Miracle-dz/UniFAR-FTUs huggingface dataset:
Vorticity generation in cosmology and the role of shell crossing

There is no source for cosmic vorticity within the cold dark matter cosmology. However, vorticity has been observed in the universe, especially on the scales of clusters, filaments, galaxies, etc. Recent results from high-resolution general relativistic N-body simulation show that the vorticity power spectrum dominates over the power spectrum of the divergence of the peculiar velocity field on scales where the effective field theory of large-scale structure breaks down. Incidentally, this scale also corresponds to the scale where shell-crossing occurs. Several studies have suggested a link between shell crossing in the dark matter fluid and the vorticity generation in the universe, however, no clear proof of how it works within general relativity exists yet. We describe for the first time how vorticity is generated in a universe such as ours with expanding and collapsing regions. We show how vorticity is generated at the boundary of the expanding and collapsing regions. Our result indicates that the amplitude of the generated vorticity is determined by the jump in gradients of the gravitational potential, pressure and the expansion rate at the boundary. In addition, we argue that the presence of vorticity in the matter fields implies a non-vanishing magnetic part of the Weyl tensor. This has implications for the generation of Maxwell's magnetic field and the dynamics of clusters. The impact of accelerated expansion of the universe and the existence of causal limit for massive particles are discussed
Haptics-Enabled Forceps with Multi-Modal Force Sensing: Towards Task-Autonomous Robotic Surgery

—Current minimally invasive surgical robots are lack- ing in force sensing that is robust to temperature and electromagnetic variation while being compatible with micro-sized instru- ments. This paper presents a multi-axis force sensing module that can be integrated with micro-sized surgical instruments such as biopsy forceps. The proposed miniature sensing module mainly consists of a ﬂexure, a camera, and a target. The deformation of the ﬂexure is obtained by the pose variation of the top-mounted target, which is estimated by the camera with a proposed pose estimation algorithm. Then, the external force is estimated using the ﬂexure’s displacement and stiffness matrix. Integrating the sensing module, we further develop a pair of haptics-enabled forceps and realize its multi-modal force sensing, including touching, grasping, and pulling when the forceps manipulate tissues. To minimize the unexpected sliding between the forceps’ clips and the tissue, we design a micro-level actuator to drive the forceps and compensate for the motion introduced by the ﬂexure’s deformation. Finally, a series of experiments are conducted to verify the feasibility of the proposed sensing module and forceps, including an automatic robotic grasping procedure on ex-vivo tissues. The results indicate the sensing module can estimate external forces accurately, and the haptics-enabled forceps can potentially realize multi-modal force sensing for task-autonomous robotic surgery. A video demonstrating the experiments can be found at https://youtu.be/4UUTT hiFcI.
Auxiliary Splines Space Preconditioning for B-Splines Finite Elements: The case of H(curl, Ω) and H(div, Ω) elliptic problems

This paper presents a study of large linear systems resulting from the regular $B$-splines finite element discretization of the $\bm{curl}-\bm{curl}$ and $\bm{grad}-div$ elliptic problems on unit square/cube domains. We consider systems subject to both homogeneous essential and natural boundary conditions. Our objective is to develop a preconditioning strategy that is optimal and robust, based on the Auxiliary Space Preconditioning method proposed by Hiptmair et al. \cite{hiptmair2007nodal}. Our approach is demonstrated to be robust with respect to mesh size, and we also show how it can be combined with the Generalized Locally Toeplitz (GLT) sequences analysis presented in \cite{mazza2019isogeometric} to derive an algorithm that is optimal and stable with respect to spline degree. Numerical tests are conducted to illustrate the effectiveness of our approach.
Direct T
 e-based Metallicities of z = 2–9 Galaxies with JWST/NIRSpec: Empirical Metallicity Calibrations Applicable from Reionization to Cosmic Noon

