Miracle-dz/UniFAR-FTUs huggingface dataset:
Anisotropic fluid cosmology: An alternative to dark matter?

We use anisotropic fluid cosmology to describe the present, dark energy-dominated, universe. Similarly to what has been proposed for galactic dynamics, the anisotropic fluid gives an effective description of baryonic matter, dark energy and their possible interaction, without assuming the presence of dark matter. The resulting anisotropic fluid spacetime naturally generates inhomogeneities at small scales, triggered by an anisotropic stress, and could therefore be responsible for structure formation at these scales. Solving the cosmological equations, we show that the dynamics of the scale factor $a$ is described by usual FLRW cosmology and decouples completely from that describing inhomogeneities. We assume that the cosmological anisotropic fluid inherits the equation of state from that used to describe galaxy rotation curves. We show that, in the large scale regime, the fluid can be described as a generalized Chaplygin gas and fits well the distance modulus experimental data of type Ia supernovae, thus correctly modelling the observed accelerated expansion of the universe. Conversely, in the small scale regime, we use cosmological perturbation theory to derive the power spectrum $P(k)$ for mass density distribution. At short wavelengths, we find a $1/k^4$ behaviour, in good accordance with the observed correlation function for matter distribution at small scales.
Map-less Navigation: A Single DRL-based Controller for Robots with Varied Dimensions

Deep reinforcement learning (DRL) has shown great potential in training control agents for map-less robot navigation. However, the trained agents are generally dependent on the employed robot in training or dimension-specific, which cannot be directly reused by robots with different dimensional configurations. To address this issue, a novel DRL-based robot navigation method is proposed in this paper. The proposed approach trains a meta-robot with DRL and then transfers the meta-skill to a robot with a different dimensional configuration (named dimension-scaled robot) using a method named dimension-variable skill transfer (DVST), referred to as DRL-DVST. During the training phase, the meta-agent learns to perform self-navigation with the meta-robot in a simulation environment. In the skill-transfer phase, the observations of the dimension-scaled robot are transferred to the meta-agent in a scaled manner, and the control policy generated by the meta-agent is scaled back to the dimension-scaled robot. Simulation and real-world experimental results indicate that robots with different sizes and angular velocity bounds can accomplish navigation tasks in unknown and dynamic environments without any retraining. This work greatly extends the application range of DRL-based navigation methods from the fixed dimensional configuration to varied dimensional configurations.
Performance Analysis of Load Balancing Policies with Memory

Joining the shortest or least loaded queue among d randomly selected queues are two fundamental load balancing policies. Under both policies the dispatcher does not maintain any information on the queue length or load of the servers. In this paper we analyze the performance of these policies when the dispatcher has some memory available to store the ids of some of the idle servers. We consider methods where the dispatcher discovers idle servers as well as methods where idle servers inform the dispatcher about their state. We focus on large-scale systems and our analysis uses the cavity method. The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with memory reduce to the performance measures for the same policy without memory provided that the arrival rate is properly scaled. Thus, we can study the performance of load balancers with memory in the same manner as load balancers without memory. In particular this entails closed form solutions for joining the shortest or least loaded queue among d randomly selected queues with memory in case of exponential job sizes. We present simulation results that support our belief that the approximation obtained by the cavity method becomes exact as the number of servers tends to infinity.
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks

There has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture. Current state-of-the-art models, via a large number of parameters and pre-training on massive text corpus, have shown impressive results on several downstream tasks. Many researchers have studied previous (non-Transformer) models to understand their actual behavior under different scenarios, showing that these models are taking advantage of clues or failures of datasets and that slight perturbations on the input data can severely reduce their performance. In contrast, recent models have not been systematically tested with adversarial-examples in order to show their robustness under severe stress conditions. For that reason, this work evaluates three Transformer-based models (RoBERTa, XLNet, and BERT) in Natural Language Inference (NLI) and Question Answering (QA) tasks to know if they are more robust or if they have the same flaws as their predecessors. As a result, our experiments reveal that RoBERTa, XLNet and BERT are more robust than recurrent neural network models to stress tests for both NLI and QA tasks. Nevertheless, they are still very fragile and demonstrate various unexpected behaviors, thus revealing that there is still room for future improvement in this field.
Perfect Sampling of Hawkes Processes and Queues with Hawkes Arrivals

