Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Inference for Spectral Mixture Kernel

A spectral mixture (SM) kernel is a flexible kernel used to model any stationary covariance function. Although it is useful in modeling data, the learning of the SM kernel is generally difficult because optimizing a large number of parameters for the SM kernel typically induces an over-fitting, particularly when a gradient-based optimization is used. Also, a longer training time is required. To improve the training, we propose an approximate Bayesian inference for the SM kernel. Specifically, we employ the variational distribution of the spectral points to approximate SM kernel with a random Fourier feature. We optimize the variational parameters by applying a sampling-based variational inference to the derived evidence lower bound (ELBO) estimator constructed from the approximate kernel. To improve the inference, we further propose two additional strategies: (1) a sampling strategy of spectral points to estimate the ELBO estimator reliably and thus its associated gradient, and (2) an approximate natural gradient to accelerate the convergence of the parameters. The proposed inference combined with two strategies accelerates the convergence of the parameters and leads to better optimal parameters.
Altruism and anxiety: Engagement with online community support initiatives (OCSIs) during Covid-19 lockdown in the UK and Ireland

Given concerns about mental health during periods of Covid-19 lockdown, it important to understand how engagement with online Covid-19 related material can affect mood. In the UK and Ireland, online community support initiatives (OCSIs) have emerged to help people manage their lives. Yet, little is known about how people engaged with these or whether they influenced subsequent mood. We conducted surveys to explore how people in the UK and Ireland engaged with OCSIs, and found that 70% did so to offer support (e.g. to provide company). Those who did so reported feeling significantly calmer afterwards, those who engaged for general concerns (e.g. in response to anti-social behaviour) reported feeling significantly more anxious afterwards, but there was no difference in reported mood for those who engaged for other reasons (e.g. to share experiences or views). Thus, engaging with an OCSI for altruistic purposes might help to make people feel calmer.
Dutch General Public Reaction on Governmental COVID-19 Measures and Announcements in Twitter Data

Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. While the staying-at-home policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment. In this paper, we collect streaming data using the Twitter API starting from the COVID-19 outbreak in the Netherlands in February 2020, and track Dutch general public reactions on governmental measures and announcements. We provide temporal analysis of tweet frequency and public sentiment over the past four months. We also identify public attitudes towards the Dutch policy on wearing face masks in a case study. By presenting those preliminary results, we aim to provide visibility into the social media discussions around COVID-19 to the general public, scientists and policy makers. The data collection and analysis will be updated and expanded over time.
Synchronization Stability Analysis and Enhancement of Grid-Tied Multi-Converter Systems

Synchronization instability of grid-tied converters during grid faults is a serious concern. For phase-locked loop (PLL) synchronized converters, synchronization stability during grid faults requires that PLL can resynchronize with the power grid. It is of great significance for meeting grid code specifications such as reactive current support. However, the synchronization in the context of high-impedance weak grid is not always readily achieved. On one hand, the terminal voltage of converters is changeable due to being susceptible to the output current. One the other hand, there is interaction between multiple converters interconnected to the grid. Therefore, loss of synchronism (LOS) of converters during grid faults likely occurs. Previous research has investigated the LOS issue of single-converter infinite-bus (SCIB) system, but mostly overlooked the interaction between multiple converters. Previous efforts to address the issue of LOS are also in doubt for multi-converter systems. This paper develops a multi-converter infinite-bus (MCIB) system model and analyzes the root cause of LOS with considering multi-converter interaction during grid faults. Moreover, a feedforward compensated PLL (FFC-PLL) method applicable for multi-converter systems is developed to address the LOS issue. Simulations results verify the effectiveness of the method.
Synthesis and Characterization of Thrombin Conjugated γ‐Fe2O3 Magnetic Nanoparticles for Hemostasis

