Miracle-dz/UniFAR-FTUs huggingface dataset:
Land use history determines non-native earthworm impacts on atmospheric methane consumption in forest soils, central New York State

Yavitt, J. B. 2015. Land use history determines non-native earthworm impacts on atmospheric methane consumption in forest soils, central New York State. Can. J. Soil Sci. 95: 321-330. I used complementary field and laboratory studies to examine the impact of two types of soil disturbance on net consumption of atmospheric methane (CH4) in forest soils near Ithaca, New York. One type of disturbance is invasion of non-native earthworms that mix soil layers, and the other is agriculture, which reduces the pit-and-mound surface topography to a flat landscape. Measurements of CH4 fluxes between soil and the atmosphere were made in autumn before seasonal leaf fall when earthworms had consumed all of the previous year's leaf fall, and revealed no impact by earthworms in the never-tilled soils. Although earthworms did lead to greater consumption of atmospheric CH4 in the post-agriculture soils, the mean consumption rate was only -0.2 mg m2 d-1. Concentrations of atmospheric CH4 in wormed soils were often greater than that in the atmosphere, suggesting that earthworms promote CH4 production. In general, earthworms decreased soil permeability to gas diffusion. Post-agriculture soils also had faster CH4 oxidation rates when incubated in the laboratory. The results taken together suggest that earthworm impacts on atmospheric CH4 consumption depend on the history of soil disturbance.
Co-creation Stage: a Web-based Tool for Collaborative and Participatory Co-located Art Performances

In recent years, artists and communities have expressed the desire to work with tools that facilitate co-creation and allow distributed community performances. These performances can be spread over several physical stages, connecting them on real-time towards a single experience with the audience distributed along them. This enables a wider remote audience consuming the performance through their own devices, and even grants the participation of remote users in the show. In this paper we introduce the Co-creation Stage, a web-based tool that allows managing heterogeneous content sources, with a particular focus on live and on-demand media, across several distributed devices. The Co-creation Stage is part of the toolset developed in the Traction H2020 project which enables community performing art shows, where professional artists and non-professional participants perform together from different stages and locations. Here we present the design process, the architecture and the main functionalities of the tool as well as the results of the first user evaluation with Opera houses and artists.
Expression of Neopterin and Chemokines in Rheumatoid Arthritis and Cardiovascular Disease

Abstract In chronic inflammatory processes like rheumatoid arthritis (RA) and cardiovascular diseases (CVD) the role of pteridines and chemokines in the immune response is of great interest. The objectives of the study were to obtain information about an involvement of specific chemokines on the inflammatory process in patients with RA and CVD with activated production of the pteridine neopterin. 113 patients with RA according to American College of Rheumatology criteria including 24.8% suffering from CVD were included in the study (23% male, 77% female). For the assessment of monocyte activation and chemotaxis as well as the proinflammatory influence of chemokines on B-cells and angiogenesis in RA, neopterin and the chemokines CCL2, CXCL13 and CX3CL1 were measured by radio- and enzyme-immunoassays in serum. Neopterin and chemokines were all found in high concentrations. Neopterin significantly correlated with all chemokines: CXCL13 (rs = 0.368; p <0.0001), CX3CL1 (rs = 0.241; p <0.02), CCL2 (rs = 231; p <0.02), demonstrating high neopterin levels in RA patients to have a strong association to the chemokine induced chemotaxis of B-cells (CXCL13) and mononuclear cells (CX3CL1, CCL2). Significantly elevated concentrations of neopterin and CXCL13 were seen in patients with RA plus CVD compared to RA without CVD (p <0.03). The high incidence of cardiovascular diseases in rheumatoid arthritis seems to be associated with enhanced mononuclear (neopterin, CCL2) and B-cell activation (CXCL13), and as a consequence increased immune system activity.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what can be restored in corrupted images, and like for all inverse problems, many potential solutions exist, and one of them must be chosen. Here, we propose DIVNOISING, a denoising approach based on fully convolutional variational autoencoders (VAEs), overcoming the problem of having to choose a single solution by predicting a whole distribution of denoised images. First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. Our approach is fully unsupervised, only requiring noisy images and a suitable description of the imaging noise distribution. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training. If desired, consensus predictions can be inferred from a set of DIVNOISING predictions, leading to competitive results with other unsupervised methods and, on occasion, even with the supervised state-of-the-art. DIVNOISING samples from the posterior enable a plethora of useful applications. We are piq showing denoising results for 13 datasets, piiq discussing how optical character recognition (OCR) applications can benefit from diverse predictions, and are piiiq demonstrating how instance cell segmentation improves when using diverse DIVNOISING predictions.
Ersatzwertbildung für stationäre Verkehrsdetektoren / A Replacement Value Procedure for Stationary TrafficDetectors

