503 research outputs found
Number of Forts in Iterated Logistic Mapping
Using the theory of complete discrimination system and the computer algebra system MAPLE V.17, we compute the number of forts for the logistic mapping fλ(x)=λx(1-x) on [0,1] parameterized by λ∈(0,4]. We prove that if 0<λ≤2 then the number of forts does not increase under iteration and that if λ>2 then the number of forts is not bounded under iteration. Furthermore, we focus on the case of λ>2 and give for each k=1,…,7 some critical values of λ for the change of numbers of forts
Relations of blood lead levels to echocardiographic left ventricular structure and function in preschool children
Lead (Pb) has been proved to exert adverse effect on human cardiovascular system. However, the cardiotoxicity of Pb on children is still unclear. The aim of this study was to evaluate left ventricular (LV) structure and function, by using echocardiographic indices, in order to elucidate the effect of Pb on low-grade inflammation related to left ventricle in healthy preschool children. We recruited a total of 486 preschool children, 310 from Guiyu (e-waste-exposed area) and 176 from Haojiang (reference area). Blood Pb levels, complete blood counts, and LV parameters were evaluated. Associations between blood Pb levels and LV parameters and peripheral leukocyte counts were analyzed using linear regression models. The median blood level of Pb and the counts of white blood cells (WBCs), monocytes, and neutrophils were higher in exposed group. In addition, the exposed group showed smaller left ventricle (including interventricular septum, LV posterior wall, and LV mass index) and impaired LV systolic function (including LV fractional shortening and LV ejection fraction) regardless gender. After adjustment for confounding factors, elevated blood Pb levels were significantly associated with higher counts of WBCs and neutrophils, and lower levels of LV parameters. Furthermore, counts of WBCs, monocytes, and neutrophils were negatively correlated with LV parameters. Taken together, smaller left ventricle and impaired systolic function were found in e-waste-exposed children and associated with chronic low-grade inflammation and elevated blood Pb levels. It indicates that the heart health of e-waste-exposed children is at risk due to the long-term environmental chemical insults. (C) 2020 Elsevier Ltd. All rights reserved
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations
Inverse reinforcement learning (IRL) aims to explicitly infer an underlying
reward function based on collected expert demonstrations. Considering that
obtaining expert demonstrations can be costly, the focus of current IRL
techniques is on learning a better-than-demonstrator policy using a reward
function derived from sub-optimal demonstrations. However, existing IRL
algorithms primarily tackle the challenge of trajectory ranking ambiguity when
learning the reward function. They overlook the crucial role of considering the
degree of difference between trajectories in terms of their returns, which is
essential for further removing reward ambiguity. Additionally, it is important
to note that the reward of a single transition is heavily influenced by the
context information within the trajectory. To address these issues, we
introduce the Distance-rank Aware Sequential Reward Learning (DRASRL)
framework. Unlike existing approaches, DRASRL takes into account both the
ranking of trajectories and the degrees of dissimilarity between them to
collaboratively eliminate reward ambiguity when learning a sequence of
contextually informed reward signals. Specifically, we leverage the distance
between policies, from which the trajectories are generated, as a measure to
quantify the degree of differences between traces. This distance-aware
information is then used to infer embeddings in the representation space for
reward learning, employing the contrastive learning technique. Meanwhile, we
integrate the pairwise ranking loss function to incorporate ranking information
into the latent features. Moreover, we resort to the Transformer architecture
to capture the contextual dependencies within the trajectories in the latent
space, leading to more accurate reward estimation. Through extensive
experimentation, our DRASRL framework demonstrates significant performance
improvements over previous SOTA methods
Cryo-EM Structure of Dodecameric Vps4p and Its 2:1 Complex with Vta1p
The type I AAA (ATPase associated with a variety of cellular activities) ATPase Vps4 and its co-factor Vta1p/LIP5 function in membrane remodeling events that accompany cytokinesis, multivesicular body biogenesis, and retrovirus budding, apparently by driving disassembly and recycling of membrane-associated ESCRT (endosomal sorting complex required for transport)-III complexes. Here, we present electron cryomicroscopy reconstructions of dodecameric yeast Vps4p complexes with and without their microtubule interacting and transport (MIT) N-terminal domains and Vta1p co-factors. The ATPase domains of Vps4p form a bowl-like structure composed of stacked hexameric rings. The two rings adopt dramatically different conformations, with the “upper” ring forming an open assembly that defines the sides of the bowl and the lower ring forming a closed assembly that forms the bottom of the bowl. The N-terminal MIT domains of the upper ring localize on the symmetry axis above the cavity of the bowl, and the binding of six extended Vta1p monomers causes additional density to appear both above and below the bowl. The structures suggest models in which Vps4p MIT and Vta1p domains engage ESCRT-III substrates above the bowl and help transfer them into the bowl to be pumped through the center of the dodecameric assembly
Dual Contrastive Network for Sequential Recommendation with User and Item-Centric Perspectives
With the outbreak of today's streaming data, sequential recommendation is a
promising solution to achieve time-aware personalized modeling. It aims to
infer the next interacted item of given user based on history item sequence.
