Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Dark Matter Halo Spin using Observations and Simulations: application to UGC 5288

Dark matter (DM) halo properties are extensively studied in cosmological simulations but are very challenging to estimate from observations. The DM halo density profile of observed galaxies is modelled using multiple probes that trace the dark matter potential. However, the angular momentum distribution of DM halos is still a subject of debate. In this study we investigate a method for estimating the halo spin and halo concentration of low surface brightness (LSB), gas-rich dwarf barred galaxy UGC 5288, by forward modelling disk properties derived from observations - stellar and gas surface densities, disk scale length, HI rotation curve, bar length and bar ellipticity. We combine semi-analytical techniques, N-body/SPH and cosmological simulations to model the DM halo of UGC 5288 with both a cuspy Hernquist profile and a flat-core pseudo-isothermal profile. We find that the best match with observations is a pseudo-isothermal halo model with a core radius of rc = 0.23 kpc, and halo spin of λ= 0.08 at the virial radius. Although our findings are consistent with previous core radius estimates of the halo density profile of UGC 5288, as well as with the halo spin profiles of similar mass analogues of UGC5288 in the high-resolution cosmological-magneto-hydrodynamical simulation TNG50, there still remain some uncertainties as we are limited in our knowledge of the formation history of the galaxy. Additionally, we find that the inner halo spin (r < 10 kpc) in barred galaxies is different from the unbarred ones, and the halo spin shows weak correlations with bar properties.
kNN-Res: Residual Neural Network with kNN-Graph coherence for point cloud registration

In this paper, we present a residual neural network-based method for point set registration that preserves the topological structure of the target point set. Similar to coherent point drift (CPD), the registration (alignment) problem is viewed as the movement of data points sampled from a target distribution along a regularized displacement vector field. While the coherence constraint in CPD is stated in terms of local motion coherence, the proposed regularization term relies on a global smoothness constraint as a proxy for preserving local topology. This makes CPD less flexible when the deformation is locally rigid but globally non-rigid as in the case of multiple objects and articulate pose registration. A Jacobian-based cost function and geometric-aware statistical distances are proposed to mitigate these issues. The latter allows for measuring misalignment between the target and the reference. The justification for the k-Nearest Neighbour(kNN) graph preservation of target data, when the Jacobian cost is used, is also provided. Further, to tackle the registration of high-dimensional point sets, a constant time stochastic approximation of the Jacobian cost is introduced. The proposed method is illustrated on several 2-dimensional toy examples and tested on high-dimensional flow Cytometry datasets where the task is to align two distributions of cells whilst preserving the kNN-graph in order to preserve the biological signal of the transformed data. The implementation of the proposed approach is available at https://github.com/MuhammadSaeedBatikh/kNN-Res_Demo/ under the MIT license.
PowerModelsADA: A Framework for Solving Optimal Power Flow Using Distributed Algorithms

This letter presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA). PowerModelsADA provides a framework to test, verify, and benchmark both existing and new ADAs. This letter demonstrates use cases for PowerModelsADA and validates its implementation with multiple OPF formulations.
Meeting effectiveness and inclusiveness: large-scale measurement, identification of key features, and prediction in real-world remote meetings

Workplace meetings are vital to organizational collaboration, yet relatively little progress has been made toward measuring meeting effectiveness and inclusiveness at scale. The recent rise in remote and hybrid meetings represents an opportunity to do so via computer-mediated communication (CMC) systems. Here, we share the results of an effective and inclusive meetings survey embedded within a CMC system in a diverse set of companies and organizations. We correlate the survey results with objective metrics available from the CMC system to identify the generalizable attributes that characterize perceived effectiveness and inclusiveness in meetings. Additionally, we explore a predictive model of meeting effectiveness and inclusiveness based solely on objective meeting attributes. Lastly, we show challenges and discuss solutions around the subjective measurement of meeting experiences. To our knowledge, this is the largest data-driven study conducted after the pandemic peak to measure, understand, and predict effectiveness and inclusiveness in real-world meetings at an organizational scale.
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. (Code and dataset are available at https://github.com/IvaBojic/framework). We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33{%/40{% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.
Force controlled pushing of nanoparticles: modeling and experiments

