Miracle-dz/UniFAR-FTUs huggingface dataset:
Is the universe older than commonly accepted?

The ages of globular clusters (GC) are conventionally constrained by models that adhere to the accepted age of the Universe, preventing their ages from exceeding approximately 13.8 Gyr. However, a recent study by llorente de Andr\'es (2023) challenges this paradigm. Drawing on the relationship between the number of blue straggler stars (BSs) and the relaxation time, the study proposes that the age of the cluster NGC104 falls between 19.04 and 20.30 Gyr. Extending this approach, the present work investigates GCs NGC 5634 and NGC 5024, finding their ages to be between 15.8 and 21.6 Gyr, surpassing the accepted age of the Universe. A plausible explanation aligns with Gupta's model (Gupta 2023), suggesting a Universe age of around 26.7 billion years, consistent with early universe observations from the James Webb Space Telescope (JWST). Additionally, four other GCs (IC 4499, NGC 6273, NGC 5824 and NGC 4833) support Gupta's model. The implications of these extended GC ages challenge our current cosmic timeline understanding, prompting a comprehensive reassessment of cosmological paradigms in light of these intriguing observational results.
Observational Feasibility of 4D Einstein-Gauss-Bonnet Cosmology: Bouncing and Non-Bouncing Universes

This paper analyzes the possibility of bouncing and non-bouncing universes in the framework of four-dimensional Einstein-Gauss-Bonnet (4D-EGB) gravity, corresponding respectively to negative and positive coupling constants $\lambda$ of the Gauss-Bonnet term. We also use the Horndeski-type scalar-tensor theory to assess the role of a scalar charge $C$ as a geometrical contribution to the radiation in the Universe. We modify the expansion history of the universe to allow for modifications induced by the 4D-EGB gravity. Using Planck measurements of the cosmic microwave background anisotropies as well as various datasets of baryonic acoustic oscillations, we set the upper bounds $\lambda \le 10^{-16} \text{(km/s/Mpc)}^{-2} $ and $\lambda \le 10^{-30} \text{(km/s/Mpc)}^{-2} $ for the non-bouncing and bouncing scenarios. The upper limit in the latter case is mainly driven by the requirement to conservatively respect the thermal history at energy scales of the standard model of particle physics. We also find that the contribution of the geometrical radiation-like term of the model cannot exceed 10\% of the current radiation in the Universe. This study shows the feasibility of a bouncing universe, even with a normal matter sector, in the 4D-EGB gravity. More theoretical investigation is required to further explore possible observational predictions of the model that can distinguish between general relativity and 4D-EGB gravity.
Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction

Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a Knowledge Graph (KG) with distinct benefits: 1) Our approach amalgamates entity context and document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on benchmark datasets - DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based Knowledge Graph link prediction techniques can enhance the performance of document-level relation extraction models.
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To alleviate this issue, we propose a novel visual GUI agent -- SeeClick, which only relies on screenshots for task automation. In our preliminary study, we have discovered a key challenge in developing visual GUI agents: GUI grounding -- the capacity to accurately locate screen elements based on instructions. To tackle this challenge, we propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate the curation of GUI grounding data. Along with the efforts above, we have also created ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. After pre-training, SeeClick demonstrates significant improvement in ScreenSpot over various baselines. Moreover, comprehensive evaluations on three widely used benchmarks consistently support our finding that advancements in GUI grounding directly correlate with enhanced performance in downstream GUI agent tasks. The model, data and code are available at https://github.com/njucckevin/SeeClick.
Homogenisation of nonlinear blood flow in periodic networks: the limit of small haematocrit heterogeneity

In this work we develop a homogenisation methodology to upscale mathematical descriptions of microcirculatory blood flow from the microscale (where individual vessels are resolved) to the macroscopic (or tissue) scale. Due to the assumed two-phase nature of blood and specific features of red blood cells (RBCs), mathematical models for blood flow in the microcirculation are highly nonlinear, coupling the flow and RBC concentrations (haematocrit). In contrast to previous works which accomplished blood-flow homogenisation by assuming that the haematocrit level remains constant, here we allow for spatial heterogeneity in the haematocrit concentration and thus begin with a nonlinear microscale model. We simplify the analysis by considering the limit of small haematocrit heterogeneity which prevails when variations in haematocrit concentration between neighbouring vessels are small. Homogenisation results in a system of coupled, nonlinear partial differential equations describing the flow and haematocrit transport at the macroscale, in which a nonlinear Darcy-type model relates the flow and pressure gradient via a haematocrit-dependent permeability tensor. During the analysis we obtain further that haematocrit transport at the macroscale is governed by a purely advective equation. Applying the theory to particular examples of two- and three-dimensional geometries of periodic networks, we calculate the effective permeability tensor associated with blood flow in these vascular networks. We demonstrate how the statistical distribution of vessel lengths and diameters, together with the average haematocrit level, affect the statistical properties of the macroscopic permeability tensor. These data can be used to simulate blood flow and haematocrit transport at the macroscale.
Addressing researcher degrees of freedom through minP adjustment

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers' analytical choices, an issue also referred to as ''researcher degrees of freedom''. Combined with selective reporting of the smallest p-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the ''minP'' adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative paO2 on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error -- and thus the risk of publishing false positive results that may not be replicable.
GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?

