Miracle-dz/UniFAR-FTUs huggingface dataset:
Supermassive black hole spin evolution in cosmological simulations with OpenGadget3

The mass and spin of massive black holes (BHs) at the centre of galaxies evolve due to gas accretion and mergers with other BHs. Besides affecting the evolution of relativistic jets, for example, the BH spin determines the efficiency with which the BH radiates energy. Using cosmological, hydrodynamical simulations, we investigate the evolution of the BH spin across cosmic time and its role in controlling the joint growth of supermassive BHs and their host galaxies. We implemented a sub-resolution prescription that models the BH spin, accounting for both BH coalescence and misaligned accretion through a geometrically thin, optically thick disc. We investigated how BH spin evolves in two idealised setups, in zoomed-in simulations and in a cosmological volume. The latter simulation allowed us to retrieve statistically robust results for the evolution and distribution of BH spins as a function of BH properties. We find that BHs with $ M BH grow through gas accretion, occurring mostly in a coherent fashion that favours spin-up. Above $ M BH the gas directions of subsequent accretion episodes are often uncorrelated with each other. The probability of counter-rotating accretion and hence spin-down increases with BH mass. In the latter mass regime, BH coalescence plays an important role. The spin magnitude displays a wide variety of histories, depending on the dynamical state of the gas feeding the BH and the relative contribution of mergers and gas accretion. As a result of their combined effect, we observe a broad range of values of the spin magnitude at the high-mass end. Reorientation of the BH spin direction occurs on short timescales ($ 10$ Myr) only during highly accreting phases ($ f Edd 0.1$). Our predictions for the distributions of BH spin and spin-dependent radiative efficiency as a function of BH mass are in very good agreement with observations.
Computing approximate symmetries of complex networks

The symmetry of complex networks is a global property that has recently gained attention since MacArthur et al. 2008 showed that many real-world networks contain a considerable number of symmetries. These authors work with a very strict symmetry definition based on the network's automorphism. The potential problem with this approach is that even a slight change in the graph's structure can remove or create some symmetry. Recently, Liu 2020 proposed to use an approximate automorphism instead of strict automorphism. This method can discover symmetries in the network while accepting some minor imperfections in their structure. The proposed numerical method, however, exhibits some performance problems and has some limitations while it assumes the absence of fixed points. In this work, we exploit alternative approaches recently developed for treating the Graph Matching Problem and propose a method, which we will refer to as Quadratic Symmetry Approximator (QSA), to address the aforementioned shortcomings. To test our method, we propose a set of random graph models suitable for assessing a wide family of approximate symmetry algorithms. The performance of our method is also demonstrated on brain networks.
Post-hoc p-values

A pervasive methodological error is the post-hoc interpretation of $p$-values. A $p$-value $p$ is the smallest significance level at which we would have rejected the null had we chosen level $p$. It is not the smallest significance level at which we reject the null. We introduce post-hoc $p$-values, that do admit such a post-hoc interpretation. We show that $p$ is a post-hoc $p$-value if and only if $1/p$ is an $e$-value, a recently introduced statistical object. The product of independent post-hoc $p$-values is a post-hoc $p$-value, making them easy to combine. Moreover, any post-hoc $p$-value can be trivially improved if we permit external randomization, but only (essentially) non-randomized post-hoc $p$-values can be arbitrarily merged through multiplication. In addition, we discuss what constitutes a `good' post-hoc $p$-value. Finally, we argue that post-hoc $p$-values eliminate the need of a pre-specified significance level, such as $\alpha = . 05$ or $\alpha = . 005$ (Benjamin et al., 2018). We believe this may take away incentives for $p$-hacking and contribute to solving the file-drawer problem, as both these issues arise from using a pre-specified significance level.
Novel View Synthesis with View-Dependent Effects from a Single Image

In this paper, we firstly consider view-dependent effects into single image-based novel view synthesis (NVS) problems. For this, we propose to exploit the camera motion priors in NVS to model view-dependent appearance or effects (VDE) as the negative disparity in the scene. By recognizing specularities"follow"the camera motion, we infuse VDEs into the input images by aggregating input pixel colors along the negative depth region of the epipolar lines. Also, we propose a `relaxed volumetric rendering' approximation that allows computing the densities in a single pass, improving efficiency for NVS from single images. Our method can learn single-image NVS from image sequences only, which is a completely self-supervised learning method, for the first time requiring neither depth nor camera pose annotations. We present extensive experiment results and show that our proposed method can learn NVS with VDEs, outperforming the SOTA single-view NVS methods on the RealEstate10k and MannequinChallenge datasets.
Axion String Source Modelling

