Miracle-dz/UniFAR-FTUs huggingface dataset:
pp‐wave string models with interacting light‐cone actions

We consider pp‐wave backgrounds for which superstring action written in light‐cone gauge have the form of an interacting 2‐d theory. All pp‐wave solutions supported by non‐constant H3 or Fp fields are exact type II superstring solutions to all orders in α′. The corresponding light‐cone gauge Green‐Schwarz actions are non‐linear in bosons but always quadratic in fermions, and describe UV finite (but, in general, non‐supersymmetric) 2‐d theories. We consider a specific example where the pp‐wave F3 background is parametrized by an arbitrary holomorphic function of one complex bosonic coordinate.
Collectively driven optical nanoantennas

Optical nanoantennas, i.e., elements transforming localized light or waveguide modes into freely propagating fields and vice versa, are vital components for modern nanophotonics. Recently optical antennas have been demonstrated to cause the Dicke superradiance effect, i.e., collective spontaneous emission of quantum sources. However, the impact of coherent excitation on the antenna performance, such as directivity, efficiency, and Purcell effect remains unexplored. Herein, we investigate an antenna excited by two coherent dipole sources and demonstrate that this leads to a plethora of remarkable effects. Namely, using full-wave numerical simulations backed by all-quantum calculations, we unveil that coherent excitation allows controlling antenna multipoles, on-demand excitation of nonradiative states, enhanced superradiance and directivity, and improves antenna radiation efficiency. In the all-quantum picture, this collective excitation corresponds to the non-Dicke states with nonzero dipole moment, where the quantum phase is well defined. The results of this work bring another degree of freedom, the collective phase of an ensemble of quantum emitters, to optical nanoantennas and, as such, pave a way to the use of collective excitations for nanophotonic devices with superb performance.
Weak lensing skew-spectrum

We introduce the skew-spectrum statistic for weak lensing convergence κ maps and test it against state-of-the-art high-resolution all-sky numerical simulations. We perform the analysis as a function of source redshift and smoothing angular scale for individual tomographic bins. We also analyse the cross-correlation between different tomographic bins. We compare the numerical results to fitting-functions used to model the bispectrum of the underlying density field as a function of redshift and scale. We derive a closed form expression for the skew-spectrum for gravity-induced secondary non-Gaussianity. We also compute the skew-spectrum for the projected κ inferred from cosmic microwave background (CMB) studies. As opposed to the low redshift case, we find the post-Born corrections to be important in the modelling of the skew-spectrum for such studies. We show how the presence of a mask and noise can be incorporated in the estimation of a skew-spectrum.
EFFECTS OF LITTER SIZE ON THE ENERGETICS OF REPRODUCTION IN A HIGHLY PRECOCIAL RODENT, THE GUINEA PIG

Abstract Litter size strongly affects reproductive output and is therefore of central interest for the understanding of life-history evolution. I investigated the effect of variation in litter size (natural and manipulated) on the energetics of reproduction in the highly precocial guinea pig (Cavia porcellus). Unlike the situation in altricial species, increasing litter size in this precocial rodent influenced maternal performance (increase in litter mass and decrease in mass of individual offspring) as strongly during gestation (68 days) as during lactation (20 days). To cover the energy cost of offspring production, mothers increased their daily food intake. Their daily increase in metabolizable energy intake varied significantly with litter size during gestation but not during lactation. Instead, offspring in large litters consumed more solid food during lactation than did offspring in small litters. The efficiency of energy conversion into offspring production was not affected by litter size, neither during gestation nor during lactation. Furthermore, manipulation of litter size did not influence a mother's total energy cost of lactation. As a consequence, offspring in enlarged litters grew significantly slower than offspring in reduced litters despite similar body masses at birth and significantly greater consumption of solid food by nursing offspring in large litters. Results suggest that the effect of litter size on the energetics of reproduction in the precocial guinea pig differs from that of altricial rodents. Consequently, ecological implications of variation in litter size in the precocial guinea pig might also be very different to those in altricial species.
A minimum dilution scenario for supernovae and consequences for extremely metal-poor stars

