Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin squeezing by tensor twisting and Lipkin-Meshkov-Glick dynamics in a toroidal Bose-Einstein condensate with spatially modulated nonlinearity

We propose a scheme for spin-squeezing in the orbital motion of a Bose-Einstein condensate (BEC) in a toroidal trap. A circular lattice couples two counter-rotating modes and squeezing is generated by the nonlinear interaction spatially modulated at half the lattice period. By varying the amplitude and phase of the modulation, various cases of the twisting tensor can be directly realized, leading to different squeezing regimes. These include one-axis twisting and the two-axis counter-twisting which are often discussed as the most important paradigms for spin squeezing. Our scheme naturally realizes the Lipkin-Meshkov-Glick model with the freedom to vary all its parameters simultaneously.
Reinforcement Learning for Nested Polar Code Construction

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar coding. Specifically, a state represents the construction of an $(N,K)$ polar code, an action specifies its reduction to an $(N,K-1)$ subcode, and reward is the decoding performance. A neural network architecture consisting of both policy and value networks is proposed to generate actions based on the observed states, aiming at maximizing the overall rewards. A loss function is defined to trade off between exploitation and exploration. To further improve learning efficiency and quality, an ''integrated learning'' paradigm is proposed. It first employs a genetic algorithm to generate a population of (sub-)optimal polar codes for each $(N,K)$, and then uses them as prior knowledge to refine the policy in RL. Such a paradigm is shown to accelerate the training process, and converge at better performances. Simulation results show that the proposed learning- based polar constructions achieve comparable, or even better, performances than the state of the art under successive cancellation list (SCL) decoders. Last but not least, this is achieved without exploiting any expert knowledge from polar coding theory in the learning algorithms.
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms

A general Intrusion Detection System (IDS) fundamentally acts based on an Anomaly Detection System (ADS) or a combination of anomaly detection and signature-based methods, gathering and analyzing observations and reporting possible suspicious cases to a system administrator or the other users for further investigation. One of the notorious challenges which even the state-of-the-art ADS and IDS have not overcome is the possibility of a very high false alarms rate. Especially in very large and complex system settings, the amount of low-level alarms easily overwhelms administrators and increases their tendency to ignore alerts.We can group the existing false alarm mitigation strategies into two main families: The first group covers the methods directly customized and applied toward higher quality anomaly scoring in ADS. The second group includes approaches utilized in the related contexts as a filtering method toward decreasing the possibility of false alarm rates.Given the lack of a comprehensive study regarding possible ways to mitigate the false alarm rates, in this paper, we review the existing techniques for false alarm mitigation in ADS and present the pros and cons of each technique. We also study a few promising techniques applied in the signature-based IDS and other related contexts like commercial Security Information and Event Management (SIEM) tools, which are applicable and promising in the ADS context.Finally, we conclude with some directions for future research.
Morphological properties of superclusters of galaxies

We studied superclusters of galaxies in a volume-limited sample extracted from the Sloan Digital Sky Survey Data Release 7 and from mock catalogues based on a semi-analytical model of galaxy evolution in the Millennium Simulation. A density field method was applied to a sample of galaxies brighter than Mr =− 21+ 5l ogh100 to identify superclusters, taking into account selection and boundary effects. In order to evaluate the influence of the threshold density, we have chosen two thresholds: the first maximizes the number of objects (D1) and the second constrains the maximum supercluster size to ∼120 h −1 Mpc (D2). We have performed a morphological analysis, using Minkowski Functionals, based on a parameter, which increases monotonically from filaments to pancakes. An anticorrelation was found between supercluster richness (and total luminosity or size) and the morphological parameter, indicating that filamentary structures tend to be richer, larger and more luminous than pancakes in both observed and mock catalogues. We have also used the mock samples to compare supercluster morphologies identified in position and velocity spaces, concluding that our morphological classification is not biased by the peculiar velocities. Monte Carlo simulations designed to investigate the reliability of our results with respect to random fluctuations show that these results are robust. Our analysis indicates that filaments and pancakes present different luminosity and size distributions.
Superconductivity, charge- or spin-density wave, and metal-nonmetal transition in BaTi2(Sb1−xBix)2O

