Miracle-dz/UniFAR-FTUs huggingface dataset:
INTERSTELLAR DUST PROPERTIES FROM A SURVEY OF X-RAY HALOS

Interstellar dust grains produce X-ray halos around bright sources due to small-angle X-ray scattering. Numerous studies have examined these halos, but no systematic study has yet tested the available halo data against the large number of well-defined dust models in circulation. We have therefore obtained the largest sample to date of X-ray dust halos from XMM-Newton and Chandra, and fitted them with 14 commonly used dust grain models, including comparisons with the optical extinction, AV, where available in the literature. Our main conclusions are summarized as follows. (1) Comparing AV with NH values measured via X-ray spectral fits, we find a ratio of AV/NH (1021 cm−2) = 0.48 ± 0.06, in agreement with previous work. (2) Out of 35 halos, 27 could be fit by one or more grain models, with the most successful models having maximum grain radius a max < 0.4 ? > μm and fewer large grains than the less successful models. This suggests that the diffuse ISM does not contain a signicant presence of grains with a max > 0.5 ? > μm. (3) Most halos were best fit assuming a single dust cloud dominated the scattering, rather than smoothly distributed dust along the sightline. (4) Eight sources could not be fit with the models considered here, most of which were along distant ( d > 5 ? > kpc) sight lines through the Galactic thin disk. (5) Some sight lines had halos with observed X-ray scattering optical depth τsca/AV that were signicantly different than expected. This may result from an inhomogeneous dust distribution across the halo extraction area.
Time to eRAASe chronic inflammation: current advances and future perspectives on renin-angiotensin-aldosterone-system and chronic intestinal inflammation in dogs and humans

Chronic idiopathic intestinal inflammation is an increasing worldwide problem that affects companion animals, especially dogs, and human patients. Although these disease entities have been intensely investigated recently, many questions remain, and alternative therapeutic options are needed. Diarrhea caused by dysregulation of intestinal electrolyte transport and subsequent fluid and electrolyte losses often leads to secondary consequences for the patient. Currently, it is not exactly clear which mechanisms are involved in the dysregulation of intestinal fluid absorption, but differences in intestinal electrolyte shifts between human and canine patients suggest species-specific regulatory or counterregulatory mechanisms. Several intestinal electrolyte transporters are differentially expressed in human patients with inflammatory bowel disease (IBD), whereas there are virtually no studies on electrolyte transporters and their endocrine regulation in canine chronic inflammatory enteropathy. An important mechanism involved in regulating fluid and electrolyte homeostasis is the renin-angiotensin-aldosterone-system (RAAS), which may affect intestinal Na+ transport. While RAAS has previously been considered a systemic regulator of blood pressure, additional complex roles of RAAS in inflammatory processes have been unraveled. These alternative RAAS pathways may pose attractive therapeutic targets to address diarrhea and, thus, electrolyte shifts in human IBD and canine chronic inflammatory enteropathy. This article comparatively summarizes the current knowledge about electrolyte transport in human IBD and canine chronic inflammatory enteropathy and the role of RAAS and offers perspectives for novel therapeutic avenues.
Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies

Climate has played a significant role in shaping the distribution of mammal species across the world. Mammal community composition can therefore be used for inferring modern and past climatic conditions. Here, we develop a novel approach for bioclimatic inference using machine learning (ML) algorithms, which allows for accurate prediction of a set of climate variables based on the composition of the faunal community. The automated dataset construction process aggregates bioclimatic variables with modern species distribution maps, and includes multiple taxonomic ranks as explanatory variables for the predictions. This yields a large dataset that can be used to produce highly accurate predictions. Various ML algorithms that perform regression have been examined. To account for spatial dependence in our data, we employed a geographical block validation approach for model validation and selection. The random forest (RF) outperformed the other evaluated algorithms. Ultimately, we used unseen modern mammal surveys to assess the high predictive performances and extrapolation abilities achieved by our trained models. This contribution introduces a framework and methodology to construct models for developing models based on neo-ecological data, which could be utilized for paleoclimate applications in the future. The study aimed to satisfy specific criteria for interpreting both modern and paleo faunal assemblages, including the ability to generate reliable climate predictions from faunal lists with varying taxonomic resolutions, without the need for published wildlife inventory data from the study area. This method demonstrates the versatility of ML techniques in climate modeling and highlights their promising potential for applications in the fields of archaeology and paleontology.
Role of insulin-like growth factor 1 (IGF1) in the regulation of mitochondrial bioenergetics in zebrafish oocytes: lessons from in vivo and in vitro investigations

