Miracle-dz/UniFAR-FTUs huggingface dataset:
Consequences of fish predation, migration, and juvenile ontogeny on zooplankton spring dynamics

In order to disentangle if and when resource supply and adult and young‐of‐the‐year (0+) fish predation affect zooplankton dynamics during spring, we monitored zooplankton during three consecutive years in a lake in southern Sweden. We also experimentally assessed 0+ fish predation rates and estimated changes in predation rates of adult fish on zooplankton. Decline in abundances of large‐sized zooplankters in early spring was not caused by 0+ fish predation. Instead, this decline was most likely a combined result of size‐selective predation from adult fish (stationary in the lake and from those returning from surrounding streams) and competition for diminishing algal food resources. On the other hand, the decline in medium‐sized zooplankton in the lake during spring was strongly affected by 0+ fish. Hence, during spring, zooplankton are facing predation both from adult fish selecting large prey and from 0+ fish, which start feeding on small‐sized prey and eventually switch to larger. Neither predation by different ontogenetic stages of fish (adult and 0+) nor resource supply shape the zooplankton spring dynamics, but rather they affect the timing and strength of these events. 0+ cyprinids tend to have stronger effect on zooplankton dynamics than other taxa of 0+ fish. A combination of predation from adult and 0+ fish during spring is the main mechanism behind the crash of the zooplankton community, which in many lakes leads to the termination of the clear‐water phase.
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning.
Uncertainty-aware Self-training for Text Classification with Few Labels

Recent success of large-scale pre-trained language models crucially hinge on fine-tuning them on large amounts of labeled data for the downstream task, that are typically expensive to acquire. In this work, we study self-training as one of the earliest semi-supervised learning approaches to reduce the annotation bottleneck by making use of large-scale unlabeled data for the target task. Standard self-training mechanism randomly samples instances from the unlabeled pool to pseudo-label and augment labeled data. In this work, we propose an approach to improve self-training by incorporating uncertainty estimates of the underlying neural network leveraging recent advances in Bayesian deep learning. Specifically, we propose (i) acquisition functions to select instances from the unlabeled pool leveraging Monte Carlo (MC) Dropout, and (ii) learning mechanism leveraging model confidence for self-training. As an application, we focus on text classification on five benchmark datasets. We show our methods leveraging only 20-30 labeled samples per class for each task for training and for validation can perform within 3% of fully supervised pre-trained language models fine-tuned on thousands of labeled instances with an aggregate accuracy of 91% and improving by upto 12% over baselines.
A Survey on the Evaluation of Clone Detection Performance and Benchmarking

There are a great many clone detection tools proposed in the literature. In this paper, we investigate the state of clone detection tool evaluation. We begin by surveying the clone detection benchmarks, and performing a multi-faceted evaluation and comparison of their features and capabilities. We then survey the existing clone detection tool and technique publications, and evaluate how the authors of these works evaluate their own tools/techniques. We rank the individual works by how well they measure recall, precision, execution time and scalability. We select the works the best evaluate all four metrics as exemplars that should be considered by future researchers publishing clone detection tools/techniques when designing the empirical evaluation of their tool/technique. We measure statistics on tool evaluation by the authors, and find that evaluation is poor amongst the authors. We finish our investigation into clone detection evaluation by surveying the existing tool comparison studies, including both the qualitative and quantitative studies.
Deformation of Hermitian metrics

In this work, we study the deformation of Hermitian metrics with Chern connection. By adapting the conformal perturbation method of Aubin and Ehrlich to Hermitian setting, we prove that Hermitian metrics with quasi-positive (resp. quasi-negative) second Chern-Ricci curvature can be deformed to one with positive (resp. negative) curvature.
A convex data-driven approach for nonlinear control synthesis

We consider a class of nonlinear control synthesis problems where the underlying mathematical models are not explicitly known. We propose a data-driven approach to stabilize the systems when only sample trajectories of the dynamics are accessible. Our method is built on the density-function-based stability certificate that is the dual to the Lyapunov function for dynamic systems. Unlike Lyapunov-based methods, density functions lead to a convex formulation for a joint search of the control strategy and the stability certificate. This type of convex problem can be solved efficiently using the machinery of the sum of squares (SOS). For the data-driven part, we exploit the fact that the duality results in the stability theory can be understood through the lens of Perron–Frobenius and Koopman operators. This allows us to use data-driven methods to approximate these operators and combine them with the SOS techniques to establish a convex formulation of control synthesis. The efficacy of the proposed approach is demonstrated through several examples.
The effect of heterogeneity on hypergraph contagion models

