Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF NEST HABITAT, FOOD, AND PARENTAL BEHAVIOR ON SHOREBIRD NEST SUCCESS

Abstract ABSTRACT In environments such as arctic tundra, where bird densities are low and habitats are comparatively homogeneous, suitable nest sites likely are not limited. Under these conditions, reproductive success of birds may be determined by factors other than the habitat characteristics of nest sites. We studied the relative influence of nest habitat, food, nest distribution, and parental behavior on the reproductive success of tundra-breeding shorebirds at East Bay, Southampton Island, Nunavut, Canada. From 2000 to 2002, we monitored the nests of five species: Black-bellied Plover (Pluvialis squatarola), Semipalmated Plover (Charadrius semipalmatus), Ruddy Turnstone (Arenaria interpres), White-rumped Sandpiper (Calidris fuscicollis), and Red Phalarope (Phalaropus fulicarius). For each species, habitat differed between nest sites and random sites. In contrast, habitat differed between successful and failed nest sites only for White-rumped Sandpipers. Shorebirds did not prefer to nest in habitats where food was most abundant. Although nest success varied among species in all years, artificial nest experiments suggested that interspecific variation in predation rate was not related to habitat type. Instead, the marked interspecific variation in nest success may have been related to incubation behavior. Species taking fewer incubation recesses had higher nest success, although these results should be viewed as preliminary. The factor with the greatest interannual influence on nest success was fluctuating predation pressure, apparently related to the abundance of predators and lemmings.
Revisiting the Great American Biotic Interchange through analyses of amphitropical bees

The Great American Biotic Interchange (GABI) is zoogeographic event characterized by the exchange of taxa between North and South America, typically associated with the rise of the Isthmus of Panama in the late Pliocene. Recent geologic evidence suggests the connections between North and South America may be much older, and that the interchange of organisms between the two continents could have therefore happened much earlier than 3 Ma. Most of the research investigating the GABI has come from tropical vertebrate taxa; little work has been done on invertebrates or on non-tropical species. To investigate how the GABI shaped the distribution of arid-adapted species, particularly those with amphitropical distributions (i.e. taxa found in South and North American xeric regions yet absent from the tropics), we examine the historical biogeography of the bee genus Diadasia using a hypothesis of Diadasia phylogenetic relationships. Nuclear and mitochondrial genetic loci are used to reconstruct a phylogeny of Diadasia, which is then used to estimate divergence dates and reconstruct ancestral area relationships. Our analyses suggest the divergence between North and South American Diadasia species occurred between 20.5 and 15 Ma, long before the formation of the Isthmus of Panama. This study is the first to show a Miocene connection for an amphitropically-distributed insect group. It suggests that the biotic connection between continents is more complicated than previously thought and may have initiated long before the late Pliocene.
Frequency Regulation with Heterogeneous Energy Resources: A Realization using Distributed Control

This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. A series of tests involved up to 69 heterogeneous active distributed energy resources consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive distributed energy resources consisting of building loads and solar photovoltaic systems. The distributed control setup consists of a set of Raspberry Pi endpoints exchanging messages via an ethernet switch. Actuation commands for the distributed energy resources are obtained by solving a power allocation problem at every regulation instant using distributed ratio-consensus, primal-dual, and Newton-like algorithms. The problem formulation minimizes the sum of distributed energy resource costs while tracking the aggregate set-point provided by the system operator. We demonstrate accurate and fast real-time distributed computation of the optimization solution and effective tracking of the regulation signal over 40 min time horizons. An economic benefit analysis confirms eligibility to participate in an ancillary services market and demonstrates up to $53k of potential annual revenue for the selected population of distributed energy resources.
U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images

Purpose: Manual medical image segmentation is an exhausting and time-consuming task along with high inter-observer variability. In this study, our objective is to improve the multi-resolution image segmentation performance of U-Net architecture. Approach: We have proposed a fully convolutional neural network for image segmentation in a multi-resolution framework. We used U-Net as the base architecture and modified that to improve its image segmentation performance. In the proposed architecture (mrU-Net), the input image and its down-sampled versions were used as the network inputs. We added more convolution layers to extract features directly from the down-sampled images. We trained and tested the network on four different medical datasets, including skin lesion photos, lung computed tomography (CT) images (LUNA dataset), retina images (DRIVE dataset), and prostate magnetic resonance (MR) images (PROMISE12 dataset). We compared the performance of mrU-Net to U-Net under similar training and testing conditions. Results: Comparing the results to manual segmentation labels, mrU-Net achieved average Dice similarity coefficients of 70.6%, 97.9%, 73.6%, and 77.9% for the skin lesion, LUNA, DRIVE, and PROMISE12 segmentation, respectively. For the skin lesion, LUNA, and DRIVE datasets, mrU-Net outperformed U-Net with significantly higher accuracy and for the PROMISE12 dataset, both networks achieved similar accuracy. Furthermore, using mrU-Net led to a faster training rate on LUNA and DRIVE datasets when compared to U-Net. Conclusions: The striking feature of the proposed architecture is its higher capability in extracting image-derived features compared to U-Net. mrU-Net illustrated a faster training rate and slightly more accurate image segmentation compared to U-Net.
Unseen Object Instance Segmentation for Robotic Environments

