Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of disruptive grazing by the mud snail Ilyanassa obsoleta on mudflat nematode populations

Population densities of the mud snail Ilyanassa obsoleta were manipulated in caging experiments on a salt marsh mudflat and in laboratory microcosms. Mud snails outcompete nematodes for food resources, but may increase resources available to deposit feeding groups. Mud snails reduce annelid (polychaete and oligochaete) populations by substrate disruption. Reduced annelid densities provide the nematode community with some release from predation and competition In mudflat sediments, the nematode community responds to both primary (predation) and secondary (environmental release, food competition) interactions. Multiple levels of interactive coupling should be considered in any systems level investigation in this habitat EFFECTS OF DISRUPTIVE GRAZING BY THE MUD SNAIL ILYANASSA OBSOLETA ON MUDFLAT NEMATODE POPULATIONS
Blendshape-augmented Facial Action Units Detection

Action units (AUs) represent descriptions of the facial muscle activation. Given the activation of facial muscles, the facial skin is deformed by following the underlying facial muscle mechanics. This paper proposes to apply the generic knowledge from the principles of the facial anatomy and 3D projection models. And the generic knowledge is combined with the data information through the proposed joint top-down and bottom-up framework. Speciﬁcally, we ﬁrst propose a top-down AU model where 3D AU blendshapes and the 3D projection models are applied as the representation of the generic knowledge. A data-driven bottom-up AU model is constructed from the observed 2D images. Finally, an integration model is proposed to perform ﬁnal AU predictions by combining the estimations from both the top-down model and the bottom-up model. The ﬁnal AU predictions are thus based on both generic knowledge and data information. Evaluations on a benchmark dataset show that by leveraging the generic knowledge, the proposed top-down and bottom-up framework can improve the AU detection performance.
A STUDY OF PROPERTIES OF POSS-MODIFIED VARNISH COATINGS UV-CURED ON A WOOD SURFACE

Research was carried out in a new topic area related to the quality of lacquer finishes on wood surfaces, such as furniture or floor elements. Selected results are presented relating to basic mechanical properties of composite varnish coatings modified with a selected commercial POSS compound, photo-cured on the surface of a chosen wood species. The test results obtained for two groups of varnish coatings differing in the varnish binder used (oligoether or oligoester) pointed to various effects of POSS modification of the varnish compositions and UV-cured coatings. These effects were dependent on the functional property considered as well as the content of POSS nanofiller in the varnish composition. In general, the presence of selected POSS molecules in the UV coating structure improved its abrasion and linear scratching resistance, but did not improve – or even slightly reduced – the adhesion of the coating to the wood surface.
Is body fluid status affected by different electrolyte solutions during endurance cycling?

Fatigue development in endurance sports has several causes; among these, dehydration can impair performance. Both water and electrolytes are lost during exercise, in particular, when exercising in hot environments. These losses must be replaced, with carbohydrate-electrolyte solutions, in different forms. This study aimed to evaluate the effects of different hydrating solutions on body fluid status. Nine male recreational cyclists performed three experimental trials on separate days: trials consisted of a two-hour endurance ride, with three different hydration solutions, consumed in a randomized order (water, water+powder, water+gel). Body weight was measured, and bioimpedance vector analysis was performed both before and after each trial. Changes from preto posttraining in the impedance vector, phase angle, total body water and extracellular water were then compared between the three conditions, using MANOVA for repeated measures. A time × treatment effect was shown for the total body water (p=0.05) and impedance vector (p=0.032), with both the powder and gel differing from the water-only condition. Powder and gel solutions have been shown to be equally effective in limiting the negative effects of dehydration, preventing the loss of total body water. However, gels may represent a practical advantage for endurance athletes, especially those who take part in long-distance races.
MODEL SELECTION IN A SETTING WITH LATENT VARIABLES

