Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon-Enhanced Metal-Poor Stars. III. Main-Sequence Turnoff Stars from the SDSS SEGUE Sample

The chemical compositions of seven CEMP turnoff stars are determined from high-resolution spectroscopy. Five are selected from the SDSS SEGUE sample of metal-poor stars. Another star chosen from the SDSS SEGUE sample has only a weak upper limit on its carbon abundance obtained from the high-resolution spectrum. The effective temperatures are all higher than 6000 K, while the metallicities, parameterized by [ Fe/H ] , are all below –2; the star with the lowest iron abundance has [ Fe/H ] = − 3.1. Six of our objects exhibit high abundance ratios of barium ([ Ba/Fe ] > + 1), suggesting large contributions of the products of former AGB companions via mass transfer across binary systems. One star (SDSS 1707+58) exhibits rapid variation in radial velocity, a strong signature that it belongs to a close binary. Combining our results with previous studies provides a total of 20 CEMP main-sequence turnoff stars for which the abundances of carbon and at least some neutron-capture elements are determined. The [ C/H ] ratios for this sample of CEMP turnoff stars are generally higher than those of CEMP giants and their dispersion is also smaller. These results indicate that the carbon-enhanced material from the companion AGB star is preserved at the surface of turnoff stars with no significant dilution, counter to expectations if processes such as thermohaline mixing have operated in unevolved CEMP stars. In contrast to the behavior of [ C/H ] , a large dispersion in the observed [ Ba/H ] is found for the CEMP turnoff stars, suggesting that the efficiency of the s-process in very metal-poor AGB stars may differ greatly from star to star. Four of the six stars from the sample exhibit kinematics associated with membership in the outer-halo population, a remarkably high fraction.
Constraints on the phase transition of early dark energy with the CMB anisotropies

Early dark energy (EDE) models have attracted attention in the context of the recent problem of the Hubble tension. Here we extend these models by taking into account the new density fluctuations generated by the EDE which decays around the recombination phase. We solve the evolution of the density perturbations in dark energy fluid generated at the phase transition of EDE as isocurvature perturbations. Assuming that the isocurvature mode is characterized by a power-law power spectrum and is uncorrelated with the standard adiabatic mode, we calculate the CMB angular power spectra. By comparing them to the Planck data using the Markov-Chain Monte Carlo method, we obtained zero-consistent values of the EDE parameters and H 0 = 67.56+0.65 -0.66 km s-1 Mpc-1 at 68 % CL. This H 0 value is almost the same as the Planck value in the ΛCDM model, H 0 = 67.36 ± 0.54 km s-1 Mpc-1, and there is still a ∼ 3. 5σ tension between the CMB and Type Ia supernovae observations. Including CMB lensing, BAO, supernovae and SH0ES data sets, we find H 0 = 68.94+0.47 -0.57 km s-1 Mpc-1 at 68 % CL. The amplitude of the fluctuations induced by the phase transition of the EDE is constrained to be less than 1–2 percent of the amplitude of the adiabatic mode. This is so small that such non-standard fluctuations cannot appear in the CMB angular spectra. In conclusion, the isocurvature fluctuations induced by our simplest EDE phase transition model do not explain the Hubble tension well.
SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.
Hybrid Simulations of the Cusp and Dayside Magnetosheath Dynamics Under Quasi‐Radial Interplanetary Magnetic Fields

Under quasi‐radial interplanetary magnetic fields (IMF), foreshock turbulence can have an impact on the magnetosheath and cusps depending on the location of the quasi‐parallel shock. We perform three‐dimensional simulations of Earth's dayside magnetosphere using the hybrid code HYPERS, and compare northward and southward quasi‐radial IMF configurations. We study the magnetic field configuration, fluctuations in the magnetosheath and the plasma in the regions around the northern cusp. Under northward IMF with Earthward Bx, there is a time‐varying plasma depletion layer immediately outside the northern cusp. In the southward IMF case, the impact of foreshock turbulence and high‐speed jets, together with magnetopause reconnection, can lead to strong density enhancements in the cusp.
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation

Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to enable probing the capabilities of NLP methods, with the aim of understanding sub-clausal negation. The test suite contains premise–hypothesis pairs where the premise contains sub-clausal negation and the hypothesis is constructed by making minimal modifications to the premise in order to reflect different possible interpretations. Aside from adopting standard NLI labels, our test suite is systematically constructed under a rigorous linguistic framework. It includes annotation of negation types and constructions grounded in linguistic theory, as well as the operations used to construct hypotheses. This facilitates fine-grained analysis of model performance. We conduct experiments using pre-trained language models to demonstrate that our test suite is more challenging than existing benchmarks focused on negation, and show how our annotation supports a deeper understanding of the current NLI capabilities in terms of negation and quantification.
Maximum Likelihood-Based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL With Grid Refinement