Optimal mitochondrial functioning is indispensable for acquiring oocyte competence and meiotic maturation, whilst mitochondrial dysfunction may lead to diminished reproductive potential and impaired fertility. The role of the intra-ovarian IGF system in ovarian follicular dynamics has been implicated earlier. Although several studies have demonstrated the role of the IGF axis in facilitating mitochondrial function over a multitude of cell lines, its role in oocyte energy metabolism remains largely unexplored. Here using zebrafish, the relative importance of IGF1 in modulating oocyte mitochondrial bioenergetics has been investigated. A dramatic increase in ovarian lhcgr and igf1 expression accompanied heightened ATP levels and mitochondrial polarization in full-grown (FG) oocytes resuming meiotic maturation and ovulation in vivo. Concomitant with elevated igf1 expression and IGF1R phosphorylation, hCG (LH analog) stimulation of FG follicles in vitro prompted a sharp increase in NRF-1 and ATP levels, suggesting a positive influence of gonadotropin action on igf1 expression vis-à-vis oocyte bioenergetics. While recombinant IGF1 administration enhanced mitochondrial function, IGF1R immunodepletion or priming with PI3K inhibitor wortmannin could abrogate NRF-1 immunoreactivity, expression of respiratory chain subunits, ΔΨM, and ATP content. Mechanistically, activation of PI3K/Akt signaling in IGF1-treated follicles corroborated well with the rapid phosphorylation of GSK3β at Ser9 (inactive) followed by PGC-1β accumulation. While selective inhibition of GSK3β promoted PGC-1β, Akt inhibition could abrogate IGF1-induced p-GSK3β (Ser9) and PGC-1β immunoreactive protein indicating Akt-mediated GSK3β inactivation and PGC-1β stabilization. The IGF1-depleted follicles showed elevated superoxide anions, subdued steroidogenic potential, and attenuated G2-M1 transition. In summary, this study highlights the importance of IGF1 signaling in oocyte bioenergetics prior to resumption of meiosis.
Seeking support through solidarity: female leader’s experiences of workplace solidarity in male-dominated professions

Introduction While there is a plethora of research that documents the numerous barriers affecting female leaders in the modern workplace, there is a lack of literature which focuses on strategies or motivating resources that women use to navigate the workplace environment. Despite facing significant barriers in their leadership journeys, there are female leaders who are able to overcome these barriers to achieve leadership positions. These women leaders draw on personal and external motivating factors to assist them in dealing with the challenges associated with being a female leader as a result, research on motivating strategies for women’s career progression is a research topic that warrants immediate attention. Female solidarity as a motivating resource has been gaining traction in the field of leadership studies and can be seen as a supportive resource that can be used by current and aspiring female leaders to progress in underrepresented environments. Although female solidarity is but only one of the many strategies that can be implemented to motivate women in leadership positions, the increase of female solidarity in the workplace is expected to alleviate the conditions that reinforce essentialist notions of the “queen bee syndrome” in which women are seen as unsupportive of each other. Method A qualitative research approach was used for this study, following an interpretive descriptive design. A total of 13 semi-structured interviews were conducted with female leaders in male-dominated professions within South Africa. Data was analysed using thematic content analysis. Results Results of the study were analyzed in line with three primary content areas, i.e., barriers to female solidarity in the workplace, benefits of female solidarity in the workplace and workplace interventions to increase solidarity. Discussion In the context of the study the predominant barriers to female solidarity within male-dominated workplaces were identified as unfair workplace behaviours, generational beliefs, societal expectations, organisational cultures, stereotypes and stigmas. The benefits of female solidarity within male-dominated workplaces were identified as career shaping mentorship, female recognition, female representation and female support. Lastly, the interventions that can be implemented to increase female solidarity within male-dominated workplaces were conceptualised as networking, transforming the company culture, socialisation and mentorship.
Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes

Background: Glycosylated hemoglobin (HbA1c) is recommended for diagnosing and monitoring type 2 diabetes. However, the monitoring frequency in real-world applications has not yet reached the recommended frequency in the guidelines. Developing machine learning models to screen patients with poor glycemic control in patients with T2D could optimize management and decrease medical service costs. Methods: This study was carried out on patients with T2D who were examined for HbA1c at the Sichuan Provincial People’s Hospital from April 2018 to December 2019. Characteristics were extracted from interviews and electronic medical records. The data (excluded FBG or included FBG) were randomly divided into a training dataset and a test dataset with a radio of 8:2 after data pre-processing. Four imputing methods, four screening methods, and six machine learning algorithms were used to optimize data and develop models. Models were compared on the basis of predictive performance metrics, especially on the model benefit (MB, a confusion matrix combined with economic burden associated with therapeutic inertia). The contributions of features were interpreted using SHapley Additive exPlanation (SHAP). Finally, we validated the sample size on the best model. Results: The study included 980 patients with T2D, of whom 513 (52.3%) were defined as positive (need to perform the HbA1c test). The results indicated that the model trained in the data (included FBG) presented better forecast performance than the models that excluded the FBG value. The best model used modified random forest as the imputation method, ElasticNet as the feature screening method, and the LightGBM algorithms and had the best performance. The MB, AUC, and AUPRC of the best model, among a total of 192 trained models, were 43475.750 (¥), 0.972, 0.944, and 0.974, respectively. The FBG values, previous HbA1c values, having a rational and reasonable diet, health status scores, type of manufacturers of metformin, interval of measurement, EQ-5D scores, occupational status, and age were the most significant contributors to the prediction model. Conclusion: We found that MB could be an indicator to evaluate the model prediction performance. The proposed model performed well in identifying patients with T2D who need to undergo the HbA1c test and could help improve individualized T2D management.
Towards Improving Verification Productivity with Circuit-Aware Translation of Natural Language to SystemVerilog Assertions

