150 research outputs found
Quantum error pre-compensation for quantum noisy channels
Most previous efforts of quantum error correction focused on either extending
classical error correction schemes to the quantum regime by performing a
perfect correction on a subset of errors, or seeking a recovery operation to
maximize the fidelity between a input state and its corresponding output state
of a noisy channel. There are few results concerning quantum error
pre-compensation. Here we design an error pre-compensated input state for an
arbitrary quantum noisy channel and a given target output state. By following a
procedure, the required input state, if it exists, can be analytically obtained
in single-partite systems. Furthermore, we also present semidefinite programs
to numerically obtain the error pre-compensated input states with maximal
fidelities between the target state and the output state. The numerical results
coincide with the analytical results.Comment: 10 pages, 3 figures
Unified and Dynamic Graph for Temporal Character Grouping in Long Videos
Video temporal character grouping locates appearing moments of major
characters within a video according to their identities. To this end, recent
works have evolved from unsupervised clustering to graph-based supervised
clustering. However, graph methods are built upon the premise of fixed affinity
graphs, bringing many inexact connections. Besides, they extract multi-modal
features with kinds of models, which are unfriendly to deployment. In this
paper, we present a unified and dynamic graph (UniDG) framework for temporal
character grouping. This is accomplished firstly by a unified representation
network that learns representations of multiple modalities within the same
space and still preserves the modality's uniqueness simultaneously. Secondly,
we present a dynamic graph clustering where the neighbors of different
quantities are dynamically constructed for each node via a cyclic matching
strategy, leading to a more reliable affinity graph. Thirdly, a progressive
association method is introduced to exploit spatial and temporal contexts among
different modalities, allowing multi-modal clustering results to be well fused.
As current datasets only provide pre-extracted features, we evaluate our UniDG
method on a collected dataset named MTCG, which contains each character's
appearing clips of face and body and speaking voice tracks. We also evaluate
our key components on existing clustering and retrieval datasets to verify the
generalization ability. Experimental results manifest that our method can
achieve promising results and outperform several state-of-the-art approaches
Genomes shed light on the evolution of Begonia, a mega‐diverse genus
Clarifying the evolutionary processes underlying species diversification and adaptation is a key focus of evolutionary biology. Begonia (Begoniaceae) is one of the most species-rich angiosperm genera with ~2,000 species, most of which are shade-adapted. Here, we present chromosome-scale genome assemblies for four species of Begonia (B. loranthoides, B. masoniana, B. darthvaderiana, and B. peltatifolia), and whole genome shot-gun data for an additional 74 Begonia representatives to investigate lineage evolution and shade adaptation of the genus. The four genome assemblies range in size from 331.75 Mb (B. peltatifolia) to 799.83 Mb (B. masoniana), and harbor 22,059 - 23,444 protein-coding genes. Synteny analysis revealed a lineage specific whole-genome duplication (WGD) that occurred just before the diversification of the Begonia. Functional enrichment of gene families retained after WGD highlight the significance of modified carbohydrate metabolism and photosynthesis possibly linked to shade-adaptation in the genus, which is further supported by expansions of gene families involved in light perception and harvesting. Phylogenomic reconstructions and genomics studies indicate that genomic introgression has also played a role in the evolution of Begonia. Overall, this study provides valuable genomic resources for Begonia and suggests potential drivers underlying the diversity and adaptive evolution of this mega-diverse clade
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