Deep gradient inversion attacks expose a serious threat to Federated Learning (FL) by accurately recovering private data from shared gradients. However, the state-of-the-art heavily relies on impractical assumptions to access excessive auxiliary data, which violates the basic data partitioning principle of FL. In this paper, a novel method, Gradient Inversion Attack using Practical Image Prior (GI-PIP), is proposed under a revised threat model. GI-PIP exploits anomaly detection models to capture the underlying distribution from fewer data, while GAN-based methods consume significant more data to synthesize images. The extracted distribution is then leveraged to regulate the attack process as Anomaly Score loss. Experimental results show that GI-PIP achieves a 16.12 dB PSNR recovery using only 3.8% data of ImageNet, while GAN-based methods necessitate over 70%. Moreover, GI-PIP exhibits superior capability on distribution generalization compared to GAN-based methods. Our approach significantly alleviates the auxiliary data requirement on both amount and distribution in gradient inversion attacks, hence posing more substantial threat to real-world FL.
Exploring Missing Modality in Multimodal Egocentric Datasets

Multimodal video understanding is crucial for analyzing egocentric videos, where integrating multiple sensory signals significantly enhances action recognition and moment localization. However, practical applications often grapple with incomplete modalities due to factors like privacy concerns, efficiency demands, or hardware malfunctions. Addressing this, our study delves into the impact of missing modalities on egocentric action recognition, particularly within transformer-based models. We introduce a novel concept -Missing Modality Token (MMT)-to maintain performance even when modalities are absent, a strategy that proves effective in the Ego4D, Epic-Kitchens, and Epic-Sounds datasets. Our method mitigates the performance loss, reducing it from its original $\sim 30\%$ drop to only $\sim 10\%$ when half of the test set is modal-incomplete. Through extensive experimentation, we demonstrate the adaptability of MMT to different training scenarios and its superiority in handling missing modalities compared to current methods. Our research contributes a comprehensive analysis and an innovative approach, opening avenues for more resilient multimodal systems in real-world settings.
LCODE: Quasistatic code for simulating long-term evolution of three-dimensional plasma wakefields

A recently developed three-dimensional version of the quasistatic code LCODE has a novel feature that enables high-accuracy simulations of the long-term evolution of waves in plasma wakefield accelerators. Equations of plasma particle motion are modified to suppress clustering and numerical heating of macroparticles, which otherwise occur because the Debye length is not resolved by the numerical grid. The previously observed effects of premature wake chaotization and wavebreaking disappear with the modified equations.
Data-oriented Coordinated Uplink Transmission for Massive IoT System

Recently, the paradigm of massive ultra-reliable low-latency IoT communications (URLLC-IoT) has gained growing interest. Reliable delay-critical uplink transmission in IoT is a challenging task since low-complex devices typically do not support multiple antennas or demanding signal processing tasks. However, in many IoT services the data volumes are small and deployments may include massive number of devices. We consider on a clustered uplink transmission with two cooperation approaches: First, we focus on scenario where location-based channel knowledge map (CKM) is applied to enable cooperation. Second, we consider a scenario where scarce channel side-information is applied in transmission. In both scenarios we also model and analyse the impact of erroneous information. In the performance evaluation we apply the recently introduced data-oriented approach that has gathered significant attention in the context of short-packet transmissions. Specifically, it introduces a transient performance metric for small data transmissions, where the amount of data and available bandwidth play crucial roles. Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range. It is noticed that the performance is heavily depending on the strength of the static channel component in the CKM based cooperation. The channel side-information based cooperation is robust against changes in the radio environment but sensitive to possible errors in the channel side-information. Even with large IoT device clusters, side-information errors may set a limit for the use of services assuming high-reliability and low-latency. Analytic results are verified against simulations, showing only minor differences at low probability levels.
EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions

Multitask learning (MTL) has gained prominence for its ability to jointly predict multiple tasks, achieving better per-task performance while using fewer per-task model parameters than single-task learning. More recently, decoder-focused architectures have considerably improved multitask performance by refining task predictions using the features of other related tasks. However, most of these refinement methods fail to simultaneously capture local and global task-specific representations, as well as cross-task patterns in a parameter-efficient manner. In this paper, we introduce the Efficient Multitask Affinity Learning Network (EMA-Net), which is a lightweight framework that enhances the task refinement capabilities of multitask networks. EMA-Net adeptly captures local, global, and cross-task interactions using our novel Cross-Task Affinity Learning (CTAL) module. The key innovation of CTAL lies in its ability to manipulate task affinity matrices in a manner that is optimally suited to apply parameter-efficient grouped convolutions without worrying about information loss. Our results show that we achieve state-of-the-art MTL performance for CNN-based decoder-focused models while using substantially fewer model parameters. Our code is publicly available at https://github.com/Armanfard-Lab/EMA-Net.
An Efficient Finite Difference-based Implicit Solver for Phase-Field Equations with Spatially and Temporally Varying Parameters

