Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicit continuation methods with L-BFGS updating formulas for linearly constrained optimization problems

This paper considers an explicit continuation method with the trusty time-stepping scheme and the limited-memory BFGS (L-BFGS) updating formula (Eptctr) for the linearly constrained optimization problem. At every iteration, Eptctr only involves three pairs of the inner product of vector and one matrix-vector product, other than the traditional and representative optimization method such as the sequential quadratic programming (SQP) or the latest continuation method such as Ptctr \cite{LLS2020}, which needs to solve a quadratic programming subproblem (SQP) or a linear system of equations (Ptctr). Thus, Eptctr can save much more computational time than SQP or Ptctr. Numerical results also show that the consumed time of EPtctr is about one tenth of that of Ptctr or one fifteenth to 0.4 percent of that of SQP. Furthermore, Eptctr can save the storage space of an $(n+m) \times (n+m)$ large-scale matrix, in comparison to SQP. The required memory of Eptctr is about one fifth of that of SQP. Finally, we also give the global convergence analysis of the new method under the standard assumptions.
Ja n 20 21 Symmetry operators for the conformal wave equation

Finnian Gray, 2, ∗ Tsuyoshi Houri, † David Kubizňák, 2, ‡ and Yukinori Yasui § Perimeter Institute, 31 Caroline Street North, Waterloo, ON, N2L 2Y5, Canada Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 National Institute of Technology, Maizuru College, Kyoto 625-8511, Japan Institute for Fundamental Sciences, Setsunan University, Osaka 572-8508, Japan (Dated: January 17, 2021)
Deep Reinforcement Learning for Active High Frequency Trading

We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for active high frequency trading. We train DRL agents to trade one unit of Intel Corporation stock by employing the Proximal Policy Optimization algorithm. The training is performed on three contiguous months of high frequency Limit Order Book data, of which the last month constitutes the validation data. In order to maximise the signal to noise ratio in the training data, we compose the latter by only selecting training samples with largest price changes. The test is then carried out on the following month of data. Hyperparameters are tuned using the Sequential Model Based Optimization technique. We consider three different state characterizations, which differ in their LOB-based meta-features. Analysing the agents' performances on test data, we argue that the agents are able to create a dynamic representation of the underlying environment. They identify occasional regularities present in the data and exploit them to create long-term profitable trading strategies. Indeed, agents learn trading strategies able to produce stable positive returns in spite of the highly stochastic and non-stationary environment.
Community Detection in Blockchain Social Networks

In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network is basically consistent with the ground-truth of betting site community which has been announced to the public. At the meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
Computational Fluid Dynamics Studies of the Effects of Temperature-Dependent Physical Properties on Fixed-Bed Heat Transfer

The influence of the temperature profile on the fluid flow and heat transfer in a fixed bed of tube to particle ratio of 2.86 was studied by solving the 3D Navier−Stokes and energy equations by means of a commercial finite element code, ANSYS/FLOTRAN. The geometry model, representing a fixed bed, consisted of an arrangement of eight spheres in a tube. The difference in heat-transfer parameters between a wall-cooled and wall-heated reactor was studied, using air as a fluid. The dimensionless wall heat-transfer coefficient, Nuw, and the radial effective conductivity ratio, kr/kf, were evaluated from the calculated temperatures at different locations in the bed by comparing these with the analytical solution of a two-dimensional pseudohomogeneous model, using a nonlinear least-squares analysis. Results were obtained for Reynolds numbers in the range 9−1450. Though for high Re there was no real difference between a wall-cooled or a wall-heated tube, for low Re a significant difference was found. The effect of.
The effects of diets containing different levels of common vetch (Vicia sativa) seed on fattening performance, carcass and meat quality characteristics of Awassi male lambs

