Miracle-dz/UniFAR-FTUs huggingface dataset:
Conformal Symmetry in Quantum Gravity

We study the problem of how to derive conformal symmetry in the framework of quantum gravity. We start with a generic gravitational theory which is invariant under both the general coordinate transformation (GCT) and Weyl transformation (or equivalently, local scale transformation), and then construct its BRST formalism by fixing the gauge symmetries by the extended de Donder gauge and scalar gauge conditions. These gauge-fixing conditions are invariant under global $GL(4)$ and global scale transformations. The gauge-fixed and BRST invariant quantum action possesses a huge Poincar\'e-like $IOSp(10|10)$ global symmetry, from which we can construct an extended conformal symmetry in a flat Minkowski background in the sense that the Lorentz symmetry is replaced with the $GL(4)$ symmetry. Moreover, we construct the conventional conformal symmetry out of this extended symmetry. With a flat Minkowski background $\langle g_{\mu\nu} \rangle = \eta_{\mu\nu}$ and a non-zero scalar field $\langle \phi \rangle \neq 0$, the $GL(4)$ and global scale symmetries are spontaneously broken to the Lorentz symmetry, thereby proving that the graviton and the dilaton are respectively the corresponding Nambu-Goldstone bosons, and therefore they must be exactly massless at nonperturbative level. One of remarkable aspects in our findings is that in quantum gravity, a derivation of conformal symmetry does not depend on a classical action, and its generators are built from only the gauge-fixing and the FP ghost actions. Finally, we address a generalized Zumino theorem in quantum gravity.
Secure MIMO Communication Relying on Movable Antennas

This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system's physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix and the MAs' positions under the constraints of the maximum transmit power and the minimum distance between MAs. To solve this non-convex problem with highly coupled optimization variables, the block coordinate descent (BCD) method is applied to alternately update the variables. Specifically, we first reformulate the SR into a tractable form by utilizing the minimum mean square error (MMSE) method, and derive the optimal TPC matrix and the AN covariance matrix with fixed MAs' positions by applying the Lagrangian multiplier method in semi-closed forms. Then, the majorization-minimization (MM) algorithm is employed to iteratively optimize each MA's position while keeping others fixed. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms and the significant advantages of the MA-aided system over conventional fixed position antenna (FPA)-based system in enhancing system's security.
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation

Existing off-policy reinforcement learning algorithms typically necessitate an explicit state-action-value function representation, which becomes problematic in high-dimensional action spaces. These algorithms often encounter challenges where they struggle with the curse of dimensionality, as maintaining a state-action-value function in such spaces becomes data-inefficient. In this work, we propose a novel off-policy trust region optimization approach, called Vlearn, that eliminates the requirement for an explicit state-action-value function. Instead, we demonstrate how to efficiently leverage just a state-value function as the critic, thus overcoming several limitations of existing methods. By doing so, Vlearn addresses the computational challenges posed by high-dimensional action spaces. Furthermore, Vlearn introduces an efficient approach to address the challenges associated with pure state-value function learning in the off-policy setting. This approach not only simplifies the implementation of off-policy policy gradient algorithms but also leads to consistent and robust performance across various benchmark tasks. Specifically, by removing the need for a state-action-value function Vlearn simplifies the learning process and allows for more efficient exploration and exploitation in complex environments
Repelling-Attracting Hamiltonian Monte Carlo

We propose a variant of Hamiltonian Monte Carlo (HMC), called the Repelling-Attracting Hamiltonian Monte Carlo (RAHMC), for sampling from multimodal distributions. The key idea that underpins RAHMC is a departure from the conservative dynamics of Hamiltonian systems, which form the basis of traditional HMC, and turning instead to the dissipative dynamics of conformal Hamiltonian systems. In particular, RAHMC involves two stages: a mode-repelling stage to encourage the sampler to move away from regions of high probability density; and, a mode-attracting stage, which facilitates the sampler to find and settle near alternative modes. We achieve this by introducing just one additional tuning parameter -- the coefficient of friction. The proposed method adapts to the geometry of the target distribution, e.g., modes and density ridges, and can generate proposals that cross low-probability barriers with little to no computational overhead in comparison to traditional HMC. Notably, RAHMC requires no additional information about the target distribution or memory of previously visited modes. We establish the theoretical basis for RAHMC, and we discuss repelling-attracting extensions to several variants of HMC in literature. Finally, we provide a tuning-free implementation via dual-averaging, and we demonstrate its effectiveness in sampling from, both, multimodal and unimodal distributions in high dimensions.
A Lagrangian approach for solving an axisymmetric thermo-electromagnetic problem. Application to time-varying geometry processes

