RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets

Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumulation of more extensive datasets, subsequently facilitating the training of larger models on these datasets. However, these datasets remain relatively under-explored. To this end, we introduce a pioneering approach known as RK-core, to empower gaining a deeper understanding of the intricate hierarchical structure within datasets. Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness. Correspondingly, samples with high coreness values make a more substantial contribution to the performance in comparison to those with low coreness values. Building upon this, we further employ RK-core to analyze the hierarchical structure of samples with different coreset selection methods. Remarkably, we find that a high-quality coreset should exhibit hierarchical diversity instead of solely opting for representative samples. The code is available at https://github.com/yaolu-zjut/Kcore

Nonlinear response of atmospheric blocking to early Winter Barents-Kara seas warming: An idealized model study

Wintertime Ural blocking (UB) has been shown to play an important role in cold extremes over Eurasia, and thus it is useful to investigate the impact of warming over the Barents–Kara Seas (BKS) on the behavior of Ural blocking. Here the response of UB to stepwise tropospheric warming over the BKS is examined using a dry dynamic core model. Nonlinear responses are found in the frequency and local persistence of UB. The frequency and local persistence of the UB increase with the strength of BKS warming in a less strong range and decrease with the further increase of BKS warming, which is linked to the UB propagation influenced by upstream background atmospheric circulation. For a weak BKS warming, the UB becomes more persistent due to its less westward movement associated with intensified upstream zonal wind and meridional potential vorticity gradient (PVy) in the North Atlantic mid-high latitudes, which corresponds to a negative height response over the North Atlantic high latitudes. When BKS warming is strong, a positive height response appears in the early winter stratosphere, and its subsequent downward propagation leads to a negative NAO response or increased Greenland blocking events, which reduces zonal wind and PVy in the high latitudes from North Atlantic to Europe, thus enhancing the westward propagation of UB and reducing its local persistence. The transition to the negative NAO phase and the retrogression of UB are not found when numerically suppressing the downward influence of weakened stratospheric polar vortex, suggesting a crucial role of the stratospheric pathway in nonlinear responses of UB to the early winter BKS warming.publishedVersio

Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning

The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization

Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpose tool for optimization, their particular instantiations can be heuristic and motivated by loose biological intuition. In this work we explore a fundamentally different approach: Given a sufficiently flexible parametrization of the genetic operators, we discover entirely new genetic algorithms in a data-driven fashion. More specifically, we parametrize selection and mutation rate adaptation as cross- and self-attention modules and use Meta-Black-Box-Optimization to evolve their parameters on a set of diverse optimization tasks. The resulting Learned Genetic Algorithm outperforms state-of-the-art adaptive baseline genetic algorithms and generalizes far beyond its meta-training settings. The learned algorithm can be applied to previously unseen optimization problems, search dimensions & evaluation budgets. We conduct extensive analysis of the discovered operators and provide ablation experiments, which highlight the benefits of flexible module parametrization and the ability to transfer (`plug-in') the learned operators to conventional genetic algorithms.Comment: 14 pages, 31 figure

Casein Kinase 1 Promotes Initiation of Clathrin-Mediated Endocytosis

SummaryIn budding yeast, over 60 proteins functioning in at least five modules are recruited to endocytic sites with predictable order and timing. However, how sites of clathrin-mediated endocytosis are initiated and stabilized is not well understood. Here, the casein kinase 1 (CK1) Hrr25 is shown to be an endocytic protein and to be among the earliest proteins to appear at endocytic sites. Hrr25 absence or overexpression decreases or increases the rate of endocytic site initiation, respectively. Ede1, an early endocytic Eps15-like protein important for endocytic initiation, is an Hrr25 target and is required for Hrr25 recruitment to endocytic sites. Hrr25 phosphorylation of Ede1 is required for Hrr25-Ede1 interaction and promotes efficient initiation of endocytic sites. These observations indicate that Hrr25 kinase and Ede1 cooperate to initiate and stabilize endocytic sites. Analysis of the mammalian homologs CK1δ/ε suggests a conserved role for these protein kinases in endocytic site initiation and stabilization

