Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetohydrodynamic turbulence and propagation of cosmic rays: theory confronted with observations

Cosmic ray propagation is determined by the properties of interstellar turbulence. The multiphase nature of interstellar medium (ISM) and diversity of driving mechanisms give rise to spatial variation of turbulence properties. Meanwhile, precision astroparticle experiments pose challenges to the conventional picture of homogeneous and isotropic transport of cosmic rays (CRs). We are opening a new horizon for CR propagation research when studies of particle transport and interstellar turbulence confront each other. Here we review our recent developement on understandings of magnetohydrodynamic (MHD) turbulence and its connection to the fundamental processes governing cosmic ray propagation, different regimes of particle transport, that are augmented with observational discovery and analysis from multi-wavelength observations.
Measured and Predicted Vapor Liquid Equilibrium of Ethanol-Gasoline Fuels with Insight on the Influence of Azeotrope Interactions on Aromatic Species Enrichment and Particulate Matter Formation in Spark Ignition Engines

A relationship has been observed between increasing ethanol content in gasoline and increased particulate matter (PM) emissions from direct injection spark ignition (DISI) vehicles. The fundamental cause of this observation is not well understood. One potential explanation is that increased evaporative cooling as a result of ethanol’s high HOV may slow evaporation and prevent sufficient reactant mixing resulting in the combustion of localized fuel rich regions within the cylinder. In addition, it is well known that ethanol when blended in gasoline forms positive azeotropes which can alter the liquid/vapor composition during the vaporization process. In fact, it was shown recently through a numerical study that these interactions can retain the aromatic species within the liquid phase impeding the in-cylinder mixing of these compounds, which would accentuate PM formation upon combustion. To better understand the role of the azeotrope interactions on the vapor/liquid composition evolution of the fuel, distillations were performed using the Advanced Distillation Curve apparatus on carefully selected samples consisting of gasoline blended with ethanol and heavy aromatic and oxygenated compounds with varying vapor pressures, including cumene, p-cymene, 4-tertbutyl toluene, anisole, and 4-methyl anisole. Samples collected during the distillation indicate an enrichment of the heavy aromatic or oxygenated additive with an increase in initial ethanol concentration from E0 to E30. A recently developed distillation and droplet evaporation model is used to explore the influence of dilution effects versus azeotrope interactions on the aromatic species enrichment. The results suggest that HOV-cooling effects as well as aromatic species enrichment behaviors should be considered in future development of predictive indices to forecast the PM potential of fuels containing oxygenated compounds with comparatively high HOV.
Improved Dynamical Masses for Six Brown Dwarf Companions Using Hipparcos and Gaia EDR3

We present comprehensive orbital analyses and dynamical masses for the substellar companions Gl 229 B, Gl 758 B, HD 13724 B, HD 19467 B, HD 33632 Ab, and HD 72946 B. Our dynamical fits incorporate radial velocities, relative astrometry, and, most importantly, calibrated Hipparcos-Gaia EDR3 accelerations. For HD 33632 A and HD 72946 we perform three-body fits that account for their outer stellar companions. We present new relative astrometry of Gl 229 B with Keck/NIRC2, extending its observed baseline to 25 yr. We obtain a <1% mass measurement of 71.4 ± 0.6 M Jup for the first T dwarf Gl 229 B and a 1.2% mass measurement of its host star (0.579 ± 0.007 M ⊙) that agrees with the high-mass end of the M-dwarf mass–luminosity relation. We perform a homogeneous analysis of the host stars’ ages and use them, along with the companions’ measured masses and luminosities, to test substellar evolutionary models. Gl 229 B is the most discrepant, as models predict that an object this massive cannot cool to such a low luminosity within a Hubble time, implying that it may be an unresolved binary. The other companions are generally consistent with models, except for HD 13724 B, which has a host star activity age 3. 8σ older than its substellar cooling age. Examining our results in context with other mass–age–luminosity benchmarks, we find no trend with spectral type but instead note that younger or lower-mass brown dwarfs are overluminous compared to models, while older or higher-mass brown dwarfs are underluminous. The presented mass measurements for some companions are so precise that the stellar host ages, not the masses, limit the analysis.
SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving

Road extraction is an essential step in building autonomous navigation systems. Detecting road segments is challenging as they are of varying widths, bifurcated throughout the image, and are often occluded by terrain, cloud, or other weather conditions. Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image which is essential to extract road connectivity. To this end, we propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps. Reasoning over spatial space extracts dependencies between different spatial regions and other contextual information. Reasoning over a projected interaction space helps in appropriate delineation of roads from other topographies present in the image. Thus, SPIN extracts long-range dependencies between road segments and effectively delineates roads from other semantics. We also introduce a SPIN pyramid which performs SPIN graph reasoning across multiple scales to extract multi-scale features. We propose a network based on stacked hourglass modules and SPIN pyramid for road segmentation which achieves better performance compared to existing methods. Moreover, our method is computationally efficient and significantly boosts the convergence speed during training, making it feasible for applying on large-scale high-resolution aerial images. Code available at: https://github.com/wgcban/SPIN_RoadMapper. git.
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning

Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL framework is composed of two components: (1) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). (2) Meta-test. Apply the trained FEM to acquire the novel data's features and recognize them. FSL relies heavily on the design of the FEM. However, various FEMs have distinct emphases. For example, several may focus more attention on the contour information, whereas others may lay particular emphasis on the texture information. The single-head feature is only a one-sided representation of the sample. Besides the negative influence of cross-domain (e.g., the trained FEM can not adapt to the novel class flawlessly), the distribution of novel data may have a certain degree of deviation compared with the ground truth distribution, which is dubbed as distribution-shift-problem (DSP). To address the DSP, we propose Multi-Head Feature Collaboration (MHFC) algorithm, which attempts to project the multi-head features (e.g., multiple features extracted from a variety of FEMs) to a unified space and fuse them to capture more discriminative information. Typically, first, we introduce a subspace learning method to transform the multi-head features to aligned low-dimensional representations. It corrects the DSP via learning the feature with more powerful discrimination and overcomes the problem of inconsistent measurement scales from different head features. Then, we design an attention block to update combination weights for each head feature automatically. It comprehensively considers the contribution of various perspectives and further improves the discrimination of features. We evaluate the proposed method on five benchmark datasets (including cross-domain experiments) and achieve significant improvements of 2.1%-7.8% compared with state-of-the-arts.
Landau Modes are Eigenmodes of Stellar Systems in the Limit of Zero Collisions

We consider the spectrum of eigenmodes in a stellar system dominated by gravitational forces in the limit of zero collisions. We show analytically and numerically using the Lenard–Bernstein collision operator that the Landau modes, which are not true eigenmodes in a strictly collisionless system (except for the Jeans unstable mode), become part of the true eigenmode spectrum in the limit of zero collisions. Under these conditions, the continuous spectrum of true eigenmodes in a collisionless system, also known as the Case–van Kampen modes, is eliminated. Furthermore, because the background distribution function in a weakly collisional system can exhibit significant deviations from a Maxwellian distribution function over long times, we show that the spectrum of Landau modes can change drastically even in the presence of slight deviations from a Maxwellian, primarily through the appearance of weakly damped modes that may be otherwise heavily damped for a Maxwellian distribution. Our results provide important insights for developing statistical theories to describe thermal fluctuations in a stellar system, which are currently a subject of great interest for N-body simulations as well as observations of gravitational systems.
A Statistical Detection of Wide Binary Systems in the Ultrafaint Dwarf Galaxy Reticulum II

Binary stars can inflate the observed velocity dispersion of stars in dark matter-dominated systems such as ultrafaint dwarf galaxies (UFDs). However, the population of binaries in UFDs is poorly constrained by observations, with preferred binary fractions for individual galaxies ranging from a few percent to near unity. Searching for wide binaries through nearest neighbor (NN) statistics (or the two-point correlation function) has been suggested in the literature, and we apply this method for the first time to detect wide binaries in a UFD. By analyzing the positions of stars in Reticulum II (Ret II) from Hubble Space Telescope images, we search for angularly resolved wide binaries in Ret II. We find that the distribution of their NN distances shows an enhancement at projected separations of ≲1″ relative to a model containing no binaries. We show that such an enhancement can be explained by a wide binary fraction of fb≈0.007−0.003+0.008 at separations of more than 3000 au. Under the assumption that the binary separation distribution is similar to that in the Milky Way, the total binary fraction in Ret II may be on the order of 50%. We also use the observed magnitude distribution of stars in Ret II to constrain the initial mass function over the mass range 0.34–0.78 M ⊙, finding that a shallow power-law slope of 1.01 ≤ α ≤ 1.15 matches the data.
Chemical kinetics analysis of NO and CO formation based on thermodynamic data from an ethanol-fueled engine computational model