In this paper, we perform an investigation into the effect of the string radius of curvature $R_\mathrm{\,Gaussian}$ on the magnitude and relative magnitude of the massive and massless radiation from axion (global) string configurations, motivated by qualitative observations from string network simulations. We construct initial conditions from travelling wave solutions on a global string for two colliding Gaussians, performing parameter scans over amplitude $A$ and standard deviation $\sigma_\mathrm{d}$. We show that the energy emitted via massless radiation obeys a power law $E_\mathrm{massless} \appropto A^{\gamma}$, where the coefficient $\gamma$ depends on the curvature regime. Massive radiation is exponentially suppressed approximately as $E_{\mathrm{massive}} \appropto e^{-\zeta R_\mathrm{\,Gaussian}}$ in the quasi-linear regime $\sigma_\mathrm{d} \gg \delta$ and exhibits power-law decay $E_{\mathrm{massive}} \appropto (R_\mathrm{\,Gaussian})^{-\gamma}$ in the nonlinear regime where $\sigma_\mathrm{d} \lesssim 2\delta$, with different $\gamma$ in different regimes of $R_\mathrm{\,Gaussian}$. In certain regions of the nonlinear regime, massive particle radiation comprises up to 50\% of the total energy emitted. Drawing on a known parallel between axion radiation from global strings and gravitational radiation from Abelian-Higgs strings, this suggests that massive particle radiation channel may become of equal significance to the massless (gravitational) channel for nonlinear burst signals where $R<\sigma_\mathrm{d}$, unless we are in the regime where additional loops are generated. We also estimate the spectral index $q$ of the axion radiation for different amplitudes, showing that a higher proportion of radiation is emitted in high frequency modes as the curvature increases, bounded by $q \gtrsim 1$ for the configurations studied.
Discontinuous solutions for the Navier-Stokes equations with density-dependent viscosity

We prove existence and uniqueness of global weak solutions of the Navier-Stokes equations that govern the motion of a compressible viscous fluid with density-dependent viscosity in two space dimensions. The initial velocity belongs to the Sobolev space $H^1(\mathbb{R}^2)$ and the initial fluid density is H\"older continuous on both sides of a $\mathcal{C}^{1+\alpha}$ interface with some geometrical assumption. We show that this configuration persists over time: the initial interface is transported by the flow to an interface that is as regular as the initial one. Our result generalizes previous known of Hoff [21], Hoff and Santos [22] concerning the propagation of regularity for discontinuity surfaces by allowing more general nonlinear pressure law and density-dependent viscosity. Moreover, it supplements the work by Danchin, Fanelli and Paicu [7] with global well-posedness, even for density-dependent viscosity and we obtain uniqueness in a large space.
The Central Kinematics and Black Hole Mass of 4C+37.11

We report on integral field unit (IFU) measurements of the host of the radio source 4C+37.11. This massive elliptical contains the only resolved double compact nucleus at parsec-scale separation, likely a bound supermassive black hole binary (SMBHB). i-band photometry and GMOS-N IFU spectroscopy show that the galaxy has a large r b = 1. ″5 core and that the stellar velocity dispersion increases inside of a radius of influence r SOI ≈ 1. ″3. Jeans Anisotropic Modeling analysis of the core infers a total SMBHB mass of 2.8−0.8+0.8×1010M⊙ , making this one of the most massive black hole systems known. Our data indicate that there has been significant scouring of the central kiloparsec of the host galaxy.
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

Offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of offline pretrained policy using only a few online samples. Built on offline RL algorithms, most O2O methods focus on the balance between RL objective and pessimism, or the utilization of offline and online samples. In this paper, from a novel perspective, we systematically study the challenges that remain in O2O RL and identify that the reason behind the slow improvement of the performance and the instability of online finetuning lies in the inaccurate Q-value estimation inherited from offline pretraining. Specifically, we demonstrate that the estimation bias and the inaccurate rank of Q-value cause a misleading signal for the policy update, making the standard offline RL algorithms, such as CQL and TD3-BC, ineffective in the online finetuning. Based on this observation, we address the problem of Q-value estimation by two techniques: (1) perturbed value update and (2) increased frequency of Q-value updates. The first technique smooths out biased Q-value estimation with sharp peaks, preventing early-stage policy exploitation of sub-optimal actions. The second one alleviates the estimation bias inherited from offline pretraining by accelerating learning. Extensive experiments on the MuJoco and Adroit environments demonstrate that the proposed method, named SO2, significantly alleviates Q-value estimation issues, and consistently improves the performance against the state-of-the-art methods by up to 83.1%.
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks

The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse during model training. This situation is further exacerbated due to the lack of transparency in local training processes, potentially leading to the unauthorized accumulation of large volumes of graph data, thereby infringing on the intellectual property rights of data owners. Existing methodologies often address either data misuse detection or mitigation, and are primarily designed for local GNN models rather than cloud-based MLaaS platforms. These limitations call for an effective and comprehensive solution that detects and mitigates data misuse without requiring exact training data while respecting the proprietary nature of such data. This paper introduces a pioneering approach called GraphGuard, to tackle these challenges. We propose a training-data-free method that not only detects graph data misuse but also mitigates its impact via targeted unlearning, all without relying on the original training data. Our innovative misuse detection technique employs membership inference with radioactive data, enhancing the distinguishability between member and non-member data distributions. For mitigation, we utilize synthetic graphs that emulate the characteristics previously learned by the target model, enabling effective unlearning even in the absence of exact graph data. We conduct comprehensive experiments utilizing four real-world graph datasets to demonstrate the efficacy of GraphGuard in both detection and unlearning. We show that GraphGuard attains a near-perfect detection rate of approximately 100% across these datasets with various GNN models. In addition, it performs unlearning by eliminating the impact of the unlearned graph with a marginal decrease in accuracy (less than 5%).
Reciprocal theorem for linear poro-viscoelastic materials

In studying the transport of particles and inclusions in multi-phase systems we are often interested in integrated quantities such as the total force and the net velocity of the particles. Here, we derive a reciprocal formulation for linear poro-viscoelastic (PVE) materials, which are composed of a linear compressible viscoelastic phase, i.e. the network phase, permeated by a viscous fluid. As an application of the reciprocal theorem, we analytically calculate the time-dependent net force on a rigid stationary sphere in response to point-forces applied to the elastic network and Newtonian fluid phases of a PVE material. We show that the net force on the sphere in response to a point-force in the fluid phase evolves over timescales that are independent of the distance of the point-force to the sphere; in comparison, when the point-force is applied to the network phase the timescale for force development becomes distance-dependent. We discuss how in both cases these relaxation times are related to the physical timescales that are determined by mechanical properties of both phases -- such as the network's Poisson ratio, permeability and shear modules, and the fluid viscosity -- as well as geometric factors, including the size of the spherical inclusion and its distance from point-forces. The reciprocal theorem presented here can be applied to a wide range of problems involving the transport of cells, organelles and condensates in biological systems composed of filamentous networks permeated by viscous fluids.
Ensuring End-to-End Security with Fine-grained Access Control for Connected and Autonomous Vehicles

As advanced V2X applications emerge in the connected and autonomous vehicle (CAV), the data communications between in-vehicle end-devices and outside nodes increase, which make the end-to-end (E2E) security to in-vehicle end-devices as the urgent issue to be handled. However, the E2E security with fine-grained access control still remains as a challenging issue for resource-constrained end-devices since the existing security solutions require complicated key management and high resource consumption. Therefore, we propose a practical and secure vehicular communication protocol for the E2E security based on a new attribute-based encryption (ABE) scheme. In our scheme, the outsourced computation is provided for encryption, and the computation cost for decryption constantly remains small, regardless of the number of attributes. The policy privacy can be ensured by the proposed ABE to support privacy-sensitive V2X applications, and the existing identity-based signature for outsourced signing is newly reconstructed. Our scheme achieves the confidentiality, message authentication, identity anonymity, unlinkability, traceability, and reconfigurable outsourced computation, and we also show the practical feasibility of our protocol via the performance evaluation.
Unveiling Parts Beyond Objects: Towards Finer-Granularity Referring Expression Segmentation

