Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital twin in virtual reality for human-vehicle interactions in the context of autonomous driving

This paper presents the results of tests of interactions between real humans and simulated vehicles in a virtual scenario. Human activity is inserted into the virtual world via a virtual reality interface for pedestrians. The autonomous vehicle is equipped with a virtual Human-Machine interface (HMI) and drives through the digital twin of a real crosswalk. The HMI was combined with gentle and aggressive braking maneuvers when the pedestrian intended to cross. The results of the interactions were obtained through questionnaires and measurable variables such as the distance to the vehicle when the pedestrian initiated the crossing action. The questionnaires show that pedestrians feel safer whenever HMI is activated and that varying the braking maneuver does not influence their perception of danger as much, while the measurable variables show that both HMI activation and the gentle braking maneuver cause the pedestrian to cross earlier.
Optical theorem detectors for active scatterers

We develop a new theory of the optical theorem for scalar fields in nonhomogeneous media which can be bounded or unbounded. It applies to arbitrary lossless backgrounds and quite general probing fields. The derived formulation holds for arbitrary passive scatterers, which can be dissipative, as well as for the more general class of active scatterers which are composed of a (passive) scatterer component and an active, radiating (antenna) component. The generalization of the optical theorem to active scatterers is relevant to many applications such as surveillance of active targets including certain cloaks and invisible scatterers and wireless communications. The derived theoretical framework includes the familiar real power optical theorem describing power extinction due to both dissipation and scattering as well as a novel reactive optical theorem related to the reactive power changes. The developed approach naturally leads to three optical theorem indicators or statistics which can be used to detect changes or targets in unknown complex media. The paper includes numerical simulation results that illustrate the application of the derived optical theorem results to change detection in complex and random media.
A JWST/NIRSpec First Census of Broad-line AGNs at z = 4–7: Detection of 10 Faint AGNs with M BH ∼ 106–108 M ⊙ and Their Host Galaxy Properties

We present the first statistical sample of faint type 1 AGNs at z > 4 identified by JWST/NIRSpec deep spectroscopy. Among the 185 galaxies at z spec = 3.8–8.9 confirmed with NIRSpec, our systematic search for broad-line emission reveals 10 type 1 AGNs at z = 4.015–6.936 whose broad component is only seen in the permitted Hα line and not in the forbidden [O iii]λ5007 line that is detected with greater significance than Hα. The broad Hα line widths of FWHM ≃ 1000–6000 km s−1 suggest that the AGNs have low-mass black holes with M BH ∼ 106–108 M ⊙, remarkably lower than those of low-luminosity quasars previously identified at z > 4 with ground-based telescopes. JWST and Hubble Space Telescope high-resolution images reveal that the majority of them show extended morphologies indicating significant contribution to the total lights from their host galaxies, except for three compact objects two of which show red spectral energy distributions, probably in a transition phase from faint AGNs to low luminosity quasars. Careful AGN-host decomposition analyses show that their host’s stellar masses are systematically lower than the local relation between the black hole mass and the stellar mass, implying a fast black hole growth consistent with predictions from theoretical simulations. A high fraction of the broad-line AGNs (∼5%), higher than z ∼ 0, indicates that the number density of such faint AGNs is higher than an extrapolation of the quasar luminosity function, implying a large population of AGNs in the early universe. Such faint AGNs contribute to cosmic reionization, while the total contribution is not large, up to ∼50% at z ∼ 6, because of their faint nature.
The evolution and subsequent dynamics of waves on a vertically falling liquid film

The spatiotemporal evolution of periodic waves on a vertically falling water film was investigated at the Reynolds number Re=15–75 via physical and numerical experiments. Small periodic waves excited at a low frequency grow directly into teardrop-shaped tall pulses, small waves of an intermediate frequency first grow into close-packed humps and then into pulses sandwiching single capillary ripples, and small waves of a high frequency grow into nearly sinusoidal waves. The initial wave evolution causes the waves to accelerate at low frequencies or to decelerate at intermediate and high frequencies. The maximum deceleration occurs with the growth into the close-packed humps which then undergo the transition to the pulses without a change of the wave frequency, associated with rapid acceleration. Subsequently, the quasisteady, nearly sinusoidal waves of small amplitudes and short-separation pulses further develop into nearly solitary tall pulses through transitions of wave coalescence, associated with growth.
Far-ultraviolet Spectroscopy of Active Galactic Nuclei with ASTROSAT/UVIT