Model selection, the task of selecting a statistical model from a certain model class given data, is an important problem in statistical learning. From another perspective model selection can also be viewed as learning a single distribution, where the parameter space includes a discrete structure parameter s which imposes further constraints on the remaining parameterization of that model, so that learning a distribution decomposes intro learning s and the corresponding probability parameters. Examples for model classes that offer this kind of structural flexibility and thus entail a model selection problem are Bayesian networks [1] (where s is the DAG), Markov chains (where s is the order), and variable order [2] and parsimonious [3] Markov models (where s is the context tree structure). Model selection has been well studied in the past decades, and one popular approach is Bayesian model selection, where candidate structures are evaluated according to their Bayesian marginal likelihood. An alternative approach is based on the Minimum Description Length principle [4], and uses the Normalized Maximum Likelihood distribution [5] or approximations thereof [6] as structure score. In the simplest case, model selection is based on the idea that all observations in the data set follow the same distribution, even though the parametric form of the distribution is not known. Another problem arises if we drop that iid assumption and assume each data point to be generated from one out C possible distributions. General examples for this setting are mixture models and hidden Markov models, more specialized applications are promoter models in computational biology [7, 8]. Parameter learning in a setting with latent variables is well studied, and one popular solution is the EM algorithm [9], which can be perceived as a soft clustering algorithm. Combining the latent variable and model selection problem in the same framework is comparatively unexplored, though. One example are mixture models where each component c also comprises a structure parameter sc, which determines the precise parameterization of the distribution of the component. If all candidate models within a component have the same dimensionality, extending the EM approach for learning latent variable models can by used for learning both the structure and the parameters of each component as shown for a mixture of tree models [10]. However, further extending this approach to model classes where candidate models have different dimensionality [11] is not as straightforward, since it can be shown that asymptotically the largest candidate structures will be selected. Here, we follow an alternative approach by extending the model selection for full observations to the presence of latent variables instead of extending the latent variable learning of fixed-structure models to variable structures.
Histopathological Study of Endometrial Polyps and its Association With Cancer: Experience from a Tertiary Care Center in Coastal India

Aim: Morphologic examination of endometrial polyps and assess the occurrence of endometrial cancer. Materials & Methods: One hundred sixty cases of endometrial polyps received at the department of Pathology of a tertiary care hospital over 31 months i.e., from January 2014 and July 2016 were analysed. The clinical data was retrieved from the medical records department. Results: Out of the 160 cases of endometrial polyps, 94.4% of the endometrial polyps were non neoplastic & 5.6% were neoplastic. The mean age of presentation of non-neoplastic polyps was 45 years (21-76 years) & neoplastic polyps was 52 years (42-62 years). The mean size of non-neoplastic polyp was 2.4 cm (0.5-9 cm) & neoplastic polyp was 4.5cm (1.5-9cm). The non-neoplastic polyps were benign endometrial glandular polyp (83.7%), leiomyomatous polyp (2.2%), adenomyomatous polyp (1.1%) & stromomyoma (0.5%). The neoplastic polyps encountered were endometrial carcinoma type 1 (1.67%), endometrial carcinoma type 2 (0.5%), endometrial stromal sarcoma (1.67%), adenosarcoma (0.5%) & malignant mixed mullerian tumor (0.5%). Post-menopausal bleeding was seen in 27% of patients. Immunohistochemistry was performed in 3 of the cases. Tamoxifen induced endometrial polyp were seen in 1.67%. Conclusion: Endometrial polyps are rarely associated with malignancy, as most of them are hyperplastic glandular polyps. Multiple risk factors such as age, multiparity, tamoxifen usage show higher association with malignancy. Post-menopausal bleeding cases needs to be thoroughly evaluated for presence of endometrial malignancy. Histopathological evaluation of resected polyps proves the nature of the disease.
MINIMALIST G -MODELING: A COMMENT ON EFRON

Efron’s elegant approach to g -modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.
Markov Models in Medical Decision Making : A Practical Guide

Markov models are useful when a decision problem involves risk that is continuous over time, when the timing of events is important, and when important events may happen more than once. Representing such clinical settings with conventional decision trees is difficult and may require unrealistic simplifying assumptions. Markov models assume that a patient is always in one of a finite number of discrete health states, called Markov states. All events
Research Toolkit for Family Speculative Play with Future Toys

In this paper, we aim to share guidelines that will help designers and researchers create meaningful, novel, and collaborative future games and toys for families. We bring together best practices from game design and the study of family intercom-munication so that family members can bond, collaborate, and ideate while having fun. We develop these learnings through a preliminary scan of existing family play-interaction guidelines and comparing them to indie as well as popular digital toys, apps
Chagas Cardiomyopathy as the Etiology of Suspected Coronary Microvascular Disease. A Comparison Study with Suspected Coronary Microvascular Disease of Other Etiologies

