Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at higher abstraction levels, resulting in more stable motion prediction. Current methods however predetermine scale levels and combine spatially proximal joints to generate coarser scales based on human priors, even though movement patterns in different motion sequences vary and do not fully comply with a fixed graph of spatially connected joints. Another problem with graph convolutional methods is mode collapse, in which predicted poses converge around a mean pose with no discernible movements, particularly in long-term predictions. To tackle these issues, we propose ResChunk, an end-to-end network which explores dynamically correlated body components based on the pairwise relationships between all joints in individual sequences. ResChunk is trained to learn the residuals between target sequence chunks in an autoregressive manner to enforce the temporal connectivities between consecutive chunks. It is hence a sequence-to-sequence prediction network which considers dynamic spatio-temporal features of sequences at multiple levels. Our experiments on two challenging benchmark datasets, CMU Mocap and Human3.6M, demonstrate that our proposed method is able to effectively model the sequence information for motion prediction and outperform other techniques to set a new state-of-the-art. Our code is available at https://github.com/MohsenZand/ResChunk.
Unsupervised Text Style Transfer with Deep Generative Models

We present a general framework for unsupervised text style transfer with deep generative models. The framework models each sentence-label pair in the non-parallel corpus as partially observed from a complete quadruplet which additionally contains two latent codes representing the content and style, respectively. These codes are learned by exploiting dependencies inside the observed data. Then a sentence is transferred by manipulating them. Our framework is able to unify previous embedding and prototype methods as two special forms. It also provides a principled perspective to explain previously proposed techniques in the field such as aligned encoder and adversarial training. We further conduct experiments on three benchmarks. Both automatic and human evaluation results show that our methods achieve better or competitive results compared to several strong baselines.
An exceptional property of the one-dimensional Bianchi-Egnell inequality

In this paper, for $d \geq 1$ and $s \in (0,\frac{d}{2})$, we study the Bianchi-Egnell quotient \[ \mathcal Q(f) = \inf_{f \in \dot{H}^s(\mathbb R^d) \setminus \mathcal B} \frac{\|(-\Delta)^{s/2} f\|_{L^2(\mathbb R^d)}^2 - S_{d,s} \|f\|_{L^{\frac{2d}{d-2s}}(\mathbb R^d)}^2}{\text{dist}_{\dot{H}^s(\mathbb R^d)}(f, \mathcal B)^2}, \qquad f \in \dot{H}^s(\mathbb R^d) \setminus \mathcal B, \] where $S_{d,s}$ is the best Sobolev constant and $\mathcal B$ is the manifold of Sobolev optimizers. By a fine asymptotic analysis, we prove that when $d = 1$, there is a neighborhood of $\mathcal B$ on which the quotient $\mathcal Q(f)$ is larger than the lowest value attainable by sequences converging to $\mathcal B$. This behavior is surprising because it is contrary to the situation in dimension $d \geq 2$ described recently in \cite{Koenig}. This leads us to conjecture that for $d = 1$, $\mathcal Q(f)$ has no minimizer on $\dot{H}^s(\mathbb R^d) \setminus \mathcal B$, which again would be contrary to the situation in $d \geq 2$. As a complement of the above, we study a family of test functions which interpolates between one and two Talenti bubbles, for every $d \geq 1$. For $d \geq 2$, this family yields an alternative proof of the main result of \cite{Koenig}. For $d =1$ we make some numerical observations which support the conjecture stated above.
Linearly Edge-Reinforced Random Walks

This thesis examines linearly edge-reinforced random walks on infinite trees. In particular, recurrence and transience of such random walks on general (fixed) trees as well as on Galton-Watson trees (i.e. random trees) is characterized, and shown to be related to the branching number of these trees and a so-called reinforcement parameter. A phase transition from transience to recurrence takes place at a critical parameter value. As a tool, random walks in random environment are introduced and known results are repeated, together with detailed proofs. A result on quasi-independent percolation is proved as a by-product. Finally, for the edge-reinforced random walk on Z, the existence of a kind of stationary / limiting distribution with finite moments is shown.
Triple Points of Gravitational AdS Solitons and Black Holes

We present a triple point of a new kind for General Relativity, at which two gravitational solitons can coexist with a planar black hole in anti de Sitter space. Working in the context of non-linear electrodynamics, we obtain simple, sensible spacetimes for which the thermodynamics can be studied in an analytic manner. The spacetimes are charged under the non-linear electrodynamics leading to an electric charge for black holes and a magnetic flux for solitons. In the grand-canonical ensemble, we show that the phase space of the theory is very rich, containing re-entrant phase transitions, as well as triple points, for small values of the coupling controlling the non-linearity of the electrodynamics Lagrangian.
UV-optical Emission of AB Aur b Is Consistent with Scattered Stellar Light

