Entanglement dynamics of two-qubit system in different types of noisy channels

In this paper, we study entanglement dynamics of a two-qubit extended Werner-like state locally interacting with independent noisy channels, i.e., amplitude damping, phase damping and depolarizing channels. We show that the purity of initial entangled state has direct impacts on the entanglement robustness in each noisy channel. That is, if the initial entangled state is prepared in mixed instead of pure form, the state may exhibit entanglement sudden death (ESD) and/or be decreased for the critical probability at which the entanglement disappear.Comment: 11 pages, 6 figure

Quantized generalized minimum error entropy for kernel recursive least squares adaptive filtering

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms

Entanglement and quantum phase transition in alternating XY spin chain with next-nearest neighbour interactions

By using the method of density-matrix renormalization-group to solve the different spin-spin correlation functions, the nearest-neighbouring entanglement(NNE) and next-nearest-neighbouring entanglement(NNNE) of one-dimensional alternating Heisenberg XY spin chain is investigated in the presence of alternating nearest neighbour interactions of exchange couplings, external magnetic fields and next-nearest neighbouring interactions. For dimerized ferromagnetic spin chain, NNNE appears only above the critical dimerized interaction, meanwhile, the dimerized interaction effects quantum phase transition point and improves NNNE to a large value. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighboring (NNN) interactions on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks NNE below and above critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always decreases NNE. The antiferromagnetic NNN interaction results to a larger value of NNNE in comparison to the case when the NNN interaction is ferromagnetic.Comment: 13 pages, 4 figures,. accepted by Chinese Physics B 2008 11 (in press

ViT-Lens: Towards Omni-modal Representations

Though the success of CLIP-based training recipes in vision-language models, their scalability to more modalities (e.g., 3D, audio, etc.) is limited to large-scale data, which is expensive or even inapplicable for rare modalities. In this paper, we present ViT-Lens that facilitates efficient omni-modal representation learning by perceiving novel modalities with a pretrained ViT and aligning to a pre-defined space. Specifically, the modality-specific lens is tuned to project multimodal signals to the shared embedding space, which are then processed by a strong ViT that carries pre-trained image knowledge. The encoded multimodal representations are optimized toward aligning with the modal-independent space, pre-defined by off-the-shelf foundation models. A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities. ViT-Lens provides a unified solution for representation learning of increasing modalities with two appealing benefits: (i) Exploiting the pretrained ViT across tasks and domains effectively with efficient data regime; (ii) Emergent downstream capabilities of novel modalities are demonstrated due to the modality alignment space. We evaluate ViT-Lens in the context of 3D as an initial verification. In zero-shot 3D classification, ViT-Lens achieves substantial improvements over previous state-of-the-art, showing 52.0% accuracy on Objaverse-LVIS, 87.4% on ModelNet40, and 60.6% on ScanObjectNN. Furthermore, we enable zero-shot 3D question-answering by simply integrating the trained 3D lens into the InstructBLIP model without any adaptation. We will release the results of ViT-Lens on more modalities in the near future.Comment: 19 pages, 4 figures and 9 table

The entanglement in one-dimensional random XY spin chain with Dzyaloshinskii-Moriya interaction

The impurities of exchange couplings, external magnetic fields and Dzyaloshinskii--Moriya (DM) interaction considered as Gaussian distribution, the entanglement in one-dimensional random $XY$ spin systems is investigated by the method of solving the different spin-spin correlation functions and the average magnetization per spin. The entanglement dynamics at central locations of ferromagnetic and antiferromagnetic chains have been studied by varying the three impurities and the strength of DM interaction. (i) For ferromagnetic spin chain, the weak DM interaction can improve the amount of entanglement to a large value, and the impurities have the opposite effect on the entanglement below and above critical DM interaction. (ii) For antiferromagnetic spin chain, DM interaction can enhance the entanglement to a steady value. Our results imply that DM interaction strength, the impurity and exchange couplings (or magnetic field) play competing roles in enhancing quantum entanglement.Comment: 12 pages, 3 figure

