Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass bias in clusters of galaxies: Projection effects on the case study of Virgo replica

When measuring the observed pressure, density, or temperature profiles of the intracluster gas, and hence the mass of clusters of galaxies, projection effects or departures from the spherical symmetry hypothesis may induce biases. To estimate how strongly the cluster's observed properties depend on the direction of observation, we use a constrained hydrodynamical simulation of the Virgo cluster that replicates the actual cluster of galaxies. In this case study, we analysed Virgo properties when projected in different directions, including along the Milky Way-Virgo axis, which mimics our observation direction. We compared the hydrostatic mass and the hydrostatic mass bias from the projection along the different observation directions to that derived from the 3D simulation. We show that projection effects impact the determination of Virgo mass. We particularly demonstrate that the mass and pressure along the line of sight correlate with the 2D- and 3D-deprojected electron density and pressure profiles intensity and thus impact the derived hydrostatic mass. We also show that the deviations to the hydrostatic equilibrium induced by pressure discontinuities within the cluster are emphasised by the deprojection process and thus make the hydrostatic mass estimation invalid at these radii.
Estimating systemic importance with missing data in input-output graphs

In the context of the Cobb-Douglas productivity model we consider the $N \times N$ input-output linkage matrix $W$ for a network of $N$ firms $f_1, f_2, \cdots, f_N$. The associated influence vector $v_w$ of $W$ is defined in terms of the Leontief inverse $L_W$ of $W$ as $v_W = \frac{\alpha}{N} L_W \vec{\mathbf{1}}$ where $L_W = (I - (1-\alpha) W')^{-1}$, $W'$ denotes the transpose of $W$ and $I$ is the identity matrix. Here $\vec{\mathbf{1}}$ is the $N \times 1$ vector whose entries are all one. The influence vector is a metric of the importance for the firms in the production network. Under the realistic assumption that the data to compute the influence vector is incomplete, we prove bounds on the worst-case error for the influence vector that are sharp up to a constant factor. We also consider the situation where the missing data is binomially distributed and contextualize the bound on the influence vector accordingly. We also investigate how far off the influence vector can be when we only have data on nodes and connections that are within distance $k$ of some source node. A comparison of our results is juxtaposed against PageRank analogues. We close with a discussion on a possible extension beyond Cobb-Douglas to the Constant Elasticity of Substitution model, as well as the possibility of considering other probability distributions for missing data.
Practical, Private Assurance of the Value of Collaboration

Two parties wish to collaborate on their datasets. However, before they reveal their datasets to each other, the parties want to have the guarantee that the collaboration would be fruitful. We look at this problem from the point of view of machine learning, where one party is promised an improvement on its prediction model by incorporating data from the other party. The parties would only wish to collaborate further if the updated model shows an improvement in accuracy. Before this is ascertained, the two parties would not want to disclose their models and datasets. In this work, we construct an interactive protocol for this problem based on the fully homomorphic encryption scheme over the Torus (TFHE) and label differential privacy, where the underlying machine learning model is a neural network. Label differential privacy is used to ensure that computations are not done entirely in the encrypted domain, which is a significant bottleneck for neural network training according to the current state-of-the-art FHE implementations. We prove the security of our scheme in the universal composability framework assuming honest-but-curious parties, but where one party may not have any expertise in labelling its initial dataset. Experiments show that we can obtain the output, i.e., the accuracy of the updated model, with time many orders of magnitude faster than a protocol using entirely FHE operations.
Finite-N corrections to dynamical Lorentz symmetry breaking

A model of $N$ 4-component massless fermions in a quartic self-interaction based on the ref. [18] is investigated in the presence of chemical potential and temperature via optimized perturbation theory that accesses finite-N contributions. We use the generating functional approach to calculate the corrections at one-loop to the effective potential of the model. The model introduces an auxiliary vector field that breaks the Lorentz symmetry in the presence of a temperature $(T)$, and of a chemical potential $(\mu)$. These thermodynamics quantities are introduced through the Matsubara formalism. Thereby, the one-loop integrals are modified and via the principle of minimum sensitivity, we obtain the gap equations of the model. The correspondent solutions of these equations define the vacuum states of the model associated with the background vector field. We discuss the solutions of the four cases in which $(T = 0,\mu = 0)$, $(T \neq 0,\mu \neq 0)$, $(T \neq 0,\mu = 0)$ and $(T = 0,\mu \neq 0)$, where the effective potential is so obtained as a function of the background vector field, the chemical potential, and the temperature. The work demonstrates the persistence of second-order phase transitions in the finite-N improvement made on the finite-$N$ self-interacting fermions.
Multi-fidelity No-U-Turn Sampling

