Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Analysis of Secondary Coexistence in a Real-World CBRS Deployment

The Federal Communications Commission (FCC) in the U.S. has made the Citizens Broadband Radio Service (CBRS) band (3.55 - 3.7 GHz) available for commercial wireless usage under a shared approach using a three-tier hierarchical architecture, where the federal incumbent is the highest priority Tier 1 user, Priority Access License (PAL) holders, who have paid for licenses, are Tier 2 users and Tier 3 users operate under General Authorized Access (GAA), without license fees or protection from higher priority users. The Spectrum Access System (SAS) ensures that higher priority users are protected from interference from lower priority users. However, the lowest priority GAA users are not given any protection from each other by the SAS and are expected to not cause any harmful interference to Tier 1 and Tier 2 users. As the deployments of GAA devices grow, the potential for secondary interference between GAA users increases, especially since the SAS architecture does not allow dynamic channel switching when faced with interference. In this paper, we present a first-of-its-kind extensive measurement campaign of a commercial CBRS network deployed in the city of South Bend, IN, that quantifies both co-channel interference (CCI) and adjacent channel interference (ACI) caused by competing GAA devices and C-band 5G, respectively. We (i) identify a particular CCI scenario and improve performance by changing the frequency allocation based on our study of other allocations in the vicinity and (ii) quantify ACI from 5G in C-band (3.7 GHz) on CBRS throughput. We conclude that (i) CCI and ACI for GAA users is not handled well by the SAS, (ii) proper frequency allocation for GAA requires additional analysis of interference from other GAA users followed by dynamical channel selection, and (iii) utilization of immediate adjacent channels by high power 5G deployments limits the performance of CBRS.
Assessment of models for nonlinear oscillatory flow through a hexagonal sphere pack

We review models for unsteady porous media flow in the volume-averaging framework and we discuss the theoretical relations between the models and the definition of the model coefficients (and the uncertainty therein). The different models are compared against direct numerical simulations of oscillatory flow through a hexagonal sphere pack. The model constants are determined based on their definition in terms of the Stokes flow, the potential flow and steady nonlinear flow. Thus, the discrepancies between the model predictions and the simulation data can be attributed to shortcomings of the models' parametrisation. We found that an extension of the dynamic permeability model of Pride et al. [Physical Review B 47(9), 1993] with a Forchheimer-type nonlinearity performs very well for linear flow and for nonlinear flow at low and medium frequencies, but the Forchheimer term with a coefficient obtained from the steady-state overpredicts the nonlinear drag at high frequencies. The model reduces to the unsteady Forchheimer equation with an acceleration coefficient based on the static viscous tortuosity for low frequencies. The unsteady Forchheimer equation with an acceleration coefficient based on the high frequency limit of the dynamic tortuosity has large errors for linear flow at medium and high frequencies, but low errors for nonlinear flow at all frequencies. This is explained by an error cancellation between the inertial and the nonlinear drag.
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions

Due to their large bandwidth, relatively low cost, and robust performance, UWB radio chips can be used for a wide variety of applications, including localization, communication, and radar. This article offers an exhaustive survey of recent progress in UWB radar technology. The goal of this survey is to provide a comprehensive view of the technical fundamentals and emerging trends in UWB radar. Our analysis is categorized into multiple parts. Firstly, we explore the fundamental concepts of UWB radar technology from a technology and standardization point of view. Secondly, we examine the most relevant UWB applications and use cases, such as device-free localization, activity recognition, presence detection, and vital sign monitoring, discussing each time the bandwidth requirements, processing techniques, algorithms, latest developments, relevant example papers, and trends. Next, we steer readers toward relevant datasets and available radio chipsets. Finally, we discuss ongoing challenges and potential future research avenues. As such, this overview paper is designed to be a cornerstone reference for researchers charting the course of UWB radar technology over the last decade.
Study and derivation of closures in the volume-filtered framework for particle-laden flows