Referring expression segmentation (RES) aims at segmenting the foreground masks of the entities that match the descriptive natural language expression. Previous datasets and methods for classic RES task heavily rely on the prior assumption that one expression must refer to object-level targets. In this paper, we take a step further to finer-grained part-level RES task. To promote the object-level RES task towards finer-grained vision-language understanding, we put forward a new multi-granularity referring expression segmentation (MRES) task and construct an evaluation benchmark called RefCOCOm by manual annotations. By employing our automatic model-assisted data engine, we build the largest visual grounding dataset namely MRES-32M, which comprises over 32.2M high-quality masks and captions on the provided 1M images. Besides, a simple yet strong model named UniRES is designed to accomplish the unified object-level and part-level grounding task. Extensive experiments on our RefCOCOm for MRES and three datasets (i.e., RefCOCO(+/g) for classic RES task demonstrate the superiority of our method over previous state-of-the-art methods. To foster future research into fine-grained visual grounding, our benchmark RefCOCOm, the MRES-32M dataset and model UniRES will be publicly available at https://github.com/Rubics-Xuan/MRES
Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation

As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent instance information) beyond the label itself should be helpful to improve performance. In this paper, we discover two types of latent labels behind the displayed label embedded in LiDAR and image data. First, in the LiDAR Branch, we propose a novel augmentation, Cylinder-Mix, which is able to augment more yet reliable samples for training. Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection. Finally, the two latent labels are embedded into the multi-modal panoptic segmentation network. The ablation of the IPSL module demonstrates its robust adaptability, and the experiments evaluated on SemanticKITTI and nuScenes demonstrate that our model outperforms the state-of-the-art method, LaserMix.
Large Language Models for Intent-Driven Session Recommendations

Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to their presumption of a uniform number of intents across all sessions. This assumption overlooks the dynamic nature of user sessions, where the number and type of intentions can significantly vary. In addition, these methods typically operate in latent spaces, thus hinder the model's transparency.Addressing these challenges, we introduce a novel ISR approach, utilizing the advanced reasoning capabilities of large language models (LLMs). First, this approach begins by generating an initial prompt that guides LLMs to predict the next item in a session, based on the varied intents manifested in user sessions. Then, to refine this process, we introduce an innovative prompt optimization mechanism that iteratively self-reflects and adjusts prompts. Furthermore, our prompt selection module, built upon the LLMs' broad adaptability, swiftly selects the most optimized prompts across diverse domains. This new paradigm empowers LLMs to discern diverse user intents at a semantic level, leading to more accurate and interpretable session recommendations. Our extensive experiments on three real-world datasets demonstrate the effectiveness of our method, marking a significant advancement in ISR systems.
Radio Signal Classification by Adversarially Robust Quantum Machine Learning

Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation of the transmitted information. Researchers have underscored the high susceptibility of ML algorithms for radio signal classification to adversarial attacks. Such vulnerability could result in severe consequences, including misinterpretation of critical messages, interception of classified information, or disruption of communication channels. Recent advancements in quantum computing have revolutionized theories and implementations of computation, bringing the unprecedented development of Quantum Machine Learning (QML). It is shown that quantum variational classifiers (QVCs) provide notably enhanced robustness against classical adversarial attacks in image classification. However, no research has yet explored whether QML can similarly mitigate adversarial threats in the context of radio signal classification. This work applies QVCs to radio signal classification and studies their robustness to various adversarial attacks. We also propose the novel application of the approximate amplitude encoding (AAE) technique to encode radio signal data efficiently. Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack. However, the converse is not true. QVCs primarily resist the attacks generated on CNNs. Overall, with comprehensive simulations, our results shed new light on the growing field of QML by bridging knowledge gaps in QAML in radio signal classification and uncovering the advantages of applying QML methods in practical applications.
The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and Guidelines

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet they are often only mentioned in passing in the scholarly literature. A lack of details surrounding pilot studies in crowdsourcing research hinders the replication of studies and the reproduction of findings, stalling potential scientific advances. We conducted a systematic literature review on the current state of pilot study reporting at the intersection of crowdsourcing and HCI research. Our review of ten years of literature included 171 articles published in the proceedings of the Conference on Human Computation and Crowdsourcing (AAAI HCOMP) and the ACM Digital Library. We found that pilot studies in crowdsourcing research (i.e., crowd pilot studies) are often under-reported in the literature. Important details, such as the number of workers and rewards to workers, are often not reported. On the basis of our findings, we reflect on the current state of practice and formulate a set of best practice guidelines for reporting crowd pilot studies in crowdsourcing research. We also provide implications for the design of crowdsourcing platforms and make practical suggestions for supporting crowd pilot study reporting.