In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset required for this task typically does not exist for most robotic settings, which motivates the use of synthetic data. Our proposed method, unseen object instance segmentation (UOIS)-Net, separately leverages synthetic RGB and synthetic depth for unseen object instance segmentation. UOIS-Net is composed of two stages: first, it operates only on depth to produce object instance center votes in 2D or 3D and assembles them into rough initial masks. Second, these initial masks are refined using RGB. Surprisingly, our framework is able to learn from synthetic RGB-D data where the RGB is nonphotorealistic. To train our method, we introduce a large-scale synthetic dataset of random objects on tabletops. We show that our method can produce sharp and accurate segmentation masks, outperforming state-of-the-art methods on unseen object instance segmentation. We also show that our method can segment unseen objects for robot grasping.
Molecular identification and phylogenetic relationships among the species of the genus Oligoryzomys (Rodentia, Cricetidae) present in Argentina, putative reservoirs of hantaviruses

The systematics and geographical distribution of the species of Oligoryzomys present in Argentina are poorly known. From some of the species different hantavirus genotypes have been recovered. In order to contribute to the accurate identification of those species and to infer their phylogenetic relationships, we analysed data of restriction sites and sequences of the mtDNA d‐loop region. The samples used represent almost all Oligoryzomys species known to occur in Argentina. The trees obtained with the two types of data were similar, showing high bootstrap values for the majority of the nodes. Our results support the idea that the specific name Oligoryzomys longicaudatus should be applied only to individuals from the south of Argentina and Chile and confirm that O. nigripes and O. delticola are conspecific. Specimens identified as O. flavescens conform three related clades, probably belonging to a species complex. This study also emphasises that the use of a DNA fragment characterised by a high evolutionary rate compare with other mitochondrial segments as the d‐loop, was appropriate to infer the phylogenetic relationships in a group originated by a rapid speciation process. We also suggest the following modifications in the rodent–hantavirus relationships: the only viral genotype associated to O. longicaudatus would be Andes Sout, while O. chacoensis would be the natural host of the genotype Oran. One of the forms of the O. flavescens complex would be the natural host of the genotype Bermejo. Our data are consistent with the hypothesis that each hantavirus genotype is associated with a specific rodent host.
Accurately constraining velocity information from spectral imaging observations using machine learning techniques

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ɪɪ 8542 Å spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ2-values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue ‘High-resolution wave dynamics in the lower solar atmosphere’.
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

Direct policy gradient methods for reinforcement learning are a successful approach for a variety of reasons: they are model free, they directly optimize the performance metric of interest, and they allow for richly parameterized policies. Their primary drawback is that, by being local in nature, they fail to adequately explore the environment. In contrast, while model-based approaches and Q-learning directly handle exploration through the use of optimism, their ability to handle model misspecification and function approximation is far less evident. This work introduces the the Policy Cover-Policy Gradient (PC-PG) algorithm, which provably balances the exploration vs. exploitation tradeoff using an ensemble of learned policies (the policy cover). PC-PG enjoys polynomial sample complexity and run time for both tabular MDPs and, more generally, linear MDPs in an infinite dimensional RKHS. Furthermore, PC-PG also has strong guarantees under model misspecification that go beyond the standard worst case $\ell_{\infty}$ assumptions; this includes approximation guarantees for state aggregation under an average case error assumption, along with guarantees under a more general assumption where the approximation error under distribution shift is controlled. We complement the theory with empirical evaluation across a variety of domains in both reward-free and reward-driven settings.
Existence, Energy Identity, and Higher Time Regularity of Solutions to a Dynamic Viscoelastic Cohesive Interface Model