The aim of this work is to introduce a thermo-electromagnetic model for calculating the temperature and the power dissipated in cylindrical pieces whose geometry var\'ies with time and undergoes large deformations; the motion will be a known data. The work will be a first step towards building a complete thermoelectromagnetic-mechanical model suitable for simulating electrically assisted forming processes, which is the main motivation of the work. The electromagnetic model will be obtained from the time-harmonic eddy current problem with an inplane current; the source will be given in terms of currents or voltages defined at sorne parts of the boundary. Finite element methods based on a Lagrangian weak formulation will be used for the numerical solution. This approach will avoid the need to compute and remesh the thermo-electromagnetic domain along the time. The numerical tools will be implemented in FEniCS and validated by using a suitable test also solved in Eulerian coordinates.
HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild

Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains. Recent benchmarks designed to assess LLM hallucinations within conventional NLP tasks, such as knowledge-intensive question answering (QA) and summarization, are insufficient for capturing the complexities of user-LLM interactions in dynamic, real-world settings. To address this gap, we introduce HaluEval-Wild, the first benchmark specifically designed to evaluate LLM hallucinations in the wild. We meticulously collect challenging (adversarially filtered by Alpaca) user queries from existing real-world user-LLM interaction datasets, including ShareGPT, to evaluate the hallucination rates of various LLMs. Upon analyzing the collected queries, we categorize them into five distinct types, which enables a fine-grained analysis of the types of hallucinations LLMs exhibit, and synthesize the reference answers with the powerful GPT-4 model and retrieval-augmented generation (RAG). Our benchmark offers a novel approach towards enhancing our comprehension and improvement of LLM reliability in scenarios reflective of real-world interactions.
Maximizing Slice-Volumes of Semialgebraic Sets using Sum-of-Squares Programming

This paper presents an algorithm to maximize the volume of an affine slice through a given semialgebraic set. This slice-volume task is formulated as an infinite-dimensional linear program in continuous functions, inspired by prior work in volume computation of semialgebraic sets. A convergent sequence of upper-bounds to the maximal slice volume are computed using the moment-Sum-of-Squares hierarchy of semidefinite programs in increasing size. The computational complexity of this scheme can be reduced by utilizing topological structure (in dimensions 2, 3, 4, 8) and symmetry. This numerical convergence can be accelerated through the introduction of redundant Stokes-based constraints. Demonstrations of slice-volume calculation are performed on example sets.
Tensor Power Flow Formulations for Multidimensional Analyses in Distribution Systems

In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of power flows in distribution systems. The presented algorithms are the base for a new TensorPowerFlow (TPF) tool and shine for their simplicity, benefiting from multicore \gls{cpu} and \gls{gpu} parallelization. We also focus on the mathematical convergence properties of the algorithm, showing that its unique solution is at the practical operational point, which is the solution of high-voltage and low-current. The proof is validated using numerical simulations showing the robustness of the FPI algorithm compared to the classical \gls{nr} approach. In the case study, a benchmark with different PF solution methods is performed, showing that for applications requiring a yearly simulation at 1-minute resolution the computation time is decreased by a factor of 164, compared to the NR in its sparse formulation.
THz-assisted microscopy of silica matrix for biological materials encapsulation: a theoretical and experimental study

In this study, we use THz-assisted atom probe tomography (APT) to analyse silica matrices used to encapsulate biomolecules. This technique provides the chemical composition and 3D structure without significantly heating the biosample, which is crucial for studying soft organic molecules such as proteins. Our results show that THz pulses and a positive static field trigger controlled evaporation of silica matrices, enabling 4D imaging with chemical sensitivity comparable to UV laser-assisted APT. To support the interpretation of these experimental results, we devise a computational model based on time-dependent density functional theory to describe the interaction between silica matrices and THz radiation. This model captures the nonlinear dynamics driven by THz-pulses and the interplay between the THz source and the static electric field in real time. This interdisciplinary approach expands the capabilities of APT and holds promise for other THz-based analyses offering new insights into material dynamics in complex biological environments.
In-n-Out: Calibrating Graph Neural Networks for Link Prediction

