Miracle-dz/UniFAR-FTUs huggingface dataset:
“It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech. Detecting offensive content is rapidly becoming one of the most important real-world NLP tasks. However, most datasets use a single binary label, e.g., for hate or incivility, even though each concept is multi-faceted. This modeling choice severely limits nuanced insights, but also performance.We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues.We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance.Our dataset not only allows for a more nuanced understanding of harmful speech online, models trained on it also outperform or match performance on benchmark datasets
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU

Debiasing language models from unwanted behaviors in Natural Language Understanding (NLU) tasks is a topic with rapidly increasing interest in the NLP community. Spurious statistical correlations in the data allow models to perform shortcuts and avoid uncovering more advanced and desirable linguistic features.A multitude of effective debiasing approaches has been proposed, but flexibility remains a major issue. For the most part, models must be retrained to find a new set of weights with debiased behavior.We propose a new debiasing method in which we identify debiased pruning masks that can be applied to a finetuned model. This enables the selective and conditional application of debiasing behaviors.We assume that bias is caused by a certain subset of weights in the network; our method is, in essence, a mask search to identify and remove biased weights.Our masks show equivalent or superior performance to the standard counterparts, while offering important benefits.Pruning masks can be stored with high efficiency in memory, and it becomes possible to switch among several debiasing behaviors (or revert back to the original biased model) at inference time. Finally, it opens the doors to further research on how biases are acquired by studying the generated masks. For example, we observed that the early layers and attention heads were pruned more aggressively, possibly hinting towards the location in which biases may be encoded.
Evaluating context-invariance in unsupervised speech representations

Unsupervised speech representations have taken off, with benchmarks (SUPERB, ZeroSpeech) demonstrating major progress on semi-supervised speech recognition, speech synthesis, and speech-only language modelling. Inspiration comes from the promise of ``discovering the phonemes'' of a language or a similar low-bitrate encoding. However, one of the critical properties of phoneme transcriptions is context-invariance: the phonetic context of a speech sound can have massive influence on the way it is pronounced, while the text remains stable. This is what allows tokens of the same word to have the same transcriptions -- key to language understanding. Current benchmarks do not measure context-invariance. We develop a new version of the ZeroSpeech ABX benchmark that measures context-invariance, and apply it to recent self-supervised representations. We demonstrate that the context-independence of representations is predictive of the stability of word-level representations. We suggest research concentrate on improving context-independence of self-supervised and unsupervised representations.
Transport across stable density interfaces in forced stratiﬁed turbulence

Understanding how turbulence enhances irreversible scalar mixing in density-stratiﬁed ﬂuids is a central problem in geophysical ﬂuid dynamics. Unstable vertical density inversions are often assumed to be an indirect proxy for mixing when direct measurements of scalar diﬀusion are unavailable. We here investigate this assumption by using a forced direct numerical simulation of stratiﬁed turbulence to consider spatial correlations between the vertical density gradient 𝜕𝜌 / 𝜕𝑧 and the dissipation rates of kinetic energy 𝜖 and scalar variance 𝜒 , the latter quantifying scalar mixing. Statistically-steady turbulence is generated by the injection of kinetic energy at large horizontal length scales, leading to a robust vertically-aligned vortex, which in turn induces vertically-layered, opposing horizontal currents in the surrounding ﬂow. Sheared interfaces at the intersection of these currents are correlated with sharp, stable density gradients separating relatively well-mixed layers. While unstable density inversions are most prevalent within the mixed layers, much of the scalar mixing is localized to the intervening interfaces, a phenomenon which would be missed if either local static instability or 𝜖 were to be used as a proxy for mixing. We highlight that while the majority of the domain is indeed characterized by the canonical ﬂux coeﬃcient Γ ≡ 𝜒 / 𝜖 = 0 . 2, often assumed in ocean mixing parameterizations, extreme values of 𝜒 within the statically-stable interfaces, associated with elevated Γ , strongly skew the bulk statistics. Our ﬁndings suggest that current parameterizations of turbulent mixing may be biased by undersampling, such that the most common, but not necessarily the most signiﬁcant, mixing events are
M3FGM: a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction

Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. In order to make better use of the power of graph model, some researchs also combine split learning(SL). However, there are still several issues left unattended: 1) Clients might not be able to access the server during inference phase; 2) The graph of clients designed manually in the server model may not reveal the proper relationship between clients. This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues. For the first issue, the server model of M$^3$FGM employs a MaskNode layer to simulate the case of clients being offline. We also redesign the decoder of the client model using a dual-sub-decoders structure so that each client model can use its local data to predict independently when offline. As for the second issue, a new GNN layer named Multi-Granularity Message Passing (MGMP) layer enables each client node to perceive global and local information. We conducted extensive experiments in two different scenarios on two real traffic datasets. Results show that M$^3$FGM outperforms the baselines and variant models, achieves the best results in both datasets and scenarios.
Nonparametric Two-Sample Testing by Betting

We study the problem of designing consistent sequential two-sample tests in a nonparametric setting. Guided by the principle of testing by betting, we reframe this task into that of selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated game. In this setting, the relative increase in the bettor’s wealth has a precise interpretation as the measure of evidence against the null, and thus our sequential test rejects the null when the wealth crosses an appropriate threshold. We develop a general framework for setting up the betting game for two-sample testing, in which the payoffs are selected by a prediction strategy as data-driven predictable estimates of the witness function associated with the variational representation of some statistical distance measures, such as integral probability metrics (IPMs). We then formally relate the statistical properties of the test (such as consistency, type-II error exponent and expected sample size) to the regret of the corresponding prediction strategy. We construct a practical sequential two-sample test by instantiating our general strategy with the kernel-MMD metric, and demonstrate its ability to adapt to the difficulty of the unknown alternative through theoretical and empirical results. Our framework is versatile, and easily extends to other problems; we illustrate this by applying our approach to construct consistent tests for the following problems: (i) time-varying two-sample testing with non-exchangeable observations, and (ii) an abstract class of “invariant” testing problems, including symmetry and independence testing.
Fully Heavy Tetraquark Spectroscopy in the Relativistic Quark Model

Masses of the ground and excited (1P, 2S, 1D, 2P, 3S) states of the fully heavy tetraquarks, composed of charm (c) and bottom (b) quarks and antiquarks, are calculated in the diquark–antidiquark picture within the relativistic quark model based on the quasipotential approach and quantum chromodynamics. The quasipotentials of the quark–quark and diquark–antidiquark interactions are constructed similarly to the previous consideration of mesons and baryons. Relativistic effects are consistently taken into account. A tetraquark is considered a bound state of a diquark and an antidiquark. The finite size of the diquark is taken into account, using the form factors of the diquark–gluon interaction. It is shown that most of the investigated states of tetraquarks lie above the decay thresholds into a meson pair; as a result, they can be observed only as broad resonances. The narrow state X(6900) recently discovered in the di-J/ψ production spectrum by the LHCb, CMS and ATLAS Collaborations corresponds to an excited state of the fully charmed tetraquark. Other recently discovered exotic heavy resonances, X(6200), X(6400), X(6600), X(7200), and X(7300), can also be interpreted as the different excitations of the fully charmed tetraquark.
Differentially Private CutMix for Split Learning with Vision Transformer

