Miracle-dz/UniFAR-FTUs huggingface dataset:
FS-BAND: A Frequency-Sensitive Banding Detector

Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc. scenarios, which largely affects the user's quality of experience (QoE). The banding distortion typically appears as relatively small pixel-wise variations in smooth backgrounds, which is difficult to analyze in the spatial domain but easily reflected in the frequency domain. In this paper, we thereby study the banding artifact from the frequency aspect and propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND). The proposed detector is able to generate a pixel-wise banding map with a perception correlated quality score. Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.
Seam-guided local alignment and stitching for large parallax images

Seam-cutting methods have been proven effective in the composition step of image stitching, especially for images with parallax. However, the effectiveness of seam-cutting usually depends on that images can be roughly aligned such that there exists a local region where a plausible seam can be found. For images with large parallax, current alignment methods often fall short of expectations. In this paper, we propose a local alignment and stitching method guided by seam quality evaluation. First, we use existing image alignment and seam-cutting methods to calculate an initial seam and evaluate the quality of pixels along the seam. Then, for pixels with low qualities, we separate their enclosing patches in the aligned images and locally align them by extracting modified dense correspondences via SIFT flow. Finally, we composite the aligned patches via seam-cutting and merge them into the original aligned result to generate the final mosaic. Experiments show that compared with the state-of-the-art seam-cutting methods, our result is more plausible and with fewer artifacts. The code will be available at https://github.com/tlliao/Seam-guided-local-alignment.
Analytic approach to astrometric perturbations of critical curves by substructures

Astrometric perturbations of critical curves in strong lens systems are thought to be one of the most promising probes of substructures down to small-mass scales. While a smooth mass distribution creates a symmetric geometry of critical curves with radii of curvature about the Einstein radius, substructures introduce small-scale distortions on critical curves, which can break the symmetry of gravitational lensing events near critical curves, such as highly magnified individual stars. We derive a general formula that connects the fluctuation of critical curves with the fluctuation of the surface density caused by substructures, which is useful when constraining models of substructures from observed astrometric perturbations of critical curves. We numerically check that the formula is valid and accurate as long as substructures are not dominated by a small number of massive structures. As a demonstration of the formula, we also explore the possibility that an anomalous position of an extremely magnified star, recently reported as `Mothra', can be explained by fluctuations in the critical curve due to substructures. We find that cold dark matter subhalos with masses ranging from $5 \times 10^7 M_{\odot}/h$ to $10^9 M_{\odot}/h$ can well explain the anomalous position of Mothra, while in the fuzzy dark matter model, the very small mass of $\sim 10^{-24}~\mathrm{eV}$ is needed to explain it.
Generalized Lyapunov conditions for k-contraction: analysis and feedback design

Recently, the concept of k-contraction has been introduced as a promising generalization of contraction for dynamical systems. However, the study of k-contraction properties has faced significant challenges due to the reliance on complex mathematical objects called matrix compounds. As a result, related control design methodologies have yet to appear in the literature. In this paper, we overcome existing limitations and propose new sufficient conditions for k-contraction which do not rely on matrix compounds. Our design-oriented conditions stem from a strong geometrical interpretation and establish a connection between kcontraction and p-dominance. Notably, these conditions are also necessary in the linear time-invariant framework. Leveraging on these findings, we propose a feedback design methodology for both the linear and the nonlinear scenarios.
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation

This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OPS). Unlike most existing libraries that focus solely on either policy learning or evaluation, SCOPE-RL seamlessly integrates these two key aspects, facilitating flexible and complete implementations of both offline RL and OPE processes. SCOPE-RL put particular emphasis on its OPE modules, offering a range of OPE estimators and robust evaluation-of-OPE protocols. This approach enables more in-depth and reliable OPE compared to other packages. For instance, SCOPE-RL enhances OPE by estimating the entire reward distribution under a policy rather than its mere point-wise expected value. Additionally, SCOPE-RL provides a more thorough evaluation-of-OPE by presenting the risk-return tradeoff in OPE results, extending beyond mere accuracy evaluations in existing OPE literature. SCOPE-RL is designed with user accessibility in mind. Its user-friendly APIs, comprehensive documentation, and a variety of easy-to-follow examples assist researchers and practitioners in efficiently implementing and experimenting with various offline RL methods and OPE estimators, tailored to their specific problem contexts. The documentation of SCOPE-RL is available at https://scope-rl.readthedocs. io/en/latest/.
Electrobaryonic axion: hair of neutron stars

