Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Gauge Fixing for 3d Loop Quantum Gravity

In this work, we review 3-dimensional gravity by canonically analyzing the HilbertPalatini action. We apply a time gauge used in 4-dimensional loop quantum gravity [14] to the simpler case of the 3-dimensional loop quantum gravity. By time gauge fixing this Hilbert-Palatini action it leads to the Gauss constraints, the spatial diffeomorphism constraints, the Hamiltonian constraint and a new constraint C. The gauge symmetries generated by this new constraints are spacetime diffeomorphism and SO(2) gauge transformations. We solve the dynamics of the theory by providing a regularization of the generalized projector operator in terms of the Hamiltonian constraint. We provide the definition of the physical scalar product which can be represented in terms of a sum over finite spinfoam amplitudes. Then we establish a clear-cut link between the canonical quantization of the new theory to the spinfoam model (Ponzano-Regge model) defined in terms of the SU(2) spin networks. iv University of Ghana http://ugspace.ug. edu.gh
Learning Task-preferred Inference Routes for Gradient De-conflict in Multi-output DNNs

Multi-output deep neural networks(MONs) contain multiple task branches, and these tasks usually share partial network filters that lead to the entanglement of different task inference routes. Due to the inconsistent optimization objectives, the task gradients used for training MONs will interfere with each other on the shared routes, which will decrease the overall model performance. To address this issue, we propose a novel gradient de-conflict algorithm named DR-MGF(Dynamic Routes and Meta-weighted Gradient Fusion) in this work. Different from existing de-conflict methods, DR-MGF achieves gradient de-conflict in MONs by learning task-preferred inference routes. The proposed method is motivated by our experimental findings: the shared filters are not equally important to different tasks. By designing the learnable task-specific importance variables, DR-MGF evaluates the importance of filters for different tasks. Through making the dominances of tasks over filters be proportional to the task-specific importance of filters, DR-MGF can effectively reduce the inter-task interference. The task-specific importance variables ultimately determine task-preferred inference routes at the end of training iterations. Extensive experimental results on CIFAR, ImageNet, and NYUv2 illustrate that DR-MGF outperforms the existing de-conflict methods both in prediction accuracy and convergence speed of MONs. Furthermore, DR-MGF can be extended to general MONs without modifying the overall network structures.
Examining risks of racial biases in NLP tools for child protective services

Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be reflective of real-world applications. At the same time, practitioners are increasingly using algorithmic tools in high-stakes settings, with particular recent interest in NLP. In this work, we focus on one such setting: child protective services (CPS). CPS workers often write copious free-form text notes about families they are working with, and CPS agencies are actively seeking to deploy NLP models to leverage these data. Given well-established racial bias in this setting, we investigate possible ways deployed NLP is liable to increase racial disparities. We specifically examine word statistics within notes and algorithmic fairness in risk prediction, coreference resolution, and named entity recognition (NER). We document consistent algorithmic unfairness in NER models, possible algorithmic unfairness in coreference resolution models, and little evidence of exacerbated racial bias in risk prediction. While there is existing pronounced criticism of risk prediction, our results expose previously undocumented risks of racial bias in realistic information extraction systems, highlighting potential concerns in deploying them, even though they may appear more benign. Our work serves as a rare realistic examination of NLP algorithmic fairness in a potential deployed setting and a timely investigation of a specific risk associated with deploying NLP in CPS settings.
PEAK: Explainable Privacy Assistant through Automated Knowledge Extraction

In the realm of online privacy, privacy assistants play a pivotal role in empowering users to manage their privacy effectively. Although recent studies have shown promising progress in tackling tasks such as privacy violation detection and personalized privacy recommendations, a crucial aspect for widespread user adoption is the capability of these systems to provide explanations for their decision-making processes. This paper presents a privacy assistant for generating explanations for privacy decisions. The privacy assistant focuses on discovering latent topics, identifying explanation categories, establishing explanation schemes, and generating automated explanations. The generated explanations can be used by users to understand the recommendations of the privacy assistant. Our user study of real-world privacy dataset of images shows that users find the generated explanations useful and easy to understand. Additionally, the generated explanations can be used by privacy assistants themselves to improve their decision-making. We show how this can be realized by incorporating the generated explanations into a state-of-the-art privacy assistant.
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models

Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, visual question answering, captioning, and more. However, the aligned image-text spaces learned by all the popular VL models are still suffering from the so-called `object bias' - their representations behave as `bags of nouns', mostly ignoring or downsizing the attributes, relations, and states of objects described/appearing in texts/images. Although some great attempts at fixing these `compositional reasoning' issues were proposed in the recent literature, the problem is still far from being solved. In this paper, we uncover two factors limiting the VL models' compositional reasoning performance. These two factors are properties of the paired VL dataset used for finetuning and pre-training the VL model: (i) the caption quality, or in other words `image-alignment', of the texts; and (ii) the `density' of the captions in the sense of mentioning all the details appearing on the image. We propose a fine-tuning approach for automatically treating these factors leveraging a standard VL dataset (CC3M). Applied to CLIP, we demonstrate its significant compositional reasoning performance increase of up to $\sim27\%$ over the base model, up to $\sim20\%$ over the strongest baseline, and by $6.7\%$ on average.
Radio continuum tails in ram pressure-stripped spiral galaxies: Experimenting with a semi-empirical model in Abell 2255

Context. Wide-field radio continuum observations of galaxy clusters are revealing an increasing number of spiral galaxies hosting tens of kiloparsec-length radio tails produced by the displacement of nonthermal interstellar medium (ISM) by ram pressure. Aims. We present a semi-empirical model for the multifrequency radio continuum emission from ram-pressure-stripped tails based on the pure synchrotron cooling of a radio plasma moving along the stripping direction with a uniform velocity. Methods. We combine LOFAR and uGMRT observations at 144 and 400 MHz to study the flux density and spectral index profiles of the radio tails of seven galaxies in Abell 2255, and use the model to reproduce the flux density and spectral index profiles, and infer the stripped radio plasma velocity. Results. For five out of these seven galaxies, we observe a monotonic decrease in both flux density and spectral index up to 30 kpc from their stellar disk. Our model reproduces the observed trends with a projected radio plasma bulk velocity of between 160 and 430 km s−1. This result represents the first indirect measurement of the stripped, nonthermal ISM velocity. The observed spectral index trends indicate that the synchrotron cooling is faster than the adiabatic expansion losses, suggesting that the stripped radio plasma can survive for a few tens of million years outside of the stellar disk. This provides a lower limit on the lifetime of the stripped ISM outside of the disk. As a proof of concept, we use the best-fit velocities to constrain the 3D velocity of the galaxies in the cluster to be in the range of 300−1300 km s−1. We estimate the ram pressure affecting these galaxies to be between 0.1 and 2.9 × 10−11 erg cm−3, and measure the inclination between their stellar disk and the ram pressure wind.
Incremental Randomized Smoothing Certification

Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method constructs a smoothed DNN model and certifies its robustness through statistical sampling, but it is computationally expensive, especially when certifying with a large number of samples. Furthermore, when the smoothed model is modified (e.g., quantized or pruned), certification guarantees may not hold for the modified DNN, and recertifying from scratch can be prohibitively expensive. We present the first approach for incremental robustness certification for randomized smoothing, IRS. We show how to reuse the certification guarantees for the original smoothed model to certify an approximated model with very few samples. IRS significantly reduces the computational cost of certifying modified DNNs while maintaining strong robustness guarantees. We experimentally demonstrate the effectiveness of our approach, showing up to 3x certification speedup over the certification that applies randomized smoothing of the approximate model from scratch.
Mid-to-late M Dwarfs Lack Jupiter Analogs

Cold Jovian planets play an important role in sculpting the dynamical environment in which inner terrestrial planets form. The core accretion model predicts that giant planets cannot form around low-mass M dwarfs, although this idea has been challenged by recent planet discoveries. Here, we investigate the occurrence rate of giant planets around low-mass (0.1–0.3 M ⊙) M dwarfs. We monitor a volume-complete, inactive sample of 200 such stars located within 15 pc, collecting four high-resolution spectra of each M dwarf over six years and performing intensive follow-up monitoring of two candidate radial velocity variables. We use TRES on the 1.5 m telescope at the Fred Lawrence Whipple Observatory and CHIRON on the Cerro Tololo Inter-American Observatory 1.5 m telescope for our primary campaign, and MAROON-X on Gemini-North for high-precision follow up. We place a 95% confidence upper limit of 1.5% (68% confidence limit of 0.57%) on the occurrence of M P sin i > 1 M J giant planets out to the water snow line and provide additional constraints on the giant planet population as a function of M P sin i and period. Beyond the snow line (100 K < T eq < 150 K), we place 95% confidence upper limits of 1.5%, 1.7%, and 4.4% (68% confidence limits of 0.58%, 0.66%, and 1.7%) for 3 M J < M P sin i < 10 M J, 0.8 M J < M P sin i < 3 M J, and 0.3 M J < M P sin i < 0.8 M J giant planets, respectively; i.e., Jupiter analogs are rare around low-mass M dwarfs. In contrast, surveys of Sun-like stars have found that their giant planets are most common at these Jupiter-like instellations.
An Empirical Study of Fault Localization in Python Programs

