Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolved Eclipsing Binaries and the Age of the Open Cluster NGC 752

We present analyses of improved photometric and spectroscopic observations for two detached eclipsing binaries at the turnoff of the open cluster NGC 752: the 1.01 days binary DS And and the 15.53 days BD +37 410. For DS And, we find M 1 = 1.692 ± 0.004 ± 0.010M ⊙, R 1 = 2.185 ± 0.004 ± 0.008R ⊙, M 2 = 1.184 ± 0.001 ± 0.003M ⊙, and R 2 = 1.200 ± 0.003 ± 0.005R ⊙. We either confirm or newly identify unusual characteristics of both stars in the binary: the primary star is found to be slightly hotter than the main-sequence turnoff and there is a more substantial discrepancy in its luminosity compared to models (model luminosities are too large by about 40%), while the secondary star is oversized and cooler compared to other main-sequence stars in the same cluster. The evidence points to nonstandard evolution for both stars, but most plausible paths cannot explain the low luminosity of the primary star. BD +37 410 only has one eclipse per cycle, but extensive spectroscopic observations and the Transiting Exoplanet Survey Satellite light curve constrain the stellar masses well: M 1 = 1.717 ± 0.011M ⊙ and M 2 = 1.175 ± 0.005M ⊙. The radius of the main-sequence primary star near 2.9R ⊙ definitively requires large convective core overshooting (>0.2 pressure scale heights) in models for its mass, and multiple lines of evidence point toward an age of 1.61 ± 0.03 ± 0.05 Gyr (statistical and systematic uncertainties). Because NGC 752 is currently undergoing the transition from nondegenerate to degenerate He ignition of its red clump stars, BD +37 410 A directly constrains the star mass where this transition occurs.
An Analysis of Fusion Functions for Hybrid Retrieval

We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In particular, we examine fusion by a convex combination of lexical and semantic scores, as well as the reciprocal rank fusion (RRF) method, and identify their advantages and potential pitfalls. Contrary to existing studies, we find RRF to be sensitive to its parameters; that the learning of a convex combination fusion is generally agnostic to the choice of score normalization; that convex combination outperforms RRF in in-domain and out-of-domain settings; and finally, that convex combination is sample efficient, requiring only a small set of training examples to tune its only parameter to a target domain.
Kinematics of the late-M giants in Baade's window

Les vitesses radiales d'un echantillon non-biaise de 239 geantes M de type avance de la fenetre du Bulbe Galactique b = -4° (fenetre de Baade) sont presentees. Cet echantillon peut etre divise en deux groupes , I < 11.8 et I ≥ 11.8, de dispersions de vitesse differentes. Les forces des raies TiO et Ca II sont determinees pour estimer l'intervalle de metallicite de ces etoiles.
Extracted BERT Model Leaks More Information than You Think!

The collection and availability of big data, combined with advances in pre-trained models (e.g. BERT), have revolutionized the predictive performance of natural language processing tasks. This allows corporations to provide machine learning as a service (MLaaS) by encapsulating fine-tuned BERT-based models as APIs. Due to significant commercial interest, there has been a surge of attempts to steal remote services via model extraction. Although previous works have made progress in defending against model extraction attacks, there has been little discussion on their performance in preventing privacy leakage. This work bridges this gap by launching an attribute inference attack against the extracted BERT model. Our extensive experiments reveal that model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies.
microAI: A machine learning tool for fast calculation of lift coefficients in microchannels

There have been multiple methods proposed to calculate lift coefficients in microfluidic channels. One of the most used methods is using Direct Numerical Simulation. DNS is a very accurate yet computationally expensive method. DNS computations comprise most of the time consumed on a microfluidic simulation done by commercial software. This paper proposes a user-friendly, fast, and accurate AI-based webapp named microAI that can calculate the microfluidic lift coefficients of channels. We have also studied the effects of different types of activation functions and optimizers in convergence and the final function's differentiability. microAI is deployed to huggingface and is accessible at \url{https://erfanhamdi.github. io/microAI/}
Bisparse Blind Deconvolution through Hierarchical Sparse Recovery

The bi-sparse blind deconvolution problem is studied -- that is, from the knowledge of $h*(Qb)$, where $Q$ is some linear operator, recovering $h$ and $b$, which are both assumed to be sparse. The approach rests upon lifting the problem to a linear one, and then applying the hierarchical sparsity framework. In particular, the efficient HiHTP algorithm is proposed for performing the recovery. Then, under a random model on the matrix $Q$, it is theoretically shown that an $s$-sparse $h \in \mathbb{K}^\mu$ and $\sigma$-sparse $b \in \mathbb{K}^n$ with high probability can be recovered when $\mu \succcurlyeq s\log(s)^2\log(\mu)\log(\mu n) + s\sigma \log(n)$.
LittleBird: Efficient Faster & Longer Transformer for Question Answering

