Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Task Pruning for Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmentation networks. There are a large number of works to compress and accelerate deep neural networks in the classification task (e.g., ResNet-50 on ImageNet), but they cannot be straightforwardly applied to the semantic segmentation network that involves an implicit multi-task learning problem. To boost the segmentation performance, the backbone of semantic segmentation network is often pre-trained on a large scale classification dataset (e.g., ImageNet), and then optimized on the desired segmentation dataset. Hence to identify the redundancy in segmentation networks, we present a multi-task channel pruning approach. The importance of each convolution filter w.r. t the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we develop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on several benchmarks illustrate the superiority of the proposed algorithm over the state-of-the-art pruning methods. Notably, we can obtain an about $2\times$ FLOPs reduction on DeepLabv3 with only an about $1\%$ mIoU drop on the PASCAL VOC 2012 dataset and an about $1.3\%$ mIoU drop on Cityscapes dataset, respectively.
Implicit Mesh Reconstruction from Unannotated Image Collections

We present an approach to infer the 3D shape, texture, and camera pose for an object from a single RGB image, using only category-level image collections with foreground masks as supervision. We represent the shape as an image-conditioned implicit function that transforms the surface of a sphere to that of the predicted mesh, while additionally predicting the corresponding texture. To derive supervisory signal for learning, we enforce that: a) our predictions when rendered should explain the available image evidence, and b) the inferred 3D structure should be geometrically consistent with learned pixel to surface mappings. We empirically show that our approach improves over prior work that leverages similar supervision, and in fact performs competitively to methods that use stronger supervision. Finally, as our method enables learning with limited supervision, we qualitatively demonstrate its applicability over a set of about 30 object categories.
Effects of ecological restoration alternative treatments on nonnative plant species establishment

Disturbances generated by forest restoration treatments have the potential for enhancing the establishment of nonnative species thereby impeding long-term native plant recovery. In a ponderosa pine forest next to the Fort Valley Experimental Forest, Arizona, we examined the establishment of nonnative species after three alternative treatments with different intensities of tree thinning, coupled with prescribed burning and an untreated control, in relation to total species abundance and richness. Pretreatment data were collected in 1998 and postreatment responses were measured from 2001 through 2006. Total herbaceous cover and richness were significantly higher in the two more intensely thinned areas compared to the control over the entire post-treatment period. Native species were the most prevalent in terms of cover (92%) and richness (90%) across all treated units, though greater understory plant responses were linked to heavier amounts of tree thinning. Nonnative species abundance and richness also increased significantly in response to restoration treatments, particularly in the two more intense treatments. The proportion of nonnative abundance to the total abundance within the two heavily treated areas decreased through time and began to converge back towards the undisturbed control unit. One year following treatments, 15% of the total cover (27%) was composed of nonnative species in the heaviest treated unit. This proportion dropped almost 50% by the fifth year following treatment. Our results suggest that disturbances associated with restoration treatments can facilitate establishment of nonnative plants, however the post-treatment plant community was increasingly dominated by native species.
Learning perturbation sets for robust machine learning

Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically studied in adversarial defenses. In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize real-world effects for robust training and evaluation. Specifically, we use a conditional generator that defines the perturbation set over a constrained region of the latent space. We formulate desirable properties that measure the quality of a learned perturbation set, and theoretically prove that a conditional variational autoencoder naturally satisfies these criteria. Using this framework, our approach can generate a variety of perturbations at different complexities and scales, ranging from baseline spatial transformations, through common image corruptions, to lighting variations. We measure the quality of our learned perturbation sets both quantitatively and qualitatively, finding that our models are capable of producing a diverse set of meaningful perturbations beyond the limited data seen during training. Finally, we leverage our learned perturbation sets to train models which are empirically and certifiably robust to adversarial image corruptions and adversarial lighting variations, while improving generalization on non-adversarial data. All code and configuration files for reproducing the experiments as well as pretrained model weights can be found at this https URL.
FIVE NEW SPECIES OF BRACONIDAE WITH HOST RECORDS OF ADDITIONAL SPECIES

In recent rearings by officers of the Entomological Branch several new species of Braconidae have been secured as well as records of species hitherto little known in Canada. The writer wishes to express his indebtedness to Dr. C. W. F. Muesebeck for his assistance in examining three of the species described below. Paratypes of all the species are deposited in the United States National Museum.
Oxidative Stability, Structure, and Physical Characteristics of Microcapsules Formed by Spray Drying of Fish Oil with Protein and Dextrin Wall Materials

