Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of C/N ratio and its frequent addition on commensal and pathogenic bacterial abundances in shrimp Litopeaneus vanname gut in a biofloc system: Ratio and frequent addition interaction matters

The environmental biotic and abiotic factors form a complicated relationship with the host intestinal microbiota. In our study, we applied different levels of C/N ratio (10, 15, 20) and frequent addition times (once, twice, triple a day) in a factorial experimental design. GC/LC analysis of filtrated biofloc (BF) samples revealed the highest relative fold change for the untargeted bioactive molecules among different treatments, whereas the 16s rRNA analysis revealed the change in the shrimp gut microbiota composition. Based on the available literature on the relationship between the bioactive molecules and the available bacteria in this study, the next bioactive molecules were discussed. Proline was associated with Bacteroidota, Flavobacteriaceae, Gammaproteobacteria, and Flavobacteriales. Plumbagine was associated with Norcardiaceae. Phytosphingosin was associated with Bacteroidota. Phosphocholine compound was associated with Bacteroidota. The monobutyl ether, benzofuran, and piperidone were associated with Micobacteriaceae and Mycobacterium. Generally, C/N 15 and 20 once a day, and C/N 20 triple a day have showed a merit over other treatments in term of low pathogenic and unfavorable bacteria, and high commensal bacterial abundances. The revealed bioactive molecule composition showed the complicity of BF as a source for novel compounds as biosecurity agents in BF system. These molecules could be developed to feed additives upgrading the biosecurity level in aquaculture systems. Other bioactive molecules require future studies to reveal novel molecules in term of aquaculture biosecurity control.
A PET-CT study on neuroinflammation in Huntington’s disease patients participating in a randomized trial with laquinimod

Abstract Microglia activation, an indicator of central nervous system inflammation, is believed to contribute to the pathology of Huntington’s disease. Laquinimod is capable of regulating microglia. By targeting the translocator protein, 11C-PBR28 PET-CT imaging can be used to assess the state of regional gliosis in vivo and explore the effects of laquinimod treatment. This study relates to the LEGATO-HD, multi-centre, double-blinded, Phase 2 clinical trial with laquinimod (US National Registration: NCT02215616). Fifteen patients of the UK LEGATO-HD cohort (mean age: 45.2 ± 7.4 years; disease duration: 5.6 ± 3.0 years) were treated with laquinimod (0.5 mg, N = 4; 1.0 mg, N = 6) or placebo (N = 5) daily. All participants had one 11C-PBR28 PET-CT and one brain MRI scan before laquinimod (or placebo) and at the end of treatment (12 months apart). PET imaging data were quantified to produce 11C-PBR28 distribution volume ratios. These ratios were calculated for the caudate and putamen using the reference Logan plot with the corpus callosum as the reference region. Partial volume effect corrections (Müller–Gartner algorithm) were applied. Differences were sought in Unified Huntington’s Disease Rating Scale scores and regional distribution volume ratios between baseline and follow-up and between the two treatment groups (laquinimod versus placebo). No significant change in 11C-PBR28 distribution volume ratios was found post treatment in the caudate and putamen for both those treated with laquinimod (N = 10) and those treated with placebo (N = 5). Over time, the patients treated with laquinimod did not show a significant clinical improvement. Data from the 11C-PBR28 PET-CT study indicate that laquinimod may not have affected regional translocator protein expression and clinical performance over the studied period.
First insight into the phylogeny of fine‐leaved Festuca in the Altai Mountain Country based on genome‐wide genotyping

Abstract Festuca is one of the largest genera within the Poaceae family. Molecular phylogenies demonstrate that Festuca s.l. comprises two groups: broad‐ and fine‐leaved species. The latter is the species‐richest and taxonomically complicated group due to being paraphyletic. Here, we provide the first insight into the phylogeny of 17 fine‐leaved species of Altai fescues. Based on genome‐wide genotyping, the examined taxa were divided into three markedly differentiated clusters. The first cluster comprises species from the F. rubra complex, the second cluster includes the F. brachyphylla complex, and the third cluster contains taxa from the groups F. ovina, F. valesiaca, and F. kryloviana. Importantly, we detected a complex genetic pattern within the groups of F. valesiaca and F. kryloviana. Moreover, our findings underline a discrepancy between morphological and molecular data for some species distributed within the Altai Mountain Country. We suggest that in order to validate the current findings on the fine‐leaved fescues, additional comprehensive research including morphological, karyological, and molecular methods is required. Nonetheless, our work provides a baseline for further investigations on the genus and studies on the floral diversity of Asia.
Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms

An accurate solar wind speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning solar wind - magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetograms, considering a solar wind source surface of $R_{\rm SS}=2. 5R_\odot$, aiming to predict the solar wind speed at the Lagrange 1 (L1) point of the Sun-Earth system. The input of our model consists of four Potential Field Source Surface (PFSS) magnetograms at $R_{\rm SS}$, which are 7, 6, 5, and 4 days before the target epoch. Reduced magnetograms are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetograms at the source surface. The model provides predictions of the continuous test dataset with an averaged correlation coefficient (CC) of 0.52 and a root mean square error (RMSE) of 80.8 km/s in an eight-fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the solar wind, which can be quantified with a general threat score of 0.39.
Geometric symmetry breaking and nonlinearity can increase thermoelectric power

Direct thermal-to-electric energy converters typically operate in the linear regime, where the ratio of actual maximum power relative to the ideal maximum power, the so-called fill factor (FF), is 0.25. By increasing the FF one can potentially increase maximum power by up to four times, but this is only possible in the nonlinear regime of transport and has previously rarely been considered. Here we show, based on fundamental symmetry considerations, that the leading order non-linear terms that can increase the FF require devices with broken spatial symmetry. To experimentally demonstrate such a system, we study nonlinear, thermoelectric transport across an asymmetric energy barrier epitaxially defined in a single semiconductor nanowire. We find in both experiment and theory that we can increase the FF above the linear-response limit of 0.25, accompanied by a drastic increase in short circuit current, open-circuit voltage and maximum power. Our results show that geometric symmetry breaking combined with the design of nonlinear behaviour represent a design strategy for increasing the performance of thermal-to-electric energy converters such as in hot-carrier photovoltaics, thermophotovoltaics or in anisotropic thermoelectric materials.
Novel Statistically Designed Qbd Methodology for Quantitative Analysisof Nisoldipine in Pharmaceutical Dosage Forms

The objective of the study was to develop superlative reproducible spectrophotometric estimation process of Nislodipine via BCG (Bromocresol green) solution using novel statistically Taguchi via factorial design methodology. Primarily, an L9 design array was experimented to find significant analytical variables and optimize their(s) best levels keeping drug amount constant. Additionally factorial i.e. Box-Behnken design (BBD) of better factors (X, Y & Z) levels at positive, null and negative spaces responses (R=absorbance) was studied to found better to best level. Moreover, polynomial quadratic equations for two and three dimensional models were designed to predict the best significant independent levels. The selected variable responses were statistically evaluated using ANOVA and showed correlation coefficient (r2=0.999) followed by good Beer’s (5-40 μg/ml) range at maximum absorption wavelength (420 nm) and was validated according to ICH guidelines. This robust novel statistically designed strategy can be considered as quality by design (QbD) quantitative methodology for Nisoldipine estimation in pharmaceutical’s.
Atomic Data Assessment with PyNeb: Radiative and Electron Impact Excitation Rates for [Fe ii] and [Fe iii]

We use the PyNeb 1.1.16 Python package to evaluate the atomic datasets available for the spectral modeling of [Fe ii] and [Fe iii], which list level energies, A-values, and effective collision strengths. Most datasets are reconstructed from the sources, and new ones are incorporated to be compared with observed and measured benchmarks. For [Fe iii], we arrive at conclusive results that allow us to select the default datasets, while for [Fe ii], the conspicuous temperature dependency on the collisional data becomes a deterrent. This dependency is mainly due to the singularly low critical density of the 3d7a4F9/2 metastable level that strongly depends on both the radiative and collisional data, although the level populating by fluorescence pumping from the stellar continuum cannot be ruled out. A new version of PyNeb (1.1.17) is released containing the evaluated datasets.
An Information-Theoretic Approach to Collaborative Integrated Sensing and Communication for Two-Transmitter Systems

