Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic analysis of spatial variability in subsurface flows: 1. Comparison of one‐ and three‐dimensional flows

The complex variation of hydraulic conductivity in natural aquifer materials is represented in a continuum sense as a spatial stochastic process which is characterized by a covariance function. Assuming statistical homogeneity, the theory of spectral analysis is used to solve perturbed forms of the stochastic differential equation describing flow through porous media with randomly varying hydraulic conductivity. From analyses of unidirectional mean flows which are perturbed by one-and three-dimensional variations of the logarithm of the hydraulic conductivity, local relationships between the head variance and the log conductivity variance are obtained. The results show that the head variance produced by three-dimensional statistical isotropic conductivity perturbations is only 5% of that in the corresponding one-dimensional case. The head variance is also strongly dependent on the correlation distance of the log conductivity covariance function. These results emphasize the importance of including spatial correlation structure and multidimensional effects in stochastic simulation of groundwater flow.
BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling

Inferring the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, it has also been extended to dynamic settings. Such methods heavily rely on implicit neural priors to regularize the problem. In this work, we take a step back and investigate how current implementations may entail deleterious effects including limited expressiveness, entanglement of light and density fields, and sub-optimal motion localization. Further, we devise a factorisation-based framework that represents the scene as a composition of bandlimited, high-dimensional signals. We demonstrate compelling results across complex dynamic scenes that involve changes in lighting, texture and long-range dynamics.
Continuous Descriptor-Based Control for Deep Audio Synthesis

Despite significant advances in deep models for music generation, the use of these techniques remains restricted to expert users. Before being democratized among musicians, generative models must first provide expressive control over the generation, as this conditions the integration of deep generative models in creative workflows. In this paper, we tackle this issue by introducing a deep generative audio model providing expressive and continuous descriptor-based control, while remaining lightweight enough to be embedded in a hardware synthesizer. We enforce the controllability of real-time generation by explicitly removing salient musical features in the latent space using an adversarial confusion criterion. User-specified features are then reintroduced as additional conditioning information, allowing for continuous control of the generation, akin to a synthesizer knob. We assess the performance of our method on a wide variety of sounds including instrumental, percussive and speech recordings while providing both timbre and attributes transfer, allowing new ways of generating sounds.
Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks

In targeted poisoning attacks, an attacker manipulates an agent-environment interaction to force the agent into adopting a policy of interest, called target policy. Prior work has primarily focused on attacks that modify standard MDP primitives, such as rewards or transitions. In this paper, we study targeted poisoning attacks in a two-agent setting where an attacker implicitly poisons the effective environment of one of the agents by modifying the policy of its peer. We develop an optimization framework for designing optimal attacks, where the cost of the attack measures how much the solution deviates from the assumed default policy of the peer agent. We further study the computational properties of this optimization framework. Focusing on a tabular setting, we show that in contrast to poisoning attacks based on MDP primitives (transitions and (unbounded) rewards), which are always feasible, it is NP-hard to determine the feasibility of implicit poisoning attacks. We provide characterization results that establish sufficient conditions for the feasibility of the attack problem, as well as an upper and a lower bound on the optimal cost of the attack. We propose two algorithmic approaches for finding an optimal adversarial policy: a model-based approach with tabular policies and a model-free approach with parametric/neural policies. We showcase the efficacy of the proposed algorithms through experiments.
LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR

Scene completion refers to obtaining dense scene representation from an incomplete perception of complex 3D scenes. This helps robots detect multi-scale obstacles and analyse object occlusions in scenarios such as autonomous driving. Recent advances show that implicit representation learning can be leveraged for continuous scene completion and achieved through physical constraints like Eikonal equations. However, former Eikonal completion methods only demonstrate results on watertight meshes at a scale of tens of meshes. None of them are successfully done for non-watertight LiDAR point clouds of open large scenes at a scale of thousands of scenes. In this paper, we propose a novel Eikonal formulation that conditions the implicit representation on localized shape priors which function as dense boundary value constraints, and demonstrate it works on SemanticKITTI and SemanticPOSS. It can also be extended to semantic Eikonal scene completion with only small modifications to the network architecture. With extensive quantitative and qualitative results, we demonstrate the benefits and drawbacks of existing Eikonal methods, which naturally leads to the new locally conditioned formulation. Notably, we improve IoU from 31.7% to 51.2% on SemanticKITTI and from 40.5% to 48.7% on SemanticPOSS. We extensively ablate our methods and demonstrate that the proposed formulation is robust to a wide spectrum of implementation hyper-parameters. Codes and models are publicly available at https://github.com/AIR-DISCOVER/LODE
Non-Iterative Solution for Coordinated Optimal Dispatch via Equivalent Projection—Part I: Theory

