Miracle-dz/UniFAR-FTUs huggingface dataset:
TESTING MODEL ATMOSPHERES FOR YOUNG VERY-LOW-MASS STARS AND BROWN DWARFS IN THE INFRARED: EVIDENCE FOR SIGNIFICANTLY UNDERESTIMATED DUST OPACITIES

We test state-of-the-art model atmospheres for young very-low-mass stars and brown dwarfs in the infrared, by comparing the predicted synthetic photometry over 1.2–24 μm to the observed photometry of M-type spectral templates in star-forming regions. We find that (1) in both early and late young M types, the model atmospheres imply effective temperatures ( T eff ? > ) several hundred Kelvin lower than predicted by the standard pre-main sequence (PMS) spectral type– T eff ? > conversion scale (based on theoretical evolutionary models). It is only in the mid-M types that the two temperature estimates agree. (2) The T eff ? > discrepancy in the early M types (corresponding to stellar masses ≳ 0.4 ? > M ⊙ ? > at ages of a few Myr) probably arises from remaining uncertainties in the treatment of atmospheric convection within the atmospheric models, whereas in the late M types it is likely due to an underestimation of dust opacity. (3) The empirical and model-atmosphere J-band bolometric corrections are both roughly flat, and similar to each other, over the M-type T eff ? > range. Thus the model atmospheres yield reasonably accurate bolometric luminosities ( L bol ? > ), but lead to underestimations of mass and age relative to evolutionary expectations (especially in the late M types) due to lower T eff ? > . We demonstrate this for a large sample of young Cha I and Taurus sources. (4) The trends in the atmospheric model J−Ks colors, and their deviations from the data, are similar at PMS and main sequence ages, suggesting that the model dust opacity errors we postulate here for young ages also apply at field ages.
Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments

Collision avoidance in the presence of dynamic obstacles in unknown environments is one of the most critical challenges for unmanned systems. In this paper, we present a method that identifies obstacles in terms of ellipsoids to estimate linear and angular obstacle velocities. Our proposed method is based on the idea of any object can be approximately expressed by ellipsoids. To achieve this, we propose a method based on variational Bayesian estimation of Gaussian mixture model, the Kyachiyan algorithm, and a refinement algorithm. Our proposed method does not require knowledge of the number of clusters and can operate in real-time, unlike existing optimization-based methods. In addition, we define an ellipsoid-based feature vector to match obstacles given two timely close point frames. Our method can be applied to any environment with static and dynamic obstacles, including ones with rotating obstacles. We compare our algorithm with other clustering methods and show that when coupled with a trajectory planner, the overall system can efficiently traverse unknown environments in the presence of dynamic obstacles.
Type II Dirac seesaw portal to the mirror sector: Connecting neutrino masses and a solution to the strong 
CP
 problem

We present a version of the Type II Seesaw mechanism for parametrically small Dirac neutrino masses. Our model starts from an SU(2) L ⊗ SU(2) ′ ⊗ U(1) X gauge extension of the Standard Model involving a sector of mirror fermions. A bidoublet scalar with a very small vacuum expectation value connects the SM leptons with their mirror counterparts and we can identify the mirror neutrino with the right-handed neutrino. Similar to the conventional Type II Seesaw, our particle spectrum features singly- and doubly-charged scalars. The strong CP problem is solved by a discrete exchange symmetry between the two sectors that forces the contributions of quarks and mirror quarks to the strong CP phase to cancel each other. We discuss the low-energy phenomenology, comment on the cosmological implications of this scenario and indicate how to realize successful Dirac leptogenesis.
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensional outputs in object-level visual tasks. In this paper, we propose an object-centric vision framework, Obj2Seq. Obj2Seq takes objects as basic units, and regards most object-level visual tasks as sequence generation problems of objects. Therefore, these visual tasks can be decoupled into two steps. First recognize objects of given categories, and then generate a sequence for each of these objects. The definition of the output sequences varies for different tasks, and the model is supervised by matching these sequences with ground-truth targets. Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks. When experimenting on MS COCO, Obj2Seq achieves 45.7% AP on object detection, 89.0% AP on multi-label classification and 65.0% AP on human pose estimation. These results demonstrate its potential to be generally applied to different visual tasks. Code has been made available at: https://github.com/CASIA-IVA-Lab/Obj2Seq.
Determination of the titanium spectral function from (e,e'p) data