The experiment was carried out to evaluate the effect of common vetch seed (CVS), used at various levels (0, 15 and 25%), as a protein source in diets replacing different proportion of barley, wheat bran and soybean meal on fattening performance, carcass and meat quality characteristics of Awassi male lambs at approximately 8 months of age. The lambs were divided into three treatment groups, T1 (control, fed a diet without CVS; n = 12), T2 (15% CVS; n = 12) and T3 (25% CVS; n = 12), at the beginning of the fattening period. The T2 and T3 lambs were fed with a concentrate including 15 and 25% of ground CVS. The main protein source was soybean meal in T1 diet treatment, partially replaced by vetch in T2 and T3 diet treatments. All groups were fed a diet consisting of concentrate mixture offered ad libitum, and 300 g of grass hay per lamb per day during the 60 day fattening period. Daily feed intake, daily weight gain and feed conversion efficiency (concentrate and hay consumption for 1 kg of live weight gain) were 1.716 kg, 0.26 kg and 6.60 for T1, 1.756 kg, 0.28 kg and 6.27 for T2 and 1.806 kg, 0.29 kg and 6.23 for T3, respectively. Growth, carcass and meat quality traits were almost the same across the groups. In conclusion, CVS supplementation to diets of Awassi lambs at a 0, 15 and 25% rates showed no statistical differences in fattening performance, wholesale cuts of carcasses, meat color parameters (L* = lightness, a* = redness, b* = yellowness, H = huge angle and C = chroma) and pH values, but there were numerical differences (improvement) in feed conversion efficiency. Copyright © 2005 Society of Chemical Industry
Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts

Multimodal generative models should be able to learn a meaningful latent representation that enables a coherent joint generation of all modalities (e.g., images and text). Many applications also require the ability to accurately sample modalities conditioned on observations of a subset of the modalities. Often not all modalities may be observed for all training data points, so semi-supervised learning should be possible. In this study, we propose a novel product-of-experts (PoE) based variational autoencoder that have these desired properties. We benchmark it against a mixture-of-experts (MoE) approach and an approach of combining the modalities with an additional encoder network. An empirical evaluation shows that the PoE based models can outperform the contrasted models. Our experiments support the intuition that PoE models are more suited for a conjunctive combination of modalities.
Computational Modeling of Aerosol Deposition in Respiratory Tract: A Review

This review article is intended to serve as an overview of the current status of the computational tools and approaches available for predicting respiratory-tract dosimetry of inhaled particulate matter. There are two groups of computational models available, depending on the intended use. The whole-lung models are designed to provide deposition prediction for the whole lung, from the oronasal cavities to the pulmonary region. The whole-lung models are generally semi-empirical and hence provide more reliable results but within the range of parameters used for empirical correlations. The local deposition or computational fluid dynamics (CFD)-based models, on the other hand, utilize comprehensive theoretical and computational approaches but are often limited to upper respiratory tracts. They are based on theoretical principles and are applicable to a wider range of parameters, but less accurate. One of the difficulties with modeling of aerosol deposition in human lung is related to the complexity of the airways geometry and the limited morphometric data available. Another difficulty corresponds to simulation of the realistic physiological conditions of lung environment. Furthermore, complex physical and chemical phenomena associated with dense and multicomponent aerosols complicate the modeling tasks. All of these issues are addressed in this review. The progress made in each area in the last three decades and the challenges ahead are discussed along with some suggestions for future direction. The following subjects are covered in this review: introduction, aerosol deposition mechanisms, elements of a computational model, respiratory-tract geometry models, whole-lung models, CFD based models, cigarette smoke deposition models, and conclusion.
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates

We propose a simple but effective modification of the discriminators, namely measure-conditional discriminators, as a plug-and-play module for different GANs. By taking the generated distributions as part of input so that the target optimum for the discriminator is stationary, the proposed discriminator is more robust than the vanilla one. A variant of the measure-conditional discriminator can also handle multiple target distributions, or act as a surrogate model of statistical distances such as KL divergence with applications to transfer learning.
Protocol for autonomous rearrangement of cold atoms into low-entropy configurations