A nano-robotics manipulation system using an atomic force microscope (AFM) probe as the pushing manipulator and a force and topology sensor has been proposed. The task is the 2D positioning of nanometer size particles on a substrate in ambient conditions. Thus, the modeling of interaction forces and dynamics during the pushing operation is analyzed for understanding the nano scale physical phenomenon which is different from macro robotics physics. Simulations and experiments are held for determining the conditions and strategy for reliable manipulation, and determining the affecting parameters. The results show that latex particles can be positioned on silicon substrates successfully.
ADVANCES IN GLOBAL PSEUDOSPECTRAL METHODS FOR OPTIMAL CONTROL

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ADVANCES IN GLOBAL PSEUDOSPECTRAL METHODS FOR OPTIMAL CONTROL By Divya Garg August 2011 Chair: Anil V. Rao Major: Mechanical Engineering A new pseudospectral method that employs global collocation at the Legendre-Gauss-Radau (LGR) points is presented for direct trajectory optimization and costate estimation of finite-horizon optimal control problems. This method provides accurate state and control approximations. Furthermore, transformations are developed that relate the Karush-Kuhn-Tucker (KKT) multipliers of the discrete nonlinear programming problem (NLP) to the costate and the Lagrange multipliers of the continuous-time optimal control problem. More precisely, it is shown that the transformed KKT multipliers of the NLP correspond to the Lagrange multipliers of the continuous problem and to a pseudospectral approximation of costate that is approximated using polynomials one degree smaller than that used for the state. The relationship between the differentiation matrices for the state equation and for the costate equation is established. Next, a unified framework is presented for the numerical solution of optimal control problems based on collocation at the Legendre-Gauss (LG) and the Legendre-Gauss-Radau (LGR) points. The framework draws from the common features and mathematical properties demonstrated by the LG and the LGR methods. The framework stresses the fact that even though LG and LGR collocation appear to be only cosmetically different from collocation at Legendre-Gauss-Lobatto (LGL) points, the LG and the LGR methods are, in fact, fundamentally different from the LGL method. Specifically, it is shown that the LG and the LGR differentiation matrices are non-square and full rank whereas the
Using gravitational light deflection in optical cavities for laser frequency stabilization

We theoretically investigate the propagation of light in the presence of a homogeneous gravitational field. To model this, we derive the solutions of the wave equation in Rindler spacetime, which account for gravitational redshift and light deflection. The developed theoretical framework is used to explore the propagation of plane light waves in a horizontal Fabry-Perot cavity. We pay particular attention to the cavity output power. It is shown that this power depends not only on the input frequency, but also on the vertical position of a detector. We state that the height-dependent detector signal arising from the cavity internal light deflection effect (CILD-effect) also opens a new alternative way to frequency stabilization in Earth-based laser experiments and to study gravitational light deflection at laboratory scales.
MULTIGROUP TRANSIENT CALCULATION WITHIN THE FRAMEWORK OF A TWO-GROUP HEXAGONAL CMFD FORMULATION

A two-group CMFD formulation capable of solving multigroup problems in hexagonal geometry is established by incorporating a dynamic condensation scheme. A multigroup one-node kernel employing a two-dimensional polynomial expansion within a triangle is derived such that it provides a nodal spectrum as well as multigroup interface currents. The accuracy and computational efficiency of the two-group CMFD based multigroup calculation method are demonstrated by solving multigroup eigenvalue benchmark problems. The improvement effects attainable by multigroup calculations are then investigated by analyzing a model core with four different group structures. The eigenvalue calculation results show that the current two-group solution approach involves only trivial errors. However, it is also shown from control rod ejection calculations that the error in the transient core power behavior can be significant due to small errors in the ejected rod worth as well as the inability of distinguishing the delayed and prompt neutron spectra in two-group calculations.
Near-Field Channel Estimation for Extremely Large-Scale Reconfigurable Intelligent Surface (XL-RIS)-Aided Wideband mmWave Systems

Near-field communications present new opportunities over near-field channels, however, the spherical wavefront propagation makes near-field signal processing challenging. In this context, this paper proposes efficient near-field channel estimation methods for wideband MIMO mmWave systems with the aid of extremely large-scale reconfigurable intelligent surfaces (XL-RIS). For the wideband signals reflected by the analog RIS, we characterize their near-field beam squint effect in both angle and distance domains. Based on the mathematical analysis of the near-field beam patterns over all frequencies, a wideband spherical-domain dictionary is constructed by minimizing the coherence of two arbitrary beams. In light of this, we formulate a two-dimensional compressive sensing problem to recover the channel parameter based on the spherical-domain sparsity of mmWave channels. To this end, we present a correlation coefficient-based atom matching method within our proposed multi-frequency parallelizable subspace recovery framework for efficient solutions. Additionally, we propose a two-dimensional oracle estimator as a benchmark and derive its lower bound across all subcarriers. Our findings emphasize the significance of system hyperparameters and the sensing matrix of each subcarrier in determining the accuracy of the estimation. Finally, numerical results show that our proposed method achieves considerable performance compared with the lower bound and has a time complexity linear to the number of RIS elements.
RePAST: Relative Pose Attention Scene Representation Transformer

