Miracle-dz/UniFAR-FTUs huggingface dataset:
Pressure-robustness in the context of optimal control

This paper studies the benefits of pressure-robust discretizations in the scope of optimal control of incompressible flows. Gradient forces that may appear in the data can have a negative impact on the accuracy of state and control and can only be correctly balanced if their $L^2$-orthogonality onto discretely divergence-free test functions is restored. Perfectly orthogonal divergence-free discretizations or divergence-free reconstructions of these test functions do the trick and lead to much better analytic a priori estimates that are also validated in numerical examples.
PercepNet+: A Phase and SNR Aware PercepNet for Real-Time Speech Enhancement

PercepNet, a recent extension of the RNNoise, an efficient, high-quality and real-time full-band speech enhancement technique, has shown promising performance in various public deep noise suppression tasks. This paper proposes a new approach, named PercepNet+, to further extend the PercepNet with four significant improvements. First, we introduce a phase-aware structure to leverage the phase information into PercepNet, by adding the complex features and complex subband gains as the deep network input and output respectively. Then, a signal-to-noise ratio (SNR) estimator and an SNR switched post-processing are specially designed to alleviate the over attenuation (OA) that appears in high SNR conditions of the original PercepNet. Moreover, the GRU layer is replaced by TF-GRU to model both temporal and frequency dependencies. Finally, we propose to integrate the loss of complex subband gain, SNR, pitch filtering strength, and an OA loss in a multi-objective learning manner to further improve the speech enhancement performance. Experimental results show that, the proposed PercepNet+ outperforms the original PercepNet significantly in terms of both PESQ and STOI, without increasing the model size too much.
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening

Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyper-spectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. In this paper, we present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively. HyperTransformer consists of three main modules, namely two separate feature extractors for PAN and HSI, a multi-head feature soft-attention module, and a spatial-spectral feature fusion module. Such a network improves both spatial and spectral quality measures of the pansharpened HSI by learning cross-feature space dependencies and long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be utilized across multiple spatial scales at the backbone for obtaining improved performance. Extensive experiments conducted on three widely used datasets demonstrate that HyperTransformer achieves significant improvement over the state-of-the-art methods on both spatial and spectral quality measures. Implementation code and pretrained weights can be accessed at https://github.com/wgcban/HyperTransformer.
A class of inverse curvature flows and $L^p$ dual Christoffel-Minkowski problem

In this paper, we consider a large class of expanding flows of closed, smooth, star-shaped hypersurface in Euclidean space R with speed ψuρf , where ψ is a smooth positive function on unit sphere, u is the support function of the hypersurface, ρ is the radial function, f is a smooth, symmetric, homogenous of degree one, positive function of the principal curvatures of the hypersurface on a convex cone. When ψ = 1, we prove that the flow exists for all time and converges to infinity if α + δ + β 6 1, β > 0 and α 6 0, while in case α + δ + β > 1, α, δ 6 0, the flow blows up in finite time, and where we assume the initial hypersurface to be strictly convex. In both cases the properly rescaled flows converge to a sphere centered the origin. In particular, the results of Gerhardt [32, 34] and Urbas [67] can be recovered by putting α = δ = 0. Our previous works [24, 25] and Hu, Mao, Tu and Wu [41] can be recovered by putting δ = 0 and α = 0 respectively. By the convergence of these flows, we can give a new proof of uniqueness theorems for solutions to L-Minkowski problem and L-Christoffel-Minkowski problem with constant prescribed data. Similarly, we pose the L dual Christoffel-Minkowski problem and prove a uniqueness theorem for solutions to L dual Minkowski problem and L dual Christoffel-Minkowski problem with constant prescribed data. At last, we focus on the longtime existence and convergence of a class of anisotropic flows (i.e. for general function ψ). The final result not only gives a new proof of many previously known solutions to L dual Minkowski problem, L-Christoffel-Minkowski problem, etc. by such anisotropic flows, but also provides solutions to L dual Christoffel-Minkowski problem with some conditions.
The Coma Cluster at LOFAR Frequencies. II. The Halo, Relic, and a New Accretion Relic

