Miracle-dz/UniFAR-FTUs huggingface dataset:
A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer’s disease-related molecules in the hippocampus

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer’s disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD’s clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer’s disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer’s disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer’s disease and other types of dementia.
Neural-circuit basis of song preference learning in fruit flies

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its’ auditory behaviors according to prior sound experiences. Female flies that have heard male courtship songs of the same species are less responsive to courtship songs of different species. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the central brain, with these neurons playing a key role in mating behavior by integrating multimodal sensory and internal information. The neural circuit basis of this GABAergic input, however, has not yet been identified. Here, we find that pCd-2 neurons, totaling four cells per hemibrain and expressing the sex-determination gene doublesex, provide the GABAergic input to pC1 neurons for song preference learning. First, RNAi-mediated knockdown of GABA production in pCd-2 neurons abolished song preference learning. Second, pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between pC1 and pCd-2 neurons. Finally, GABAergic and dopaminergic inputs to pCd-2 neurons are necessary for song preference learning. Together, this study suggests that reciprocal circuits between pC1 and pCd-2 neurons serve as a sensory and internal state-integrated hub, allowing flexible control over female copulation. Consequently, this provides a neural circuit model that underlies experience-dependent auditory plasticity. Significance To find a suitable mate, an organism must adapt its behavior based on past experiences. In the case of Drosophila, female assessments of male song signals, which contain information about the status and species of the sender, are experience dependent. Here, we show that reciprocal circuits in the central brain modulate the female’s song response depending on her previous auditory experiences. These circuits exhibit feedback and lateral inhibition motifs, and are regulated by dopaminergic and GABAergic inputs. While the effects of prior auditory experiences on sound responsiveness have been extensively studied in other species, our research advances the use of Drosophila as a model for dissecting the circuitry underlying experience-dependent auditory plasticity at single-cell resolution.
Geographical Erasure in Language Generation

Large language models (LLMs) encode vast amounts of world knowledge. However, since these models are trained on large swaths of internet data, they are at risk of inordinately capturing information about dominant groups. This imbalance can propagate into generated language. In this work, we study and operationalise a form of geographical erasure, wherein language models underpredict certain countries. We demonstrate consistent instances of erasure across a range of LLMs. We discover that erasure strongly correlates with low frequencies of country mentions in the training corpus. Lastly, we mitigate erasure by finetuning using a custom objective.
Moderating Effects of Governance on Open Government Data Quality and Open Government Data Utilization: Analysis Based on the Resource Complementarity Perspective

ABSTRACT Open government data (OGD) has great potential to produce political, social and economic values, but its utilization is far from expectations. This paper intends to study the factors and mechanisms that affect open data utilization. Drawing from the resource complementarity perspective of the resource-based theory, this study examines the moderating effect of the six dimensions of governance on open data quality and open data utilization. The model is empirically tested by the partial least squares (PLS) method using sample data from 115 countries. The results show that the five dimensions of governance (including voice and accountability, government effectiveness, rule of law, regulatory quality and control of corruption) moderated the relationship of open data quality with open data utilization in a positive direction. In addition, the impact of open data quality on open data utilization still holds true at the global level. The conclusion is that governance, as an important complementary resource to promote open data activities, can better promote open data utilization and value generation when combined with open data resources of various countries. Different governance levels are an important explanatory variable for the large differences of open data utilization among countries. In open data practice, governments should not only improve open data quality, but also improve the national governance level to provide complementary resource support for the utilization and value generation of open data in their countries.
Pre-eruption thermal rejuvenation and stirring of a partly crystalline rhyolite pluton revealed by the Earthquake Flat Pyroclastics deposits, New Zealand