A series of theoretical analyses of pollutant formation has been undertaken on a computer program based on temperature and volume data from a two-zone internal combustion engine model. The objective was to predict the formation of nitric oxide and carbon monoxide during combustion process in an ethanol-fueled engine before the three-way catalyst operation. The predictions were based on a chemical kinetics model, which considered 12 chemical species and 22 chemical reactions. The model calculated the reaction rates from the beginning of the combustion process until chemical kinetics calculations showed no effect on the gases composition during expansion. The developed analysis tested some temperatures in order to discover until when chemical kinetics effectiveness was considerable. This analysis involved the study of NO and CO formation rates. The considered range of temperatures was between 2500 and 1500K, typical engine temperatures during combustion and expansion processes. Results showed that NO and CO formation were qualitatively coherent when compared to known formation measures by ICEs presented on literature. The studied temperatures allow the possibility of maintaining constant the concentration of some species from specific moments during expansion process, without changing significantly formation results.
Normalization of congruence of bitangents to a hypersurface in $\mathbb P^3$

A congruence is a surface in the Grassmannian Gr(2, 4). In this paper, we consider the normalization of congruence of bitangents to a hypersurface in P. We call it the Fano congruence of bitangents. We give a criterion for smoothness of the Fano congruence of bitangents and describe explicitly their degenerations in a general Lefschetz pencil in the space of hypersurfaces in P.
Formation and evolution of protostellar accretion discs – II. From 3D simulation to a simple semi-analytic model of Class 0/I discs

We use a 3D radiative non-ideal magnetohydrodynamic simulation to investigate the formation and evolution of a young protostellar disc from a magnetized pre-stellar core. The simulation covers the first ${\sim }10\, {\rm kyr}$ after protostar formation and shows a massive, weakly magnetized disc with radius that initially grows and then saturates at ${\sim }30\, {\rm au}$. The disc is gravitationally unstable with prominent large-amplitude spiral arms. We use our simulation results and a series of physical arguments to construct a predictive and quantitative physical picture of Class 0/I protostellar disc evolution from several aspects, including (i) the angular-momentum redistribution in the disc, self-regulated by gravitational instability to make most of the disc marginally unstable; (ii) the thermal profile of the disc, well-approximated by a balance between radiative cooling and accretion heating; and (iii) the magnetic-field strength and magnetic-braking rate inside the disc, regulated by non-ideal magnetic diffusion. Using these physical insights, we build a simple 1D semi-analytic model of disc evolution. We show that this 1D model, when coupled to a computationally inexpensive simulation for the evolution of the surrounding pseudo-disc, can be used reliably to predict disc evolution in the Class 0/I phase. The predicted long-term evolution of disc size, which saturates at ${\sim }30\, {\rm au}$ and eventually shrinks, is consistent with a recent observational survey of Class 0/I discs. Such hierarchical modelling of disc evolution circumvents the computational difficulty of tracing disc evolution through Class 0/I phase with direct, numerically converged simulations.
Ultimate phase sensitivity in Surface PlasmonResonance sensors by tuning critical couplingwith phase change materials

Plasmonic sensing is an established technology for real-time biomedical diagnostics and air-quality monitoring. While intensity and wavelength tracking are the most commonly used interrogation methods for Surface Plasmon Resonance (SPR), several works indicate the potential superiority of phase interrogation in detection sensitivity. Here, we theoretically and numerically establish the link between ultra-high sensitivities in phase interrogation SPR sensors and the critical coupling condition. However, reaching this condition requires a technically infeasible angstrom-level precision in the metal layer thickness. We propose a robust solution to overcome this limitation by coupling the SPR with a phase-change material (PCM) thin film. By exploiting the multilevel reconfigurable phase states of PCM, we theoretically demonstrate ultra-high phase sensitivities with a limit of detection as low as $10^{-10}$ refractive index unit (RIU). Such a PCM-assisted SPR sensor platform paves the way for unprecedented sensitivity sensors for the detection of trace amounts of low molecular weight species in biomedical sensing and environmental monitoring.
Dala: a simple capability-based dynamic language design for data race-freedom