We study accretion disk emission from eight Seyfert 1–1.5 active galactic nuclei (AGN) using far-ultraviolet (FUV) (1300–1800 Å) slitless grating spectra acquired with AstroSat/UVIT. We correct for the Galactic and intrinsic extinction, contamination from the host galaxies, narrow and broad-line regions, Fe ii emission, and Balmer continuum, and derive the intrinsic continua. We use Hubble Space Telescope COS/FOS spectra to account for the emission/absorption lines in the low-resolution UVIT spectra. We find generally redder power-law (f ν ∝ ν α ) slopes (α ∼ −1.1 to 0.3) in the FUV band than predicted by the standard accretion disk model in the optical/UV band. We fit accretion disk models such as the multitemperature disk blackbody (DISKBB) and relativistic disk (ZKERRBB, OPTXAGNF) models to the observed intrinsic continuum emission. We measure the inner disk temperatures using the DISKBB model for seven AGN. These temperatures in the range ∼3.6–5.8 eV are lower than the peak temperatures predicted for standard disks around maximally spinning supermassive black holes accreting at Eddington rates. The inner disks in two AGN, NGC 7469, and Mrk 352, appear to be truncated at ∼35–125 and 50–135 r g , respectively. While our results show that the intrinsic FUV emission from the AGN is consistent with the standard disks, it is possible that UV continua may be affected by the presence of soft X-ray excess emission, X-ray reprocessing, and thermal Comptonization in the hot corona. Joint spectral modeling of simultaneously acquired UV/X-ray data may be necessary to further investigate the nature of accretion disks in AGN.
Better Understanding Differences in Attribution Methods via Systematic Evaluations

Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth attributions exist. We thus propose three novel evaluation schemes to more reliably measure the faithfulness of those methods, to make comparisons between them more fair, and to make visual inspection more systematic. To address faithfulness, we propose a novel evaluation setting (DiFull) in which we carefully control which parts of the input can influence the output in order to distinguish possible from impossible attributions. To address fairness, we note that different methods are applied at different layers, which skews any comparison, and so evaluate all methods on the same layers (ML-Att) and discuss how this impacts their performance on quantitative metrics. For more systematic visualizations, we propose a scheme (AggAtt) to qualitatively evaluate the methods on complete datasets. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models. Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability.
Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images

Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3

Knowledge-based visual question answering (VQA) involves questions that require world knowledge beyond the image to yield the correct answer. Large language models (LMs) like GPT-3 are particularly helpful for this task because of their strong knowledge retrieval and reasoning capabilities. To enable LM to understand images, prior work uses a captioning model to convert images into text. However, when summarizing an image in a single caption sentence, which visual entities to describe are often underspecified. Generic image captions often miss visual details essential for the LM to answer visual questions correctly. To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. The prompt contains a question that the caption should aid in answering. To avoid extra annotation, PromptCap is trained by examples synthesized with GPT-3 and existing datasets. We demonstrate Prompt-Cap’s effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. Prompt-Cap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60.4% on OK-VQA and 59.6% on A-OKVQA). Zero-shot results on WebQA show that PromptCap generalizes well to unseen domains.1
Equiangular lines via matrix projection

In 1973, Lemmens and Seidel posed the problem of determining the maximum number of equiangular lines in $\mathbb{R}^r$ with angle $\arccos(\alpha)$ and gave a partial answer in the regime $r \leq 1/\alpha^2 - 2$. At the other extreme where $r$ is at least exponential in $1/\alpha$, recent breakthroughs have led to an almost complete resolution of this problem. In this paper, we introduce a new method for obtaining upper bounds which unifies and improves upon previous approaches, thereby yielding bounds which bridge the gap between the aforementioned regimes and are best possible either exactly or up to a small multiplicative constant. Our approach relies on orthogonal projection of matrices with respect to the Frobenius inner product and as a byproduct, it yields the first extension of the Alon-Boppana theorem to dense graphs, with equality for strongly regular graphs corresponding to $\binom{r+1}{2}$ equiangular lines in $\mathbb{R}^r$. Applications of our method in the complex setting will be discussed as well.
Mapping effective connectivity by virtually perturbing a surrogate brain