Assertion-based verification is a technique to ensure that a circuit design conforms to its specification and help detect errors early in the design process. It is enabled by powerful industry and open-source model-checking tools that automatically prove or disprove an assertion for a given circuit design. Formalizing a circuits requirement, however, involves a significant manual effort by verification engineers to translate requirements in natural language into a formal assertion language. In this extended abstract, we introduce a framework that utilizes Large Language Models (LLMs) pre-trained on natu-ral language and code to improve verification productivity by automating the formalization process. In particular, we report on the current progress of developing nl2sva , a framework for circuit-aware translations of natural language to the most frequently used assertion language, SystemVerilog Assertions (SVA). We introduce a methodology that (1) generates the SVA for a specific circuit out of a generic circuit property in natural language and (2) implements a model checker and human in the loop that interactively provides feedback to the verification engineer and the underlying LLM to facilitate debugging the design.
Scaling-up the Resources for a Freely Available Swedish VADER (svVADER)

With widespread commercial applications in various domains, sentiment analysis has become a success story for Natural Language Processing (NLP). Still, although sentiment analysis has rapidly progressed during the last years, mainly due to the application of modern AI technologies, many approaches apply knowledge-based strategies, such as lexicon-based, to the task. This is particularly true for analyzing short social media content, e.g., tweets. Moreover, lexicon-based sentiment analysis approaches are usually preferred over learning-based methods when training data is unavailable or insufficient. Therefore, our main goal is to scale-up and apply a lexicon-based approach which can be used as a baseline to Swedish sentiment analysis. All scaled-up resources are made available, while the performance of this enhanced tool is evaluated on two short datasets, achieving adequate results.
Stein Variational Belief Propagation for Decentralized Multi-Robot Control

—Decentralized control for multi-robot systems involves planning in complex, high-dimensional spaces. The planning problem is particularly challenging in the presence of potential collisions between robots and obstacles, and different sources of uncertainty such as inaccurate dynamic models and sensor noise. A multi-robot system can be represented as a graphical model, in which nodes represent individual robots and edges represent communication between robots. This representation enables the use of graphical inference algorithms for solving multi-robot control. In this short paper, we introduce Stein Variational Belief Propogation (SVBP), a novel algorithm for performing inference over the marginal distributions of nodes in a graph. We present simulation results which demonstrate that our method can represent complex, multi-modal distributions in localization and control tasks.
Exploring universe polymorphism in Omega

Ωmega extends Haskell with novel features for practical functional programming: GADT’s, extensible kinds, and type functions. With both extensible types and extensible kinds in place, there is a tendency for redundant datatype definitions; likewise for functions that operate over these structures. Universe polymorphism is a way to abstract over levels in the typing hierarchy, unifying these redundant constructs. In this paper, we use Ωmega’s novel features to encode simplified models of Ωmega as an object language, and then use these models to begin exploring the design space for universe polymorphism in Ωmega.
Operational Tau method for nonlinear multi-order FDEs

This paper presents an operational formulation of the Tau method based upon orthogonal polynomials by using a reduced set of matrix operations for the numerical solution of nonlinear multi-order fractional differential equations(FDEs). The main characteristic behind the approach using this technique is that it reduces such problems to those of solving a system of non-linear algebraic equations. Some numerical examples are provided to demonstrate the validity and applicability of the method.
Tiny Attention: A Simple yet Effective Method for Learning Contextual Word Embeddings

Contextual Word Embedding (CWE) obtained via the Attention Mechanism in Transformer (AMT) models is one of the key drivers of the current revolution in Natural Language Processing. Previous techniques for learning CWEs are not only inferior to AMT but also are largely subpar to the simple bag-of-words base-line. Though there have been many variants of the Transformer model, the attention mechanism itself remains unchanged and is largely opaque. We introduce a new method for leaning CWEs that uses a simple and transparent attention mechanism. Our method is derived from the SVD based Syntagmatic Word Embeddings, which capture word associations. We test our model on the Word-in-Context dataset, and show that it outperforms the simple but tough-to-beat baseline by a substantial margin.
Large-scale assessment of ecological and anthropogenic drivers of beetle communities in Laos shows that conversion of natural forests into plantations leads to a decline in abundance

