Miracle-dz/UniFAR-FTUs huggingface dataset:
The doomsday of black hole evaporation

By assuming simultaneously the unitarity of the Hawking evaporation and the universality of Bekenstein entropy bound as well as the validity of cosmic censorship conjecture, we have found that the black hole evaporation rate could evolve from the usual inverse square law in black hole mass into a constant evaporation rate near the end of the Hawking evaporation before quantum gravity could come into play, inferring a slightly longer lifetime for lighter black holes.
A dynamical holographic QCD model for spontaneous chiral symmetry breaking and confinement

In this paper, we present a holographic realization of spontaneous chiral symmetry breaking and confinement. The latter is realized by building a solution of 5d Einstein-dilaton gravity leading to a confining quark antiquark potential. The 4d currents and the quark mass operator associated with chiral symmetry breaking and creation of meson states are mapped to 5d fields whose dynamics is given by a non-Abelian Higgs action. We introduce a non-minimal dilaton coupling to the tachyon potential which has two parameters, one of them controlling the presence or absence of spontaneous chiral symmetry breaking and the other controlling the sign of the chiral condensate as the quark mass grows. We calculate the masses of vector, scalar, axial-vector and pseudoscalar mesons, focusing on the effect of chiral symmetry breaking on the spectrum. In the chiral limit we identify the emergence of a massless state in the pseudoscalar meson spectrum, i.e., the pion. We calculate all meson decay constants and confirm that the pion satisfies the Gell-Mann-Oakes relation in the light quark regime.
Emotion Embeddings - Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets

Human emotion is expressed in many communication modalities and media formats and so their computational study is equally diversified into natural language processing, audio signal analysis, computer vision, etc. Similarly, the large variety of representation formats used in previous research to describe emotions (polarity scales, basic emotion categories, dimensional approaches, appraisal theory, etc.) have led to an ever proliferating diversity of datasets, predictive models, and software tools for emotion analysis. Because of these two distinct types of heterogeneity, at the expressional and representational level, there is a dire need to unify previous work on increasingly diverging data and label types. This article presents such a unifying computational model. We propose a training procedure that learns a shared latent representation for emotions, so-called emotion embeddings, independent of different natural languages, communication modalities, media or representation label formats, and even disparate model architectures. Experiments on a wide range of heterogeneous affective datasets indicate that this approach yields the desired interoperability for the sake of reusability, interpretability and flexibility, without penalizing prediction quality. Code and data are archived under https://doi.org/10.5281/zenodo.7405327 .
Order-based Structure Learning with Normalizing Flows

Estimating the causal structure of observational data is a challenging combinatorial search problem that scales super-exponentially with graph size. Existing methods use continuous relaxations to make this problem computationally tractable but often restrict the data-generating process to additive noise models (ANMs) through explicit or implicit assumptions. We present Order-based Structure Learning with Normalizing Flows (OSLow), a framework that relaxes these assumptions using autoregressive normalizing flows. We leverage the insight that searching over topological orderings is a natural way to enforce acyclicity in structure discovery and propose a novel, differentiable permutation learning method to find such orderings. Through extensive experiments on synthetic and real-world data, we demonstrate that OSLow outperforms prior baselines and improves performance on the observational Sachs and SynTReN datasets as measured by structural hamming distance and structural intervention distance, highlighting the importance of relaxing the ANM assumption made by existing methods.
Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels

Motion mode (M-mode) recording is an essential part of echocardiography to measure cardiac dimension and function. However, the current diagnosis cannot build an automatic scheme, as there are three fundamental obstructs: Firstly, there is no open dataset available to build the automation for ensuring constant results and bridging M-mode echocardiography with real-time instance segmentation (RIS); Secondly, the examination is involving the time-consuming manual labelling upon M-mode echocardiograms; Thirdly, as objects in echocardiograms occupy a significant portion of pixels, the limited receptive field in existing backbones (e.g., ResNet) composed from multiple convolution layers are inefficient to cover the period of a valve movement. Existing non-local attentions (NL) compromise being unable real-time with a high computation overhead or losing information from a simplified version of the non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode echocardiography measurement scheme, contributes three aspects to answer the problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance segmentation, to enable consistent results and support the development of an automatic scheme; 2) propose panel attention, local-to-global efficient attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS scheme toward big object detection with global receptive field; 3) develop and implement AMEM, an efficient algorithm of automatic M-mode echocardiography measurement enabling fast and accurate automatic labelling among diagnosis. The experimental results show that RAMEM surpasses existing RIS backbones (with non-local attention) in PASCAL 2012 SBD and human performances in real-time MEIS tested. The code of MEIS and dataset are available at https://github.com/hanktseng131415go/RAME.
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction

Automated Feature Engineering (AutoFE) has become an important task for any machine learning project, as it can help improve model performance and gain more information for statistical analysis. However, most current approaches for AutoFE rely on manual feature creation or use methods that can generate a large number of features, which can be computationally intensive and lead to overfitting. To address these challenges, we propose a novel convolutional method called FeatGeNN that extracts and creates new features using correlation as a pooling function. Unlike traditional pooling functions like max-pooling, correlation-based pooling considers the linear relationship between the features in the data matrix, making it more suitable for tabular data. We evaluate our method on various benchmark datasets and demonstrate that FeatGeNN outperforms existing AutoFE approaches regarding model performance. Our results suggest that correlation-based pooling can be a promising alternative to max-pooling for AutoFE in tabular data applications.
RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models

In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of the state-of-the-art ATLAS model and identify its limitations in in-context learning, primarily due to a mismatch between pretraining and testing, as well as a restricted context length. To address these issues, we propose RAVEN, a model that combines retrieval-augmented masked language modeling and prefix language modeling. We further introduce Fusion-in-Context Learning to enhance the few-shot performance by enabling the model to leverage more in-context examples without requiring additional training or model modifications. Through extensive experiments, we demonstrate that RAVEN significantly outperforms ATLAS and achieves results comparable to the most advanced language models in certain scenarios, despite having substantially fewer parameters. Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning and encourages further research in this direction.
Corundum and corundum-hibonite grains discovered by cathodoluminescence in the matrix of Acfer 094 meteorite

IN THE MATRIX OF ACFER 094 METEORITE. T. M. Nakamura, N. Sugiura, M. Kimura, A. Miyazaki, and A. N. Krot, Department of Earth and Planetary Science, Graduated School of Science, University of Tokyo, Tokyo 113-0033, Japan. (takahiro@eps.s.u-tokyo.ac.jp), Faculty of Science, Ibaraki University, Mito 310-8512, Japan, Hawai’i Institute of Geophysics and Planetology, School of Ocean and Earth Science and Technology, University of Hawai’i at Manoa, Honolulu, HI 96822, USA.
CALYPSO: LLMs as Dungeon Masters' Assistants

The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players' interactions with the scene. Doing all of these tasks while maintaining consistency within the narrative and story world is no small feat of human cognition, making the task tiring and unapproachable to new players. Large language models (LLMs) like GPT-3 and ChatGPT have shown remarkable abilities to generate coherent natural language text. In this paper, we conduct a formative evaluation with DMs to establish the use cases of LLMs in D&D and tabletop gaming generally. We introduce CALYPSO, a system of LLM-powered interfaces that support DMs with information and inspiration specific to their own scenario. CALYPSO distills game context into bite-sized prose and helps brainstorm ideas without distracting the DM from the game. When given access to CALYPSO, DMs reported that it generated high-fidelity text suitable for direct presentation to players, and low-fidelity ideas that the DM could develop further while maintaining their creative agency. We see CALYPSO as exemplifying a paradigm of AI-augmented tools that provide synchronous creative assistance within established game worlds, and tabletop gaming more broadly.
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation

Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of obtaining large scale real-world data, computer graphics are typically leveraged for constructing datasets. However, there is a common belief that synthetic-to-real domain gaps limit generalization to real scenes. In this paper, we show that the required characteristics in an optical flow dataset are rather simple and present a simpler synthetic data generation method that achieves a certain level of realism with compositions of elementary operations. With 2D motion-based datasets, we systematically analyze the simplest yet critical factors for generating synthetic datasets. Furthermore, we propose a novel method of utilizing occlusion masks in a supervised method and observe that suppressing gradients on occluded regions serves as a powerful initial state in the curriculum learning sense. The RAFT network initially trained on our dataset outperforms the original RAFT on the two most challenging online benchmarks, MPI Sintel and KITTI 2015.
Macroevolutionary patterns in the pelvis, stylopodium and zeugopodium of megalosauroid theropod dinosaurs and their importance for locomotor function