We report detections of the [O iii]λ4364 auroral emission line for 16 galaxies at z = 2.1–8.7, measured from JWST/NIRSpec observations obtained as part of the Cosmic Evolution Early Release Science (CEERS) survey program. We combine this CEERS sample with 9 objects from the literature at z = 4−9 with auroral-line detections from JWST/NIRSpec and 21 galaxies at z = 1.4−3.7 with auroral-line detections from ground-based spectroscopy. We derive electron temperature (T e) and direct-method oxygen abundances for the combined sample of 46 star-forming galaxies at z = 1.4−8.7. We use these measurements to construct the first high-redshift empirical T e-based metallicity calibrations for the strong-line ratios [O iii]/Hβ, [O ii]/Hβ, R23 = ([O iii]+[O ii])/Hβ, [O iii]/[O ii], and [Ne iii]/[O ii]. These new calibrations are valid over 12+log(O/H) = 7.4−8.3 and can be applied to samples of star-forming galaxies at z = 2−9, leading to an improvement in the accuracy of metallicity determinations at Cosmic Noon and in the Epoch of Reionization. The high-redshift strong-line relations are offset from calibrations based on typical z ∼ 0 galaxies or H ii regions, reflecting the known evolution of ionization conditions between z ∼ 0 and z ∼ 2. Deep spectroscopic programs with JWST/NIRSpec promise to improve statistics at the low and high ends of the metallicity range covered by the current sample, as well as to improve the detection rate of [N ii]λ6585 and thus allow the future assessment of N-based indicators. These new high-redshift calibrations will enable accurate characterizations of metallicity scaling relations at high redshift, improving our understanding of feedback and baryon cycling in the early Universe.
Box-Constrained $L_1/L_2$ Minimization in Single-View Tomographic Reconstruction

We present a note on the implementation and efficacy of a box-constrained $L_1/L_2$ regularization in numerical optimization approaches to performing tomographic reconstruction from a single projection view. The constrained $L_1/L_2$ minimization problem is constructed and solved using the Alternating Direction Method of Multipliers (ADMM). We include brief discussions on parameter selection and numerical convergence, as well as detailed numerical demonstrations against relevant alternative methods. In particular, we benchmark against a box-constrained TVmin and an unconstrained Filtered Backprojection in both cone and parallel beam (Abel) forward models. We consider both a fully synthetic benchmark, and reconstructions from X-ray radiographic image data.
Simulation of gas dynamic behavior in dry-wall inertial fusion energy chambers

Dry-wall chambers are currently the favored approach for laser-driven inertial fusion energy (IFE). Understanding the gas dynamic response in dry-wall chambers after a target explosion is essential in order to resolve the critical issues of chamber clearing, wall response, and propagation of targets and beams for the following shot. In order to address these critical issues, a program of experiments is anticipated in which ignition conditions will be simulated with pulsed energy sources in scaled chambers. Therefore, understanding the scaling of gas dynamic responses is critical. In this work, chamber gas dynamic responses to pulsed energy deposition were modeled numerically for different shapes, sizes and initial energy distributions in order to characterize the dependencies and to help establish the validity of size scaling for simulation experiments.
Homogenization theory of random walks among deterministic conductances

We study asymptotic laws of random walks on $\mathbb Z^d$ ($d\ge1$) in deterministic reversible environments defined by an assignment of a positive and finite conductance to each edge of $\mathbb Z^d$. The walk jumps across an edge with probability proportional to its conductance. We identify a deterministic set of conductance configurations for which an Invariance Principle (i.e., convergence in law to Brownian motion under diffusive scaling of space and time) provably holds. This set is closed under translations and zero-density perturbations and carries all ergodic conductance laws subject to certain moment conditions. The proofs are based on martingale approximations whose control relies on the conversion of averages in time and physical space under the deterministic environment to those in a suitable stochastic counterpart. Our study sets up a framework for proofs of"deterministic homogenization"in other motions in disordered media.
Generating symbolic music using diffusion models

Denoising Diffusion Probabilistic models have emerged as simple yet very powerful generative models. Unlike other generative models, diffusion models do not suffer from mode collapse or require a discriminator to generate high-quality samples. In this paper, a diffusion model that uses a binomial prior distribution to generate piano rolls is proposed. The paper also proposes an efficient method to train the model and generate samples. The generated music has coherence at time scales up to the length of the training piano roll segments. The paper demonstrates how this model is conditioned on the input and can be used to harmonize a given melody, complete an incomplete piano roll, or generate a variation of a given piece. The code is publicly shared to encourage the use and development of the method by the community.
Acoustic Source Localization in the Spherical Harmonics Domain Exploiting Low-Rank Approximations

Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays. A very important task is that concerning the localization of active sound sources. In this paper, we propose a simple yet effective method to localize prominent acoustic sources in adverse acoustic scenarios. By using a proper normalization and arrangement of the estimated spherical harmonic coefficients, we exploit low-rank approximations to estimate the far field modal directional pattern of the dominant source at each time-frame. The experiments confirm the validity of the proposed approach, with superior performance compared to other recent SHD-based approaches.
PyExaFMM: An Exercise in Designing High-Performance Software With Python and Numba

