanism of emotional adaptation aftereffects. Consistent with previous studies, we observed typical emotional adaptation effects in behavior. Specifically, for the same morphed facial images, participants perceived increased sadness after adapting to a happy facial image and increased happiness after adapting to a sad facial image. More crucially, by contrasting neural responses to ambiguous morphed facial images (i.e., facial images of intermediate morph levels) following adaptation to happy and sad expressions, we demonstrated a neural mechanism of emotional aftereffects supported by the left amygdala/insula, right angular gyrus, and right inferior frontal gyrus. These results suggest that the aftereffects of emotional adaptation are supported not only by brain regions subserving emotional processing but also by those subserving cognitive control.
Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

This laboratory experimental study investigates the temporal evolution of the size distribution of subsurface oil droplets generated as breaking waves entrain oil slicks. The measurements are performed for varying wave energy, as well as large variations in oil viscosity and oil-water interfacial tension, the latter achieved by premixing the oil with dispersant. In situ measurements using digital inline holography at two magnifications are applied for measuring the droplet sizes and Particle Image Velocimetry (PIV) for determining the temporal evolution of turbulence after wave breaking. All early (2–10 s) size distributions have two distinct size ranges with different slopes. For low dispersant to oil ratios (DOR), the transition between them could be predicted based on a turbulent Weber (We) number in the 2–4 range, suggesting that turbulence plays an important role. For smaller droplets, all the number size distributions have power of about −2.1, and for larger droplets, the power decreases well below −3. The measured steepening of the size distribution over time is predicted by a simple model involving buoyant rise and turbulence dispersion. Conversely, for DOR 1:100 and 1:25 oils, the diameter of slope transition decreases from ∼1 mm to 46 and 14 µm, respectively, much faster than the We-based prediction, and the size distribution steepens with increasing DOR. Furthermore, the concentration of micron-sized droplets of DOR 1:25 oil increases for the first 10 min after entrainment. These phenomena are presumably caused by the observed formation and breakup oil microthreads associated with tip streaming.
Exploring the Experiences, Challenges, and Perspectives in Digital Literacy Training of Older Thai EFL Teachers with Low Digital Literacy

Although technology has been seamlessly integrated into classroom teaching, there is room for improvement in the way professional development is implemented. The aim of this study was to explore the experiences of Thai teachers of English as a Foreign Language (EFL) in implementing digital literacy training. Focusing on technologically illiterate teachers (and particularly older ones), the study aimed to investigate the production of online videos by these teachers to be used in their classrooms. The study was conducted in Trang, Thailand, and involved 36 Thai EFL teachers who underwent a one-month training programme in 2020. Data was collected through a questionnaire, observation, and interviews. The results showed that the participants required additional training time, and faced challenges such as teaching style, computer specifications, and Internet accessibility. Despite these challenges, the teachers reported a positive perceptions of teaching English with technology, and found it was easier than they had anticipated. These findings have pedagogical implications for future implementations of digital literacy training for somewhat technologically illiterate language teachers.
Numerical simulations of supercells in convective boundary layers

Numerical simulations of supercell thunderstorms in horizontally homogeneous environments (e.g., Wilhelmson and Klemp 1978a, b; Weisman and Klemp 1982, 1984; McCaul and Weisman 2001) have demonstrated the importance of CAPE and wind shear in influencing storm type. With respect to supercells, numerical simulations have provided considerable insight into the dynamics of supercell rotation, maintenance, and propagation. It is well-known that significant horizontal variability in CAPE and wind shear can exist on the meso-α and meso-β scales (e.g., Brooks et al. 1994; Markowski et al. 1998). Recent studies have explored (e.g. Kost and Richardson 2004; Kron 2004; Richardson et al. 2007) the sensitivity of simulated convective storms to mesoβ-scale moisture and wind shear variations, and have found these heterogeneities can significantly influence the structure and evolution of deep, moist convection. Investigations into the influence of radiative effects on supercells (e.g., Markowski and Harrington 2005; Frame and Markowski 2008) have indicated a significant change in the low-level kinematic (i.e. vertical vorticity) and thermodynamic fields due to radiative cooling beneath the thunderstorm anvil. These alterations to the near-storm environment surrounding a supercell can significantly influence its subsequent morphology. ____________________________________________ * Corresponding author address: Kent H. Knopfmeier, Department of Meteorology, Pennsylvania State University, 503 Walker Building, University Park, PA, 16803; email: khk124@psu.edu Considerable variability on the meso-γ scale, in large part as a result of dry boundary layer convection, also has been observed (e.g., Markowski and Richardson 2007). Markowski and Richardson (2007) documented marked horizontal heterogeneity in the magnitude of the 0-1 km shear vector and of the 0-1 km storm-relative helicity within CBLs during the International H2O Project (Weckwerth et al. 2004). However, their observational study could not address whether the meso-γ scale wind shear variability had any effect on storms. All of these aforementioned studies suggest a need to include environmental heterogeneities in numerical simulations of convective storms. Although, as mentioned above, some prior studies have included meso-β-scale heterogeneity, even fewer numerical studies of deep, moist convection have included surface heat fluxes and convective boundary layers (e.g., Carpenter et al. 1998; this study was limited to cumulus congestus clouds, however). If desired, the computing capabilities of today now allow for high-resolution, 3D simulations of convective storms in convective boundary layers. To this end, our ongoing work explores the effects of the kinematic and thermodynamic variability of a convective boundary layer on thunderstorms. Preliminary results comparing the evolution of a supercell in a horizontally homogeneous environment to its evolution in a convective boundary layer are presented herein.
Growth Yield and Yield Attributes of Some White Maize Varieties as Influenced by Planting Configuration