Coordinated optimal dispatch is of utmost importance for the efficient and secure operation of hierarchically structured power systems. Conventional coordinated optimization methods, such as the Lagrangian relaxation and Benders decomposition, require iterative information exchange among subsystems. Iterative coordination methods have drawbacks including slow convergence, risk of oscillation and divergence, and incapability of multi-level optimization problems. To this end, this paper aims at the non-iterative coordinated optimization method for hierarchical power systems. The theory of the equivalent projection (EP) is proposed, which makes external equivalence of the optimal dispatch model of the subsystem. Based on the EP theory, a coordinated optimization framework is developed, where each subsystem submits the EP model as a substitute for its original model to participate in the cross-system coordination. The proposed coordination framework is proven to guarantee the same optimality as the joint optimization, with additional benefits of avoiding iterative information exchange, protecting privacy, compatibility with practical dispatch scheme, and capability of multi-level problems.
On n-Hausdorff homogeneous and n-Urysohn homogeneous spaces

In this paper we study $n$-Hausdorff homogeneous and $n$-Urysohn homogeneous spaces. We give some upper bounds for the cardinality of these kind of spaces and give examples. Additionally we show that for every $n>2$, there is no $n$-Hausdorff 2-homogeneous space. Finally, for any $n$-Hausdorff space we construct an $n$-Hausdorff homogeneous extension which is the union of countably many $n$-H-closed spaces.
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image. In 2022, we witnessed another significant improvement in the pose estimation accuracy – the state of the art, which was 56.9 ARC in 2019 (Vidal et al.) and 69.8 ARC in 2020 (CosyPose), moved to new heights of 83.7 ARC (GDRNPP). Out of 49 pose estimation methods evaluated since 2019, the top 18 are from 2022. Methods based on point pair features, which were introduced in 2010 and achieved competitive results even in 2020, are now clearly outperformed by deep learning methods. The synthetic-to-real domain gap was again significantly reduced, with 82.7 ARC achieved by GDRNPP trained only on synthetic images from BlenderProc. The fastest variant of GDRNPP reached 80.5 ARC with an average time per image of 0.23s. Since most of the recent methods for 6D object pose estimation begin by detecting/segmenting objects, we also started evaluating 2D object detection and segmentation performance based on the COCO metrics. Compared to the Mask R-CNN results from CosyPose in 2020, detection improved from 60.3 to 77.3 APC and segmentation from 40.5 to 58.7 APC. The online evaluation system stays open and is available at: bop.felk. cvut.cz.
Isolated Suborders and their Application to Counting Closure Operators

In this paper we investigate the interplay between isolated suborders and closures. Isolated suborders are a special kind of suborders and can be used to diminish the number of elements of an ordered set by means of a quotient construction. The decisive point is that there are simple formulae establishing relationships between the number of closures in the original ordered set and the quotient thereof induced by isolated suborders. We show how these connections can be used to derive a recursive algorithm for counting closures, provided the ordered set under consideration contains suitable isolated suborders.
Unusual Hard X-Ray Flares Caught in NICER Monitoring of the Binary Supermassive Black Hole Candidate AT2019cuk/Tick Tock/SDSS J1430+2303

The nuclear transient AT2019cuk/Tick Tock/SDSS J1430+2303 has been suggested to harbor a supermassive black hole (SMBH) binary near coalescence. We report results from high-cadence NICER X-ray monitoring with multiple visits per day from 2022 January to August, as well as continued optical monitoring during the same time period. We find no evidence of periodic/quasiperiodic modulation in the X-ray, UV, or optical bands; however, we do observe exotic hard X-ray variability that is unusual for typical active galactic nuclei (AGN). The most striking feature of the NICER light curve is repetitive hard (2–4 keV) X-ray flares that result in distinctly harder X-ray spectra compared to the nonflaring data. In its nonflaring state, AT2019cuk looks like a relatively standard AGN, but it presents the first case of day-long, hard X-ray flares in a changing-look AGN. We consider a few different models for the driving mechanism of these hard X-ray flares, including (1) corona/jet variability driven by increased magnetic activity, (2) variable obscuration, and (3) self-lensing from the potential secondary SMBH. We prefer the variable corona model, as the obscuration model requires rather contrived timescales and the self-lensing model is difficult to reconcile with a lack of clear periodicity in the flares. These findings illustrate how important high-cadence X-ray monitoring is to our understanding of the rapid variability of the X-ray corona and necessitate further high-cadence, multiwavelength monitoring of changing-look AGN like AT2019cuk to probe the corona-jet connection.
A performance analysis of trellis coded modulation schemes in non-independent Rician fading channels