We have performed an isovalent substitution study in a layered titanium oxypnictide system BaTi$_{2}$(Sb$_{1-x}$Bi$_{x}$)$_{2}$O (0$\leq x\leq$ 0.40) by the measurements of x-ray diffraction, electrical resistivity and magnetic susceptibility. The parent compound BaTi$_{2}$Sb$_{2}$O is confirmed to exhibit superconductivity at 1.5 K as well as charge- or spin-density wave (CDW/SDW) ordering below 55 K. With the partial substitution of Sb by Bi, the lattice parameters $a$, $c$ and $c/a$ all increase monotonically, indicating negative chemical pressure and lattice distortion on the (super)conducting Ti$_2$Sb$_2$O-layers. The Bi doping elevates the superconducting transition temperature to its maximum $T_c$=3.7 K at $x=$0.17, and then $T_c$ decreases gradually with additional Bi doping. A metal-to-nonmetal transition takes place around $x$=0.3, and superconductivity at $\sim$1K exists at the nonmetal side. The CDW/SDW anomaly, in comparison, is rapidly suppressed by the Bi doping, and vanishes for $x\geq$0.17. The results are discussed in terms of negative chemical pressure and disorder effect.
Numerical Simulation of Roughness Effects of Ice Accretion on Wind Turbine Airfoils

One of the emerging problems in modern computational fluid dynamics is the simulation of flow over rough surfaces. A good example of these rough surfaces is wind turbine blades with ice formation on its leading edge. Instead of resolving the airflow field using a fine computational grid near the wall, rough wall functions (RWFs) can be used to model the flow behavior in case of the presence of roughness. This work aims to investigate the performance of state-of-the-art RWFs to show which of these models can provide the most accurate results with the lowest computational cost possible. This aim is achieved by comparing coefficients of lift and pressure resulting from CFD simulations with wind tunnel results of an airfoil with actual ice profiles collected from the site. The RWFs are used to simulate airflow field over the airfoil profiles with ice profile attached to its leading edge using OpenFOAM CFD framework. The comparison of the numerical simulations and the wind tunnel measurements showed that the Colebrook RWF provided the best agreement between simulation and experimental results while using about 20% of the number of cells used with smooth RWF.
ON THE BARYON FRACTIONS IN CLUSTERS AND GROUPS OF GALAXIES

We present the baryon fractions of 2MASS groups and clusters as a function of cluster richness using total and gas masses measured from stacked ROSAT X-ray data and stellar masses estimated from the infrared galaxy catalogs. We detect X-ray emission even in the outskirts of clusters, beyond r200 for richness classes with X-ray temperatures above 1 keV. This enables us to more accurately determine the total gas mass in these groups and clusters. We find that the optically selected groups and clusters have flatter temperature profiles and higher stellar-to-gas mass ratios than the individually studied, X-ray bright clusters. We also find that the stellar mass in poor groups with temperatures below 1 keV is comparable to the gas mass in these systems. Combining these results with individual measurements for clusters, groups, and galaxies from the literature, we find a break in the baryon fraction at ∼1 keV. Above this temperature, the baryon fraction scales with temperature as fb ∝ T0.20±0.03. We see significantly smaller baryon fractions below this temperature and the baryon fraction of poor groups joins smoothly onto that of systems with still shallower potential wells such as normal and dwarf galaxies where the baryon fraction scales with the inferred velocity dispersion as fb ∝ σ1. 6. The small scatter in the baryon fraction at any given potential well depth favors a universal baryon loss mechanism and a preheating model for the baryon loss. The scatter is, however, larger for less massive systems. Finally, we note that although the broken power-law relation can be inferred from data points in the literature alone, the consistency between the baryon fractions for poor groups and massive galaxies inspires us to fit the two categories of objects (galaxies and clusters) with one relation.
THE IMPACT OF STELLAR FEEDBACK ON THE STRUCTURE, SIZE, AND MORPHOLOGY OF GALAXIES IN MILKY-WAY-SIZED DARK MATTER HALOS