Thrombin is the final protease produced in the clotting pathways. Thrombin has been used in the clinic more than six decades for topical hemostasis and wound management. In human plasma the half‐life of thrombin is shorter than 15 seconds due to close control by inhibitors. In order to stabilize thrombin, this enzyme was conjugated covalently and physically to γ‐Fe2O3 magnetic nanoparticles. The physical conjugation was accomplished through adsorption of thrombin to BSA coating on the nanoparticles. The coagulant activity of the covalently bound thrombin was significantly lower than that of the physically adsorbed thrombin. Leakage of the physically bound thrombin into PBS containing 4% HSA was negligible. The physical conjugation of thrombin onto the nanoparticles stabilized the thrombin against its major inhibitor antithrombin III and improved its storage stability. At optimal CaCl2 concentration, the clotting time by the bound thrombin is shorter than that of the free enzyme. This novel conjugated thrombin may be an efficient candidate for topical hemostasis and wound healing.
The imprint of dark subhaloes on the circumgalactic medium

The standard model of cosmology, the Λ cold dark matter (ΛCDM) model, robustly predicts the existence of a multitude of dark matter ‘subhaloes’ around galaxies like the Milky Way. A wide variety of observations have been proposed to look for the gravitational effects such subhaloes would induce in observable matter. Most of these approaches pertain to the stellar or cool gaseous phases of matter. Here we propose a new approach, which is to search for the perturbations that such dark subhaloes would source in the warm/hot circumgalactic medium (CGM) around normal galaxies. With a combination of analytic theory, carefully controlled high-resolution idealized simulations, and full cosmological hydrodynamical simulations (the artemis simulations), we calculate the expected signal and how it depends on important physical parameters (subhalo mass, CGM temperature, and relative velocity). We find that dark subhaloes enhance both the local CGM temperature and density and, therefore, also the pressure. For the pressure and density, the fluctuations can vary in magnitude from tens of per cent (for subhaloes with Msub = 1010 M⊙) to a few per cent (for subhaloes with Msub = 108 M⊙), although this depends strongly on the CGM temperature. The subhaloes also induce fluctuations in the velocity field ranging in magnitude from a few km s−1 up to 25 km s−1. We propose that X-ray, Sunyaev–Zel’dovich effect, radio dispersion measure, and quasar absorption line observations can be used to measure these fluctuations and place constraints on the abundance and distribution of dark subhaloes, thereby placing constraints on the nature of dark matter.
Anthropogenic stress may affect aquatic hyphomycete diversity more than leaf decomposition in a low-order stream

Fungal diversity and microbial decomposition of leaf litter were examined in a low-order stream at a reference and an impacted site. The latter, 10 km downstream of the reference site, has high nutrient loads from domestic sewage and agriculture, and increased heavy metal levels in the stream water and sediments. At the polluted site aquatic hyphomycete diversity and sporulation were reduced, whereas fungal biomass and leaf decomposition rate were not. Articulospora tetracladia and Flagellospora sp. were the dominant species at the reference site, and Dimorphospora foliicola was dominant at the polluted site. Biomass of bacteria was higher at the polluted site, but only approached 10 % of fungal biomass, indicating that fungi remained the main microbial decomposers. In addition, the results suggest that aquatic hyphomycete communities may respond to stress according to the redundancy model, in which overall function remains stable because increased biomasses of tolerant species compensate for the loss of sensitive species.
MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Designing deep neural networks is an art that often involves an expensive search over candidate architectures. To overcome this for recurrent neural nets (RNNs), we establish a connection between the hidden state dynamics in an RNN and gradient descent (GD). We then integrate momentum into this framework and propose a new family of RNNs, called {\em MomentumRNNs}. We theoretically prove and numerically demonstrate that MomentumRNNs alleviate the vanishing gradient issue in training RNNs. We study the momentum long-short term memory (MomentumLSTM) and verify its advantages in convergence speed and accuracy over its LSTM counterpart across a variety of benchmarks, with little compromise in computational or memory efficiency. We also demonstrate that MomentumRNN is applicable to many types of recurrent cells, including those in the state-of-the-art orthogonal RNNs. Finally, we show that other advanced momentum-based optimization methods, such as Adam and Nesterov accelerated gradients with a restart, can be easily incorporated into the MomentumRNN framework for designing new recurrent cells with even better performance. The code is available at \url{this https URL}.
Robust Optimization and Inference on Manifolds