We consider the parametric measurement model employed in applications such as line spectral or direction-of-arrival estimation with the goal to estimate the underlying parameter in a gridless manner. We focus on the stochastic maximum likelihood estimation (MLE) framework and overcome the model complexities of the past by reparameterization of the objective and exploiting the sparse Bayesian learning (SBL) approach. SBL is shown to be a correlation-aware method and, for the underlying problem, a grid-based technique for recovering a structured covariance matrix of the measurements. For the case when measurements are spatial (or temporal) samples at regular intervals, the structured matrix is expressible as a sampled Toeplitz matrix. In this case, additional constraints and reparameterization of the SBL objective leads to the proposed structured matrix recovery technique based on MLE. The optimization problem is non-convex and a majorization-minimization based iterative procedure is proposed to estimate the structured matrix; each iteration solves a semidefinite program. We recover the parameter of interest in a gridless manner by appealing to the Carathéodory-Féjer result on decomposition of positive semidefinite Toeplitz matrices. For the general case of irregularly spaced samples, we propose an iterative SBL procedure that refines grid points to increase resolution near potential source locations, while maintaining a low per iteration complexity. We provide numerical results to compare the performance of the proposed techniques with other gridless techniques, and the Cramér-Rao bound. The proposed correlation-aware approach is more robust to issues such as fewer snapshots, correlated or closely separated sources, and improves sources identifiability.
Improving Large-scale Paraphrase Acquisition and Generation

This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks. We present a new Multi-Topic Paraphrase in Twitter (MultiPIT) corpus that consists of a total of 130k sentence pairs with crowdsoursing (MultiPIT_crowd) and expert (MultiPIT_expert) annotations using two different paraphrase definitions for paraphrase identification, in addition to a multi-reference test set (MultiPIT_NMR) and a large automatically constructed training set (MultiPIT_Auto) for paraphrase generation. With improved data annotation quality and task-specific paraphrase definition, the best pre-trained language model fine-tuned on our dataset achieves the state-of-the-art performance of 84.2 F1 for automatic paraphrase identification. Furthermore, our empirical results also demonstrate that the paraphrase generation models trained on MultiPIT_Auto generate more diverse and high-quality paraphrases compared to their counterparts fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT.
An Investigation into Whitening Loss for Self-supervised Learning

A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of positive pairs under the conditioning that the embeddings from different views are whitened. In this paper, we propose a framework with an informative indicator to analyze whitening loss, which provides a clue to demystify several interesting phenomena as well as a pivoting point connecting to other SSL methods. We reveal that batch whitening (BW) based methods do not impose whitening constraints on the embedding, but they only require the embedding to be full-rank. This full-rank constraint is also sufficient to avoid dimensional collapse. Based on our analysis, we propose channel whitening with random group partition (CW-RGP), which exploits the advantages of BW-based methods in preventing collapse and avoids their disadvantages requiring large batch size. Experimental results on ImageNet classification and COCO object detection reveal that the proposed CW-RGP possesses a promising potential for learning good representations. The code is available at https://github.com/winci-ai/CW-RGP.
Reachability Analysis and Safety Verification of Neural Feedback Systems via Hybrid Zonotopes

Hybrid zonotopes generalize constrained zonotopes by introducing additional binary variables and possess some unique properties that make them convenient to represent nonconvex sets. This paper presents novel hybrid zonotope-based methods for the reachability analysis and safety verification of neural feedback systems. Algorithms are proposed to compute the input-output relationship of each layer of a feed-forward neural network, as well as the exact reachable sets of neural feedback systems. It is shown that a ReLU-activated feed-forward neural network can be exactly represented by a hybrid zonotope. In addition, a sufficient and necessary condition is formulated as a mixed-integer linear program to certify whether the trajectories of a neural feedback system can avoid unsafe regions. The proposed approach is shown to yield a formulation that provides the tightest convex relaxation for the reachable sets of the neural feedback system. Complexity reduction techniques for the reachable sets are developed to balance the computation efficiency and approximation accuracy. Two numerical examples demonstrate the superior performance of the proposed approach compared to other existing methods.
Supercell low-level mesocyclones: Origins of inflow and vorticity

