Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave Filtering Using Forward Brillouin Scattering in Photonic-Phononic Emit-Receive Devices

Microwave photonic systems are compelling for their ability to process signals at high frequencies and over extremely wide bandwidths as a basis for next generation communication and radar technologies. However, many applications also require narrow-band $(\sim\! \text{MHz})$ filtering operations that are challenging to be implemented using optical filtering techniques, as this requires reliable integration of ultra-high quality factor $(\sim\! 10^8)$ optical resonators. One way to address this challenge is to utilize long-lived acoustic resonances, taking advantage of their narrow-band frequency response to filter microwave signals. In this article, we examine new strategies to harness a narrow-band acoustic response within a microwave-photonic signal processing platform through the use of light-sound coupling. Our signal processing scheme is based on a recently demonstrated photon-phonon emitter-receiver device, which transfers information between the optical and acoustic domains using Brillouin interactions, and produces narrow-band filtering of a microwave signal. To understand the best way to use this device technology, we study the properties of a microwave-photonic link using this filtering scheme. We theoretically analyze the noise characteristics of this microwave-photonic link, and explore the parameter space for the design and optimization of such systems.
On the origin of the chemical bimodality of disc stars: a tale of merger and migration

The Milky Way’s (MW) stellar disc exhibits a bimodality in the [Fe/H] versus [α/Fe] plane, showing a distinct high- and low-α sequences whose origin is still under debate. We examine the [Fe/H]–[α/Fe] abundance plane in cosmological hydrodynamical simulations of MW-like galaxies from the NIHAO-UHD project and show that the bimodal α-sequence is a generic consequence of a gas-rich merger at some time in the Galaxy’s evolution. The high-α sequence evolves first in the early galaxies, extending to high metallicities, while it is the low-α sequence that is formed after the gas-rich merger. The merger brings in fresh metal-poor gas diluting the interstellar medium’s metallicity while keeping the [α/Fe] abundance almost unchanged. The kinematic, structural, and spatial properties of the bimodal α-sequence in our simulations reproduces that of observations. In all simulations, the high-α disc is old, radially concentrated towards the galaxy’s centre and shows large scale heights. In contrast, the low-α disc is younger, more radially extended and concentrated to the disc mid-plane. Our results show that the abundance plane is well described by these two populations that have been distributed radially across the disc by migration: at present day in the solar neighbourhood, low-α stars originate from both the inner and outer discs, while most of the high-α stars have migrated from the inner disc. We show that age dating the stars in the [Fe/H]–[α/Fe] plane can constrain the time of the low-α sequence forming merger and conclude that α-bimodality is likely a not uncommon feature of disc galaxies.
Investigating Early-type Galaxy Evolution with a Multiwavelength Approach. III. Insights from SPH Simulations with Chemophotometric Implementation

We are exploring galaxy evolution in low-density environments exploiting smooth particle hydrodynamic simulations, including chemophotometric implementation. From a large grid of simulations of galaxy encounters and mergers starting from triaxial halos of gas and dark matter, we single out the simulations matching the global properties of our targets. These simulations are used to give insights into their evolution. We focus on 11 early-type galaxies selected because of their nearly passive stage of evolution in the nuclear region. However, a variety of UV features are detected in more than half of these galaxies. We find no significant differences in the formation mechanisms between galaxies with or without UV features. Major and minor mergers are able to reproduce their peculiar UV morphologies, and galaxy encounters are more suitable for “normal” early-type galaxies. Their star formation rate self-quenches several gigayears later than the merger/encounter occurred via gas exhaustion and stellar feedback, moving the galaxy from blue to red colors and driving the galaxy transformation. The length of the quenching is mass-dependent and lasts from 1 to 5 Gyr or more in the less massive systems. All of our targets are gas-rich at redshift 1. Three of them assembled at most 40% of their current stellar mass at z > 1, and seven assembled more than 40% between redshift 0.5 and 1. Their stellar mass grows by 4% by crossing the green valley before reaching their current position on the NUV−r versus Mr diagram.
Finite-density QCD transition in a magnetic background field