The correct and complete recording of traffic counts constitutes an important basis for controlling and monitoring traffic flows. To avoid disruptions of dependent systems during malfunctions or failures of measuring equipment, replacement value procedures are applied. In this paper, a clusterbased approach for identifying adequate replacement values for traffic counts from stationary traffic detectors is introduced. Statistical and spatial aspects are considered in the approach. Both absolute values as well as temporal dynamics of traffic counts could be determined with high accuracy.
Effective Abstract Reasoning with Dual-Contrast Network

As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate feature representation, we only use the ground truth answer of each question for model learning, aiming for an intelligent agent to have a strong learning capability with a small amount of supervision. Based on the RPM problem formulation, the correct answer filled into the missing entry of the third row/column has to best satisfy the same rules shared between the first two rows/columns. Thus we design a simple yet effective Dual-Contrast Network (DCNet) to exploit the inherent structure of RPM puzzles. Specifically, a rule contrast module is designed to compare the latent rules between the filled row/column and the first two rows/columns; a choice contrast module is designed to increase the relative differences between candidate choices. Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%. Further experiments on few training samples and model generalization also show the effectiveness of DCNet. Code is available at https://github.com/visiontao/dcnet.
Work Functions of Pristine and Heteroatom-Doped Graphenes under Different External Electric Fields: An ab Initio DFT Study

Low contact barrier electrodes and various field-emitting devices require a tunable work function, and graphene is a dream material for these applications. In this work, the theoretical investigations on the variation of the work function for monolayer graphene doped with different kinds of atoms from groups IIA–VIA of the Periodic Table are reported. The geometry, density of states, dipole moment, and work function of each heteroatom-doped graphene are calculated using ab initio density functional theory with a dispersion correction. The obtained formation energy of the heteroatom-doped graphenes is in the order: N < B < P < O < S < Si < As < Se < Ge < Al < Ga. The work functions without an electric field abide by a periodic law in terms of doping atoms except for O-doped graphene. The calculated results demonstrate that the work functions of all heteroatom-doped graphenes are a linear function of the applied external electric field intensity, and the slopes of the lines deviate from the ideal value to a.
Monte-Carlo Planning and Learning with Language Action Value Estimates

Interactive Fiction (IF) games provide a useful testbed for language-based reinforcement learning agents, posing significant challenges of natural language understanding, commonsense reasoning, and non-myopic planning in the combinatorial search space. Agents using standard planning algorithms struggle to play IF games due to the massive search space of language actions. Thus, languagegrounded planning is a key ability of such agents, since inferring the consequence of language action based on semantic understanding can drastically improve search. In this paper, we introduce Monte-Carlo planning with Language Action Value Estimates (MC-LAVE) that combines Monte-Carlo tree search with language-driven exploration. MC-LAVE concentrates search effort on semantically promising language actions using locally optimistic language value estimates, yielding a significant reduction in the effective search space of language actions. We then present a reinforcement learning approach built on MC-LAVE, which alternates between MC-LAVE planning and supervised learning of the selfgenerated language actions. In the experiments, we demonstrate that our method achieves new high scores in various IF games.
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

commonly used algorithms in protein learningwere specifically designed for protein data, and are able tocapture all relevant structural levels of a protein during learning. To fill this gap,we propose two new learning operators, specifically designed to process proteinstructures. First, we introduce a novel convolution operator that considers theprimary, secondary, and tertiary structure of a protein by usingn-D convolutionsdefined on both the Euclidean distance, as well as multiple geodesic distancesbetween the atoms in a multi-graph. Second, we introduce a set of hierarchicalpooling operators that enable multi-scale protein analysis. We further evaluate theaccuracy of our algorithms on common downstream tasks, where we outperformstate-of-the-art protein learning algorithms.
Auxiliary Task Update Decomposition: the Good, the Bad and the neutral