The dynamics of network social contagion processes such as opinion formation and epidemic spreading are often mediated by interactions between multiple nodes. Previous results have shown that these higher-order interactions can profoundly modify the dynamics of contagion processes, resulting in bistability, hysteresis, and explosive transitions. In this paper, we present and analyze a hyperdegree-based mean-field description of the dynamics of the susceptible–infected–susceptible model on hypergraphs, i.e., networks with higher-order interactions, and illustrate its applicability with the example of a hypergraph where contagion is mediated by both links (pairwise interactions) and triangles (three-way interactions). We consider various models for the organization of link and triangle structures and different mechanisms of higher-order contagion and healing. We find that explosive transitions can be suppressed by heterogeneity in the link degree distribution when links and triangles are chosen independently or when link and triangle connections are positively correlated when compared to the uncorrelated case. We verify these results with microscopic simulations of the contagion process and with analytic predictions derived from the mean-field model. Our results show that the structure of higher-order interactions can have important effects on contagion processes on hypergraphs.
DEEPACC:Automate Chromosome Classification Based On Metaphase Images Using Deep Learning Framework Fused With Priori Knowledge

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of the model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of priori knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criteria as a prior-knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. A private metaphase image dataset from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baseline models.
The Regulation Of Intervertebral Disc Cell Interactions With Their Surrounding Microenvironment

Title of Document: THE REGULATION OF INTERVERTEBRAL DISC CELL INTERACTIONS WITH THEIR SURROUNDING MICROENVIRONMENT. Anshu Rastogi Doctor of Philosophy, 2010 Directed By: Assistant Professor Adam H. Hsieh, Fischell Department of Bioengineering Intervertebral disc degeneration is the major cause of back pain in the US, which can be both physically debilitating and costly to treat. Current treatments include invasive surgeries, which can be effective in ameliorating pain, but also contain the risk of complications. Additionally, these strategies target clinical manifestations of disc degeneration, rather than examine the cause of degenerative changes. Therefore, current research focuses on finding minimally invasive treatments for disc disease such as gene therapy. Regulating intervertebral disc cell interactions with their immediate environment can be a useful tool in the development of therapeutic strategies. This was explored through environmental changes to assess shifts in cell phenotype as well as genetic modulation to elucidate alterations in cell function. Biochemical, nutritional, and physical factors were examined in immature nucleus pulposus cells to assess changes in gene expression, attachment, and proliferation. It was found that nutritional and physical factors can alter gene expression levels of NP cells, thereby altering cell phenotype. In addition, down-regulation of the proteolytic enzyme MMP-2 was explored through RNAi interference. Five shRNA lentiviral vectors were designed and validated for the sustained gene silencing of MMP-2. Silencing MMP-2 activity resulted in the inability of disc cells to focally degrade gelatin films as well as reduced ability of disc cells to remodel fibers in type I collagen gels, resulting in weakened gel architecture. These functional consequences were further explored in an in vivo study utilizing an annular needle-puncture model of disc degeneration. Injection of the shMMP lentiviral construct lead to decreased expression of MMP-2 in the disc, as well as improved disc height and morphology. Thus, the functional consequences of silencing MMP-2 were examined, elucidating its role in the degradative pathway leading to degenerative disc disease. The results of these studies can lay the foundation for developing therapeutic treatments for intervertebral disc degeneration. THE REGULATION OF INTERVERTEBRAL DISC CELL INTERACTIONS WITH THEIR SURROUNDING MICROENVIRONMENT
Early warning signals indicate a critical transition in Betelgeuse

Context.Critical transitions occur in complex dynamical systems when the system dynamics undergoes a regime shift. These can often occur with little change in the mean amplitude of the system response prior to the actual time of transition. The recent dimming and brightening event in Betelgeuse occurred as a sudden shift in the brightness and has been the subject of much debate. Internal changes or an external dust cloud have been suggested as reasons for this change in variability.Aims. We examine whether the dimming and brightening event of 2019–20 could be due to a critical transition in the pulsation dynamics of Betelgeuse by studying the characteristics of the light curve prior to transition.Methods. We calculated the quantifiers hypothesized to rise prior to a critical transition for the light curve of Betelgeuse up to the dimming event of 2019–20. These included the autocorrelation at lag-1, variance, and the spectral coefficient calculated from detrended fluctuation analysis, in addition to two measures that quantify the recurrence properties of the light curve. Significant rises are confirmed using the Mann-Kendall trend test.Results. We see a significant increase in all quantifiers (p < 0.05) prior to the dimming event of 2019–20. This suggests that the event was a critical transition related to the underlying nonlinear dynamics of the star.Conclusions. Together with results that suggest a minimal change inTeffand IR flux, a critical transition in the pulsation dynamics might be a reason for the unprecedented dimming of Betelgeuse. The rise in the quantifiers we studied prior to the dimming event supports this possibility.
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Local Attention