Using numerical simulations of lattice QCD with physical quark masses, we reveal the influence of magnetic-field background on chiral and deconfinement crossovers in finite-temperature QCD at low baryonic density. In the absence of thermodynamic singularity, we identify these transitions with inflection points of the approximate order parameters: normalized light-quark condensate and renormalized Polyakov loop, respectively. We show that the quadratic curvature of the chiral transition temperature in the ``temperature--chemical potential'' plane depends rather weakly on the strength of the background magnetic field. At weak magnetic fields, the thermal width of the chiral crossover gets narrower as the density of the baryon matter increases, possibly indicating a proximity to a real thermodynamic phase transition. Remarkably, the curvature of the chiral thermal width flips its sign at $eB_{\mathrm{fl}} \simeq 0.6\,\mathrm{GeV}^2$, so that above the flipping point $B > B_{\mathrm{fl}}$, the chiral width gets wider as the baryon density increases. Approximately at the same strength of magnetic field, the chiral and deconfining crossovers merge together at $T \approx 140\,\mathrm{MeV}$. The phase diagram in the parameter space ``temperature-chemical potential-magnetic field'' is outlined, and single-quark entropy and single-quark magnetization are explored. The curvature of the chiral thermal width allows us to estimate an approximate position of the chiral critical endpoint at zero magnetic field: $(T_c^{\text{CEP}}, \mu_B^{\text{CEP}})= (100(25)\, \text{MeV},\ 800(140)\,\text{MeV})$.
Metric-Based Few-Shot Learning for Video Action Recognition

In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gone into few-shot learning for image classification, little work has been done on few-shot video classification. In this work, we address the task of few-shot video action recognition with a set of two-stream models. We evaluate the performance of a set of convolutional and recurrent neural network video encoder architectures used in conjunction with three popular metric-based few-shot algorithms. We train and evaluate using a few-shot split of the Kinetics 600 dataset. Our experiments confirm the importance of the two-stream setup, and find prototypical networks and pooled long short-term memory network embeddings to give the best performance as few-shot method and video encoder, respectively. For a 5-shot 5-way task, this setup obtains 84.2% accuracy on the test set and 59.4% on a special "challenge" test set, composed of highly confusable classes.
Sparse regularization of inverse problems by biorthogonal frame thresholding

We analyze sparse frame based regularization of inverse problems by means of a biorthogonal frame decomposition (BFD) for the forward operator. The BFD allows to define a non-iterative (direct) frame thresholding approach which we show to provide a convergent regularization method with linear convergence rates. These results will be compared to the well-known analysis and synthesis variants of sparse $\ell^1$-regularization which are usually implemented thorough iterative schemes. If the frame is a basis (non-redundant case), the three versions of sparse regularization, namely synthesis and analysis variants of $\ell^1$-regularization as well as the BFD thresholding are equivalent. However, in the redundant case, those three approaches are pairwise different.
A Critical Analysis of Biased Parsers in Unsupervised Parsing

A series of recent papers has used a parsing algorithm due to Shen et al. (2018) to recover phrase-structure trees based on proxies for "syntactic depth. " These proxy depths are obtained from the representations learned by recurrent language models augmented with mechanisms that encourage the (unsupervised) discovery of hierarchical structure latent in natural language sentences. Using the same parser, we show that proxies derived from a conventional LSTM language model produce trees comparably well to the specialized architectures used in previous work. However, we also provide a detailed analysis of the parsing algorithm, showing (1) that it is incomplete---that is, it can recover only a fraction of possible trees---and (2) that it has a marked bias for right-branching structures which results in inflated performance in right-branching languages like English. Our analysis shows that evaluating with biased parsing algorithms can inflate the apparent structural competence of language models.
SANVis: Visual Analytics for Understanding Self-Attention Networks