Markov Chain Monte Carlo (MCMC) methods often take many iterations to converge for highly correlated or high-dimensional target density functions. Methods such as Hamiltonian Monte Carlo (HMC) or No-U-Turn Sampling (NUTS) use the first-order derivative of the density function to tackle the aforementioned issues. However, the calculation of the derivative represents a bottleneck for computationally expensive models. We propose to first build a multi-fidelity Gaussian Process (GP) surrogate. The building block of the multi-fidelity surrogate is a hierarchy of models of decreasing approximation error and increasing computational cost. Then the generated multi-fidelity surrogate is used to approximate the derivative. The majority of the computation is assigned to the cheap models thereby reducing the overall computational cost. The derivative of the multi-fidelity method is used to explore the target density function and generate proposals. We select or reject the proposals using the Metropolis Hasting criterion using the highest fidelity model which ensures that the proposed method is ergodic with respect to the highest fidelity density function. We apply the proposed method to three test cases including some well-known benchmarks to compare it with existing methods and show that multi-fidelity No-U-turn sampling outperforms other methods.
Graph Neural Networks and Time Series as Directed Graphs for Quality Recognition

Graph Neural Networks (GNNs) are becoming central in the study of time series, coupled with existing algorithms as Temporal Convolutional Networks and Recurrent Neural Networks. In this paper, we see time series themselves as directed graphs, so that their topology encodes time dependencies and we start to explore the effectiveness of GNNs architectures on them. We develop two distinct Geometric Deep Learning models, a supervised classifier and an autoencoder-like model for signal reconstruction. We apply these models on a quality recognition problem.
Tight Sampling in Unbounded Networks

The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to prioritize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.
AVO Impedance: A New Attribute for Lithology and Fluid Discrimination

AVOImpedance is a data adaptive attribute based on a weighted combination of AI (acoustic impedance) and EI (elastic impedance). Using a well dataset from the Central North Sea, the AVOImpedance attribute is shown to be comparable (in terms of its predicting power) to other attributes based on projections of AI and GI and MR (μρ) and L/M (λ/μ). The simplicity of the AVOI attribute and the direct relevance to the outputs of near and far stack inversions is compelling.
R-LGP: A Reachability-guided Logic-geometric Programming Framework for Optimal Task and Motion Planning on Mobile Manipulators

This paper presents an optimization-based solution to task and motion planning (TAMP) on mobile manipulators. Logic-geometric programming (LGP) has shown promising capabilities for optimally dealing with hybrid TAMP problems that involve abstract and geometric constraints. However, LGP does not scale well to high-dimensional systems (e.g. mobile manipulators) and can suffer from obstacle avoidance issues due to local minima. In this work, we extend LGP with a sampling-based reachability graph to enable solving optimal TAMP on high-DoF mobile manipulators. The proposed reachability graph can incorporate environmental information (obstacles) to provide the planner with sufficient geometric constraints. This reachability-aware heuristic efficiently prunes infeasible sequences of actions in the continuous domain, hence, it reduces replanning by securing feasibility at the final full path trajectory optimization. Our framework proves to be time-efficient in computing optimal and collision-free solutions, while outperforming the current state of the art on metrics of success rate, planning time, path length and number of steps. We validate our framework on the physical Toyota HSR robot and report comparisons on a series of mobile manipulation tasks of increasing difficulty. Videos of the experiments are available at https://youtu.be/NEVVHEhQnOQ.
Extracting Rules from Event Data for Study Planning

In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students. The main goal is to offer valuable guidance for their study planning. We employ process and data mining techniques to explore the impact of sequences of taken courses on academic success. Through the use of decision tree models, we generate data-driven recommendations in the form of rules for study planning and compare them to the recommended study plan. The evaluation focuses on RWTH Aachen University computer science bachelor program students and demonstrates that the proposed course sequence features effectively explain academic performance measures. Furthermore, the findings suggest avenues for developing more adaptable study plans.
Multimodal Question Answering for Unified Information Extraction