The Scene Representation Transformer (SRT) is a recent method to render novel views at interactive rates. Since SRT uses camera poses with respect to an arbitrarily chosen reference camera, it is not invariant to the order of the input views. As a result, SRT is not directly applicable to large-scale scenes where the reference frame would need to be changed regularly. In this work, we propose Relative Pose Attention SRT (RePAST): Instead of fixing a reference frame at the input, we inject pairwise relative camera pose information directly into the attention mechanism of the Transformers. This leads to a model that is by definition invariant to the choice of any global reference frame, while still retaining the full capabilities of the original method. Empirical results show that adding this invariance to the model does not lead to a loss in quality. We believe that this is a step towards applying fully latent transformer-based rendering methods to large-scale scenes.
The Effect of Counterfactuals on Reading Chest X-rays

This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to rate their confidence that the model's prediction is correct using a 5 point scale. Half of the predictions are false positives. Each prediction is explained twice, once using traditional attribution methods and once with a counterfactual explanation. The overall results indicate that counterfactual explanations allow a radiologist to have more confidence in true positive predictions compared to traditional approaches (0.15$\pm$0.95 with p=0.01) with only a small increase in false positive predictions (0.04$\pm$1.06 with p=0.57). We observe the specific prediction tasks of Mass and Atelectasis appear to benefit the most compared to other tasks.
Non-Hermitian superintegrable systems

A non-Hermitian generalisation of the Marsden–Weinstein reduction method is introduced to construct families of quantum -symmetric superintegrable models over an n-dimensional sphere Sn . The mechanism is illustrated with one- and two-dimensional examples, related to u(2) and u(3) Lie algebras respectively, providing new quantum models with real spectra and spontaneous -symmetric breaking. In certain limits, the models reduce to known non-Hermitian systems and complex extensions of previously studied real superintegrable systems.
Pulsed $^{87}$Rb vector magnetometer using a fast rotating field

There are diverse set approaches for vector magnetic field measurements involving condensed matter and atomic physics systems. However, they all suffer from various limitations, so the most widely used high-sensitivity vector magnetometers are fluxgates using soft magnetically-saturable materials. Here we describe a vector magnetometer by applying an external rotating magnetic field to a scalar atomic magnetometer. Such an approach provides simultaneous measurements of the total magnetic field and two polar angles relative to the plane of magnetic field rotation. Crucially, it avoids several metrological difficulties associated with vector magnetometers and gradiometers. We describe in detail the fundamental, systematic, and practical limits of such vector magnetometers. We use a field rotation rate faster than the spin relaxation rate. We show that it eliminates a class of systematic effects associated with heading errors in alkali-metal scalar magnetometers. We investigate several other systematic effects, such as Berry's phase frequency shift and the effects of eddy currents in nearby conductors. We also derive fundamental limits on the sensitivity of such sensors and show that the vector sensitivity can approach the sensitivity of scalar atomic magnetometers.
A new indirect measurement method of the electron temperature for the Protosphera's pinch plasma

This article presents a new method for estimating the electron temperature of the Protosphera's screw pinch. The temperature radial profile is obtained by a self-consistent modeling of a 1D MHD equilibrium along with a 0D power balance of the plasma column, given measurements and estimates of the axial pinch plasma current, of the plasma rotational frequency and, at the equatorial plane, of the electron density radial profile, of the edge poloidal magnetic field, of the edge electron temperature and of the neutrals pressure in the vacuum vessel. The plasma is considered in equilibrium with its neutral phase and in constant rotation. A MATLAB code has been developed with the aim of estimating the MHD radial equilibrium profiles, the thermodynamic plasma state and the neutrals profile. The numerical estimates are compared with available experimental data showing a good agreement.
Topological polarisation states