In this paper we develop to our best knowledge the first perfect sampling algorithm for queues with Hawkes input (i.e., single-server queues with Hawkes arrivals and independent and identically distributed service times of general distribution). In addition to the stability condition, we also assume the excitation function of the Hawkes process has a light tail and the service time has finite moment-generating function in the neighborhood of the origin. In this procedure, we also propose a new perfect sampling algorithm for Hawkes processes with improved computational efficiency compared with the existing algorithm. Theoretical analysis and numerical tests on the algorithms’ correctness and efficiency are also included.
Interactive Text-to-Speech via Semi-supervised Style Transfer Learning

With increasing interests in interactive speech systems, speech emotion recognition and multi-style text-to-speech (TTS) synthesis are becoming increasingly important research areas. In this paper, we combine both. We present a method to extract speech style embed-dings from input speech queries and apply this embedding as conditional input to a TTS voice so that the TTS response matches the speaking style of the input query. Specifically, we first train a multi-modal style classification model using acoustic and textual features of speech utterances. Due to a limited amount of labeled data, we combined the emotional recognition dataset: the interactive emotional dyadic motion capture database (IEMOCAP) with a small labeled subset of our internal TTS dataset for style model training. We take the softmax layer from the style classifier as style embedding and then apply this style embedding extraction model to generate soft style labels for our unlabelled internal TTS dataset. With this semi-supervised approach, reliable style embeddings are extracted to train a multi-style TTS system. As a result, we developed a controllable multi-style TTS system whose response matches the given target styles embedding, which could be extracted from the input query or manually assigned.
Meta-Learning Extractors for Music Source Separation

We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-sharing, while still allowing for instrument-specific parameterization. Meta-TasNet is shown to be more effective than the models trained independently or in a multi-task setting, and achieve performance comparable with state-of-the-art methods. In comparison to the latter, our extractors contain fewer parameters and have faster run-time performance. We discuss important architectural considerations, and explore the costs and benefits of this approach.
Can we constrain the extragalactic magnetic field from very high energy observations of GRB 190114C?

Primary very high energy $\ensuremath{\gamma}$ rays from $\ensuremath{\gamma}$-ray bursts (GRBs) are partially absorbed on extragalactic background light photons with subsequent formation of intergalactic electromagnetic cascades. Characteristics of the observable cascade $\ensuremath{\gamma}$-ray signal are sensitive to the strength and structure of the extragalactic magnetic field (EGMF). GRB 190114C was recently detected with the MAGIC imaging atmospheric Cherenkov telescopes, for the first time allowing us to estimate the observable cascade intensity. We inquire whether any constraints on the EGMF strength and structure could be obtained from publicly available $\ensuremath{\gamma}$-ray data on GRB 190114C. We present detailed calculations of the observable cascade signal for various EGMF configurations. We show that the sensitivity of the Fermi-LAT space $\ensuremath{\gamma}$-ray telescope is not sufficient to obtain such constraints on the EGMF parameters. However, next-generation space $\ensuremath{\gamma}$-ray observatories such as MAST would be able to detect pair echoes from GRBs similar to GRB 190114C for the EGMF strength below ${10}^{\ensuremath{-}17}--{10}^{\ensuremath{-}18}\text{ }\text{ }\mathrm{G}$.
Polarization-adjusted Convolutional (PAC) Codes: Fano Decoding vs List Decoding

In the Shannon lecture at the 2019 International Symposium on Information Theory (ISIT), Arikan proposed to employ a one-to-one convolutional transform as a pre-coding step before polar transform. The resulting codes of this concatenation are called {\em polarization-adjusted convolutional (PAC) codes}. In this scheme, a pair of polar mapper and demapper as pre- and post-processing devices are deployed around a memoryless channel, which provides polarized information to an outer decoder leading to improved error correction performance of outer code. In this paper, the implementations of list decoding and Fano decoding for PAC codes are first investigated. Then, in order to reduce the complexity of sequential decoding of PAC/polar codes, we propose (i) an adaptive heuristic metric, (ii) tree search constraints for backtracking to avoid exploration of unlikely sub-paths, and (iii) tree search strategies consistent with the pattern of error occurrence in polar codes. These contribute to reducing the average decoding time complexity up to 85\%, with only a relatively small degradation in error correction performance. Additionally, as an important ingredient in Fano decoding of PAC/polar codes, an efficient computation method for the intermediate LLRs and partial sums is provided. This method is necessary for backtracking and avoids storing the intermediate information or restarting the decoding process.
Modeling and simulations of supernova remnants: A short review focused on recent progress in morphological studies

