Miracle-dz/UniFAR-FTUs huggingface dataset:
Anthropogenic food use and diet overlap between red foxes (Vulpes vulpes) and arctic foxes (Vulpes lagopus) in Prudhoe Bay, Alaska

Red foxes (Vulpes vulpes (L., 1758)) recently expanded into the oil fields at Prudhoe Bay, Alaska, USA, and we hypothesized that the availability of anthropogenic foods may contribute to their success and persistence there. This study assessed the importance of anthropogenic foods to the diets of red foxes and arctic foxes (Vulpes lagopus (L., 1758)), and competition for food resources between the two species in Prudhoe Bay. We used stable isotope analysis of fox tissues to infer diet during summer and winter for both red and arctic foxes, and lifetime diet for red fox. While the contribution of anthropogenic foods in late summer for both species’ diets was low (~10% to 15%), the contribution in late winter was higher and varied between species (red foxes = 49%; arctic foxes = 39%). Estimates of lifetime diet in red foxes suggest consistent use of anthropogenic foods. We found moderate overlap of fox diets, although red foxes appeared to be more specialized on lemmings, whereas arctic foxes had a more div.
Hybridization between the cyprinid fishes Luxilus albeolus, L. cornutus, and L. cerasinus with comments on the proposed hybrid origin of L. albeolus

Allozyme electrophoresis, restriction endonuclease analysis of mitochondrial DNA (mtDNA), and principal component analysis (PCA) of scale counts were used to characterize hybridization between Luxilus cornutus and L. cerasinus at a single locality (Barbour Creek, James River drainage, southwestern Virginia). PCA provided some separation between these species and L. albeolus from allopatric populations; however, overlap among all three species prevented complete discrimination of these forms with morphological characters alone. Application of morphological and genetic characters diagnostic for L. cornutus and L. cerasinus identified many hybrids in Barbour Creek. Most were F,s (as indicated by heterozygous genotypes at all four diagnostic allozyme loci) and exhibited mtDNAs of L. cerasinus. These data were contrasted with other hybridizing species of the genus Luxilus, which showed more extensive introgression. In addition, one individual exhibited morphological and genetic characteristics consistent with its identification as L. albeolus, providing the first report of this species in the James River. These results do not refute or support the hypothesis of hybrid origin for L. albeolus but highlight the difficulty of discriminating among ancestral polymorphism, convergence, and past hybridization.
Geometric series expansion of the Neumann–Poincaré operator: Application to composite materials

The Neumann-Poincare operator, a singular integral operator on the boundary of a domain, naturally appears when one solves a conductivity transmission problem via the boundary integral formulation. Recently, a series expression of the Neumann-Poincare operator was developed in two dimensions based on geometric function theory. In this paper, we investigate geometric properties of composite materials by using this series expansion. In particular, we obtain explicit formulas for the polarization tensor and the effective conductivity for an inclusion or a periodic array of inclusions of arbitrary shape with extremal conductivity, in terms of the associated exterior conformal mapping. Also, we observe by numerical computations that the spectrum of the Neumann--Poincare operator has a monotonic behavior with respect to the shape deformation of the inclusion. Additionally, we derive inequality relations of the coefficients of the Riemann mapping of an arbitrary Lipschitz domain by using the properties of the polarization tensor corresponding to the domain.
A stationary solution for the mixed turbulence shell model

The aim of this work is to prove an existence result on the mixed shell model extending the classic standard existence results from $\ell^2$ initial conditions to $\mu$-almost every initial conditions, where $\mu$ is a Gaussian measure on the infinite dimensional space of initial conditions. A similar result is also shown to hold for turbulence models where a dyadic tree structure replaces the linear one.
Bump Attractors and Waves in Networks of Leaky Integrate-and-Fire Neurons

Bump attractors are wandering localised patterns observed in in vivo experiments of spatially-extended neurobiological networks. They are important for the brain's navigational system and specific memory tasks. A bump attractor is characterised by a core in which neurons fire frequently, while those away from the core do not fire. We uncover a relationship between bump attractors and travelling waves in a classical network of excitable, leaky integrate-and-fire neurons. This relationship bears strong similarities to the one between complex spatiotemporal patterns and waves at the onset of pipe turbulence. Waves in the spiking network are determined by a firing set, that is, the collection of times at which neurons reach a threshold and fire as the wave propagates. We define and study analytical properties of the voltage mapping, an operator transforming a solution's firing set into its spatiotemporal profile. This operator allows us to construct localised travelling waves with an arbitrary number of spikes at the core, and to study their linear stability. A homogeneous "laminar" state exists in the network, and it is linearly stable for all values of the principal control parameter. Sufficiently wide disturbances to the homogeneous state elicit the bump attractor. We show that one can construct waves with a seemingly arbitrary number of spikes at the core; the higher the number of spikes, the slower the wave, and the more its profile resembles a stationary bump. As in the fluid-dynamical analogy, such waves coexist with the homogeneous state, are unstable, and the solution branches to which they belong are disconnected from the laminar state; we provide evidence that the dynamics of the bump attractor displays echoes of the unstable waves, which form its building blocks.
Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks

It is well observed that in deep learning and computer vision literature, visual data are always represented in a manually designed coding scheme (eg., RGB images are represented as integers ranging from 0 to 255 for each channel) when they are input to an end-to-end deep neural network (DNN) for any learning task. We boldly question whether the manually designed inputs are good for DNN training for different tasks and study whether the input to a DNN can be optimally learned end-to-end together with learning the weights of the DNN. In this paper, we propose the paradigm of {\em deep collective learning} which aims to learn the weights of DNNs and the inputs to DNNs simultaneously for given tasks. We note that collective learning has been implicitly but widely used in natural language processing while it has almost never been studied in computer vision. Consequently, we propose the lookup vision networks (Lookup-VNets) as a solution to deep collective learning in computer vision. This is achieved by associating each color in each channel with a vector in lookup tables. As learning inputs in computer vision has almost never been studied in the existing literature, we explore several aspects of this question through varieties of experiments on image classification tasks. Experimental results on four benchmark datasets, i.e., CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet (ILSVRC2012) have shown several surprising characteristics of Lookup-VNets and have demonstrated the advantages and promise of Lookup-VNets and deep collective learning.
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support

Empathy is critical to successful mental health support. Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. Because millions of people use text-based platforms for mental health support, understanding empathy in these contexts is crucial. In this work, we present a computational approach to understanding how empathy is expressed in online mental health platforms. We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations. We collect and share a corpus of 10k (post, response) pairs annotated using this empathy framework with supporting evidence for annotations (rationales). We develop a multi-task RoBERTa-based bi-encoder model for identifying empathy in conversations and extracting rationales underlying its predictions. Experiments demonstrate that our approach can effectively identify empathic conversations. We further apply this model to analyze 235k mental health interactions and show that users do not self-learn empathy over time, revealing opportunities for empathy training and feedback.
Emergence and expansion of embryonic definitive haematopoietic stem cells

De novo generation and physiological expansion of long-term repopulating definitive haematopoietic stem cells (LTR-dHSCs) occurs exclusively during embiyogenesis and is initiated within the dorsal aortalpara-aortic mesenchyme (Ao) of the Eli. 5 aorta-gonad-mesonephros (AGM) region. Elucidation of the in vivo mechanisms involved in LTR-dHSC emergence and expansion is likely to be a prerequisite to the success of generating LTR-dHSCs from non-haematopoietic materials and facilitating an effective expansion of pre-existing LTR-dHSC in vitro. Using flow cytometry and the in vivo long-term haematopoietic reconstitution assay we herein describe the endothelial affiliation of early LTR-dHSCs as they emerge within the embryonic aorta-gonad-mesonephros (AGM) region and yolk sac, fundamentally defined by a VE-cadherinCD45 irnmunophenotype, and the successive restriction to a haematopoietic identity upon hepatic colonisation. Well defined haematopoietic colony forming unit-culture and endothelial network forming assays were used to reveal that the endothelio-haematopoietic plasticity observed in the early embryo is lost at mid-gestation in favour of exclusive lineage commitment. A functional benefit from haematopoietic-endothelial interaction is alluded to by experiments using a novel liquid suspension culture system in which successful multilineage haematopoietic differentiation of HSC/progenitor cells is dependent on the presence of endothelium. To directly examine the hypothesis of an anatomical polarity of haematopoietic activity in the AGM region the dorsal aorta (Ao) from the E1O.5-1l.5 AGM region was bisected along the dorsoventral axis. The ventral (AoV) and dorsal
Breeding of Useful Killer Wine Yeasts

For producing a good wine by pure culture as in brewing beer, useful killer yeast strains of Saccharomyces cerevisiae were bred by back-crossing KL-88, a wild killer sake yeast, with a repetitive parent, OC-2, one of the best wine yeasts. Haploids of KL-88 were crossed with haploids of OC-2 to yield first-generation hybrids (1HYs). Haploids possessing both killing activity and SO 2 tolerance were selected from 1HYs and back-crossed with haploids of OC-2 to yield second-generation hybrids (2HYs). Several strains of killer hybrids were comparable to OC-2 in fermentation ability, SO 2 tolerance, TTC stain, and growth in a β-alanine medium at 35°C. One strain (2HY-1) was further confirmed to produce wine of as good quality as that of OC-2 in fermentation on a subindustrial scale. The hybrids could kill only Saccharomyces yeasts in grape must. In the last stage of fermenting musts with the killer hybrid 2HY-1, no sensitive yeasts and few neutral yeasts ( Kloeckera, Pichia, Hansenula , etc.) were detected; the former were killed by the killer yeast and the latter were markedly inhibited by the SO 2 added and the alcohol formed. Furthermore, no film formation occurred during storage for 60 days at 25°C on the wine made with the hybrid because pseudo-film-forming Saccharomyces ( S. bayanus, S. oviformis, S. fermentati , etc.) had been killed by the killer hybrid, whereas film formation was abundant on wine made with OC-2 unless SO 2 was added after fermentation.
Discond-VAE: Disentangling Continuous Factors from the Discrete