We propose a robust and scalable procedure for general optimization and inference problems on manifolds leveraging the classical idea of `median-of-means' estimation. This is motivated by ubiquitous examples and applications in modern data science in which a statistical learning problem can be cast as an optimization problem over manifolds. Being able to incorporate the underlying geometry for inference while addressing the need for robustness and scalability presents great challenges. We address these challenges by first proving a key lemma that characterizes some crucial properties of geometric medians on manifolds. In turn, this allows us to prove robustness and tighter concentration of our proposed final estimator in a subsequent theorem. This estimator aggregates a collection of subset estimators by taking their geometric median over the manifold. We illustrate bounds on this estimator via calculations in explicit examples. The robustness and scalability of the procedure is illustrated in numerical examples on both simulated and real data sets.
DFENet: Deep Feature Enhancement Network for Accurate Calculation of Instantaneous Wave-Free Ratio

Accurate iFR calculation can provide important clinical information for intracoronary functional assessment without administration of adenosine, which needs to locate object points in the pressure waveforms: peak, the dichrotic notch and the pressure nadir at the end of diastole. We propose a DFENet that is capable of locating object points to calculate iFR accurately. We first design a SFRA into DFENet with the idea of DenseNet. To avoid overfitting when dealing with sparse signals, we set appropriate number of network layers, growth rate of dense blocks and compression rate of transition blocks in 1D DenseNet. Then, we introduce a feature enhancement mechanism named 1D SE block for enhancing inconspicuous but vital features from SFRA, which guides DFENet to focus on these important features via feature recalibration. Finally, we prove an effective interaction mode between SFRA and 1D SE block to locate object points accurately. Adequate experiments demonstrate that DFENet reaches a high accuracy of 94.22%, error of 5.6 on object point localization of 1D pressure waveforms that include 1457 samples from 100 subjects via a cross-validation of Leave-One-Out. Comparison experiment demonstrates that the accuracy of DFENet exceeds other state-of-the-art methods by 3.35%, and ablation experiment demonstrates that the accuracy of SFRA and cSE exceed the other variations by 6.63% and 2.56% respectively. Importantly, we reveal how the DFENet enhance inconspicuous but vital feature by applying gradient-weighted class activation maps. DFENet can locate object points accurately, which is applicable to other signal processing tasks, especially in health sensing.
Ambient temperature and algal prey type affect essential fatty acid incorporation and trophic upgrading in a herbivorous marine copepod

The essential fatty acids (EFA) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are critical nutrients for all organisms, and the temperature sensitivity of their trophic transfer in marine systems is of concern because of rising ocean temperatures. Laboratory-reared copepodites of the marine calanoid Calanus finmarchicus were used to test the effects of temperature (at 6°C, 12°C and increasing temperature stress) and prey type (the dinoflagellate Heterocapsa triquetra and the diatom Thalassiosira weissflogii) on the extent and efficiency of dietary EPA and DHA incorporation from phytoplankton to copepods in a set of feeding experiments using 13C labelling. Temperature was a significant determinant of C. finmarchicus copepodites' EFA incorporation and gross growth efficiency, defined as the fraction of ingested EFA retained in copepod tissue. Ingestion and incorporation of both EFA were higher at warmer temperature, except in the case of DHA in copepods feeding on diatoms. DHA-associated growth efficiency was higher at the higher temperature for copepodites consuming the dinoflagellate, but temperature-related variation in algal EFA content was also a predictive factor. Moreover, our results strongly suggest that copepodites are capable of synthesizing EPA when consuming an EPA-depleted diet. Our study implies that the copepod link of marine food webs is resilient in terms of EFA transfer when confronted with alterations of ambient temperature and prey type availability. Measurements presented here are critical for estimating how EFA transfer dynamics respond to intra- and interannual environmental variability. This article is part of the theme issue ‘The next horizons for lipids as ‘trophic biomarkers’: evidence and significance of consumer modification of dietary fatty acids’.
The potential of fatty acid isotopes to trace trophic transfer in aquatic food-webs