Chagas Cardiomyopathy as the Etiology of Suspected Coronary Microvascular Disease. A Comparison Study with Suspected Coronary Microvascular Disease of Other Etiologies Felipe Araujo Campos,1 Mariana L. Magalhães,1 Henrique Turin Moreira,1 Rafael B. Pavão,1 Moyses O. Lima-Filho,1 Igor M. Lago,1 André V. Badran,1 João R. A. Chierice,1 André Schmidt,1 José Antonio Marin-Neto Universidade de São Paulo Faculdade de Medicina de Ribeirao Preto,1 Ribeirão Preto, SP Brazil
Dynamics of Water in Solvation Shells and Intersolute Regions of C60: A Molecular Dynamics Simulation Study

We present dynamical characteristics of the solvent in various solvation shells of a C60 molecule as well as intersolute region between two fullerene molecules. Results are obtained from extensive atomistic molecular dynamics simulations of aqueous solutions of C60 in the isothermal−isobaric ensemble. Information about translational mobilities of water in and around fullerene molecules are obtained from velocity auto correlation function and mean square displacements as calculated from simulation data. Reorientational behavior also is investigated by analyzing first and second rank dipole−dipole correlation functions as calculated from simulation trajectories. Both the translational and rotational mobilities of water in the first solvation shell around a C60 molecule are found to be much slower than the same in the second and third solvation shells. Slower decay of the occupation time correlation function for the water in the first hydration shell indicates slower exchange of these water molecules with th.
Differentiable Nonparametric Belief Propagation

We present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm. Existing nonparametric belief propagation methods rely on domain-specific features encoded in the probabilistic factors of a graphical model. In this work, we replace each crafted factor with a differentiable neural network enabling the factors to be learned using an efficient optimization routine from labeled data. By combining differentiable neural networks with an efficient belief propagation algorithm, our method learns to maintain a set of marginal posterior samples using end-to-end training. We evaluate our differentiable nonparametric belief propagation (DNBP) method on a set of articulated pose tracking tasks and compare performance with a recurrent neural network. Results from this comparison demonstrate the effectiveness of using learned factors for tracking and suggest the practical advantage over hand-crafted approaches. The project webpage is available at: progress.eecs. umich.edu/projects/dnbp.
Constraining Type Ia Supernova Delay Time with Spatially Resolved Star Formation Histories

We present the delay time distribution (DTD) estimates of Type Ia supernovae (SNe Ia) using spatially resolved SN Ia host galaxy spectra from MUSE and MaNGA. By employing a grouping algorithm based on k-means and earth mover’s distances (EMDs), we separated the host galaxy stellar population age distributions (SPADs) into spatially distinct regions and used maximum likelihood method to constrain the DTD of SN Ia progenitors. When a power-law model of the form DTD(t) ∝ t s (t > τ) is used, we find an SN rate decay slope s=−1.41−0.33+0.32 and a delay time τ=120−83+142Myr . Moreover, we tested other DTD models, such as a broken power-law model and a two-component power-law model, and found no statistically significant support for these alternative models.
Structured Bayesian variable selection for multiple correlated response variables and high-dimensional predictors

It is becoming increasingly common to study complex associations between multiple phenotypes and high-dimensional genomic features in biomedicine. However, it requires ﬂexi-ble and eﬃcient joint statistical models if there are correlations between multiple response variables and between high-dimensional predictors. We propose a structured multivariate Bayesian variable selection model to identify sparse predictors associated with multiple correlated response variables. The approach makes use of known structure information between the multiple response variables and high-dimensional predictors via a Markov random ﬁeld (MRF) prior for the latent indicator variables of the coeﬃcient matrix of a sparse seemingly unrelated regressions (SSUR). The structure information included in the MRF prior can improve the model performance (i.e., variable selection and response prediction) compared to other common priors. In addition, we employ random eﬀects to capture heterogeneity of grouped samples. The proposed approach is validated by simulation studies and applied to a pharmacogenomic study which includes pharmacological proﬁling and multi-omics data (i.e., gene expression,
Joint Positioning and Tracking via NR Sidelink in 5G-Empowered Industrial IoT: Releasing the Potential of V2X Technology