We use cosmological zoom-in simulations of galaxy formation in a Milky-Way-sized halo started from identical initial conditions to investigate the evolution of galaxy sizes, baryon fractions, morphologies, and angular momenta in runs with different parameters of the star formation–feedback cycle. Our fiducial model with a high local star formation efficiency, which results in efficient feedback, produces a realistic late-type galaxy that matches the evolution of basic properties of late-type galaxies: stellar mass, disk size, morphology dominated by a kinematically cold disk, stellar and gas surface density profiles, and specific angular momentum. We argue that feedback’s role in this success is twofold: (1) removal of low angular momentum gas, and (2) maintaining a low disk-to-halo mass fraction, which suppresses disk instabilities that lead to angular momentum redistribution and a central concentration of baryons. However, our model with a low local star formation efficiency, but large energy input per supernova, chosen to produce a galaxy with a similar star formation history as our fiducial model, leads to a highly irregular galaxy with no kinematically cold component, overly extended stellar distribution, and low angular momentum. This indicates that only when feedback is allowed to become vigorous via locally efficient star formation in dense cold gas do resulting galaxy sizes, gas/stellar surface density profiles, and stellar disk angular momenta agree with observed z = 0 galaxies.
THE LANDSCAPE OF THE NEUTRINO MECHANISM OF CORE-COLLAPSE SUPERNOVAE: NEUTRON STAR AND BLACK HOLE MASS FUNCTIONS, EXPLOSION ENERGIES, AND NICKEL YIELDS

If the neutrino luminosity from the proto-neutron star formed during a massive star core collapse exceeds a critical threshold, a supernova (SN) results. Using spherical quasi-static evolutionary sequences for hundreds of progenitors over a range of metallicities, we study how the explosion threshold maps onto observables, including the fraction of successful explosions, the neutron star (NS) and black hole (BH) mass functions, the explosion energies (ESN) and nickel yields (MNi), and their mutual correlations. Successful explosions are intertwined with failures in a complex pattern that is not simply related to initial progenitor mass or compactness. We predict that progenitors with initial masses of 15 ± 1, 19 ± 1, and ∼21–26 M☉ are most likely to form BHs, that the BH formation probability is non-zero at solar-metallicity and increases significantly at low metallicity, and that low luminosity, low Ni-yield SNe come from progenitors close to success/failure interfaces. We qualitatively reproduce the observed ESN–MNi correlation, we predict a correlation between the mean and width of the NS mass and ESN distributions, and that the means of the NS and BH mass distributions are correlated. We show that the observed mean NS mass of ≃ 1.33 M☉ implies that the successful explosion fraction is higher than 0.35. Overall, we show that the neutrino mechanism can in principle explain the observed properties of SNe and their compact objects. We argue that the rugged landscape of progenitors and outcomes mandates that SN theory should focus on reproducing the wide ranging distributions of observed SN properties.
750 GeV diphoton excess confronted with a top-pion in the TTM model

The latest LHC data suggest an intriguing excess at mγγ = 750GeV which apparently requires an explanation from the beyond standard model physics. In this paper, we explore the possibility for this signal to arise from a top-pion in the Top Triangle Moose model which can be viewed as a dimensional-deconstruction version of the top-color assisted technicolor model. We demonstrate that the observed excess can be accommodated by and has important implications for this interesting model.
Accelerating Deep Convolutional Neural Networks Using Number Theoretic Transform