Axion-like particles are predicted in many physics scenarios beyond the Standard Model (SM). Their interactions with SM particles may arise from the triangle anomaly of the associated global symmetry, along with other SM global and gauge symmetries, including anomalies with the global baryon number and electromagnetic gauge symmetries. We initiate the phenomenological study of the corresponding ``electrobaryonic axion", a particle that couples with both the baryon chemical potential and the electromagnetic field. Neutron stars, particularly magnetars, possessing high baryon density and strong magnetic fields, can naturally develop a thin axion hair around their surface. In this study, we calculate this phenomenon, considering the effects of neutron star rotation and general relativity. For axion particles lighter than the neutron star rotation frequency, the anomalous interaction can also induce the emission of axion particles from the neutron star. This emission, in the light axion regime, can have a significant contribution to the neutron star cooling rate.
Advancing Attack-Resilient Scheduling of Integrated Energy Systems with Demand Response via Deep Reinforcement Learning

Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, a state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10\% improvement in economic performance under cyber-attack scenarios.
In vivo detection of DNA secondary structures using Permanganate/S1 Footprinting with Direct Adapter Ligation and Sequencing (PDAL-Seq)

DNA secondary structures are essential elements of the genomic landscape, playing a critical role in regulating various cellular processes. These structures refer to G-quadruplexes, cruciforms, Z-DNA or H-DNA structures, amongst others (collectively called ‘non-B DN’), which DNA molecules can adopt beyond the B conformation. DNA secondary structures have significant biological roles, and their landscape is dynamic and can rearrange due to various factors, including changes in cellular conditions, temperature, and DNA-binding proteins. Understanding this dynamic nature is crucial for unraveling their functions in cellular processes. Detecting DNA secondary structures remains a challenge. Conventional methods, such as gel electrophoresis and chemical probing, have limitations in terms of sensitivity and specificity. Emerging techniques, including next-generation sequencing and single-molecule approaches, offer promise but face challenges since these techniques are mostly limited to only one type of secondary structure. Here we describe an updated version of a technique permanganate/S1 nuclease footprinting, which uses potassium permanganate to trap single-stranded DNA regions as found in non-B structures, in combination with S1 nuclease digest and adapter ligation to detect genome-wide non-B formation. To overcome technical hurdles, we combined this method with direct adapter ligation and sequencing (PDAL-Seq). Furthermore, we established a user-friendly pipeline available on Galaxy to standardize PDAL-Seq data analysis. This optimized method allows the analysis of many types of DNA secondary structures that form in a living cell and will advance our knowledge of their roles in health and disease.
Eco-evolutionary dynamics of partially migratory metapopulations in spatially and seasonally varying environments

Predicting population responses to environmental changes requires understanding interactions among environmentally induced phenotypic variation, selection, demography and genetic variation, and thereby predicting eco-evolutionary dynamics emerging across diverse temporal and spatial scales. Partially migratory metapopulations (PMMPs), featuring seasonal coexistence of resident and migrant individuals across multiple spatially distinct subpopulations, have clear potential for complex spatio-seasonal eco-evolutionary dynamics through impacts of selection on migration on spatial population dynamics, and feedbacks resulting from ongoing micro-evolution. However, the key genetic and environmental conditions that maintain migratory polymorphisms, and eco-evolutionary dynamics of PMMPs under stochastic environmental variation and strong seasonal perturbations, have not yet been resolved. Accordingly, we present a general individual-based model that tracks eco-evolutionary dynamics in PMMPs inhabiting spatially structured, seasonally varying landscapes, with migration formulated as a quantitative genetic threshold trait. Our simulations show that such genetic and landscape structures, which commonly occur in nature, can readily produce a variety of stable partially migratory systems given diverse regimes of spatio-seasonal environmental variation. Typically, partial migration is maintained whenever sites differ in non-breeding season suitability resulting from variation in density-dependence, causing ‘ideal free’ non-breeding distributions where residents and migrants occur with frequencies that generate similar survival probabilities. Yet, stable partial migration can also arise without any fixed differences in non-breeding season density-dependence among sites, and even without density-dependence at all, through risk-spreading given sufficiently large stochastic environmental fluctuations among sites and years. Finally, we show that local non-breeding season mortality events, as could result from extreme climatic events, can generate eco-evolutionary dynamics that ripple out to affect breeding and non-breeding season space use of subpopulations throughout the PMMP, on both short and longer timeframes. Such effects result from spatially divergent selection on both the occurrence and destinations of migration. Our model thus shows how facultative seasonal migration can act as a key mediator of eco-evolutionary dynamics in spatially and seasonally structured environments, providing key steps towards predicting responses of natural partially migratory populations to ongoing changes in spatio-seasonal patterns of environmental variation.
A new conceptual framework explaining spatial variation in soil nitrous oxide emissions