The development and intensification of low-level mesocyclones in supercell thunderstorms has often been attributed, at least in part, to augmented streamwise vorticity generated baroclinically in the forward flank of supercells. However, the ambient streamwise vorticity of the environment (often quantified via storm-relative helicity), especially near the ground, is particularly skillful at discriminating between nontornadic and tornadic supercells. This study investigates whether the origins of the inflow air into supercell low-level mesocyclones, both horizontally and vertically, can help explain the dynamical role of environmental versus storm-generated vorticity in the development of low-level mesocyclone rotation. Simulations of supercells, initialized with wind profiles common to supercell environments observed in nature, show that the air bound for the low-level mesocyclone primarily originates from the ambient environment (rather than from along the forward flank) and from very close to the ground, often in the lowest 200 - 400 m of the atmosphere. Given that the near-ground environmental air comprises the bulk of the inflow into low-level mesocyclones, this likely explains the forecast skill of environmental streamwise vorticity in the lowest few hundred meters of the atmosphere. The low-level mesocyclone does not appear to require much augmentation from the development of additional horizontal vorticity in the forward flank. Instead, the dominant contributor to vertical vorticity within the low-level mesocyclone is from the environmental horizontal vorticity. This study provides further context to the on-going discussion regarding the development of rotation within supercell low-level mesocyclones.
Exponential Approximation of Band-limited Functions from Nonuniform Sampling by Regularization Methods

Reconstructing a band-limited function from its ﬁnite sample data is a fundamental task in signal analysis. A Gaussian regularized Shannon sampling series has been proved to be able to achieve exponential convergence for uniform sampling. Whether such an exponential convergence can also be achieved for nonuniform sampling by regularization methods was unresolved. In this paper, we give an aﬃrmative answer to this question. Speciﬁcally, we show that one can recover a band-limited function by Gaussian or hyper-Gaussian regularized nonuniform sampling series with an exponential convergence rate. Our analysis is based on the residue theorem in complex analysis, which is used to represent the truncated error by a contour integral. Several concrete examples of nonuniform sampling with exponential convergence will be presented.
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose feature sharing across neural tuning curves which significantly improves performance and helps optimization. We also propose a solution to the ensemble detection problem, where different groups of neurons, i.e., ensembles, can be modulated by different latent manifolds. Achieved through a soft clustering of neurons during training, this allows for the separation of mixed neural populations in an unsupervised manner. These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds. Finally, we apply our method on a recently published grid cell dataset, and recover distinct ensembles, infer toroidal latents and predict neural tuning curves in a single integrated modeling framework.
Stellar Flyby Analysis for Spiral Arm Hosts with Gaia DR3

Scattered-light imaging studies have detected nearly two dozen spiral arm systems in circumstellar disks, yet the formation mechanisms for most of them are still under debate. Although existing studies can use motion measurements to distinguish leading mechanisms such as planet–disk interaction and disk self-gravity, close-in stellar flybys can induce short-lived spirals and even excite arm-driving planets into highly eccentric orbits. With unprecedented stellar location and proper-motion measurements from Gaia Data Release 3 (DR3), here we study for known spiral arm systems their flyby history with their stellar neighbors by formulating an analytical on-sky flyby framework. For stellar neighbors currently located within 10 pc of the spiral hosts, we restrict the flyby time to within the past 104 yr and the flyby distance to within 10 times the disk extent in scattered light. Among a total of 12,570 neighbors that are identified in Gaia DR3 for 20 spiral systems, we do not identify credible flyby candidates for isolated systems. Our analysis suggests that a close-in recent flyby is not the dominant formation mechanism for isolated spiral systems in scattered light.
Reasoning and Thinking

Reasoning and Thinking: A Four-way Introduction. Deduction: Experiments with Syllogisms. Deduction: Experiments with "if" and other Connectives. Deduction: Biases and Content Effects. Theories of Deduction. Hypothesis Testing. Induction. Judging Probability. Decision Making. Reasoning, Thinking, and Rationality.
RIS-Aided Localization Under Position and Orientation Offsets in the Near and Far Field