Recently, vision transformer (ViT) has started to outpace the conventional CNN in computer vision tasks. Considering privacy-preserving distributed learning with ViT, federated learning (FL) communicates models, which becomes ill-suited due to ViT’s large model size and computing costs. Split learning (SL) detours this by communicating smashed data at a cut-layer, yet suffers from data privacy leakage and large communication costs caused by high similarity between ViT’s smashed data and input data. Motivated by this problem, we propose DP-CutMixSL , a differentially private (DP) SL framework by developing DP patch-level randomized CutMix (DP-CutMix) , a novel privacy-preserving inter-client interpolation scheme that replaces randomly selected patches in smashed data. By experiment, we show that DP-CutMixSL not only boosts privacy guarantees and communication efficiency, but also achieves higher accuracy than its Vanilla SL counterpart. Theoretically, we analyze that DP-CutMix amplifies Rényi DP (RDP), which is upper-bounded by its Vanilla Mixup counterpart.
FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes

<italic>Context</italic>: Machine learning software can generate models that inappropriately discriminate against specific protected social groups (e.g., groups based on gender, ethnicity, etc.). Motivated by those results, software engineering researchers have proposed many methods for mitigating those discriminatory effects. While those methods are effective in mitigating bias, few of them can provide explanations on what is the root cause of bias. <italic>Objective</italic>: We aim to better detect and mitigate algorithmic discrimination in machine learning software problems. <italic>Method</italic>: Here we propose <inline-formula><tex-math notation="LaTeX">${{\sf FairMask}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FairMask</mml:mi></mml:math><inline-graphic xlink:href="peng-ieq1-3220713.gif"/></alternatives></inline-formula>, a <italic>model-based</italic> extrapolation method that is capable of both mitigating bias and explaining the cause. In our <inline-formula><tex-math notation="LaTeX">${{\sf FairMask}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FairMask</mml:mi></mml:math><inline-graphic xlink:href="peng-ieq2-3220713.gif"/></alternatives></inline-formula> approach, protected attributes are represented by models learned from the other independent variables (and these models offer extrapolations over the space between existing examples). We then use the extrapolation models to relabel protected attributes later seen in testing data or deployment time. Our approach aims to offset the biased predictions of the classification model by rebalancing the distribution of protected attributes. <italic>Results</italic>: The experiments of this paper show that, without compromising (original) model performance, <inline-formula><tex-math notation="LaTeX">${{\sf FairMask}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FairMask</mml:mi></mml:math><inline-graphic xlink:href="peng-ieq3-3220713.gif"/></alternatives></inline-formula> can achieve significantly better group and individual fairness (as measured in different metrics) than benchmark methods. Moreover, compared to another instance-based rebalancing method, our model-based approach shows faster runtime and thus better scalability. <italic>Conclusion</italic>: Algorithmic decision bias can be removed via extrapolation that corrects the misleading latent correlation between the protected attributes and other non-protected ones. As evidence for this, our proposed <inline-formula><tex-math notation="LaTeX">${{\sf FairMask}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">FairMask</mml:mi></mml:math><inline-graphic xlink:href="peng-ieq4-3220713.gif"/></alternatives></inline-formula> is not only performance-wise better (measured by fairness and performance metrics) than two state-of-the-art fairness algorithms. <italic>Reproduction Package</italic>: In order to better support open science, all scripts and data used in this study are available online at <uri>https://github.com/anonymous12138/biasmitigation</uri>.
FUSE Observations of Interstellar and Intergalactic Absorption toward the X-Ray-bright BL Lacertae Object Markarian 421