Discovering Evolution Strategies via Meta-Black-Box Optimization

Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies. While highly general, their learning dynamics are often times heuristic and inflexible - exactly the limitations that meta-learning can address. Hence, we propose to discover effective update rules for evolution strategies via meta-learning. Concretely, our approach employs a search strategy parametrized by a self-attention-based architecture, which guarantees the update rule is invariant to the ordering of the candidate solutions. We show that meta-evolving this system on a small set of representative low-dimensional analytic optimization problems is sufficient to discover new evolution strategies capable of generalizing to unseen optimization problems, population sizes and optimization horizons. Furthermore, the same learned evolution strategy can outperform established neuroevolution baselines on supervised and continuous control tasks. As additional contributions, we ablate the individual neural network components of our method; reverse engineer the learned strategy into an explicit heuristic form, which remains highly competitive; and show that it is possible to self-referentially train an evolution strategy from scratch, with the learned update rule used to drive the outer meta-learning loop.Comment: 22 pages, 21 figure

NTCP Deficiency Affects the Levels of Circulating Bile Acids and Induces Osteoporosis

BackgroundThe p.Ser267Phe mutation in the SLC10A1 gene can cause NTCP deficiency. However, the full clinical presentation of p.Ser267Phe homozygous individuals and its long-term consequences remain unclear. Hence, in the present study, we characterized the phenotypic characteristics of NTCP deficiency and evaluated its long-term prognosis.MethodsTen NTCP p.Ser267Phe homozygous individuals were recruited and a comprehensive medical evaluation with a 5-year follow-up observation was performed. The phenotypic characteristics of NTCP deficiency were also demonstrated using an NTCP-global knockout mouse model.ResultsDuring the 5-year follow-up observation of 10 NTCP p.Ser267Phe homozygous adults, we found that the most common phenotypic features of NTCP deficiency in adults were hypercholanemia, vitamin D deficiency, bone loss, and gallbladder abnormalities. The profile of bile acids (BAs) in the serum was significantly altered in these individuals and marked by both elevated proportion and concentration of primary and conjugated BAs. Moreover, the NTCP deficiency led to increased levels of serum BAs, decreased levels of vitamin D, and aggravated the osteoporotic phenotype induced by estrogen withdrawal in mice.ConclusionsBoth mice and humans with NTCP deficiency presented hypercholanemia and were more prone to vitamin D deficiency and aggravated osteoporotic phenotype. Therefore, we recommend monitoring the levels of BAs and vitamin D, bone density, and abdominal ultrasounds in individuals with NTCP deficiency

Determination of 4 Kinds of β-Agonists Residues in Braised Meat by Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry

An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS) method was developed for the determination of four β-agonists (terbutaline, clenbuterol, ractopamine, salbutamol) in braised meat. Samples were hydrolyzed by β-glucuronidase and cleaned up by an SLS solid phase extraction column. The separation was performed on a Thermo Hypersil Gold C18 column with a gradient elution of 0.1% formic acid water and acetonitrile as mobile phases, ESI+ was used for multiple response monitoring (MRM) and quantitative analysis by internal standard method. The linear relationship of the four β-agonists was good in the concentration range of 0.5 μg/L to 9.5 μg/L, and the correlation coefficient (r) was greater than 0.9988. The limit of detection (LOD) was 0.1 μg/kg, and the limit of quantitation (LOQ) was 0.3 μg/kg. The recoveries were 87.9%~113.7% and RSDs were 1.48%~9.32% at three spiked levels (1, 5 and 9 μg/kg). In a total of 162 batches of braised meat samples, one sample of braised pig’s trotter was found to contain 1.51 μg/kg of clenbuterol and 3.65 μg/kg of ractopamine. Additionally, another sample of braised lamb was found to contain 11.5 μg/kg of clenbuterol. The method is rapid and accurate, and can be used for qualitative and quantitative determination of four β-agonists (terbutaline, clenbuterol, ractopamine, salbutamol) in braised meat