The preparation of low-entropy starting conditions is a key requirement for many experiments involving neutral atoms. Here, we propose a method to autonomously assemble arbitrary spatial conﬁgurations of atoms within arrays of optical tweezers or lattice sites, enabled by a combination of tunneling and ground-state laser cooling. In contrast to previous methods, our protocol does not rely on either imaging or evaporative cooling. This circumvents limitations associated with imaging ﬁdelity and loss, especially in systems with small spatial scales, while providing a substantial improvement in speed relative to evaporative approaches. These features may make it well-suited for preparing arbitrary initial conditions for Bose-Hubbard or Rydberg interacting systems.
Robustness to Augmentations as a Generalization metric

Generalization is the ability of a model to predict on unseen domains and is a fundamental task in machine learning. Several generalization bounds, both theoretical and empirical have been proposed but they do not provide tight bounds . In this work, we propose a simple yet effective method to predict the generalization performance of a model by using the concept that models that are robust to augmentations are more generalizable than those which are not. We experiment with several augmentations and composition of augmentations to check the generalization capacity of a model. We also provide a detailed motivation behind the proposed method. The proposed generalization metric is calculated based on the change in the output of the model after augmenting the input. The proposed method was the first runner up solution for the NeurIPS competition on Predicting Generalization in Deep Learning.
CO Excitation, Molecular Gas Density, and Interstellar Radiation Field in Local and High-redshift Galaxies

We study the carbon monoxide (CO) excitation, mean molecular gas density, and interstellar radiation field (ISRF) intensity in a comprehensive sample of 76 galaxies from local to high redshift (z ∼ 0–6), selected based on detections of their CO transitions J = 2 → 1 and 5 → 4 and their optical/infrared/(sub)millimeter spectral energy distributions (SEDs). We confirm the existence of a tight correlation between CO excitation as traced by the CO (5–4)/(2–1) line ratio R 52 and the mean ISRF intensity U as derived from infrared SED fitting using dust SED templates. By modeling the molecular gas density probability distribution function (PDF) in galaxies and predicting CO line ratios with large velocity gradient radiative transfer calculations, we present a framework linking global CO line ratios to the mean molecular hydrogen gas density nH2 and kinetic temperature T kin. Mapping in this way observed R 52 ratios to nH2 and T kin probability distributions, we obtain positive U – nH2 and U –T kin correlations, which imply a scenario in which the ISRF in galaxies is mainly regulated by T kin and (nonlinearly) by nH2 . A small fraction of starburst galaxies showing enhanced nH2 could be due to merger-driven compaction. Our work demonstrates that ISRF and CO excitation are tightly coupled and that density–PDF modeling is a promising tool for probing detailed ISM properties inside galaxies.
AMFFCN: Attentional Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement

Audio-visual speech enhancement system is regarded to be one of promising solutions for isolating and enhancing speech of desired speaker. Conventional methods focus on predicting clean speech spectrum via a naive convolution neural network based encoder-decoder architecture, and these methods a) not adequate to use data fully and effectively, b) cannot process features selectively. The proposed model addresses these drawbacks, by a) applying a model that fuses audio and visual features layer by layer in encoding phase, and that feeds fused audio-visual features to each corresponding decoder layer, and more importantly, b) introducing soft threshold attention into the model to select the informative modality softly. This paper proposes attentional audio-visual multi-layer feature fusion model, in which soft threshold attention unit are applied on feature mapping at every layer of decoder. The proposed model demonstrates the superior performance of the network against the state-of-the-art models.
Discrete Double Fibrations

Presheaves on a small category are well-known to correspond via a category of elements construction to ordinary discrete fibrations over that same small category. Work of R. Paré proposes that presheaves on a small double category are certain lax functors valued in the double category of sets with spans. This paper isolates the discrete fibration concept corresponding to this presheaf notion and shows that the category of elements construction introduced by Paré leads to an equivalence of virtual double categories.
DFOGraph: an I/O- and communication-efficient system for distributed fully-out-of-core graph processing