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in human due to being invasive and limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in the rest of regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of EC on a brain-wide scale. Our validation of NPI, using models with established EC, shows its superiority over Granger Causality and Dynamic Causal Modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Applications on a disease-specific dataset highlight the potential of using personalized EC as biomarkers for neurological diseases. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and can facilitate neuroscience research and clinical applications.
Food and Nutrition Surveillance System (SISVAN) coverage, nutritional status of older adults and its relationship with social inequalities in Brazil, 2008-2019: an ecological time-series study

Abstract Objective: to analyze the temporal trend of Food and Nutrition Surveillance System (Sistema de Vigilância Alimentar e Nutricional - SISVAN) coverage and the nutritional status of older adults, and its correlation with indicators of social inequality in Brazil between 2008-2019. Methods: this was an ecological study using records from SISVAN, related to the population aged 60 years and older; the temporal trend of coverage and the correlation between indicators of social inequality and increment rate of nutritional status were analyzed; slope index of inequality and concentration index were used to measure absolute and relative inequalities. Results: 11,587,933 records were identified; national coverage increased from 0.1% (2008) to 2.9% (2019), with a statistically significant upward trend; a moderate inverse correlation with an annual increment rate of overweight between human development index and gross domestic product per capita, was found. Conclusion: there was an increasing trend in SISVAN coverage; the increase in overweight was associated with social inequality.
Machine Learning for Brain Disorders: Transformers and Visual Transformers

Transformers were initially introduced for natural language processing (NLP) tasks, but fast they were adopted by most deep learning fields, including computer vision. They measure the relationships between pairs of input tokens (words in the case of text strings, parts of images for visual Transformers), termed attention. The cost is exponential with the number of tokens. For image classification, the most common Transformer Architecture uses only the Transformer Encoder in order to transform the various input tokens. However, there are also numerous other applications in which the decoder part of the traditional Transformer Architecture is also used. Here, we first introduce the Attention mechanism (Section 1), and then the Basic Transformer Block including the Vision Transformer (Section 2). Next, we discuss some improvements of visual Transformers to account for small datasets or less computation(Section 3). Finally, we introduce Visual Transformers applied to tasks other than image classification, such as detection, segmentation, generation and training without labels (Section 4) and other domains, such as video or multimodality using text or audio data (Section 5).
Social and paternal female choice for male MHC genes in golden snub‐nosed monkeys (Rhinopithecus roxellana)

Genes of the major histocompatibility complex (MHC) play important roles in vertebrate immunocompetence. MHC genes thus offer females indirect benefits to mate choice through the production of offspring of an optimal MHC genotype. Females may choose males with specific MHC haplotypes, dissimilar MHC genotypes, MHC heterozygous males or MHC‐diverse males. We tested these four alternatives for both female social and paternal choice in wild golden snub‐nosed monkeys (Rhinopithecus roxellana) by examining overall genetic variability (via microsatellites) and four MHC‐genes (DRB1, DRB2, DQA1 and DQB1). Monte Carlo randomization tests showed that MHC dissimilarity was favoured for social choice (males to which females were socially affiliated) and intermediate MHC dissimilarity was favoured in paternal choice (fathers of offspring). No evidence of inbreeding avoidance was found for either social or paternal mates. We found that MHC heterozygotes, higher microsatellite multilocus heterozygosity and higher microsatellites diversity were favoured for social mates, and higher microsatellite diversity was favoured for paternal mates. Independent of male age, we found that the formation of male–female social pairings is significantly predicted by compatibility based on the sharing of MHC haplotypes. However, we found no evidence of independent genetic effects on the duration of male–female social pairings, male social status (achieving OMU leader male status or not), the number of females with which individual leader males paired, the likelihood of potential male–female pairings producing offspring, or whether males fathered offspring or not. Overall, our findings suggest different genetic factors are involved in social and paternal choice in R. roxellana.
Dietary inflammatory and insulinemic potential, risk of hepatocellular carcinoma, and chronic liver disease mortality