To date no metal-free stars have been identified by direct observations. The most common method of constraining their properties is searching the spectra of the most metal-poor stars for the chemical elements created in the first stars and their supernova (SN). In this approach, modelled SN yields are compared to the observed abundance patterns in extremely metal-poor stars. The method typically only uses the abundance ratios, i.e. the yields are diluted to the observed level. Following the usual assumption of spherical symmetry we compute a simple lower limit of the mass an SN can mix with and find that it is consistent with all published simulations of early chemical enrichment in the interstellar medium. For three different cases, we demonstrate that this dilution limit can change the conclusions from the abundance fitting. There is a large discrepancy between the dilution found in simulations of SN explosions in minihaloes and the dilution assumed in many abundance fits. Limiting the dilution can significantly alter the likelihood of which supernovae are possible progenitors of observed CEMP-no stars. In particular, some of the faint, very low yield SNe, which have been suggested as models for the abundance pattern of SMSS0313−6708, cannot explain the measured metal abundances, as their predicted metal yields are too small by two orders of magnitude. Altogether, the new dilution model presented here emphasizes the need to better understand the mixing and dilution behaviour of aspherical SNe.
All Holographic Four-Point Functions in All Maximally Supersymmetric CFTs

We present a constructive derivation of holographic four-point correlators of arbitrary half-BPS operators for all maximally supersymmetric conformal field theories in $d>2$. This includes holographic correlators in 3d ${\cal N}=8$ ABJM theories, 4d ${\cal N}=4$ SYM theory and the 6d ${\cal N}=(2,0)$ theory, dual to tree-level amplitudes in 11D supergravity on $AdS_4 \times S^7$, 10D supergravity on $AdS_5 \times S^5$ and 11D supergravity on $AdS_7 \times S^4$, respectively. We introduce the concept of Maximally R-symmetry Violating (MRV) amplitude, which corresponds to a special configuration in the R-symmetry space. In this limit the amplitude drastically simplifies, but at the same time the entire polar part of the full amplitude can be recovered from this limit. Furthermore, for a specific choice of the polar part, contact terms can be shown to be absent, by using the superconformal Ward identities and the flat space limit.
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport

The problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets is becoming increasingly important. A key challenge is to design an approach that overcomes the covariate and target shift both among the sources, and between the source and target domains. In this paper, we address this problem from a new perspective: instead of looking for a latent representation invariant between source and target domains, we exploit the diversity of source distributions by tuning their weights to the target task at hand. Our method, named Weighted Joint Distribution Optimal Transport (WJDOT), aims at finding simultaneously an Optimal Transport-based alignment between the source and target distributions and a re-weighting of the sources distributions. We discuss the theoretical aspects of the method and propose a conceptually simple algorithm. Numerical experiments indicate that the proposed method achieves state-of-the-art performance on simulated and real-life datasets.
New Examples Of Shrinking Laplacian Solitons

We give a one-parameter family of examples of shrinking Laplacian solitons, which are the second known solutions to the closed $G_2$-Laplacian flow with a finite-time singularity. The torsion forms and the Laplacian and Ricci operators of a large family of $G_2$-structures on different Lie groups are also studied. We apply these formulas to prove that, under a suitable extra condition, there is no closed eigenform for the Laplacian on such family.
Phylogenetic relationships and patterns of character change in the tribe Lactuceae (Asteraceae) based on chloroplast DNA restriction site variation

The Lactuceae is perhaps the most easily recognizable tribe in the Asteraceae, distinguished by the presence of milky latex and of ligulate florets in the inflorescence. Three existing taxonomic treatments of the tribe establish subtribal classifications but fail to resolve relationships among major lineages. Our study of chloroplast DNA restriction site variation sampled 60 Lactuceae taxa. We detected 1268 mutations, 612 of which are phylogenetically informative. Despite the large amount of variation detected, little resolution of relationships among major lineages was obtained from parsimony analyses, although the monophyly of many groups is strongly supported. These results, when considered along with data from morphological analyses of other workers, suggest that rapid diversification played an important role in early stages of the tribe's evolution. Our examination of character change further reveals that as noted by other workers, restriction site variation is not evenly distributed across the chlor.
Acoustic analog of Hawking radiation in quantized circular superflows of Bose-Einstein condensates

We propose emulation of Hawking radiation by means of acoustic excitations propagating on top of a persistent current in an atomic Bose-Einstein condensate loaded in an annular confining potential. The setting admits realization of sonic black and white event horizons. It is found that density-density correlations, representing the acoustic analogue of the Hawking radiation, are strongly affected by the perimeter of the ring-shaped configuration and number of discrete acoustic modes admitted by it. Remarkably, there is a minimum radius of the ring which admits the emulation of the Hawking radiation. We also discuss a possible similarity of properties of the matter-wave sonic black holes to the known puzzle of the stability of Planck-scale primordial black holes in quantum gravity.
PHYLOGENETIC RETICULATIONS AND CLADISTICS: DISCUSSION OF METHODOLOGICAL CONCEPTS

