Dissipative dynamics in a tunable Rabi dimer with periodic harmonic driving

Recent progress on qubit manipulation allows application of periodic driving signals on qubits. In this study, a harmonic driving field is added to a Rabi dimer to engineer photon and qubit dynamics in a circuit quantum electrodynamics device. To model environmental effects, qubits in the Rabi dimer are coupled to a phonon bath with a sub-Ohmic spectral density. A non-perturbative treatment, the Dirac-Frenkel time-dependent variational principle together with the multiple Davydov D$_2$ {\it Ansatz} is employed to explore the dynamical behavior of the tunable Rabi dimer. In the absence of the phonon bath, the amplitude damping of the photon number oscillation is greatly suppressed by the driving field, and photons can be created thanks to resonances between the periodic driving field and the photon frequency. In the presence of the phonon bath, one still can change the photon numbers in two resonators, and indirectly alter the photon imbalance in the Rabi dimer by directly varying the driving signal in one qubit. It is shown that qubit states can be manipulated directly by the harmonic driving. The environment is found to strengthen the interqubit asymmetry induced by the external driving, opening up a new venue to engineer the qubit states

Practical Research on the Dual River Leaders System in the Context of Watershed Governance : A case study of Guiyang city, Guizhou province

This study focuses on the Dual River Leaders System, which can improve the water environment and combat the increasing basin pollution in China. It was born in the reform of the government's internal administrative system, and developed during the process of seeking cooperation between government and nongovernment. In particular, this study investigates the Dual River Leaders System in Guiyang City, Guizhou Province, where it was implemented in 2010, ahead of the rest of the country. We examined the changes over time through participatory survey methods and participation in practice as an environmental volunteer. This study discusses not only the improvement of water pollution but also the watershed governance of multi-subjects from the perspective of the local community. Moreover, we present new insights that challenge the inherent structure of China's environmental governance. We found that the Governmental River Leader System and the Non-Governmental River Leader System play important roles and have a complementary relationship. In addition, the Non-governmental River Leader System centers on the Civilian River Leaders who composed of intellectuals. In the process of continuous exploration, selecting suitable individuals from local village residents to serve as Civilian Environmental Supervisors has not only helped resolve regional conflicts but has also increased the environmental awareness of local residents. Thus gradually established a Watershed Governance Mode. The Non-Governmental River Leader System considers the relationship between nature and human beings and creates interactions between individuals that change the environmental consciousness and behavioral habits of local residents, while also fundamentally improving pollution. Furthermore, it tries to protect the environment of the local river basin by promoting cooperation among nongovernmental entities. Therefore, the Dual River Leaders System is not imitated Japan, Europe, and the United States, as a means of civilian participation, but it can empower local residents to participate in Watershed Governance, and create a new kind of "Chinese-style civilian participation." In addition, the Dual River Leaders System has played a critical role in civic behavior. The fi ndings of this study suggest that its implementation in Guiyang City is favorable for the development of Dual River Leaders Systems throughout China

Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation

Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes. Previous efforts are dedicated to mining segmentation-guiding visual cues from a constrained set of support samples. However, they still struggle to address the pronounced intra-class differences in RS images, as sparse visual cues make it challenging to establish robust class-specific representations. In this paper, we propose a holistic semantic embedding (HSE) approach that effectively harnesses general semantic knowledge, i.e., class description (CD) embeddings.Instead of the naive combination of CD embeddings and visual features for segmentation decoding, we investigate embedding the general semantic knowledge during the feature extraction stage.Specifically, in HSE, a spatial dense interaction module allows the interaction of visual support features with CD embeddings along the spatial dimension via self-attention.Furthermore, a global content modulation module efficiently augments the global information of the target category in both support and query features, thanks to the transformative fusion of visual features and CD embeddings.These two components holistically synergize general CD embeddings and visual cues, constructing a robust class-specific representation.Through extensive experiments on the standard FSS benchmark, the proposed HSE approach demonstrates superior performance compared to peer work, setting a new state-of-the-art

Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text

Few-shot learning aims to generalize the recognizer from seen categories to an entirely novel scenario. With only a few support samples, several advanced methods initially introduce class names as prior knowledge for identifying novel classes. However, obstacles still impede achieving a comprehensive understanding of how to harness the mutual advantages of visual and textual knowledge. In this paper, we propose a coherent Bidirectional Knowledge Permeation strategy called BiKop, which is grounded in a human intuition: A class name description offers a general representation, whereas an image captures the specificity of individuals. BiKop primarily establishes a hierarchical joint general-specific representation through bidirectional knowledge permeation. On the other hand, considering the bias of joint representation towards the base set, we disentangle base-class-relevant semantics during training, thereby alleviating the suppression of potential novel-class-relevant information. Experiments on four challenging benchmarks demonstrate the remarkable superiority of BiKop. Our code will be publicly available