With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While existing distributed out-of-core solutions have made it possible, they suffer from limited performance due to excessive I/O and communication costs. We present DFOGraph, a distributed fully-out-of-core graph processing system that applies and assembles multiple techniques to enable I/O- and communication-efficient processing. DFOGraph builds upon two-level partitions with adaptive compressed representations to allow fine-grained selective computation and communication. Our evaluation shows DFOGraph outperforms Chaos and HybridGraph significantly (>12.94× and >10.82×) when scaling out to eight nodes.
The X-Ray Pulsar XTE J1858+034 Observed with NuSTAR and Fermi/GBM: Spectral and Timing Characterization plus a Cyclotron Line

Accreting X-ray pulsars (XRPs) undergo luminous X-ray outbursts during which the spectral and timing behavior of the neutron star can be studied in detail. We analyze a NuSTAR observation of the XRP XTE J1858+034 during its outburst in 2019. The spectrum is fit with a phenomenological, a semiempirical, and a physical spectral model. A candidate cyclotron line is found at 48 keV, implying a magnetic field of 5.4 × 1012 G at the site of emission. This is also supported by the physical best-fit model. We propose an orbital period of about 81 days based on the visual inspection of the X-ray outburst recurrence time. Based on Fermi/GBM data, the standard disk accretion-torque theory allowed us to infer a distance of 10.9 ± 1.0 kpc. Pulse profiles are single-peaked and show a pulsed fraction that is strongly energy-dependent at least up to 40 keV.
Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRC

The purpose of this project is to evaluate three language models named BERT, ALBERT, and LongFormer on the Question Answering dataset called DuoRC. The language model task has two inputs, a question, and a context. The context is a paragraph or an entire document while the output is the answer based on the context. The goal is to perform grid search hyperparameter fine-tuning using DuoRC. Pretrained weights of the models are taken from the Huggingface library. Different sets of hyperparameters are used to fine-tune the models using two versions of DuoRC which are the SelfRC and the ParaphraseRC. The results show that the ALBERT (pretrained using the SQuAD1 dataset) has an F1 score of 76.4 and an accuracy score of 68.52 after fine-tuning on the SelfRC dataset. The Longformer model (pretrained using the SQuAD and SelfRC datasets) has an F1 score of 52.58 and an accuracy score of 46.60 after fine-tuning on the ParaphraseRC dataset. The current results outperformed the results from the previous model by DuoRC.
Ultrahigh energy cosmic ray deflection by the intergalactic magnetic field

Andrés Arámburo-Garćıa, ∗ Kyrylo Bondarenko, 3, † Alexey Boyarsky, ‡ Dylan Nelson, § Annalisa Pillepich, ¶ and Anastasia Sokolenko ∗∗ Institute Lorentz, Leiden University, Niels Bohrweg 2, Leiden, NL-2333 CA, the Netherlands Theoretical Physics Department, CERN, Geneva 23, CH-1211, Switzerland L’Ecole polytechnique fédérale de Lausanne, 1015 Lausanne, Switzerland Universität Heidelberg, Zentrum für Astronomie, Institut für theoretische Astrophysik, Albert-Ueberle-Str. 2, 69120 Heidelberg, Germany Max-Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany Institute of High Energy Physics, Austrian Academy of Sciences, Nikolsdorfergasse 18, 1050 Vienna, Austria (Dated: January 19, 2021)
A Multiwavelength Look at the GJ 9827 System: No Evidence of Extended Atmospheres in GJ 9827b and d from HST and CARMENES Data