While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other tasks. In this case, careful consideration is needed to select tasks and model parameterizations such that updates from the auxiliary tasks actually help the primary task. We seek to alleviate this burden by formulating a model-agnostic framework that performs fine-grained manipulation of the auxiliary task gradients. We propose to decompose auxiliary updates into directions which help, damage or leave the primary task loss unchanged. This allows weighting the update directions differently depending on their impact on the problem of interest. We present a novel and efficient algorithm for that purpose and show its advantage in practice. Our method leverages efficient automatic differentiation procedures and randomized singular value decomposition for scalability. We show that our framework is generic and encompasses some prior work as particular cases. Our approach consistently outperforms strong and widely used baselines when leveraging out-of-distribution data for Text and Image classification tasks.
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability

Current methods for the interpretability of discriminative deep neural networks commonly rely on the model’s input-gradients, i.e., the gradients of the output logits w.r. t. the inputs. The common assumption is that these input-gradients contain information regarding pθ(y | x), the model’s discriminative capabilities, thus justifying their use for interpretability. However, in this work we show that these input-gradients can be arbitrarily manipulated as a consequence of the shiftinvariance of softmax without changing the discriminative function. This leaves an open question: if input-gradients can be arbitrary, why are they highly structured and explanatory in standard models? We investigate this by re-interpreting the logits of standard softmax-based classifiers as unnormalized log-densities of the data distribution and show that input-gradients can be viewed as gradients of a class-conditional density model pθ(x | y) implicit within the discriminative model. This leads us to hypothesize that the highly structured and explanatory nature of input-gradients may be due to the alignment of this class-conditional model pθ(x | y) with that of the ground truth data distribution pdata(x | y). We test this hypothesis by studying the effect of density alignment on gradient explanations. To achieve this density alignment, we use an algorithm called score-matching, and propose novel approximations to this algorithm to enable training large-scale models. Our experiments show that improving the alignment of the implicit density model with the data distribution enhances gradient structure and explanatory power while reducing this alignment has the opposite effect. This also leads us to conjecture that unintended density alignment in standard neural network training may explain the highly structured nature of input-gradients observed in practice. Overall, our finding that input-gradients capture information regarding an implicit generative model implies that we need to re-think their use for interpreting discriminative models.
When to Hold ’Em

Summary We consider the age-old question: how do I win my fortune at poker? First a disclaimer: this is a poor career choice. But thinking about it involves some great math! Of course, you need to know how good your hand is, which leads to some super-fun counting and probability, but you are still left with questions: Should I hold ’em? Should I fold ’em? Should I bet all my money? It can be pretty hard to decide! Here we get some insight into these questions by thinking about simplified games. Along the way we introduce some ideas from game theory, including the idea of Nash equilibrium.
A Commentary on Coordinating Genetic and Archaeology Studies of First Peoples

ABSTRACT Over the past few decades several genetic models have been published to explain the peopling of the Americas. Most of these models have not been fully reconciled with prior ones and with local and regional archaeological records. The implications of this and other concerns are discussed and solutions offered to better coordinate the joint approach of the two disciplines in the study of First Peoples and in archaeology and genetics in general.
Dark tourism segmentation by tourists' motivations for visiting earthquake sites in Nepal: implications for dark tourism

ABSTRACT This study explores visitors' experiences, post-visit attitudes, and benefits in relation to dark tourism destinations. A total of 464 responses were collected at seismic memorial sites, and the responses were grouped into different clusters by their motivations using hierarchical cluster analysis. The study results indicate that motivations for visiting dark tourism sites significantly affect tourists’ experiences and positive post-visit intentions and are mainly related to tourists’ demographic characteristics (e.g., nationality and traveling pattern). The findings advance our understanding and insights on tourists’ motivations for visiting dark tourism sites, enabling organizers and marketers to better manage and develop attractive dark tourism destinations.
Changes in EEG Brain Connectivity Caused by Short-Term BCI Neurofeedback-Rehabilitation Training: A Case Study