Inhibition of fast axonal transport by pathogenic SOD1 involves activation of p38 MAP kinase

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS ONE 8 (2013): e65235, doi:10.1371/journal.pone.0065235.Dying-back degeneration of motor neuron axons represents an established feature of familial amyotrophic lateral sclerosis (FALS) associated with superoxide dismutase 1 (SOD1) mutations, but axon-autonomous effects of pathogenic SOD1 remained undefined. Characteristics of motor neurons affected in FALS include abnormal kinase activation, aberrant neurofilament phosphorylation, and fast axonal transport (FAT) deficits, but functional relationships among these pathogenic events were unclear. Experiments in isolated squid axoplasm reveal that FALS-related SOD1 mutant polypeptides inhibit FAT through a mechanism involving a p38 mitogen activated protein kinase pathway. Mutant SOD1 activated neuronal p38 in mouse spinal cord, neuroblastoma cells and squid axoplasm. Active p38 MAP kinase phosphorylated kinesin-1, and this phosphorylation event inhibited kinesin-1. Finally, vesicle motility assays revealed previously unrecognized, isoform-specific effects of p38 on FAT. Axon-autonomous activation of the p38 pathway represents a novel gain of toxic function for FALS-linked SOD1 proteins consistent with the dying-back pattern of neurodegeneration characteristic of ALS.Support was provided by 2007/2008 Marine Biological Laboratory summer fellowships and NIH (NS066942A) grants to GM; Howard Hughes Medical Institute-USE Grant #52006287 to Hunter College of CUNY (LM); Muscular Dystrophy Association (MDA) and NIH (R01NS44170) grants to LJH; MDA and NIH (NS23868, NS23320, NS41170) grants to STB; NIH grant MH066179 to GB; NIH grants R01AG031311 and R01NS055951 to DMW; NIH (U01NS05225, R01NS050557, 1RC1NS068391, 1RC2NS070342) grants to RHB; R01NS067206 to DAB; ALS Association grants to GM, AT, RHB, and STB; and ALS/CVS Therapy Alliance grants to RHB, GM, AT, LJH, and DAB. RHB and AT received support from the Angel Fund. RHB also received support from the DeBourgknecht Fund for ALS Research, P2ALS and Project ALS

Research progress on the correlation between platelet aggregation and tumor progression

Platelets are generally considered as the main functional unit of the coagulation system. However, more and more studies have confirmed that platelets also have an important relationship with tumor progression. Tumor cells can utilize platelets to promote their own infiltration and hematogenous metastasis, and platelets are activated and aggregated in this process. Therefore, platelet aggregation may be a concomitant marker of tumor progression. This is of great significance for predicting tumor metastasis before timely treatments

Optimal wholesale price and technological innovation under dual credit policy on carbon emission reduction in a supply chain

The adoption of new energy vehicles (NEVs) can effectively reduce vehicle exhaust emissions and achieve carbon peaking and carbon neutrality goals in the transportation sector. To facilitate the development of NEVs, the Chinese government issued the dual credit policy (DCP). However, whether the DCP can promote the technological innovation of NEVs and effectively reduce carbon emissions in the transportation sector remains to be studied. This study constructed the decision-making model of NEVs under the DCP and obtained the optimal strategy to study the impact of the DCP on carbon emissions. Furthermore, we constructed a bargaining game model based on an alliance strategy to demonstrate the coordination of the NEV supply chain. The results showed that implementing the DCP can effectively reduce carbon emissions in the transportation field. The higher the technological innovation credit coefficient or credit price, the more significant the DCP’s incentive effect on reducing carbon emissions. Decentralized decision-making weakens the DCP’s incentive effect on reducing carbon emissions. The bargaining game based on alliance negotiation can enable independent companies to achieve carbon emission reduction when making centralized decisions so that the DCP’s incentive effect on reducing carbon emissions is optimized. The alliance between manufacturers is not to increase profits but to enhance their product advantages. However, suppliers can gain higher profits by participating in the alliance, which provides a theoretical reference for the alliance’s cooperation in decision-making

KidneyRegNet: A Deep Learning Method for 3DCT-2DUS Kidney Registration during Breathing

This work proposed a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which consists of a feature network, and a 3D-2D CNN-based registration network. The feature network has handcraft texture feature layers to reduce the semantic gap. The registration network is encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with retrospective datasets cum training data generation strategy, then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite application. The experiment was on 132 U/S sequences, 39 multiple phase CT and 210 public single phase CT images, and 25 pairs of CT and U/S sequences. It resulted in mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. For datasets with small transformations, it resulted in MCD of 0.82 and 1.02 mm respectively. For large transformations, it resulted in MCD of 1.10 and 1.28 mm respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategy.Comment: 15 pages, 8 figures, 9 table