The paper presents a new upper bound on the pairwise error probability (PEP) of trellis-coded modulation (TCM) schemes over nonindependent Rician fading channels. The bound is quite general, e.g., applicable to ideal coherent and pilot-tone aided detection, and differential detection, etc. Being quite accurate, the bound can be used in conjunction with a truncated union bound to estimate the bit error probability when ideal interleaving is not possible. <<ETX>>
Single-layer feed waveguide consisting of posts for plane TEM wave excitation in parallel plates

The authors propose a novel feed structure to excite a plane TEM wave in a parallel-plate waveguide. The feed waveguide is composed of densely arrayed posts on the same layer as the parallel plate. The posts can be easily fabricated at low cost by making metalized via holes in a grounded dielectric substrate. Such a procedure results in a quite simple fabrication of the antenna. The feed waveguide is designed to obtain a uniform division, which is confirmed by measurements on a 40-GHz band model.
Optoacoustic monitoring of real-time lesion formation during radiofrequency catheter ablation

Current radiofrequency cardiac ablation procedures lack real-time lesion monitoring guidance, limiting the reliability and efficacy of the treatment. The objective of this work is to demonstrate that optoacoustic imaging can be applied to develop a diagnostic technique applicable to radiofrequency ablation for cardiac arrhythmia treatment with the capabilities of real-time monitoring of ablated lesion size and geometry. We demonstrate an optoacoustic imaging method using a 256-detector optoacoustic imaging probe and pulsed-laser illumination in the infrared wavelength range that is applied during radiofrequency ablation in excised porcine myocardial tissue samples. This technique results in images with high contrast between the lesion volume and unablated tissue, and is also capable of capturing time-resolved image sequences that provide information on the lesion development process. The size and geometry of the imaged lesion were shown to be in excellent agreement with the histological examinations. This study demonstrates the first deep-lesion real-time monitoring for radiofrequency ablation generated lesions, and the technique presented here has the potential for providing critical feedback that can significantly impact the outcome of clinical radiofrequency ablation procedures.
Onthel: Online Delivery App to Induce Green Lifestyle Awareness

Since the pandemic first appeared in 2020, there has been a growth in Indonesia's aggregators of delivery services. The convenience and promptness of this service are reasons why customers appreciate it. On the other hand, a large number of service vehicles contribute to the global warming and an increase in carbon emissions. These emerge as opportunities for us to design the Onthel. An environmentally friendly online delivery application using the bicycle fleet and also induce consumers to practice an eco-friendly lifestyle. We use an interaction design approach—a science that intersects with HCI—to help developers improve products and user interactions. The interaction design stage starts by establishing requirements, designing alternatives, prototyping, and evaluating. We tested the prototype that had been made through task scenario analysis, which is one of the stages of usability testing. The evaluation's findings indicate that this research can guide in developing a system to promote consumer behavioral change; in this example, induce customer behavior towards eco-friendly lifestyle.
Stable Autoregressive Models and Signal Estimation

This article studies the problem of model identification and estimation for stable autoregressive process observed in a symmetric stable noise environment. A new tool called partial auto-covariation function is introduced to identify the stable autoregressive signals. The signal and noise parameters are estimated using a modified version of Generalized Yule Walker type method and the method of moments. The proposed methods are illustrated through data simulated from autoregressive signals with symmetric stable innovations. The new technique is applied to analyze the time series of sea surface temperature anomaly and compared with its Gaussian counterpart.
Monte Carlo turbulence simulation using rational approximations to von Karman spectra

Turbulence simulation is computational ly much simpler using rational spectra, but turbulence falls off as y-s/3 jn frequency ranges of interest to aircraft response and as predicted by von Karman's model. Rational approximations to von Karman spectra should satisfy three requirements: 1) The rational spectra should provide a good approximation to the von Karman spectra in the frequency range of interest; 2) for stability, the resulting rational transfer function should have all its poles in the left half-plane; and 3) at high frequencies, the rational spectra must fall off as an integer power of frequency, and since the - 2 power is closest to the - 5/3 power, the rational approximation should roll off as the - 2 power at high frequencies. Rational approximations to von Karman spectra that satisfy these three criteria are presented, along with spectra from simulated turbulence. Agreement between the spectra of the simulated turbulence and von Karman spectra is excellent.
First-Principles Calculations of the Atomic and Electronic Structure of CaTiO3 (111) Surfaces

The results of calculations of CaTiO3 polar (111) surface relaxations, rumplings, energetics, optical band gaps, and charge distribution using the ab initio code CRYSTAL and a hybrid description of exchange and correlation are presented. Using a hybrid B3LYP approach, the surface relaxation for the two possible Ti and CaO3 CaTiO3 (111) surface terminations are calculated. For both Ti and CaO3-terminated CaTiO3 (111) surfaces upper layer atoms relax inwards, while the second layer atoms, with the sole exception of CaO3-terminated surface Ca atom, relax outwards. Calculated surface relaxation energy for Ti-terminated CaTiO3 (111) surface is more than five times larger than the surface relaxation energy for CaO3-terminated CaTiO3 (111) surface. The surface energy for Ti-terminated CaTiO3 (111) surface (4.18 eV/cell) is smaller, than the surface energy for CaO3-terminated CaTiO3 (111) surface (5.86 eV/ cell).
Quantitative process measures in interventions to improve employees’ mental health: A systematic literature review and the IPEF framework