Deep neural networks are notoriously miscalibrated, i.e., their outputs do not reflect the true probability of the event we aim to predict. While networks for tabular or image data are usually overconfident, recent works have shown that graph neural networks (GNNs) show the opposite behavior for node-level classification. But what happens when we are predicting links? We show that, in this case, GNNs often exhibit a mixed behavior. More specifically, they may be overconfident in negative predictions while being underconfident in positive ones. Based on this observation, we propose IN-N-OUT, the first-ever method to calibrate GNNs for link prediction. IN-N-OUT is based on two simple intuitions: i) attributing true/false labels to an edge while respecting a GNNs prediction should cause but small fluctuations in that edge's embedding; and, conversely, ii) if we label that same edge contradicting our GNN, embeddings should change more substantially. An extensive experimental campaign shows that IN-N-OUT significantly improves the calibration of GNNs in link prediction, consistently outperforming the baselines available -- which are not designed for this specific task.
VAEMax: Open-Set Intrusion Detection based on OpenMax and Variational Autoencoder

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on system or equipment. This paper aims to develop an open-set intrusion detection model to classify known attacks as well as inferring unknown ones. To achieve this, we employ OpenMax and variational autoencoder to propose a dual detection model, VAEMax. First, we extract flow payload feature based on one-dimensional convolutional neural network. Then, the OpenMax is used to classify flows, during which some unknown attacks can be detected, while the rest are misclassified into a certain class of known flows. Finally, use VAE to perform secondary detection on each class of flows, and determine whether the flow is an unknown attack based on the reconstruction loss. Experiments performed on dataset CIC-IDS2017 and CSE-CIC-IDS2018 show our approach is better than baseline models and can be effectively applied to realistic network environments.
Why Online Reinforcement Learning is Causal

Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while the goal of reinforcement learning is to select interventions that maximize the rewards the agent receives from the environment. Reinforcement learning includes the two most powerful sources of information for estimating causal relationships: temporal ordering and the ability to act on an environment. This paper examines which reinforcement learning settings we can expect to benefit from causal modelling, and how. In online learning, the agent has the ability to interact directly with their environment, and learn from exploring it. Our main argument is that in online learning, conditional probabilities are causal, and therefore offline RL is the setting where causal learning has the most potential to make a difference. Essentially, the reason is that when an agent learns from their {\em own} experience, there are no unobserved confounders that influence both the agent's own exploratory actions and the rewards they receive. Our paper formalizes this argument. For offline RL, where an agent may and typically does learn from the experience of {\em others}, we describe previous and new methods for leveraging a causal model, including support for counterfactual queries.
A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems

Indoor human positioning has become increasingly important for applications such as health monitoring, breath monitoring, human identification, safety and rescue operations, and security surveillance. However, achieving robust indoor human positioning remains challenging due to various constraints. Numerous attempts have been made in the literature to develop efficient indoor positioning systems (IPSs), with a growing focus on machine learning (ML) based techniques. This paper aims to compare and analyze current ML-based wireless techniques and approaches for indoor positioning, providing a comprehensive review of enabling technologies for human detection, positioning, and activity recognition. The study explores different input measurement data, including RSSI, TDOA, etc., for various IPSs. Key positioning techniques such as RSSI-based fingerprinting, Angle-based, and Time-based approaches are examined in conjunction with various ML methods. The survey compares the positioning accuracy, scalability, and algorithm complexity, with the goal of determining the suitable technology in various services. Finally, the paper compares distinct datasets focused on indoor localization, which have been published using diverse technologies. Overall, the paper presents a comprehensive comparison of existing techniques and localization models.
Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis

This paper presents a comprehensive survey of research works on the topic of form understanding in the context of scanned documents. We delve into recent advancements and breakthroughs in the field, highlighting the significance of language models and transformers in solving this challenging task. Our research methodology involves an in-depth analysis of popular documents and forms of understanding of trends over the last decade, enabling us to offer valuable insights into the evolution of this domain. Focusing on cutting-edge models, we showcase how transformers have propelled the field forward, revolutionizing form-understanding techniques. Our exploration includes an extensive examination of state-of-the-art language models designed to effectively tackle the complexities of noisy scanned documents. Furthermore, we present an overview of the latest and most relevant datasets, which serve as essential benchmarks for evaluating the performance of selected models. By comparing and contrasting the capabilities of these models, we aim to provide researchers and practitioners with useful guidance in choosing the most suitable solutions for their specific form understanding tasks.
YYDS: Visible-Infrared Person Re-Identification with Coarse Descriptions

