Miracle-dz/UniFAR-FTUs huggingface dataset:
Singing voice synthesis based on convolutional neural networks

The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. In these systems, the relationship between musical score feature sequences and acoustic feature sequences extracted from singing voices is modeled by DNNs. Then, an acoustic feature sequence of an arbitrary musical score is output in units of frames by the trained DNNs, and a natural trajectory of a singing voice is obtained by using a parameter generation algorithm. As singing voices contain rich expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs. An acoustic feature sequence is generated in units of segments that consist of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Experimental results in a subjective listening test show that the proposed architecture can synthesize natural sounding singing voices.
Heat transfer between anisotropic nanoparticles: Enhancement and switching

We theoretically study heat transfer between two anisotropic nanoparticles in vacuum, and derive closed expressions in terms of the anisotropic dipole polarizabilities. We show that transfer between two small spheroids can be many times as large as the one for two spheres of same volumes. Such increase with anisotropy is also found for the heat emission of an isolated small spheroid. Furthermore, we observe a strong dependence of transfer on the relative orientation, yielding the interpretation as a heat transfer switch. The switch quality, given as the ratio of transfer in the “on” and “off” positions, is observed to be as large as 103 in the near field and even larger in the far field.
The IR obstruction to UV completion for Dante's Inferno model with higher-dimensional gauge theory origin

We continue our investigation of large field inflation models obtained from higher-dimensional gauge theories, initiated in our previous study [1]. We focus on Dante's Inferno model which was the most preferred model in our previous analysis. We point out the relevance of the IR obstruction to UV completion, which constrains the form of the potential of the massive vector field, under the current observational upper bound on the tensor to scalar ratio. We also show that in simple examples of the potential arising from DBI action of a D5-brane and that of an NS5-brane that the inflation takes place in the field range which is within the convergence radius of the Taylor expansion. This is in contrast to the well known examples of axion monodromy inflation where inflaton takes place outside the convergence radius of the Taylor expansion. This difference arises from the very essence of Dante's Inferno model that the effective inflaton potential is stretched in the inflaton field direction compared with the potential for the original field.
SYNCHROTRON LIGHTCURVES OF BLAZARS IN A TIME-DEPENDENT SYNCHROTRON-SELF COMPTON COOLING SCENARIO

Blazars emit non-thermal radiation in all frequency bands from radio to γ-rays. Additionally, they often exhibit rapid flaring events at all frequencies with doubling timescale of the TeV and X-ray flux on the order of minutes, and such rapid flaring events are difficult to explain theoretically. We explore the effect of the synchrotron-self Compton cooling, which is inherently time-dependent, leading to a rapid cooling of the electrons. Having intensively discussed the resulting effects of this cooling scenario on the spectral energy distribution of blazars in previous papers, the effects of the time-dependent approach on the synchrotron lightcurve are investigated here. Taking into account the retardation due to the finite size of the source and the source geometry, we show that the time-dependent synchrotron-self Compton (SSC) cooling still has profound effects on the lightcurve compared to the usual linear (synchrotron and external Compton) cooling terms. This is most obvious if the SSC cooling takes longer than the light crossing timescale. Then, in most frequency bands, the variability timescale is up to an order of magnitude shorter than under linear cooling conditions. This is yet another strong indication that the time-dependent approach should be taken into account for modeling blazar flares from compact emission regions.
Inverse Faraday effect driven by radiation friction

A collective, macroscopic signature to detect radiation friction in laser–plasma experiments is proposed. In the interaction of superintense circularly polarized laser pulses with high density targets, the effective dissipation due to radiative losses allows the absorption of electromagnetic angular momentum, which in turn leads to the generation of a quasistatic axial magnetic field. This peculiar ‘inverse Faraday effect’ is investigated by analytical modeling and three-dimensional simulations, showing that multi-gigagauss magnetic fields may be generated at laser intensities > 10 23 W cm − 2 .
The effect of baryonic streaming motions on the formation of the first supermassive black holes

Observations of quasars at redshifts z > 6 reveal that 10^9 Msol supermassive black holes (SMBHs) had already formed when the Universe was 20. This is the regime where baryonic streaming motions--the relative velocities of baryons with respect to dark matter in the early Universe--most strongly inhibit star formation by suppressing gas infall and cooling. We investigate the impact of this effect on the growth of the first SMBHs using a suite of high-fidelity, ellipsoidal-collapse Monte Carlo merger-tree simulations. We find that the suppression of seed BH formation by the streaming motions significantly reduces the number density of the most massive BHs at z > 15, but the residual effect at lower redshifts is essentially negligible. The streaming motions can reduce by a factor of few the number density of the most luminous quasars at z ~ 10-11, where such objects could be detected by the James Webb Space Telescope. We conclude, with minor theoretical caveats, that baryonic streaming motions are unlikely to pose a significant additional obstacle to the formation of the observed high-redshift quasar SMBHs. Nor do they appreciably affect the heating and reionization histories of the Universe or the merger rates of nuclear BHs in the mass and redshift ranges of interest for proposed gravitational-wave detectors.
Near-Infrared Spectroscopy of Quasars at z~3 and Estimates of Their Supermassive Black Hole Masses