Abstract— Some problems associated with a cladistic treatment of complex taxa (hybrids and holobionts) are reviewed and discussed. The authors recommend aplication of standard cladistic analysis to all taxa, regardless of their supposed mode of origin. The position of a complex taxon depends entirely on the set of characters it shares with other taxa. This approach is preferable, because there are no means to distinguish deductively between various kinds of character conflicts (the recognition of one character as incongruous with others says nothing about its evolutionary origin). All other approaches have been proposed so as to reflect the topology of reticulate phylogenetic trees, but, at the same time, they require prior knowledge of the complex nature of a taxon.
Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks

Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like hearing aids (HAs). This, however, is currently not possible with state-of-the-art methods. They either require a lot of parameters and computational power and thus are only feasible using modern CPUs. Or they are not suitable for online processing, which requires constraints like low-latency by the filter bank and the algorithm itself. In this work, we propose a mask-based noise reduction approach. Using hierarchical recurrent neural networks, we are able to drastically reduce the number of neurons per layer while including temporal context via hierarchical connections. This allows us to optimize our model towards a minimum number of parameters and floating-point operations (FLOPs), while preserving noise reduction quality compared to previous work. Our smallest network contains only 5k parameters, which makes this algorithm applicable on embedded devices. We evaluate our model on a mixture of EUROM and a real-world noise database and report objective metrics on unseen noise.
Combinatorial Pure Exploration of Dueling Bandit

In this paper, we study combinatorial pure exploration for dueling bandits (CPE-DB): we have multiple candidates for multiple positions as modeled by a bipartite graph, and in each round we sample a duel of two candidates on one position and observe who wins in the duel, with the goal of finding the best candidate-position matching with high probability after multiple rounds of samples. CPE-DB is an adaptation of the original combinatorial pure exploration for multi-armed bandit (CPE-MAB) problem to the dueling bandit setting. We consider both the Borda winner and the Condorcet winner cases. For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round. For Condorcet winner, we first design a fully polynomial time approximation scheme (FPTAS) for the offline problem of finding the Condorcet winner with known winning probabilities, and then use the FPTAS as an oracle to design a novel pure exploration algorithm CAR-Cond with sample complexity analysis. CAR-Cond is the first algorithm with polynomial running time per round for identifying the Condorcet winner in CPE-DB.
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability. To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem. The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network. The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data. The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time.
Statistical Mechanics of Generalization in Kernel Regression

Generalization beyond a training dataset is a main goal of machine learning. We investigate generalization error in kernel regression using statistical mechanics and derive an analytical expression for it applicable to any kernel. Focusing on the broad class of rotation invariant kernels, which is relevant to training deep neural networks in the infinite-width limit, we show several phenomena. When data is drawn from a spherically symmetric distribution and the number of input dimensions, $D$, is large, we find that multiple learning stages exist, one for each scaling of the number of training samples with $\mathcal{O}_D(D^K)$ with $K\in Z^+$. In each stage $\mathcal{O}_D(D^K)$ degenerate spectral modes corresponding to the $K$-th kernel eigenvalue are learned. The mathematical analysis of a learning stage reduces to that of a solvable model with the dimensionality of the feature space extensive in the number of samples and a white kernel spectrum, including linear regression as a special case. The behavior of the learning curve in each stage is governed by an effective regularizer and an effective target noise that are related to the tail of the kernel and the target function spectra. When effective regularization is zero, we identify a first order phase transition that corresponds to a divergence in the generalization error. Each learning stage can exhibit sample-wise \textit{double-descent}, where learning curves show non-monotonic sample size dependence. For each stage an optimal value of effective regularizer exists, equal to the effective noise variance, that gives minimum generalization error.
Micropropagation and conservation of rare medicinal plant Wattakaka volubilis (Linn.) Stapf

A micropropagation protocol for the important medicinal plant species Wattakaka volubilis was developed using shoot tip and nodal explants by culturing on Murashige-Skoog (MS) media fortified with 1.5 mg/L BAP in combination with 0.7 mg/L Kn that resulted in 5.3±1.4 multiple shoots per explant. Maximum shoot length observed was 5.8±1.1 cm on media supplemented with 1.5 mg/L BAP and 0.6 mg/L Kn. The in vitro raised micro shoots produced highest percentage of rooting (85%) in MS media augmented with 1.0 mg/L IBA. The hardened plantlets were acclimatized in the green house conditions and then reintroduced in the Herbal garden attached to the Centre in which 76% of the plants survived.
Grasp State Assessment of Deformable Objects Using Visual-Tactile Fusion Perception