The Earthquake Flat Pyroclastics form a c. 10 km3 rhyolite deposit erupted at c. 50 ka from the margin of Okataina Volcanic Centre, immediately following the caldera-forming eruption of the Rotoiti Pyroclastics (c. 100 km3) from vents c. 20 km to the NE. Earthquake Flat Pyroclastics deposits display textural and compositional complexity on a crystal-scale consistent with rejuvenation of a near-crystalline pluton in the upper crust. Quartz and plagioclase crystals are resorbed, whereas hornblende and biotite are euhedral. Fe–Ti oxides indicate large variations in pre-eruption temperatures (702–805 °C). Differences of up to 70 °C within pumice lapilli show that crystals were chaotically juxtaposed during magma stirring and evacuation. Chemical zoning within hornblende crystals is consistent with rimward increases of c. 50 °C. These features are consistent with a convective self-stirring process. Previous isotope studies demonstrate a long (>100 ka) crystallization history for the magma. Resorption of crystals deep in the magma may have produced a Ca-, Fe- and Mg-enriched rhyolite melt that allowed the growth of reverse-zoned hornblende. Microdiorite lithic fragments in the Earthquake Flat Pyroclastics and Rotoiti deposits and a basaltic eruption that immediately preceded the Rotoiti eruption suggest that mafic underplating beneath Okataina Volcanic Centre provided a major thermal and volatile pulse to drive the caldera eruptions.
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation

Label aggregation such as majority voting is commonly used to resolve annotator disagreement in dataset creation. However, this may disregard minority values and opinions. Recent studies indicate that learning from individual annotations outperforms learning from aggregated labels, though they require a considerable amount of annotation. Active learning, as an annotation cost-saving strategy, has not been fully explored in the context of learning from disagreement. We show that in the active learning setting, a multi-head model performs significantly better than a single-head model in terms of uncertainty estimation. By designing and evaluating acquisition functions with annotator-specific heads on two datasets, we show that group-level entropy works generally well on both datasets. Importantly, it achieves performance in terms of both prediction and uncertainty estimation comparable to full-scale training from disagreement, while saving up to 70% of the annotation budget.
Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?

Large Language Models (LLMs), such as ChatGPT, have drawn a lot of attentions recently in the legal domain due to its emergent ability to tackle a variety of legal tasks. However, it is still unknown if LLMs are able to analyze a legal case and perform reasoning in the same manner as lawyers. Therefore, we constructed a novel corpus consisting of scenarios pertain to Contract Acts Malaysia and Australian Social Act for Dependent Child. ChatGPT is applied to perform analysis on the corpus using the IRAC method, which is a framework widely used by legal professionals for organizing legal analysis. Each scenario in the corpus is annotated with a complete IRAC analysis in a semi-structured format so that both machines and legal professionals are able to interpret and understand the annotations. In addition, we conducted the first empirical assessment of ChatGPT for IRAC analysis in order to understand how well it aligns with the analysis of legal professionals. Our experimental results shed lights on possible future research directions to improve alignments between LLMs and legal experts in terms of legal reasoning.
Machine Learning Enabled Mixed Reality Systems - For Evaluation and Validation of Augmented Experience in Aircraft Maintenance

In this paper, a framework to assess the usability of mixed reality (MR) systems in terms of physical load metric is presented. The participants used a MR system for an aircraft maintenance task, while a computer vision (CV) based machine learning (ML) method extracted the skeletal data of the participants. A bi-manual musculoskeletal computational model of human upper-limb was overlayed onto the skeletal data to derive upper-limb muscle forces/energy spent during the aircraft maintenance tasks. The experiments were conducted with and without the use of MR devices, while CV based ML was applied to detect the energy used of aircraft maintainers in real-time while they performed tasks on an MRH90 helicopter. An analysis of augmented experience has been presented, which will act as a driver for the use of MR based applications in aircraft maintenance as well as battle-ready platforms. Preliminary results are encouraging and we envisage to use the proposed methodology for an objective assessment and detection of physical workload.
Subjective Study of 3D Mesh Quality Scores in Virtual Reality