The phase field method is an effective tool for modeling microstructure evolution in materials. Many efficient implicit numerical solvers have been proposed for phase field simulations under uniform and time-invariant model parameters. We use Eyre's theorem to develop an unconditionally stable implicit solver for spatially non-uniform and time-varying model parameters. The accuracy, unconditional stability, and efficiency of the solver is validated against benchmarking examples. In its current form, the solver requires a uniform mesh and may only be applied to problems with periodic, Neumann, or mixed periodic and Neumann boundary conditions.
Mass Superinflation from Enhanced Cauchy Horizon Singularity in a Reissner-Nordstr\"om Black Hole

Ever since Penrose and Simpson contradicted Novikov's prediction that an infalling passenger would emerge into an asymptotically flat universe, there have been a continued interest in predicting the nature of singularity at the Cauchy horizon of a Reissner-Nordstrom blackhole. This prediction was first confirmed by Poisson and Israel using cross-stream of massless particles, suggesting the phenomenon of mass inflation. Ori however obtained a weaker singularity using a null shell of radiation. In this work, we consider a massive scalar field coupled to the Reissner-Nordstrom geometry and analyze the nature of singularity at the Cauchy horizon. To study the asymptotic behavior of the mass function and the scalar field near the Cauchy horizon, we perturbatively solve the coupled dynamical equations emplyoing the Adomian decomposition method. Our analysis shows that the mass function exhibits a very rapid and unbounded double-exponential growth, called herein mass superinflation, which is enormously stronger than previously obtained singularities. The scalar field is also found to undergo a very strong blueshift near the Cauchy horizon.
Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits

We introduce a distributionally robust approach that enhances the reliability of offline policy evaluation in contextual bandits under general covariate shifts. Our method aims to deliver robust policy evaluation results in the presence of discrepancies in both context and policy distribution between logging and target data. Central to our methodology is the application of robust regression, a distributionally robust technique tailored here to improve the estimation of conditional reward distribution from logging data. Utilizing the reward model obtained from robust regression, we develop a comprehensive suite of policy value estimators, by integrating our reward model into established evaluation frameworks, namely direct methods and doubly robust methods. Through theoretical analysis, we further establish that the proposed policy value estimators offer a finite sample upper bound for the bias, providing a clear advantage over traditional methods, especially when the shift is large. Finally, we designed an extensive range of policy evaluation scenarios, covering diverse magnitudes of shifts and a spectrum of logging and target policies. Our empirical results indicate that our approach significantly outperforms baseline methods, most notably in 90% of the cases under the policy shift-only settings and 72% of the scenarios under the general covariate shift settings.
Unsupervised Learning of Graph from Recipes

Cooking recipes are one of the most readily available kinds of procedural text. They consist of natural language instructions that can be challenging to interpret. In this paper, we propose a model to identify relevant information from recipes and generate a graph to represent the sequence of actions in the recipe. In contrast with other approaches, we use an unsupervised approach. We iteratively learn the graph structure and the parameters of a $\mathsf{GNN}$ encoding the texts (text-to-graph) one sequence at a time while providing the supervision by decoding the graph into text (graph-to-text) and comparing the generated text to the input. We evaluate the approach by comparing the identified entities with annotated datasets, comparing the difference between the input and output texts, and comparing our generated graphs with those generated by state of the art methods.
Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM

Reconstructing 3D humans from a single image has been extensively investigated. However, existing approaches often fall short on capturing fine geometry and appearance details, hallucinating occluded parts with plausible details, and achieving generalization across unseen and in-the-wild datasets. We present Human-LRM, a diffusion-guided feed-forward model that predicts the implicit field of a human from a single image. Leveraging the power of the state-of-the-art reconstruction model (i.e., LRM) and generative model (i.e Stable Diffusion), our method is able to capture human without any template prior, e.g., SMPL, and effectively enhance occluded parts with rich and realistic details. Our approach first uses a single-view LRM model with an enhanced geometry decoder to get the triplane NeRF representation. The novel view renderings from the triplane NeRF provide strong geometry and color prior, from which we generate photo-realistic details for the occluded parts using a diffusion model. The generated multiple views then enable reconstruction with high-quality geometry and appearance, leading to superior overall performance comparing to all existing human reconstruction methods.
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis

We explore a stochastic contextual linear bandit problem where the agent observes a noisy, corrupted version of the true context through a noise channel with an unknown noise parameter. Our objective is to design an action policy that can approximate"that of an oracle, which has access to the reward model, the channel parameter, and the predictive distribution of the true context from the observed noisy context. In a Bayesian framework, we introduce a Thompson sampling algorithm for Gaussian bandits with Gaussian context noise. Adopting an information-theoretic analysis, we demonstrate the Bayesian regret of our algorithm concerning the oracle's action policy. We also extend this problem to a scenario where the agent observes the true context with some delay after receiving the reward and show that delayed true contexts lead to lower Bayesian regret. Finally, we empirically demonstrate the performance of the proposed algorithms against baselines.