Miracle-dz/UniFAR-FTUs huggingface dataset:
Q-Learning for Mean-Field Controls

Multi-agent reinforcement learning (MARL) has been applied to many challenging problems including two-team computer games, autonomous drivings, and real-time biddings. Despite the empirical success, there is a conspicuous absence of theoretical study of different MARL algorithms: this is mainly due to the curse of dimensionality caused by the exponential growth of the joint state-action space as the number of agents increases. Mean-field controls (MFC) with infinitely many agents and deterministic flows, meanwhile, provide good approximations to $N$-agent collaborative games in terms of both game values and optimal strategies. In this paper, we study the collaborative MARL under an MFC approximation framework: we develop a model-free kernel-based Q-learning algorithm (CDD-Q) and show that its convergence rate and sample complexity are independent of the number of agents. Our empirical studies on MFC examples demonstrate strong performances of CDD-Q. Moreover, the CDD-Q algorithm can be applied to a general class of Markov decision problems (MDPs) with deterministic dynamics and continuous state-action space.
Dielectric Nanoantennas for Strain Engineering in Atomically Thin Two-Dimensional Semiconductors

Atomically thin two-dimensional semiconducting transition metal dichalcogenides (TMDs) can withstand large levels of strain before their irreversible damage occurs. This unique property offers a promising route for control of the optical and electronic properties of TMDs, for instance by depositing them on nano-structured surfaces, where position-dependent strain can be produced on the nano-scale. Here, we demonstrate strain-induced modifications of the optical properties of mono- and bilayer TMD WSe$_2 $ placed on photonic nano-antennas made from gallium phosphide (GaP). Photoluminescence (PL) from the strained areas of the TMD layer is enhanced owing to the efficient coupling with the confined optical mode of the nano-antenna. Thus, by following the shift of the PL peak, we deduce the changes in the strain in WSe$_2$ deposited on the nano-antennas of different radii. In agreement with the presented theory, strain up to $\approx 1.4 \%$ is observed for WSe$_2$ monolayers. We also estimate that $>3\%$ strain is achieved in bilayers, accompanied with the emergence of a direct bandgap in this normally indirect-bandgap semiconductor. At cryogenic temperatures, we find evidence of the exciton confinement in the most strained nano-scale parts of the WSe$_2$ layers, as also predicted by our theoretical model. Our results, of direct relevance for both dielectric and plasmonic nano-antennas, show that strain in atomically thin semiconductors can be used as an additional parameter for engineering light-matter interaction in nano-photonic devices.
Semi-Supervised Few-Shot Learning with Prototypical Random Walks

Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach, dubbed as Prototypical Random Walk Networks(PRWN), built on top of Prototypical Networks (PN). We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated. Our work is related to the very recent development on graph-based approaches for few-shot learning. However, we show that compact and well-separated class representations can be achieved by modeling our prototypical random walk notion without needing additional graph-NN parameters or requiring a transductive setting where collective test set is provided. Our model outperforms prior art in most benchmarks with significant improvements in some cases. For example, in a mini-Imagenet 5-shot classification task, we obtain 69.65$\%$ accuracy to the 64.59$\%$ state-of-the-art. Our model, trained with 40$\%$ of the data as labelled, compares competitively against fully supervised prototypical networks, trained on 100$\%$ of the labels, even outperforming it in the 1-shot mini-Imagenet case with 50.89$\%$ to 49.4$\%$ accuracy. We also show that our model is resistant to distractors, unlabeled data that does not belong to any of the training classes, and hence reflecting robustness to labelled/unlabelled class distribution mismatch. We also performed a challenging discriminative power test, showing a relative improvement on top of the baseline of $\approx$14\% on 20 classes on mini-Imagenet and $\approx$60\% on 800 classes on Omniglot. Code will be made available.
Two new non-negativity preserving iterative regularization methods for ill-posed inverse problems