Visible-infrared person re-identification (VI-ReID) is challenging due to considerable cross-modality discrepancies. Existing works mainly focus on learning modality-invariant features while suppressing modality-specific ones. However, retrieving visible images only depends on infrared samples is an extreme problem because of the absence of color information. To this end, we present the Refer-VI-ReID settings, which aims to match target visible images from both infrared images and coarse language descriptions (e.g.,"a man with red top and black pants") to complement the missing color information. To address this task, we design a Y-Y-shape decomposition structure, dubbed YYDS, to decompose and aggregate texture and color features of targets. Specifically, the text-IoU regularization strategy is firstly presented to facilitate the decomposition training, and a joint relation module is then proposed to infer the aggregation. Furthermore, the cross-modal version of k-reciprocal re-ranking algorithm is investigated, named CMKR, in which three neighbor search strategies and one local query expansion method are explored to alleviate the modality bias problem of the near neighbors. We conduct experiments on SYSU-MM01, RegDB and LLCM datasets with our manually annotated descriptions. Both YYDS and CMKR achieve remarkable improvements over SOTA methods on all three datasets. Codes are available at https://github.com/dyhBUPT/YYDS.
Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics : a multi-model analysis

The responses of carbon dioxide (CO 2) and other climate variables to an emission pulse of CO 2 into the atmosphere are often used to compute the Global Warming Potential (GWP) and Global Temperature change Potential (GTP), to characterize the response timescales of Earth System models, and to build reduced-form models. In this carbon cycleclimate model intercomparison project, which spans the full model hierarchy, we quantify responses to emission pulses of different magnitudes injected under different conditions. The CO2 response shows the known rapid decline in the first few decades followed by a millennium-scale tail. For a 100 Gt-C emission pulse added to a constant CO 2 concentration of 389 ppm, 25± 9 % is still found in the atmosphere after 1000 yr; the ocean has absorbed 59 ± 12 % and the land the remainder (16± 14 %). The response in global mean surface air temperature is an increase by 0.20 ± . 12C within the first twenty years; thereafter and until year 1000, temperature decreases only slightly, whereas ocean heat content and sea level continue to rise. Our best es imate for the Absolute Global Warming Potential, given by the time-integrated Published by Copernicus Publications on behalf of the European Geosciences Union. 2794 F. Joos et al.: A multi-model analysis response in CO2 at year 100 multiplied by its radiative efficiency, is 92.5× 10−15 yr W m−2 per kg-CO2. This value very likely (5 to 95 % confidence) lies within the range of (68 to 117)× 10−15 yr W m−2 per kg-CO2. Estimates for timeintegrated response in CO 2 published in the IPCC First, Second, and Fourth Assessment and our multi-model best estimate all agree within 15 % during the first 100 yr. The integrated CO2 response, normalized by the pulse size, is lower for pre-industrial conditions, compared to present day, and lower for smaller pulses than larger pulses. In contrast, the response in temperature, sea level and ocean heat content is less sensitive to these choices. Although, choices in pulse size, background concentration, and model lead to uncertainties, the most important and subjective choice to determine AGWP of CO2 and GWP is the time horizon.
of the Past

Radiocarbon production, solar activity, total solar irradiance (TSI) and solar-induced climate change are re-constructed for the Holocene (10 to 0 kyr BP), and TSI is predicted for the next centuries. The IntCal09/SHCal04 ra-diocarbon and ice core CO 2 records, reconstructions of the geomagnetic dipole, and instrumental data of solar activity are applied in the Bern3D-LPJ, a fully featured Earth system model of intermediate complexity including a 3-D dynamic ocean, ocean sediments, and a dynamic vegetation model, and in formulations linking radiocarbon production, the solar modulation potential, and TSI. Uncertainties are assessed us-ing Monte Carlo simulations and bounding scenarios. Transient climate simulations span the past 21 thousand years, thereby considering the time lags and uncertainties associated with the last glacial termination. Our carbon-cycle-based modern estimate of radiocarbon production of 1.7 atoms cm − 2 s − 1 is lower than previously reported for the cosmogenic nuclide production model by Masarik and Beer (2009) and is more in-line with Kovaltsov et al. (2012). In contrast to earlier studies, periods of high solar activity were quite common not only in recent millennia, but throughout the Holocene. Notable deviations compared to earlier reconstructions are also found on decadal to centennial timescales. We show that earlier Holocene reconstructions, not accounting for the interhemispheric gradients in radiocarbon, are biased low. Solar activity is during 28 % of the time higher than the modern average (650 MeV), but the absolute values remain weakly constrained due to uncertainties in the normalisation of the solar modulation to instrumental data. A recently published solar activity–TSI relationship yields small changes in Holocene TSI of the order of 1 W m − 2 with a Maunder Minimum irradiance reduction of 0.85 ± 0.16 W m − 2 . Related solar-induced variations in global mean surface air temperature are simulated to be within 0.1 K. Autoregressive modelling suggests a declining trend of solar activity in the 21st century towards average Holocene conditions.