Numba is a game-changing compiler for high-performance computing with Python. It produces machine code that runs outside of the single-threaded Python interpreter, and that fully utilizes the resources of modern CPUs. This means support for parallel multithreading and auto-vectorization if available, as with compiled languages such as C++ or Fortran. In this article, we document our experience developing PyExaFMM, a multithreaded Numba implementation of the fast multipole method, an algorithm with a nonlinear data structure and a large amount of data organization. We find that designing performant Numba code for complex algorithms can be as challenging as writing in a compiled language.
Generating contingency tables with fixed marginal probabilities and dependence structures described by loglinear models

We present a method to generate contingency tables that follow loglinear models with prescribed marginal probabilities and dependence structures. We make use of (loglinear) Poisson regression, where the dependence structures, described using odds ratios, are implemented using an offset term. We apply this methodology to carry out simulation studies in the context of population size estimation using dual system and triple system estimators, popular in official statistics. These estimators use contingency tables that summarise the counts of elements enumerated or captured within lists that are linked. The simulation is used to investigate these estimators in the situation that the model assumptions are fulfilled, and the situation that the model assumptions are violated.
Existence of financial equilibria : space of transfers of fixed dimension

In this article, we consider the classical two-period exchange model with the same level of generality as the Arrow-Debreu model with non ordered preferences as in Gale and Mas Colell. So, the preferences of the consumers are not assumed to be complete or transitive and no assumption of monotonicity or free disposal is made. We present an existence theorem of pseudo-equilibria which implies the existence of financial equilibria under an assumption of regularity of the space of admissible transfers. In particular, we obtain the existence in the case of numeraire assets, of Arrow-Debreu contingent markets, thus generalizing the previous work by Due Shaer.
Dimension and variance reduction for Monte Carlo methods for high-dimensional models in finance

Abstract One-way coupling often occurs in multi-dimensional models in finance. In this paper, we present a dimension reduction technique for Monte Carlo (MC) methods, referred to as drMC, that exploits this structure for pricing plain-vanilla European options under an N-dimensional one-way coupled model, where N is arbitrary. The dimension reduction also often produces a significant variance reduction. The drMC method is a dimension reduction technique built upon (i) the conditional MC technique applied to one of the factors which does not depend on any other factors in the model, and (ii) the derivation of a closed-form solution to the conditional partial differential equation (PDE) that arises via Fourier transforms. In the drMC approach, the option price can be computed simply by taking the expectation of this closed-form solution. Hence, the approach results in a powerful dimension reduction from N to one, which often results in a significant variance reduction as well, since the variance associated with the other factors in the original model are completely removed from the drMC simulation. Moreover, under the drMC framework, hedging parameters, or Greeks, can be computed in a much more efficient way than in traditional MC techniques. A variance reduction analysis of the method is presented and numerical results illustrating the method’s efficiency are provided.
Blind Estimation of Audio Processing Graph

Musicians and audio engineers sculpt and transform their sounds by connecting multiple processors, forming an audio processing graph. However, most deep-learning methods overlook this real-world practice and assume fixed graph settings. To bridge this gap, we develop a system that reconstructs the entire graph from a given reference audio. We first generate a realistic graph-reference pair dataset and train a simple blind estimation system composed of a convolutional reference encoder and a transformer-based graph decoder. We apply our model to singing voice effects and drum mixing estimation tasks. Evaluation results show that our method can reconstruct complex signal routings, including multi-band processing and sidechaining.
Query-guided Attention in Vision Transformers for Localizing Objects Using a Single Sketch

In this work, we investigate the problem of sketch-based object localization on natural images, where given a crude hand-drawn sketch of an object, the goal is to localize all the instances of the same object on the target image. This problem proves difficult due to the abstract nature of hand-drawn sketches, variations in the style and quality of sketches, and the large domain gap existing between the sketches and the natural images. To mitigate these challenges, existing works proposed attention-based frameworks to incorporate query information into the image features. However, in these works, the query features are incorporated after the image features have already been independently learned, leading to inadequate alignment. In contrast, we propose a sketch-guided vision transformer encoder that uses cross-attention after each block of the transformer-based image encoder to learn query-conditioned image features leading to stronger alignment with the query sketch. Further, at the output of the decoder, the object and the sketch features are refined to bring the representation of relevant objects closer to the sketch query and thereby improve the localization. The proposed model also generalizes to the object categories not seen during training, as the target image features learned by our method are query-aware. Our localization framework can also utilize multiple sketch queries via a trainable novel sketch fusion strategy. The model is evaluated on the images from the public object detection benchmark, namely MS-COCO, using the sketch queries from QuickDraw! and Sketchy datasets. Compared with existing localization methods, the proposed approach gives a $6.6\%$ and $8.0\%$ improvement in mAP for seen objects using sketch queries from QuickDraw! and Sketchy datasets, respectively, and a $12.2\%$ improvement in AP@50 for large objects that are `unseen' during training.
HeRAFC: Heuristic Resource Allocation and Optimization in MultiFog-Cloud Environment