GJ 9827 is a bright star hosting a planetary system with three transiting planets. As a multiplanet system with planets that sprawl within the boundaries of the radius gap between terrestrial and gaseous planets, GJ 9827 is an optimal target to study the evolution of the atmospheres of close-in planets with a common evolutionary history and their dependence from stellar irradiation. Here we report on the Hubble Space Telescope (HST) and CARMENES transit observations of GJ 9827 planets b and d. We performed a stellar and ISM characterization from the ultraviolet HST spectra, obtaining fluxes for Lyα and Mg ii of F(Lyα) = (5.42 ) × 10−13 erg cm−2 s−1 and F(Mgii) = (5.64 ± 0.24) × 10−14 erg cm−2 s−1. We also investigated a possible absorption signature in Lyα in the atmosphere of GJ 9827b during a transit event from HST spectra, as well as Hα and He i signature for the atmosphere of GJ 9827b and d from CARMENES spectra. We found no evidence of an extended atmosphere in either of the planets. This result is also supported by our analytical estimations of mass loss based on the measured radiation fields for all three planets of this system, which led to a mass-loss rate of 0.4, 0.3, and 0.1 planetary masses per Gyr for GJ 9827b, c, and d, respectively. These values indicate that the planets could have lost their volatiles quickly in their evolution and probably do not retain an atmosphere at the current stage.
Performance Analysis and Codebook Design for mmWave Beamforming System With Beam Squint

Beamforming technology is widely used in millimeter wave systems to combat path losses, and beamformers are usually selected from a predefined codebook. Unfortunately, the traditional codebook design neglects the beam squint effect, and this will cause severe performance degradation when the bandwidth is large. In this letter, we consider that a codebook with fixed size is adopted in the wideband beamforming system. First, we analyze how beam squint affects system performance when all beams have the same width. The expression of average spectrum efficiency is derived based on the ideal beam pattern. Next, we formulate the optimization problem to design the optimal codebook. Simulation results demonstrate that the proposed codebook deals with beam squint by spreading the beam coverage and significantly mitigates the performance degradation.
Generation of GelSight Tactile Images for Sim2Real Learning

Most current works in Sim2Real learning for robotic manipulation tasks leverage camera vision that may be significantly occluded by robot hands during the manipulation. Tactile sensing offers complementary information to vision and can compensate for the information loss caused by the occlusions. However, the use of tactile sensing is restricted in the Sim2Real research due to no simulated tactile sensors being available. To mitigate the gap, we introduce a novel approach for simulating a GelSight tactile sensor in the commonly used Gazebo simulator. Similar to the real GelSight sensor, the simulated sensor can produce high-resolution images from depth-maps captured by a simulated optical sensor, and reconstruct the interaction between the touched object and an opaque soft membrane. It can indirectly sense forces, geometry, texture and other properties of the object and enables Sim2Real learning with tactile sensing. Preliminary experimental results have shown that the simulated sensor could generate realistic outputs similar to the ones captured by a real GelSight sensor. All the materials used in this letter are available at https://danfergo.github. io/gelsight-simulation.
TIPTOP: a new tool to efficiently predict your favorite AO PSF

The Adaptive Optics (AO) performance significantly depends on the available Natural Guide Stars (NGSs) and a wide range of atmospheric conditions (seeing, Cn2, windspeed, . . . ). In order to be able to easily predict the AO performance, we have developed a fast algorithm - called TIPTOP - producing the expected AO Point Spread Function (PSF) for any of the existing AO observing modes (SCAO, LTAO, MCAO, GLAO), and any atmospheric conditions. This TIPTOP tool takes its roots in an analytical approach, where the simulations are done in the Fourier domain. This allows to reach a very fast computation time (few seconds per PSF), and efficiently explore the wide parameter space. TIPTOP has been developed in Python, taking advantage of previous work developed in different languages, and unifying them in a single framework. The TIPTOP app is available on GitHub at: https://github.com/FabioRossiArcetri/TIPTOP, and will serve as one of the bricks for the ELT Exposure Time Calculator.