The volume-filtering of the Navier-Stokes equations allows to consider the effect that particles have on the fluid without further assumptions, but closures arise of which the implications are not fully understood. In the present paper, we carefully study every closure in the volume-filtered fluid momentum equation and investigate their impact on the momentum and energy transfer dependent on the filtering characteristics. We provide an analytical expression for the viscous closure that arises because filter and spatial derivative in the viscous term do not commute. An analytical expression for the regularization of the particle momentum source of a single sphere in the Stokes regime is derived. Furthermore, we propose a model for the subfilter stress tensor, which originates from filtering the advective term. The model for the subfilter stress tensor is shown to agree well with the subfilter stress tensor for small filter widths relative to the size of the particle. We show that the subfilter stress tensor requires modeling and should not be neglected. For small filter widths, we find that the commonly applied Gaussian regularization of the particle momentum source is a poor approximation of the spatial distribution of the particle momentum source, but for larger filter widths the spatial distribution approaches a Gaussian. Furthermore, we propose a modified advective term in the volume-filtered momentum equation that consistently circumvents the common stability issues observed at locally small fluid volume fractions and identify inconsistencies in previous studies of the phase-averaged kinetic energy of the volume-filtered fluid velocity. Finally, we propose a generally applicable form of the volume-filtered momentum equation and its closures based on clear and well-founded assumptions and propose guidelines for point-particle simulations based on the new findings.
SPAD : Spatially Aware Multiview Diffusers

We present SPAD, a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation, we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions, and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-attention proposed in prior work (e.g. MVDream) leads to content copying between views. Therefore, we explicitly constrain the cross-view attention based on epipolar geometry. To further enhance 3D consistency, we utilize Plucker coordinates derived from camera rays and inject them as positional encoding. This enables SPAD to reason over spatial proximity in 3D well. In contrast to recent works that can only generate views at fixed azimuth and elevation, SPAD offers full camera control and achieves state-of-the-art results in novel view synthesis on unseen objects from the Objaverse and Google Scanned Objects datasets. Finally, we demonstrate that text-to-3D generation using SPAD prevents the multi-face Janus issue. See more details at our webpage: https://yashkant.github. io/spad
Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn't adhere to software design best practices. [Goal] In order to better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. [Method] We conducted a controlled experiment with three independent trials involving 100 data scientists. We restructured real industrial ML code that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. [Results] The study results provide statistically significant evidence that the adoption of the SOLID design principles can improve code understanding within the realm of ML projects. [Conclusion] We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the maintainability of ML code.
The Unruh-DeWitt model and its joint interacting Hilbert space

In this work we make the connection between the Unruh-DeWitt particle detector model applied to quantum field theory in curved spacetimes and the rigorous construction of the spin-boson model. With some modifications, we show that existing results about the existence of a spin-boson ground state can be adapted to the Unruh-DeWitt model. In the most relevant scenario involving massless scalar fields in (3+1)-dimensional globally hyperbolic spacetimes, where the Unruh-DeWitt model describes a simplified model of light-matter interaction, we argue that common choices of the spacetime smearing functions regulate the ultraviolet behaviour of the model but can still exhibit infrared divergences. In particular, this implies the well-known expectation that the joint interacting Hilbert space of the model cannot be described by the tensor product of a two-dimensional complex Hilbert space and the Fock space of the vacuum representation. We discuss the conditions under which this problem does not arise and the relevance of the operator-algebraic approach for better understanding of particle detector models and their applications.
Classifying Nodes in Graphs without GNNs

Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph, but they have several undesirable attributes stemming from their message passing architecture. Recently, distillation methods succeeded in eliminating the use of GNNs at test time but they still require them during training. We perform a careful analysis of the role that GNNs play in distillation methods. This analysis leads us to propose a fully GNN-free approach for node classification, not requiring them at train or test time. Our method consists of three key components: smoothness constraints, pseudo-labeling iterations and neighborhood-label histograms. Our final approach can match the state-of-the-art accuracy on standard popular benchmarks such as citation and co-purchase networks, without training a GNN.
Triadic Resonance in Columnar Vortices

Employing a poloidal-toroidal projection technique, a multi-scale analysis of resonant wave triads in columnar vortices is performed to obtain the governing equations of the triadically coupled wave amplitudes. For inviscid flows, we establish that resonance between neutral, smooth waves is conservative, and the temporal evolution of wave amplitudes is either bounded or explosively unstable based on the signs of the triad's interaction coefficients. Assessing the onset of weakly nonlinear instabilities through the pseudoenergy criterion introduced by Cairns (1979, J. Fluid Mech., vol. 92), we use the large-axial-wavenumber asymptotic approach by Le Dizes and Lacaze (2005, J. Fluid Mech., vol. 542) to evaluate each triad member's pseudoenergy and argue against the possibility of explosive conservative three-wave resonance involving only regular Kelvin waves. Additionally, extending our investigation to specific vortices, such as the Lamb-Oseen vortex and the Batchelor vortex, we find that triadic resonance among their neutral modes consistently results in bounded behaviour.
Examining Gender and Racial Bias in Large Vision–Language Models Using a Novel Dataset of Parallel Images