Attention networks, a deep neural network architecture inspired by humans’ attention mechanism, have seen significant success in image captioning, machine translation, and many other applications. Recently, they have been further evolved into an advanced approach called multi-head self-attention networks, which can encode a set of input vectors, e.g., word vectors in a sentence, into another set of vectors. Such encoding aims at simultaneously capturing diverse syntactic and semantic features within a set, each of which corresponds to a particular attention head, forming altogether multi-head attention. Meanwhile, the increased model complexity prevents users from easily understanding and manipulating the inner workings of models. To tackle the challenges, we present a visual analytics system called SANVis, which helps users understand the behaviors and the characteristics of multi-head self-attention networks. Using a state-of-the-art self-attention model called Transformer, we demonstrate usage scenarios of SANVis in machine translation tasks. Our system is available at http://short.sanvis. org.
3D characterization of the primary Al$_3$Sc phases in an Al-Sc alloy using Synchrotron X-ray tomography and electron microscopy

The three-dimensional structures of the primary Al3Sc particles in an Al-2Sc master alloy were studied by synchrotron X-ray microtomography, scanning and transmission electron microscopy. The Al3Sc phases were found to be a single cube and a cluster of cubes. The surface area, equivalent diameter of the Al3Sc cubes increased with the increasing of cube volume, but the specific surface area decreases. The primary Al3Sc cubes and Al-matrix have the same crystal orientation, indicating that the Al3Sc phases are the heterogeneous nucleation sites for Al. The experimental results show that {\alpha}-Al2O3 are the possible nucleation sites for the Al3Sc cubes.
Redirection Controller Using Reinforcement Learning

There is a growing demand for redirected walking (RDW) techniques and their application to various virtual and real environments. To apply appropriate RDW methods and manipulation according to the real and virtual environments, the RDW controllers are predominantly used. There are three types of RDW controllers: direct scripted controller, generalized controller, and predictive controller. The scripted controller type pre-scripts the mapping between the real and virtual environments. The generalized controller type employs the RDW method and manipulation quantities according to a certain procedure depending on the user’s position in relation to the real space. This approach has the potential to be reused in any environment; however, it is not fully optimized. The predictive controller type predicts the user’s future path using the user’s behavior and manages RDW techniques. This approach is highly anticipated to be very effective and versatile; however, it has not been sufficiently developed. This paper proposes a novel RDW controller using reinforcement learning (RL) with advanced plannability/versatility. Our simulation experiments indicate that the proposed method can reduce the number of reset manipulations, which is one of the indicators of the effectiveness of the RDW controller, compared to the generalized controller under real environments with many obstacles. Meanwhile, the experimental results also showed that the gain output by the proposed method oscillates. The results of a user study conducted showed that the proposed RDW controller can reduce the number of resets compared to the conventional generalized controller. Furthermore, no adverse effects such as cybersickness associated with the oscillation of the output gain were evinced. The simulation and user studies demonstrate that the proposed RDW controller with RL outperforms the existing generalized controllers and can be applied to users as it only causes cybersickness comparable to that in the case of conventional generalized controllers.
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction

This work proposes a method for depth completion of sparse LiDAR data using a convolutional neural network which can be used to generate semi-dense depth maps and "almost" full 3D point-clouds with significantly lower root mean squared error (RMSE) over state-of-the-art methods. We add an "Error Prediction" unit to our network and present a novel and simple end-to-end method that learns to predict an error-map of depth regression task. An "almost" dense high-confidence/low-variance point-cloud is more valuable for safety-critical applications specifically real-world autonomous driving than a full point-cloud with high error rate and high error variance. Using our predicted error-map, we demonstrate that by up-filling a LiDAR point cloud from 18,000 points to 285,000 points, versus 300,000 points for full depth, we can reduce the RMSE error from 1004 to 399. This error is approximately 60% less than the state-of-the-art and 50% less than the state-of-the-art with RGB guidance (we did not use RGB guidance in our algorithm). In addition to analyzing our results on Kitti depth completion dataset, we also demonstrate the ability of our proposed method to extend to new tasks by deploying our "Error Prediction" unit to improve upon the state-of-the-art for monocular depth estimation. Codes and demo videos are available at this http URL
Colloquium
: Spintronics in graphene and other two-dimensional materials

