Miracle-dz/UniFAR-FTUs huggingface dataset:
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones for decentralized execution. VDN and QMIX address only a fraction of factorizable MARL tasks due to their structural constraint in factorization such as additivity and monotonicity. In this paper, we propose a new factorization method for MARL, QTRAN, which is free from such structural constraints and takes on a new approach to transforming the original joint action-value function into an easily factorizable one, with the same optimal actions. QTRAN guarantees more general factorization than VDN or QMIX, thus covering a much wider class of MARL tasks than does previous methods. Our experiments for the tasks of multi-domain Gaussian-squeeze and modified predator-prey demonstrate QTRAN's superior performance with especially larger margins in games whose payoffs penalize non-cooperative behavior more aggressively.
Effect of Chitosan Dispersion and Microparticles on Older Streptococcus mutans Biofilms

(1) Background: The effectiveness of chitosan to improve the action of antimicrobial compounds against planktonic bacteria and young biofilms has been widely investigated in Dentistry, where the biofilm lifecycle is a determining factor for the success of antibacterial treatment. In the present study, mature Streptococcus mutans biofilms were treated with chitosan dispersion (CD) or chitosan microparticles (CM). (2) Methods: CD at 0.25% and 1% were characterized by texture analysis, while CD at 2% was spray-dried to form CM, which were characterized with respect to particle size distribution, zeta potential, and morphology. After determining the minimum inhibitory and bactericidal concentrations, S. mutans biofilms were grown on glass slides exposed 8×/day to 10% sucrose and 2×/day to CD or CM at 0.25% and 1%. Biofilm viability and acidogenicity were determined, using appropriate control groups for each experiment. (3) Results: CD had high viscosity and CM were spherical, with narrow size distribution and positive zeta potential. CM affected bacterial viability and acidogenicity in mature S. mutans biofilms more strongly than CD, especially at 1%. (4) Conclusions: Both chitosan forms exerted antimicrobial effect against mature S. mutans biofilms. CM at 1% can reduce bacterial viability and acidogenicity more effectively than CD at 1%, and thereby be more effective to control the growth of mature biofilms in vitro.
Wearable Bio-Signal(PPG)-Based Personal Authentication Method Using Random Forest and Period Setting Considering the Feature of PPG Signals

A study regarding personal authentication based on PPG has been conducted using the Random Forest algorithm. In order to ensure correct authentication, data features must be consistent. This consistency is provided through the normalization of the PPG signal using maximum-minimum normalization and spline interpolation. The threshold is set by using normalized data and the highest value among the points above the threshold is set as the peak of the period. After establishing candidates of valley with values below the threshold, a shifted period is calculated by designating the last valley next to the following peak to the valley of the period. In order to reduce errors in the one period PPG data, the data is averaged after overlapping. A discrete cosine transform (DCT) is used to extract features from the preprocessed data. Thus, the extracted features are used as the input variables for machine learning techniques such as Decision Tree, KNN, Random Forest. The accuracy of these algorithms is 93%, 98%, and 99% respectively.
Multiscale simulation of the effects of atrioventricular block and valve diseases on heart performance

A new mathematical model of the cardiovascular system is proposed. The left ventricle is described by an axisymmetric multiscale model where myocardium is treated as an incompressible transversely isotropic medium with a realistic distribution of fibre orientation. Active tension and its regulation by Ca2+ ions are described by our recent kinetic model. A lumped parameter model is used for the simulation of blood circulation, in which the left and right atria and the right ventricle are described by a system of ordinary differential equations for active pressure‐volume relationships. The stress and strain of the left ventricle myocardium were calculated by the finite element method implemented by the authors. The changes in the haemodynamics upon changes in preload of a healthy heart, upon physical exercise, and in case of atrioventricular block with different types of arrhythmias were simulated. To simulate the effect of stenosis or regurgitation of the aortic or mitral valves, the hydraulic and inertial flow resistances of the heart valves were set as functions of their orifice areas. The model reproduced a number of phenomena observed in clinical practice, including the classification of the severity of valve disease.
Hierarchically Structured Meta-learning

In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning, we propose a hierarchically structured meta-learning (HSML) algorithm that explicitly tailors the transferable knowledge to different clusters of tasks. Inspired by the way human beings organize knowledge, we resort to a hierarchical task clustering structure to cluster tasks. As a result, the proposed approach not only addresses the challenge via the knowledge customization to different clusters of tasks, but also preserves knowledge generalization among a cluster of similar tasks. To tackle the changing of task relationship, in addition, we extend the hierarchical structure to a continual learning environment. The experimental results show that our approach can achieve state-of-the-art performance in both toy-regression and few-shot image classification problems.
Intermediate mass black holes in globular clusters: effects on jerks and jounces of millisecond pulsars