We present the results of new infrared spectroscopic observations of 37 quasars at z~3, selected based on the optical r'-band magnitude and the availability of nearby bright stars for future imaging follow-up with Adaptive Optics system. The supermassive black hole (SMBH) masses (M_BH) were successfully estimated in 28 out of 37 observed objects from the combination of the H_beta emission linewidth and continuum luminosity at rest-frame 5100A. Comparing these results with those from previous studies of quasars with similar redshift, our sample exhibited slightly lower (~ -0.11 dex in median) Eddington ratios; and, the SMBH masses are slightly (~ 0.38 dex in median) higher. The SMBH growth time, t_grow, was calculated by dividing the estimated SMBH mass by the mass accretion rate measured using optical luminosity. We found, given reasonable assumptions, that t_grow was smaller than the age of the universe at the redshift of individual quasars for a large fraction of observed sources, suggesting that the SMBHs in many of our observed quasars are in growing phase with high accretion rates. A comparison of the SMBH masses derived from our H_beta data and archived CIV data indicated considerable scattering, as indicated in previous studies. All quasars with measured SMBH masses have at least one nearby bright star, such that they are suitable targets for adaptive optics observations to study the mass relationship between SMBHs and host galaxies' stellar component at high redshift.
Lattice gauge theory simulations in the quantum information era

The many-body problem is ubiquitous in the theoretical description of physical phenomena, ranging from the behaviour of elementary particles to the physics of electrons in solids. Most of our understanding of many-body systems comes from analysing the symmetric properties of Hamiltonian and states: the most striking examples are gauge theories such as quantum electrodynamics, where a local symmetry strongly constrains the microscopic dynamics. The physics of such gauge theories is relevant for the understanding of a diverse set of systems, including frustrated quantum magnets and the collective dynamics of elementary particles within the standard model. In the last few years, several approaches have been put forward to tackle the complex dynamics of gauge theories using quantum information concepts. In particular, quantum simulation platforms have been put forward for the realisation of synthetic gauge theories, and novel classical simulation algorithms based on quantum information concepts have been formulated. In this review, we present an introduction to these approaches, illustrating the basics concepts and highlighting the connections between apparently very different fields, and report the recent developments in this new thriving field of research.
The rates and modes of gas accretion on to galaxies and their gaseous haloes

We study the rate at which gas accretes on to galaxies and haloes and investigate whether the accreted gas was shocked to high temperatures before reaching a galaxy. For this purpose we use a suite of large cosmological, hydrodynamical simulations from the OWLS project. We improve on previous work by considering a wider range of halo masses and redshifts, by distinguishing accretion on to haloes and galaxies, by including important feedback processes, and by comparing simulations with different physics. Gas accretion is mostly smooth, with mergers only becoming important for groups and clusters. The specific rate of gas accretion on to haloes is, like that for dark matter, only weakly dependent on halo mass. For halo masses Mhalo � 10 11 Mit is relatively insensitive to feedback processes. In contrast, accretion rates on to galaxies are determined by radiative cooling and by outflows driven by supernovae and active galactic nuclei. Galactic winds increase the halo mass at which the central galaxies grow the fastest by about two orders of magnitude to Mhalo � 10 12 M� . Gas accretion is bimodal, with maximum past temperatures either of order the virial temperature or . 10 5 K. The fraction of gas accreted on to haloes in the hot mode is insensitive to feedback and metal-line cooling. It increases with redshift, but is mostly determined by halo mass, increasing gradually from less than 10% for � 10 11 Mto greater than 90% at � 10 13 M� . In contrast, for accretion on to galaxies the cold mode is always significant and the relative contributions of the two accretion modes are more sensitive to feedback and metal-line cooling. On average, the majority of stars present in any mass halo at any redshift were formed from gas accreted in the cold mode, although the hot mode contributes typically over 10% for Mhalo & 10 11 M� . Thus, while gas accretion on to haloes can be robustly predicted, the rate of accretion on to galaxies is sensitive to uncertain feedback processes. Nevertheless, it is clear that galaxies, but not their gaseous haloes, are predominantly fed by gas that did not experience an accretion shock when it entered the host halo.
FATSO: A family of operators for variable selection in linear models