Modern deep convolutional neural networks (CNNs) suffer from high computational complexity due to excessive convolution operations. Recently, fast convolution algorithms such as fast Fourier transform (FFT) and Winograd transform have gained attention to address this problem. They reduce the number of multiplications required in the convolution operation by replacing it with element-wise multiplication in the transform domain. However, fast convolution-based CNN accelerators have three major concerns: expensive domain transform, large memory overhead, and limited flexibility in kernel size. In this paper, we present a novel CNN accelerator based on number theoretic transform (NTT), which overcomes the existing limitations. We propose the low-cost NTT and inverse-NTT converter that only use adders and shifters for on-chip domain transform, which solves the inflated bandwidth problem and enables more parallel computations in the accelerator. We also propose the accelerator architecture that includes multiple tile engines with the optimized data flow and mapping. Finally, we implement the proposed NTT-based CNN accelerator on the Xilinx Alveo U50 FPGA and evaluate it for popular deep CNN models. As a result, the proposed accelerator achieves 2859.5, 990.3, and 805.6 GOPS throughput for VGG-16, GoogLeNet, and Darknet-19, respectively. It outperforms the existing fast convolution-based CNN accelerators up to $9.6\times $ .
Slow wave phenomena in photonic crystals

Slow light in photonic crystals and other periodic structures is associated with stationary points of the photonic dispersion relation, where the group velocity of light vanishes. It is shown that in certain cases, the vanishing group velocity is accompanied by the so‐called frozen mode regime, when the incident light can be completely converted into the slow mode with huge diverging amplitude. The frozen mode regime is a qualitatively new wave phenomenon – it does not reduce to any known electromagnetic resonance. Formally, the frozen mode regime is not a resonance, in a sense that it is not particularly sensitive to the size and shape of the photonic crystal. The frozen mode regime is more robust and powerful, compared to any known slow‐wave resonance. It has much higher tolerance to absorption and structural imperfections.
DISCOVERY OF γ-RAY EMISSION FROM THE RADIO-INTERMEDIATE QUASAR III ZW 2: VIOLENT JET ACTIVITY WITH INTRADAY γ-RAY VARIABILITY

III Zw 2 is the prototype of radio-intermediate quasars. Although there is the evidence of possessing strong jet, significant γ-ray emission has not been reported before. In this work, we carry out a detailed analysis of the latest Fermi-LAT Pass 8 data. No significant γ-ray signal has been detected in the time-averaged 7-year Fermi-LAT data of III Zw 2; however, we have identified two distinct γ-ray flares with isotropic luminosities of ∼1045 erg s−1. Multiwavelength data analysis (also including the optical photometric observations from Yunnan Observatories) are presented and the main finding is simultaneous optical and γ-ray flares of III Zw 2 appearing in 2009 November. Violent γ-ray variability with a doubling timescale of 2.5 hr was detected in another γ-ray flare in May 2010, for which the 3-hr γ-ray peak flux is ∼250 times of the average flux in 7 years. Rather similar behaviors are observed in blazars and the blazar model can reasonably reproduce the spectral energy distribution of III Zw 2 in a wide energy range, strongly suggesting that its central engine resembles that of blazars. In view of its core, which shares radio similarities with young radio sources, together with weak extended radio lobe emission, we suggest that III Zw 2 harbors a recurrent activity core and thus serves as a valuable target for investigating the fueling and triggering of the activity in radio-loud active galactic nuclei.
Accelerating a Morphology-Preserving Adsorption Model by Deep Learning

Physico-chemical simulations are often based on time consuming statistical microstructures models. Recent works show that mathematical morphology and non-linear image processing operators could provide less time-consuming alternatives [1]. Even though these models allow a significant improvement of computation cost, the solving time for a single run on a large microstructure may still require many hours. This paper proposes a two-step approach to overcome this issue in the field of gas adsorption modeling. The first step is to encompass physico-chemical calculation data into 3D adsorption maps. The second step is to train an encoder-decoder convolutional neural network on these maps, to estimate adsorption maps. Using this new approach, the computation time of the adsorption curve for a single run (512 voxels) was reduced by a factor of more than 30.
ANALYTICAL STAR FORMATION RATE FROM GRAVOTURBULENT FRAGMENTATION