: Maltodextrins and a highly branched cyclic dextrin (HBCD) were tested for their ability to serve as wall materials for microcapsules with proteins. HBCD or a maltodextrin of DE18 with sodium caseinate (SC) improved the oxidative stability of encapsulated fish oil; however, the DE18/SC wall system had 2 disadvantages: browning induced by the Maillard reaction and agglomeration. The oil load level and the selection of dextrin strongly affected the outer topography and the inner structure, as well as the ratio of the oil to dextrin on the surface of the microcapsules. It is stated that drying speeds of dextrin and oil load levels were shown to be likely related to the structural difference in the microcapsules.
Effects of radiotransmitters on body mass, feed consumption, and energy expenditure of northern bobwhites

Abstract Radiotelemetry is commonly used in northern bobwhite (Colinus virginianus) research. An underlying assumption is that radiomarked individuals provide unbiased estimates of population parameters. Our objectives were to evaluate the effects of radiotransmitters on body mass and feed consumption of radiomarked and banded-only pen-raised bobwhites in a controlled environment and to compare daily energy expenditure (kJ/g/day) between treatments in a simulated field setting. We randomly assigned a treatment (i.e., radiomarked or banded-only) to 40 pen-raised bobwhites and placed them in individual cages (51- × 27- × 28-cm) within an environmental chamber. We conducted 2 separate 21-day experiments to simulate summer (35°C daytime high, 23.9°C nighttime low, 15-hour photoperiod) and winter conditions (18.3°C daytime high, 4.4°C nighttime low, 10-hour photoperiod) typical for south Texas. For the field experiment, we evaluated energy expenditure via doubly labeled water for radiomarked (n=5) and banded-only pen-raised bobwhites (n=5) in a flight pen (50- × 30- × 4-m). We documented no difference in change in body mass or feed consumption between radiomarked and banded-only bobwhites during either experiment (P>0.05). We also found no difference in daily energy expenditure between radiomarked (0.839 ± 0.056 kJ/g/day) and banded-only pen-raised bobwhites (0.804 ± 0.014 kJ/g/day; P=0.77).
KAM theory for some dissipative systems

Dissipative systems play a very important role in several physical models, most notably in Celestial Mechanics, where the dissipation drives the motion of natural and artificial satellites, leading them to migration of orbits, resonant states, etc. Hence the need to develop theories that ensure the existence of structures such as invariant tori or periodic orbits and device efficient computational methods. In this work we concentrate on the existence of invariant tori for the specific case of dissipative systems known as "conformally symplectic" systems, which have the property that they transform the symplectic form into a multiple of itself. To give explicit examples of conformally symplectic systems, we will present two different models: a discrete system, the standard map, and a continuous system, the spin-orbit problem. In both cases we will consider the conservative and dissipative versions, that will help to highlight the differences between the symplectic and conformally symplectic dynamics. For such dissipative systems we will present a KAM theorem in an a-posteriori format. The method of proof is based on extending geometric identities originally developed in [39] for the symplectic case. Besides leading to streamlined proofs of KAM theorem, this method provides a very efficient algorithm which has been implemented. Coupling an efficient numerical algorithm with an a-posteriori theorem, we have a very efficient way to provide rigorous estimates close to optimal. Indeed, the method gives a criterion (the Sobolev blow up criterion) that allows to compute numerically the breakdown. We will review this method as well as an extension of J. Greene's method and present the results in the conservative and dissipative standard maps. Computing close to the breakdown, allows to discover new mathematical phenomena such as the "bundle collapse mechanism".
Data-driven effective model shows a liquid-like deep learning

Geometric structure of an optimization landscape is argued to be fundamentally important to support the success of deep learning. However, recent research efforts focused on either of toy random models with unrealistic assumptions and numerical evidences about different shapes of the optimization landscape, thereby lacking a unified view about the nature of the landscape. Here, we propose a statistical mechanics framework by directly building a least structured model of the high-dimensional weight space, considering realistic structured data, stochastic gradient descent algorithms, and the computational depth of the network parametrized by weight parameters. We also consider whether the number of network parameters outnumbers the number of supplied training data, namely, over- or under-parametrization. Our least structured model predicts that the weight spaces of the under-parametrization and over-parameterization cases belong to the same class. These weight spaces are well-connected without any heterogeneous geometric properties. In contrast, the shallow-network has a shattered weight space, characterized by discontinuous phase transitions in physics, thereby clarifying roles of depth in deep learning. Our effective model also predicts that inside a deep network, there exists a liquid-like central part of the architecture in the sense that the weights in this part behave as randomly as possible. Our work may thus explain why deep learning is unreasonably effective in terms of the high-dimensional weight space, and how deep networks are different from shallow ones.
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation

Recent years have brought great advancement in 2D human pose estimation. However, bottom-up approaches that do not rely on external detectors to generate person crops, tend to have large model sizes and intense computational requirements, making them ill-suited for applications where large computation costs can be prohibitive. Lightweight approaches are exceedingly rare and often come at the price of massive accuracy loss. In this paper, we present EfficientHRNet, a family of lightweight 2D human pose estimators that unifies the high-resolution structure of state-of-the-art HigherHRNet with the highly efficient model scaling principles of EfficientNet to create high accuracy models with significantly reduced computation costs compared to other state-of-the-art approaches. In addition, we provide a formulation for jointly scaling our backbone EfficientNet below the baseline B0 and the rest of EfficientHRNet with it. Ultimately, we are able to create a family of highly accurate and efficient 2D human pose estimators that is flexible enough to provide a lightweight solution for a variety of application and device requirements. Baseline EfficientHRNet achieves a 0.4% accuracy improvement when compared to HRNet while having a 34% reduction in floating point operations. When compared to Lightweight OpenPose, a compressed network designed specifically for lightweight inference, one EfficientHRNet model outperforms it by over 10% in accuracy while reducing overall computation by 15%, and another model, while only having 2% higher accuracy than Lightweight OpenPose, is able to further reduce computational complexity by 63%. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.
A second-order accurate structure-preserving scheme for the Cahn-Hilliard equation with a dynamic boundary condition

We propose a structure-preserving finite difference scheme for the Cahn–Hilliard equation with a dynamic boundary condition using the discrete variational derivative method (DVDM) proposed by Furihata and Matsuo [14]. In this approach, it is important and essential how to discretize the energy which characterizes the equation. By modifying the conventional manner and using an appropriate summation-by-parts formula, we can use a standard central difference operator as an approximation of an outward normal derivative on the discrete boundary condition of the scheme. We show that our proposed scheme is second-order accurate in space, although the previous structure-preserving scheme proposed by Fukao–Yoshikawa–Wada [13] is first-order accurate in space. Also, we show the stability, the existence, and the uniqueness of the solution for our proposed scheme. Computation examples demonstrate the effectiveness of our proposed scheme. Especially through computation examples, we confirm that numerical solutions can be stably obtained by our proposed scheme.
Variance and heritability of the capped developmental stage in European Apis mellifera L. and its correlation with increased Varroa jacobsoni Oud. infestation.

SummaryA time-saving method for determining the duration of the capped stage (sealed brood) of large numbers of colonies is described. The results of 112 colonies covering 22 different origins and hybrids of European honeybee races are presented. Differences up to 9 h between strains and up to 19 h within individual colonies could be detected. Influenced by seasonal effects, the average capped period is about 7 h shorter in early than in late summer. For one group of test colonies (n= 21) the Varroa infestation after 18 months of undisturbed colony and mite population development has been determined. The correlation between the capped period and the susceptibility of the colonies to mites is calculated as r = 0·48. By linear regression, an 8·7% reduction of the final mite infestation is calculated for a 1-h reduction of the capped period. The heritability of the duration of the capped period is estimated with h2 = 0·232. This may be a realistic value for test populations of European honeybee under field c.
R EPRESENTATION Q UALITY O F N EURAL N ETWORKS L INKS T O A DVERSARIAL A TTACKS AND D EFENCES

Neural networks have been shown vulnerable to a variety of adversarial algorithms. A crucial step for understanding the rationale behind this lack of robustness is to assess the potential of the neural networks’ representation to encode the existing features. Here, we propose a method to understand the representation quality of the neural networks using a novel test based on Zero-Shot Learning, entitled Raw Zero-Shot. The principal idea is that if an algorithm learns rich features, such features should be able to interpret ‘new or unknown’ classes as a combination of previously learned features. This is because unknown classes usually share several regular features with recognised (learned) classes, given that the features learned are general enough. We further introduce two metrics to assess this learned representation which interprets unknown classes. One is based on inter-cluster validation technique, while the other is based on the difference in the representation between the case when the class is unknown and the case when it is known to the classifier. Experiments suggest that several adversarial defences not only decrease the attack accuracy of some attacks but also improve the representation quality of the classifiers. Further, a low p-value of the paired-samples t-test suggests that several adversarial defences, in general, change the representation quality significantly. Moreover, experiments also reveal a relationship between the proposed metrics and adversarial attacks (a high Pearson correlation coefficient and low p-value).
Pneumococcal Behavior and Host Responses during Bronchopneumonia Are Affected Differently by the Cytolytic and Complement-Activating Activities of Pneumolysin