By bringing computing capacity from a remote cloud environment closer to the user, fog computing is introduced. As a result, users can access the services from more nearby computing environments, resulting in better quality of service and lower latency on the network. From the service providers' point of view, this addresses the network latency and congestion issues. This is achieved by deploying the services in cloud and fog computing environments. The responsibility of service providers is to manage the heterogeneous resources available in both computing environments. In recent years, resource management strategies have made it possible to efficiently allocate resources from nearby fog and clouds to users' applications. Unfortunately, these existing resource management strategies fail to give the desired result when the service providers have the opportunity to allocate the resources to the users' application from fog nodes that are at a multi-hop distance from the nearby fog node. The complexity of this resource management problem drastically increases in a MultiFog-Cloud environment. This problem motivates us to revisit and present a novel Heuristic Resource Allocation and Optimization algorithm in a MultiFog-Cloud (HeRAFC) environment. Taking users' application priority, execution time, and communication latency into account, HeRAFC optimizes resource utilization and minimizes cloud load. The proposed algorithm is evaluated and compared with related algorithms. The simulation results show the efficiency of the proposed HeRAFC over other algorithms.
Greybody factors for higher-dimensional non-commutative geometry inspired black holes

Greybody factors are computed for massless fields of spin 0, 1/2, 1, and 2 emitted from higher-dimensional non-commutative geometry inspired black holes. Short-range potentials are used with path-ordered matrix exponentials to numerically calculate transmission coefficients. The resulting absorption cross sections and emission spectra are computed on the brane and compared with the higher-dimensional Schwarzschild–Tangherlini black hole. A non-commutative black hole at its maximum temperature in seven extra dimensions will radiate a particle flux and power of 0.72–0.81 and 0.75–0.81, respectively, times lower than a Schwarzschild–Tangherlini black hole of the same temperature. A non-commutative black hole at its maximum temperature in seven extra dimensions will radiate a particle flux and power of 0.64–0.72 and 0.60–0.64, respectively, times lower than a Schwarzschild–Tangherlini black hole of the same mass.
Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring

This paper deals with Audio-Visual Speech Recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, clean visual inputs are not always accessible and can even be corrupted by occluded lip regions or noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-Visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations.
How GPT-3 responds to different publics on climate change and Black Lives Matter: A critical appraisal of equity in conversational AI

Autoregressive language models, which use deep learning to produce human-like texts, have become increasingly widespread. Such models are powering popular virtual assistants in areas like smart health, finance, and autonomous driving. While the parameters of these large language models are improving, concerns persist that these models might not work equally for all subgroups in society. Despite growing discussions of AI fairness across disciplines, there lacks systemic metrics to assess what equity means in dialogue systems and how to engage different populations in the assessment loop. Grounded in theories of deliberative democracy and science and technology studies, this paper proposes an analytical framework for unpacking the meaning of equity in human-AI dialogues. Using this framework, we conducted an auditing study to examine how GPT-3 responded to different sub-populations on crucial science and social topics: climate change and the Black Lives Matter (BLM) movement. Our corpus consists of over 20,000 rounds of dialogues between GPT-3 and 3290 individuals who vary in gender, race and ethnicity, education level, English as a first language, and opinions toward the issues. We found a substantively worse user experience with GPT-3 among the opinion and the education minority subpopulations; however, these two groups achieved the largest knowledge gain, changing attitudes toward supporting BLM and climate change efforts after the chat. We traced these user experience divides to conversational differences and found that GPT-3 used more negative expressions when it responded to the education and opinion minority groups, compared to its responses to the majority groups. We discuss the implications of our findings for a deliberative conversational AI system that centralizes diversity, equity, and inclusion.
Electron trajectories and gain in free electron laser with ion channel guiding

A detailed analysis of electron trajectories and gain in a helical wiggler free electron laser (FEL) with ion channel focusing using single particle dynamics is presented. Conditions for stability of electron orbit have been obtained and stable regimes have been identified. Gain has been evaluated in the low-gain-per-pass limit with the help of the self-consistent pendulum equation. It is seen that the presence of ion channel leads to significant gain enhancement under appropriate conditions.