BERT has shown a lot of sucess in a wide variety of NLP tasks. But it has a limitation dealing with long inputs due to its attention mechanism. Longformer, ETC and BigBird addressed this issue and effectively solved the quadratic dependency problem.However we find that these models are not sufficient, and propose LittleBird, a novel model based on BigBird with improved speed and memory footprint while maintaining accuracy.In particular, we devise a more flexible and efficient position representation method based on Attention with Linear Biases(ALiBi). We also show that replacing the method of global information represented in the BigBird with pack and unpack attention is more effective.The proposed model can work on long inputs even after being pre-trained on short inputs, and can be trained efficiently reusing existing pre-trained language model for short inputs. This is a significant benefit for low-resource languages where large amounts of long text data are difficult to obtain.As a result, our experiments show that LittleBird works very well in a variety of languages, achieving high performance in question answering tasks, particularly in KorQuAD2.0, Korean Question Answering Dataset for long paragraphs.
Competing Bandits in Time Varying Matching Markets

We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting where one side of the market, the arms, has fixed known set of preferences over the other side, the players. While this problem has been studied when the players have fixed but unknown preferences, in this work we study the problem of how to learn when the preferences of the players are time varying and unknown. Our contribution is a methodology that can handle any type of preference structure and variation scenario. We show that, with the proposed algorithm, each player receives a uniform sub-linear regret of {$\widetilde{\mathcal{O}}(L^{1/2}_TT^{1/2})$} up to the number of changes in the underlying preferences of the agents, $L_T$. Therefore, we show that the optimal rates for single-agent learning can be achieved in spite of the competition up to a difference of a constant factor. We also discuss extensions of this algorithm to the case where the number of changes need not be known a priori.
Physical Layer Security in Random NOMA-Enabled Heterogeneous Networks

The performance of physical layer secrecy approach in non-orthogonal multiple access (NOMA)-enabled heterogeneous networks (HetNets) is analyzed in this paper. A $K$-tier multi-cell HetNet is considered, comprising NOMA adopted in all tiers. The base stations, legitimate users (in a two-user NOMA setup), and passive eavesdroppers, all with single-antenna, are randomly distributed. Assuming independent Poisson point processes for node distribution, stochastic geometry approaches are exploited to characterize the ergodic secrecy rate. A lower bound on the ergodic secrecy rate along with closed-form expressions for the lower bound on the ergodic rates of the legitimate users in a special case are derived. Moreover, simpler expressions for the ergodic secrecy rate are obtained in the interference-limited regime with vanishing noise variance. The effect of multi-tier technology, NOMA, and physical layer secrecy are investigated using numerical results. The results reveal that applying HetNet to a secure multi-cell NOMA system improves the spectrum efficiency performance.
A convergent genus expansion for the plateau

We conjecture a formula for the spectral form factor of a double-scaled matrix integral in the limit of large time, large density of states, and fixed temperature. The formula has a genus expansion with a nonzero radius of convergence. To understand the origin of this series, we compare to the semiclassical theory of"encounters"in periodic orbits. In Jackiw-Teitelboim (JT) gravity, encounters correspond to portions of the moduli space integral that mutually cancel (in the orientable case) but individually grow at low energies. At genus one we show how the full moduli space integral resolves the low energy region and gives a finite nonzero answer.
Data-Driven Quickest Change Detection in Markov Models

The problem of quickest change detection in Markov models is studied. A sequence of samples are generated from a Markov model, and at some unknown time, the transition kernel of the Markov model changes. The goal is to detect the change as soon as possible subject to false alarm constraints. The data-driven setting is investigated, where neither the pre-nor the post-change Markov transition kernel is known. A kernel based data-driven algorithm is developed, which applies to general state space and is recursive and computationally efficient. Performance bounds on the average running length and worst-case average detection delay are derived. Numerical results are provided to validate the performance of the proposed algorithm.
The rotational dynamics of Mercury and the state of its core

Data on the rotational dynamics of Mercury are examined together with possible events that could lead to the current state of rotation. It is shown that the dynamical evolution of Mercury's spin angular momentum controlled by the dissipative processes of tidal friction and relative motion between a solid mantle and a liquid core would lead naturally to the current state of rotation of the Mercury planet. To investigate the possibility that Mercury has a molten core, an experiment is designed for the measurement of the core properties of Mercury. It is shown that it is technically feasible to measure the four parameters necessary for the determination of the existence and the extent of a Mercurian molten core, including the amplitude of the physical vibration about the resonance spin rate, the obliquity, and the gravitational harmonic coefficients.
Enhancement of quantum speed limit time due to cooperative effects in multilevel systems