The E12-14-012 experiment, performed in Jefferson Lab Hall A, has measured the (e,e'p) cross section in parallel kinematics using a natural titanium target. Here, we report the full results of the analysis of the data set corresponding to beam energy 2.2 GeV, and spanning the missing momentum and missing energy range 15<= pm<= 250 MeV/c and 12<= Em<= 80 MeV. The reduced cross section has been measured with ~7% accuracy as function of both missing momentum and missing energy. We compared our data to the results of a Monte Carlo simulations performed using a model spectral function and including the effects of final state interactions. The overall agreement between data and simulations is quite good (chi2/d.o.f. = 0.9).
A STATISTICAL STUDY OF H i GAS IN NEARBY NARROW-LINE AGN-HOSTING GALAXIES

As a quenching mechanism, active galactic nucleus (AGN) feedback could suppress on going star formation in host galaxies. On the basis of a sample of galaxies selected from the Arecibo Legacy Fast ALFA (ALFALFA) H i survey, the dependence of the H i mass (MH i), stellar mass (M*), and H i-to-stellar mass ratio (MH i/M*) on various tracers of AGN activity are presented and analyzed in this paper. Almost all the AGN hostings in this sample are gas-rich galaxies, and there is not any evidence to indicate that the AGN activity could increase or decrease either MH i or MH i/M*. The position of the cold neutral gas cannot be fixed accurately based only on available H i data, due to the large beam size of ALFALFA survey. In addition, even though AGN hostings are more easily detected by an H i survey compared with absorption line galaxies, these two types of galaxies show similar star formation history. If an AGN hosting would ultimately evolve into an old red galaxy with low cold gas, then when and how the gas has been exhausted must be solved by future hypotheses and observations.
Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results

—Humour is a substantial element of human affect and cognition. Its automatic understanding can facilitate a more naturalistic human-device interaction and the humanisation of artiﬁcial intelligence. Current methods of humour detection are solely based on staged data making them inadequate for ‘real-world’ applications. We address this deﬁciency by introducing the novel Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of about 11 hours of recordings. The Passau-SFCH dataset is annotated for the presence of humour and its dimensions (sentiment and direction) as proposed in Martin’s Humor Style Questionnaire. We conduct a series of experiments, employing pretrained Transformers, convolutional neural networks, and expert-designed features. The performance of each modality (text, audio, video) for spontaneous humour recognition is analysed and their complementarity is investigated. Our ﬁndings suggest that for the automatic analysis of humour and its sentiment, facial expressions are most promising, while humour direction can be best modelled via text-based features. The results reveal considerable differences among various subjects, highlighting the individuality of humour usage and style. Further, we observe that a decision-level fusion yields the best recognition result. Finally, we make our code publicly available at https://www.github. com/EIHW/passau-sfch. The Passau-SFCH dataset is available upon request.
Meta-Learning in Games

In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from routing problems to online advertising auctions -- evolve dynamically, thereby leading to many similar games to be solved. To address this gap, we introduce meta-learning for equilibrium finding and learning to play games. We establish the first meta-learning guarantees for a variety of fundamental and well-studied classes of games, including two-player zero-sum games, general-sum games, and Stackelberg games. In particular, we obtain rates of convergence to different game-theoretic equilibria that depend on natural notions of similarity between the sequence of games encountered, while at the same time recovering the known single-game guarantees when the sequence of games is arbitrary. Along the way, we prove a number of new results in the single-game regime through a simple and unified framework, which may be of independent interest. Finally, we evaluate our meta-learning algorithms on endgames faced by the poker agent Libratus against top human professionals. The experiments show that games with varying stack sizes can be solved significantly faster using our meta-learning techniques than by solving them separately, often by an order of magnitude.
Signed Latent Factors for Spamming Activity Detection

Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These works have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is valida
Gas fraction and star formation efficiency at z < 1.0

After new observations of 39 galaxies at z = 0.6-1.0 obtained at the IRAM 30m telescope, we present our full CO line survey covering the redshift range 0.2 < z < 1. Our aim is to determine the driving factors accounting for the steep decline in the star formation rate during this epoch. We study both the gas fraction, defined as Mgas/(Mgas+Mstar), and the star formation efficiency (SFE) defined by the ratio between far-infrared luminosity and molecular gas mass (LFIR/M(H2), i.e. a measure for the inverse of the gas depletion time. The sources are selected to be ultra-luminous infrared galaxies (ULIRGs), with LFIR greater than 10^12 Lo and experiencing starbursts. When we adopt a standard ULIRG CO-to-H2 conversion factor, their molecular gas depletion time is less than 100 Myr. Our full survey has now filled the gap of CO observations in the 0.2
Feature sets in just-in-time defect prediction: an empirical evaluation