Rapid economic development can pose a threat to the biodiversity of tropical countries. In Laos, this is manifested by the conversion of natural forests into plantations, even though this area is one of the biodiversity hotspots of Southeast Asia. Beetle communities can be good indicators of the impact of anthropogenic pressure on natural ecosystems. In this study, we analyzed for the first time a countrywide inventory of Coleoptera to assess the ecological and anthropogenic drivers of beetle communities in Laos. We examined beetle communities (described at the family level) across the country, located in distinct habitat types, in order to understand the impact a rapid increase in human activities has on the region’s biodiversity. We found that beetle abundance had declined in plantations compared to natural forests. At the same time, we observed fewer beetle families in plantations overall, but at the scale of sampling sites there was no difference in local diversity compared to natural forests, suggesting a homogenization of beetle communities in anthropogenic habitats. Although results are certainly sensitive to our coarse classification of beetle specimens into families, the negative impact of the conversion of natural tropical forests into agriculture area can still be clearly demonstrated. Our findings highlight that it is possible to make use of unstructured large-scale inventories to explore how beetle communities responds to landscape changes induced by human activities. We suggest that sampling beetle communities can be used as an ecological indicator to monitor anthropogenic impacts on tropical ecosystems. Original research paper Large-scale assessment of ecological and anthropogenic drivers of beetle communities in Laos shows that conversion of natural forests into plantations leads to a decline in abundance Bounsanong Chouangthavy1, Yoan Fourcade2 1Entomology Laboratory, Faculty of Agriculture, National University of Laos, P.O. Box 7322, Vientiane, Laos 2Univ. Paris Est Creteil, Sorbonne Université, Univ Paris Cité, CNRS, IRD, INRAE, Institut d’écologie et des sciences de l’environnement, IEES, F-94010 Créteil, France Correspondence Bounsanong Chouangthavy, 1Entomology Laboratory, Faculty of Agriculture, National University of Laos, P.O. Box 7322, Vientiane, Laos Email: bsnchouangthavy@gmail.com
P ERSONALIZING TEXT - TO - IMAGE GENERATION WITH VISUAL PROMPTS USING BLIP-2

In recent years, text-to-image generation has received significant attention as researchers aim to automatically generate realistic images from textual descriptions. Despite the promising results of diffusion models in producing high-quality images, they often struggle to capture the richness and diversity of natural language expressions, making it difficult to generate images that align with user intentions. To tackle this challenge, personalization has been proposed as a potential solution. Personalization involves fine-tuning pre-trained text-to-image generation models using user-provided images that contain specific concepts or subjects, enabling the models to generate images with the desired subject or style. However, existing personalization techniques typically require direct fine-tuning of the text encoder, diffusion model, or both, which can result in high computational costs for incorporating user-provided concepts and potentially compromise the model’s knowledge. This paper introduces a novel approach to personalizing a text-toimage model by leveraging a BLIP-2 encoder. We provide the image that contains objects we wish to generate using the Stable Diffusion model as inputs to the BLIP-2 encoder. Then, we use the output queries of the BLIP-2 Q-former as visual prompts to guide the Stable Diffusion model to generate images that capture the visual representations of the input image.
Monocular 3D Pose Estimation with Uncertainty Estimation for Handling Occlusion.

Supervised methods have been quite successful in the recent years for estimating 3D human poses from single view images, provided that enough 3D annotated data is available [12, 16–18]. However such methods usually fail in in-the-wild images captured from a wide number of scenarios involving unusual activities, for which acquiring the annotations become a challenging task. Thus, much of the recent focus has shifted to Semi/Weakly/Self supervised methods [1, 7, 8, 11], which are aimed to provide a reliable pseudo-label estimate for the unannotated single view images. However, the Semi/Weakly/Self supervised methods are highly vulnerable to (a) occlusion, (b) changes in illumination and viewing angles, (c) low image resolution etc., thereby resulting in noisy 2D estimates and 3D pseudo targets; out of which occlusion remains the primary and a major source of such noisy estimates. A number of works aim to tackle occlusions using multi-view camera setup where the pseudo targets are obtained using multi-view geometry of well calibrated cameras [6, 9, 15]. However, they do not generalize well to single, potentially moving, cameras. A number of approaches also attempt to overcome these limitations by exploiting temporal consistency in monocular videos [2, 11]. Some works also impose prior pose and kinematic priors on the estimated 3D poses to handle such noisy detections as an additional module [13, 14]. However, the aforementioned algorithms do not explicitly model occlusion, in order to enhance the robustness of the underlying networks against it in their learning framework.