This paper considers information-theoretic models for integrated sensing and communication (ISAC) over multi-access channels (MAC) and device-to-device (D2D) communication. The models are general and include as special cases scenarios with and without perfect or imperfect state-information at the MAC receiver as well as causal state-information at the D2D terminals. For both setups, we propose collaborative sensing ISAC schemes where terminals not only convey data to the other terminals but also state-information that they extract from their previous observations. This state-information can be exploited at the other terminals to improve their sensing performances. Indeed, as we show through examples, our schemes improve over previous non-collaborative schemes in terms of their achievable rate-distortion tradeoffs. For D2D we propose two schemes, one where compression of state information is separated from channel coding and one where it is integrated via a hybrid coding approach.
A novel probabilistic framework for event‐based speech recognition

One of the reasons for unsatisfactory performance of the state‐of‐the‐art automatic speech recognition (ASR) systems is the inferior acoustic modeling of low‐level acoustic‐phonetic information in the speech signal. An acoustic‐phonetic approach to ASR, on the other hand, explicitly targets linguistic information in the speech signal, but such a system for continuous speech recognition (CSR) is not known to exist. A probabilistic and statistical framework for CSR based on the idea of the representation of speech sounds by bundles of binary valued articulatory phonetic features is proposed. Multiple probabilistic sequences of linguistically motivated landmarks are obtained using binary classifiers of manner phonetic features—syllabic, sonorant and continuant—and the knowledge‐based acoustic parameters (APs) that are acoustic correlates of those features. The landmarks are then used for the extraction of knowledge‐based APs for source and place phonetic features and their binary classification. Probabilisti.
Conformalized Unconditional Quantile Regression

We develop a predictive inference procedure that combines conformal prediction (CP) with unconditional quantile regression (QR) -- a commonly used tool in econometrics that involves regressing the recentered influence function (RIF) of the quantile functional over input covariates. Unlike the more widely-known conditional QR, unconditional QR explicitly captures the impact of changes in covariate distribution on the quantiles of the marginal distribution of outcomes. Leveraging this property, our procedure issues adaptive predictive intervals with localized frequentist coverage guarantees. It operates by fitting a machine learning model for the RIFs using training data, and then applying the CP procedure for any test covariate with respect to a ``hypothetical'' covariate distribution localized around the new instance. Experiments show that our procedure is adaptive to heteroscedasticity, provides transparent coverage guarantees that are relevant to the test instance at hand, and performs competitively with existing methods in terms of efficiency.
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction

Recent works on 3D reconstruction from posed images [17], [23], [24] have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency. However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coarse without fine geometric details. To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions. We first present a resolution-agnostic TSDF supervision strategy to provide the network with a more accurate learning signal during training, avoiding the pitfalls of TSDF interpolation seen in previous work. We then introduce a depth guidance strategy using multi-view depth estimates to enhance the scene representation and recover more accurate surfaces. Finally, we develop a novel architecture for the final layers of the network, conditioning the output TSDF prediction on high-resolution image features in addition to coarse voxel features, enabling sharper reconstruction of fine details. Our method, FineRecon1, produces smooth and highly accurate reconstructions, showing significant improvements across multiple depth and 3D reconstruction metrics.
Functional Knowledge Transfer with Self-supervised Representation Learning

This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization of self-supervised learning pseudo task and supervised learning task, improving supervised learning task performance. Recent progress in self-supervised learning uses a large volume of data, which becomes a constraint for its applications on small-scale datasets. This work shares a simple yet effective joint training framework that reinforces human-supervised task learning by learning self-supervised representations just-in-time and vice versa. Experiments on three public datasets from different visual domains, Intel Image, CIFAR, and APTOS, reveal a consistent track of performance improvements on classification tasks during joint optimization. Qualitative analysis also supports the robustness of learnt representations. Source code and trained models are available on GitHub1.
A Method for Animating Children’s Drawings of the Human Figure

Children’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing over 178,000 amateur drawings and corresponding user-accepted character bounding boxes, segmentation masks, and joint location annotations.
The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects

Wavelength-dependent atmospheric effects impact photometric supernova flux measurements for ground-based observations. We present corrections on supernova flux measurements from the Dark Energy Survey Supernova Program’s 5YR sample (DES-SN5YR) for differential chromatic refraction (DCR) and wavelength-dependent seeing, and we show their impact on the cosmological parameters w and Ω m . We use g − i colors of Type Ia supernovae to quantify astrometric offsets caused by DCR and simulate point-spread functions (PSFs) using the GalSIM package to predict the shapes of the PSFs with DCR and wavelength-dependent seeing. We calculate the magnitude corrections and apply them to the magnitudes computed by the DES-SN5YR photometric pipeline. We find that for the DES-SN5YR analysis, not accounting for the astrometric offsets and changes in the PSF shape cause an average bias of +0.2 mmag and −0.3 mmag, respectively, with standard deviations of 0.7 mmag and 2.7 mmag across all DES observing bands (griz) throughout all redshifts. When the DCR and seeing effects are not accounted for, we find that w and Ω m are lower by less than 0.004 ± 0.02 and 0.001 ± 0.01, respectively, with 0.02 and 0.01 being the 1σ statistical uncertainties. Although we find that these biases do not limit the constraints of the DES-SN5YR sample, future surveys with much higher statistics, lower systematics, and especially those that observe in the u band will require these corrections as wavelength-dependent atmospheric effects are larger at shorter wavelengths. We also discuss limitations of our method and how they can be better accounted for in future surveys.
USTC FLICAR: A sensors fusion dataset of LiDAR-inertial-camera for heavy-duty autonomous aerial work robots

In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To fill this gap, we create the “Giraffe” mapping robot based on a bucket truck, which is equipped with a variety of well-calibrated and synchronized sensors: four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the millimeter-level ground truth positions. We also make its ground twin, the “Okapi” mapping robot, to gather data for comparison. The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes, demonstrating the potential of combining autonomous driving perception systems with bucket trucks to create a versatile autonomous aerial working platform. Moreover, based on the Segment Anything Model (SAM), we produce the Semantic FLICAR dataset, which provides fine-grained semantic segmentation annotations for multimodal continuous data in both temporal and spatial dimensions. The dataset is available for download at: https://ustc-flicar.github. io/.
The Economic Effect of Gaining a New Qualification Later in Life

Pursuing educational qualifications later in life is an increasingly common phenomenon within OECD countries since technological change and automation continues to drive the evolution of skills needed in many professions. We focus on the causal impacts to economic returns of degrees completed later in life, where motivations and capabilities to acquire additional education may be distinct from education in early years. We find that completing an additional degree leads to more than \$3000 (AUD, 2019) extra income per year compared to those who do not complete additional study. For outcomes, treatment and controls we use the extremely rich and nationally representative longitudinal data from the Household Income and Labour Dynamics Australia survey (HILDA). To take full advantage of the complexity and richness of this data we use a Machine Learning (ML) based methodology for causal effect estimation. We are also able to use ML to discover sources of heterogeneity in the effects of gaining additional qualifications. For example, those younger than 45 years of age when obtaining additional qualifications tend to reap more benefits (as much as \$50 per week more) than others.
How Regional Wind Characteristics Affect CNN-based wind predictions: Insights from Spatiotemporal Correlation Analysis

This paper investigates how incorporating spatio-temporal data dimensions can improve the precision of a wind forecasting model developed using a neural network. While previous studies have shown that including spatial data can enhance the accuracy of such models, little research has explored the impact of diﬀerent spatial scales and optimal temporal lengths of input data on their predictive performance. To address this gap, we employ data with various spatio-temporal dimensions as inputs when forecasting wind using 3D-Convolutional Neural Networks (3D-CNN), and assess their predictive performance. We demonstrate that using spatial data of the surrounding area and multi-time data of past wind information during 3D-CNN training favorably aﬀects the predictive performance of the model. Moreover, we pro-pose correlation analyses, including auto-and Pearson correlation analyses, to reveal the inﬂuence of spatio-temporal wind phenomena on the prediction performance of the 3D-CNN model. We show that local geometric and seasonal wind conditions can signiﬁcantly inﬂuence the forecast capability of the
Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking

In this paper, we follow a data-centric philosophy and propose a novel motion annotation method based on the inherent representativeness of motion data in a given dataset. Specifically, we propose a Representation-based Representativeness Ranking R3 method that ranks all motion data in a given dataset according to their representativeness in a learned motion representation space. We further propose a novel dual-level motion constrastive learning method to learn the motion representation space in a more informative way. Thanks to its high efficiency, our method is particularly responsive to frequent requirements change and enables agile development of motion annotation models. Experimental results on the HDM05 dataset against state-of-the-art methods demonstrate the superiority of our method.