Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Chandra X-Ray Observatory Study of the Millisecond Pulsar Population in the Globular Cluster Terzan 5

We present an analysis of 745.8 ks of archival Chandra X-Ray Observatory Advanced CCD Imaging Spectrometer data accumulated between 2000 and 2016 of the millisecond pulsar (MSP) population in the rich Galactic globular cluster Terzan 5. Eight of the 38 MSPs with precise positions are found to have plausible X-ray source matches. Despite the deep exposure, the remaining MSPs are either marginally detected or have no obvious X-ray counterparts, which can be attributed to the typically soft thermal spectra of rotation-powered MSPs, which are strongly attenuated by the high intervening absorbing column (∼1022 cm−2) toward the cluster, and in some instances to severe source crowding/blending. For the “redback” MSP binaries PSR J1748−2446P and PSR J1748−2446ad and the “black widow” binary system PSR J1748−2446O, we find clear evidence for large-amplitude X-ray variability at the orbital period consistent with an intrabinary shock origin. The third redback MSP in the cluster, PSR J1748−2446A, shows order-of-magnitude variations in flux on timescales of years, possibly due to state transitions or intense flaring episodes from a magnetically active secondary star.
AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy

Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD). They tend to converge excellently at the beginning of training but are weak at the end. An immediate idea is to complement the strengths of these algorithms with SGD. However, a truncated replacement of optimizer often leads to a crash of the update pattern, and new algorithms often require many iterations to stabilize their search direction. Driven by this idea and to address this problem, we design and present a regularized natural gradient optimization algorithm with look-ahead strategy, named asymptotic natural gradient (ANG). According to the total iteration step, ANG dynamic assembles NG and Euclidean gradient, and updates parameters along the new direction using the intensity of NG. Validation experiments on CIFAR10 and CIFAR100 data sets show that ANG can update smoothly and stably at the second-order speed, and achieve better generalization performance.
Differential Capacitance of Electric Double Layers: A Poisson-Bikerman Formula

We propose a Poisson-Bikerman (PBik) formula for calculating the differential capacitance (DC) of electrical double layers (EDLs) in aqueous electrolytes or ionic liquids. The PBik theory is a generalization of the classical Poisson-Boltzmann theory to include different steric energies of different-sized ions and water similar to different electrical energies for different-charged ions. Water and ions with interstitial voids in this molecular mean field theory have their physical volumes as they do in molecular dynamics simulations. The PBik formula derived from Fermi distributions of ions and water in arbitrary shape and volume reduces to the Bikerman-Freise formula derived from the lattice model of equal-sized ions. The DC curves predicted by the Gouy-Chapman formula are U-shaped (for point-like ions with zero volume and very dilute solutions). The curves change from U shape to camel shape (Bactrian) and then to bell shape (for finite size ions) as the volume fraction of ions and water changes from zero to medium value then to large value. The transition is characterized by critical and inflection voltages in terms of the particle volume fraction. These voltages determine steric and electrical energies that describe the space/charge competition and saturation properties of ions and water packed in the condensed layer of EDLs under high field conditions. Steric energy is as important as electrical energy in these conditions. PBik computes symmetric DC curves from delicately balanced steric interactions of asymmetric-size ions and water like the experimental data of KPF6 in aqueous solution. It computes asymmetric curves and captures delicately balanced steric or electrical interactions of ions having different volumes or charges in ionic liquids.
Generating Long-term Continuous Multi-type Generation Profiles using Generative Adversarial Network

Today, the adoption of new technologies has increased power system dynamics significantly. Traditional long-term planning studies that most utility companies perform based on discrete power levels such as peak or average values cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies. As a result, long-term future continuous profiles such as the 8760 hourly profiles are required to enable time-series based long-term planning studies. However, unlike short-term profiles used for operation studies, generating long-term continuous profiles that can reflect both historical time-varying characteristics and future expected power magnitude is very challenging. Current methods such as average profiling have major drawbacks. To solve this challenge, this paper proposes a completely novel approach to generate such profiles for multiple generation types using Generative Adversarial Networks (GAN). A multi-level profile synthesis process is proposed to capture time-varying characteristics at different time levels. Both Single-type GAN and a modified Conditional GAN systems are developed. Unique profile evaluation metrics are proposed. The proposed approach was evaluated based on a public dataset and demonstrated great performance and application value for generating long-term continuous multi-type generation profiles.
Extremely Energetic Supernova Explosions Embedded in a Massive Circumstellar Medium: The Case of SN 2016aps