We present LOw Frequency ARray observations of the Coma Cluster field at 144 MHz. The cluster hosts one of the most famous radio halos, a relic, and a low surface brightness bridge. We detect new features that allow us to make a step forward in the understanding of particle acceleration in clusters. The radio halo extends for more than 2 Mpc, which is the largest extent ever reported. To the northeast of the cluster, beyond the Coma virial radius, we discover an arc-like radio source that could trace particles accelerated by an accretion shock. To the west of the halo, coincident with a shock detected in the X-rays, we confirm the presence of a radio front, with different spectral properties with respect to the rest of the halo. We detect a radial steepening of the radio halo spectral index between 144 and 342 MHz, at ∼30′ from the cluster center, that may indicate a non-constant re-acceleration time throughout the volume. We also detect a mild steepening of the spectral index toward the cluster center. For the first time, a radial change in the slope of the radio–X-ray correlation is found, and we show that such a change could indicate an increasing fraction of cosmic-ray versus thermal energy density in the cluster outskirts. Finally, we investigate the origin of the emission between the relic and the source NGC 4789, and we argue that NGC 4789 could have crossed the shock originating the radio emission visible between its tail and the relic.
Why Are There Still So Many Jobs? The History and Future of Workplace

In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for laborâ��as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.
Fast Neural Architecture Search for Lightweight Dense Prediction Networks

We present LDP, a lightweight dense prediction neural architecture search (NAS) framework. Starting from a pre-defined generic backbone, LDP applies the novel Assisted Tabu Search for efficient architecture exploration. LDP is fast and suitable for various dense estimation problems, unlike previous NAS methods that are either computational demanding or deployed only for a single subtask. The performance of LPD is evaluated on monocular depth estimation, semantic segmentation, and image super-resolution tasks on diverse datasets, including NYU-Depth-v2, KITTI, Cityscapes, COCO-stuff, DIV2K, Set5, Set14, BSD100, Urban100. Experiments show that the proposed framework yields consistent improvements on all tested dense prediction tasks, while being $5\%-315\%$ more compact in terms of the number of model parameters than prior arts.
Actuator stiction compensation via model predictive control for nonlinear processes

The problem of valve stiction is addressed, which is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. A model predictive control (MPC) stiction compensation formulation is developed including detailed dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions due to stiction. Although developed with a focus on stiction, the MPC-based compensation method presented is general and has potential to compensate for other nonlinear valve dynamics which have some similarities to those caused by stiction. Feasibility and closed-loop stability of the proposed MPC formulation are proven for a sufficiently small sampling period when Lyapunov-based constraints are incorporated. Using a chemical process example with an economic model predictive controller (EMPC), the selection of appropriate constraints for the proposed method is demonstrated. The example verified the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC causes it to select set-points that the valve output can reach and causes the operating constraints to be met. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2004–2023, 2016
Longitudinal Analysis of Normative Energy Use Feedback on Dormitory Occupants

Behavior interventions aiming to reduce energy consumption in the built environment are becoming increasingly common. The application of individual feedback and use of social norms have shown promise in the short-term at improving occupant energy use behavior. Yet, the long-term effects, role of messaging duration, and relative effect of normative elements in feedback messages remain less clear. This paper attempts to address these gaps in the literature by conducting and analyzing two yearlong field experiments that test the effect of normative feedback messages on energy consumption in dormitories. The analysis finds that adding normative elements to feedback messages had no effect on energy consumption in the short-term. When examining behavior change conditional on an occupant’s concern for social norms, it was found that individuals with a high concern for social norms positively responded to the normative elements with an estimated treatment effect of −14% (consumed less). Conversely, individuals with a low concern for social norms had the opposite reaction, with an estimated treatment effect of 5%. Further, it was discovered that the duration of messaging positively influenced the long-term durability of behavior change. The long-term effect of behavior change was twice as prevalent in individuals with high concern for social norms.
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification

Nuclei segmentation and classification is a crucial step utilized throughout various microscopy medical analysis applications. However, it has several challenges such as the segmentation of small objects, highly imbalanced data, and subtle differences between types of nuclei. In this paper, we propose a method to tackle the aforementioned problems. First, a new encoder “ConvNeXt” is proposed for the HoVer-Net architecture to leverage the major components of vision transformer to improve the network's encoding capabilities. Second, to better distinguish the differences between nuclei, a multi-channel color space-based approach is proposed to aid the model in extracting discriminative features. Third, affine transformations such as scaling and shearing are utilized to improve the model's gen-eralizability. Finally, Unified Focal Loss (UFL) and Sharpness-Aware Minimization (SAM) are tested in attempt to tackle the foreground-backward imbalance and further improve gen-eralizability, respectively. We show how our proposed method (ConvNeXt-S encoder + affine transformation + multi-channel color stacking) outperforms the original HoVer-Net network on the “Lizard” dataset by 2.6% mPQ+.
Design and Fabrication of a Differential MOEMS Accelerometer Based on Fabry–Pérot Micro-Cavities

In this paper, a differential micro-opto-electro-mechanical system (MOEMS) accelerometer based on the Fabry-Pérot (FP) micro-cavities is presented. The optical system of the device consists of two FP cavities and the mechanical system is composed of a proof mass that is suspended by four springs. The applied acceleration tends to move the PM from its resting position. This mechanical displacement can be measured by the FP interferometer formed between the proof mass cross-section and the optical fiber end-face. The proposed sensor is fabricated on a silicon-on-insulator wafer using the bulk micromachining method. The results of the sensor characterization show that the accelerometer has a linear response in the range of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\pm }1\text{g}$ </tex-math></inline-formula>. Also, the optical sensitivity and resolution of the sensor in the static characterization are 6.52 nm/g and <inline-formula> <tex-math notation="LaTeX">$153 \boldsymbol {\mu }\text{g}$ </tex-math></inline-formula>. The sensor sensitivity in the power measurement is 49.6 mV/g and its resonant frequency is at 1372 Hz. Using the differential measurement method increases the sensitivity of the accelerometer. Based on the experimental data, the optical sensitivity in static mode is two times as high as that of a similar MOEMS accelerometer with one FP cavity.
Multijoints and Factorisation

We solve the dual multijoint problem and prove the existence of so-called"factorisations"for arbitrary fields and multijoints of $k_j$-planes. More generally, we deduce a discrete analogue of a theorem due in essence to Bourgain and Guth. Our result is a universal statement which describes a property of the discrete wedge product without any explicit reference to multijoints and is stated as follows: Suppose that $k_1 + \ldots + k_d = n$. There is a constant $C=C(n)$ so that for any field $\mathbb{F}$ and for any finitely supported function $S : \mathbb{F}^n \rightarrow \mathbb{R}_{\geq 0}$, there are factorising functions $s_{k_j} : \mathbb{F}^n\times \mathrm{Gr}(k_j, \mathbb{F}^n)\rightarrow \mathbb{R}_{\geq 0}$ such that $$(V_1 \wedge\cdots\wedge V_d)S(p)^d \leq C\prod_{j=1}^d s_{k_j}(p, V_j),$$ for every $p\in \mathbb{F}^n$ and every tuple of planes $V_j\in \mathrm{Gr}(k_j, \mathbb{F}^n)$, and $$\sum_{p\in \pi_j} s(p, e(\pi_j)) =||S||_d$$ for every $k_j$-plane $\pi_j\subset \mathbb{F}^n$, where $e(\pi_j)\in \mathrm{Gr}(k_j,\mathbb{F}^n)$ denotes the translate of $\pi_j$ that contains the origin and $\wedge$ denotes the discrete wedge product.
Local thermodynamic description of isothermal single-phase flow in porous media