A field experiment was conducted at Agronomy field of Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka during the period from November to April (winter season) 2015-2016. This study investigated the combined effects of four Chinese white maize varieties (V1 = Changnau-1, V2 = Q-Xiangnau-1, V3 = Changnau-6, V4 = Youngnau-7) and two planting configurations (S1 =70 cm x 25 cm and S2 = 60 cm x 25 cm) on their growth, yield, and yield attributes. The experiment was laid out in a factorial randomized complete block design (RCBD) with three replications. Significant variations were observed among the varieties for all measured parameters. Changnau-6 (V3) produced the longest cob (17.54 cm), maximum grains cob-1 (418.36), highest grain yield (8.3670 t ha-1), stover yield (9.6921 t ha-1) and biological yield (18.059 t ha-1) while Youngnau-7 (V4) had the shortest cob (12.683 cm), maximum grains cob-1 (247.53), lowest grain yield (4.8469 t ha-1), stover yield (7.957 t ha-1), biological yield (10.982 t ha-1) shortest maturation time. Planting at 70 cm x 25 cm (S1) spacing resulted in taller plants, more leaves, and longer cobs compared to 60 cm x 25 cm (S2) spacing, but yielded less grain, stover, and biomass. The interaction between variety and planting configuration further influenced the traits. Changnau-1 (V1) planted at 70 cm x 25 cm (S1) had the highest plant height, number of leaves, and days to maturity, while Changnau-6 (V3) planted at 60 cm x 25 cm (S2) achieved the highest grain yield (8.7645 t ha-1), stover yield (10.089 t ha-1) biological yield (18.853 t ha-1) and harvest index (46.487 %). Overall, the choice of variety and planting configuration significantly impacted the growth, yield, and yield attributes of white maize. Changnau-6 (V3) variety with 60 cm x 25 cm (S2) plant spacing was found to be the most promising combination for maximizing grain yield and harvest index under the conditions of this field trial.
QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning

Transformer-based models have gained widespread popularity in both the computer vision (CV) and natural language processing (NLP) fields. However, significant challenges arise during post-training linear quantization, leading to noticeable reductions in inference accuracy. Our study focuses on uncovering the underlying causes of these accuracy drops and proposing a quantization-friendly fine-tuning method, \textbf{QuantTune}. Firstly, our analysis revealed that, on average, 65\% of quantization errors result from the precision loss incurred by the dynamic range amplification effect of outliers across the target Transformer-based models. Secondly, \textbf{QuantTune} adjusts weights based on the deviation of outlier activations and effectively constrains the dynamic ranges of the problematic activations. As a result, it successfully mitigates the negative impact of outliers on the inference accuracy of quantized models. Lastly, \textbf{QuantTune} can be seamlessly integrated into the back-propagation pass in the fine-tuning process without requiring extra complexity in inference software and hardware design. Our approach showcases significant improvements in post-training quantization across a range of Transformer-based models, including ViT, Bert-base, and OPT. QuantTune reduces accuracy drops by 12.09\% at 8-bit quantization and 33.8\% at 7-bit compared to top calibration methods, outperforming state-of-the-art solutions by over 18.84\% across ViT models.
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have shown that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets, but their methods rely heavily on handcrafted text prompts about defects, making them difficult to generalize to anomalies in other applications, e.g., medical image anomalies or semantic anomalies in natural images. In this work, we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly. To this end, we introduce a novel approach that learns an in-context residual learning model for GAD, termed InCTRL. It is trained on an auxiliary dataset to discriminate anomalies from normal samples based on a holistic evaluation of the residuals between query images and few-shot normal sample prompts. Regardless of the datasets, per definition of anomaly, larger residuals are expected for anomalies than normal samples, thereby enabling InCTRL to generalize across different domains without further training. Comprehensive experiments on nine AD datasets are performed to establish a GAD benchmark that encapsulate the detection of industrial defect anomalies, medical anomalies, and semantic anomalies in both one-vs-all and multi-class setting, on which InCTRL is the best performer and significantly outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/InCTRL.
Symmetry Resolved Measures in Quantum Field Theory: a Short Review