Prior work in multi-task learning has mainly focused on predictions on a single image. In this work, we present a new approach for multi-task learning from videos via efficient inter-frame local attention (MILA). Our approach contains a novel inter-frame attention module which allows learning of task-specific attention across frames. We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate. We also propose an effective adversarial learning strategy to encourage the slow and fast network to learn similar features. Our approach ensures low-latency multi-task learning while maintaining high quality predictions. Experiments show competitive accuracy compared to state-of-the-art on two multi-task learning benchmarks while reducing the number of floating point operations (FLOPs) by up to 70\%. In addition, our attention based feature propagation method (ILA) outperforms prior work in terms of task accuracy while also reducing up to 90\% of FLOPs.
Integrative species delimitation and conservation of tarantulas (Araneae, Mygalomorphae, Theraphosidae) from a North American biodiversity hotspot

The Madrean Sky Islands of northern Mexico and the south‐western United States are located in the Madrean pine–oak woodlands ‘biodiversity hotspot’. This area is renowned for its exceptional flora and fauna, but comparatively little is known about arthropod diversity in this region. We obtained specimens of the tarantula genus Aphonopelma from five different mountain ranges (Huachucas, Pajaritos, Peloncillos, Santa Catalinas, and Santa Ritas) to assess patterns of diversity within the group. Species delimitation in tarantulas is difficult because they possess few informative morphological features. We implemented a rigorous methodological framework for delimiting species that incorporated multiple lines of evidence (molecules, distributional information, morphology, and breeding period) in an explicitly hypothesis‐driven manner. Our integrative taxonomic approach delimited three species (all undescribed): one endemic to the Peloncillos, one endemic to the Santa Catalinas, and one that is more widely distributed in the Huachucas, Santa Ritas, and Pajaritos. These patterns of diversity and endemicity are comparable to other arachnids in the region and suggest that species‐level diversity of stenotypic/dispersal‐limited arthropods throughout the Madrean Sky Island Region may be underestimated. Ecological data are not widely available for Madrean Sky Island tarantulas and it is difficult to evaluate their conservation status. Several intrinsic (patterns of endemicity, limited vagility, and life history) and extrinsic (habitat degradation, commercial exploitation, and climate change) factors, however, suggest that these spiders may be of conservation concern. The approach taken in this study highlights the importance of considering multiple lines of evidence when species delimitation has direct implications on conservation.
OpenDVC: An Open Source Implementation of the DVC Video Compression Method

We introduce an open source Tensorflow implementation of the Deep Video Compression (DVC) method in this technical report. DVC is the first end-to-end optimized learned video compression method, achieving better MS-SSIM performance than the Low-Delay P (LDP) very fast setting of x265 and comparable PSNR performance with x265 (LDP very fast). At the time of writing this report, several learned video compression methods are superior to DVC, but currently none of them provides open source codes. We hope that our OpenDVC codes are able to provide a useful model for further development, and facilitate future researches on learned video compression. Different from the original DVC, which is only optimized for PSNR, we release not only the PSNR-optimized re-implementation, denoted by OpenDVC (PSNR), but also the MS-SSIM-optimized model OpenDVC (MS-SSIM). Our OpenDVC (MS-SSIM) model provides a more convincing baseline for MS-SSIM optimized methods, which can only compare with the PSNR optimized DVC in the past. The OpenDVC source codes and pre-trained models are publicly released at this https URL.
Descriptions of four new records of plant-parasitic nematode species from commercial cut-flowers in Ethiopia

Soil samples were collected from the rhizosphere of gypsophila and rose plants from greenhouses in Debrezeit and Ziway localities in Ethiopia. Among the many specimens of plant-parasitic nematodes extracted from the soil, four were identified as new records of Ethiopia, namely Mesocriconema sphaerocephaloides; Longidorus laevicapitatus; Paratylenchus obtusicaudatus, and Nanidorus minor. A comprehensive analysis of morphological and morphometrical characters is provided and species-specific variability in those characters between populations originating from different regions is discussed. Overall, this updated information will improve identification of this species in the future and also increase awareness of those species as a potential threat in cut-flower production.
Central limit theorems for local network statistics