We study the dynamics of visco-elastic materials coupled by a common cohesive interface (or, equivalently, {two single domains separated by} a prescribed cohesive crack) in the anti-plane setting. We consider a general class of traction-separation laws featuring an activation threshold on the normal stress, softening and elastic unloading. In strong form, the evolution is described by a system of PDEs coupling momentum balance (in the bulk) with transmission and Karush-Kuhn-Tucker conditions (on the interface). We provide a detailed analysis of the system. We first prove existence of a weak solution, employing a time discrete approach and a regularization of the initial data. Then, we prove our main results: the energy identity and the existence of { solutions} with acceleration in $L^\infty (0,T; L^2)$.
Far-red Light Absorbing Photoselective Plastic Films Affect Growth and Flowering of Chrysanthemum Cultivars

Growth and development re spons es of three chrysanthemum (Dendranthema ·grandifl ora (Ramat.) Kitam. (syn. Chrysanthemum ·morifolium Ramat.)) cultivars ('Bright Golden Anneʼ, 'Iridonʼ, and 'Yellow Snowdonʼ) to photoselective plastic fi lms with varying concentrations (0 to 0.22 g·m-2 ) of a far-red (FR) light absorbing dye were investigated under greenhouse con di tions. Photoselective fi lms reduced stem elon ga tion of all three cultivars. The greater the dye con cen tra tion in the fi lm, the greater and earlier the reduction in stem elongation. After 4 weeks, 'Yellow Snowdenʼ, 'Bright Gold en Anneʼ, and 'Iridonʼ plants grown under the fi lm with the highest dye concentration (A fr3 fi lm) were 21%, 26%, and 26% shorter than control plants, respectively. Height reduction under photoselective fi lms was caused by short er internodes. Photoselective covers were most effective in reducing the stem elongation during the early vegetative period. Fol low ing transition to the re pro duc tive stage, weekly stem elongation rates were reduced. At the time of fl owering, 'Yellow Snowdenʼ, 'Bright Golden Anneʼ, and 'Iridonʼ plants grown un der the fi lm with the highest dye concentration (A fr3 fi lm) were 12%, 7%, and 14% shorter than con trol plants, respectively. Photoselective cov ers did not affect the an the sis of chry san the mum cultivars, but resulted in a 10% to 14% reduction in fl ower diameter de pend ing on the cultivar. Although the fi lms with higher dye concentration were more ef fec tive in reducing stem elongation of chry san the mum, increased dye con cen tra tion re duced light transmission. Thus, photoselective covers that reduce light trans mis sion over 25% would not be suitable for com mer cial pro duc tion.
$USp(32)$ Special Grand Unification

We discuss a grand unified theory (GUT) based on a $USp(32)$ GUT gauge group broken to its subgroups including a special subgroup. A GUT based on an $SO(32)$ GUT gauge group has been discussed on six-dimensional (6D) orbifold space $M^4\times T^2/\mathbb{Z}_2$. It is inspired by the $SO(32)$ string theory behind the $SU(16)$ GUT whose $SU(16)$ is broken to a special subgroup $SO(10)$. Alternative direction is to embed an $SU(16)$ gauge group into a $USp(32)$ GUT gauge group, which is inspired by a non-supersymmetric symplectic-type $USp(32)$ string theory. In a $USp(32)$ GUT, one generation of the SM fermions is embedded into a 6D bulk Weyl fermion in a $USp(32)$ defining representation. For a three generation model, all the 6D and 4D gauge anomalies in the bulk and on the fixed points are canceled out without exotic chiral fermions at low energies. The SM Higgs scalar is embedded into a 6D bulk scalar field in a $USp(32)$ adjoint representation.
Challenge report: VIPriors Action Recognition Challenge

This paper is a brief report to our submission to the VIPriors Action Recognition Challenge. Action recognition has attracted many researchers attention for its full application, but it is still challenging. In this paper, we study previous methods and propose our method. In our method, we are primarily making improvements on the SlowFast Network and fusing with TSM to make further breakthroughs. Also, we use a fast but effective way to extract motion features from videos by using residual frames as input. Better motion features can be extracted using residual frames with SlowFast, and the residual-frame-input path is an excellent supplement for existing RGB-frame-input models. And better performance obtained by combining 3D convolution(SlowFast) with 2D convolution(TSM). The above experiments were all trained from scratch on UCF101.
An exact solution for the 2 + 1 Kardar-Parisi-Zhang exponents

The Kardar-Parisi-Zhang (KPZ) equation in the d+1 dimensional space has been connected with a large number of stochastic process in physics and chemistry. Therefore, the quest for its universal exponents has been a very intensive field of research. In this work using geometric analytical methods, we associate these exponents with the fractal dimension of the rough surface. Consequently, we obtain the exponents exactly for 2 + 1 dimensions and, for higher dimensions, we set theoretical limits for them. We prove as well that the KPZ model has no upper critical dimension.
Asymptotic enumeration and limit laws for multisets: The subexponential case

