Miracle-dz/UniFAR-FTUs huggingface dataset:
PAC Model Checking of Black-Box Continuous-Time Dynamical Systems

In this article, we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning. The black-box dynamical systems are the ones, for which no model is given but whose states changing continuously through time within a finite-time interval can be observed at some discrete-time instants for a given input. The new model checking approach is termed as the PAC model checking due to the incorporation of learned models with correctness guarantees expressed using the terms error probability and confidence. Based on the error probability and confidence level, our approach provides statistically formal guarantees that the time-evolving trajectories of the black-box dynamical system over finite-time horizons fall within the range of the learned model plus a bounded interval, contributing to insights on the reachability of the black-box system and thus on the satisfiability of its safety requirements. The learned model together with the bounded interval is obtained by scenario optimization, which boils down to a linear programming problem. Three examples demonstrate the performance of our approach.
Lipschitz stability for an inverse source scattering problem at a fixed frequency

This paper is concerned with an inverse source problem for the three-dimensional Helmholtz equation by a single boundary measurement at a fixed frequency. We show the uniqueness and a Lipschitz-type stability estimate under the assumption that the source function is piecewise constant on a domain which is made of a union of disjoint convex polyhedral subdomains.
On Coresets for Fair Clustering in Metric and Euclidean Spaces and Their Applications

Fair clustering is a constrained variant of clustering where the goal is to partition a set of colored points, such that the fraction of points of any color in every cluster is more or less equal to the fraction of points of this color in the dataset. This variant was recently introduced by Chierichetti et al. [NeurIPS, 2017] in a seminal work and became widely popular in the clustering literature. In this paper, we propose a new construction of coresets for fair clustering based on random sampling. The new construction allows us to obtain the first coreset for fair clustering in general metric spaces. For Euclidean spaces, we obtain the first coreset whose size does not depend exponentially on the dimension. Our coreset results solve open questions proposed by Schmidt et al. [WAOA, 2019] and Huang et al. [NeurIPS, 2019]. The new coreset construction helps to design several new approximation and streaming algorithms. In particular, we obtain the first true constant-approximation algorithm for metric fair clustering, whose running time is fixed-parameter tractable (FPT). In the Euclidean case, we derive the first $(1+\epsilon)$-approximation algorithm for fair clustering whose time complexity is near-linear and does not depend exponentially on the dimension of the space. Besides, our coreset construction scheme is fairly general and gives rise to coresets for a wide range of constrained clustering problems. This leads to improved constant-approximations for these problems in general metrics and near-linear time $(1+\epsilon)$-approximations in the Euclidean metric.
Object-Centric Multi-View Aggregation

We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid. Key to our approach is an object-centric canonical 3D coordinate system into which views can be lifted, without explicit camera pose estimation, and then combined -- in a manner that can accommodate a variable number of views and is view order independent. We show that computing a symmetry-aware mapping from pixels to the canonical coordinate system allows us to better propagate information to unseen regions, as well as to robustly overcome pose ambiguities during inference. Our aggregate representation enables us to perform 3D inference tasks like volumetric reconstruction and novel view synthesis, and we use these tasks to demonstrate the benefits of our aggregation approach as compared to implicit or camera-centric alternatives.
Learning Adaptive Sampling and Reconstruction for Volume Visualization

A central challenge in data visualization is to understand which data samples are required to generate an image of a data set in which the relevant information is encoded. In this article, we make a first step towards answering the question of whether an artificial neural network can predict where to sample the data with higher or lower density, by learning of correspondences between the data, the sampling patterns and the generated images. We introduce a novel neural rendering pipeline, which is trained end-to-end to generate a sparse adaptive sampling structure from a given low-resolution input image, and reconstructs a high-resolution image from the sparse set of samples. For the first time, to the best of our knowledge, we demonstrate that the selection of structures that are relevant for the final visual representation can be jointly learned together with the reconstruction of this representation from these structures. Therefore, we introduce differentiable sampling and reconstruction stages, which can leverage back-propagation based on supervised losses solely on the final image. We shed light on the adaptive sampling patterns generated by the network pipeline and analyze its use for volume visualization including isosurface and direct volume rendering.
Disseminated avian tuberculosis in captive Ara macao