Polarisation states are described by spin expectation values, known as Stokes parameters, the trajectories of which in a rotationally symmetric system form a sphere named after Poincaré. Here, we show that the trajectories of broken rotational symmetric systems can exhibit distinct topological structures in polarisation states. We use a phase-shifter to form a polarisation circle (S1), which interferes with the original input due to the phase change of the output state upon rotation. By rotating the circle using a rotator, the trajectories become a polarisation torus (S1×S1), which was experimentally confirmed in a simple setup using passive optical components together with the Mach–Zehnder interferometer. We also discuss the realisations of other topological features, such as a Möbius strip, a trefoil knot, Hopf links, and topological Dirac bosons, with a bulk-edge correspondence.
A Two-stage Multiband WiFi Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference

Multiband fusion enhances WiFi sensing by jointly utilizing signals from multiple non-contiguous frequency bands. However, in the multi-band WiFi sensing signal model, there are many local optimums in the associated likelihood function due to the existence of high frequency component and phase distortion factors, posing challenges for high-accuracy parameter estimation. To address this, we propose a two-stage scheme equipped with different signal models derived from the original model, where the first-stage coarse estimation is performed using a weighted root MUSIC algorithm to narrow down the search range for the subsequent stage, and the second-stage refined estimation utilizes a Bayesian approach to avoid convergence to bad suboptimal solutions. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike conventional particle-based VBI (PVBI) that optimizes only particle probability and incurs exponential per-iteration complexity with particle count, our more flexible SPVBI algorithm optimizes both the position and probability of each particle. Additionally, it utilizes block SSCA to significantly improve sampling efficiency by averaging over iterations, making it suitable for high-dimensional problems. Extensive simulations demonstrate the superiority of our proposed algorithm over various baseline methods.
Self-Ordering Point Clouds

In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering. Only a few works have tried to address this challenging vision problem, all with the help of hard to obtain point and cloud labels. Different from these works, we introduce the task of point-wise ordering in 3D point clouds through self-supervision, which we call selfordering. We further contribute the first end-to-end trainable network that learns a point-wise ordering in a selfsupervised fashion. It utilizes a novel differentiable point scoring-sorting strategy and it constructs an hierarchical contrastive scheme to obtain self-supervision signals. We extensively ablate the method and show its scalability and superior performance even compared to supervised ordering methods on multiple datasets and tasks including zeroshot ordering of point clouds from unseen categories.
NeMF: Inverse Volume Rendering with Neural Microflake Field

Recovering the physical attributes of an object’s appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering. Recent approaches adopt the emerging implicit scene representations and have shown impressive results. However, they unanimously adopt a surface-based representation, and hence can not well handle scenes with very complex geometry, translucent object and etc. In this paper, we propose to conduct inverse volume rendering, in contrast to surface-based, by representing a scene using microflake volume, which assumes the space is filled with infinite small flakes and light reflects or scatters at each spatial location according to microflake distributions. We further adopt the coordinate networks to implicitly encode the microflake volume, and develop a differentiable microflake volume renderer to train the network in an end-to-end way in principle. Our NeMF enables effective recovery of appearance attributes for highly complex geometry and scattering object, enables high-quality relighting, material editing, and especially simulates volume rendering effects, such as scattering, which is infeasible for surface-based approaches. Our data and code are available at: https://github.com/YoujiaZhang/NeMF.
Predictive power of daily viscacha and vicu\~na sightings on Simons Array site work results

We studied the predictive power of daily animal sightings on site work outcomes at the Polarbear and Simons Array experiment site in the Atacama Desert, Chile. Specifically, we observed the number of viscacha and vicuna sightings during a two-month period, totaling 31 observation days, and analyzed their relationship with site work outcomes using machine learning techniques. Our results show that there was no significant correlation between the number of animal sightings and site work outcomes. The feather importance score for viscacha and vicuna were 0.71068 and 0.057762, respectively. Future research may include expanding the analysis to include other animal species, investigating the impact of human activity on site work outcomes, and exploring alternative machine learning models or statistical techniques.
Functional role of coupling the endothelial TRPV4 and KCa3.1 channels in regulating coronary vascular tone

High‐fat diet (HFD) induces dysregulated pathways in coronary artery endothelial cells (CAECs), which leads to altered regulation of vascular tone, tissue perfusion and increases the risk of coronary artery diseases. Ca2+‐activated K+ (KCa) channels are known to be associated with transient receptor potential (TRP) channels, which are important for regulating endothelial function. But how TRPV4 channels interacts with KCa channels in regulating coronary vascular tone in HFD mice requires further exploration.