Dynamic languages like Erlang, Clojure, JavaScript, and E adopted data-race freedom by design. To enforce data-race freedom, these languages either deep copy objects during actor (thread) communication or proxy back to their owning thread. We present Dala, a simple programming model that ensures data-race freedom while supporting efficient inter-thread communication. Dala is a dynamic, concurrent, capability-based language that relies on three core capabilities: immutable values can be shared freely; isolated mutable objects can be transferred between threads but not aliased; local objects can be aliased within their owning thread but not dereferenced by other threads. Objects with capabilities can co-exist with unsafe objects, that are unchecked and may suffer data races, without compromising the safety of safe objects. We present a formal model of Dala, prove data race-freedom and state and prove a dynamic gradual guarantee. These theorems guarantee data race-freedom when using safe capabilities and show that the addition of capabilities is semantics preserving modulo permission and cast errors.
Solar Orbiter/RPW antenna calibration in the radio domain and its application to type III burst observations

In order to allow for a comparison with the measurements from other antenna systems, the voltage power spectral density measured by the Radio and Plasma waves receiver (RPW) on board Solar Orbiter needs to be converted into physical quantities that depend on the intrinsic properties of the radiation itself.The main goal of this study is to perform a calibration of the RPW dipole antenna system that allows for the conversion of the voltage power spectral density measured at the receiver's input into the incoming flux density. We used space observations from the Thermal Noise Receiver (TNR) and the High Frequency Receiver (HFR) to perform the calibration of the RPW dipole antenna system. Observations of type III bursts by the Wind spacecraft are used to obtain a reference radio flux density for cross-calibrating the RPW dipole antennas. The analysis of a large sample of HFR observations (over about ten months), carried out jointly with an analysis of TNR-HFR data and prior to the antennas' deployment, allowed us to estimate the reference system noise of the TNR-HFR receivers. We obtained the effective length of the RPW dipoles and the reference system noise of TNR-HFR in space, where the antennas and pre-amplifiers are embedded in the solar wind plasma. The obtained $l_{eff}$ values are in agreement with the simulation and measurements performed on the ground. By investigating the radio flux intensities of 35 type III bursts simultaneously observed by Solar Orbiter and Wind, we found that while the scaling of the decay time as a function of the frequency is the same for the Waves and RPW instruments, their median values are higher for the former. This provides the first observational evidence that Type III radio waves still undergo density scattering, even when they propagate from the source, in a medium with a plasma frequency that is well below their own emission frequency.
Associative Memories via Predictive Coding

Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this paper, we present a novel neural model for realizing associative memories, which is based on a hierarchical generative network that receives external stimuli via sensory neurons. It is trained using predictive coding, an error-based learning algorithm inspired by information processing in the cortex. To test the model's capabilities, we perform multiple retrieval experiments from both corrupted and incomplete data points. In an extensive comparison, we show that this new model outperforms in retrieval accuracy and robustness popular associative memory models, such as autoencoders trained via backpropagation, and modern Hopfield networks. In particular, in completing partial data points, our model achieves remarkable results on natural image datasets, such as ImageNet, with a surprisingly high accuracy, even when only a tiny fraction of pixels of the original images is presented. Our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances. To this end, we present CONTaiNER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. Instead of optimizing class-specific attributes, CONTaiNER optimizes a generalized objective of differentiating between token categories based on their Gaussian-distributed embeddings. This effectively alleviates overfitting issues originating from training domains. Our experiments in several traditional test domains (OntoNotes, CoNLL’03, WNUT ‘17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that on average, CONTaiNER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance.
Joint ergodicity of Hardy field sequences

We study mean convergence of multiple ergodic averages, where the iterates arise from smooth functions of polynomial growth that belong to a Hardy field. Our results include all logarithmico-exponential functions of polynomial growth, such as the functions $t^{3/2}, t\log t$ and $e^{\sqrt{\log t}}$. We show that if all non-trivial linear combinations of the functions $a_1,. ,a_k$ stay logarithmically away from rational polynomials, then the $L^2$-limit of the ergodic averages $\frac{1}{N} \sum_{n=1}^{N}f_1(T^{\lfloor{a_1(n)}\rfloor}x)\cdots f_k(T^{\lfloor{a_k(n)}\rfloor}x)$ exists and is equal to the product of the integrals of the functions $f_1,. ,f_k$ in ergodic systems, which establishes a conjecture of Frantzikinakis. Under some more general conditions on the functions $a_1,. ,a_k$, we also find characteristic factors for convergence of the above averages and deduce a convergence result for weak-mixing systems.