Following on recent advances in large language models (LLMs) and subsequent chat models, a new wave of large vision–language models (LVLMs) has emerged. Such models can incorporate images as input in addition to text, and perform tasks such as visual question answering, image captioning, story generation, etc. Here, we examine potential gender and racial biases in such systems, based on the perceived characteristics of the people in the input images. To accomplish this, we present a new dataset PAIRS (PArallel Images for eveRyday Scenarios). The PAIRS dataset contains sets of AI-generated images of people, such that the images are highly similar in terms of background and visual content, but differ along the dimensions of gender (man, woman) and race (Black, white). By querying the LVLMs with such images, we observe significant differences in the responses according to the perceived gender or race of the person depicted.
Bridging the Gap between Luminous Red Novae and Common Envelope Evolution: The Role of Recombination Energy and Radiation Force

Luminous red novae and their connection to common envelope evolution (CEE) remain elusive in astrophysics. Here, we present a radiation hydrodynamic model capable of simulating the light curves of material ejected during a CEE. For the first time, the radiation hydrodynamic model incorporates complete recombination physics for hydrogen and helium. The radiation hydrodynamic equations are solved with Guangqi. With time-independent ejecta simulations, we show that the peaks in the light curves are attributed to radiation-dominated ejecta, while the extended plateaus are produced by matter-dominated ejecta. To showcase our model’s capability, we fit the light curve of AT 2019zhd. The central mass object of 6 M ⊙ is assumed based on observations and scaling relations. Our model demonstrates that the ejecta mass of AT 2019zhd falls within the range of 0.04–0.1 M ⊙. Additionally, we demonstrate that recombination energy and radiation force acceleration significantly impact the light curves, whereas dust formation has a limited effect during the peak and plateau phases.
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving

Deep learning solvers for partial differential equations typically have limited accuracy. We propose to overcome this problem by using them as preconditioners. More specifically, we apply discretization-invariant neural operators to learn preconditioners for the flexible conjugate gradient method (FCG). Architecture paired with novel loss function and training scheme allows for learning efficient preconditioners that can be used across different resolutions. On the theoretical side, FCG theory allows us to safely use nonlinear preconditioners that can be applied in $O(N)$ operations without constraining the form of the preconditioners matrix. To justify learning scheme components (the loss function and the way training data is collected) we perform several ablation studies. Numerical results indicate that our approach favorably compares with classical preconditioners and allows to reuse of preconditioners learned for lower resolution to the higher resolution data.
Learning pseudo-contractive denoisers for inverse problems

Deep denoisers have shown excellent performance in solving inverse problems in signal and image processing. In order to guarantee the convergence, the denoiser needs to satisfy some Lipschitz conditions like non-expansiveness. However, enforcing such constraints inevitably compromises recovery performance. This paper introduces a novel training strategy that enforces a weaker constraint on the deep denoiser called pseudo-contractiveness. By studying the spectrum of the Jacobian matrix, relationships between different denoiser assumptions are revealed. Effective algorithms based on gradient descent and Ishikawa process are derived, and further assumptions of strict pseudo-contractiveness yield efficient algorithms using half-quadratic splitting and forward-backward splitting. The proposed algorithms theoretically converge strongly to a fixed point. A training strategy based on holomorphic transformation and functional calculi is proposed to enforce the pseudo-contractive denoiser assumption. Extensive experiments demonstrate superior performance of the pseudo-contractive denoiser compared to related denoisers. The proposed methods are competitive in terms of visual effects and quantitative values.
Real-time Holistic Robot Pose Estimation with Unknown States

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles, which are not always available in real-world scenarios. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work addresses the urgent need for efficient robot pose estimation with unknown states. We propose an end-to-end pipeline for real-time, holistic robot pose estimation from a single RGB image, even in the absence of known robot states. Our method decomposes the problem into estimating camera-to-robot rotation, robot state parameters, keypoint locations, and root depth. We further design a corresponding neural network module for each task. This approach allows for learning multi-facet representations and facilitates sim-to-real transfer through self-supervised learning. Notably, our method achieves inference with a single feedforward, eliminating the need for costly test-time iterative optimization. As a result, it delivers a 12-time speed boost with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code is available at https://oliverbansk.github. io/Holistic-Robot-Pose/.
Establishing degrees of closeness between audio recordings along different dimensions using large-scale cross-lingual models