ABSTRACT Interventions to improve mental health can target individuals, working groups, their leaders, or organisations, also known as the Individual, Group, Leader, and Organisational (IGLO) levels of intervention. Evaluating such interventions in organisational settings is complex and requires sophisticated evaluation designs taking into account the intervention process. In the present systematic literature review, we present state of the-art of quantitative measures of process evaluation. We identified 39 papers. We found that measures had been developed to explore the organisational context, the intervention design, and the mental models of the intervention and its activities. Quantitative process measures are often poorly validated, and only around half of the studies linked the process to intervention outcomes. Fifteen studies used mixed methods for process evaluation. Most often, a qualitative process evaluation was used to understand unexpected intervention outcomes. Despite the existence of theoretical process evaluation frameworks, these were not often employed, and even when included, frameworks were rarely acknowledged, and only selected elements were included. Based on our synthesis, we propose a new framework for evaluating interventions, the Integrative Process Evaluation Framework (IPEF), together with reflections on how we may optimise the use of quantitative process evaluation in conjunction with a qualitative process evaluation.
A re-analysis of the spectrum of QSO 2206 – 199

Recently Pettini, Hunstead, Smith and Mar, (PHSM 1991) performed an analysis of the Ly-alpha forest of QSO 2206-199N at very high resolution. On the basis of their observations they concluded that most Doppler parameters b = (square root of 2)(sigma) of Ly-alpha forest lines are below 22 km/s, with a range down to a few km/s and a median of 17 km/s. They also found a strong intrinsic correlation between Doppler parameter b and column density N. These results are in contrast to those of a similar study by Carswell, Lanzetta, Parnell, and Webb (CLPW 1991) at comparable resolution with the same instruments, who find that most of the Ly-alpha lines towards QSO 1100-264 have Doppler parameters above 15 km/s (median b = 34 km/s), and that there is no significant correlation between b and N. Whilst an intrinsic difference between the lines of sight to 2206-199 and to other QSO's can not be excluded a priori, previous disagreement between Doppler parameter estimates obtained by both groups pointed to a potential difference in estimation techniques and in the interpretation of the results. To investigate this possibility, the AAT/UCLES spectrum of 2206-199 obtained by PHSM were reanalyzed. The spectrum was extracted from the raw data and determined the line parameters using the method described by CLPW.
Fermi acceleration of Lyman-alpha photons by shocks

The repeated scattering of Ly-alpha radiation across a shock front results in a systematic blueshift which may greatly exceed the shock velocity vs and is proportional to cube root of (Nvs), where N is the column density of hydrogen atoms on either side of the shock front. The blueshifting process is similar to the Fermi acceleration of cosmic rays and may be responsible for the blue asymmetric line profiles that have been observed in high-redshift Ly-alpha galaxies. The Ly-alpha line profile in 3C 326.1 is accounted for by a model in which shocks, driven into a population of interstellar clouds by a radio lobe, trigger the formation of ionizing stars and Fermi accelerate the Ly-alpha radiation emitted by H II regions surrounding those stars. Galaxy mergers, particularly between galaxies with low dust content, should produce Ly-alpha lines with strong blue wings. 14 references.
Adaptation of Dynamics in Default Mode Network Dependent on Mental States

We suggest a novel analysis method called one-to-many dynamic functional connectivity (DFC) analysis to detect adaptations in dynamics of a brain network dependent on mental states with a single case study. The analysis method assesses the major direction of the variance in the partial correlations of a center that are time-windowed functional correlations of a brain region (a center) to the rest of the regions in a brain network. This analysis method uses principal component analysis (PCA) to define an orthonormal basis of the variance in the partial correlations of a center. We assume that the weights of each correlation in the partial correlations to a chosen major principal component define a configuration of the brain network corresponding to its functional state. We compare how similar the major principal components in the same mental states or distinct mental states are by taking the configurations. In the current study, we collected fMRI BOLD signals from a blind subject whom with advanced ability in mental imagery while the subject was resting and then retrieving a morning routine three times. The default mode network (DMN), which is known for its high correlation to episodic memory retrieval and resting-state tasks, was analyzed with one-to-many DFC analysis. We compare the configurations of the DMN in the two mental states. The results imply that the dynamic adaption in the DMN in the two mental states could be well depicted when the posterior cingulate cortex (PCC) is the center of the analysis in this single case study.