During the Mesozoic, non-avian theropods represented one of the most successful clades globally distributed, with a wide diversity of forms. An example is the clade Megalosauroidea, which included medium- to large-bodied forms. Here, we analyse the macroevolution of the locomotor system in early Theropoda, emphasizing the Megalosauroidea. We scored the Spinosaurus neotype in a published taxon-character matrix and described the associated modifications in character states, mapping them onto a phylogeny and using these to study disparity. In the evolution of Megalosauroidea, there was the mosaic emergence of a low swollen ridge; enlargement of the posterior brevis fossa and emergence of a posterodorsal process on the ilium in some megalosauroids; emergence of a femoral head oriented anteromedially and medially angled, and appearance of posterolaterally oriented medial femoral condyles in spinosaurids. The greatest morphological disparity is in the ilium of megalosaurids; the ischium seems to have a high degree of homoplasy; there is a clear distinction in the femoral morphospace regarding megalosauroids and other theropods; piatnitzkysaurids show considerable disparity of zeugopodial characters. These reconstructions of osteological evolution form a stronger basis on which other studies could build, such as mapping of pelvic/appendicular musculature and/or correlating skeletal traits with changes in locomotor function.
Computational modelling distinguishes diverse contributors to aneurysmal progression in the Marfan aorta

Thoracic aortic aneurysms are characterized by a progressive loss of biomechanical functionality resulting from degenerative changes in wall composition, microstructure, and mechanical properties. Among the many causes of these lesions, Marfan syndrome is the most common heritable condition, resulting from mutations to the gene that codes the elastin-associated glycoprotein fibrillin-1. Key histopathological features of the aneurysmal Marfan aorta include extensive degradation of elastic fibres, significant remodelling of fibrillar collagens and compromised smooth muscle cell function. Computational growth and remodelling models have confirmed the importance of compromised elastic fibre integrity in aneurysmal dilatation, but we show here that this contributor alone is not sufficient to describe biomechanical data collected from the two most common mouse models of Marfan syndrome. Rather, our simulations suggest that compromised mechanosensing and mechanoregulation of extracellular matrix by mural cells also play central roles in the natural history. Determination of optimal disease-contributing parameters further suggests a rapid reduction in cellular mechanosensing and mechanoregulation relative to diminished elastic fibre integrity, highlighting the importance of inter- and intra-lamellar elastin in the Marfan aorta. Aneurysmal dilatation in Marfan syndrome thus results from multiple contributors to progressive degeneration of the aortic wall, and computational mechanobiological models can help disentangle these contributions.
How intelligence interviewees mentally identify relevant information

This research explored how intelligence interviewees mentally identify the relevant information at their disposal. We theorized that interviewees estimate the interviewer's objectives based on how they frame any attempt to solicit information. Then interviewees organize the information they possess into item designations that pragmatically correspond to the perceived interviewer-objective. The more an interviewer specifies what they want to know, the more the interviewee will mentally designate information items corresponding with that objective. To examine the theory, we conducted two identical experiments wherein participants assumed the role of an informant with one of two dispositions. They were to be cooperative or resistant when undergoing an interview. The interviewer posed specific or ambiguous questions. In Study 1 (N = 210), interviewees identified applicable information items based on their interviewer's questions. And interviewees answered their interviewer's questions in Study 2 (N = 199). We aimed to demonstrate that question type influences mental designations and disposition affects disclosures. Disposition had a stronger influence on interviewees' disclosure than when reasoning about what the interviewer wants to know. But contrary to our expectations, mental designation preferences indicated that interviewees generally assume interviewers want to know complete details, irrespective of question specificity. We suggest avenues for future research.
Designing for User's Digital Wellbeing: Co-creating Nudges with Designers: Designing for user's digital wellbeingCo-creating nudges with designers

Digital wellbeing (DWB) became a prominent topic since the use of technology evoked a concern over its impact on users’ mental health and wellbeing. Hence, raising designers' interest and recall of DWB is necessary. As nudges approved their effectiveness in changing individuals’ behavior in different domains including users’ addictive behaviors online, they have not, yet, been used to change designers’ behaviors towards wellbeing-sensitive designs. In this study, we conducted a co-design workshop with digital designers to identify what type of nudges they prefer to ensure a more inclusive and wellbeing sensitive design. Basic psychological needs of autonomy, competence and relatedness were used to represent wellbeing. We performed template analysis on the workshop outputs where 24 nudge ideas were produced by three mixed groups of designers, Human Computer Interaction researchers and psychologists. We identified three themes representing nudge characteristics which are encouraging reflective thinking, facilitating actions, and stimulating heuristic behavior, and six subthemes representing design categories. Our results show that nudges encouraging reflective thinking represent the majority of the suggestions. This result indicates that designers have ownership for reminding themselves to incorporate basic human needs within the design through transparent nudges where the intention of behavior change is clearly identified.
Data-Driven Communicative Behaviour Generation: A Survey