Many inverse problems are concerned with the estimation of non-negative parameter functions. In this paper, in order to obtain non-negative stable approximate solutions to ill-posed linear operator equations in a Hilbert space setting, we develop two novel non-negativity preserving iterative regularization methods. They are based on fixed point iterations in combination with preconditioning ideas. In contrast to the projected Landweber iteration, for which only weak convergence can be shown for the regularized solution when the noise level tends to zero, the introduced regularization methods exhibit strong convergence. There are presented convergence results, even for a combination of noisy right-hand side and imperfect forward operators, and for one of the approaches there are also convergence rates results. Specifically adapted discrepancy principles are used as a posteriori stopping rules of the established iterative regularization algorithms. For an application of the suggested new approaches, we consider a biosensor problem, which is modelled as a two dimensional linear Fredholm integral equation of the first kind. Several numerical examples, as well as a comparison with the projected Landweber method, are presented to show the accuracy and the acceleration effect of the novel methods. Case studies of a real data problem indicate that the developed methods can produce meaningful featured regularized solutions.
Machine Learning Approaches for Motor Learning: A Short Review

Machine learning approaches have seen a considerable number of applications in human movement modeling but remain limited for motor learning. Motor learning requires that motor variability be taken into account and poses new challenges because the algorithms need to be able to differentiate between new movements and variation in known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.
Feature Importance Estimation with Self-Attention Networks

Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret. Recently, the attention mechanism was introduced, offering insights into the inner workings of neural language models. This paper explores the use of attention-based neural networks mechanism for estimating feature importance, as means for explaining the models learned from propositional (tabular) data. Feature importance estimates, assessed by the proposed Self-Attention Network (SAN) architecture, are compared with the established ReliefF, Mutual Information and Random Forest-based estimates, which are widely used in practice for model interpretation. For the first time we conduct scale-free comparisons of feature importance estimates across algorithms on ten real and synthetic data sets to study the similarities and differences of the resulting feature importance estimates, showing that SANs identify similar high-ranked features as the other methods. We demonstrate that SANs identify feature interactions which in some cases yield better predictive performance than the baselines, suggesting that attention extends beyond interactions of just a few key features and detects larger feature subsets relevant for the considered learning task.
Channel Estimation: Unified View of Optimal Performance and Pilot Sequences

Channel estimation is of paramount importance in most communication systems in order to optimize the data rate/energy consumption tradeoff. In modern systems, the possibly large number of transmit/receive antennas and subcarriers makes this task difficult. Designing pilot sequences of reasonable size yielding good performance is thus critical. Classically, the number of pilots is reduced by viewing the channel as a random vector and assuming knowledge of its distribution. In practice, this requires estimating the channel covariance matrix, which can be computationally costly and not adapted to scenarios with high mobility. In this paper, an alternative view is considered, in which the channel is a function of unknown deterministic parameters. In this setting, the problem of designing optimal pilot sequences of smallest possible size is studied for any parametric channel model. To do so, the Cramér-Rao bound (CRB) for this general channel estimation problem is given, highlighting its key dependency on the introduced variation space. Then, the minimal size of pilot sequences and minimal value of the CRB are determined. Moreover, a general strategy to build optimal minimal length power constrained pilots sequences is given, based on an estimation of the variation space. The theoretical results are finally illustrated in a massive MIMO system context. They conveniently allow to retrieve well known previous results, but also to exhibit minimal length optimal pilot sequences for a new strategy based on a nonlinear physical model.
Almost critical regularity of non-abelian Chern-Simons-Higgs system in the Lorenz gauge