Avian mycobacteriosis was diagnosed in a captive scarlet macaw (Ara macao) that presented multifocal granulomas on subcutaneous tissue, sciatic nerves, infraorbital sinus, trachea, air sacs, muscles, spleen and liver. Microscopically, central areas of caseous necrosis surrounded by epithelioid macrophage, multinucleated giant cells, and lymphocytes were observed. Acid-fast bacilli were demonstrated by Ziehl-Neelsen stain. Inoculation into Lowenstein– Jensen, Stonebrink and Petragnani media, yielded Mycobacterium spp, which was identified as Mycobacterium avium by polymerase chain reaction technique (PCR).
Parcels and particles: Markov blankets in the brain

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions—and analyses—of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.
Scale-dependent statistics of inertial particle distribution in high Reynolds number turbulence

Multiscale statistical analyses of inertial particle distributions are presented to investigate the statistical signature of clustering and void regions in particle-laden incompressible isotropic turbulence. Three-dimensional direct numerical simulations of homogeneous isotropic turbulence at high Reynolds number ($Re_\lambda \gtrsim 200$) with up to $10^9$ inertial particles are performed for Stokes numbers ranging from $0.05$ to $5.0$. Orthogonal wavelet analysis is then applied to the computed particle number density fields. Scale-dependent skewness and flatness values of the particle number density distributions are calculated and the influence of Reynolds number $Re_\lambda$ and Stokes number $St$ is assessed. For $St \sim 1.0$, both the scale-dependent skewness and flatness values become larger as the scale decreases, suggesting intermittent clustering at small scales. For $St \le 0.2$, the flatness at intermediate scales, i.e. for scales larger than the Kolmogorov scale and smaller than the integral scale of the flow, increases as $St$ increases, and the skewness exhibits negative values at the intermediate scales. The negative values of the skewness are attributed to void regions. These results indicate that void regions at the intermediate sales are pronounced and intermittently distributed for such small Stokes numbers. As $Re_\lambda$ increases, the flatness increases slightly. For $Re_\lambda \ge 328$, the skewness shows negative values at large scales, suggesting that void regions are pronounced at large scales, while clusters are pronounced at small scales.
NPCFace: A Negative-Positive Cooperation Supervision for Training Large-scale Face Recognition

Deep face recognition has made remarkable advances in the last few years, while the training scheme still remains challenging in the large-scale data situation where many hard cases occur. Especially in the range of low false accept rate (FAR), there are various hard cases in both positives ($\textit{i.e.}$ intra-class) and negatives ($\textit{i.e.}$ inter-class). In this paper, we study how to make better use of these hard samples for improving the training. The existing training methods deal with the challenge by adding margins in either the positive logit (such as SphereFace, CosFace, ArcFace) or the negative logit (such as MV-softmax, ArcNegFace, CurricularFace). However, the correlation between hard positive and hard negative is overlooked, as well as the relation between the margin in positive logit and the margin in negative logit. We find such correlation is significant, especially in the large-scale dataset, and one can take advantage from it to boost the training via relating the positive and negative margins for each training sample. To this end, we propose an explicit cooperation between positive and negative margins sample-wisely. Given a batch of hard samples, a novel Negative-Positive Cooperation loss, named NPCFace, is formulated, which emphasizes the training on both the negative and positive hard cases via a cooperative-margin mechanism in the softmax logits, and also brings better interpretation of negative-positive hardness correlation. Besides, the negative emphasis is implemented with an improved formulation to achieve stable convergence and flexible parameter setting.We validate the effectiveness of our approach on various benchmarks of large-scale face recognition and outperform the previous methods especially in the low FAR range.
Pharmacological Effects of Ephedrine Alkaloids on Human α1- and α2-Adrenergic Receptor Subtypes

Ephedra species of plants have both beneficial and adverse effects primarily associated with the presence of ephedrine alkaloids. Few reports have appeared that examine the direct actions of ephedrine alkaloids on human subtypes of adrenergic receptors (ARs). In the present study, ephedrine alkaloids were evaluated for their binding affinities on human α1A-, α1B-, α1D-, α2A-, α2B-, and α2C-AR subtypes expressed in HEK and Chinese hamster ovary cells. Cell-based reporter gene assays were used to establish functional activity of ephedrine alkaloids at α1A-, α2A-, and α2C-ARs. The data showed that ephedrine alkaloids did not activate α1- and α2-ARs and that they antagonized the agonist-mediated effects of phenylephrine and medetomidine on α1- and α2-ARs, respectively. As in the binding studies, 1R,2R- and 1R,2S-ephedrine showed greater functional antagonist activity than the 1S,2R- and 1S,2S-isomers. The rank order of affinity for the isomers was 1R,2R > 1R,2S > 1S,2R > 1S,2S. The rank order of potencies of alkaloids containing a 1R,2S-configuration was norephedrine ≥ ephedrine ≫ N-methylephedrine. These studies have demonstrated that orientation of the β-hydroxyl group on the ethylamino side chain and the state of N-methyl substitution are important for α-AR binding and functional activity of the ephedrine alkaloids. In conclusion, the ephedrine isomers and analogs studied did not exhibit any direct agonist activity and were found to possess moderate antagonist activities on cloned human α-ARs. The blockade of presynaptic α2A- and α2C-ARs may have a pharmacological role in the direct actions of Ephedra alkaloids.
Bayesian optimization for automatic design of face stimuli