ABSTRACT Pneumolysin, a multifunctional toxin produced by all clinical isolates of Streptococcus pneumoniae, is strongly implicated in the pathogenesis of pneumococcal bronchopneumonia and septicemia. Using isogenic mutant strains, we examined the effect of deletion of the cytotoxic activity or complement-activating activity of pneumolysin on bacterial growth in lungs and blood, histological changes in infected lung tissue, and the pattern of inflammatory cell recruitment. Both of the activities of pneumolysin contributed to the pathology in the lungs, as well as the timing of the onset of bacteremia. Histological changes in the lungs were delayed after infection with either mutant compared to the changes seen after infection with the wild-type pneumococcus. The complement-activating activity of pneumolysin affected the accumulation of T cells, whereas the toxin's cytolytic activity influenced neutrophil recruitment into lung tissue.
Relationship of Seed Mass to Oil and Protein Contents in Peanut (Arachis hypogaea L.)

Abstract Seed mass, oil and protein contents are important quality traits in peanut (Arachis hypogaea L.). Sixty-four genotypes were grown for four seasons to study genetic variation and character association between these three traits. Graded seed samples of 33 genotypes were further studied for possible variation within genotype among grades for oil and protein contents. No significant association of seed mass with percent oil or protein contents was observed among the 64 genotypes. However, oil and protein contents were significantly negatively associated. Oil content variation within a genotype showed a significant linear increase as the seed mass increased in the graded samples, but no such relationship was observed with protein content. Genotypes with desirable traits for confectionery and/or oil types were identified and may be used for germplasm enhancement.
Differentiation of Enterobacter aerogenes from Klebsiellae by Deoxyribonucleic Acid Reassociation

Polynucleotide sequence relatedness tests were carried out to determine the extent ofdeoxyribonucleic acid (DNA) divergence among species of Klebsiella and Enterobacter aerogenes strains. Labeled, denatured DNA fragments from K. pneumoniae type 2 and E. aerogenes 1627-66 were each incubated with an excess of unlabeled DNA fragments from Klebsiella species and strains of E. aerogenes. Reassociated DNA duplexes were separated from unreactedDNA on hydroxyapatite. The stability of reassociated DNA duplexes was determined in a series of thermal elutions from hydroxyapatite. Relative reassociation of DNA from 5 Klebsiella strains to K. pneumoniae type 2 was 80 to 91%. In no case did the related DNA duplexes exhibit evidence of greater than 1.2% unpaired bases. Similarly, DNA from 10 strains of E. aerogenes exhibited 83 to 100% relative reassociation with DNA from E. aerogenes 1627-66. In this case, 2.2% was the largest amount of unpaired bases within a reassociated DNA duplex. Conversely, only 56% relative relatedness was observed in 18 reactions between klebsiellae and E. aerogenes. In these reactions, related DNA duplexes exhibited an average of 9% unpaired bases. We conclude that klebsiellae and E. aerogenes each are highly related groups of strains and that these two groups have diverged significantly from one another. An E. cloacae strain exhibited some 40% relative relatedness with some 12% unpaired bases in reactions with K. pneumoniae and E. aerogenes.
Student pharmacist personal and work experiences with people displaying warning signs of suicidal ideation

Introduction Student pharmacists, in their roles as trainees, technicians, and peers, may interact with people displaying suicide warning signs. Providing suicide gatekeeping training to student pharmacists may prepare them to engage people at risk. Measuring the extent to which student pharmacists have encountered people displaying warning signs of suicide may help contextualize the potential importance of training student pharmacists in suicide gatekeeping. The objective was to describe student pharmacists' awareness of someone they know having attempted or died by suicide and whether they have heard statements suggesting suicide risk in their personal and work life. Methods An anonymous electronic survey was administered to 111 student pharmacists before engaging in question-persuade-refer training as part of their second-year pharmacy curriculum. Respondents were asked for demographics and if Someone ever told you something concerning where you wondered if they were thinking about suicide for both work and one's personal life. Descriptive statistics and chi-squared tests were used to compare items by gender. Results There were 111 responses to the survey for a 100% response rate. Concerning statements related to suicide were reported by 71.2% of respondents in their personal life and by 34.2% of students while at work. There were no differences based on gender. Discussion A significant proportion of student pharmacists have personal, peer, and professional exposure related to people with potential suicidal ideation. These findings emphasize the need for broad approaches for training students and pharmacists in suicide gatekeeping as a new public health role.