In this paper we consider a Cauchy problem on the self-dual relativistic non-abelian Chern-Simons-Higgs model, which is the system of equations of $\mathfrak{su}(n)\, (n \ge 2)$-valued matter field $\phi$ and gauge field $A$. Since the system is non-abelian, it is extremely hard to reveal all the null structure. Instead we use angular Whitney decomposition of \cite{selb} based on the frequency localization as well as the null structure. By a technical reason we take $\ell^1$ summation over the localized frequencies, which results in the local well-posedness in Besov space $B_{2, 1}^\frac34 \times B_{2, 1}^\frac14$. We also prove that the solution flow map $(\phi(0), A(0)) \mapsto (\phi(t), A(t))$ fails to be $C^2$ at the origin of $H^s \times H^\sigma$ when $\sigma < \frac14$ regardless of $s \in \mathbb R$. This means the regularity $B_{2, 1}^\frac14$ is almost critical.
On the Noisy Gradient Descent that Generalizes as SGD

The gradient noise of SGD is considered to play a central role in the observed strong generalization abilities of deep learning. While past studies confirm that the magnitude and the covariance structure of gradient noise are critical for regularization, it remains unclear whether or not the class of noise distributions is important. In this work we provide negative results by showing that noises in classes different from the SGD noise can also effectively regularize gradient descent. Our finding is based on a novel observation on the structure of the SGD noise: it is the multiplication of the gradient matrix and a sampling noise that arises from the mini-batch sampling procedure. Moreover, the sampling noises unify two kinds of gradient regularizing noises that belong to the Gaussian class: the one using (scaled) Fisher as covariance and the one using the gradient covariance of SGD as covariance. Finally, thanks to the flexibility of choosing noise class, an algorithm is proposed to perform noisy gradient descent that generalizes well, the variant of which even benefits large batch SGD training without hurting generalization.
Non-Autoregressive Neural Dialogue Generation

Maximum Mutual information (MMI), which models the bidirectional dependency between responses ($y$) and contexts ($x$), i.e., the forward probability $\log p(y|x)$ and the backward probability $\log p(x|y)$, has been widely used as the objective in the \sts model to address the dull-response issue in open-domain dialog generation. Unfortunately, under the framework of the \sts model, direct decoding from $\log p(y|x) + \log p(x|y)$ is infeasible since the second part (i.e., $p(x|y)$) requires the completion of target generation before it can be computed, and the search space for $y$ is enormous. Empirically, an N-best list is first generated given $p(y|x)$, and $p(x|y)$ is then used to rerank the N-best list, which inevitably results in non-globally-optimal solutions. In this paper, we propose to use non-autoregressive (non-AR) generation model to address this non-global optimality issue. Since target tokens are generated independently in non-AR generation, $p(x|y)$ for each target word can be computed as soon as it's generated, and does not have to wait for the completion of the whole sequence. This naturally resolves the non-global optimal issue in decoding. Experimental results demonstrate that the proposed non-AR strategy produces more diverse, coherent, and appropriate responses, yielding substantive gains in BLEU scores and in human evaluations.
Sexual Reproduction via a 1-Aminocyclopropane-1-Carboxylic Acid-Dependent Pathway Through Redox Modulation in the Marine Red Alga Pyropia yezoensis (Rhodophyta)

The transition from the vegetative to sexually reproductive phase is the most dynamic change to occur during a plant's life cycle. In the present study, we showed that the ethylene precursor 1-aminocylopropane-1-carboxylic acid (ACC) induces sexual reproduction in the marine red alga Pyropia yezoensis independently from ethylene. Exogenous application of ACC, which contains a three membered carbocyclic ring, promoted the formation of spermatia and carporspores in gametophytes, whereas ethephon, an ethylene-releasing compound, did not stimulate sexual reproduction. In addition, an ACC analog, 1-aminocyclobutane-1-carboxylic acid (ACBC), which contains a four membered carbocyclic ring, promoted sexual reproduction and enhanced tolerance to oxidative stress in the same manner as ACC, but 1-aminocyclopentane-1-carboxylic acid (cycloleucine; which contains a cyclopentane ring) did not. The application of ACC increased the generation of reactive oxygen species (ROS) and induced the expression of PyRboh gene encoding NADPH oxidase. ACC also stimulated the synthesis of ascorbate (AsA) by inducing transcripts of PyGalLDH, which encodes galactono-1,4-lactone dehydrogenase, the catalyst for the final enzymatic step of the AsA biosynthetic pathway. Conversely, ACC caused a decrease in the synthesis of glutathione (GSH) by repressing transcripts of PyGCL, which encodes glutamate cysteine ligase, the catalyst for the rate-limiting step in the formation of GSH. These results suggest a possible role played by ACC as a signaling molecule independent from ethylene in the regulation of sexual reproduction through alterations to the redox state in P. yezoensis.
Association Between BDNF Gene Variant Rs6265 and the Severity of Depression in Antidepressant Treatment-Free Depressed Patients