QUERCETIN: A POTENTIAL NATURAL DRUG FOR ADJUVANT TREATMENT OF RHEUMATOID ARTHRITIS

Rheumatoid arthritis (RA) is the rheumatism mainly manifested as disabling joint disease and mainly involves hands, wrists, feet and other small joints. Recurrent arthritis attacks, synovial cell hypertrophy and hyperplasia and bone and cartilage damages eventually lead to joint dysfunction and other complications, and there is no cure. Quercetin (QU) is a kind of natural flavonoids, with lipid-lowering, anti-inflammatory and other pharmacological activities, and minor toxic side effects. Thus, we assume that QU may be an adjuvant natural drug for treatment of RA. The possible mechanism is through regulation of NF-κB, to inhibit the transcription of joint synovitis factors, hinder the generation of inflammatory factors, and inhibit the inflammatory reaction; through inhibiting the activities of VEGF, bFGF, MMP-2 and other cytokines, to inhibit angiogenesis in multiple links and inhibit synovial pannus formation. QU may be an adjuvant natural drug for treatment of RA

Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment

Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth object to one prediction, for end-to-end detection without NMS post-processing. It is known that one-to-many assignment, assigning one ground-truth object to multiple predictions, succeeds in detection methods such as Faster R-CNN and FCOS. While the naive one-to-many assignment does not work for DETR, and it remains challenging to apply one-to-many assignment for DETR training. In this paper, we introduce Group DETR, a simple yet efficient DETR training approach that introduces a group-wise way for one-to-many assignment. This approach involves using multiple groups of object queries, conducting one-to-one assignment within each group, and performing decoder self-attention separately. It resembles data augmentation with automatically-learned object query augmentation. It is also equivalent to simultaneously training parameter-sharing networks of the same architecture, introducing more supervision and thus improving DETR training. The inference process is the same as DETR trained normally and only needs one group of queries without any architecture modification. Group DETR is versatile and is applicable to various DETR variants. The experiments show that Group DETR significantly speeds up the training convergence and improves the performance of various DETR-based models. Code will be available at \url{https://github.com/Atten4Vis/GroupDETR}.Comment: ICCV23 camera ready versio

D-box-binding protein alleviates vascular calcification in rats with chronic kidney disease by activating microRNA-195-5p and downregulating cyclin D1

Vascular calcification (VC) is a critical complication in chronic kidney disease (CKD), where transcription factors (TFs) and microRNAs (miRs) could potentially play a pivotal role in its pathogenesis and progression. To explore the potential molecular mechanism by which the TF D-box-binding protein (DBP) regulates the miR-195-5p/cyclin D1 (CCND1) axis and its impact on aortic VC in CKD rats, we established a rat model of CKD with VC through a 5/6 nephrectomy procedure. This model was treated with lentivirus overexpressing DBP or CCND1 to analyze their roles in aortic VC. Additionally, an in vitro cell model of VC was induced by high phosphorus. This model underwent transfection with lentivirus overexpressing DBP or miR-195-5p mimic/inhibitor to confirm their regulatory roles in aortic VC in vitro. We assessed the interactions between DBP and miR-195-5p, as well as between miR-195-5p and CCND1. Our results indicated that the expression of DBP and miR-195-5p was reduced, while CCND1 levels were elevated in both the rat and cell models.  Overexpression of miR-195-5p inhibited VC in vascular smooth muscle cells (VSMCs). Bioinformatics prediction and dual luciferase assays confirmed that DBP could act as a TF to enhance miR-195-5p expression, with Ccnd1 identified as a downstream target gene of miR-195-5p. Overexpression of DBP inhibited aortic calcification in CKD rats, whereas overexpression of CCND1 produced the opposite effect. In conclusion, the TF DBP can inhibit CCND1 expression through transcriptional activation of miR-195-5p, thereby preventing VC in rats with CKD

Low carbon transition of global power sector enhances sustainable development goals

Low-carbon power transition, key to combatting climate change, brings far-reaching effects on achieving Sustainable Development Goals (SDGs), in terms of resources use, environmental emissions, employment, and many more. Here we assessed the potential impacts of power transition on 49 regional multiple SDGs progress under three different climate scenarios. We found that power transition could increase global SDG index score from 72.36 in 2015 to 74.38 in 2040 under the 1.5℃ scenario, compared with 70.55 and 71.44 under ‘Coal-dependent’ and ‘Middle of the road’ scenario, respectively. The power transition related global SDG progress would mainly come from switching to renewables in developing economies. Power transition also improves the overall SDG in most developed economies under all scenarios, while undermining their employment-related SDG progress. The global SDG progress would be jeopardized by power transition related international trade changes under ‘Coal-dependent’ and ‘Middle of the road’ scenario, while improved under the 1.5℃ scenario.<br/