In linear models it is common to have situations where several regression coefficients are zero. In these situations a common tool to perform regression is a variable selection operator. One of the most common such operators is the LASSO operator, which promotes point estimates which are zero. The LASSO operator and similar approaches, however, give little in terms of easily interpretable parameters to determine the degree of variable selectivity. In this paper we propose a new family of selection operators which builds on the geometry of LASSO but which yield an easily interpretable way to tune selectivity. These operators correspond to Bayesian prior densities and hence are suitable for Bayesian inference. We present some examples using simulated and real data, with promising results.
Review of the Fault-Tolerant Strategy for Space Manipulators With Joint Failure

The fault-tolerant strategy of space manipulator is very important to guarantee the task completion and safety after locked joint failure (LJF) occurs. According to the different research stages, the fault-tolerant strategy of the space manipulator with locked joint failure (SMLJF) is divided into the fault-tolerant strategy in design stage and use stage. In view of the fault-tolerant strategy in the design phase, the system-level design and component-level design are reviewed, the fault-tolerant design methods of the structure and joints of the space manipulator are introduced, and the problems existing in these methods are pointed out. In view of the fault-tolerant strategy in the use phase, it can be divided into fault-tolerant strategy before and after LJF, respectively. The fault-tolerant strategy before LJF, such as artificial joint limits, global fault-tolerant path planning, and the fault-tolerant strategy after LJF including online fault-tolerant strategy and offline fault-tolerant strategy is reviewed in detail. The current research status of fault-tolerant strategy for SMLJF is introduced systematically, and the problems of these strategies are also analyzed in detail in this article. Finally, all the existing problems of fault-tolerant strategy for SMLJF are summarized, and the research direction of fault-tolerant strategy is prospected.
UNRESOLVED FINE-SCALE STRUCTURE IN SOLAR CORONAL LOOP-TOPS

New and advanced space-based observing facilities continue to lower the resolution limit and detect solar coronal loops in greater detail. We continue to discover even finer substructures within coronal loop cross-sections, in order to understand the nature of the solar corona. Here, we push this lower limit further to search for the finest coronal loop substructures, through taking advantage of the resolving power of the Swedish 1 m Solar Telescope/CRisp Imaging Spectro-Polarimeter (CRISP), together with co-observations from the Solar Dynamics Observatory/Atmospheric Image Assembly (AIA). High-resolution imaging of the chromospheric Hα 656.28 nm spectral line core and wings can, under certain circumstances, allow one to deduce the topology of the local magnetic environment of the solar atmosphere where its observed. Here, we study post-flare coronal loops, which become filled with evaporated chromosphere that rapidly condenses into chromospheric clumps of plasma (detectable in Hα) known as a coronal rain, to investigate their fine-scale structure. We identify, through analysis of three data sets, large-scale catastrophic cooling in coronal loop-tops and the existence of multi-thermal, multi-stranded substructures. Many cool strands even extend fully intact from loop-top to footpoint. We discover that coronal loop fine-scale strands can appear bunched with as many as eight parallel strands within an AIA coronal loop cross-section. The strand number density versus cross-sectional width distribution, as detected by CRISP within AIA-defined coronal loops, most likely peaks at well below 100 km, and currently, 69% of the substructure strands are statistically unresolved in AIA coronal loops.
The Potential for Placental Activation of PPARγ to Improve the Angiogenic Profile in Preeclampsia

Preeclampsia (PE) is one of the most common causes of maternal-fetal morbidity and mortality world-wide. While the underlying causes of PE remain elusive, aberrant trophoblast differentiation and function are thought to cause an imbalance of secreted angiogenic proteins resulting in systemic endothelial dysfunction and organ damage in the mother. The placental dysfunction is also characterized by a reduction of the transcription factor, peroxisome proliferator activated receptor γ (PPARγ) which normally promotes trophoblast differentiation and healthy placental function. This study aimed to understand how placental activation of PPARγ effects the secretion of angiogenic proteins and subsequently endothelial function. To study this, healthy and PE placental tissues were cultured with or without the PPARγ agonist, Rosiglitazone, and a Luminex assay was performed to measure secreted proteins from the placenta. To assess the angiogenic effects of placental activation of PPARγ, human umbilical vein endothelial cells (HUVECs) were cultured with the placental conditioned media and the net angiogenic potential of these cells was measured by a tube formation assay. This is the first study to show PPARγ’s beneficial effect on the angiogenic profile in the human preeclamptic placenta through the reduction of anti-angiogenic angiopoietin-2 and soluble endoglin and the upregulation of pro-angiogenic placental growth factor, fibroblast growth factor-2, heparin-binding epidermal growth factor, and follistatin. The changes in the angiogenic profile were supported by the increased angiogenic potential observed in the HUVECs when cultured with conditioned media from rosiglitazone-treated preeclamptic placentas. The restoration of these disrupted pathways by activation of PPARγ in the preeclamptic placenta offers potential to improve placental and endothelial function in PE.
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