Compound-specific isotope analyses (CSIA) of fatty acids (FA) constitute a promising tool for tracing energy flows in food-webs. However, past applications of FA-specific carbon isotope analyses have been restricted to a relatively coarse food-source separation and mainly quantified dietary contributions from different habitats. Our aim was to evaluate the potential of FA-CSIA to provide high-resolution data on within-system energy flows using algae and zooplankton as model organisms. First, we investigated the power of FA-CSIA to distinguish among four different algae groups, namely cyanobacteria, chlorophytes, haptophytes and diatoms. We found substantial within-group variation but also demonstrated that δ13C of several FA (e.g. 18:3ω3 or 18:4ω3) differed among taxa, resulting in group-specific isotopic fingerprints. Second, we assessed changes in FA isotope ratios with trophic transfer. Isotope fractionation was highly variable in daphnids and rotifers exposed to different food sources. Only δ13C of nutritionally valuable poly-unsaturated FA remained relatively constant, highlighting their potential as dietary tracers. The variability in fractionation was partly driven by the identity of food sources. Such systematic effects likely reflect the impact of dietary quality on consumers' metabolism and suggest that FA isotopes could be useful nutritional indicators in the field. Overall, our results reveal that the variability of FA isotope ratios provides a substantial challenge, but that FA-CSIA nevertheless have several promising applications in food-web ecology. This article is part of the theme issue ‘The next horizons for lipids as ‘trophic biomarkers’: evidence and significance of consumer modification of dietary fatty acids’.
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots’ states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent’s steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google. com/view/hybridmrca.
Bioinformatics Analysis of Hsp20 Sequences in Proteobacteria

Heat shock proteins are a class of molecular chaperones that can be found in nearly all organisms from Bacteria, Archaea and Eukarya domains. Heat shock proteins experience increased transcription during periods of heat induced osmotic stress and are involved in protein disaggregation and refolding as part of a cell's danger signaling cascade. Heat shock protein, Hsp20 is a small molecular chaperone that is approximately 20kDa in weight and is hypothesized to prevent aggregation and denaturation. Hsp20 can be found in several strains of Proteobacteria, which comprises the largest phyla of the Bacteria domain and also contains several medically significant bacterial strains. Genomic analyses were performed to determine a common evolutionary pattern among Hsp20 sequences in Proteobacteria. It was found that Hsp20 shared a common ancestor within and among the five subclasses of Proteobacteria. This is readily apparent from the amount of sequence similarities within and between Hsp20 protein sequences as well as phylogenetic analysis of sequences from proteobacterial and non-proteobacterial species.
Lipid‐suppressed and tissue‐fraction corrected metabolic distributions in human central brain structures using 2D 1H magnetic resonance spectroscopic imaging at 7 T

Magnetic resonance spectroscopic imaging (MRSI) has the potential to add a layer of understanding of the neurobiological mechanisms underlying brain diseases, disease progression, and treatment efficacy. Limitations related to metabolite fitting of low SNR data, signal variations due to partial volume effects, acquisition and extra-cranial lipid artefacts, along with clinically relevant aspects such as scan-time constraints, are among the factors that hinder the widespread implementation of in vivo MRSI. The aim of this work was to address these factors and to develop an acquisition, reconstruction and post-processing pipeline to derive lipid suppressed metabolite values based on Free Induction Decay (FID-MRSI) measurements made using a 7 tesla MR scanner. Anatomical images were used to perform high-resolution (1mm3) partial-volume correction to account for grey matter, white matter and cerebral-spinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the MNI standard atlas facilitated the creation of high-resolution average metabolite maps of several clinically relevant metabolites in central brain regions, while accounting for macromolecular distributions. Reported metabolite values include glutamate, choline, (phospo)creatine, myo-inositol, glutathione, N-acetyl aspartyl glutamate(and glutamine) and N-acetyl aspartate. MNI-registered average metabolite maps facilitate group-based analysis; thus offering the possibility to mitigate uncertainty in variable MRSI.
Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in “information spreading” (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.