For a given combinatorial class $\mathcal{C}$ we study the class $\mathcal{G} = \mathrm{MSET}(\mathcal{C})$ satisfying the multiset construction, that is, any object in $\mathcal{G}$ is uniquely determined by a set of $\mathcal{C}$-objects paired with their multiplicities. For example, $\mathrm{MSET}(\mathbb{N})$ is just the class of number partitions of positive integers, a prominent and well-studied case. The multiset construction appears naturally in the study of unlabelled objects, for example graphs or various objects related to number partitions. Our main result establishes the asymptotic size of the set $\mathcal{G}_{n,N}$ that contains all multisets in $\mathcal{G}$ having size $n$ and being comprised of $N$ objects from $\mathcal{C}$, as $n$ and $N$ tend to infinity and when the counting sequence of $\mathcal{C}$ is governed by subexponential growth; this is a particularly important setting in combinatorial applications. Moreover, we study the component distribution of typical objects from $\mathcal{G}_{n,N}$ and we discover a unique phenomenon that we baptise extreme condensation: taking away the largest component as well as all the components of the smallest possible size, we are left with an object which converges in distribution as $n, N \rightarrow\infty$. The exact distribution of the limiting object is also retrieved. Moreover and rather surprisingly, in stark contrast to analogous results for labelled objects, the results here hold uniformly in $N$.
Less is More: A privacy-respecting Android malware classifier using federated learning

Abstract In this paper we present LiM (‘Less is More’), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users’ devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly identify whether an app is malicious). To tackle this, LiM uses a safe semi-supervised ensemble that maximizes classification accuracy with respect to a baseline classifier trained by the service provider (i.e. the cloud). We implement LiM and show that the cloud server has F1 score of 95%, while clients have perfect recall with only 1 false positive in > 100 apps, using a dataset of 25K clean apps and 25K malicious apps, 200 users and 50 rounds of federation. Furthermore, we conduct a security analysis and demonstrate that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server. Further experiments with Ma-MaDroid’s dataset confirm resistance against poisoning attacks and a performance improvement due to the federation.
On Power Laws in Deep Ensembles

Ensembles of deep neural networks are known to achieve state-of-the-art performance in uncertainty estimation and lead to accuracy improvement. In this work, we focus on a classification problem and investigate the behavior of both non-calibrated and calibrated negative log-likelihood (CNLL) of a deep ensemble as a function of the ensemble size and the member network size. We indicate the conditions under which CNLL follows a power law w.r. t. ensemble size or member network size, and analyze the dynamics of the parameters of the discovered power laws. Our important practical finding is that one large network may perform worse than an ensemble of several medium-size networks with the same total number of parameters (we call this ensemble a memory split). Using the detected power law-like dependencies, we can predict (1) the possible gain from the ensembling of networks with given structure, (2) the optimal memory split given a memory budget, based on a relatively small number of trained networks. We describe the memory split advantage effect in more details in arXiv:2005.07292
β‐Cryptoxanthin suppresses the growth of immortalized human bronchial epithelial cells and non‐small‐cell lung cancer cells and up‐regulates retinoic acid receptor β expression

Recent findings of an inverse association between β‐cryptoxanthin and lung cancer risk in several observational epidemiologic studies suggest that β‐cryptoxanthin could potentially act as a chemopreventive agent against lung cancer. However, the biological activity of β‐cryptoxanthin and molecular mechanism(s) by which β‐cryptoxanthin affects lung tumourigenesis have not been studied. In the present study, we found that β‐cryptoxanthin inhibited the growth of A549 cells, a non‐small‐cell lung cancer cell line and BEAS‐2B cells, an immortalized human bronchial epithelial cell line in a dose‐dependent manner. β‐Cryptoxanthin suppressed the protein levels of cyclin D1 and cyclin E, up‐regulated the cell cycle inhibitor p21, increased the number of lung cancer cells in theG1/G0 phase and decreased those in the S phase of the cell cycle. Consistent with inhibition of the lung cancer cell growth, β‐cryptoxanthin induced the mRNA levels of retinoic acid receptor β (RARβ) in BEAS‐2B cells, although this effect was less pronounced in A549 cells. Furthermore, β‐cryptoxanthin transactivated RAR‐mediated transcription activity of the retinoic acid response element. These findings suggest a mechanism of anti‐proliferative action of β‐cryptoxanthin and indicate that β‐cryptoxanthin may be a promising chemopreventive agent against lung cancer. © 2006 Wiley‐Liss, Inc.