High-quality Far Ultraviolet Spectroscopic Explorer (FUSE) observations at 20 km s-1 resolution of interstellar and intergalactic absorption from 910 to 1187 Å are presented for the X-ray-bright BL Lac object Mrk 421. These observations are supplemented with FUSE data for the distant halo stars BD +38°2182 and HD 93521 near the Mrk 421 line of sight, in order to obtain information about the distance to absorbing structures in the Milky Way toward Mrk 421. The FUSE ISM observations provide measures of absorption by O VI and many other species commonly found in warm neutral and warm ionized gas, including H I, C II, C III, O I, N I, N II, Fe II, and Fe III. In this study we consider the O VI absorption between -140 and 165 km s-1 and its relationship to the lower ionization absorption and strong absorption produced by O VII and O VIII at X-ray wavelengths. The O VI absorption extending from -140 to 60 km s-1 is associated with strong low-ionization gas absorption and originates in the Galactic thick disk/halo. This O VI appears to be produced by a combination of processes, including conductive interfaces between warm and hot gas and possibly cooling Galactic fountain gas and hot halo gas bubbles. The O VI absorption extending from 60 to 165 km s-1 has unusual ionization properties in that there is very little associated low-ionization absorption, with the exception of C III. This absorption is not observed toward the two halo stars, implying that it occurs in gas more distant than 3.5 kpc from the Galactic disk. Over the 60-165 km s-1 velocity range, O VI and C III absorption have the same kinematic behavior. The ratio N(O )/N(C ) = 10 ± 3 over the 60-120 km s-1 velocity range. Given the association of O VI with C III, it is unlikely that the high-velocity O VI coexists with the hotter gas responsible for the O VII and O VIII absorption. The O VI positive velocity absorption wing might be tracing cooler gas entrained in a hot Galactic fountain outflow. The O VII and O VIII absorption observed by Chandra and XMM-Newton may trace the hot gas in a highly extended (~100 kpc) Galactic corona or hot gas in the Local Group. The low resolution of the current X-ray observations (~750-900 km s-1) and the kinematical complexity of the O VI absorption along typical lines of sight through the Milky Way halo make it difficult to clearly associate the O VI absorption with that produced by O VII and O VIII. A search for metal lines associated with the Lyα absorber at z = 0.01, which is situated in a galactic void, was unsuccessful.
Ionized gas in the Scutum spiral arm as traced in [N II] and [C II]

Determining the properties of the warm ionized medium (WIM) at the leading edge of spiral arms is important for understanding its dynamics and cloud formation. The inner edge of the Scutum arm tangency is a unique location in which to disentangle the WIM from other components. We use high spectral resolution [C II] 158 micron and [N II] 205 micron fine structure line observations taken with the upGREAT and GREAT instruments on SOFIA, along with auxiliary HI and 13CO observations. The observations were in and out of the Galactic plane along 18 lines of sight between longitude 30deg and 32deg. We detect strong [N II] emission throughout the Scutum tangency. At VLSR = 110 to 125 km/s, where there is little, if any, 13CO, we could disentangle the [N II] and [C II] emission that arises from the WIM at the inner edge. We find an average electron density, about 0.9 cm{-3} in the plane, and about 0.4 cm{-3} above the plane. For VLSR about 10 to 20 times those of the interarm WIM, which is best explained by a model in which the interarm WIM is compressed as it falls into the potential well of the arm. The widespread distribution of [N II] in the molecular layers shows that high density ionized gas is distributed throughout the Scutum arm. The n(e) derived from [N II] for these molecular cloud regions are about 30 cm{-3}, and probably arise in the ionized boundary layers of clouds. This [N II] emission from the ionized boundary layers is probably the result of the shock compression of the WIM as it impacts the arm's neutral gas, but some could arise from extended HII regions.
L1188: A Promising Candidate for Cloud–Cloud Collisions Triggering the Formation of Low- and Intermediate-mass Stars

We present a new large-scale (2° × 2°) simultaneous 12CO, 13CO, and C18O (J = 1–0) mapping of L1188 with the Purple Mountain Observatory 13.7 m telescope. Our observations have revealed that L1188 consists of two nearly orthogonal filamentary molecular clouds at two clearly separated velocities. Toward the intersection showing large velocity spreads, we find several bridging features connecting the two clouds in velocity, and an open arc structure that exhibits high excitation temperatures, enhanced 12CO and 13CO emission, and broad 12CO line wings. This agrees with the scenario that the two clouds are colliding with each other. The distribution of young stellar object (YSO) candidates implies an enhancement of star formation in the intersection of the two clouds. We suggest that a cloud–cloud collision happened in L1188 about 1 Myr ago, possibly triggering the formation of low- and intermediate-mass YSOs in the intersection.
Spectral Methods for Partial Differential Equations