This paper presents a rigorous Bayesian analysis of the information in the signal (consisting of both the line-of-sight (LOS) path and reflections from multiple reconfigurable intelligent surfaces (RISs)) that originate from a single base station (BS) and is received by a user equipment (UE). For a comprehensive Bayesian analysis, both near and far field regimes are considered. The Bayesian analysis views both the location of the RISs and previous information about the UE as a priori information for UE localization. With outdated a priori information, the position and orientation offsets of the RISs become parameters that need to be estimated and fed back to the BS for correction. We first show that when the RIS elements have a half wavelength spacing, this RIS orientation offset is a factor in the pathloss of the RIS paths. Subsequently, we show through the Bayesian equivalent Fisher information matrix (EFIM) for the channel parameters that the RIS orientation offset cannot be corrected when there is an unknown phase offset in the received signal in the far-field regime. However, the corresponding EFIM for the channel parameters in the received signal observed in the near-field shows that this unknown phase offset does not hinder the estimation of the RIS orientation offset when the UE has more than one receive antenna. Furthermore, we use the EFIM for the UE location parameters to present bounds for UE localization in the presence of RIS uncertainty. We rigorously show that regardless of size and propagation regime, the RISs are only helpful for localization when there is a priori information about the location of the RISs. Finally, through numerical analysis of the EFIM and its smallest eigenvalue, we demonstrate the loss in information when the far-field model is incorrectly applied to the signals received at a UE experiencing near-field propagation.
FAST Observations of an Extremely Active Episode of FRB 20201124A: I. Burst Morphology

We report the properties of more than 600 bursts (including cluster-bursts) detected from the repeating fast radio burst (FRB) source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode on UTC 2021 September 25–28, in a series of four papers. The observations were carried out in the band of 1.0–1.5 GHz by using the center beam of the L-band 19-beam receiver. We monitored the source in sixteen 1 hr sessions and one 3 hr session spanning 23 days. All the bursts were detected during the first four days. In this first paper of the series, we perform a detailed morphological study of 624 bursts using the two-dimensional frequency-time “waterfall” plots, with a burst (or cluster-burst) defined as an emission episode during which the adjacent emission peaks have a separation shorter than 400 ms. The duration of a burst is therefore always longer than 1 ms, with the longest up to more than 120 ms. The emission spectra of the sub-bursts are typically narrow within the observing band with a characteristic width of ∼277 MHz. The center frequency distribution has a dominant peak at about 1091.9 MHz and a secondary weak peak around 1327.9 MHz. Most bursts show a frequencydownward-drifting pattern. Based on the drifting patterns, we classify the bursts into five main categories: downward drifting (263) bursts, upward drifting (3) bursts, complex (203), no drifting (35) bursts, and no evidence for drifting (121) bursts. Subtypes are introduced based on the emission frequency range in the band (low, middle, high and wide) as well as the number of components in one burst (1, 2, or multiple). We measured a varying scintillation bandwidth from about 0.5 MHz at 1.0 GHz to 1.4 MHz at 1.5 GHz with a spectral index of 3.0.
A Simulation Study Comparing Two Methods Of Evaluating Differential Test Functioning (DTF): DFIT and the Mantel-Haenszel/Liu-Agresti Variance

This study uses simulated data to compare two methods of calculating Differential Test Functioning (DTF): Raju’s DFIT, a parametric method that measures the squared difference between two Test Characteristic Curves (Raju, van der Linden & Fleer, 1995), and a variance estimator based on the Mantel-Haenszel/Liu-Agresti method, a non-parametric method enabled in the DIFAS (Penfield, 2005) program. Most research has been done on Differential Item Functioning (DIF; Pae & Park, 2006), and theory and empirical studies indicate that DTF is the summation of DIF in a test (Donovan, Drasgow & Probst; 2000, Ellis & Mead, 2000; Nandakumar, 1993). Perhaps because of this, measurement of DTF is under-investigated. A number of reasons can be given why the study of DTF is important. From a statistical viewpoint, items, when compared to tests, are small and unreliable samples (Gierl, Bisanz, Bisanz, Boughton, & Khaliq, 2001). As an aggregate measure of DIF, DTF can present an overall view of the effect of differential functioning, even when no single item exhibits significant DIF (Shealy & Stout, 1993b). Decisions about examinees are made at the test level, not the item level (Ellis & Raju, 2003; Jones, 2000; Pae & Park, 2006; Roznowski & Reith, 1999; Zumbo, 2003). Overall both methods performed as expected with some exceptions. DTF tended to increase with DIF magnitude and with sample size. The MH/LA method generally showed greater rates of DTF than DFIT. It was also especially sensitive to group distribution differences (impact) identifying it as DTF where DFIT did not. An empirical cutoff value seemed to work as a method of determining statistical significance for the MH/LA method. Plots of the MH/LA DTF indicator showed a tendency towards and F-distribution for equal Reference and focal group sizes, and a normal distribution for unequal sample sizes. Areas for future research are identified. INDEX WORDS: DTF, Differential Test Functioning, DFIT, Mantel-Haenszel A SIMULATION STUDY COMPARING TWO METHODS OF EVALUATING DIFFERENTIAL TEST FUNCTIONING (DTF): DFIT AND THE MANTELHAENSZEL/LIU-AGRESTI VARIANCE by Charles Vincent Hunter, Jr.