After the first unequivocal demonstration of spin transport in graphene (Tombros et al., 2007), surprisingly at room temperature, it was quickly realized that this novel material was relevant for both fundamental spintronics and future applications. Over the decade since, exciting results have made the field of graphene spintronics blossom, and a second generation of studies has extended to new two-dimensional (2D) compounds. This Colloquium reviews recent theoretical and experimental advances on electronic spin transport in graphene and related 2D materials, focusing on emergent phenomena in van der Waals heterostructures and the new perspectives provided by them. These phenomena include proximity-enabled spin-orbit effects, the coupling of electronic spin to light, electrical tunability, and 2D magnetism.
Alternative Analysis Methods for Time to Event Endpoints Under Nonproportional Hazards: A Comparative Analysis

Abstract The log-rank test is most powerful under proportional hazards (PH). In practice, non-PH patterns are often observed in clinical trials, such as in immuno-oncology; therefore, alternative methods are needed to restore the efficiency of statistical testing. Three categories of testing methods were evaluated, including weighted log-rank tests, Kaplan–Meier curve-based tests (including weighted Kaplan–Meier and restricted mean survival time), and combination tests (including Breslow test, Lee’s combo test, and MaxCombo test). Nine scenarios representing the PH and various non-PH patterns were simulated. The power, Type I error, and effect estimate of each method were compared. In general, all tests control Type I error well. There is not a single most powerful test across all scenarios. In the absence of prior knowledge regarding the underlying or non-PH patterns, the MaxCombo test is relatively robust across patterns. Since the treatment effect changes over time under non-PH, the overall profile of the treatment effect may not be represented comprehensively based on a single measure. Thus, multiple measures of the treatment effect should be prespecified as sensitivity analyses to describe the totality of the data. Supplementary materials for this article are available online.
The impact of peculiar velocities on the estimation of the Hubble constant from gravitational wave standard sirens

In this work, we investigate the systematic uncertainties that arise from the calculation of the peculiar velocity when estimating the Hubble constant (H0) from gravitational wave standard sirens. We study the GW170817 event and the estimation of the peculiar velocity of its host galaxy, NGC 4993, when using Gaussian smoothing over nearby galaxies. NGC 4993 being a relatively nearby galaxy, at ∼40 Mpc away, is subject to a significant effect of peculiar velocities. We demonstrate a direct dependence of the estimated peculiar velocity value on the choice of smoothing scale. We show that when not accounting for this systematic, a bias of ${\sim }200~{\rm km\, s^{-1}}$ in the peculiar velocity incurs a bias of ${\sim }4~{\rm km\, s^{-1}\, Mpc^{-1}}$ on the Hubble constant. We formulate a Bayesian model that accounts for the dependence of the peculiar velocity on the smoothing scale and by marginalizing over this parameter we remove the need for a choice of smoothing scale. The proposed model yields $H_0 = 68.6 ^{+14.0} _{-8.5}~{\rm km\, s^{-1}\, Mpc^{-1}}$. We demonstrate that under this model a more robust unbiased estimate of the Hubble constant from nearby GW sources is obtained.
Can We Derive Quantum Mechanics as an Emergent Theory from a Modified Relativity

A new model of Deformed Special Relativity (DSR) was proposed to derive a modified General Relativity at the sub-quantum level towards the Planck scale. This was used as a basis for a new approach to Emergent Quantum Mechanics from which the standard mathematical formalism of Relativistic Quantum Mechanics was derived. Lastly, a new interpretation for the so-called quantum wave was suggested.
Multiprocessor Real-Time Locking Protocols: A Systematic Review

We systematically survey the literature on analytically sound multiprocessor real-time locking protocols from 1988 until 2018, covering the following topics: progress mechanisms that prevent the lock-holder preemption problem, spin-lock protocols, binary semaphore protocols, independence-preserving (or fully preemptive) locking protocols, reader-writer and k-exclusion synchronization, support for nested critical sections, and implementation and system-integration aspects. A special focus is placed on the suspension-oblivious and suspension-aware analysis approaches for semaphore protocols, their respective notions of priority inversion, optimality criteria, lower bounds on maximum priority-inversion blocking, and matching asymptotically optimal locking protocols.
Complications majeures du diabète sucré en Afrique