Supernova remnants (SNRs) are the outcome of supernovae (SNe, either core-collapse or thermonuclear). The remnant results from the interaction between the stellar ejecta and the ambient medium around the progenitor star. Young SNRs are characterized by strong shocks that heat and ionize the gas, generate magneto-hydrodynamic turbulence, and accelerate particles to relativistic energies. They radiate at all wavelengths, especially in the X-ray domain, where spectro-imaging observations can provide a wealth of information. This paper presents recent progress in the modelling of SNRs, particularly by the means of numerical simulations, and with a focus on three-dimensional aspects. In the first part we will consider SNRs as producers of cosmic rays (CRs). If SNRs are accelerators efficient enough to power the Galactic component of CRs, this must have a visible impact on their dynamics, and therefore on the thermal emission from the plasma, as well as on their non-thermal emission. In the second part we will consider SNRs as probes of the explosion mechanism. The time has come to connect multi-dimensional simulations of SNe and simulations of SNRs, opening the possibility to study the explosion mechanism via the dynamics and morphology of SNRs.
Models of communication and control for brain networks: distinctions, convergence, and future outlook

Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.
When to Limit Market Entry under Mandatory Purchase

We study a problem inspired by regulated health insurance markets, such as those created by the government in the Affordable Care Act Exchanges or by employers when they contract with private insurers to provide plans for their employees. The market regulator can choose to do nothing, running a Free Market, or can exercise her regulatory power by limiting the entry of providers (decreasing consumer welfare by limiting options, but also decreasing revenue via enhanced competition). We investigate whether limiting entry increases or decreases the utility (welfare minus revenue) of the consumers who purchase from the providers, specifically in settings where the outside option of "purchasing nothing" is prohibitively undesirable. We focus primarily on the case where providers are symmetric. We propose a sufficient condition on the distribution of consumer values for (a) a unique symmetric equilibrium to exist in both markets and (b) utility to be higher with limited entry. (We also establish that these conclusions do not necessarily hold for all distributions, and therefore some condition is necessary.) Our techniques are primarily based on tools from revenue maximization, and in particular Myerson's virtual value theory. We also consider extensions to settings where providers have identical costs for providing plans, and to two providers with an asymmetric distribution.
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach

In this paper, we investigate the possibilities of utilizing deep learning for cardinality estimation of similarity selection. Answering this problem accurately and efficiently is essential to many data management applications, especially for query optimization. Moreover, in some applications the estimated cardinality is supposed to be consistent and interpretable. Hence a monotonic estimation w.r. t. the query threshold is preferred. We propose a novel and generic method that can be applied to any data type and distance function. Our method consists of a feature extraction model and a regression model. The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w.r. t. the threshold for both accuracy and monotonicity. We develop a training strategy tailored to our model as well as techniques for fast estimation. We also discuss how to handle updates. We demonstrate the accuracy and the efficiency of our method through experiments, and show how it improves the performance of a query optimizer.
How fast can you update your MST? (Dynamic algorithms for cluster computing)

Imagine a large graph that is being processed by a cluster of computers, e.g., described by the $k$-machine model or the Massively Parallel Computation Model. The graph, however, is not static; instead it is receiving a constant stream of updates. How fast can the cluster process the stream of updates? The fundamental question we want to ask in this paper is whether we can update the graph fast enough to keep up with the stream. We focus specifically on the problem of maintaining a minimum spanning tree (MST), and we give an algorithm for the $k$-machine model that can process $O(k)$ graph updates per $O(1)$ rounds with high probability. (And these results carry over to the Massively Parallel Computation (MPC) model. ) We also show a lower bound, i.e., it is impossible to process $k^{1+\epsilon}$ updates in $O(1)$ rounds. Thus we provide a nearly tight answer to the question of how fast a cluster can respond to a stream of graph modifications while maintaining an MST.
Correlated Adversarial Imitation Learning

A novel imitation learning algorithm is introduced by applying a game-theoretic notion of correlated equilibrium to the generative adversarial imitation learning. This imitation learning algorithm is equipped with queues of discriminators and agents, in contrast with the classical approach, where there are single discriminator and single agent. The achievement of a correlated equilibrium is due to a mediating neural architecture, which augments the observations that are being seen by queues of discriminators and agents. At every step of the training, the mediator network computes feedback using the rewards of discriminators and agents, to augment the next observations accordingly. By interacting in the game, it steers the training dynamic towards more suitable regions. The resulting imitation learning provides three important benefits. First, it makes adaptability and transferability of the learned model to new environments straightforward. Second, it is suitable for imitating a mixture of state-action trajectories. Third, it avoids the difficulties of non-convex optimization faced by the discriminator in the generative adversarial type architectures.
Color Tuning by Spontaneous Propagation of Gap Surface Plasmons in Epsilon Near Zero Nano-Cavity