Globular clusters may host intermediate mass black holes (IMBHs) at their centres. Here we propose a new method for their identification using millisecond pulsars (MSPs) as probes. We show that measuring the first (jerk) and second (jounce) derivatives of the accelerations of an ensemble of MSPs will let us infer the presence of an IMBH in a globular cluster better than measuring the sole accelerations. We test this concept by simulating a set of star clusters with and without a central IMBH to extract the distributions of the stellar jerks and jounces. We then apply this technique to the ensemble of MSPs in the Galactic globular cluster 47 Tucanae. Current timing observations are insufficient to constrain the presence of an IMBH and can only be used to pose upper limits on its mass. But, with few more years of observations it will be possible to test for the presence of a central IMBH with mass smaller than $\sim$ 1000 M$_{\odot}$. We conclude that jerks and jounces help significantly in reducing the upper limit of the mass of IMBHs in Galactic globular clusters.
X-Ray Binary Luminosity Function Scaling Relations for Local Galaxies Based on Subgalactic Modeling

We present new Chandra constraints on the X-ray luminosity functions (XLFs) of X-ray binary (XRB) populations, as well as their scaling relations, for a sample of 38 nearby galaxies (D = 3.4–29 Mpc). Our galaxy sample is drawn primarily from the Spitzer Infrared Nearby Galaxies Survey (SINGS) and contains a wealth of Chandra (5.8 Ms total) and multiwavelength data, allowing for star formation rates (SFRs) and stellar masses (M⋆) to be measured on subgalactic scales. We divided the 2478 X-ray-detected sources into 21 subsamples in bins of specific SFR (sSFR ≡ SFR/M⋆) and constructed XLFs. To model the XLF dependence on sSFR, we fit a global XLF model, containing contributions from high-mass XRBs (HMXBs), low-mass XRBs (LMXBs), and background sources from the cosmic X-ray background that respectively scale with SFR, M⋆, and sky area. We find an HMXB XLF that is more complex in shape than previously reported and an LMXB XLF that likely varies with sSFR, potentially due to an age dependence. When applying our global model to XLF data for each individual galaxy, we discover a few galaxy XLFs that significantly deviate from our model beyond statistical scatter. Most notably, relatively low-metallicity galaxies have an excess of HMXBs above ≈1038 erg s−1, and elliptical galaxies that have relatively rich populations of globular clusters (GCs) show excesses of LMXBs compared to the global model. Additional modeling of how the XRB XLF depends on stellar age, metallicity, and GC specific frequency is required to sufficiently characterize the XLFs of galaxies.
A Flexible Multi-Facility Capacity Expansion Problem with Risk Aversion

This paper studies flexible multi-facility capacity expansion with risk aversion. In this setting, the decision maker can periodically expand the capacity of facilities given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem. We express risk aversion in this problem through conditional value-at-risk (CVaR), and we formulate a mean-CVaR objective. To solve the multi-stage problem, we optimize over decision rules. In particular, we approximate the full policy space of the problem with a tractable family of if-then policies. Subsequently, a decomposition algorithm is proposed to optimize the decision rule. This algorithm decomposes the model over scenarios and it updates solutions via the subgradients of the recourse function. We demonstrate that this algorithm can quickly converge to high-performance policies. To illustrate the practical effectiveness of this method, a case study on the waste-to-energy system in Singapore is presented. These simulation results show that by adjusting the weight factor of the objective function, decision makers are able to trade off between a risk-averse policy that has a higher expected cost but a lower value-at-risk, and a risk-neutral policy that has a lower expected cost but a higher value-at-risk risk.
Entity-Relation Extraction as Multi-Turn Question Answering

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and elations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.6 (+1.2), 60.3 (+0.7) and 69.2 (+1.4), respectively. Additionally, we construct and will release a newly developed dataset RESUME, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.
Nuclear FAM21 participates in NF-&kgr;B-dependent gene regulation in pancreatic cancer cells