Abstract Background Diet modulates inflammation and insulin response and may be an important modifiable factor in the primary prevention of hepatocellular carcinoma (HCC) and chronic liver disease (CLD). We developed the empirical dietary inflammatory pattern (EDIP) and empirical dietary index for hyperinsulinemia (EDIH) scores to assess the inflammatory and insulinemic potentials of diet. We prospectively examined the associations of EDIP and EDIH at baseline with the following HCC risk and CLD mortality. Design We followed 485 931 individuals in the National Institutes of Health–American Association of Retired Persons Diet and Health Study since 1995. Cox proportional hazards regression was used to calculate multivariable hazard ratios (HRs) and 95% confidence intervals (CIs). Results We confirmed 635 incident HCC cases and 993 CLD deaths. Participants in the highest compared with those in the lowest EDIP quartile had a 1.35 times higher risk of developing HCC (95% CI = 1.08 to 1.70, Ptrend = .0005) and a 1.70 times higher CLD mortality (95% CI = 1.41 to 2.04, Ptrend < .0001). For the same comparison, participants with the highest EDIH were at increased risk of HCC (HR = 1.53, 95% CI = 1.20 to 1.95, Ptrend = .0004) and CLD mortality (HR = 1.72, 95% CI = 1.42 to 2.01, Ptrend < .0001). Similar positive associations of scores with HCC risk and CLD mortality were observed for both women and men. Moreover, individuals in both the highest EDIP and EDIH tertiles had a 92% increased HCC risk (95% CI = 1.43 to 2.58) and 98% increased CLD mortality (95% CI = 1.27 to 3.08) compared with those in both lowest tertiles. Conclusions Our findings suggest that inflammation and hyperinsulinemia are potential mechanisms linking diet to HCC development and CLD mortality.
Score tests for association under response-dependent sampling designs for expensive covariates

Response-dependent sampling is widely used in settings where certain variables are expensive to obtain. Estimation has been thoroughly investigated but recent applications have emphasized tests of association for expensive covariates and a response variable. We consider testing and provide easily implemented likelihood score tests for generalized linear models under a broad range of sampling plans. We show that when there are no additional covariates, the score statistics are identical for conditional and full likelihood approaches, and are of the same form as for ordinary random sampling. Applications in genetics are discussed briefly.
Zinc finger protein 189 promotes the differentiation of lamina propria T helper 17.1 cells in dextran sulfate sodium-induced colitis

Abstract The factors regulating the heterogeneity of interleukin-17A (IL-17A)-expressing CD4+ T cells in inflammatory bowel diseases remain unclear. In the current study, we characterised the expression and function of zinc finger protein 189 (ZFP189) in a murine colitis model. Mice were given dextran sulphate sodium to induce acute colitis. Flow cytometry was applied to recognise and enrich Th17 and Th17.1 cells based on the expression of IL-17A, interferon-γ (IFN-γ), C-X-C motif chemokine receptor 3 (CXCR3), and C-C motif chemokine receptor 4 (CCR4). The expression of ZFP189 in Th17 and Th17.1 cells was determined by Immunoblotting. Lentivirus-mediated ZFP189 knockdown was conducted to evaluate the effect of ZFP189 on the differentiation of Th17 and Th17.1 cells. The adoptive transfer was performed to analyse the pathogenicity of Th17.1 cells in vivo. We found that ZFP189 was mildly up-regulated in IL-17A-expressing CD4+ T cells in colonic lamina propria. Lamina propria Th17.1 cells expressed higher ZFP189 than Th17 cells. In vitro ZFP189 knockdown in CD4+ T cells did not impact Th17 cell differentiation but suppressed Th17.1 cell differentiation, as evidenced by lower T-box expressed in T cells (T-bet) and IFN-γ. When adoptively transferred into mice, ZFP189-deficient Th17.1 cells produced fewer IFN-γ, tumour necrosis factor-alpha (TNF-α), and granulocyte-macrophage colony-stimulating factor (GM-CSF) than ZFP189-expressing Th17.1 cells. Moreover, ZFP189-deficient Th17.1 cells induced less severe colitis than ZFP189-expressing Th17.1 cells, as evidenced by less body weight loss, a lower disease activity index, and a lower colon histological score. In summary, ZFP189 acts as a positive regulator of the differentiation and pathogenicity of lamina propria Th17.1 cells in colitis.