En Afrique, l'augmentation des complications du diabete risque de suivre l'elevation de la prevalence de cette affection posant des maintenant l'opportunite de l'evaluation de l'ampleur de ces complications. Le diabete sucre constitue une charge importante pour les ressources limitees des pays en voie de developpement comme l'Afrique. La mortalite totale du diabete est estimee a 14,9 pour 1 000 personnes/annee de diabete avec un âge moyen de deces de 56,1 ans chez la femme et de 57,6 ans chez l'homme, la duree moyenne du diabete connu etant de 12,5 ans. De plus, 30 % de tous ces deces sont dus aux complications metaboliques aigues, aux infections et aux accidents vasculaires cerebraux. L'acidocetose survenant dans 24 % des diabetes connus de l'enfant est revelatrice dans 76 % des cas realisant une forme clinique comateuse dans 34 % des cas. Une prevalence de 34 % d'acidocetose concerne le diabete insulinodependant avec un caractere revelateur. L'infection des membres inferieurs est particulierement grave et frequente et meme fatale realisant une quasi-specificite tropicale du fait de l'implication a la fois du staphylocoque et des germes gram negatifs ; l'anemie et l'hyperleucocytose aggravante sont correlees a un echec de l'antibiotherapie. Par ailleurs, la tuberculose pulmonaire est preoccupante occupant la neuvieme position parmi les complications les plus frequentes du diabete. Pres de la moitie des insulinodependants ont une retinopathie qui se caracterise par un âge moyen estime a 50,33 ± 12,8 ans, un sex ratio de 2 hommes pour une femme avec une association independante a la microalbuminurie particulierement dans sa forme non proliferante. Cependant, des differences ethniques tiennent plutot aux conditions economiques mauvaises qui favorisent un mauvais equilibre glycemique. La part de 32 % de retinopathie dans les complications oculaires du diabete est comparable aux observations africaines et d'ailleurs en milieu ophtalmologique. Quant a la neuropathie, sa grande prevalence est unanimement reconnue (70 % des complications microangiopathiques). L'impuissance concerne 48,7 % d'une population de diabetiques ayant un âge moyen de 41,4 ± 15 ans. La neuropathie autonome pourrait bien etre impliquee dans cette impuissance. L'existence d'une atteinte coronarienne est reconnue avec un role aggravant des hemoglobinopathies particulierement quand la coronarographie est normale. L'arteriopathie des membres inferieurs concerne 18 % des diabetiques.
An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces Based on a Minimum Variance Unbiased Estimator

In a wireless system with Intelligent Reflective Surfaces (IRS) containing many passive elements, we consider the problem of channel estimation. All the links from the transmitter to the receiver via each IRS elements (or groups) are estimated. We show that the estimation performance are dependent on the setting of the IRS, and design an optimal channel estimation scheme where the IRS elements follow an optimal series of activation patterns. The optimal design is guided by results for the minimum variance unbiased estimation. The IRS setting during the channel estimation period mimics the discrete Fourier transforms. We show theoretically and with simulations that the estimation variance is one order smaller compared to existing methods with on/off IRS activation patterns as proposed in the literature.
Life Satisfaction and Theories of Bureaucratic Rents

The monopoly position of the public bureaucracy in providing public services allows government employees to acquire rents. Those rents can involve higher wages, monetary and non-monetary fringe benefits and bribes. We propose a direct measure to capture the total of these rents: the difference in reported subjective well-being between bureaucrats and private sector employees. In a sample of 38 countries, we find large variations in the extent of rents in the public bureaucracy. The extent of rents is determined by differences in institutional constraints and correlates with perceptions of corruption. We find judicial independence to be of major relevance for a tamed bureaucracy. JEL classification: D72, D73, I31, J30, J45, K42, H11, H83