We propose a variant of VAE capable of disentangling both variations within each class and variations shared across all classes. To represent these generative factors of data, we introduce two sets of continuous latent variables, private variable and public variable. Our proposed framework models the private variable as a Mixture of Gaussian and the public variable as a Gaussian, respectively. Each mode of the private variable is responsible for a class of the discrete variable. Most of the previous attempts to integrate the discrete generative factors to disentanglement assume statistical independence between the continuous and discrete variables. However, this assumption does not hold in general. Our proposed model, which we call Discond-VAE, DISentangles the class-dependent CONtinuous factors from the Discrete factors by introducing the private variables. The experiments show that Discond-VAE can discover the private and public factors from data qualitatively and quantitatively.
Sparsification Lower Bounds for List H-Coloring

We investigate the List H-Coloring problem, the generalization of graph coloring that asks whether an input graph G admits a homomorphism to the undirected graph H (possibly with loops), such that each vertex v ∈ V(G) is mapped to a vertex on its list L(v) ⊆ V(H). An important result by Feder, Hell, and Huang [JGT 2003] states that List H-Coloring is polynomial-time solvable if H is a so-called bi-arc graph, and NP-complete otherwise. We investigate the NP-complete cases of the problem from the perspective of polynomial-time sparsification: can an n-vertex instance be efficiently reduced to an equivalent instance of bitsize \(\mathcal {O} (n^{2-\varepsilon })\) (n2-ɛ) for some ɛ > 0? We prove that if H is not a bi-arc graph, then List H-Coloring does not admit such a sparsification algorithm unless \(\mathsf {NP \subseteq coNP/poly}\) . Our proofs combine techniques from kernelization lower bounds with a study of the structure of graphs H which are not bi-graphs.
A robust regional MPC approach

Recently, a number of regional MPC approaches have been proposed that exploit the piecewise affine structure of the optimal solution (without computing the entire explicit solution before). Here, regional refers to the idea of using the affine feedback law that is optimal in a vicinity of the current state of operation, and therefore provides the optimal input signal without requiring to solve a QP. We extend the original approach from Jost et al. (2015) to linear systems with bounded additive disturbances by replacing the underlying classical MPC problem by a min-max MPC problem. We analyse the performance of the new approach and compare it to an existing regional tube-based approach.
Deforming the Loss Surface to Affect the Behaviour of the Optimizer

In deep learning, it is usually assumed that the optimization process is conducted on a shape-fixed loss surface. Differently, we first propose a novel concept of deformation mapping in this paper to affect the behaviour of the optimizer. Vertical deformation mapping (VDM), as a type of deformation mapping, can make the optimizer enter a flat region, which often implies better generalization performance. Moreover, we design various VDMs, and further provide their contributions to the loss surface. After defining the local M region, theoretical analyses show that deforming the loss surface can enhance the gradient descent optimizer's ability to filter out sharp minima. With visualizations of loss landscapes, we evaluate the flatnesses of minima obtained by both the original optimizer and optimizers enhanced by VDMs on CIFAR-100. The experimental results show that VDMs do find flatter regions. Moreover, we compare popular convolutional neural networks enhanced by VDMs with the corresponding original ones on ImageNet, CIFAR-10, and CIFAR-100. The results are surprising: there are significant improvements on all of the involved models equipped with VDMs. For example, the top-1 test accuracy of ResNet-20 on CIFAR-100 increases by 1.46%, with insignificant additional computational overhead.
MStream: Fast Streaming Multi-Aspect Group Anomaly Detection

Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream, which can detect unusual group anomalies as they occur, in a dynamic manner. MStream has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MStream is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.
Suction-based Soft Robotic Gripping of Rough and Irregular Parts