Multimodal information extraction (MIE) aims to extract structured information from unstructured multimedia content. Due to the diversity of tasks and settings, most current MIE models are task-specific and data-intensive, which limits their generalization to real-world scenarios with diverse task requirements and limited labeled data. To address these issues, we propose a novel multimodal question answering (MQA) framework to unify three MIE tasks by reformulating them into a unified span extraction and multi-choice QA pipeline. Extensive experiments on six datasets show that: 1) Our MQA framework consistently and significantly improves the performances of various off-the-shelf large multimodal models (LMM) on MIE tasks, compared to vanilla prompting. 2) In the zero-shot setting, MQA outperforms previous state-of-the-art baselines by a large margin. In addition, the effectiveness of our framework can successfully transfer to the few-shot setting, enhancing LMMs on a scale of 10B parameters to be competitive or outperform much larger language models such as ChatGPT and GPT-4. Our MQA framework can serve as a general principle of utilizing LMMs to better solve MIE and potentially other downstream multimodal tasks.
Recovery of Training Data from Overparameterized Autoencoders: An Inverse Problem Perspective

We study the recovery of training data from overparameterized autoencoder models. Given a degraded training sample, we define the recovery of the original sample as an inverse problem and formulate it as an optimization task. In our inverse problem, we use the trained autoencoder to implicitly define a regularizer for the particular training dataset that we aim to retrieve from. We develop the intricate optimization task into a practical method that iteratively applies the trained autoencoder and relatively simple computations that estimate and address the unknown degradation operator. We evaluate our method for blind inpainting where the goal is to recover training images from degradation of many missing pixels in an unknown pattern. We examine various deep autoencoder architectures, such as fully connected and U-Net (with various nonlinearities and at diverse train loss values), and show that our method significantly outperforms previous methods for training data recovery from autoencoders. Importantly, our method greatly improves the recovery performance also in settings that were previously considered highly challenging, and even impractical, for such retrieval.
Coherence of group-IV color centers

Group-IV color centers in diamond (SiV, GeV, SnV) have emerged as leading solid-state spin-photon interfaces for quantum information processing applications. However, these qubits require cryogenic temperatures to achieve high fidelity operation due to interactions with the thermal phonon bath. In this work, we: (i) derive a detailed model of the decoherence from first-order acoustic phonon processes acting on the spin-orbit fine structure of these color centers; (ii) demonstrate agreement of the model's predicted coherence times with previous measurements; (iii) identify regimes to suppress phonon-mediated decoherence by changing magnetic-field and strain bias to allow higher temperature operation. This methodology enables prediction of decoherence processes in other color centers and solid-state qubit systems coupled to a thermal bath via a parasitic two-level system. By experiment-anchored decoherence models, we facilitate optimizing qubit coherence for specific applications and devices.
Entropy of flat space cosmologies from celestial dual

We construct a one-dimensional dual theory that effectively describes the sector of the (2+1)D flat gravity phase space near a Flat Space Cosmology (FSC) saddle labeled by definite mass and angular momentum. This Schwarzian type action describes the dynamics of the (Pseudo-) Goldstone Bosons of BMS$_3$ algebra on a circle as the symmetry is spontaneously and anomalously broken. This 1D theory, living on the celestial circle, provides an explicit construction of a celestial dual in (2+1)D. We use it to calculate the semiclassical entropy of Flat Space Cosmologies and find perfect agreement with existing literature.
Agricultural management impacts on groundwater: simulations of existing and alternative management options in Peninsular India

Understanding the principal causes and possible solutions for groundwater depletion in India is important for its water security, especially as it relates to agriculture. A study was conducted in an agricultural watershed in Andhra Pradesh, India to assess the impacts on groundwater of current and alternative agricultural management. Hydrological simulations were used as follows: (1) to evaluate the recharge benefits of water‐harvesting tillage through a modified Soil and Water Assessment Tool (SWAT) model and (2) to predict the groundwater response to changing extent and irrigation management of rice growing areas. The Green–Ampt infiltration routine was modified in SWAT was modified to represent water‐harvesting tillage using maximum depression storage parameter. Water‐harvesting tillage in rainfed croplands was shown to increase basin‐scale groundwater recharge by 3% and decrease run‐off by 43% compared with existing conventional tillage. The groundwater balance (recharge minus irrigation withdrawals), negative 11 mm/year under existing management changed to positive (18–45 mm/year) when rice growing areas or irrigation depths were reduced. Groundwater balance was sensitive to changes in rice cropland management, meaning even small changes in rice cropland management had large impacts on groundwater availability. The modified SWAT was capable of representing tillage management of varying maximum depression storage, and tillage for water‐harvesting was shown to be a potentially important strategy for producers to enhance infiltration and groundwater recharge, especially in semi‐arid regions where rainfall may be becoming increasingly variable. This enhanced SWAT could be used to evaluate the landscape‐scale impacts of alternative tillage management in other regions that are working to develop strategies for reducing groundwater depletion. Copyright © 2013 John Wiley & Sons, Ltd.
The role of electron inertia in laser-plasma interaction