Background Brain-derived neurotrophic factor (BDNF) plays an important role in neuronal plasticity, and its dysregulation has been associated with the pathogenesis of mood and anxiety disorders. Prolactin (PRL) is a pituitary hormone which is also produced as a cytokine by immune cells and could be a neurotrophic factor regulating the functional activity of stress-related mechanisms. Aim To investigate the possible relationship between depressive state and BDNF and PRL genotypes or levels with special reference to severity of depression. Methods Participants of 18–70 years with a clinical diagnosis of depressive disorder of at least moderate severity were included. These patients had not been treated with antidepressant drugs before admission to hospital during the preceding period of the last 6 months, and 54.5% had never been treated with antidepressant drugs during their entire life. The DNA was genotyped for rs1341239 within the prolactin and for rs6265, rs7124442, and rs11030104 within the BDNF gene. Rs11030104 violated the Hardy-Weinberg equilibrium distribution and was excluded from further analyses. BDNF and prolactin concentration was measured in serum by MAGPIX multiplex analyzer (Luminex, USA) using MILLIPLEX® MAP kit (Merck, Germany). Genetic associations were determined by sequentially regressing prolactin, BDNF, 17-items Hamilton's Depression (HAMD-17) and Clinical Global Impression scale, Severity (CGI-S) ratings, and depression (absent/present) on the available SNPs. Genetic associations were evaluated assuming an additive model. Results A total of 186 depressed patients (of which 169 were women) and 94 healthy controls (67 women) were genotyped. After excluding subjects without genetic information on all three study SNPs, 217 remained of whom 138 suffered from depression. Within depressed patients we observed an association of rs6265 with HAMD-17: mean difference (MD) 2.33 (95%CI 0.49; 4.16; p = 0.014) and CGI-S: MD 0.38 (95%CI 0.09; 0.66; p = 0.011). No significant association was observed between the prolactin SNP rs1341239 and prolactin levels. Similarly the mean differences of BDNF SNPs did not show an association with BDNF: rs6265 −0.042 ln(pg/ml) (95%CI −0.198; 0.113), and rs7124442 0.006 ln(pg/ml) (95%CI −0.117; 0.130). No other association reached statistical significance. Conclusion We observed a significant association between BDNF gene variant rs6265 and the severity of depression in newly admitted, antidepressant treatment-free, depressed patients. Actual PRL and BDNF levels were not elevated sufficiently in depressed patients to reach statistical significance and were not associated with the studied genotypes.
Gastric Damage and Cancer-Associated Biomarkers in Helicobacter pylori-Infected Children