The proposed protoplanet AB Aur b is a spatially concentrated emission source imaged in the millimeter-wavelength disk gap of the Herbig Ae/Be star AB Aur. Its near-infrared spectrum and absence of strong polarized light have been interpreted as evidence supporting the protoplanet interpretation. However, the complex scattered-light structures in the AB Aur disk pose challenges in resolving the emission source and interpreting the true nature of AB Aur b. We present new images of the AB Aur system obtained using the Hubble Space Telescope Wide Field Camera 3 in the ultraviolet (UV) and optical bands. AB Aur b and the known disk spirals are recovered in the F336W, F410M, and F645N bands. The spectral energy distribution of AB Aur b shows absorption in the Balmer jump, mimicking that of early-type stars. By comparing the colors of AB Aur b to those of the host star, the disk spirals, and predictions from scattered light and self-luminous models, we find that the emission from AB Aur b is inconsistent with planetary photospheric or accretion shock models. Instead, it is consistent with those measured in the circumstellar disks that trace scattered light. We conclude that the UV and visible emission from AB Aur b does not necessitate the presence of a protoplanet. We synthesize observational constraints on AB Aur b and discuss inconsistent interpretations among different data sets. Considering the significance of the AB Aur b discovery, we advocate for further observational evidence to verify its planetary nature.
The Longitudinal Effect of Ultra-Processed Food on the Development of Dyslipidemia/Obesity as Assessed by the NOVA System and Food Compass Score.

SCOPE Ultra-processing food (UPF) has been a nutrition and health interest. This study is aimed to investigate the association between UPF consumption and the risk of obesity or dyslipidemia. METHODS AND RESULTS This study is performed using an ongoing cohort study including 17 310 individuals aged ≥40 years in South Korea. UPF is categorized by the NOVA system and FCS, respectively. After an average 5-year follow-up, there is a positive association between NOVA-defined UPF and dyslipidemia. The risk of the Q4 group is almost 20% higher than that of the Q1 group (men, adjusted HR = 1.209 [95% CI 1.039-1.407], women, adjusted HR = 1.195 [95% CI 1.096-1.303]). Consuming high-FCS foods (less processed and healthier foods) show a lower risk for dyslipidemia in both sexes and lower obesity risk in women compared to low-FCS consumption (men, dyslipidemia, adjusted HR = 0.857 [95% CI 0.744-0.988]; women, dyslipidemia, adjusted HR = 0.919 [95% CI 0.850-0.993], obesity, adjusted HR = 0.759 [95% CI 0.628-0.916]). CONCLUSION Higher UPF intakes assessed by the NOVA system and FCS are associated with increased incidences of dyslipidemia and obesity. Furthermore, NOVA-defined UPF shows a statistically significant negative association with AMED score, indicating poor diet quality.
Evaluating the accuracy of data extracted from electronic health records into MedicineInsight, a national Australian general practice database

Abstract Introduction MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. Previous research validated algorithms used to derive medical condition flags in MedicineInsight, but the accuracy of data fields following EHR extractions from clinical practices and data warehouse transformation processes have not been formally validated. Objectives To examine the accuracy of the extraction and transformation of EHR fields for selected demographics, observations, diagnoses, prescriptions, and tests into MedicineInsight. Methods We benchmarked MedicineInsight values against those recorded in original EHRs. Forty-six general practices contributing data to MedicineInsight met our eligibility criteria, eight were randomly selected, and four agreed to participate. We randomly selected 200 patients >18 years of age within each participating practice from MedicineInsight. Trained staff reviewed the original EHRs for the selected patients and recorded data from the relevant fields. We calculated the percentage of agreement (POA) between MedicineInsight and EHR data for all fields; Cohen’s Kappa for categorical and intra-class correlation (ICC) for continuous measures; and sensitivity, specificity, and positive and negative predictive values (PPV/NPV) for diagnoses. Results A total of 796 patients were included in our analysis. All demographic characteristics, observations, diagnoses, prescriptions and random pathology test results had excellent (>90%) POA, Kappa, and ICC. POA for most recent pathology/imaging test was moderate (81%, [95% CI: 78% to 84%]). Sensitivity, specificity, PPV, and NPV were excellent (>90%) for all but one of the examined diagnoses which had a poor PPV. Conclusions Overall, our study shows good agreement between the majority of MedicineInsight data and those from original EHRs, suggesting MedicineInsight data extraction and warehousing procedures accurately conserve the data in these key fields. Discrepancies between test data may have arisen due to how data from pathology, radiology and other imaging providers are stored in EHRs and MedicineInsight and this requires further investigation.
Agricultural Restructuring and Sustainability: A Geographical Perspective