Incorporating lasers in macroscopic plasma models is made challenging by the fast temporal and small spatial scales when electrons interact with laser fields. When ion motion can be neglected, like in laser wakefield acceleration, particle-in-cell models capture most of features present in the plasma dynamics. But when the ion dynamics takes center stage, only multiple-fluid models can be used, as particle-in-cell simulations become too expensive to run. In this letter, we show that extended magnetohydrodynamics can be used instead, as it captures exactly the macroscopic coupling between laser fields, electron flows and the ion dynamics, on the ion time scale. While it replicates several features usually associated with explicit electron physics, such as cut-off density, or electromagnetic wave reflection and absorption, it does so with little computational overhead compared to standard magnetohydrodynamics. Since this approach computes the electron dynamics implicitly using generalized Ohm's law, rather than electron momentum conservation, the spatial and temporal scales of extended magnetohydrodynamics fit well between multi-fluid and standard magnetohydrodynamics scales, allowing to study a new class of problems that would be too expensive to run otherwise.
Addressing Quantum's"Fine Print": State Preparation and Information Extraction for Quantum Algorithms and Geologic Fracture Networks

Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.
Rediscovering the World: Gridded Cartograms of Human and Physical Space

'We need new maps' is the central claim made in this thesis. In a world increasingly influenced by human action and interaction, we still rely heavily on mapping techniques that were invented to discover unknown places and explore our physical environment. Although the traditional concept of a map is currently being revived in digital environments, the underlying mapping approaches are not capable of making the complexity of human-environment relationships fully comprehensible. Starting from how people can be put on the map in new ways, this thesis outlines the development of a novel technique that stretches a map according to quantitative data, such as population. The new maps are called gridded cartograms as the method is based on a grid onto which a density-equalising cartogram technique is applied. The underlying grid ensures the preservation of an accurate geographic reference to the real world. It allows the gridded cartograms to be used as basemaps onto which other information can be mapped. This applies to any geographic information from the human and physical environment. As demonstrated through the examples presented in this thesis, the new maps are not limited to showing population as a defining element for the transformation, but can show any quantitative geospatial data, such as wealth, rainfall, or even the environmental conditions of the oceans. The new maps also work at various scales, from a global perspective down to the scale of urban environments. The gridded cartogram technique is proposed as a new global and local map projection that is a viable and versatile alternative to other conventional map projections. The maps based on this technique open up a wide range of potential new applications to rediscover the diverse geographies of the world. They have the potential to allow us to gain new perspectives through detailed cartographic depictions.
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety. While previous studies have limitations in using only depth or height information, we find both depth and height matter and they are in fact complementary. The depth feature encompasses precise geometric cues, whereas the height feature is primarily focused on distinguishing between various categories of height intervals, essentially providing semantic context. This insight motivates the development of Complementary-BEV (CoBEV), a novel end-to-end monocular 3D object detection framework that integrates depth and height to construct robust BEV representations. In essence, CoBEV estimates each pixel's depth and height distribution and lifts the camera features into 3D space for lateral fusion using the newly proposed two-stage complementary feature selection (CFS) module. A BEV feature distillation framework is also seamlessly integrated to further enhance the detection accuracy from the prior knowledge of the fusion-modal CoBEV teacher. We conduct extensive experiments on the public 3D detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D, as well as the private Supremind-Road dataset, demonstrating that CoBEV not only achieves the accuracy of the new state-of-the-art, but also significantly advances the robustness of previous methods in challenging long-distance scenarios and noisy camera disturbance, and enhances generalization by a large margin in heterologous settings with drastic changes in scene and camera parameters. For the first time, the vehicle AP score of a camera model reaches 80% on DAIR-V2X-I in terms of easy mode. The source code will be made publicly available at https://github.com/MasterHow/CoBEV.