Safe reinforcement learning has many variants and it is still an open research problem. Here, we focus on how to use action guidance by means of a non-expert demonstrator to avoid catastrophic events in a domain with sparse, delayed, and deceptive rewards: the recently-proposed multi-agent benchmark of Pommerman. This domain is very challenging for reinforcement learning (RL) --- past work has shown that model-free RL algorithms fail to achieve significant learning. In this paper, we shed light into the reasons behind this failure by exemplifying and analyzing the high rate of catastrophic events (i.e., suicides) that happen under random exploration in this domain. While model-free random exploration is typically futile, we propose a new framework where even a non-expert simulated demonstrator, e.g., planning algorithms such as Monte Carlo tree search with small number of rollouts, can be integrated to asynchronous distributed deep reinforcement learning methods. Compared to vanilla deep RL algorithms, our proposed methods both learn faster and converge to better policies on a two-player mini version of the Pommerman game.
Hydrodynamic Torques in Circumbinary Accretion Disks

Gaseous disks have been proposed as a mechanism for facilitating mergers of binary black holes. We explore circumbinary disk systems to determine the evolution of the central binary. To do so, we perform 3D, hydrodynamic, locally isothermal simulations of circumbinary disks on a Cartesian grid. We focus on binaries of equal mass ratios on fixed circular orbits. To investigate the orbital evolution of the binary, we examine the various torques exerted on the system. For the case where the disk plane and binary orbital plane are aligned, we find that the total torque is positive so that the semimajor axis of the binary increases. For the misaligned case, we run simulations with the binary orbital plane and disk midplane misaligned by 45° and find the same results—the binary grows. The timescale for the circumbinary disk to realign to the plane of the binary is consistent with the global viscous timescale of the disk.
Towards More Accurate Matching of Contactless Fingerprints With a Deep Geometric Graph Convolutional Network

Contactless fingerprint recognition has attracted increasing attention because of its higher data safety and hygiene than traditional contact-based fingerprints. However, accurate matching of contactless fingerprints is still challenging due to the inevitable perspective distortions and variations of illumination and poses. Traditional matching methods are mainly based on minutiae topology and hand-designed descriptors, which cannot work well on contactless fingerprints. Towards more accurate matching of contactless fingerprints, this paper proposes a deep geometric graph neural network to jointly learn the multi-level minutia features and their similarities in an end-to-end fashion. First, given a set of minutia points, a convolutional neural network is built on fingerprint images to extract the low level features of minutiae. Second, we propose a geometric graph neural network with minutia points as nodes and the adjacent minutiae connected with edges, which can pass and aggregate messages of minutia nodes to learn the high level geometric features. Third, minutia matching is converted into binary classification problem with a focal loss function to learn the similarity of minutia pairs. Finally, fingerprint matching is performed by integrating the similarities of matched minutia pairs. Our method is tested on contactless fingerprints from two public datasets and one wet dataset collected in our laboratory. Experimental results show that our method performs better than other state-of-the-art methods for contactless fingerprint matching.
Dissipative phase transition in a pair of coupled noisy two-level systems

We study the renormalization group (RG) equations of a pair of spin-boson systems coupled in the z-direction with each other. Each spin is coupled to a different bath of harmonic oscillators. We introduce a systematic adiabatic RG, which generalizes the first-order adiabatic renormalization previously used for the single spin-boson model, and we obtain the flow equations for the tunneling constant, the dissipation strength and the inter-spin coupling up to third order in the tunneling. If one of the two spins is treated as a constant magnetization the other spin is described by a biased spin-boson Hamiltonian. In this case the RG equations we find coincide with the ones obtained via a mapping to a long-range Ising chain. If the whole Ohmic two-spin system is considered the Kosterlitz-Thouless phase transition is replaced by a second-order phase transition. In the case of a sub-Ohmic bath our approach predicts that the two-spin system is always localized.
Bulletin of Electrical Engineering and Informatics

Received Jun 25, 2022 Revised Aug 18, 2022 Accepted Oct 26, 2022 In this work we are focusing on listing out various works in the understanding of various parameters and context to get the overview of stock market analysis in the context of machine learning (ML) and deep learning (DL) models. The work focusses on the stock market analysis along with methodologies and algorithms used to understand the trends and the corresponding results as part of those studies. The importance of this work is to summarize and analyse the parameters which are highly influenced the understandingof the stock market trends. The outcome of the work is understanding the important factors which directly and indirectly influences the stock value raise and drop. The work highlights the methodologies and the algorithms used to stock market data analysis and efficient and effective recommendation of stable stocks to the customers. Further we are listing out the research gaps and future enhancements of the studies which are left over in the earlier works. The work pops up the limitations of some of the works in the existing works along with significance of hyper parameter techniques to clearly identify the features through which we can get more possibilities of better analysis of the data.