We present an analytical determination of the star formation rate (SFR) in molecular clouds, based on a time-dependent extension of our analytical theory of the stellar initial mass function. The theory yields SFRs in good agreement with observations, suggesting that turbulence is the dominant, initial process responsible for star formation. In contrast to previous SFR theories, the present one does not invoke an ad hoc density threshold for star formation; instead, the SFR continuously increases with gas density, naturally yielding two different characteristic regimes, thus two different slopes in the SFR versus gas density relationship, in agreement with observational determinations. Besides the complete SFR derivation, we also provide a simplified expression, which reproduces the complete calculations reasonably well and can easily be used for quick determinations of SFRs in cloud environments. A key property at the heart of both our complete and simplified theory is that the SFR involves a density-dependent dynamical time, characteristic of each collapsing (prestellar) overdense region in the cloud, instead of one single mean or critical freefall timescale. Unfortunately, the SFR also depends on some ill-determined parameters, such as the core-to-star mass conversion efficiency and the crossing timescale. Although we provide estimates for these parameters, their uncertainty hampers a precise quantitative determination of the SFR, within less than a factor of a few.
Toward Stress Detection During Gameplay: A Survey

In day-to-day life, stress can arise due to various factors including work–life demands, external situations, and health issues. Stress becomes a concern when it affects a person's mental health, and sometimes it can even result in other chronic illnesses. Currently, stress-detection studies in the literature are often limited to laboratory studies or to specific situations. However, daily stressors are ongoing, and therefore, detection of stress in everyday life (outside the laboratory environments) is important to improve the wellbeing of individuals. Computer games are entertainment media that can be found in almost every household in the modern society. Statistics show that people spend hours playing computer games daily. The amount of data that gameplay generates and interactivity they provide via various human computer interfaces have a lot of potential in identifying behavior patterns of the players that could assist in the process of stress detection. As such, this survey attempts to identify the extent to which computer games can be used as a medium for stress detection. Toward this end, this survey reviews the existing stress-detection studies, both laboratory techniques as well as the techniques that can be used in a home-based environment. Finally, it summarizes the stress-detection techniques that can be used within games in order to make it an everyday technology that can be used to detect and monitor stress. In addition, it is expected that development of such a technology will be useful in providing objective data to the healthcare professionals for intervention and management. Such a technology is even more required in the current unprecedented situation the world has faced due to the COVID-19 pandemic as it can be developed as a technology to manage mental health issues people are facing due to home isolation.
Autoimmune Hepatitis Following mRNA COVID-19 Vaccination in a Very Old Patient With Preexisting Sjögren’s Syndrome: A Case Report

A case of autoimmune hepatitis (AIH) following COVID-19 vaccination in a very old patient is presented. An 85-year-old woman who had preexisting Sjögren’s syndrome (SS) but had never shown evidence of liver disease was admitted to our hospital due to jaundice and liver dysfunction. Further laboratory tests, imaging studies, and liver histology proved this to be a case of definite AIH. Eight weeks before the disease onset, she had received the second dose of mRNA COVID-19 vaccination. To our knowledge, this is the first case of AIH following COVID-19 vaccination in a patient with a history of SS.
LONG-TERM X-RAY MONITORING OF THE YOUNG PULSAR PSR B1509−58

It has long been thought that the pulsed X-ray properties of rotation-powered pulsars are stable on long timescales. However, long-term, systematic studies of individual sources have been lacking. Furthermore, dramatic X-ray variability has now been observed from two pulsars having inferred sub-critical dipole magnetic fields. Here we present an analysis of the long-term pulsed X-ray properties of the young, energetic pulsar PSR B1509−58 using data from the Rossi X-ray Timing Explorer. We measured the 2–50 keV pulsed flux for 14.7 yr of X-ray observations and found that it is consistent with being constant on all relevant timescales, and place a 3σ upper limit on day-to-week variability of <28%. In addition, we searched for magnetar-like X-ray bursts in all observations and found none, which we use to constrain the measurable burst rate to less than one per 750 ks of observations. We also searched for variability in the pulse profile and found that it is consistent with being stable on timescales of days to decades. This supports the hypothesis that X-ray properties of rotation-powered X-ray pulsars can be stable on decade-long timescales. In addition, we extend the existing timing solution by 7.1 yr to a total of 28.4 yr and report updated values of the braking index, n = 2.832 ± 0.003, and the second braking index, m = 17.6 ± 1.9.