Subgraph counts, in particular the number of occurrences of small shapes such as triangles, characterize properties of random networks. As a result, they have seen wide use as network summary statistics. Subgraphs are typically counted globally, making existing approaches unable to describe vertex-specific characteristics. In contrast, rooted subgraphs focus on vertex neighbourhoods, and are fundamental descriptors of local network properties. We derive the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model which generalizes most statistical network models. This result enables a shift in the statistical analysis of graphs, from estimating network summaries, to estimating models linking local network structure and vertex-specific covariates. As an example, we consider a school friendship network and show that gender and race are significant predictors of local friendship patterns.
TFNet: Multi-Semantic Feature Interaction for CTR Prediction

The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in this field, such as Logistic Regression (LR), Factorization Machines (FM) and deep learning based methods like Wide&Deep, Neural Factorization Machines (NFM) and DeepFM. However, such approaches generally use the vector-product of each pair of features, which have ignored the different semantic spaces of the feature interactions. In this paper, we propose a novel Tensor-based Feature interaction Network (TFNet) model, which introduces an operating tensor to elaborate feature interactions via multi-slice matrices in multiple semantic spaces. Extensive offline and online experiments show that TFNet: 1) outperforms the competitive compared methods on the typical Criteo and Avazu datasets; 2) achieves large improvement of revenue and click rate in online A/B tests in the largest Chinese App recommender system, Tencent MyApp.
Interpreting and Disentangling Feature Components of Various Complexity from DNNs

This paper aims to define, quantify, and analyze the feature complexity that is learned by a DNN. We propose a generic definition for the feature complexity. Given the feature of a certain layer in the DNN, our method disentangles feature components of different complexity orders from the feature. We further design a set of metrics to evaluate the reliability, the effectiveness, and the significance of over-fitting of these feature components. Furthermore, we successfully discover a close relationship between the feature complexity and the performance of DNNs. As a generic mathematical tool, the feature complexity and the proposed metrics can also be used to analyze the success of network compression and knowledge distillation.
Phyto-agglutinin, total proteins and amino assimilating enzymatic activity of indigeneous chickpea (Cicer arietinum L.) cultivars

Biochemical studies were carried out with in vitro micropropagated plantlets of two indigenous cultivars of chickpea (Cicer arietinum L.), KK-1 and Hassan-2K, where extract from shoots, leaves, roots, and reproductive organs were determined for human erythrocyte agglutination (by mixing the lectin containing extract 1:1 with a 2% erythrocyte suspension), protein profiling and the amino assimilating (glutamine synthetase) enzymatic activity. Both of KK-1 and Hassan-2K cultivars extract showed highly phyto-agglutination of human erythrocytes with reproductive organs and other tissues, which represents the presence of potent lectins (phyto-agglutinin). The amino assimilating enzymatic activity of green leaves of Hasan-2K was found higher (3.545 GS μmol.gfw -1 .min -1 ) as compared to KK-1 (2.873 GS μmol.gfw -1 .min -1 ). Protein content profile showed variation in bulk proteins of various tissues, higher (12.634 μg/gfw) and (2.987 μg/gfw) in reproductive organs of KK-1 and Hasan-2K, respectively.
Evaluation of native and introduced grasses for low-input pastures in temperate Australia: experimental approach, site and genotype descriptions

This paper describes the experimental methodology, sites, seasonal conditions and germplasm used in the Australian Native and Low Input Grass Network (NLIGN). In 1998, eight sites were established across the temperate pastoral zone of southern Australia. These were located at Armidale, Binya, Sutton and Trangie in NSW; Springhurst in Victoria; Jericho in Tasmania; Flaxley in South Australie and Kendenup in Western Australia. A total of 62 lines were evaluated, of which, 29 were Australian native grasses and 33 were introduced. With differences in seed size among species and a lack of information on dormancy and germination characteristics of the native plants, seedlings were transplanted into the field on weed-mat as spaced plants. Lines were compared over a 3-year period from 1998 to 2001. Methods used for determination of forage production, persistence and palatability are described. Information detailing the original collection sites of the germplasm, a list of NLIGN sites where each genotype was evaluated, as well as a detailed description of sites and seasonal conditions is also presented.