3D meshes are widely used in Virtual Reality as essential graphical elements for creating immersive virtual environments. In practice, the 3D meshes being used are often subject to some manipulations, where some details may be lost and some noise could be introduced, e.g., due to the limited transmission bandwidth. While existing studies have considered 3D mesh quality measures in the desktop setting, we consider how different 3D distortion types affect the perceptual quality of 3D shapes when viewed in a Virtual Reality setup (with users wearing a Meta/Oculus headset). Our experiment collected mean opinion scores (MOS) for each distorted shape by showing both distorted and reference shapes to users, and compared the results with meshes viewed in the traditional display. This paper aims to understand the effect of different types and levels of 3D mesh distortions on perceived quality and user experience in VR. We analyse correlations of two settings (VR and desktop) using the Pearson and Spearman correlation coefficients, which show a positive relationship between the two settings. However, in virtual reality, perception appears more sensitive to particular distortions than others, compared with the desktop setting, which can provide helpful guidance for downstream applications.
A Study of Real-World Information Mapping for Information Sharing Using Edge Devices

We are developing a system that detects "objects" and "events" in the real-world using edge devices that can share the detected information among them in the cyber-space (virtual world) so that the users can remotely obtain real-world information from distant locations. We aim for the system can serve as a foundation for services that construct and utilize a digital twin of the real world. In this paper, we report our findings by implementing "objects" and "things" detection in edge devices (smartphones) and making experiments for sharing the detected "objects" and "things" registered on a digital map.
Polarimetric Radar Quantitative Precipitation Estimation

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.
Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs

Many real-world computations involve sparse data structures in the form of sparse matrices. A common strategy for optimizing sparse matrix operations is to reorder a matrix to improve data locality. However, it's not always clear whether reordering will provide benefits over the unordered matrix, as its effectiveness depends on several factors, such as structural features of the matrix, the reordering algorithm and the hardware that is used. This paper aims to establish the relationship between matrix reordering algorithms and the performance of sparse matrix operations. We thoroughly evaluate six different matrix reordering algorithms on 490 matrices across eight multicore architectures, focusing on the commonly used sparse matrix-vector multiplication (SpMV) kernel. We find that reordering based on graph partitioning provides better SpMV performance than the alternatives for a large majority of matrices, and that the resulting performance is explained through a combination of data locality and load balancing concerns.
Real-time Multi-CNN based Emotion Recognition System for Evaluating Museum Visitors’ Satisfaction

Conventional studies on the satisfaction of museum visitors focus on collecting information through surveys to provide a one-way service to visitors, and thus it is impossible to obtain feedback on the real-time satisfaction of visitors who are experiencing the museum exhibition program. In addition, museum practitioners lack research on automated ways to evaluate a produced content program's life cycle and its appropriateness. To overcome these problems, we propose a novel multi-convolutional neural network (CNN), called VimoNet, which is able to recognize visitors emotions automatically in real-time based on their facial expressions and body gestures. Furthermore, we design a user preference model of content and a framework to obtain feedback on content improvement for providing personalized digital cultural heritage content to visitors. Specifically, we define seven emotions of visitors and build a dataset of visitor facial expressions and gestures with respect to the emotions. Using the dataset, we proceed with feature fusion of face and gesture images trained on the DenseNet-201 and VGG-16 models for generating a combined emotion recognition model. From the results of the experiment, VimoNet achieved a classification accuracy of 84.10%, providing 7.60% and 14.31% improvement, respectively, over a single face and body gesture-based method of emotion classification performance. It is thus possible to automatically capture the emotions of museum visitors via VimoNet, and we confirm its feasibility through a case study with respect to digital content of cultural heritage.
Noninvasive Cuﬄ ess Blood Pressure Monitoring. How Mechanism-Driven and Data-Driven Models Can Help in Clinical Practice