ABSTRACT The pentameric WASH complex is best known for its role in regulating receptor trafficking from retromer-rich endosomal subdomains. FAM21 functions to stabilize the WASH complex through its N-terminal head domain and localizes it to endosomes by directly binding the retromer through its extended C-terminal tail. Herein, we used affinity purification combined with mass spectrometry to identify additional FAM21-interacting proteins. Surprisingly, multiple components of the nuclear factor &kgr;B (NF-&kgr;B) pathway were identified, including the p50 and p65 (RelA) NF-&kgr;B subunits. We show that FAM21 interacts with these components and regulates NF-&kgr;B-dependent gene transcription at the level of p65 chromatin binding. We further demonstrate that FAM21 contains a functional monopartite nuclear localization signal sequence (NLS) as well as a CRM1/exportin1-dependent nuclear export signal (NES), both of which work jointly with the N-terminal head domain and C-terminal retromer recruitment domain to regulate FAM21 cytosolic and nuclear subcellular localization. Finally, our findings indicate that FAM21 depletion sensitizes pancreatic cancer cells to gemcitabine and 5-fluorouracil. Thus, FAM21 not only functions as an integral component of the cytoplasmic WASH complex, but also modulates NF-&kgr;B gene transcription in the nucleus.
The Galactic spiral structure as revealed by O- and early B-type stars

We investigate the morphology and kinematics of the Galactic spiral structure based on a new sample of O- and early B-type stars. We select 6,858 highly confident OB star candidates from the combined data of the VST Photometric H$\alpha$ Survey Data Release 2 (VPHAS+ DR2) and the Gaia Data Release 2 (Gaia DR2). Together with the O-B2 stars from the literature, we build a sample consisting of 14,880 O- and early B-type stars, all with Gaia parallax uncertainties smaller than 20 per cent. The new sample, hitherto the largest one of O- and early B-type stars with robust distance and proper motion estimates, covers the Galactic plane of distances up to $\sim$ 6 kpc from the Sun. The sample allows us to examine the morphology of the Scutum, Sagittarius, Local and Perseus Arms in great detail. The spiral structure of the Milky Way as traced by O- and early B-type stars shows flocculent patterns. Accurate structure parameters, as well as the means and dispersions of the vertical velocity distributions of the individual Spiral Arms are presented.
An Improved Self-supervised GAN via Adversarial Training

We propose to improve unconditional Generative Adversarial Networks (GAN) by training the self-supervised learning with the adversarial process. In particular, we apply self-supervised learning via the geometric transformation on input images and assign the pseudo-labels to these transformed images. (i) In addition to the GAN task, which distinguishes data (real) versus generated (fake) samples, we train the discriminator to predict the correct pseudo-labels of real transformed samples (classification task). Importantly, we find out that simultaneously training the discriminator to classify the fake class from the pseudo-classes of real samples for the classification task will improve the discriminator and subsequently lead better guides to train generator. (ii) The generator is trained by attempting to confuse the discriminator for not only the GAN task but also the classification task. For the classification task, the generator tries to confuse the discriminator recognizing the transformation of its output as one of the real transformed classes. Especially, we exploit that when the generator creates samples that result in a similar loss (via cross-entropy) as that of the real ones, the training is more stable and the generator distribution tends to match better the data distribution. When integrating our techniques into a state-of-the-art Auto-Encoder (AE) based-GAN model, they help to significantly boost the model's performance and also establish new state-of-the-art Frechet Inception Distance (FID) scores in the literature of unconditional GAN for CIFAR-10 and STL-10 datasets.
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining

Removing rain effects from an image automatically has many applications such as autonomous driving, drone piloting and photo editing and still draws the attention of many people. Traditional methods use heuristics to handcraft various priors to remove or separate the rain effects from an image. Recently end-to-end deep learning based deraining methods have been proposed to offer more flexibility and effectiveness. However, they tend not to obtain good visual effect when encountered images with heavy rain. Heavy rain brings not only rain streaks but also haze-like effect which is caused by the accumulation of tiny raindrops. Different from previous deraining methods, in this paper we model rainy images with a new rain model to remove not only rain streaks but also haze-like effect. Guided by our model, we design a two-branch network to learn its parameters. Then, an SPP structure is jointly trained to refine the results of our model to control the degree of removing the haze-like effect flexibly. Besides, a subnetwork which can localize the rainy pixels is proposed to guide the training of our network. Extensive experiments on several datasets show that our method outperforms the state-of-the-art in both objectives assessments and visual quality.
Multivariate Ranks and Quantiles using Optimal Transportation and Applications to Goodness-of-fit Testing