Helicobacter pylori (H. pylori) is well-known to be involved in gastric carcinogenesis, associated with deregulation of cell proliferation and epigenetic changes in cancer-related genes. H. pylori infection is largely acquired during childhood, persisting long-term in about half of infected individuals, a subset of whom will go on to develop peptic ulcer disease and eventually gastric cancer, however, the sequence of events leading to disease is not completely understood. Knowledge on carcinogenesis and gastric damage-related biomarkers is abundant in adult populations, but scarce in children. We performed an extensive literature review focusing on gastric cancer related biomarkers identified in adult populations, which have been detected in children infected with H. pylori. Biomarkers were related to expression levels (RNA or protein) and/or methylation levels (DNA) in gastric tissue or blood of infected children as compared to non-infected controls. In this review, we identified 37 biomarkers of which 24 are over expressed, three are under expressed, and ten genes are significantly hypermethylated in H. pylori-infected children compared to healthy controls in at least 1 study. Only four of these biomarkers (pepsinogen I, pepsinogen II, gastrin, and SLC5A8) have been studied in asymptomatically infected children. Importantly, 13 of these biomarkers (β-catenin, C-MYC, GATA-4, DAPK1, CXCL13, DC-SIGN, TIMP3, EGFR, GRIN2B, PIM2, SLC5A8, CDH1, and VCAM-1.) are consistently deregulated in infected children and in adults with gastric cancer. Future studies should be designed to determine the clinical significance of these changes in infection-associated biomarkers in children and their persistence over time. The effect of eradication therapy over these biomarkers in children if proven significant, could lead to modifications in treatment guidelines for younger populations, and eventually promote the development of preventive strategies, such as vaccination, in the near future.
The Methods of Lymph Node Examination Make a Difference to Node Staging and Detection of N3b Node Status for Gastric Cancer

Background: The number of retrieved lymph nodes (RLNs) affects the likelihood of detecting metastatic lymph nodes (MLNs) for gastric cancer (GC), but the retrieval of LNs is not satisfactory worldwide. There is no standard for LN examination. Methods: We retrospectively analyzed 2,163 patients diagnosed with GC who underwent surgery at Nanfang Hospital between October 2004 and September 2016. According to the methods of LN examination, patients were classified into two groups: LN detection by pathologists (pathologist group) and LN examination by surgicopathologic team (surgicopathologist group). The relationship between RLNs and LN staging accuracy as well as the factors influencing the detection of MLNs were evaluated. Results: There were 472 males in pathologist group and 467 males in surgicopathologist group. The number of RLNs and MLNs in surgicopathologist group was significantly higher than that in pathologist group (RLNs: 53.8 ± 20.9 vs. 18.8 ± 11.5, p < 0.001; MLNs: 5.6 ± 9.8 vs. 3.9 ± 5.7, p < 0.001). Notably, the detection of N3b node status was significantly improved in surgicopathologist group [83 (11.9%) vs. 34 (4.8%), p < 0.001]. Additionally, the detection rate of N3b status gradually increased from 0 in patients with 1-16 RLNs to 16.6% in patients with more than 49 RLNs. The MLNs detected increased gradually from 2.3 ± 3.0 in patients with 1-16 RLNs to 7.3 ± 11.7 in patients with more than 49 RLNs. Univariate and multivariate analyses indicated that LN examination by surgicopathologic team, more advanced pT, tumor size ≥5 cm and combined organ(s) resection were related to detecting more MLNs. Conclusions: The retrieval of nodes immediately postoperatively by the surgicopathologic team could significantly improve the number of RLNs, detect more MLNs, and screen more patients with N3b node status.
Residential care and care to community-dwelling parents: out-selection, in-selection and diffusion of responsibility

ABSTRACT Research suggests that adult children are less likely to provide care to community-dwelling parents when beds in residential care settings are more widely available. The underlying mechanisms are not well understood. Drawing on data from the Survey of Health, Ageing and Retirement in Europe (SHARE) on 1,214 impaired parent–child dyads from 12 countries, we find that adult children are less likely to provide care in countries where beds in residential care settings are more widely available because (a) parents’ care needs are less severe in such countries (out-selection hypothesis) and (b) adult children and impaired parents are less likely to share a household in such countries (in-selection hypothesis). Finally (c), after taking these two factors into account, adult children remain less likely to provide care in countries where beds in residential care settings are more widely available (diffusion of responsibility hypothesis). Plausibly, being able to rely on residential care undermines adult children's sense of urgency to step in and provide care to their parents.