12,718 research outputs found
Unification of Flavor SU(3) Analyses of Heavy Hadron Weak Decays
Analyses of heavy mesons and baryons hadronic charmless decays using the
flavor SU(3) symemtry can be formulated in two different forms. One is to
construct the SU(3) irreducible representation amplitude (IRA) by decomposing
effective Hamiltonian, and the other is to draw the topological diagrams (TDA).
In the flavor SU(3) limit, we study various ,
decays, and two-body nonleptonic decays of beauty/charm baryons, and
demonstrate that when all terms are included these two ways of analyzing the
decay amplitudes are completely equivalent. Furthermore we clarify some
confusions in drawing topological diagrams using different ways of describing
beauty/charm baryons.Comment: 36 pages, 6 figures, 16 table
Effects of Fertilizer Applications on Seed Yield and Quality of \u3cem\u3eElymus sibiricus\u3c/em\u3e in a Rain-Fed Condition
Siberian wildrye grass (Elymus sibiricus L.) is widely used for reseeding as part of grassland improvement programs in Inner Mongolia. Shortage of seed supply has been a problem that limits wider use of E. sibiricus in Northern China steppes. In this research, we investigate the effects of fertilizer application on the seed yield and seed quality of E. sibiricus grown under rain-fed conditions in Inner Mongolia
Safe Deep Policy Adaptation
A critical goal of autonomy and artificial intelligence is enabling
autonomous robots to rapidly adapt in dynamic and uncertain environments.
Classic adaptive control and safe control provide stability and safety
guarantees but are limited to specific system classes. In contrast, policy
adaptation based on reinforcement learning (RL) offers versatility and
generalizability but presents safety and robustness challenges. We propose
SafeDPA, a novel RL and control framework that simultaneously tackles the
problems of policy adaptation and safe reinforcement learning. SafeDPA jointly
learns adaptive policy and dynamics models in simulation, predicts environment
configurations, and fine-tunes dynamics models with few-shot real-world data. A
safety filter based on the Control Barrier Function (CBF) on top of the RL
policy is introduced to ensure safety during real-world deployment. We provide
theoretical safety guarantees of SafeDPA and show the robustness of SafeDPA
against learning errors and extra perturbations. Comprehensive experiments on
(1) classic control problems (Inverted Pendulum), (2) simulation benchmarks
(Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate
great superiority of SafeDPA in both safety and task performance, over
state-of-the-art baselines. Particularly, SafeDPA demonstrates notable
generalizability, achieving a 300% increase in safety rate compared to the
baselines, under unseen disturbances in real-world experiments.Comment: 8 pages, 7 figure
Triminimal Parametrization of Quark Mixing Matrix
Starting from a new zeroth order basis for quark mixing (CKM) matrix based on
the quark-lepton complementarity and the tri-bimaximal pattern of lepton
mixing, we derive a triminimal parametrization of CKM matrix with three small
angles and a CP-violating phase as its parameters. This new triminimal
parametrization has the merits of fast convergence and simplicity in
application. With the quark-lepton complementary relations, we derive relations
between the two unified triminimal parametrizations for quark mixing obtained
in this work and for lepton mixing obtained by Pakvasa-Rodejohann-Weiler.
Parametrization deviating from quark-lepton complementarity is also discussed.Comment: 9 pages, no figur
Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification
Visible-Infrared person re-identification (VI-ReID) is an important and
challenging task in intelligent video surveillance. Existing methods mainly
focus on learning a shared feature space to reduce the modality discrepancy
between visible and infrared modalities, which still leave two problems
underexplored: information redundancy and modality complementarity. To this
end, properly eliminating the identity-irrelevant information as well as making
up for the modality-specific information are critical and remains a challenging
endeavor. To tackle the above problems, we present a novel mutual information
and modality consensus network, namely CMInfoNet, to extract modality-invariant
identity features with the most representative information and reduce the
redundancies. The key insight of our method is to find an optimal
representation to capture more identity-relevant information and compress the
irrelevant parts by optimizing a mutual information bottleneck trade-off.
Besides, we propose an automatically search strategy to find the most prominent
parts that identify the pedestrians. To eliminate the cross- and intra-modality
variations, we also devise a modality consensus module to align the visible and
infrared modalities for task-specific guidance. Moreover, the global-local
feature representations can also be acquired for key parts discrimination.
Experimental results on four benchmarks, i.e., SYSU-MM01, RegDB,
Occluded-DukeMTMC, Occluded-REID, Partial-REID and Partial\_iLIDS dataset, have
demonstrated the effectiveness of CMInfoNet
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