In this paper we study multivariate ranks and quantiles, defined using the theory of optimal transportation, and build on the work of Chernozhukov et al. (2017) and del Barrio et al. (2018). We study the characterization, computation and properties of the multivariate rank and quantile functions and their empirical counterparts. We derive the uniform consistency of these empirical estimates to their population versions, under certain assumptions. In fact, we prove a Glivenko-Cantelli type theorem that shows the asymptotic stability of the empirical rank map in any direction. We provide easily verifiable sufficient conditions that guarantee the existence of a continuous and invertible population quantile map --- a crucial assumption for our main consistency result. We provide a framework to derive the local uniform rate of convergence of the estimated quantile and ranks functions and explicitly illustrate the technique in a special case. Further, we propose multivariate (nonparametric) goodness-of-fit tests --- a two-sample test and a test for mutual independence --- based on our notion of quantiles and ranks. Asymptotic consistency of these tests are also shown. Additionally, we derive many properties of (sub)-gradients of convex functions and their Legendre-Fenchel duals that may be of independent interest.
The Type II Secretion System and Its Ubiquitous Lipoprotein Substrate, SslE, Are Required for Biofilm Formation and Virulence of Enteropathogenic Escherichia coli

ABSTRACT Enteropathogenic Escherichia coli (EPEC) is a major cause of diarrhea in infants in developing countries. We have identified a functional type II secretion system (T2SS) in EPEC that is homologous to the pathway responsible for the secretion of heat-labile enterotoxin by enterotoxigenic E. coli. The wild-type EPEC T2SS was able to secrete a heat-labile enterotoxin reporter, but an isogenic T2SS mutant could not. We showed that the major substrate of the T2SS in EPEC is SslE, an outer membrane lipoprotein (formerly known as YghJ), and that a functional T2SS is essential for biofilm formation by EPEC. T2SS and SslE mutants were arrested at the microcolony stage of biofilm formation, suggesting that the T2SS is involved in the development of mature biofilms and that SslE is a dominant effector of biofilm development. Moreover, the T2SS was required for virulence, as infection of rabbits with a rabbit-specific EPEC strain carrying a mutation in either the T2SS or SslE resulted in significantly reduced intestinal colonization and milder disease.
Diabetic neuropathy in children and adolescents with type 1 diabetes mellitus: Diagnosis, pathogenesis, and associated genetic markers

Diabetic neuropathy (DN) is a common long‐term complication of type 1 (T1D) and type 2 (T2D) diabetes mellitus, with significant morbidity and mortality. DN is defined as impaired function of the autonomic and/or peripheral nervous system, often subclinical, particularly in children and adolescents with T1D. Nerve conduction studies (NCS) and skin biopsies are considered gold‐standard methods in the assessment of DN. Multiple environmental and genetic factors are involved in the pathogenesis of DN. Specifically, the role of metabolic control and glycemic variability is of paramount importance. A number of recently identified genes, including the AKR1B1, VEGF, MTHFR, APOE, and ACE genes, contribute significantly in the pathogenesis of DN. These genes may serve as biomarkers to predict future DN development or treatment response. In addition, they may serve as the basis for the development of new medications or gene therapy. In this review, the diagnostic evaluation, pathogenesis, and associated genetic markers of DN in children and adolescents with T1D are presented and discussed.
Mitochondrial Transport and Turnover in the Pathogenesis of Amyotrophic Lateral Sclerosis

Neurons are high-energy consuming cells, heavily dependent on mitochondria for ATP generation and calcium buffering. These mitochondrial functions are particularly critical at specific cellular sites, where ionic currents impose a large energetic burden, such as at synapses. The highly polarized nature of neurons, with extremely large axoplasm relative to the cell body, requires mitochondria to be efficiently transported along microtubules to reach distant sites. Furthermore, neurons are post-mitotic cells that need to maintain pools of healthy mitochondria throughout their lifespan. Hence, mitochondrial transport and turnover are essential processes for neuronal survival and function. In neurodegenerative diseases, the maintenance of a healthy mitochondrial network is often compromised. Numerous lines of evidence indicate that mitochondrial impairment contributes to neuronal demise in a variety of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), where degeneration of motor neurons causes a fatal muscle paralysis. Dysfunctional mitochondria accumulate in motor neurons affected by genetic or sporadic forms of ALS, strongly suggesting that the inability to maintain a healthy pool of mitochondria plays a pathophysiological role in the disease. This article critically reviews current hypotheses on mitochondrial involvement in the pathogenesis of ALS, focusing on the alterations of mitochondrial axonal transport and turnover in motor neurons.