Soil emissions of nitrous oxide (N2O), a potent greenhouse gas, contribute substantially to global warming from agriculture. Spatial variation in N2O emissions within agricultural fields leads to high uncertainty in the benefits of climate-smart agricultural practices. Here, we present a new conceptual framework explaining spatial variation in soil N2O emissions developed from high spatial resolution automated measurements of soil N2O emissions together with measurements of gross N2O fluxes and soil physicochemical properties in two separately managed maize fields in central Illinois, USA. We found that sub-field locations with consistently low N2O emissions had distinct biogeochemical properties compared to locations where high emissions occurred episodically, leading to spatial variation in which factors control N2O production rates. In the consistent N2O cold spots, soil nitrate (NO3-) and dissolved organic carbon (DOC) constrained N2O production irrespective of changes in soil moisture. In contrast, in the episodic N2O hot spots which had higher soil NO3- and DOC availability, N2O production was stimulated by increases in soil moisture. These findings form the ‘cannon model’ which conceptualizes how sub-field scale variation in soil NO3- and DOC determines where increases in soil moisture can trigger high soil N2O emissions within agricultural fields.
A simple agent-based hybrid model to simulate the biophysics of glioblastoma multiforme cells and the concomitant evolution of the oxygen field

Background and objectives: Glioblastoma multiforme (GBM) is one of the most aggressive cancers of the central nervous system. It is characterized by a high mitotic activity and an infiltrative ability of the glioma cells, neovascularization and necrosis. GBM evolution entails the continuous interplay between heterogeneous cell populations, chemotaxis, and physical cues through different scales. In this work, an agent-based hybrid model is proposed to simulate the coupling of the multiscale biological events involved in the GBM invasion, specifically the individual and collective migration of GBM cells and the concurrent evolution of the oxygen field and phenotypic plasticity. An asset of the formulation is that it is conceptually and computationally simple but allows to reproduce the complexity and the progression of the GBM micro-environment at cell and tissue scales simultaneously. Methods: The migration is reproduced as the result of the interaction between every single cell and its micro-environment. The behavior of each individual cell is formulated through genotypic variables whereas the cell micro-environment is modeled in terms of the oxygen concentration and the cell density surrounding each cell. The collective behavior is formulated at a cellular scale through a flocking model. The phenotypic plasticity of the cells is induced by the micro-environment conditions, considering five phenotypes. Results: The model has been contrasted by benchmark problems and experimental tests showing the ability to reproduce different scenarios of glioma cell migration. In all cases, the individual and collective cell migration and the coupled evolution of both the oxygen field and phenotypic plasticity have been properly simulated. This simple formulation allows to mimic the formation of relevant hallmarks of glioblastoma multiforme, such as the necrotic cores, and to reproduce experimental evidences related to the mitotic activity in pseudopalisades. Conclusions: In the collective migration, the survival of the clusters prevails at the expense of cell mitosis, regardless of the size of the groups, which delays the formation of necrotic foci and reduces the rate of oxygen consumption.
Biomass and Productivity of Microphytobenthos in Temperate Coastal Areas