This work presents numerical and experimental results of plasmonic Metal-Insulator optical nano-cavities. The systems are composed of Silver as metal, polyvinylpyrrolidone or indium tin oxide or zinc oxide as insulator. The proposed nano-cavities exhibit extraordinary effects as colors changing in function of the incident/view angles, enhancement of the experimental real and imaginary parts of the pseudo dielectric constant, an extraordinary transmission of 50$\%$ and zero reflection for both polarizations at the resonant wavelengths. These results lead to the formation and propagation of surface plasmon polaritons and their hybridization in gap surface plasmons. The concurrence of these effects allow studying the Goos-Hanchen shift by exploiting the very narrow $\Delta$ and $\Psi$ ellipsometer parameters. Thank to their optical properties, the proposed nano-cavities could be applied in several fields such tunable color filters, optics, photonics and sensing.
Investigating the role of Cu foil orientation in the growth of large BN films synthesized by reactive RF magnetron sputtering

Two dimensional materials are an emerging class of materials which is transforming the present day research activity on a phenomenal scale. Hexagonal boron nitride is a wide band gap 2D material which is an excellent substrate for graphene based electronics. To achieve the full potential of hBN scalable and high yield growth procedures are required. Here, we demonstrate the synthesis of hBN by reactive R.F magnetron sputtering over copper foil. Copper foil preparation conditions determines the phase selectivity of BN films. Deposition of hBN on non-electropolished Cu foils with predominant (100) orientation resulted in growth of BN islands with mixed cubic and hexagonal BN phase. On electropolished Cu foils with high symmetry hexagonal (111) surface termination we get growth of continuous hexagonal BN films, while on Cu foils having (100) and (110) orientation with lower symmetry growth of cubic BN films are observed.
G411(P) Relationship between maternal and cord blood zinc levels at term

Aims Zinc deficiency is one of the most common micronutrient deficiencies in developing countries. Maternal zinc deficiency is associated with foetal growth retardation and other adverse foetal outcomes. The study aimed to determine serum zinc concentrations of study subjects, provide much needed information on the prevalence of both maternal and cord blood zinc deficiency, as well as the relationship between maternal and cord blood zinc levels. Methods This cross-sectional study was conducted with 386 consecutive pregnant women recruited at term (37–42 weeks gestation). Maternal and cord blood samples were taken at delivery. Estimation of serum zinc concentration was by atomic absorption spectrophotometry. Results The mean serum zinc concentration in the maternal blood and cord blood were 9.0 (6.1) mmol/L and 13.1 (7.8) mmol/L respectively. One hundred and ninety-nine (51.6%) mothers had normal zinc levels while 187 (48.4%) mothers had hypozincaemia (serum zinc concentration below 7.6 µmol/L15. Also, 233 (60.4%) of the 386 babies had normal zinc levels while 153 (39.6%) had hypozincaemia (serum zinc concentration below 9.9 mmol/L)15. The mean cord serum zinc concentration was significantly higher than maternal serum zinc concentration (t = 33.12, p = 0.000). There was a significant association between the prevalence of low maternal and cord serum zinc levels; and normal maternal and cord serum zinc levels (X2 = 71.105, df = 1, p = 0.000) (Table 1). There was a positive correlation between paired maternal and cord blood zinc concentrations (r = 0.403, p = 0.000) (Figure 1). No significant correlation existed between gestational age and serum zinc levels in the maternal or cord blood.Abstract G411(P) Figure 1 Scatter plot of maternal and cord serum zinc with line of best fit (r = 0.403, p = 0.000)Abstract G411(P) Table 1 Association between maternal and cord serum zinc Conclusion There is a high prevalence of maternal and cord blood zinc deficiency in the study location which however did not reflect in the same magnitude in the newborn. Therefore, the role of zinc supplementation in pregnant women in developing countries still appears controversial. Reference Hotz C, Peerson JM, Brown KH. Suggested lower cutoffs of serum zinc cconcentrations for assessing zinc status: reanalysis of the second National Health and Nutrition Examination Survey data (1976 - 1980). Am J Clin Nutr. 2003; 78:756–764