Recently, suction-based robotic systems with microscopic features or active suction components have been proposed to grip rough and irregular surfaces. However, sophisticated fabrication methods or complex control systems are required for such systems, and robust attachment to rough real-world surfaces still remains a grand challenge. Here, we propose a fully soft robotic gripper, where a flat elastic membrane is used to conform and contact parts or surfaces well, where an internal negative pressure exerted on the air-sealed membrane induces the suction-based gripping. 3D printing in combination with soft molding techniques enable the fabrication of the soft gripper. Robust attachment to complex 3D and rough surfaces is enabled by the surface-conformable soft flat membrane, which generates strong and robust suction at the contact interface. Such robust attachment to rough and irregular surfaces enables manipulation of a broad range of real-world objects, such as an egg, lime, and foiled package, without any physical damage. Compared to the conventional suction cup designs, the proposed suction gripper design shows a four-fold increase in gripping performance on rough surfaces. Furthermore, the structural and material simplicity of the proposed gripper architecture facilitates its system-level integration with other soft robotic peripherals, which can enable broader impact in diverse fields, such as digital manufacturing, robotic manipulation, and medical gripping applications.
Regional predictive control with suboptimally extended regions of validity

Model predictive control (MPC) is based on perpetually solving optimization problems. The solution of the optimization is usually interpreted as the optimal input for the current state. However, the solution of the optimization does not just provide an optimal input, but an entire optimal affine feedback law and a polytope on which this law is optimal. We recently proposed to use this feedback law as long as the system remains in its polytope. This can be interpreted as an eventbased approach, where leaving the current polytope is the event that triggers the next optimization. This approach is especially appropriate for a networked control setting since the feedback laws and their polytopes can be evaluated with a low computational effort on lean local nodes. In this article the region of validity for a feedback law is extended. More precisely, the optimal polytopes are extended to nonlinearly bounded regions of validity resulting from the intersection of a feasibility and stability region. As a result, fewer quadratic programs need to be solved compared to the optimal approach. The new validity regions are still suitable for the evaluation on a lean local node. Moreover, the regions can be adjusted for a desired closed-loop performance.
Finding Effective Security Strategies through Reinforcement Learning and Self-Play

We present a method to automatically find security strategies for the use case of intrusion prevention. Following this method, we model the interaction between an attacker and a defender as a Markov game and let attack and defense strategies evolve through reinforcement learning and self-play without human intervention. Using a simple infrastructure configuration, we demonstrate that effective security strategies can emerge from self-play. This shows that self-play, which has been applied in other domains with great success, can be effective in the context of network security. Inspection of the converged policies show that the emerged policies reflect common-sense knowledge and are similar to strategies of humans. Moreover, we address known challenges of reinforcement learning in this domain and present an approach that uses function approximation, an opponent pool, and an autoregressive policy representation. Through evaluations we show that our method is superior to two baseline methods but that policy convergence in self-play remains a challenge.
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent Loss

Self-supervised representation learning is an emerging research topic for its powerful capacity in learning with unlabeled data. As a mainstream self-supervised learning method, augmentation-based contrastive learning has achieved great success in various computer vision tasks that lack manual annotations. Despite current progress, the existing methods are often limited by extra cost on memory or storage, and their performance still has large room for improvement. Here we present a self-supervised representation learning method, namely AAG, which is featured by an auxiliary augmentation strategy and GNT-Xent loss. The auxiliary augmentation is able to promote the performance of contrastive learning by increasing the diversity of images. The proposed GNT-Xent loss enables a steady and fast training process and yields competitive accuracy. Experiment results demonstrate the superiority of AAG to previous state-of-the-art methods on CIFAR10, CIFAR100, and SVHN. Especially, AAG achieves 94.5% top-1 accuracy on CIFAR10 with batch size 64, which is 0.5% higher than the best result of SimCLR with batch size 1024.
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks

In this paper, we introduce a simple yet effective approach that can boost the vanilla ResNet-50 to 80%+ Top-1 accuracy on ImageNet without any tricks. Generally, our method is based on the recently proposed MEAL, i.e., ensemble knowledge distillation via discriminators. We further simplify it through 1) adopting the similarity loss and discriminator only on the final outputs and 2) using the average of softmax probabilities from all teacher ensembles as the stronger supervision for distillation. One crucial perspective of our method is that the one-hot/hard label should not be used in the distillation process. We show that such a simple framework can achieve state-of-the-art results without involving any commonly-used techniques, such as 1) architecture modification; 2) outside training data beyond ImageNet; 3) autoaug/randaug; 4) cosine learning rate; 5) mixup/cutmix training; 6) label smoothing; etc. On ImageNet, our method obtains 80.67% top-1 accuracy using a single crop-size of 224X224 on the vanilla ResNet-50, outperforming the previous state-of-the-arts by a remarkable margin under the same network structure. Our result can be regarded as a new strong baseline on ResNet-50 using knowledge distillation. To our best knowledge, this is the first work that is able to boost vanilla ResNet-50 to surpass 80% on ImageNet without architecture modification or additional training data. Our code and models are available at: this https URL.