Deriving minimum evolution times is of paramount importance in quantum mechanics. Bounds on the speed of evolution are given by the so called quantum speed limit (QSL). In this work we use quantum optimal control methods to study the QSL for driven many level systems which exhibit local two-level interactions in the form of avoided crossings (ACs). Remarkably, we find that optimal evolution times are proportionally smaller than those predicted by the well-known two-level case, even when the ACs are isolated. We show that the physical mechanism for such enhancement is due to non-trivial cooperative effects between the AC and other levels, which are dynamically induced by the shape of the optimized control field.
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals

For text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. Among recent models aiming to avoid this spurious pattern problem, adding extra counterfactual samples to the training data has proven to be very effective. Yet, counterfactual data generation is costly since it relies on human annotation. Thus, we propose a novel solution that only requires annotation of a small fraction (e.g., 1%) of the original training data, and uses automatic generation of extra counterfactuals in an encoding vector space. We demonstrate the effectiveness of our approach in sentiment classification, using IMDb data for training and other sets for OOD tests (i.e., Amazon, SemEval and Yelp). We achieve noticeable accuracy improvements by adding only 1% manual counterfactuals: +3% compared to adding +100% in-distribution training samples, +1.3% compared to alternate counterfactual approaches.
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs

Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. This has recently been linked with the curvature and spectral gap of the graph. On the other hand, adding edges to the message-passing graph can lead to increasingly similar node representations and a problem known as oversmoothing. We propose a computationally efficient algorithm that prevents oversquashing by systematically adding edges to the graph based on spectral expansion. We combine this with a relational architecture, which lets the GNN preserve the original graph structure and provably prevents oversmoothing. We find experimentally that our algorithm outperforms existing graph rewiring methods in several graph classification tasks.
Automatic Cattle Identification using YOLOv5 and Moasic Augmentation: A Comparative Analysis

You Only Look Once (YOLO) is a single-stage object detection model popular for real-time object detection, accuracy, and speed. This paper investigates the YOLOv5 model to identify cattle in the yards. The current solution to cattle identification includes radio-frequency identification (RFID) tags. The problem occurs when the RFID tag is lost or damaged. A biometric solution identifies the cattle and helps to assign the lost or damaged tag or replace the RFID-based system. Muzzle patterns in cattle are unique biometric solutions like a fingerprint in humans. This paper aims to present our recent research in utilizing five popular object detection models, looking at the architecture of YOLOv5, investigating the performance of eight backbones with the YOLOv5 model, and the influence of mosaic augmentation in YOLOv5 by experimental results on the available cattle muzzle images. Finally, we concluded with the excellent potential of using YOLOv5 in automatic cattle identification. Our experiments show YOLOv5 with transformer performed best with mean Average Precision (mAP)_0. 5 (the average of AP when the IoU is greater than 50%) of 0.995, and mAP_0. 5:0.95 (the average of AP from 50% to 95% IoU with an interval of 5%) of 0.9366. In addition, our experiments show the increase in accuracy of the model by using mosaic augmentation in all backbones used in our experiments. Moreover, we can also detect cattle with partial muzzle images.
Cold gas in a complete sample of group-dominant early-type galaxies

We present IRAM 30 m and APEX telescope observations of CO(1-0) and CO(2-1) lines in 36 group-dominant early-type galaxies, completing our molecular gas survey of dominant galaxies in the Complete Local-volume Groups Sample. We detect CO emission in 12 of the galaxies at > 4σ significance, with molecular gas masses in the range ∼ 0.01 − 6 × 108 M⊙, as well as CO in absorption in the non-dominant group member galaxy NGC 5354. In total 21 of the 53 CLoGS dominant galaxies are detected in CO and we confirm our previous findings that they have low star formation rates (0.01–1 M⊙yr−1) but short depletion times (>1 Gyr) implying rapid replenishment of their gas reservoirs. Comparing molecular gas mass with radio luminosity, we find that a much higher fraction of our group-dominant galaxies (60 ± 16%) are AGN-dominated than is the case for the general population of ellipticals, but that there is no clear connection between radio luminosity and the molecular gas mass. Using data from the literature, we find that at least 27 of the 53 CLoGS dominant galaxies contain H I, comparable to the fraction of nearby non-cluster early type galaxies detected in H I and significantly higher that the fraction in the Virgo cluster. We see no correlation between the presence of an X-ray detected intra-group medium and molecular gas in the dominant galaxy, but find that the H I-richest galaxies are located in X-ray faint groups. Morphological data from the literature suggests the cold gas component most commonly takes the form of a disk, but many systems show evidence of galaxy-galaxy interactions, indicating that they may have acquired their gas through stripping or mergers. We provide improved molecular gas mass estimates for two galaxies previously identified as being in the centres of cooling flows, NGC 4636 and NGC 5846, and find that they are relatively molecular gas poor compared to our other detected systems.
Blind Polynomial Regression

Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at all, rendering conventional regression approaches not applicable. In this paper, we formally state the (potentially partial) blind regression problem, illustrate some of its theoretical properties, and propose an algorithmic approach to solve it. As a case-study, we apply our methods to a jitter-correction problem and corroborate its performance.
All Politics is Local: Redistricting via Local Fairness

In this paper, we propose to use the concept of local fairness for auditing and ranking redistricting plans. Given a redistricting plan, a deviating group is a population-balanced contiguous region in which a majority of individuals are of the same interest and in the minority of their respective districts; such a set of individuals have a justified complaint with how the redistricting plan was drawn. A redistricting plan with no deviating groups is called locally fair. We show that the problem of auditing a given plan for local fairness is NP-complete. We present an MCMC approach for auditing as well as ranking redistricting plans. We also present a dynamic programming based algorithm for the auditing problem that we use to demonstrate the efficacy of our MCMC approach. Using these tools, we test local fairness on real-world election data, showing that it is indeed possible to find plans that are almost or exactly locally fair. Further, we show that such plans can be generated while sacrificing very little in terms of compactness and existing fairness measures such as competitiveness of the districts or seat shares of the plans.
Statistical Study and Live Catalog of Multispacecraft 3He-rich Time Periods over Solar Cycles 23, 24, and 25

Using ion measurements from Ultra-Low-Energy Isotope Spectrometer observations on board Advanced Composition Explorer and Solar Isotope Spectrometer observations on board the Solar Terrestrial Observatory (STEREO)-A and STEREO-B spacecraft, we have identified 854 3He-rich time periods between 1997 September and 2021 March. We include all event types with observed 3He enhancements such as corotating interaction regions, gradual solar energetic particle (SEP) events, interplanetary shocks, and impulsive SEP events. We employ two different mass separation techniques to obtain 3He, 4He, Fe, and O fluences for each event, and we determine the 3He/4He and Fe/O abundance ratios between 0.32–0.45 and 0.64–1.28 MeV nucleon−1. We find a clear correlation in the 3He/4He and Fe/O abundance ratios between both energy ranges. We find two distinct trends in the 3He/4He versus Fe/O relation. For low 3He/4He values, there is a positive linear correlation between 3He/4He and Fe/O. However, at 3He/4He ∼ 0.3, Fe/O appears to reach a limit and the correlation weakens significantly. We provide a live catalog of 3He-rich time periods that includes the robust determination of the onset and end times of the 3He enhancements in SEP-associated periods for different types of events observed by multiple spacecraft. This catalog is available for public use. New releases will follow after major additions such as adding new periods from new missions (e.g., Parker Solar Probe and Solar Orbiter), identifying event types (impulsive SEP events, etc.), or adding new parameters such as remote observations detailing characteristics of the events’ active regions.
HDHumans

Photo-real digital human avatars are of enormous importance in graphics, as they enable immersive communication over the globe, improve gaming and entertainment experiences, and can be particularly beneficial for AR and VR settings. However, current avatar generation approaches either fall short in high-fidelity novel view synthesis, generalization to novel motions, reproduction of loose clothing, or they cannot render characters at the high resolution offered by modern displays. To this end, we propose HDHumans, which is the first method for HD human character synthesis that jointly produces an accurate and temporally coherent 3D deforming surface and highly photo-realistic images of arbitrary novel views and of motions not seen at training time. At the technical core, our method tightly integrates a classical deforming character template with neural radiance fields (NeRF). Our method is carefully designed to achieve a synergy between classical surface deformation and a NeRF. First, the template guides the NeRF, which allows synthesizing novel views of a highly dynamic and articulated character and even enables the synthesis of novel motions. Second, we also leverage the dense pointclouds resulting from the NeRF to further improve the deforming surface via 3D-to-3D supervision. We outperform the state of the art quantitatively and qualitatively in terms of synthesis quality and resolution, as well as the quality of 3D surface reconstruction.