Continuous noninvasive cuﬄ ess blood pressure (BP) monitoring is essential for early detection and treatment of hypertension. In this paper, we provide an overview of the recent advancements in cuﬄ ess BP sensors. These include contact wearable sensors such as electrocardiography (ECG), photoplethysmography (PPG), contact non-wearable sensors such as ballistocardiography (BCG), and contactless sensors such as video plethysmography (VPG). These sensors employ diﬀ erent measuring mechanisms such as pulse arrival time (PAT), pulse transit time (PTT), and pulse wave analysis (PWA) to estimate BP. However, challenges exist in the eﬀ ective use and interpretation of signal features to obtain clinically reliable BP measurements. The correlations between signal features and BP are obtained by mechanism-driven models which use physiological principles to identify mathematical correlations, and data-driven models which use machine learning algorithms to analyze observational data to identify multidimensional correlations. On the one hand, applying mechanism-driven models to non-linear scenarios and incomplete or noisy data is challenging On the other hand, data-driven models require a large amount of data in order to prevent physically inconsistent predictions, resulting in poor generalization. From this perspective, this paper proposes to combine the strengths of mechanism-driven and data-driven approaches to obtain a more comprehensive approach, the physiology-informed machine-learning approach, with the goal of enhancing the accuracy, interpretability, and scalability of continuous cuﬄ ess BP monitoring. This holds promise for personalized clinical applications and the advancement of hypertension management.
Green Credit Development: Current Status and Policy Recommendations in Vietnam

The banking industry can play a decisive role in promoting environmentally sustainable development and community responsibility investments. In that context, green credit development is considered one of the essential strategies to move closer to the "green banking" model. Although it has great potential and brings opportunities for technological innovation to the economy, currently, green credit still faces many obstacles. With the goal of green credit for sustainable development, the introduction of Directive 03/CT-NHNN on promoting green credit growth and managing environmental and social risks in operations providing credit sent a strong message about the responsibility of the banking industry towards environmental protection. Thus, the research objective is to systematize the general theoretical issues of green credit and green credit development. Analyze the importance of green credit for the banking business by surveying 150 managers related to green credit at commercial banks. Based on the research results, this study proposes some policy recommendations to support the development of green credit for commercial banks in Vietnam.
Explanation of discrepancies among satellite observations of the aerosol indirect effects

Satellite‐based remote sensing instruments for measuring the aerosol indirect effect (IE = −d ln re/d ln τa where re is the cloud drop effective radius and τa is the aerosol optical depth) show large disparities in the magnitude of the effect for similar regions of the globe. Over the oceans, the Advanced Very High Resolution Radiometer (AVHRR) measures an indirect effect twice that measured by the POLarization and Directionality of the Earth Reflectances (POLDER) (0.17 vs. 0.085). We address possible reasons for these disparities. It is argued that AVHRR misses the optically thin and broken clouds, especially over land, while POLDER misses clouds with variable top heights in its field of view. POLDER is also biased to thinner, less turbulent clouds. The sensitivity of the indirect effect to cloud turbulence therefore biases POLDER to lower values. POLDER measures an indirect effect over the ocean that is about twice that over the land (0.085 vs. 0.04). By considering factors such as dynamics, variability in cloud liquid water path, decoupling of the boundary layer, and the effect of salt particles, we argue that this could be an artifact, and that the indirect effect on cloud microstructure may be stronger over land than over the ocean.
Use of crop sanitation for the management of the banana weevil in Uganda

Removal and destruction of banana crop residues after harvest has been widely recommended for the control of banana weevil, Cosmopolites sordidus control. However, there is scanty information of the actual effect of this practice on the banana weevil populations and damage. This study aimed at investigating the effect of crop sanitation on the population dynamics of this pest and its associated damage. Laboratory and field studies were conducted in Uganda to study the biology and ecology of this pest on crop residues. We found that corms are the most attractive of all residues parts. Fresh residues were most attractive but oviposition occurred on residues up to 120 days after harvest. An on-farm study of the effect of crop sanitation on populations and corm damage was conducted through farmer participatory trials in Ntungamo district, Uganda. Increase in sanitation level from low to high significantly reduced banana weevil adult populations, lowered corm damage, increased plant maturation rates and increased yields. The data suggest that improved sanitation management can contribute to C. sordidus control and improved banana productivity.