In this short review we present the key definitions, ideas and techniques involved in the study of symmetry resolved entanglement measures, with a focus on the symmetry resolved entanglement entropy. In order to be able to define such entanglement measures, it is essential that the theory under study possess an internal symmetry. Then, symmetry resolved entanglement measures quantify the contribution to a particular entanglement measure that can be associated to a chosen symmetry sector. Our review focuses on conformal (gapless/massless/critical) and integrable (gapped/massive) quantum field theories, where the leading computational technique employs symmetry fields known as (composite) branch point twist fields.
The Effect of Visual Reasoning on Arithmetic Word Problem Solving

First-grade students often encounter challenges in understanding and solving arithmetic word problems due to their limited reading comprehension abilities. Despite these difficulties, students may employ arbitrary strategies, such as combining numbers based on specific keywords, even if they lack a full understanding of the problems. Research suggests that effective mathematical reasoning involves the use of visual mental representations during the problem-solving process. To address this, some studies have explored methods to enhance students’ comprehension of word problems. Building on this, the current study explores the impact of first-grade pupils creating visual representations of problem situations on their comprehension and the number of correct solutions. In a typical math class, 45 first graders received a paper-and-pencil task, and, in a visual context, they solved similar problems after reading and illustrating the situation. The findings reveal that while most participants correctly represented the problem situations through drawing, about half struggled to determine the numeric solutions. Nevertheless, the visual context led to an increase in the number of correct problem solutions compared to the normal context, suggesting the potential benefits of incorporating visual representations in enhancing comprehension and problem-solving skills.
Neural dynamics of predictive timing and motor engagement in music listening

Why do humans spontaneously dance to music? To test the hypothesis that motor dynamics reflect predictive timing during music listening, we built melodies with varying degrees of rhythmic predictability. Magnetoencephalography data showed that while auditory regions track the rhythm of melodies, intrinsic neural dynamics at delta (1.4 Hz) and beta (20-30 Hz) rates in the dorsal auditory pathway embody the experience of groove. Critically, neural dynamics are organized along this pathway in a spectral gradient, with the left sensorimotor cortex acting as a hub coordinating groove-related delta and beta activity. Combined with predictions of a neurodynamic model, this indicate that spontaneous motor engagement during music listening is a manifestation of predictive timing effected by interaction of neural dynamics along the dorsal auditory pathway. One-Sentence Summary Interacting neural dynamics along the dorsal auditory pathway effect the experience of groove during music listening.
I Was Merely a Brick in the Game: A Qualitative Study on Registered Nurses’ Reasons for Quitting Their Jobs in Hospitals

The aim was to explore why registered nurses (RNs) in Sweden choose to quit their jobs in hospitals, also in relation to experienced patient safety. Previous research has shown that nurse turnover, especially in hospital settings, is a serious challenge for society and health care globally. Insufficient staffing of RNs is linked to poorer patient outcomes and a general patient safety at risk. It is, therefore, important to continually explore how nurses describe their reasons for quitting their jobs. The study was conducted using a qualitative descriptive design, based on 11 semistructured interviews with RNs. The analysis generated four categories describing the results: Feeling that the profession is not valued; Psychological and physical symptoms related to work; An insufficient and unsupportive organization; and Unsatisfying leadership and teamwork. Specifically, the RNs participating in this study described a range of reasons for quitting, where the feeling of not being valued and treated as a respected and autonomous profession was a common thread throughout the results. RNs experienced that, overall, the insufficient work conditions, also resulting in lower patient safety, ultimately led to their decision to quit. The findings highlight the crucial need for employers to develop working conditions for RNs, to make sure that the profession is valued according to professional standards and provide the potential for autonomous nursing practice. To reduce nurse turnover, and instead attract and retain nurses, leadership and management in nursing need to be adjusted to meet the demands of a modern academic profession.
The Effect of Using Edpuzzle-Based Video in Electrolyte and Nonelectrolyte Solutions Learning on Student Learning Outcomes