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java -- the most common choices for fault localization research -- carry over to other languages, whether the dynamic nature of Python and how the language is used in practice affect the capabilities of classic fault localization approaches remain open questions to investigate. This paper is the first multi-family large-scale empirical study of fault localization on real-world Python programs and faults. Using Zou et al.'s recent large-scale empirical study of fault localization in Java as the basis of our study, we investigated the effectiveness (i.e., localization accuracy), efficiency (i.e., runtime performance), and other features (e.g., different entity granularities) of seven well-known fault-localization techniques in four families (spectrum-based, mutation-based, predicate switching, and stack-trace based) on 135 faults from 13 open-source Python projects from the BugsInPy curated collection. The results replicate for Python several results known about Java, and shed light on whether Python's peculiarities affect the capabilities of fault localization. The replication package that accompanies this paper includes detailed data about our experiments, as well as the tool FauxPy that we implemented to conduct the study.
Mitigating Test-Time Bias for Fair Image Retrieval

We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the utility (performance) of the underlying vision-language (VL) model. Previous methods aim to disentangle learned representations of images and text queries from gender and racial characteristics. However, we show these are inadequate at alleviating bias for the desired equal representation result, as there usually exists test-time bias in the target retrieval set. So motivated, we introduce a straightforward technique, Post-hoc Bias Mitigation (PBM), that post-processes the outputs from the pre-trained vision-language model. We evaluate our algorithm on real-world image search datasets, Occupation 1 and 2, as well as two large-scale image-text datasets, MS-COCO and Flickr30k. Our approach achieves the lowest bias, compared with various existing bias-mitigation methods, in text-based image retrieval result while maintaining satisfactory retrieval performance. The source code is publicly available at \url{https://anonymous.4open. science/r/Fair_Text_based_Image_Retrieval-D8B2}.
Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment

This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in contrast with previous methods which often rely on frame-level information only. Our approach begins with a frame-level prediction module which estimates framewise action classes via a transformer encoder. The frame-level prediction module is trained in an unsupervised manner via temporal optimal transport. To exploit segment-level information, we utilize a segment-level prediction module and a frame-to-segment alignment module. The former includes a transformer decoder for estimating video transcripts, while the latter matches frame-level features with segment-level features, yielding permutation-aware segmentation results. Moreover, inspired by temporal optimal transport, we introduce simple-yet-effective pseudo labels for unsupervised training of the above modules. Our experiments on four public datasets, i.e., 50 Salads, YouTube Instructions, Breakfast, and Desktop Assembly show that our approach achieves comparable or better performance than previous methods in unsupervised activity segmentation.
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis

We present a new framework for deriving bounds on the generalization bound of statistical learning algorithms from the perspective of online learning. Specifically, we construct an online learning game called the"generalization game", where an online learner is trying to compete with a fixed statistical learning algorithm in predicting the sequence of generalization gaps on a training set of i.i. d. data points. We establish a connection between the online and statistical learning setting by showing that the existence of an online learning algorithm with bounded regret in this game implies a bound on the generalization error of the statistical learning algorithm, up to a martingale concentration term that is independent of the complexity of the statistical learning method. This technique allows us to recover several standard generalization bounds including a range of PAC-Bayesian and information-theoretic guarantees, as well as generalizations thereof.
AFGL 5180 and AFGL 6366S: sites of hub–filament systems at the opposite edges of a filamentary cloud

We present a multiscale and multiwavelength study to unveil massive star formation (MSF) processes around sites AFGL 5180 and AFGL 6366S, both hosting a Class ii 6.7 GHz methanol maser emission. The radio continuum map at 8.46 GHz reveals a small cluster of radio sources towards AFGL 5180. Signatures of the early stages of MSF in our target sites are spatially seen at the opposite edges of a filamentary cloud (length ∼5 pc), which is observed in the submillimetre dust continuum maps. Using the near-infrared photometric data, the spatial distribution of young stellar objects is found towards the entire filament, primarily clustered at its edges. The getsf utility on the Herschel far-infrared images reveals a hub–filament system (HFS) towards each target site. The analysis of the molecular line data, which benefits from large area coverage (∼1° × 1°), detects two cloud components with a connection in both position and velocity space. This supports the scenario of a cloud–cloud collision (CCC) that occurred ∼1 Myr ago. The filamentary cloud, connecting AFGL 5180 and AFGL 6366S, seems spatially close to an H ii region Sh 2−247 excited by a massive O9.5 star. Based on the knowledge of various pressures exerted by the massive star on its surroundings, the impact of its energetic feedback on the filamentary cloud is found to be insignificant. Overall, our observational outcomes favour the possibility of the CCC scenario driving MSF and the formation of HFSs towards the target sites.
Adapting Fairness Interventions to Missing Values