In the highly constrained context of low-resource language studies, we explore vector representations of speech from a pretrained model to determine their level of abstraction with regard to the audio signal. We propose a new unsupervised method using ABX tests on audio recordings with carefully curated metadata to shed light on the type of information present in the representations. ABX tests determine whether the representations computed by a multilingual speech model encode a given characteristic. Three experiments are devised: one on room acoustics aspects, one on linguistic genre, and one on phonetic aspects. The results confirm that the representations extracted from recordings with different linguistic/extra-linguistic characteristics differ along the same lines. Embedding more audio signal in one vector better discriminates extra-linguistic characteristics, whereas shorter snippets are better to distinguish segmental information. The method is fully unsupervised, potentially opening new research avenues for comparative work on under-documented languages.
Understanding electronic excited states in BiFeO$_3$ via ab initio calculations and symmetry analysis

BiFeO$_3$ is a technologically relevant multiferroic perovskite featuring ferroelectricity and antiferromagnetism. Its lattice, magnetic, and ferroelectric degrees of freedoms are coupled to its optically active excitations and thus hold the potential to be reversible probed and controlled by light. In this work, we combine ab initio density functional and many-body perturbation theory methods with an extensive symmetry and atomic-orbital analysis to describe and understand the electronic excited states spectrum and its imprint on the optical absorption spectrum with quantitative accuracy and qualitative insights. We find that the optical absorption spectrum of BiFeO$_3$ contain several strongly bound and spatially localized electronic transitions in which the spin-degree of freedom is almost fully flipped. With our analysis we thoroughly characterize these localized spin-flip transitions in terms of the unusual crystal field splitting of Fe-$3d$ single-electron orbitals. Our symmetry analysis further allows us to thoroughly explain how the spin content and the energetic fine structure of these strongly bound excitons are dictated by the interplay between crystal symmetry, electron-hole attraction, and the spin-orbit coupling.
TMS-EEG Reliability: Bridging the Gap to Clinical Use

Concurrent transcranial magnetic stimulation (TMS) and electroencephalography (EEG), or TMS-EEG, holds the potential to broaden the clinical applications of TMS beyond its traditional role in evaluating the cortico-spinal tract and motor cortices. TMS-evoked potentials (TEPs) have emerged as valuable tools in clinical research, enabling the assessment of cortical excitability and effective connectivity between cortical regions in various psychiatric and neurological disorders and are increasingly recognized as a promising candidate biomarker for aiding in diagnosis and prognosis. Despite the well-established diagnostic utility of TMS, the clinical implementation of TMS-EEG has yet to meet the necessary standards. One critical aspect that often remains unaddressed is the reliability of TEP measurements. In this context, we outline the crucial reliability assessments required to determine the clinical applicability of TEPs. Firstly, we conduct a comprehensive review of the existing literature on reliability, encompassing both theoretical and statistical considerations. Subsequently, we present the current state of knowledge on TEP reliability. We emphasize the specific elements of reliability that must be incorporated to facilitate a unified, evidence-derived assessment of TMS-EEG as a clinical tool.
A Bandit Approach with Evolutionary Operators for Model Selection

This paper formulates model selection as an infinite-armed bandit problem. The models are arms, and picking an arm corresponds to a partial training of the model (resource allocation). The reward is the accuracy of the selected model after its partial training. In this best arm identification problem, regret is the gap between the expected accuracy of the optimal model and that of the model finally chosen. We first consider a straightforward generalization of UCB-E to the stochastic infinite-armed bandit problem and show that, under basic assumptions, the expected regret order is $T^{-\alpha}$ for some $\alpha \in (0,1/5)$ and $T$ the number of resources to allocate. From this vanilla algorithm, we introduce the algorithm Mutant-UCB that incorporates operators from evolutionary algorithms. Tests carried out on three open source image classification data sets attest to the relevance of this novel combining approach, which outperforms the state-of-the-art for a fixed budget.
Geometric characterizations of Lipschitz stability for convex optimization problems

In this paper, we mainly study tilt stability and Lipschitz stability of convex optimization problems. Our characterizations are geometric and fully computable in many important cases. As a result, we apply our theory to the group Lasso problem and the nuclear norm minimization problem and reveal that the Lipschitz stability of the solution mapping in these problems is automatic whenever the solution mapping is single-valued.