SECTION I: Conceptualizing Agricultural Restructuring and Sustainability 1.1: Sustainable Development: A Critical Review of Rural Land-use Policy in the UK, R Munton 1.2: Sustainability, Spatial Hierarchies and Land-based Production, C Cocklin, G Blunden and W Moran 1.3: Greening and Globalizing: Agriculture in 'the New Times', G Robinson SECTION II: Family Farming and Farming Culture 2.1: Sustainable Technologies, Sustainable Farms: Farms, Households and Structural Change, R Roberts and G Hollander 2.2: Environmental Change and Farm Restructuring in Britain: The Impact of the Farm Family Life Cycle, C Potter 2.3: The Construction of Environmental Meanings Within 'Farming Culture' in the UK: The Implications for Agri- environmental Research, C Morris and C Andrews 2.4: Community-level Worldviews and the Sustainability of Agriculture, J M Curry-Roper SECTION III: Diversification and Alternative Agriculture 3.1: Rural Re-regulation and Institutional Sustainability: A Case Study of Alternative Farming Systems in England, G Clark, I Bowler, A Crockett, B Ilbery and A Shaw 3.2: On and Off-farm Business Diversification by Farm Households in England, B Ilbery, M Healey and J Higginbottom 3.3: Great Plains Agroecologies: The Continuum from Conventional to Alternative Agriculture in Colorado, L A Duram SECTION IV: Agricultural Sustainability and Climate Change 4.1: Agricultural System Response to Environmental Stress, J Smithers and B Smit 4.2: Adaptability of Agriculture Systems to Global Climate Change: A Renfrew County, Ontario, Canada Pilot Study, M Brklacich, D McNabb, C Bryant and J Dumanski 4.3: Agricultural Response to Climate Change: A Preliminary Investigation of Farm-level Adaptation in Southern Alberta, Q Chiotti, T Johnston, B Smit and B Ebel SECTION V: Sustainable Agriculture and Environmental Policy 5.1: Policy, Sustainability and Scale: The US Conservation Reserve Programme, D Nellis, L Harrington and J Sheeley 5.2: Something Old, New, Borrowed and Blue: The Marriage of Agriculture and Conservation in England, N Evans 5.3: Farmer Reaction to Agrienvironmental Schemes: A Study of Participants in South-West England and the Implications for Research and Policy Development, A W Gilg and M R J Battershill 5.4: Achieving Sustainability in Rural Land Management Through Landowner Involvement in Stewardship Programmes, S Hilts SECTION VI: Sustainability and Restructuring the Agricultural System 6.1: Scale Change, Discontinuity and Polarization in Canadian Farm-based Rural Systems, M Troughton 6.2: Sustainability Issues in the Industrialization of Hog Production in the United States, O J Furuseth 6.3: Sustainable Agriculture and Its Social Geographic Context in Ontario, G Walker 6.4: Restructuring for Rural Sustainability: Overcoming Scale Conflicts and Cultural Biases, D Napton
A critical examination of Danish norms for the Strengths and Difficulties Questionnaire (SDQ)

Abstract Objective The objective was to describe observed differences between the official Danish Strengths and Difficulties Questionnaire (SDQ)-norms and data from a sample based on the Danish National Birth Cohort (DNBC) including children born between 1996 and 2003. We compared the risk classification, cut-off bandings and the group sizes between the Danish norms and the values found in our sample at ages 7, 11 and 18 years. Results Two sets of norms are used in Denmark: Arnfred’s norms, based on a sample from one single Danish municipality and Niclasen’s norms, based on multiple Danish cohorts, including the DNBC. Inconsistencies were found between banding scores in the two existing norms and the banding scores identified in our sample from DNBC: discrepancies included banding scores for several of the problem scales for children and preadolescents. For adolescents, we found less apparent inconsistencies between Arnfred’s sample and the DNBC. Results demonstrate that the existing SDQ norms do not apply well to a large-scale cohort sample in Denmark. The usefulness of the SDQ as a screening instrument for mental health problems depends on appropriate norms. We therefore urge that the current Danish SDQ norms are used with caution, and preferably they should be revised.
Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features

In echocardiographic view classification, accurately detecting out-of-distribution (OOD) data is essential but challenging, especially given the subtle differences between in-distribution and OOD data. While conventional OOD detection methods, such as Mahalanobis distance (MD) are effective in far-OOD scenarios with clear distinctions between distributions, they struggle to discern the less obvious variations characteristic of echocardiographic data. In this study, we introduce a novel use of label smoothing to enhance semantic feature representation in echocardiographic images, demonstrating that these enriched semantic features are key for significantly improving near-OOD instance detection. By combining label smoothing with MD-based OOD detection, we establish a new benchmark for accuracy in echocardiographic OOD detection.
BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation

Semantic image segmentation is particularly vital for the advancement of autonomous vehicle technologies. However, this domain faces substantial challenges under adverse conditions like rain or darkness, which remain underrepresented in most datasets. The generation of additional training data for these scenarios is not only costly but also fraught with potential inaccuracies, largely attributable to the aleatoric uncertainty inherent in such conditions. We introduce BTSeg, an innovative, semi-supervised training approach enhancing semantic segmentation models in order to effectively handle a range of adverse conditions without requiring the creation of extensive new datasets. BTSeg employs a novel application of the Barlow Twins loss, a concept borrowed from unsupervised learning. The original Barlow Twins approach uses stochastic augmentations in order to learn useful representations from unlabeled data without the need for external labels. In our approach, we regard images captured at identical locations but under varying adverse conditions as manifold representation of the same scene (which could be interpreted as"natural augmentations"), thereby enabling the model to conceptualize its understanding of the environment. We evaluate our approach on the ACDC dataset, where it performs favorably when compared to the current state-of-the-art methods, while also being simpler to implement and train. For the new challenging ACG benchmark it shows cutting-edge performance, demonstrating its robustness and generalization capabilities. We will make the code publicly available post-acceptance.
Nucleus-Aware Self-Supervised Pretraining Using Unpaired Image-to-Image Translation for Histopathology Images

Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the extraction of nucleus-level information, which is essential for pathologic analysis. In this work, we propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation between histopathology images and pseudo mask images. The generation process is modulated by both conditional and stochastic style representations, ensuring the reality and diversity of the generated histopathology images for pretraining. Further, an instance segmentation guided strategy is employed to capture instance-level information. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks with the transfer learning protocol, and yields superior results than other self-supervised approaches on 8 semi-supervised tasks. Our project is publicly available at https://github.com/zhiyuns/UNITPathSSL.
Comprehensive Questionnaire in Postpartum Women to Assess Women’s Knowledge of the Current Weight Gain Guidelines during Pregnancy in Lower Saxony

Introduction: Maternal body mass index and gestational weight gain (GWG) are important factors for maternal and neonatal health. The objective of this study was to assess women’s knowledge and examine adherence to the Institute of Medicine (IOM) criteria for weight gain during pregnancy by evaluating the information received from obstetricians and women’s knowledge about GWG. Methods: This is an analytical semi-longitudinal observational study. Weight data from a nonconsecutive convenience sample of 389 women who gave birth at the Hannover Medical School in the period from August 2020 to July 2021 were taken from their maternal records. Immediately after giving birth, the whole collective (n = 389) was asked to participate in a questionnaire study including questions that were taken from the EMat Health Survey inquiring about their knowledge and received information about GWG and about their eating behavior. Here, a subset of 202 women participated. Results: Sixty-five percent of the participants who answered the questionnaire reported that they had not been informed by their obstetrician about GWG recommendations. Additionally, a minority of women knew the correct IOM GWG category based on their pre-pregnancy weight. Meeting the IOM GWG guidelines did not depend on whether or not women received GWG recommendations or knew about the correct GWG category. The majority of women were not concerned about gaining too much weight during pregnancy. 20.7% of all women participating in the study were affected by obesity pre-pregnancy. According to the IOM criteria for GWG, 50.4% gained too much weight. The proportion of women exceeding IOM recommendations was highest in women with pre-pregnancy overweight and obesity (67%). Discussion: Weight gain outside of the IOM recommendations is widespread in our survey. Information received and knowledge about GWG recommendations were inadequate in our sample. Considering the fact that GWG outside recommended ranges can contribute to short- and long-term health complications, especially when a woman enters pregnancy already with overweight or obesity, identifying ways of achieving a healthier GWG is warranted.
PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction

Vectorized high-definition map online construction has garnered considerable attention in the field of autonomous driving research. Most existing approaches model changeable map elements using a fixed number of points, or predict local maps in a two-stage autoregressive manner, which may miss essential details and lead to error accumulation. Towards precise map element learning, we propose a simple yet effective architecture named PivotNet, which adopts unified pivot-based map representations and is formulated as a direct set prediction paradigm. Concretely, we first propose a novel Point-to-Line Mask module to encode both the subordinate and geometrical point-line priors in the network. Then, a well-designed Pivot Dynamic Matching module is proposed to model the topology in dynamic point sequences by introducing the concept of sequence matching. Furthermore, to supervise the position and topology of the vectorized point predictions, we propose a Dynamic Vectorized Sequence loss. Extensive experiments and ablations show that PivotNet is remarkably superior to other SOTAs by 5.9 mAP at least. The code will be available soon.