The development of data-driven behaviour generating systems has recently become the focus of considerable attention in the fields of human-agent interaction (HAI) and human-robot interaction (HRI). Although rule-based approaches were dominant for years, these proved inflexible and expensive to develop. The difficulty of developing production rules, as well as the need for manual configuration in order to generate artificial behaviours, places a limit on how complex and diverse rule-based behaviours can be. In contrast, actual human-human interaction data collected using tracking and recording devices makes human-like multimodal co-speech behaviour generation possible using machine learning and specifically, in recent years, deep learning. This survey provides an overview of the state-of-the-art of deep learning-based co-speech behaviour generation models and offers an outlook for future research in this area.
Changing Your Future?: An Experimental Study on the Persuasive Effects of an Interactive Climate Change Documentary

Interactive digital narratives (IDNs) have the potential to adequately and effectively represent the highly relevant and complex issue of climate change. The interactivity in interactive digital narratives (IDNs) can increase narrative engagement, as IDNs require active participation. Such narrative engagement, in turn, is a well-known mediator of narrative persuasion. One recent IDN is the award-winning interactive Cli-Fi (‘Climate Fiction’) documentary ‘De eeuw van mijn opa’ (DEVMO; ‘Grandfather's century’), by filmmaker Sam van Zoest. Comparing the original interactive version with a non-interactive version created by the researchers, we used a between-subjects experiment (n=62) to test whether interactivity (yes/no) had an effect on narrative engagement and narrative persuasion. Perceived effectance and perceived autonomy were included as control variables. The results showed that both versions of the documentary had a persuasive effect when comparing scores before versus after exposure. However, the interactive version was not significantly more persuasive compared to the non-interactive version. Furthermore, no evidence was found of narrative engagement as a mediating factor, although narrative engagement did positively affect narrative persuasion. Surprisingly, no differences were found in control variables perceived effectance and perceived autonomy between the conditions with and without interactivity. We discuss several explanations for our findings relating to the study's power and the operationalization of interactivity in ‘De eeuw van mijn opa’.
Pan-antiviral effects of a PIKfyve inhibitor on respiratory virus infection in human nasal epithelium and mice

ABSTRACT Endocytosis, or internalization through endosomes, is a major cell entry mechanism used by respiratory viruses. Phosphoinositide 5-kinase (PIKfyve) is a critical enzyme for the synthesis of phosphatidylinositol (3, 5)biphosphate (PtdIns (3, 5)P2) and has been implicated in virus trafficking via the endocytic pathway. In fact, antiviral effects of PIKfyve inhibitors against SARS-CoV-2 and Ebola have been reported, but there is little evidence regarding other respiratory viruses. In this study, we demonstrated the antiviral effects of PIKfyve inhibitors on influenza virus and respiratory syncytial virus in vitro and in vivo. PIKfyve inhibitors Apilimod mesylate (AM) and YM201636 concentration-dependently inhibited several influenza strains in an MDCK cell-cytopathic assay. AM also reduced the viral load and cytokine release, while improving the cell integrity of human nasal air-liquid interface cultured epithelium infected with influenza PR8. In PR8-infected mice, AM (2 mg/mL), when intranasally treated, exhibited a significant reduction of viral load and inflammation and inhibited weight loss caused by influenza infection, with effects being similar to oral oseltamivir (10 mg/kg). In addition, AM demonstrated antiviral effects in RSV A2-infected human nasal epithelium in vitro and mouse in vivo, with an equivalent effect to that of ribavirin. AM also showed antiviral effects against human rhinovirus and seasonal coronavirus in vitro. Thus, PIKfyve is found to be involved in influenza and RSV infection, and PIKfyve inhibitor is a promising molecule for a pan-viral approach against respiratory viruses.
Effect of Aqueous Garlic Extract on Hyperlipidaemia in Rats: A Randomised Case-Control Study

This study aimed to determine the effect of garlic extract on hyperlipidemia in rats. This was a randomised case-control study in 42 rats comprising seven groups with six animals per group. Group I consisted of rats fed with 100% Normal rat chow serving as control, Group II: Butter diet, Group III: Butter diet + Atorvastatin, Group IV: Butter diet + Garlic extract, Group V: rats injected with dexamethasone, Group VI: Dexamethasone + Atorvastatin, Group VII: Dexamethasone + Garlic extract. Blood was collected from all the animals and the separated serum was subjected to the estimations of lipoproteins levels and coronary risk. There was a significant increase in the concentration of TC in the butter control group (BCG) compared to the concentration of the LDL-C (p=0.028) of the normal control group (NCG). The concentration of TG was significantly higher in DCG when compared with TG concentration in NCG (p<0.0001) and BCG (p=0.0002). Total Cholesterol level was significantly higher in BCG when compared to BGG (p<0.0001), BAG (p=0.0001) and NCG (p=0.023) which typifies hypercholesterolemia. Hyperlipidemia was induced by the long-term high-fat diet but lipid levels were reduced after the administration of aqueous garlic extract and atorvastatin.