Experimental Realization of Br\"{u}schweiler's exponentially fast search algorithm in a homo-nuclear system

Compared with classical search algorithms, Grover quantum algorithm [ Phys. Rev. Lett., 79, 325(1997)] achieves quadratic speedup and Bruschweiler hybrid quantum algorithm [Phys. Rev. Lett., 85, 4815(2000)] achieves an exponential speedup. In this paper, we report the experimental realization of the Bruschweiler$ algorithm in a 3-qubit NMR ensemble system. The pulse sequences are used for the algorithms and the measurement method used here is improved on that used by Bruschweiler, namely, instead of quantitatively measuring the spin projection of the ancilla bit, we utilize the shape of the ancilla bit spectrum. By simply judging the downwardness or upwardness of the corresponding peaks in an ancilla bit spectrum, the bit value of the marked state can be read out, especially, the geometric nature of this read-out can make the results more robust against errors.Comment: 10 pages and 3 figure

Poly[bis­(μ-azido-κ2 N 1:N 1)[μ-1,2-bis­(imid­azol-1-yl)ethane-κ2 N 3:N 3′]cadmium]

In the title three-dimensional coordination polymer, [Cd(N3)2(C8H10N4)]n, the coordination geometry around the CdII atom is distorted octa­hedral. The CdII atom is coordinated by two N atoms from two cis-positioned bridging 1,2-bis­(imidazol-1-yl)ethane (bime) ligands and four N atoms from four azide anions. Each azide ligand acts in an end-on bridging coordination mode. The azide ligands and CdII atoms form a one-dimensional zigzag chain constructed from four-membered [Cd(N3)2]n metallacycles extending along the a axis. These inorganic chains are connected with four other chains via bridging bime ligands to form a three-dimensional coordination network

High-Order Calibration and Data Analysis in Chromatography

Multiway data analysis and tensorial calibration are gaining widespread acceptance with the rapid development of multichannel chromatographic instruments. By combining chromatographic techniques with chemometrics based on high-order calibration methods, some traditional problems in analysis, such as complicated pretreatment steps, long elution times, or even worse analysis results, can be avoided/improved. This chapter presents an overview from second-order to third-order data that cover theories and applications together with corresponding data processing in chromatography

Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models

Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology

PILOT: A Pre-Trained Model-Based Continual Learning Toolbox

While traditional machine learning can effectively tackle a wide range of problems, it primarily operates within a closed-world setting, which presents limitations when dealing with streaming data. As a solution, incremental learning emerges to address real-world scenarios involving new data's arrival. Recently, pre-training has made significant advancements and garnered the attention of numerous researchers. The strong performance of these pre-trained models (PTMs) presents a promising avenue for developing continual learning algorithms that can effectively adapt to real-world scenarios. Consequently, exploring the utilization of PTMs in incremental learning has become essential. This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT also fits typical class-incremental learning algorithms (e.g., DER, FOSTER, and MEMO) within the context of pre-trained models to evaluate their effectiveness.Comment: Code is available at https://github.com/sun-hailong/LAMDA-PILO

Diversity of interstitial nemerteans of the genus Ototyphlonemertes (Nemertea: Monostilifera: Ototyphlonemertidae) in the South China Sea, with a comment on the distribution pattern of the genus

The genus Ototyphlonemertes Diesing, 1863, consisting of 33 named species and numerous unnamed morphospecies/molecular entities, is a unique group of nemerteans that possess cerebral statocysts and specifically live in coarse-grained sands. Only eight named species of this genus have yet been recorded from the Indo-Polynesian biogeographic province, which harbors the highest marine biodiversity in the world. In recent years, Ototyphlonemertes were collected from eight sites along the South China Sea coasts. Nine species/entities were revealed by four phylogenetic markers (COI, 16S, 18S, 28S) analyzed by three species delimitation methods: Automatic Barcode Gap Discovery (ABGD), Poisson Tree Process (PTP), and Generalized Mixed Yule Coalescent model (GMYC). Six entities are described as new species based on integration of morphological and molecular species delimitations: Ototyphlonemertes conicobasis sp. nov., Ototyphlonemertes coralli sp. nov., Ototyphlonemertes similis sp. nov., Ototyphlonemertes sinica sp. nov., Ototyphlonemertes subrubra sp. nov., and Ototyphlonemertes yingge sp. nov. No morphological differences were detected between two entities and Ototyphlonemertes chernyshevi Kajihara et al., 2018, despite large genetic differences, so are treated as candidate species. Ototyphlonemertes ani Chernyshev, 2007 is first recorded in China. Based mostly on results of phylogenetic analyses, two previously established subgenera are re-defined, and a new subgenus, Procso subgen. nov., is established. Through reviewing the existing studies, we recognize 101 species/entities of Ototyphlonemertes, which are distributed in 18 marine biogeographic provinces. Most (88.1%) of them are endemic to a single biogeographic province, and evolutionary lineages endemic to a geographic area are not uncommon. Maximum diversity has been recorded in the Indo-Polynesian Province (22 species), though sampling to date has covered only a small part of the biogeographic province