I review the distribution of microphytobenthic biomass and production in the intertidal, shallow subtidal (+5 m), and o#shore (5 30 m) zones of temperate coastal regions by compiling previous studies, with special emphasis on the light regime as a driving force behind the observed patterns. In the intertidal zone, which is periodically exposed to direct sunlight, the mean biomass and production of microphytobenthos reach 84,61 mg Chl-a m-2 and 121,100 g C m-2 yr-1, respectively. The biomass and production of intertidal microphytobenthos generally exceed those of phytoplankton in the overlying water column on an areal basis. Although subtidal microphytobenthos communities are never exposed to direct sunlight, both the microphytobenthic biomass and production of the shallow subtidal zone, mainly in microtidal areas, are similar to those of the intertidal zone. In contrast to intertidal habitats, however, the benthic microalgal productivity in the shallow subtidal zone is often less than that of the overlying phytoplankton. The dynamics of o#shore microphytobenthos are probably a#ected by phytoplankton in the overlying water column due to a shading e#ect. The magnitude of both the biomass and the production of microphytobenthos in the o#shore zone is relatively low, but microphytobenthic production can be nearly equivalent to the phytoplanktonic production in certain locations where the waters are characterized by low densities of phytoplankton and consequently by high transparency. Changes in the distribution of microphytobenthic biomass and production, and in the relative importance of the microphytobenthos and phytoplankton, are likely to a#ect the availability of food for the macrobenthos, with implications for the entire benthic food web. To achieve a better understanding of the benthic ecosystem in coastal regions, it is therefore necessary to conduct further quantitative studies of the microphytobenthos, including the development of suitable methodology. Key Words: benthic microalgae, biomass, coastal area, production, microphytobenthos, phytoplankton . / 0 1/ 2/ 3 4 5 67 89 4/ :; <= >? 6@ ABC ? DEF " GH. / I J KLM 10 mmNO P QRSTUS V WXYZ/ [ \]^A_L/ ` a Cb cdeLf gLC " h ijk4P lm no pAqrL/ s / Z tu s dvwxo yzA6? {|d} Z ~fHgLC (Riaux-Gobin et al. 1987; Cariou-Le Gall & Blanchard 1995; Facca et al. 2002; Safi 2003; Totti 2003; Cartaxana et al. 2006). / d Af? < ~f/  1_  Y~~6C 191 m E/  [~fHgLC (McGee et al. 2008). > . /  6 Ay ?  z 1530   HgL / 3 P . ~ C (Cahoon et al. 1993).  . / ¡¢ YLM 10 mNOE A £ ¤ YL / ¥ . ¦6 § ̈ ©aA «¬ ­? [ 6®L C ̄ ° ± 2002; ±° 20052" d Y3 £ . / ́ ́ μ¶A qrL/ y ¬ ́ nK·gLCZ ̧ 1fgLC " C : Nzno . / IJ KL 7 9NO HgL  (Montani et al. 2003; Sundbäck & Miles 2000; Baird & Middleton 2004; Cook et al. Received 1 Oct. 2010 Accepted 28 May 2011 Corresponding author E-mail: hitomiyjp@yahoo.co.jp, Tel: 087 891 3144 Japanese Journal of Benthology  oSU» 1⁄41⁄2 66: 1 21 (2011)
Dual-Stage OOD Detection Learning with an Unsupervised Start

When deep neural networks (DNNs) are deployed in real-world applications, their effectiveness is threatened by unexpected input data that do not reflect the training data distribution, i.e., out-of-distribution (OOD) data. Ideally, there should be relatively large training sets, including a diverse set of priori-unknown OOD data that are sufficiently different from the set of training distributions. However, this is often not the case in real-world scenarios and finite training data sampled from unknown distribution(s) may not be representative, leading to poor model generalizability. In this paper, we present a new two-stage training procedure for improving OOD detection. The proposed method utilizes unsupervised contrastive representation learning, in the first stage, to leverage unlabeled data to learn invariant features. Then, in the second stage, we train the same model with label smoothing technique, by minimizing cross-entropy. We evaluate our method by considering the case of an OOD detection task in image classification using the CIFAR-10, SVHN, TinyImageNet, and LSUN benchmark data sets, where there are relatively little labeled training data. The proposed method is shown to improve the generalizability and OOD detection of two frequently used architectures of convolutional neural networks. Our experiments show that label smoothing improves model calibration, thereby enhancing the reliability of OOD detection.
Distributed MIMO network optimization based on duality and local message passing

In a communication network, it is often impractical for each node to learn the global channel knowledge (network connectivity and channel state information of each link). In this paper, we address distributed rate optimization for Time-Division Duplex (TDD) Multiple-Input Multiple-Output (MIMO) networks when part of the local channel knowledge is learned via message passing between each transmitter and its intended receivers. The distributed optimization algorithm is based on a rate duality and the corresponding input covariance matrix transformation between the forward and reverse links of TDD MIMO networks under the assumption of global channel knowledge. Noting that the key information required by the proposed transformation is the interference-plus-noise covariance matrix, we propose a local covariance matrix transformation such that each node can distributedly optimize its input covariance matrix by only exchanging interference-plus-noise covariance matrix locally. It is observed from the simulation that the proposed algorithm achieves a performance close to the one with global channel knowledge and outperforms the existing distributed algorithms.