The limitation of using learning media atSMAN 1 Kalasan causes the learning of electrolyte and nonelectrolyte solutions to be less interesting and less effective. Therefore, learning can be supported by attractive video design in the form of Edpuzzle which hasthe potential to build better learning processes and outcomes. This research aims to evaluate the effect of Edpuzzle-based video implementation in electrolyte and nonelectrolyte solutions lessons on student learning outcomes. In form ofa Quasi-Experimental Design, this research was carried out by One-Group Pretest-Posttest Design. The research sample is students of X MIPA 2 that selected by simple random sampling. The instruments used were validation sheets, lesson plans, pretest, and posttest items, questions in Edpuzzle video, an observation sheet of Edpuzzle-based video implementation, and a students’ response questionnaire. Research data were analyzed by SPSS 25, Aiken’s Statistics, and descriptively. The results showed that using Edpuzzle-based video had an influence on students’ learning outcomes that support by Sig value. (2-tailed) < 0.05 andthe average of N-Gain is 0.8 (high). The average post-test is 96.7 (excellent). During Edpuzzle video implementation on learning, the cognitive achievement of C1, C2, and C3respectively levelcompleted are 100%, 100%, and 91.66%, while, C4 and C5 are respectively 22.22% and 41.66%, In addition, the achievement of learning outcomes indicator is also supported by observation for psychomotor and affective domains, with a respectively average scores 96.06% and 95.48%. Edpuzzle-based video can be used as support for electrolyte and nonelectrolyte learning in senior high school.
US fiscal policy shocks: Proxy‐SVAR overidentification via GMM

Using external instruments, one can recover the effects of individual shocks without fully identifying a vector autoregression (VAR). We show that fully or almost fully instrumenting a VAR—that is, using an instrument for each shock—allows one to overidentify the model by incorporating the condition that the structural shocks are uncorrelated, via the generalized method of moments (GMM). We apply our approach to a fiscal VAR for the United States over 1948–2019, where the overidentifying restrictions are not rejected. The overidentified structural vector autoregression (SVAR) yields (a) greater precision in estimating impulse response functions and multipliers and (b) measures of the effects of nonfiscal shocks even when there is no instrument for them.
On the Diminishing Returns of Width for Continual Learning

While deep neural networks have demonstrated groundbreaking performance in various settings, these models often suffer from \emph{catastrophic forgetting} when trained on new tasks in sequence. Several works have empirically demonstrated that increasing the width of a neural network leads to a decrease in catastrophic forgetting but have yet to characterize the exact relationship between width and continual learning. We design one of the first frameworks to analyze Continual Learning Theory and prove that width is directly related to forgetting in Feed-Forward Networks (FFN). Specifically, we demonstrate that increasing network widths to reduce forgetting yields diminishing returns. We empirically verify our claims at widths hitherto unexplored in prior studies where the diminishing returns are clearly observed as predicted by our theory.
Leaf- and ecosystem-scale photosynthetic parameters for the overstory and understory of boreal forests in interior Alaska

Photosynthetic parameters are key for predicting the carbon cycle and fluxes in terrestrial ecosystems. In northern high-latitude regions where cold temperatures limit available nitrogen for plants, the photosynthetic parameters are tightly linked with the nitrogen content. Here, we present the leaf- and ecosystem-scale photosynthetic parameters, the maximum carboxylation rate ( vc max25 ) , based on leaf chamber measurements and eddy covariance data from two mature black spruce forests and one young aspen-birth forest in interior Alaska. The leaf carbon to nitrogen ratio ( C:N ratio ) explained most of the variations in vc max25 across the five vascular plants ( Picea mariana , Betula glandulosa , Ledum groenlandicum , Betula papyrifera , and Populus tremuloides )( R 2 = 0.68; p < 0.01 ) , including the overstory and understory, from the three forests. Each plant had a small within-species variability of vc max25 and leaf C:N, suggesting that plants have a specific potential for available N in photosynthesis. This result indicates that the canopy structure and community composition are presumably important factors for modeling the canopy photosynthesis across boreal forests in interior Alaska. The ecosystem-scale vc max25 determined from the eddy covariance data further supported this hypothesis. evaluated the photosynthetic parameters for overstory and understory plants of two black spruce and one young aspen-birch forests in interior Alaska, based on leaf chamber measurements for the leaf scale and an inversion of a canopy photosynthesis model using eddy covariance fluxes ( Ueyama et al ., 2016 ) for the ecosystem scale. We show a single relationship between the maximum carboxylation rates and the leaf carbon to nitrogen ratios ( C:N ratio ) within and between the forests, and discuss the linkage of the leaf- and ecosystem-scale photosynthetic parameters.