Investigating the cognitive and neural mechanisms involved with face processing is a fundamental task in modern neuroscience and psychology. To date, the majority of such studies have focused on the use of pre-selected stimuli. The absence of personalized stimuli presents a serious limitation as it fails to account for how each individual face processing system is tuned to cultural embeddings or how it is disrupted in disease. In this work, we propose a novel framework which combines generative adversarial networks (GANs) with Bayesian optimization to identify individual response patterns to many different faces. Formally, we employ Bayesian optimization to efficiently search the latent space of state-of-the-art GAN models, with the aim to automatically generate novel faces, to maximize an individual subject's response. We present results from a web-based proof-of-principle study, where participants rated images of themselves generated via performing Bayesian optimization over the latent space of a GAN. We show how the algorithm can efficiently locate an individual's optimal face while mapping out their response across different semantic transformations of a face; inter-individual analyses suggest how the approach can provide rich information about individual differences in face processing.
Elevated Interleukin-6 Levels within 72 Hours Post Admission Are Associated with Disease Progression in Nonseptic Critically Ill Children

It has been established that the innate and adaptive immune suppression was heterogeneous in septic and nonseptic critically ill patients, while the value of immune function in pediatric patients with nonseptic critical illness is limited. We conducted a single-center retrospective study to explore this issue. A total of 65 children with nonseptic illnesses were studied for lymphocyte subpopulations, immunoglobulin concentrations, complement concentrations, and cytokines in peripheral blood in the next 72 hours after admission to our Pediatric Intensive Care Unit (PICU). When compared to clinically recovered patients, patients with disease progression had a numerically lower but not significantly different median pediatric critical illness score and longer PICU median stays. The analysis of serum immunoglobulin (IgG, IgM, and IgA), serum complement (C3, C4) concentrations, and lymphocyte subpopulations showed no significantly difference between patients with and without relieved clinical symptoms by day 4. For the cytokine analysis, the level of IL-6 was significantly higher in patients with disease progression than that in patients who clinically recovered (p = 0.046). In the univariate Cox regression analysis, plasma IL-6 levels were associated with outcome. Multivariate analysis evidenced that the level of plasma IL-6 was one of the factors determining the length of hospital stays. In conclusions, our results demonstrate that increased IL-6 levels in the initial 72 hours post admission are associated with prolonged stays and disease progression in nonseptic critically ill children in the PICU.
Intertidal Mosaics: Patch Size, Propagule Availability, and Spatially Variable Patterns of Succession

Localized disturbances transform most assemblages of sessile organisms into mosaics of patches differing in characteristics such as size and age (time since last disturbed). This mosaic nature of natural communities is especially evident on exposed intertidal shores along the northwest coast of North America, where the competitively dominant mussel, Mytilus californianus, occupies much of the space at mid-tidal levels. Nearly continuous beds of this species are interrupted by patches of open space generated mainly by the shearing forces of winter storm waves. These patches serve as foci for the recruitment, growth, and reproduction of many competitively inferior, "fugitive" species, including both algae and sessile invertebrates. These species are doomed to local extinction as the lateral encroachment of adult mussels closes the patch and excludes them from the area. This study examined the dynamics of algal succession within experimental patches cleared in mussel beds. In particular, two potentially important sources of variation in successional dynamics were investigated: (1) the size of the patch when first created, and (2) the location of the patch with respect to potential sources of propagules. The size of a cleared patch was found to influence strongly the course of algal succession. This effect was largely indirect, resulting from an interaction between patch size and grazing intensity. Small patches support higher densities of grazers, especially limpets, than do large patches. As a consequence, the assemblages of algae that develop within small and large patches differ markedly. The assemblage in small patches includes grazer-resistant but apparently competitively inferior species, whereas that in large patches is composed of grazer-vulnerable but competitively superior species. Small patches appear to serve as refuges from competition for grazer-resistant species. Recruitment was variable among the experimental patches. Percent cover of several species was found to be highly correlated with the cover of epizoic nonspecific adults within 1 m of the edge of the patch. This result suggests that a number of the species inhabiting patches within mussel beds may disperse their propagules over relatively short distances. For such species, patch dispersion may influence the regional dynamics of their populations.
Effects of Interaction Level, Framerate, Field of View, 3D Content Feedback, Previous Experience on Subjective User eXperience and Objective Usability in Immersive Virtual Environment