We perform one-dimensional radiation-hydrodynamic simulations of energetic supernova (SN) ejecta colliding with a massive circumstellar medium (CSM) aimed at explaining SN 2016aps, likely the brightest SN observed to date. SN 2016aps was a superluminous Type IIn SN, which released as much as erg of thermal radiation. Our results suggest that the multiband light curve of SN 2016aps is well explained by the collision of a SN ejecta with the explosion energy of 1052 erg and a wind-like CSM with the outer radius of 1016 cm, that is, a hypernova explosion embedded in a massive CSM. This finding indicates that very massive stars with initial masses larger than , which supposedly produce highly energetic SNe, occasionally eject their hydrogen-rich envelopes shortly before the core collapse. We suggest that the pulsational pair-instability SNe may provide a natural explanation for the massive CSM and the energetic explosion. We also provide the relations among the peak luminosity, the radiated energy and the rise time for interacting SNe with the kinetic energy of 1052 erg, which can be used for interpreting SN 2016aps–like objects in future surveys.
GD 424 – a helium-atmosphere white dwarf with a large amount of trace hydrogen in the process of digesting a rocky planetesimal

The photospheric metal pollution of white dwarfs is now well established as the signature of the accretion of planetary debris. However, the origin of the trace hydrogen detected in many white dwarfs with helium atmospheres is still debated. Here, we report the analysis of GD 424: a metal-polluted, helium-atmosphere white dwarf with a large amount of trace hydrogen. We determined the atmospheric parameters using a hybrid analysis that combines the sensitivity of spectroscopy to the atmospheric composition, log(H/He), with that of photometry and astrometry to the effective temperature, Teff, and surface gravity, log g. The resulting white dwarf mass, radius, and cooling age are $\mbox{$M_{\mathrm{WD}}$}=0.77\pm 0.01\, \mbox{$\mathrm{M}_{\odot }$}$, $\mbox{$R_{\mathrm{WD}}$}=0.0109\pm 0.0001\, \mbox{$\mathrm{R}_{\odot }$}$, and τcool = 215 ± 10 Myr, respectively. We identified and measured the abundances of 11 photospheric metals and argue that the accretion event is most likely either in the increasing or in steady state, and that the disrupted planetesimal resembles either CI chondrites or the bulk Earth in terms of its composition. We suggest that the observed 1.33 × 1022 g of trace hydrogen in GD 424 was at least partly acquired through accretion of water-rich planetary debris in an earlier accretion episode.
Linear-optical dynamics of one-dimensional anyons

We study the dynamics of bosonic and fermionic anyons defined on a one-dimensional lattice, under the effect of Hamiltonians quadratic in creation and annihilation operators, commonly referred to as linear optics. These anyonic models are obtained from deformations of the standard bosonic or fermionic commutation relations via the introduction of a non-trivial exchange phase between different lattice sites. We study the effects of the anyonic exchange phase on the usual bosonic and fermionic bunching behaviors. We show how to exploit the inherent Aharonov-Bohm effect exhibited by these particles to build a deterministic, entangling two-qubit gate and prove quantum computational universality in these systems. We define coherent states for bosonic anyons and study their behavior under two-mode linear-optical devices. In particular we prove that, for a particular value of the exchange factor, an anyonic mirror can generate cat states, an important resource in quantum information processing with continuous variables.
Interpolating Points on a Non-Uniform Grid using a Mixture of Gaussians

In this work, we propose an approach to perform non-uniform image interpolation based on a Gaussian Mixture Model. Traditional image interpolation methods, like nearest neighbor, bilinear, Hamming, Lanczos, etc. assume that the coordinates you want to interpolate from, are positioned on a uniform grid. However, it is not always the case in practice and we develop an interpolation method that is able to generate an image from arbitrarily positioned pixel values. We do this by representing each known pixel as a 2D normal distribution and considering each output image pixel as a sample from the mixture of all the known ones. Apart from the ability to reconstruct an image from arbitrarily positioned set of pixels, this also allows us to differentiate through the interpolation procedure, which might be helpful for downstream applications. Our optimized CUDA kernel and the source code to reproduce the benchmarks is located at https://github.com/universome/non-uniform-interpolation.
The Age of Incorrect Information: An Enabler of Semantics-Empowered Communication

In this paper, we introduce the Age of Incorrect Information (AoII) as an enabler for semantics-empowered communication, a newly advocated communication paradigm centered around data’s role and its usefulness to the communication’s goal. First, we shed light on how the traditional communication paradigm, with its role-blind approach to data, is vulnerable to performance bottlenecks. Next, we highlight the shortcomings of several proposed performance measures destined to deal with the traditional communication paradigm’s limitations, namely the Age of Information (AoI) and the error-based metrics. We also show how the AoII addresses these shortcomings and captures more meaningfully the purpose of data. Afterward, we consider the problem of minimizing the average AoII in a transmitter-receiver pair scenario. We prove that the optimal transmission strategy is a randomized threshold policy, and we propose an algorithm that finds the optimal parameters. Furthermore, we provide a theoretical comparison between the AoII framework and the standard error-based metrics counterpart. Interestingly, we show that the AoII-optimal policy is also error-optimal for the adopted information source model. Concurrently, the converse is not necessarily true. Finally, we implement our policy in various applications, and we showcase its performance advantages compared to both the error-optimal and the AoI-optimal policies.
Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters

Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages. In this paper, and from an algorithmic perspective, we explore if the chosen architecture, when trained with the same data, influences the gender bias accuracy. Experiments in four language pairs show that Language-Specific encoders-decoders exhibit less bias than the Shared encoder-decoder architecture. Further interpretability analysis of source embeddings and the attention shows that, in the Language-Specific case, the embeddings encode more gender information, and its attention is more diverted. Both behaviors help in mitigating gender bias.
Physical training improves physical activity levels but is associated with amplification of sedentary behavior in older women

Physical activity level (PAL) and sedentary behavior (SB) are independent predictors of mortality. It is unclear how these predictors interact with each other and health variables. Investigate the bidirectional relationship between PAL and SB, and their impact and health variables of women aged 60 to 70 years. One hundred forty-two older adults women (66.3 ± 2.9 years) considered insufficiently active were submitted to 14 weeks of multicomponent training (MT), multicomponent training with flexibility (TMF), or the control group (CG). PAL variables were analyzed by accelerometry and QBMI questionnaire, physical activity (PA) light, moderate, vigorous and CS by accelerometry, 6 min walk (CAM), SBP, BMI, LDL, HDL, uric acid, triglycerides, glucose and cholesterol total. In linear regressions, CS was associated with glucose (B:12.80; CI:9.31/20.50; p < 0.001; R2:0.45), light PA (B:3.10; CI:2, 41/4.76; p < 0.001; R2:0.57), NAF by accelerometer (B:8.21; CI:6.74/10.02; p < 0.001; R2:0.62), vigorous PA (B:794.03; CI:682.11/908.2; p < 0.001; R2:0.70), LDL (B:13.28; CI:7.45/16.75; p < 0.002; R2:0.71) and 6 min walk (B:3.39; CI:2.96/8.75; p < 0.004; R2:0.73). NAF was associated with mild PA (B:0.246; CI:0.130/0.275; p < 0.001; R2:0.624), moderate PA (B:0.763; CI:0.567/0.924; p < 0.001; R2:0.745), glucose (B:−0.437; CI:−0.789/−0.124; p < 0.001; R2:0.782), CAM (B:2.223; CI:1.872/4.985; p < 0.002; R2:0.989) and CS (B:0.253; CI: 0.189/0.512; p < 0.001; R2:1.94). The NAF can enhance CS. Build a new look at how these variables are independent but dependent simultaneously, being able to influence the quality of health when this dependence is denied.
Stochastic Steepest Descent Methods for Linear Systems: Greedy Sampling & Momentum

Recently proposed adaptive Sketch&Project (SP) methods connect several well-known projection methods such as Randomized Kaczmarz (RK), Randomized Block Kaczmarz (RBK), Motzkin Relaxation (MR), Randomized Coordinate Descent (RCD), Capped Coordinate Descent (CCD), etc. into one framework for solving linear systems. In this work, we first propose a Stochastic Steepest Descent (SSD) framework that connects SP methods with the well-known Steepest Descent (SD) method for solving positive-definite linear system of equations. We then introduce two greedy sampling strategies in the SSD framework that allow us to obtain algorithms such as Sampling Kaczmarz Motzkin (SKM), Sampling Block Kaczmarz (SBK), Sampling Coordinate Descent (SCD), etc. In doing so, we generalize the existing sampling rules into one framework and develop an efficient version of SP methods. Furthermore, we incorporated the Polyak momentum technique into the SSD method to accelerate the resulting algorithms. We provide global convergence results for both the SSD method and the momentum induced SSD method. Moreover, we prove $\mathcal{O}(\frac{1}{k})$ convergence rate for the Cesaro average of iterates generated by both methods. By varying parameters in the SSD method, we obtain classical convergence results of the SD method as well as the SP methods as special cases. We design computational experiments to demonstrate the performance of the proposed greedy sampling methods as well as the momentum methods. The proposed greedy methods significantly outperform the existing methods for a wide variety of datasets such as random test instances as well as real-world datasets (LIBSVM, sparse datasets from matrix market collection). Finally, the momentum algorithms designed in this work accelerate the algorithmic performance of the SSD methods.
Dissipative particle dynamics modeling of a graphene nanosheet and its self-assembly with surfactant molecules

A coarse-grained graphene nanosheet (CGNS) is modeled with dissipative particle dynamics to investigate its self-assembly with surfactant molecules adsorbing onto the graphene nanosheet in aqueous solution. The adsorption dynamics of coarse-grained sodium dodecyl sulfate (CSDS) and a micelle collapse are observed with the CGNS. Different morphologies for the equilibrium structures are obtained by varying the concentration of CSDS in a simulation box. It is found that the arrangement of the CSDS molecules onto the CGNS changed from the edge to the central region of the CGNS to minimize the exposure of the hydrophobic segments of the CSDS molecules to water. In particular, the CSDS surfactants tend to be located beside the CGNS edge at a high ratio of surfactant molecules to the graphene surface area.
Representing Partial Programs with Blended Abstract Semantics

Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next. We introduce a general technique for representing partially written programs in a program synthesis engine. We take inspiration from the technique of abstract interpretation, in which an approximate execution model is used to determine if an unfinished program will eventually satisfy a goal specification. Here we learn an approximate execution model implemented as a modular neural network. By constructing compositional program representations that implicitly encode the interpretation semantics of the underlying programming language, we can represent partial programs using a flexible combination of concrete execution state and learned neural representations, using the learned approximate semantics when concrete semantics are not known (in unfinished parts of the program). We show that these hybrid neuro-symbolic representations enable execution-guided synthesizers to use more powerful language constructs, such as loops and higher-order functions, and can be used to synthesize programs more accurately for a given search budget than pure neural approaches in several domains.
Improved in vitro dissolution parameters and in vivo hypolipidemic efficiency of atorvastatin calcium through the formation of hydrophilic inclusion complex with cyclodextrins

An aim of the present study was to improve the dissolution of the inherently low water solubility hypolipidemic agent, Atorvastatin calcium (ATC), through the preparation and characterization of ATC with cyclodextrins (CDs) inclusion complexes employing different techniques. A second goal was to study the in vivo hypolipidemic efficacy of ATC‐complexes with enhanced dissolution characteristics. Inclusion complexation of ATC with β‐cyclodextrin (β‐CD) and hydroxypropyl‐β‐cyclodextrin (HP‐β‐CD) was evaluated in aqueous and solid states. ATC formed inclusion complexes with β‐CD and HP‐β‐CD depending to a great extent upon ATC ionization state. Evaporation and freeze‐drying were the most efficient techniques to achieve complexation. In contrast, kneading was an inefficient tool to create true inclusion complexes, which could reflect the hindrance of drug–CDs interactions in the semisolid medium. The ATC:CD ratio of 1:2 showed better dissolution characteristics compared to a 1:1 ratio. Moreover, the in vivo hypolipidemic activities of ATC‐CDs (β‐CD and HP‐β‐CD) complexes were greater (P<0.05) than the other investigated formulations. Thus the nature of the carrier did not play a critical role in the dissolution characteristics of the inclusion system. In contrast, the carrier molar ratio, and the employed complexation technique were found to be key factors in enhancing the ATC dissolution rate, yielding performances as well as the in vivo hypolipidemic efficacies. Drug Dev Res 72: 379–390, 2011. © 2011 Wiley‐Liss, Inc.
Generalizable multi-parameter quantum metrology

Han Xu, 2 Lingna Wang, Haidong Yuan, ∗ and Xin Wang † Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China, and City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong 518057, China School of Physics and Technology, Wuhan University, Wuhan 430072, China Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China (Dated: December 25, 2020)
A Hybrid Bandit Framework for Diversified Recommendation

The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set. However, the investigation of users' personalized preferences on the diversity properties of an item set is usually ignored. To overcome this problem, we propose the Linear Modular Dispersion Bandit (LMDB) framework, which is an online learning setting for optimizing a combination of modular functions and dispersion functions. Specifically, LMDB employs modular functions to model the relevance properties of each item, and dispersion functions to describe the diversity properties of an item set. Moreover, we also develop a learning algorithm, called Linear Modular Dispersion Hybrid (LMDH) to solve the LMDB problem and derive a gap-free bound on its n-step regret. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed LMDB framework in balancing the recommendation accuracy and diversity.
Toward the Use of Temporary Tattoo Electrodes for Impedancemetric Respiration Monitoring and Other Electrophysiological Recordings on Skin

The development of dry, ultra-conformable and unperceivable temporary tattoo electrodes (TTEs), based on the ink-jet printing of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on top of commercially available temporary tattoo paper, has gained increasing attention as a new and promising technology for electrophysiological recordings on skin. In this work, we present a TTEs epidermal sensor for real time monitoring of respiration through transthoracic impedance measurements, exploiting a new design, based on the application of soft screen printed Ag ink and magnetic interlink, that guarantees a repositionable, long-term stable and robust interconnection of TTEs with external “docking” devices. The efficiency of the TTE and the proposed interconnection strategy under stretching (up to 10%) and over time (up to 96 h) has been verified on a dedicated experimental setup and on humans, fulfilling the proposed specific application of transthoracic impedance measurements. The proposed approach makes this technology suitable for large-scale production and suitable not only for the specific use case presented, but also for real time monitoring of different bio-electric signals, as demonstrated through specific proof of concept demonstrators.