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and the standard procedure for handling missing values where first data is imputed, then the imputed data is used for classification -- a procedure referred to as"impute-then-classify"-- can exacerbate discrimination. In this paper, we analyze how missing values affect algorithmic fairness. We first prove that training a classifier from imputed data can significantly worsen the achievable values of group fairness and average accuracy. This is because imputing data results in the loss of the missing pattern of the data, which often conveys information about the predictive label. We present scalable and adaptive algorithms for fair classification with missing values. These algorithms can be combined with any preexisting fairness-intervention algorithm to handle all possible missing patterns while preserving information encoded within the missing patterns. Numerical experiments with state-of-the-art fairness interventions demonstrate that our adaptive algorithms consistently achieve higher fairness and accuracy than impute-then-classify across different datasets.
On the relevance of fatigue in the risk of failure of structural elements exposed to bottom wave slamming

This study aims to investigate whether the fatigue damage induced by bottom wave slamming is a failure mode, important to consider when sizing a marine structural element. The shape and structural arrangement of the body exposed to water-wave slamming are considered to be such that the rise time of wave-impact hydrodynamic loads, $t_{\rm on}$, is short compared to the vibratory response timescale, $t_{\rm vib}$, of the structure, $t_{\rm on} \ll t_{\rm vib}$. Without further specification of the details of the body, two asymptotic structural response regimes are considered: (i) a first regime where the typical wave impact duration, $t_{\rm imp}$, is much shorter than the vibratory response timescale, $t_{\rm imp} \ll t_{\rm vib}$; (ii) a second opposite regime where $t_{\rm imp} \gg t_{\rm vib}$. In the first regime, fatigue is found to be the dominant failure mechanism, and accounting for the risk of failure due to fatigue damage yields design constraints which are significantly more conservative than the constraints obtained from the risk of ultimate strength exceedance. In the second regime, a detailed modeling of the risk of failure poses various challenges.
Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models

In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence through MHNG is proven to be a decentralized Bayesian inference of representations shared by the agents. However, the previously proposed MHNG is limited to a two-agent scenario. This paper extends MHNG to an N-agent scenario. The main contributions of this paper are twofold: (1) we propose the recursive Metropolis-Hastings naming game (RMHNG) as an N-agent version of MHNG and demonstrate that RMHNG is an approximate Bayesian inference method for the posterior distribution over a latent variable shared by agents, similar to MHNG; and (2) we empirically evaluate the performance of RMHNG on synthetic and real image data, i.e., YCB object dataset, enabling multiple agents to develop and share a symbol system. Furthermore, we introduce two types of approximations—one-sample and limited-length—to reduce computational complexity while maintaining the ability to explain communication in a population of agents. The experimental findings showcased the efficacy of RMHNG as a decentralized Bayesian inference for approximating the posterior distribution concerning latent variables, which are jointly shared among agents, akin to MHNG, although the improvement in ARI and κ coefficient is smaller in the real image dataset condition. Moreover, the utilization of RMHNG elucidated the agents' capacity to exchange symbols. Furthermore, the study discovered that even the computationally simplified version of RMHNG could enable symbols to emerge among the agents.
Permutoric Promotion: Gliding Globs, Sliding Stones, and Colliding Coins

The first author recently introduced toric promotion, an operator that acts on the labelings of a graph $G$ and serves as a cyclic analogue of Sch\"utzenberger's promotion operator. Toric promotion is defined as the composition of certain toggle operators, listed in a natural cyclic order. We consider more general permutoric promotion operators, which are defined as compositions of the same toggles, but in permuted orders. We settle a conjecture of the first author by determining the orders of all permutoric promotion operators when $G$ is a path graph. In fact, we completely characterize the orbit structures of these operators, showing that they satisfy the cyclic sieving phenomenon. The first half of our proof requires us to introduce and analyze new broken promotion operators, which can be interpreted via globs of liquid gliding on a path graph. For the latter half of our proof, we reformulate the dynamics of permutoric promotion via stones sliding along a cycle graph and coins colliding with each other on a path graph.