449 research outputs found
Zeros of polynomials over finite Witt rings
Let denote the finite field of characteristic and order
. Let denote the unramified extension of the -adic
rational integers with residue field . Given two
positive integers , define a box to be a subset of
with elements such that modulo
is equal to . For a collection of
nonconstant polynomials and
positive integers , define the set of common zeros inside the
box to be V=\{X\in \mathcal B_m:\; f_i(X)\equiv 0\mod
{p^{m_i}}\mbox{ for all } 1\leq i\leq s\}. It is an interesting problem to
give the sharp estimates for the -divisibility of . This problem has
been partially solved for the three cases: (i) , which is
just the Ax-Katz theorem, (ii) , which was solved by Katz,
Marshal and Ramage, and (iii) , and , which was
recently solved by Cao, Wan and Grynkiewicz. Based on the multi-fold addition
and multiplication of the finite Witt rings over , we investigate
the remaining unconsidered case of and for some , and finally provide a complete answer to this problem
QTL variations for growth-related traits in eight distinct families of common carp (Cyprinus carpio)
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty
Knowledge distillation is an effective paradigm for boosting the performance
of pocket-size model, especially when multiple teacher models are available,
the student would break the upper limit again. However, it is not economical to
train diverse teacher models for the disposable distillation. In this paper, we
introduce a new concept dubbed Avatars for distillation, which are the
inference ensemble models derived from the teacher. Concretely, (1) For each
iteration of distillation training, various Avatars are generated by a
perturbation transformation. We validate that Avatars own higher upper limit of
working capacity and teaching ability, aiding the student model in learning
diverse and receptive knowledge perspectives from the teacher model. (2) During
the distillation, we propose an uncertainty-aware factor from the variance of
statistical differences between the vanilla teacher and Avatars, to adjust
Avatars' contribution on knowledge transfer adaptively. Avatar Knowledge
Distillation AKD is fundamentally different from existing methods and refines
with the innovative view of unequal training. Comprehensive experiments
demonstrate the effectiveness of our Avatars mechanism, which polishes up the
state-of-the-art distillation methods for dense prediction without more extra
computational cost. The AKD brings at most 0.7 AP gains on COCO 2017 for Object
Detection and 1.83 mIoU gains on Cityscapes for Semantic Segmentation,
respectively.Comment: Accepted by ACM MM 202
Multi-stable and spatiotemporal staggered patterns in a predator-prey model with predator-taxis and delay
The effects of predator-taxis and conversion time delay on formations of spatiotemporal patterns in a predator-prey model are explored. First, the well-posedness, which implies global existence of classical solutions, is proved. Then, we establish critical conditions for the destabilization of the coexistence equilibrium via Turing/Turing-Turing bifurcations by describing the first Turing bifurcation curve; we also theoretically predict possible bistable/multi-stable spatially heterogeneous patterns. Next, we demonstrate that the coexistence equilibrium can also be destabilized via Hopf, Hopf-Hopf and Turing-Hopf bifurcations; also possible stable/bistable spatially inhomogeneous staggered periodic patterns and bistable spatially inhomogeneous synchronous periodic patterns are theoretically predicted. Finally, numerical experiments also support theoretical predictions and partially extend them. In a word, theoretical analyses indicate that, on the one hand, strong predator-taxis can eliminate spatial patterns caused by self-diffusion; on the other hand, the joint effects of predator-taxis and conversion time delay can induce complex survival patterns, e.g., bistable spatially heterogeneous staggered/synchronous periodic patterns, thus diversifying populations' survival patterns
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