This paper investigates the effects of interaction level, framerate, field of view, 3D content feedback and previous experience on subjective User eXperience (i.e., presence, engagement, immersion, flow, emotion, skill, experience consequence, judgement, technology adoption) and objective usability in immersive virtual environment. Data were collected from a series of five sub- experiments (i.e., one for each influential factor) that involved a total of 152 individuals. The participants were asked to use the "Think and Shoot" immersive virtual environment and to complete a User eXperience questionnaire. Their subjective perceptions and objective measures were collected and analyzed. The results revealed that interactivity level and previous experience had an effect on subjective User eXperience and on objective usability. Framerate and field of view had an effect on objective usability. Finally, 3D content feedback had no significant influence on User eXperience. From these findings, key points for User eXperience practitioners are proposed.
Emergence of Buprestidae, Cerambycidae, and Scolytinae (Coleoptera) from Mountain Pine Beetle-Killed and Fire-Killed Ponderosa Pines in the Black Hills, South Dakota, USA

ABSTRACT Wood borers (Coleoptera: Cerambycidae and Buprestidae) and bark beetles (Coleoptera: Curculionidae) infest ponderosa pines, Pinus ponderosa P. Lawson and C. Lawson, killed by mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, and fire. No data is available comparing wood borer and bark beetle densities or species guilds associated with MPB-killed or fire-killed trees from the Black Hills of South Dakota, USA. We compared species assemblages of wood borers (Cerambycidae and Buprestidae) and bark beetles (Dendroctonus spp. and Ips spp. (Coleoptera: Curculionidae)) emerging from bolts collected from trees that had been killed by MPB or fire one or two years prior to harvesting. Significantly more wood borers emerged from bolts of fire-killed trees than from bolts of MPB-killed trees. Wood borer density in fire-killed trees was 13.8 per m2, compared to 4.4 per m2 in MPB-killed trees. Six wood borer species, Acanthocinus obliquus (LeConte), Buprestis consularis Gory, Chrysobothris sp. , Melanophila acuminata (De Geer), Monochamus clamator LeConte, and Phaenops gentilis (LeConte), emerged from fire-killed tree bolts. Four species of wood borers, A. obliquus, Rhagium inquisitor (L.), Tetropium cinnamopterum Kirby, and Xylotrechus longitarsis Casey, emerged from MPB-killed tree bolts. Acanthocinus obliquus was the only species that emerged from both MPB-killed and fire-killed tree bolts. The bark beetles D. ponderosae, Dendroctonus Valens LeConte, Ips knausi Swaine, and Ips pini (Say) emerged from one-year-old, MPB-killed trees, while I. pini was the only species that emerged from a fire-killed tree.
Evolution of Alternative Reproductive Strategies: Frequency-Dependent Sexual Selection in Male Bluegill Sunfish

This study provides empirical evidence in a wild population for frequency-dependent sexual selection between alternative male reproductive strategies. The bluegill sunfish (Lepomis macrochirus) has two male reproductive strategies, cuckolder or parental, used by different males to compete in fertilizing the same eggs. As the density of cuckolders in colonies of parental males increases, the average mating success of cuckolders initially peaks but then declines. The cuckolder density at which their success peaks is determined by ecological characteristics of each colony. A theoretical analysis assuming random and omniscient cuckolder distributions among ecologically different colonies shows that cuckolders will fertilize decreasing proportions of eggs, relative to parental males, as cuckolders increase in frequency in the population. This supports evolutionary models that assume negative frequency-dependent selection between the competing strategies. Cuckolder and parental strategies may therefore have evolved as an Evolutionarily Stable State (ESSt).
Phenotypic Response to Photoperiod and Temperature in a Tropical Pierid Butterfly

Photoperiod and temperature during development were shown to control the phenotype of adults of Catopsilia pomona pomona in a population in Queensland. These environmental cues controlled pattern and colour elements of the phenotype to different extents, and the responses of males and females differed. Weather experienced in the 5th larval instar and pupal stage determined the adult phenotype. Differences in the responses of the progeny of February and July adults may have reflected differential survival of different genotypes over the dry season, through selection for the seasonally appropriate phenotype. The adaptive significance of the 2 seasonal forms is not known.