Humans can quickly determine the force required to grasp a deformable object to prevent its sliding or excessive deformation through vision and touch, which is still a challenging task for robots. To address this issue, we propose a novel 3D convolution-based visual-tactile fusion deep neural network (C3D-VTFN) to evaluate the grasp state of various deformable objects in this paper. Specifically, we divide the grasp states of deformable objects into three categories of sliding, appropriate and excessive. Also, a dataset for training and testing the proposed network is built by extensive grasping and lifting experiments with different widths and forces on 16 various deformable objects with a robotic arm equipped with a wrist camera and a tactile sensor. As a result, a classification accuracy as high as 99.97% is achieved. Furthermore, some delicate grasp experiments based on the proposed network are implemented in this paper. The experimental results demonstrate that the C3D-VTFN is accurate and efficient enough for grasp state assessment, which can be widely applied to automatic force control, adaptive grasping, and other visual-tactile spatiotemporal sequence learning problems.
Environment Shaping in Reinforcement Learning using State Abstraction

One of the central challenges faced by a reinforcement learning (RL) agent is to effectively learn a (near-)optimal policy in environments with large state spaces having sparse and noisy feedback signals. In real-world applications, an expert with additional domain knowledge can help in speeding up the learning process via \emph{shaping the environment}, i.e., making the environment more learner-friendly. A popular paradigm in literature is \emph{potential-based reward shaping}, where the environment's reward function is augmented with additional local rewards using a potential function. However, the applicability of potential-based reward shaping is limited in settings where (i) the state space is very large, and it is challenging to compute an appropriate potential function, (ii) the feedback signals are noisy, and even with shaped rewards the agent could be trapped in local optima, and (iii) changing the rewards alone is not sufficient, and effective shaping requires changing the dynamics. We address these limitations of potential-based shaping methods and propose a novel framework of \emph{environment shaping using state abstraction}. Our key idea is to compress the environment's large state space with noisy signals to an abstracted space, and to use this abstraction in creating smoother and more effective feedback signals for the agent. We study the theoretical underpinnings of our abstraction-based environment shaping, and show that the agent's policy learnt in the shaped environment preserves near-optimal behavior in the original environment.
Association between dietary inflammatory index and serum C-reactive protein concentrations in the Japan Collaborative Cohort Study

ABSTRACT Diet plays an important role in the regulation of chronic inflammation, which is linked to cardiovascular disease (CVD) and several cancers. The dietary inflammatory index (DII®) was developed to estimate the inflammatory potential of an individual’s diet. We examined the association between DII scores and serum high-sensitivity C-reactive protein (hs-CRP) concentrations using the baseline data from the Japan Collaborative Cohort Study (JACC Study). Data were from 1176 control subjects (650 men and 526 women) in a nested case-control study of several cancers and CVD in the JACC Study who were free of cancer and CVD at baseline. DII scores were calculated from 26 food parameters that were derived from a validated food frequency questionnaire administered at the baseline. Energy-adjusted DII scores were calculated using the residual method. Serum hs-CRP concentrations were measured by latex-enhanced nephelometry or enzyme-immunoassay. In multivariable logistic regression analysis adjusting for potential confounders including sex, age, smoking habits, drinking habits, body mass index, and history of hypertension, the odds ratio (OR) and 95% confidence intervals (CI) for high serum hs-CRP concentrations (>1.0 mg/L) was significantly higher in the highest versus the lowest DII quartile (ORQuartile4vs1 = 1.32, 95% CI = 1.01 to 2.52). Likewise, a 1-point increase in DII score was associated with a 14% increased risk of high serum hs-CRP concentrations (ORContinuous = 1.09, 95%CI = 1.01 to 1.19). A pro-inflammatory diet, as represented by high DII scores, was associated with high serum hs-CRP concentrations in this Japanese population.
Online Learning: A Panacea in the Time of COVID-19 Crisis

Educational institutions (schools, colleges, and universities) in India are currently based only on traditional methods of learning, that is, they follow the traditional set up of face-to-face lectures in a classroom. Although many academic units have also started blended learning, still a lot of them are stuck with old procedures. The sudden outbreak of a deadly disease called Covid-19 caused by a Corona Virus (SARS-CoV-2) shook the entire world. The World Health Organization declared it as a pandemic. This situation challenged the education system across the world and forced educators to shift to an online mode of teaching overnight. Many academic institutions that were earlier reluctant to change their traditional pedagogical approach had no option but to shift entirely to online teaching–learning. The article includes the importance of online learning and Strengths, Weaknesses, Opportunities, & Challenges (SWOC) analysis of e-learning modes in the time of crisis. This article also put some light on the growth of EdTech Start-ups during the time of pandemic and natural disasters and includes suggestions for academic institutions of how to deal with challenges associated with online learning.