Darcy’s law for porous media transport is given a new local thermodynamic basis in terms of the grand potential of confined fluids. The local effective pressure gradient is determined using non-equilibrium molecular dynamics, and the hydraulic conductivity and permeability are investigated. The transport coefficients are determined for single-phase flow in face-centered cubic lattices of solid spheres. The porosity changed from that in the closest packing of spheres to near unity in a pure fluid, while the fluid mass density varied from that of a dilute gas to a dense liquid. The permeability varied between 5.7 × 10−20 m and 5.5 × 10−17 m, showing a porosity-dependent Klinkenberg effect. Both transport coefficients depended on the average fluid mass density and porosity but in different ways. These results set the stage for a non-equilibrium thermodynamic investigation of coupled transport of multi-phase fluids in complex media.
Constant-roll, cosmic acceleration, and massive neutrinos

We propose a model to describe the late-time cosmic acceleration in the context of the constant-roll model. By considering a coupling between massive neutrinos and the quintessence, the onset of evolution of the quintessence is related to the neutrinos' behavior. When the neutrinos become non-relativistic, the dark energy density increases from zero and results in late-time cosmic acceleration alleviating the coincidence problem. The quintessence potential is such that it evolves under the constant-roll condition giving rise to persistent late-time acceleration.
Sustainable Packaging of Organic Food: Myth or Reality?

Previous research has argued that packaging has a great potential to contribute to sustainable development, but how to put this potential into practice is not yet fully explored. The theoretical foundation of the research presented is at the intersection of packaging logistics and sustainable development. This foundation was chosen because it offers established models that are well linked to the holistic view of sustainable packaging of this thesis. The thesis has two purposes. The first is to explore and elaborate on the potential contribution of packaging to sustainable development from a packaging logistics perspective. This is done in two theoretical studies. They are based on data collected via literature reviews and analyzed according to a content analysis procedure. The first theoretical study examines how this contribution of packaging is supported in formalized approaches provided by academia and trade associations. The analysis shows that in general, the approaches fall short in their consideration of packaging functions and the indirect effects packaging functions have on sustainable development. The second theoretical study explores in greater depth how the onsideration of packaging functions can contribute to sustainable development. This resulted in extensive lists of how the 19 packaging features of three packaging functions can contribute to sustainable development in 14 different ways. Several practical examples are given. The second purpose of the thesis is to explore and elaborate on the potential contributions that organic standardization organizations, consumers and brand owners can make to the use and development of sustainable packaging in practice in an organic food context. The data were primarily collected in four studies through interviews and a survey. Content and statistical analyses were applied to the data. The studies show that organic standardization organizations, consumers and brand owners face challenges that limit their ability to contribute to the use and development of sustainable packaging. However, the research also points out additional abilities for these stakeholders that are particularly linked to increased consideration of the important functions of packaging in regulations, purchases, and in packaging selection and development processes. Overall, this thesis emphasizes the need to integrate consideration of packaging functions and a holistic approach to sustainable packaging in an effort to enhance the sustainable packaging of organic food.
GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called “Graph Complete Network (GCNet),” filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, “Speaker GNN” and “Temporal GNN,” to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning.
Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance.

Bayesian linear mixed-effects models (LMMs) and Bayesian analysis of variance (ANOVA) are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how to specify the data and the model correctly is often not clear. In Bayesian approaches, many authors use data aggregation at the by-subject level and estimate Bayes factors on aggregated data. Here, we use simulation-based calibration for model inference applied to several example experimental designs to demonstrate that, as with frequentist analysis, such null hypothesis tests on aggregated data can be problematic in Bayesian analysis. Specifically, when random slope variances differ (i.e., violated sphericity assumption), Bayes factors are too conservative for contrasts where the variance is small and they are too liberal for contrasts where the variance is large. Running Bayesian ANOVA on aggregated data can-if the sphericity assumption is violated-likewise lead to biased Bayes factor results. Moreover, Bayes factors for by-subject aggregated data are biased (too liberal) when random item slope variance is present but ignored in the analysis. These problems can be circumvented or reduced by running Bayesian LMMs on nonaggregated data such as on individual trials, and by explicitly modeling the full random effects structure. Reproducible code is available from https://osf.io/mjf47/. (PsycInfo Database Record (c) 2024 APA, all rights reserved).