Abstract : Origins of spectral methods, especially their relation to the Method of Weighted Residuals, are surveyed. Basic Fourier, Chebyshev, and Legendre spectral concepts are reviewed, and demonstrated through application to simple model problems. Both collocation and tau methods are considered. These techniques are then applied to a number of difficult, nonlinear problems of hyperbolic, parabolic, elliptic, and mixed type. Fluid-dynamical applications are emphasized.
Effect of stellar wind induced magnetic fields on planetary obstacles of non-magnetized hot Jupiters

We investigate the interaction between the magnetized stellar wind plasma and the partially ionized hydrodynamic hydrogen outflow from the escaping upper atmosphere of non- or weakly magnetized hot Jupiters. We use the well-studied hot Jupiter HD 209458b as an example for similar exoplanets, assuming a negligible intrinsic magnetic moment. For this planet, the stellar wind plasma interaction forms an obstacle in the planet's upper atmosphere, in which the position of the magnetopause is determined by the condition of pressure balance between the stellar wind and the expanded atmosphere, heated by the stellar extreme ultraviolet (EUV) radiation. We show that the neutral atmospheric atoms penetrate into the region dominated by the stellar wind, where they are ionized by photo-ionization and charge exchange, and then mixed with the stellar wind flow. Using a 3D magnetohydrodynamic (MHD) model, we show that an induced magnetic field forms in front of the planetary obstacle, which appears to be much stronger compared to those produced by the solar wind interaction with Venus and Mars. Depending on the stellar wind parameters, because of the induced magnetic field, the planetary obstacle can move up to ~0.5-1 planetary radii closer to the planet. Finally, we discuss how estimations of the intrinsic magnetic moment of hot Jupiters can be inferred by coupling hydrodynamic upper planetary atmosphere and MHD stellar wind interaction models together with UV observations. In particular, we find that HD 209458b should likely have an intrinsic magnetic moment of 10-20% that of Jupiter.
The Near-infrared Tip of the Red Giant Branch. II. An Absolute Calibration in the Large Magellanic Cloud

We present a new empirical JHK absolute calibration of the tip of the red giant branch (TRGB) in the Large Magellanic Cloud (LMC). We use published data from the extensive Near-Infrared Synoptic Survey containing 3.5 million stars, 65,000 of which are red giants that fall within one magnitude of the TRGB. Adopting the TRGB slopes from a companion study of the isolated dwarf galaxy IC 1613, as well as an LMC distance modulus of μ0 = 18.49 mag from (geometric) detached eclipsing binaries, we derive absolute JHK zero points for the near-infrared TRGB. For a comparison with measurements in the bar alone, we apply the calibrated JHK TRGB to a 500 deg2 area of the 2MASS survey. The TRGB reveals the 3D structure of the LMC with a tilt in the direction perpendicular to the major axis of the bar, which is in agreement with previous studies.
Probing Universality in the Gravity Duals of N=1 SYM by gamma-deformations

Recently, a one-parameter deformation of the Maldacena-N´u˜nez dual of N = 1 SYM theory was constructed in hep-th/0505100. According to the Lunin-Maldacena conjecture, the background is dual to pure N = 1 SYM in the IR coupled to a KK sector whose dynamics is altered by a dipole deformation that is proportional to the deformation parameter γ . Thus, the deformation serves to identify the aspects of the gravity backgrounds that bear the eﬀects of the KK sector, hence non-universal in the dual gauge theory. We make this idea concrete by studying a Penrose limit of the deformed MN theory. We obtain an exactly solvable pp-wave that is conjectured to describe the IR dynamics of KK-hadrons in the ﬁeld theory. The spectrum, the thermal partition function and the Hagedorn temperature are calculated. Interestingly, the Hagedorn temperature turns out to be independent of the deformation parameter.