Just-in-time defect prediction assigns a defect risk to each new change to a software repository in order to prioritize review and testing efforts. Over the last decades different approaches were proposed in literature to craft more accurate prediction models. However, defect prediction is still not widely used in industry, due to predictions with varying performance. In this study, we evaluate existing features on six open-source projects and propose two new features sets, not yet discussed in literature. By combining all feature sets, we improve MCC by on average 21%, leading to the best performing models when compared to state-of-the-art approaches. We also evaluate effort-awareness and find that on average 14% more defects can be identified, inspecting 20% of changed lines.
Optimally Biased Expertise

This paper shows that the principal can strictly benefit from delegating a decision to an agent whose opinion differs from that of the principal. We consider a"delegated expertise"problem, in which the agent has an advantage in information acquisition relative to the principal, as opposed to having preexisting private information. When the principal is ex ante predisposed towards some action, it is optimal for her to hire an agent who is predisposed towards the same action, but to a smaller extent, since such an agent would acquire more information, which outweighs the bias stemming from misalignment. We show that belief misalignment between an agent and a principal is a viable instrument in delegation, performing on par with contracting and communication in a class of problems.
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention

The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection. To accelerate detection speed, lightweight detectors always have few convolution layers using single-path backbone. Single-path architecture, however, involves continuous pooling and downsampling operations, always resulting in coarse and inaccurate feature maps that are disadvantageous to locate objects. On the other hand, due to limited network capacity, recent lightweight networks are often weak in representing large scale visual data. To address these problems, this paper presents a dual-path network, named DPNet, with a lightweight attention scheme for real-time object detection. The dual-path architecture enables us to parallelly extract high-level semantic features and low-level object details. Although DPNet has nearly duplicated shape with respect to single-path detectors, the computational costs and model size are not significantly increased. To enhance representation capability, a lightweight self-correlation module (LSCM) is designed to capture global interactions, with only few computational overheads and network parameters. In neck, LSCM is extended into a lightweight crosscorrelation module (LCCM), capturing mutual dependencies among neighboring scale features. We have conducted exhaustive experiments on MS COCO and Pascal VOC 2007 datasets. The experimental results demonstrate that DPNet achieves state-of the-art trade-off between detection accuracy and implementation efficiency. Specifically, DPNet achieves 30.5% AP on MS COCO test-dev and 81.5% mAP on Pascal VOC 2007 test set, together mwith nearly 2.5M model size, 1.04 GFLOPs, and 164 FPS and 196 FPS for 320 x 320 input images of two datasets.
Validation of TOI-1221 b: A Warm Sub-Neptune Exhibiting Transit Timing Variations around a Sun-like Star

We present a validation of a long-period ( 91.68278−0.00041+0.00032 days) transiting sub-Neptune planet, TOI-1221 b (TIC 349095149.01), around a Sun-like (m V = 10.5) star. This is one of the few known exoplanets with a period >50 days, and belongs to the even smaller subset of which have bright enough hosts for detailed spectroscopic follow-up. We combine Transiting Exoplanet Survey Satellite light curves and ground-based time-series photometry from the Perth Exoplanet Survey Telescope (0.3 m) and Las Cumbres Observatory global telescope network (1.0 m) to analyze the transit signals and rule out nearby stars as potential false-positive sources. High-contrast imaging from the Southern Astrophysical Research Telescope and Gemini/Zorro rule out nearby stellar contaminants. Reconnaissance spectroscopy from CHIRON sets a planetary scale upper mass limit on the transiting object (1.1 and 3.5 M Jup at 1σ and 3σ, respectively) and shows no sign of a spectroscopic binary companion. We determine a planetary radius of Rp=2.91−0.12+0.13R⊕ , placing it in the sub-Neptune regime. With a stellar insolation of S=6.06−0.77+0.85S⊕ , we calculate a moderate equilibrium temperature of T eq = 440 K, assuming no albedo and perfect heat redistribution. We find a false-positive probability from the TRICERATOPS tool of FPP = 0.0014 ± 0.0003 as well as other qualitative and quantitative evidence to support the statistical validation of TOI-1221 b. We find significant evidence (>5σ) of oscillatory transit timing variations, likely indicative of an additional nontransiting planet.
Audio Retrieval with WavText5K and CLAP Training

Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant natural language descriptions. Most of the literature train retrieval systems with one audio captioning dataset, but evaluating the benefit of training with multiple datasets is underexplored. Moreover, retrieval systems have to learn the alignment between elaborated sentences describing audio content of variable length ranging from a few seconds to several minutes. In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval. First, we provide a new collection of about five thousand web audio-text pairs that we refer to as WavText5K. When used to train our retrieval system, WavText5K improved performance more than other audio captioning datasets. Second, our framework learns to connect language and audio content by using a text encoder, two audio encoders, and a contrastive learning objective. Combining both audio encoders helps to process variable length audio. The two contributions beat state of the art performance for AudioCaps and Clotho on Text-Audio retrieval by a relative 2% and 16%, and Audio-Text retrieval by 6% and 23%.
Devouring the Milky Way Satellites: Modeling Dwarf Galaxies with Galacticus

Dwarf galaxies are ubiquitous throughout the universe and are extremely sensitive to various forms of internal and external feedback. Over the last two decades, the census of dwarf galaxies in the Local Group and beyond has increased markedly. While hydrodynamic simulations (e.g., FIRE II, Mint Justice League) have reproduced the observed dwarf properties down to the ultrafaints, such simulations require extensive computational resources to run. In this work, we constrain the standard physical implementations in the semianalytic model Galacticus to reproduce the observed properties of the Milky Way satellites down to the ultrafaint dwarfs found in the Sloan Digital Sky Survey. We run Galacticus on merger trees from our high-resolution N–body simulation of a Milky Way analog. We determine the best-fit parameters by matching the cumulative luminosity function and luminosity–metallicity relation from both observations and hydrodynamic simulations. With the correct parameters, the standard physics in Galacticus can reproduce the observed luminosity function and luminosity–metallicity relation of the Milky Way dwarfs. In addition, we find a multidimensional match with half-light radii, velocity dispersions, and mass to light ratios at z = 0 down to M V ≤ −6 (L ≥ 104 L ⊙). In addition to successfully reproducing the properties of the z = 0 Milky Way satellite population, our modeled dwarfs have star formation histories that are consistent with those of the Local Group dwarfs.
MeWEHV: Mel and Wave Embeddings for Human Voice Tasks

A recent trend in speech processing is the use of embeddings created through machine learning models trained on a specific task with large datasets. By leveraging the knowledge already acquired, these models can be reused in new tasks where the amount of available data is small. This paper proposes a pipeline to create a new model, called Mel and Wave Embeddings for Human Voice Tasks (MeWEHV), capable of generating robust embeddings for speech processing. MeWEHV combines the embeddings generated by a pre-trained raw audio waveform encoder model, and deep features extracted from Mel Frequency Cepstral Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate the performance of MeWEHV on three tasks: speaker, language, and accent identification. For the first one, we use the VoxCeleb1, and VBHIR datasets and present YouSpeakers204, a new and publicly available dataset for English speaker identification that contains 19607 audio clips from 204 persons speaking in six different accents, allowing other researchers to work with a very balanced dataset, and to create new models that are robust to multiple accents. For evaluating the language identification task, we use the VoxForge, Common Language, and the LRE17 datasets. Finally, for accent identification, we use the Latin American Spanish Corpora (LASC), Common Voice, and the NISP datasets. Our approach allows a significant increase in the performance of state-of-the-art embedding generation models on all the tested datasets, with a low additional computational cost.
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems

As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe. This paper presents a set of backward reachability approaches for safety certification of neural feedback loops (NFLs), i.e., closed-loop systems with NN control policies. While backward reachability strategies have been developed for systems without NN components, the nonlinearities in NN activation functions and general noninvertibility of NN weight matrices make backward reachability for NFLs a challenging problem. To avoid the difficulties associated with propagating sets backward through NNs, we introduce a framework that leverages standard forward NN analysis tools to efficiently find over-approximations to backprojection (BP) sets, i.e., sets of states for which an NN policy will lead a system to a given target set. We present frameworks for calculating BP over-approximations for both linear and nonlinear systems with control policies represented by feedforward NNs and propose computationally efficient strategies. We use numerical results from a variety of models to showcase the proposed algorithms, including a demonstration of safety certification for a 6D system.