Some recent works tend to improve the sequential recommendation via randomly
masking on the history item so as to generate self-supervised signals. But such
approach will indeed result in sparser item sequence and unreliable signals.
Besides, the existing sequential recommendation is only user-centric, i.e.,
based on the historical items by chronological order to predict the probability
of candidate items, which ignores whether the items from a provider can be
successfully recommended. The such user-centric recommendation will make it
impossible for the provider to expose their new items and result in popular
bias.
In this paper, we propose a novel Dual Contrastive Network (DCN) to generate
ground-truth self-supervised signals for sequential recommendation by auxiliary
user-sequence from item-centric perspective. Specifically, we propose dual
representation contrastive learning to refine the representation learning by
minimizing the euclidean distance between the representations of given
user/item and history items/users of them. Before the second contrastive
learning module, we perform next user prediction to to capture the trends of
items preferred by certain types of users and provide personalized exploration
opportunities for item providers. Finally, we further propose dual interest
contrastive learning to self-supervise the dynamic interest from next item/user
prediction and static interest of matching probability. Experiments on four
benchmark datasets verify the effectiveness of our proposed method. Further
ablation study also illustrates the boosting effect of the proposed components
upon different sequential models.Comment: 23 page
Coral-algal interactions at Weizhou Island in the northern South China Sea: variations by taxa and the exacerbating impact of sediments trapped in turf algae
Competitive interactions between corals and benthic algae are increasingly frequent on degrading coral reefs, but the processes and mechanisms surrounding the interactions, as well as the exacerbating effects of sediments trapped in turf algae, are poorly described. We surveyed the frequency, proportion, and outcomes of interactions between benthic algae (turf algae and macroalgae) and 631 corals (genera: Porites, Favites, Favia, Platygyra, and Pavona) on a degenerating reef in the northern South China Sea, with a specific focus on the negative effects of algal contact on corals. Our data indicated that turf algae were the main algal competitors for each surveyed coral genus and the proportion of algal contact along the coral edges varied significantly among the coral genera and the algal types. The proportions of algal wins between corals and turf algae or macroalgae differed significantly among coral genera. Compared to macroalgae, turf algae consistently yielded more algal wins and fewer coral wins on all coral genera. Amongst the coral genera, Porites was the most easily damaged by algal competition. The proportions of turf algal wins on the coral genera increased 1.1–1.9 times in the presence of sediments. Furthermore, the proportions of algal wins on massive and encrusting corals significantly increased with the combination of sediments and turf algae as the algal type. However, the variation in proportions of algal wins between massive and encrusting corals disappeared as sediments became trapped in turf algae. Sediments bound within turf algae further induced damage to corals and reduced the competitive advantage of the different coral growth forms in their competitive interactions with adjacent turf algae
Frequency-Domain Hydrodynamic Modelling of Dense and Sparse Arrays of Wave Energy Converters
In this work, we develop a frequency-domain model to study the hydrodynamic behaviour of a floater blanket (FB), i.e., an array of floater elements individually connected to power take-off (PTO) systems, which constitutes the core technology of the novel Ocean Grazer (OG) wave energy converter (WEC). The boundary element method open-source code NEMOH is used to solve the scattering and radiation problem. The aforementioned floater elements that comprise the FB are mechanically interconnected via (cylindrical, revolutional or spring) joints, which add extra constraint equations to the multibody problem. Various scenarios are investigated to understand the hydrodynamic response of the FB. The variation of the capture factor, PTO damping coeffcients, q-factor and response amplitude operator (RAO) of each scenario are analysed, in order to quantify the device performance. A new concept based on a negative-stiffness spring joint is proposed to increase the energy output of the FB. Attention is also paid to the anti-resonance that is found in the numerical simulations. This study provides further insight into the hydrodynamic behaviour of dense or sparse, interconnected arrays of WECs, which is fundamental for the design and optimisation of the OG-WEC
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