The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems. In this article, we address the prospects of 5G New Radio (NR) sidelink based V2X networks and their applicability for increasing the situational awareness, in terms of continuous tracking of moving connected machines and vehicles, in industrial systems. For increased system flexibility and fast deployments, we assume that the locations of the so-called anchor nodes are unknown, and describe an extended Kalman filter-based joint positioning and tracking framework in which the locations of both the anchor nodes and the target nodes can be estimated simultaneously. We assess and demonstrate the achievable 3D positioning and tracking performance in the context of a realistic industrial warehouse facility, through extensive ray-tracing based evaluations at the 26 GHz NR band. Our findings show that when both angle-based and time-based measurements are utilized, reaching sub-1 meter accuracy is realistic and that the system is also relatively robust against different node geometries. Finally, several research challenges towards achieving robust, high-performance and cost-efficient positioning solutions are outlined and discussed, identifying various potential directions for future work.
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics, i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data, make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of TML for HSIC and then we will acquaint the superiority of DL to address these problems. This article breaks down the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial–spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
DiffPD: Differentiable Projective Dynamics with Contact

We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods. Sim-ulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method to solve the expensive linearized dynamics. Inspired by Projective Dynamics (PD), we present DiffPD, an efficient differentiable soft-body simulator with implicit time integration. The key idea in DiffPD is to speed up backpropagation by exploiting the prefactorized Cholesky decomposition in PD to achieve a super-linear convergence rate. To handle contacts, DiffPD solves contact forces by analyzing a linear complementarity problem (LCP) and its gradients. With the assumption that contacts occur on a small number of nodes, we develop an efficient method for gradient computation by exploring the low-rank structure in the linearized dynamics. We evaluate the performance of DiffPD and observe a speedup of 4-19 times compared to the standard Newton’s method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control.
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

Semantic segmentation is a key technology for autonomous vehicles to understand the surrounding scenes. The appealing performances of contemporary models usually come at the expense of heavy computations and lengthy inference time, which is intolerable for self-driving. Using light-weight architectures (encoder-decoder or two-pathway) or reasoning on low-resolution images, recent methods realize very fast scene parsing, even running at more than 100 FPS on a single 1080Ti GPU. However, there is still a significant gap in performance between these real-time methods and the models based on dilation backbones. To tackle this problem, we proposed a family of efficient backbones specially designed for real-time semantic segmentation. The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed. Additionally, we design a new contextual information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) to enlarge effective receptive fields and fuse multi-scale context based on low-resolution feature maps. Our method achieves a new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset. In particular, on a single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 102 FPS on Cityscapes test set and 74.7% mIoU at 230 FPS on CamVid test set. With widely used test augmentation, our method is superior to most state-of-the-art models and requires much less computation. Codes and trained models are available online.
MOF-Derived Hierarchically Porous Carbon with Exceptional Porosity and Hydrogen Storage Capacity

Highly porous carbon has played an important role in tackling down the energy and environmental problems due to their attractive features such as high specific surface area (SSA), stability, and mass productivity. Especially, the desirable characteristics of the highly porous carbon such as lightweight, fast adsorption/desorption kinetics, and high SSA have attracted extensive attention in the “hydrogen storage” application which is a main bottleneck for the realization of on-board hydrogen fuel cell vehicles. We herein presented porous carbon with hierarchical pore structure derived from highly crystalline metal organic frameworks (denoted as MOF-derived carbon: MDC) without any carbon source and showed it as a promising hydrogen storage adsorbent. MDCs can be fabricated by a simple heat adjustment of MOFs without complicated process and environmental burden. The MDC displayed hierarchical pore structures with high ultramicroporosity, high SSA, and very high total pore volume. Due to its exceptional poro.
Empirical Evaluation of Supervision Signals for Style Transfer Models

Text style transfer has gained increasing attention from the research community over the recent years. However, the proposed approaches vary in many ways, which makes it hard to assess the individual contribution of the model components. In style transfer, the most important component is the optimization technique used to guide the learning in the absence of parallel training data. In this work we empirically compare the dominant optimization paradigms which provide supervision signals during training: backtranslation, adversarial training and reinforcement learning. We find that backtranslation has model-specific limitations, which inhibits training style transfer models. Reinforcement learning shows the best performance gains, while adversarial training, despite its popularity, does not offer an advantage over the latter alternative. In this work we also experiment with Minimum Risk Training, a popular technique in the machine translation community, which, to our knowledge, has not been empirically evaluated in the task of style transfer. We fill this research gap and empirically show its efficacy.