Background In combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns’ changes after a short-term rehabilitation training. Materials and Methods Using an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal’s Mu band power’s attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG’s Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain. Results Results showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group’s ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group’s Mu band power’s attenuation increased but not significantly (fitting line slope = 0.2, t-test value p > 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = −0.4, t-test value p > 0.05). In the ERP-based brain network analysis, the neurofeedback group’s network parameters were attenuated in all scales significantly (t-test value: p < 0.01); while the non-feedback group’s most network parameters didn’t change significantly (t-test value: p > 0.05). Conclusion The MI-BCI training’s short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.
SENP3-Mediated PPARγ2 DeSUMOylation in BM-MSCs Potentiates Glucocorticoid-Induced Osteoporosis by Promoting Adipogenesis and Weakening Osteogenesis

Glucocorticoid-induced osteoporosis (GIOP) is the most common secondary osteoporosis and reduced bone formation was the main pathological change in GIOP. Our previous studies have shown that there was an imbalance between adipogenic and osteogenic differentiation in GIOP BM-MSCs and peroxisome proliferator-activated receptor γ2 (PPARγ2) played a vital role in this disorders. Here, we reported that there was an increase in ROS level and SENP3 expression in Dex-induced osteoporotic BM-MSCs, and enhanced adipogenesis and weakened osteogenesis in osteoporotic BM-MSCs might be caused by upregulated SENP3. Then we found that SENP3 de-SUMOylated PPARγ2 on K107 site to potentiate adipogenesis and weaken osteogenesis. These results may provide new strategy and target in the clinical diagnosis and treatment of GIOP.
Identification and Characterization of Genes Encoding the Hydroxypyruvate Reductases in Chlamydomonas Reveal Their Distinct Roles in Photorespiration

Photorespiration plays an important role in maintaining normal physiological metabolism in higher plants and other oxygenic organisms, such as algae. The unicellular eukaryotic organism Chlamydomonas is reported to have a photorespiration system different from that in higher plants, and only two out of nine genes encoding photorespiratory enzymes have been experimentally characterized. Hydroxypyruvate reductase (HPR), which is responsible for the conversion of hydroxypyruvate into glycerate, is poorly understood and not yet explored in Chlamydomonas. To identify the candidate genes encoding hydroxypyruvate reductases in Chlamydomonas (CrHPR) and uncover their elusive functions, we performed sequence comparison, enzyme activity measurement, subcellular localization, and analysis of knockout/knockdown strains. Together, we identify five proteins to be good candidates for CrHPRs, all of which are detected with the activity of hydroxypyruvate reductase. CrHPR1, a nicotinamide adenine dinucleotide (NADH)-dependent enzyme in mitochondria, may function as the major component of photorespiration. Its deletion causes severe photorespiratory defects. CrHPR2 takes part in the cytosolic bypass of photorespiration as the compensatory pathway of CrHPR1 for the reduction of hydroxypyruvate. CrHPR4, with NADH as the cofactor, may participate in photorespiration by acting as the chloroplastidial glyoxylate reductase in glycolate-quinone oxidoreductase system. Therefore, the results reveal that CrHPRs are far more complex than previously recognized and provide a greatly expanded knowledge base for studies to understand how CrHPRs perform their functions in photorespiration. These will facilitate both modification of photorespiration and genetic engineering for crop improvement by synthetic biology.
Hydrogen, Bicarbonate, and Their Associated Exchangers in Cell Volume Regulation

Cells lacking a stiff cell wall, e.g., mammalian cells, must actively regulate their volume to maintain proper cell function. On the time scale that protein production is negligible, water flow in and out of the cell determines the cell volume variation. Water flux follows hydraulic and osmotic gradients; the latter is generated by various ion channels, transporters, and pumps in the cell membrane. Compared to the widely studied roles of sodium, potassium, and chloride in cell volume regulation, the effects of proton and bicarbonate are less understood. In this work, we use mathematical models to analyze how proton and bicarbonate, combined with sodium, potassium, chloride, and buffer species, regulate cell volume upon inhibition of ion channels, transporters, and pumps. The model includes several common, widely expressed ion transporters and focuses on obtaining generic outcomes. Results show that the intracellular osmolarity remains almost constant before and after cell volume change. The steady-state cell volume does not depend on water permeability. In addition, to ensure the stability of cell volume and ion concentrations, cells need to develop redundant mechanisms to maintain homeostasis, i.e., multiple ion channels or transporters are involved in the flux of the same ion species. These results provide insights for molecular mechanisms of cell volume regulation with additional implications for water-driven cell migration.